Health Care Utilisation in Jamaica

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					Health Care Utilisation in Jamaica

         Paul A. Bourne
Health Care Utilisation in Jamaica




      Paul Andrew Bourne




                i
© Paul A. Bourne, 2011

First Published in Jamaica, 2011 by
Paul Andrew Bourne
66 Long Wall Drive
Stony Hill,
Kingston 9,
St. Andrew


National Library of Jamaica Cataloguing Data


Health Care Utilisation in Jamaica



Includes index

ISBN

Bourne, Paul Andrew


All rights reserved. Published, 2011

Cover designed by Paul Andrew Bourne




                                       ii
CONTENTS
                                                                               page

Preface                                                                            iv
Chapter 1                                                                           1

Health insurance coverage in Jamaica: Multivariate Analyses using two cross-sectional
survey data for 2002 and 2007

Chapter 2                                                                          31

Hospital Healthcare Utilisation in middle-income developing country

Chapter 3                                                                          66

Health Inequality in Jamaica, 1988-2007

Chapter 4                                                                         101

Inflation, Public Health Care and Utilization in Jamaica

Chapter 5                                                                         146

Public Health Behaviour-Change Intervention Model for Jamaicans: Charting the Way
Forward in Public Health

Chapter 6                                                                         166

Biosocial determinants of health and health seeking behaviour of male youths in
Jamaica

Chapter 7                                                                         193

Socio-demographic determinants of Health care-seeking behaviour, self-reported illness
and Self-evaluated Health status in Jamaica

Chapter 8                                                                         235

Knowledge, attitude and practices of adults of the reproductive years on reproductive
health matters, with emphasis on HIV infected people in a Caribbean society


                                            iii
Chapter 9                                                                               256

Perception, attitude and practices of women towards pelvic examination and Pap Smear
in Jamaica

Chapter 10                                                                               285

Impact of poverty, not seeking medical care, unemployment, inflation, self-reported
illness, and health insurance on mortality in Jamaica

Chapter 11                                                                               322

Retesting and refining theories on the association between illness, chronic illness and
poverty: Are there other disparities?

Chapter 12                                                                               347

Health, lifestyle and health care utilization among health professionals

Chapter 13                                                                               371

Health literacy and health seeking behaviour among older men in a middle-income
nation

Chapter 14                                                                               402

Healthcare providers in St. Catherine, Jamaica: Older men’s satisfaction (or
dissatisfaction) with their healthcare service delivery

Chapter 15                                                                               430

Public-private healthcare utilization differentials in Jamaica

Chapter 16                                                                               457

Health status and Medical Care-Seeking Behaviour of the poorest 20% in Jamaica

Chapter 17                                                                               487

Disparities in self-rated health, health care utilization, illness, chronic illness and other
socioeconomic characteristics of the Insured and Uninsured

Chapter 18                                                                               516

Good Health Status of Rural Women in the Reproductive Ages

                                             iv
Chapter 19                                                                          555

Health of children less than 5 years old in an Upper Middle Income Country: Parents’
views

Chapter 20                                                                          581

The uninsured ill in a developing nation

Chapter 21                                                                          614


Self-reported health and medical care-seeking behaviour of uninsured Jamaicans

Chapter 22                                                                          639

Variations in health, illness and health care-seeking behaviour of those in the upper
social hierarchies in a Caribbean society

Chapter 23                                                                          665

Social determinants of physical exercise in older men in Jamaica



Chapter 24                                                                          697

Self-rated health of the educated and uneducated classes in Jamaica



Chapter 25                                                                          723

Demographic shifts in health conditions of adolescents aged 10-19 years, Jamaica:
Using cross-sectional data for 2002 and 2007


Chapter 26                                                                          749

Health of females in Jamaica: using two cross-sectional surveys




                                           v
Preface

This book is written primarily for people who are interested, or working, in the health
care system (including administrators, members of health authorities, secretaries and
community workers) as well as health demographers, sociologists, social workers, policy
planners, teachers, developmentalists, insurance agents, actuaries, and epidemiologists.
The volume is in response to paucity of information on health insurance and health
utilisation in Jamaica, and the importance of those topics to development and health
planning.



The book emerged in response to an assignment in a capricious inner-city community in
St. Andrew, Jamaica, August. As a member of a group of persons assigned to address
the economic and health needs of a female in poverty, many of the difficulties faced by
the individual were because of effect of poverty on all other facets of live. The choices
made were based on survivability needs of the family constraint by the lack of health
insurance and how this affects health care utilisation. This brings into focus, the issue
“Does money affect health?” Or, “Does money affect health insurance, health care
utilisation and finally health outcome?” In seeking to understanding how to address the
concerns of a poor ill female household head, with no health insurance and
socioeconomic difficulties of survival, I realize that even if we were to buy health
insurance for our client, health care utilisation may not be increased because the
problems were complex and interchangeable. So much so that I asked the question
“How can health care utilisation be increased among the poor, economically
disadvantaged, insuranceless, sick, unemployed sick, and those in inner-city
communities?”



In searching for answers to understand the complex problems faced by my sick, poor,
head of household and resident of an inner-city community, I looked for books on
health insurance and health care utilisation in Jamaica, which revealed none. This led
to literature (or studies) outside of Jamaica. While Jamaicans share similar
characteristics as many other English-Speaking Caribbean nations, Africans, African
Americans and peoples of the developing world, there are differences (including
cultures, perceptions) that are different and make for its own set of propositions.
                                             vi
Many people to whom I spoke for assistance for my colleagues pointed to the need for
studies in Jamaica. It was in response to the paucity of information that many studies
were germinated that provided material for this book. It is therefore written with many
technical statistical principles. However, the author hopes that it will stimulate and aid
thinking in health needs, policy focus and details for planning health services. Even
though there are many statistical technicalities in this book, the reader is still able to
understand the material without having done a course in introductory or advanced
statistics. For further knowledge on many of the statistical techniques used in this
volume, the author is asking those who desire a comprehensive understanding to read
an introductory statistical text as well as logistic and multiple regressions in an,
advanced statistical text. I have tried to simply the materials to bring this to the general
readers. However, with the depth of analyses that were need sometimes technicalities
were unavoidable.



This book contains 22 chapters. Chapter 1 commences with an examination of health
insurance coverage over two years, 2002 and 2007. Chapter 2 investigates hospital
healthcare utilisation in middle-income developing country; chapter 3 evaluates Health
Inequality in Jamaica, 1988-2007; chapter 4 explores inflation, public health care and
utilization in Jamaica; chapter 5 forwards Public Health Behaviour-Change Intervention
Model for Jamaicans: Charting the Way Forward in Public Health; chapter 6 examines
Biosocial determinants of health and health seeking behaviour of male youths in
Jamaica, and many other chapters evaluating health care utilisation in Jamaica. The
chapter 22 explores the variations in health, illness and health care-seeking behaviour
of those in the upper social hierarchies in a Caribbean society.



Any discussion on health care utilisation cannot conclude with an examination of the
educated and uneducated class in regard to their health status, health care utilisation,
illness profile and health insurance coverage. Chapter 24 evaluates the aforementioned
issues; provide knowledge on the subjects and information on disparities and inequality
between the two cohorts. The current study revealed that the educated cohort was
least likely to dwell in rural areas, more likely to be the holders of health insurance
policies. However, they are not healthy than the uneducated group.



                                            vii
All the chapters of this book are published works in various journals. These journals
include North American Journal of Medical Sciences, Health, International Journal of
Collaborative Research on Internal Medicine and Public Health, and Asian Journal of
Medical Sciences. There are a few chapters that I co-authored with other Caribbean
and International Scholars. I would like to extend my sincere appreciation to the
aforementioned institutions and authors who collaborated with me to examine some of
the issues forwarded in this manuscript.



I hope that this book will contribute to the discourse of health, health care utilisation,
health insurance, gender disparity in health, health inequality and health care treatment
in Jamaica. In addition, the volume is intended to aid policy formulation and
intervention programmes as well as shape research. The road to effectively addressing
the concerns of the poor as it relates to health care utilisation is going to be a long one.
But, I hope this book will contribute to the progress and process.



                                                                  Paul Andrew Bourne
                                                                                 Director
                                                          Socio-Medical Research Institute




                                            viii
Health Care Utilisation in Jamaica




                ix
                                                                         Chapter
                                                                                           1
Health insurance coverage in Jamaica: Multivariate Analyses using two cross-
                   sectional survey data for 2002 and 2007


Health insurance is established as an indicator of health care-seeking behaviour. Despite this
reality, no study existed in Jamaica that examines those factors that determine private health
insurance coverage. This study bridges the gap in the literature as it seeks to determine
correlates of private health insurance coverage. The aim of this study is to understand those who
possess Health insurance coverage in Jamaica so as to aid public health policy formulation.
Health insurance coverage can be predicted by socio-demographic factors (such as area of
residence; education, marital status, social support, social class, gender, age), and economic
(consumption and income). The findings revealed some similarities and dissimilarities between
data for 2002 and 2007. Area of residence, consumption, educational level, marital status,
income and social support were determinants over the two periods. Asset ownership was a
factor in 2002 but not in 2007. For 2007, age, gender and social class were factors and not for
2002. A dissimilarity in this study was with social support. It was found that in 2002, social
support was negatively correlated with Health insurance coverage and this shifts to a positive
correlate in 2007. In 2002, age and gender were not associated with Health insurance coverage
but these became significant predictors in 2007. Interestingly, poor health status is not
correlated with private health insurance coverage. More health insurance coverage is owned by
urban than by other town or rural residents. Health insurance coverage is more structured for
employed people who are in the private or public sectors more within urban and other towns
than rural areas indicating that rural residents, who are faced high poverty and self-
employment, will be more likely in continuing their choice in home remedy or non-traditional
medicine in order to address their ill-health. Health which is strongly correlated with income
means that poor individuals, families, societies, nations, will be less healthy and will need
assistance in the form of health insurance to be able to reduce mortality.


Introduction
Health is more than the absence of diseases (WHO, 1948); as the absence of diseases is an

antithesis (negative definition) of health and does not capture the positive aspects to this

                                               1
phenomenon. In the preamble to its Constitution in 1946, the WHO noted that health includes

social, psychological and physical wellbeing; indicating that any measurement of health must

include non-epidemiologic factors and that this must recognize the positive ingredients in the

construction of health. One scholar coined the terms ‘Biopsychosocial model’ to explain the

different facets that must be understood, evaluated and treated in addressing the care of

unhealthy patients (Engel, 1960). Engel’s ‘Biopsychosocial model’ was employed to mean that

health includes biological, social, psychology and other determinants. While one scholar opined

that this definition of health as forwarded by the WHO as well as by extension Engel was too

broad and elusive, and creates a difficulty to measure (Bok, 2004), the WHO’s conceptual

definition of health recognizes the importance of social and behavioural factors in determining

health status. They cannot be omitted in medical care treatment nor should we seek a

measurement in order to operationalizing health as this will not be in keeping with the construct

of the comprehensive phenomenon.


       Caldwell (1993) wrote that the behavioural and lifestyle practices are a major determinant

in health (see also, Bourne, 2009), and that this in explaining mortality is not new. Caldwell’s

perspective does not only highlight the role that people play in their own quality of life; but that

their actions (or inactions) hold a crucible part of their health status. Smoking, alcohol

consumption, physical inactivity, wreckless driving, unhealthy diets and other choices are all

decisions people take in life that will either negatively or positively influence their health status,

and later will become a public health challenge. The tendency of people to become involved in

particular lifestyle practices account for pre-mature mortality for many of them. Material

deprivation, psychosocial stressors, high levels of risky behaviour, unhealthy living conditions,

social exclusion, perceived lack of control, limited access to good-quality health care,
                                                  2
constrained choices and physical inactivity account for higher levels of dysfunctions. According

to the WHO (2005), 60% of all death are owing to chronic illness, and that 80% of chronic

dysfunctions occur in low-to-middle income countries, which speaks to the growing lifestyle

practices (or lack). Material deprivation and psychosocial stressors increased the risk of diseases

for poor people and people in general which is embedded in the statistics of the WHO

publication.


       According to the WHO (2005, p. 66), 95% of Jamaicans with chronic dysfunctions

experienced financial difficulties owing to their illness “…and [that] a high proportion of people

admitting such difficulties avoided some medical treatment as a result (p. 66). It was also noted

that in India diabetic patients spent significantly more of their annual salary on medical care. The

statistics from the WHO (2005) showed that 25% of the poor’s annual income is spent on private

care compared to 4% of people with higher incomes. People are aware that illnesses are

inevitable, owing to the high cost of medical care in order to access health care services they will

then use health insurance coverage. Health care costs can be so high that people become poor

owing to the additional burden of health care cost (Pau & Maharaj, 1989); and the recurring

nature of some ailments can deplete people’s income and wealth to the point of poverty. It is this

reality that accounts for health insurance coverage. Health insurance coverage is a by-product for

people because it is demanded for lower treatment costing when illnesses occur. Therefore,

health insurance coverage not only lowers treatment cost of illnesses but also lowers the

psychosocial stressor on income, and the family’s wellbeing.


       Morrison (2000) titled an article ‘Diabetes and hypertension: Twin Trouble’ in which he

established that diabetes mellitus and hypertension have now become two problems for

                                                 3
Jamaicans and in the wider Caribbean. This situation was equally collaborated by Callender

(2000) at the 6th International Diabetes and Hypertension Conference, which was held in Jamaica

in March 2000. The researcher found that there was a positive association between diabetic and

hypertensive patients - 50% of individuals with diabetes had a history of hypertension

(Callender, 2000, p. 67). Those diseases are not only lifestyle causing, they can be expensive to

treat especially if they are severe. Hence, health insurance coverage is sought in keeping with the

probability of illness.


        Health insurance is therefore a health care-seeking behaviour and it can be used to

indicate people’s perception of a futuristic likelihood of illness. It can estimate people’s fear of

their inability to afford medical costs, their preparation for not wanting to deplete income, lower

wealth and the lack of it can account for some premature mortality. From the findings of a cross-

sectional study conducted by Powell et al. (2007) of some 1,338 Jamaicans, 19.0% of

respondents perceived that their economic wellbeing to be ‘very bad’. In addition, when they

asked, “Does your salary and the total of your family’s salary allow you to satisfactorily cover

your needs?” 57.4% of them felt that this “does not cover” their expenses (Powell et al., 2007, p.

29). In addition, out of a maximum score of 10, those in the lower class scored 5.9 for how do

they ‘feel about the state of their health’ compared to a score of 6.6 for those in the upper class

and a score of 6.7 for the middle class. This again goes to the rationale of demanding health

insurance coverage for the poor people. Bourne (2009) found that there is no significant

statistical relationship between health insurance and health care seeking behaviour or health

insurance and good health of Jamaicans, suggesting that it is not inaffordability of health care

that drives health insurance coverage; but something else.


                                                 4
       An extensive review of health literature in Jamaica found no study that has examined

determinants of health insurance coverage. Health insurance in Jamaica was a private good up to

2007, and so it could only be had by those who were employed. Hence using data up to 2007

would be examining Health insurance coverage of employed Jamaicans. The aim of this study is

to have an understanding of those who possess Health insurance coverage in Jamaica, so as to

aid public health policy formulation. In keeping with the aim, this study sought to determine

correlates of Health insurance coverage in Jamaica, using cross-sectional data for 2002 and 2007.




Methods


This study used two secondary cross-sectional data from the Jamaica Survey of Living

Conditions (JSLC). The JSLC was commissioned by the Planning Institute of Jamaica (PIOJ)

and the Statistical Institute of Jamaica (STATIN) in 1988. These two organizations are

responsible for planning, data collection and policy guideline for Jamaica, and have been

conducting the JSLC annually since 1989. The two cross-sectional surveys used for this study

were conducted in 2002 and 2007 (World Bank, 2002; PIOJ & STATIN, 2003; PIOJ & STATIN,

2008). The surveys were taken from a national cross-sectional survey of 25 018 respondents (for

2002) and 6,782 people (for 2007) from the 14 parishes across Jamaica. The surveys used

stratified random probability sampling technique to drawn the original sample of respondents.

The non-response rate for the 2002 survey was 29.7% and 26.2% for the 2007 survey. The

sample was weighted to reflect the population (World Bank, 2002; PIOJ & STATIN, 2003; PIOJ

& STATIN, 2008).



                                               5
       The JSLC is a self-administered questionnaire where respondents are asked to recall

detailed information on particular activities. The questionnaire was modelled from the World

Bank’s Living Standards Measurement Study (LSMS) household survey.                  There are some

modifications to the LSMS, as JSLC is more focused on policy impacts (World Bank, 2002).

The questionnaire covers demographic variables, health, immunization of children 0–59 months,

education, daily expenses, non-food consumption expenditure, housing conditions, inventory of

durable goods and social assistance. Interviewers are trained to collect the data from household

members. The survey is conducted between April and July annually.


       Descriptive statistics such as mean, standard deviation (SD), frequency and percentage

were used to analyze the socio-demographic characteristics of the sample. Chi-square was used

to examine the association between non-metric variables, and an Analysis of Variance

(ANOVA) was used to test the relationships between metric and non-dichotomous categorical

variables. Logistic regression examined the relationship between the dependent variable and

some predisposed independent (explanatory) variables, because the dependent variable was a

binary one (self-reported health status: 1 if reported good health status and 0 if poor health).


       The results were presented using unstandardized B-coefficients, Wald statistics, Odds

ratio and confidence interval (95% CI). The predictive power of the model was tested using the

Omnibus Test of Model and Hosmer & Lemeshow (2000) to examine goodness of fit. The

correlation matrix was examined in order to ascertain whether autocorrelation (or

multicollinearity) existed between variables. Based on Cohen & Holliday (1982) correlation can

be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. This was used to

exclude (or allow) a variable in the model. In addition, variables were excluded from the model

                                                  6
if they had in excess of 20% of the cases missing. Odds Ratio (OR) was used to interpret each

significant variable.


         Multivariate regression framework (Asnani et al., 2008; Hambleton et al., 2005) was

utilized to assess the relative importance of various demographic, socio-economic characteristics,

physical environment and psychological characteristics, in determining the health status of

Jamaicans; and this has also been employed outside of Jamaica (Cohen & Holliday, 1982; James,

2001; Ross et al., 1990). This approach allowed for the analysis of a number of variables

simultaneously; and is used to examine health insurance coverage. Secondly, the dependent

variable is a binary dichotomous one and this statistic technique has been utilized in the past to

do similar studies. Having identified the determinants of health status from previous studies,

using logistic regression techniques, final models were built for Jamaicans as well as for each of

the geographical sub-regions (rural, peri-urban and urban areas) and sex of respondents using

only those predictors.


Models
The current study will employ multivariate analyses in the study of health and medical care
seeking behaviour of Jamaicans. The use of this approach is better than bivariate analyses as
many variables can be tested simultaneously for their impact (if any) on a dependent variable.
HIt =f(H t , A i , G i , HH i , AR i , lnC, ∑D i , ED i, MR i , S i , HTi , lnY, CR i , MC t , SS i , Ti , CIi , P i , En i ,
HSB,                                                                                                                    εi)
(1)

         Where HIi is health insurance coverage of person i, H t (ie self-rated current health status
         in time t) is a function of age of respondents, A i ; sex of individual i, G i ; household head
         of individual i, HH i ; area of residence, AR i ; house tenure of individual i, HTi ; logged
         consumption per person per household member, lnC; summation of durable goods and
         asset owned, ∑D i ; Education level of individual i, ED i ; marital status of person i, MR i ;
         social class of person i, S i ;; logged income, lnY; crowding of individual i, CRi; medical
         expenditure of individual i in time period t, MC t ; social support of individual i, SS i ;
         social assistance (ie welfare) individual i, Ti ; crime index, CIi ; physical environment of
         individual i, En i , health care seeking behaviour and an error term (ie. residual error).

                                                             7
       The final models that were derived from the general Equation (1) that can be used to
predict Health insurance coverage of Jamaicans are Equation (2) and Equation (3):

       HI t(Jamaicans,     2002)     =f(AR i ,      lnC,        ED i,      MR i,    lnY,    SS i,      ∑D i ,     HSB,   ε i)
(2)

      HI t(Jamaicans,    2007)     =f(AR i,      lnC,   ED i,      MR i,     lnY,   SS i,   A i,    G i,   S i,   HSB,   ε i)
(3)



Measures


An explanation of some of the variables in the model is provided here. Self-reported is a dummy

variable, where 1 (good health) = not reporting an ailment or dysfunction or illness in the last

4 weeks, which was the survey period; 0 (poor health) if there were no self-reported ailments,

injuries or illnesses (Bourne & Rhule, 2009). While self-reported ill-health is not an ideal

indicator of actual health conditions because people may underreport, it is still an accurate proxy

of ill-health and mortality (Idler & Kasl, 1991; Idler & Benyamini, 1997; Bourne & Rhule,

2009). Social supports (or networks) denote different social networks with which the individual

is involved (1 = membership of and/or visits to civic organizations or having friends who visit

ones home or with whom one is able to network, 0 = otherwise). Psychological conditions are

the psychological state of an individual, and this is subdivided into positive and negative

affective psychological conditions (Diener, 2000; Harris & Lightsey, 2005). Positive affective

psychological condition is the number of responses with regard to being hopeful, optimistic

about the future and life generally. Negative affective psychological condition is number of

responses from a person on having lost a breadwinner and/or family member, having lost

property, being made redundant or failing to meet household and other obligations. Health status

is a binary measure (1=good to excellent health; 0= otherwise) which is determined from

                                                                  8
“Generally, how do you feel about your health”? Answers for this question are in a Likert scale

matter ranging from excellent to poor. Health care-seeking behaviour is derived from the

question: Have you visited a health care practitioner, pharmacist or healer in the past four 4

weeks, with an option of yes or no. For the purpose of the regression was coded as 1=yes,

0=otherwise. Crowding is the total number of individuals in the household divided by the

number of rooms (excluding kitchen, verandah and bathroom). Age is a continuous variable in

years.


Results

Demographic characteristic and bivariate analyses

In 2002 the sample was 25,018 respondents: 12,332 males (49.3%) and 12,675 females (50.7%).

In 2007 the sample was 6,782 respondents with there being marginally more females (51.3%)

than males (48.7%) (Table 1.1). The findings in Table 1.1 revealed that urbanization was taken

place in 2002, there were 13.4% of respondents living in urban zones and this shifted to 29.5% in

2007. The percentage of Jamaicans dwelling in rural areas declined from 61% in 2002 to 49.0%

in 2007. In 2002, 12.5% of respondents indicated that they had an illness in the 4-week survey

period and this increased by 2.4% in 2007. Sixty-four percent of respondents reported having

visited a health care facility (including a healer), and this increased to 66% in 2007. The social

class categorization of Jamaicans remained relatively the same over the studied period; and the

percentage of respondents who had health insurance coverage increased from 11.0% in 2002 to

20.2% in 2007. The mean number of visits made to health care institutions (including healers)

declined from 1.7 days to 1.4 days. On the other hand, crowding increased by 135% in 2007 over

2002; and medical care expenditure also increased by 29.1% over the period (Table 1.2).


                                                9
       The mean annual income of respondents in 2002 was Ja $331,488 and this increased by

109% in 2007 to Ja $691,560. On disaggregating income by area of residence, it was revealed

that there was significant statistical difference between income of respondents and their area of

residents. On average, urban respondents received 1.6 times more income than rural residents in

2007 and this was similar in 2002 (approximately 1.5 times more). The disparity in income

between urban and other town respondents was lower (in 2007 – 1.1 times more and this was the

same in 2002) than that between urban and rural dwellers.

       A significant association was found between health status and self-reported illness

(p<0.001) (Table 1.3). An individual who indicated poor health status was nine times more

likely to have an illness than those who did not. On the other hand, an individual who indicated

good health status was twice more likely not to report an illness than those who did not indicate

an ailment. More males than female (85% vs. 79%; p<0.001) reported good health status and the

opposite was true for poor health status (4.2% vs. 5.5%; p < 0.001).

       There was a change in pattern of the 5-leading recurring illnesses in Jamaica (Table 1.4).

In 2002, hypertension was the leading cause of self-reported dysfunctions (21.6%) followed by

cold (19.9%); unspecified ailments (18.1%); diabetes mellitus (11.6%) and asthma (9.6%).

However in 2007, the leading prevalence of self-reported ailments shifted to unspecified

ailments (23.4%) followed by hypertension (20.6%); cold (14.9%), diabetes mellitus (12.3%)

and 9.5% asthma cases. Furthermore, a significant statistical relationship was found between

diagnosed recurring illness was gender in both years: In 2002 (χ2(df = 1) = 125.469, p < 0.001)

and in 2007 (χ2(df = 1) = 40.916, p < 0.001) (Table 1.4). Table 1.4 showed that diabetes mellitus

and hypertension were significant more among for females than males and that arthritis,

unspecified illnesses, asthma diarrhoea and cold were more prevalent among males than females.

                                                10
       Table 1.5 showed that there was a significant statistical correlation between medical care-

seeking behaviours and gender: In 2002 (χ2(df = 1) = 9.006, p = 0.003) and in 2007, (χ2(df = 1) =

3.004, p < 0.048). In 2002 more females sought medical care (66%) than males (60.7%); and

this was the case in 2007: 67.6% for females and 62.3% for males (Table 1.5).



In 2007, there was a significant statistical relationship between health care-seeking behaviour of

Jamaicans and health insurance coverage (χ2(df = 1) = 16.712, p < 0.001). The association was a

very weak one (r = 0.128). However, the findings showed that 76.2% (n = 189) of people with

private health insurance visited a health care practitioner compared to 62.0% (n = 468) those who

do not have health insurance coverage.


Multivariate analyses

In 2007, health insurance coverage in was correlated with logged consumption (OR = 1.90, 95%

CI = 1.12 - 3.23); logged income (OR = 1.71, 95% CI = 1.02 - 2.87); durable goods (OR = 1.09,

95% CI = 1.02 - 1.17); marital status (married: OR = 3.91, 95% CI = 2.47 - 6.20); area of

residence (urban areas: OR = 2.24, 95% CI = 1.23 - 4.09); education (secondary: OR = 2.97,

95% CI = 1.46 - 6.00; tertiary: OR = 18.76, 95% CI = 8.12 - 43.43); and social support (OR =

0.54, 95% CI = 0.36 - 0.80) (Table 1.7).

       For 2002, health insurance coverage model was a predictive model (χ2 (df = 24) =

451.35, p < 0.001; Hosmer and Lemeshow goodness of fit χ2=5.91, P = 0.66), with 92.4% of the

data being correctly classified (41.1% - correct classification of cases of self-rated Health

insurance coverage and 98.4% of cases of self-rated no private health insurance coverage) (Table




                                               11
1.7). The model (Table 1.7) can explain 44.7% of the variability in Health insurance coverage of

Jamaicans (for 2002).

       Health insurance coverage in Jamaica for 2007 can be determined by 10 variables. These

were logged consumption (OR = 1.00, 95% CI = 1.00 - 1.00); logged income (OR = 1.00, 95%

CI = 1.00 - 1.00); marital status (married: OR = 1.84, 95% CI = 1.52 - 2.22); area of residence

(urban areas: OR = 1.30, 95% CI = 1.08 - 1.57); education (secondary or tertiary: OR = 1.45,

95% CI = 1.09 - 1.92); and social support (OR = 1.33, 95% CI = 1.04 - 1.70); age (OR = 1.01,

95% CI = 1.01 - 1.02); social class (upper class: OR = 1.61, 95% CI = 1.08 - 1.57) and by gender

(male: OR = 0.81, 95% CI = 0.69 - 0.95).

       For 2007, the factors that determine health insurance coverage in Jamaica is a predictive

model (χ2 (df = 20) =590.07, p < 0.001; Hosmer and Lemeshow goodness of fit χ2=7.25, P =

0.51), with 79.4% of the data being correctly classified (40.4% - correct classification of cases of

self-rated Health insurance coverage and 96.4% of cases of self-rated no private health insurance

coverage). For 2007, the model can explain 49.1% of the variability in private health insurance

coverage.


Discussion

There are some sociodemographic determinants of health insurance coverage in Jamaica that

have remained predictors. These include area of residence, consumption, education, marital

status, income and social support. Durable goods were a predictor of health insurance coverage

in 2002; however, this is ceased to be the case in 2007. Over time, health insurance coverage

was determined by some additional factors such as age, gender, and social class. Of the 6

predictors of Health insurance coverage in Jamaica that continued to be factors in both periods,


                                                12
there is dissimilarity. Social support which was a negative determinant in 2002 reversed to a

positive one in 2007. It is expected that those with more social support would be less likely to

purchase health insurance coverage as there is a higher probability that they can be assisted in

times of medical needs by the social networks with which they are apart. The church, civic

associations and societies, family, friends and associates are more likely to extend a helping hand

in time of medical need, and this account for the unwillingness of people to purchase private

health insurance because this socio-economic support is present.

        In 2007, the findings revealed that Health insurance coverage was positively correlated to

social support which invalidates the aforementioned perspective. The inflation rate in Jamaica

rose by 194% in 2007 over 2006, which indicates that net disposable individual and household

income would have fallen substantially and that each individual would have seen an erosion of

his purchasing power coupled with higher cost of living. The direct correlation between social

support and Health insurance coverage can be explained by social institutions encouraging its

members to purchase insurance to offset the increased costs. They probably may be less likely to

offer the same level of assistance to all its members like the previous period when costings were

lower. The economic cost will create a challenge for those social networks to spread their limited

financial resources over a wider cross-section of people with diverse needs. This then is a part of

the explanation why Health insurance coverage was the highest in Jamaica in 2007 (21.2%) over

the 2 decades; and in 2007, medical care-seeking behaviour was 66% which fell by 5.7% over

2006.

        The current study revealed that married people were more likely to purchased private

health insurance than those who were never married and that there is no significant difference in

purchase of health insurance between those who were divorced, separated or widowed and those

                                                13
who were never married. In 2002, the findings showed that married people were 4 times more

buy Health insurance coverage compared to those who were never married and that this ratio fell

to 2 times more in 2007. This lower of disparity in ownership of Health insurance coverage

between the married and never married cohorts in Jamaica is an indication of people’s

willingness to subsidize medical care cost with private health insurance coverage; the lowering

of their disposable income owing to increased cost of living; increased awareness of seeking

medical care and the high cost of doing so; and the changing typology of diseases which require

continuous monitoring by health care practitioners and how this is likely to erode income and

wealth, and that this would be best mitigated against through the provision of health insurance.

       An another interest finding that is embedded in the disparity of more married than

unmarried people owning private health insurance is the explanation for why married people

have a greater health status than unmarried people. Health insurance coverage is an indicator of

health care-seeking behaviour, which goes to the core of married people’s willing to address

health concerns owing to their recognition of the family (ie children and spouse) depending on

them for care, protection and financial support. According to Moore et al. (1997, 29), people who

reside with a spouse have a different base of support that those in other social arrangements (See

also Smith & Waitzman 1994; Lillard & Panis 1996).           Cohen & Wills (1985) found that

perceived support from one’s spouse increased wellbeing (see also Smith & Waitzman 1994),

while Ganster et al. (1986) reported that support from supervisors, family members and friends

was related to low health complaints. Koo, Rie & Park (2004) findings revealed that being

married was a ‘good’ cause for an increase in psychological and subjective wellbeing in old age.

Smith & Waitzman(1994) offered the explanation that wives found dissuade their husband from

particular risky behaviours such as the use of alcohol and drugs, and would ensure that they

                                                14
maintain a strict medical regimen coupled with proper eating habit (see also Ross et al., 1990;

Gore, 1973). In an effort to contextualize the psychosocial and biomedical health status of

particular marital status, one demography cited that the death of a spouse meant a closure to

daily communicate and shared activities, which sometimes translate into depression that affect

the wellbeing more of the elderly who would have had investment must in a partner (Delbés &

Gaymu 2002, p. 905).

       Embedded in Smith and Waitzman finding is the positive effecting of marriage on men’s

health status. This speaks to culture of men’s unwillingness to seek medical care, and the role of

the spouse in reducing this practice. The current study found that men were 19.2% less likely that

women own health insurance, indicating once again their unwillingness to seek medical care.

Health literature has established that women are more likely to seek medical care than men

(Stekelenburg et al., 2009; PIOJ & STATIN, 2001) and that this was concurred by the current

study. Interestingly, in 2002, for every 156 females that sought medical care there were 100

males; but in 2007, the ratio widens to 160 females for every 100 males. Although females

sought more health care services than males, statistics revealed that the latter group spent more

days in illness (mean = 10.3 days) than females (mean number of days suffered from illness =

9.3 days) (PIOJ & STATIN, 2008).

       Poor health status which is an indicator of health conditions means that females were

more likely to seek medical care to address those concerns compared to males who were

suffering from the different illnesses. Of the 3 specified chronic illnesses (arthritis, diabetes

mellitus, and hypertension) females are influenced by the more severe types, and thus explain the

greater probability of them seeking medical care and buying health insurance coverage than

males. This research found that in 2002, females were 2.1 times more likely to report having

                                               15
hypertension and 1.5 times more likely to claim that they have diabetes mellitus than males. In

2007, the disparity in self-reported hypertension fell to 1.7 times and increased to 2 times for

diabetes mellitus. For arthritis, the disparity was narrowly greater for males than females. In

2002, for every 120 males that reported arthritis there were 100 females and this was 111 males

for every 100 females in 2007.

       Men are not only unwilling culturally to display emotions, fear, weakness and illness,

they are equally reserved about speaking of their health conditions. Such a position is embedded

in the culture, which states that boys should ‘suppress reaction to pain’ and to speak of illness to

lower ones maleness (Chevannes, 2001, p. 37). Chevannes’s work explains the current findings

as well to provide in-depth information on statistics published in the Jamaica Survey of Living

Conditions (JSLC). The JSLC (2000) reported that men were 0.7 times less likely to self-report

sicknesses, injuries and/or ailments compared to their female counterparts. In a number of

societies, traditional females seek health-care more than males, which allow for a better

monitoring and diagnostic assessment of their health conditions as against men.

       Higher income means the individual, family, society and nation has more to it disposable

to cover non-consumption items such as health insurance. Easterlin argued that “those with

higher income will be better able to fulfill their aspiration and, and other things being equal, on

an average, feel better off” (Easterlin, 2001a, p. 472), indicating a bivariate relationship between

subjective well-being and income. Stutzer & Frey (2003) found that the association between

subjective wellbeing and income to be a non-linear one. According to Stutzer & Frey (2003) “In

the data set for Germany, for example, the simple correlation is 0.11 based on 12, 979

observations” (p. 9). The current study concur with Easterlin that greater income can purchase

other goods, which accounts for the positive correlation between income and private health
                                                16
insurance coverage. This is also in keeping with Brown et al.’s study (2008) which had income

as a predictor of health care-seeking behaviour. The current research went further than Brown et

al (2008) and Easterlin (2001) studies as it found that those who consume more on food and non-

food items are more likely to own Health insurance coverage than those who consume less.

Hence, it is expected that wealthy will be significantly more likely to own Health insurance

coverage than the poor.

       In Jamaica, statistics from the Planning Institute of Jamaica and Statistical Institute of

Jamaica (2007) revealed that poverty is substantially a rural phenomenon and that the more of

the wealthy live in urban area, then more urban dwellers having Health insurance coverage is

reinforcing the literature that more money provide access to a wider spread of goods and services

outside of basic necessities. The current research has provided more interest information in the

literature as wide gap that existed in 2002 between the wealthy and the poor in regards to

ownership of private health insurance, narrowed in 2007.

       Another interesting finding of this study is the positive significant correlation between

health insurance coverage and educational attainment. In 2002, those with tertiary level

education were 19 times more likely to own health insurance coverage in Jamaica and this

narrowed substantially to 1.4 times more than those with primary and below education. The

narrowing of the gap of those who owned health insurance coverage between the tertiary and the

primary level education can due to knowledge of ill-health, lowered income, the role of the

media in information the populace about the role of health insurance coverage in reducing

medical cost on seeking health care. Interestingly private health insurance companies in Jamaica

have expanded health insurance schemes to Credit Unions, and so this is giving greater access of

this product to the poor who are mostly members of the Union.

                                               17
       The positive significant correlation of age and health insurance coverage in Jamaica can

be accounted for by the biological changes and the high cost of medical care due to this futuristic

probability. Organism aged naturally, which explains biological ageing. Ageing is synonymous

with reduced functional limitations (or increased health conditions), suggesting that the older

people become they will be more willing to purchase Health insurance coverage due to the future

cost of medical care and the high likeliness of illness because of health conditions. Gompertz’s

law in Gavriolov & Gavrilova (2001) showed that there is fundamental quantitative theory of

ageing and mortality of certain species (the examples here are as follows – humans, human lice,

rat mice, fruit flies, and flour beetles (see also, Gavriolov & Gavrilova, 1991). Gompertz’s law

went further to establish that human mortality increase twofold with every 8 years of an adult

life, which means that ageing increases in geometric progression. This phenomenon means that

human mortality increases with age of the human adult, but that this becomes less progress in

advance ageing. Thus, biological ageing is a process where the human cells degenerate with

years (i.e. the cells die with increasing in age), which is well established in evolutionary biology

(Medawar 1946; Carnes and Olshansky, 1993; Carnes et al., 1999; Charlesworth, 1994).

       A study on the elderly in the Caribbean Food and Nutrition Institute’s magazine Cajanus

found that 70% of individuals who were patients within different typologies of health services in

Jamaica were senior citizens (Caribbean Food and Nutrition Institute1999; Anthony 1999), and

this emphasize the need of elderly to purchase health insurance in order to cover the cost of

health care. A study conducted by Costa, using secondary data drawn from the records of the

Union Army (UA) pension programme that covered some 85% of all UA, showed there is an

association between chronic conditions and functional limitation – which include difficulty

walking, bending, blindness in at least one eye and deafness (Costa 2002). Again this is

                                                18
reiterating the need to seek medical care owing to ageing, and justifying the positive correlation

between age and health insurance coverage in this study.



       Interestingly health insurance is among the greatest predictor of health care-seeking

behaviour in the United States (Call & Ziegenfuss, 2007), and this is not the case in Jamaica as

only 21 out of every 100 Jamaicans possessed health insurance coverage in 2007. However of

those who claimed to have private health insurance coverage, 8 out of 10 visited health care

facilities, suggesting that those with this facility would be a great predictor of health care-seeking

behaviour. It should be noted that Jamaica does not have a national health insurance coverage

which is opened to the general populace. Instead (in 2007), the government introduced a national

health insurance coverage in which people with particular ailments can access services and

medication at particular public institutions free and a national health insurance scheme which

caters to the elderly Jamaicans (ages 60 years and older).


Conclusion

The socioeconomic determinants of Health insurance coverage in Jamaica have expanded in

2007 over 2002. Area of residence, consumption, income, educational attainment, marital status

and social support have remained factors in 2007 over 2002; but age, gender and social class are

currently new sociodemographic variables that explain private health insurance in Jamaica.

Furthermore, females seeking more medical care in Jamaica has been fundamentally linked to

culture and this is undoubtedly so; but this study has found that the typology of their health

conditions is another pivotal rationale for this disparity. The reported health conditions with

which males reported more of than females are illnesses that can be substantially over the


                                                 19
counter with non-traditional medicine, and so further goes to the reason for their low access of

traditional health care services.



       In Jamaica, the employment typology in area of residents is different and contributes to

the disparity in private health insurance coverage. Employment in rural area is substantially self-

employment (ie farming) and this type of employment is not designed around private health

insurance coverage. Health insurance coverage is more structured for employed people who are

in the private or public sectors more within urban and other towns than rural areas indicating that

rural residents, who are faced high poverty and self-employment, will be more likely in

continuing their choice in home remedy or non-traditional medicine in order to address their ill-

health. Health which is strongly correlated with income means that poor individuals, families,

societies, nations, will be less healthy and will need assistance in the form of health insurance to

be able to reduce mortality. In concluding, the information with which this provided can be used

by public health services in formulating programmes that can be address the concerns of males

and rural poor.




                                                20
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                                               23
Table 1.1. Demographic characteristic of samples 2002 and 2007

Variable                                                         2002                         2007
                                                      Number            Percent      Number          Percent
Gender
     Male                                                  12,332             49.3      3,303              48.7
    Female                                                 12,675             50.7      3,479              51.3
Area of residence
    Urban                                                   3,357             13.4      2,002              29.5
    Other                                                   6,401             25.6      1,458              21.5
    Rural                                                  15,260             61.0      3,322              49.0
Illness
    Yes                                                     3,010             12.5        980              14.9
    No                                                     21,103             87.5      5,609              85.1
Visits health care facilities
    Yes                                                     1,966             63.9        658              65.5
    No                                                      1,113             36.1        347              34.5
Social class
    Poor                                                    9,931             39.7      2,697              39.4
    Middle                                                  4,984             19.9      1,351              19.9
    Upper                                                  10,099             40.0      2,734              40.3
Private health insurance coverage
    Yes                                                     2,671             11.0      1,314              20.2
    No                                                     21,546             89.0      5,203              79.8
Health status
   Good                                                                                 5,397              82.2
   Fair                                                                                   848              12.9
   Poor                                                                                   320               4.9




                                                      24
Table 1.2. Income, crowding, age, by area of residence 2002 and 2007
Characteristic            Year          Category         Mean          SD            p-value
Income Ja$                2002†         Urban            440,4521      $521,519.38   <0.001
                                        Other towns      385,626       $276,644.12
                                        Rural            284,810       $231,540.04
                                        Total            331,488       $304,040.77
                          2007††        Urban            865,674       $673,512.10   <0.001
                                        Other towns      771,301       $597,582.65
                                        Rural            551,634       $389,765.68
                                        Total            691,560       $128,742.65
Crowding (persons)        2002          Urban            2.0           1.4 persons   0.097
                                        Other towns      2.0           1.4 persons
                                        Rural            2.0           1.4 persons
                                        Total            2.0           1.4 persons
                          2007          Urban            4.3           2.4 persons   <0.001
                                        Other towns      4.6           2.3 persons
                                        Rural            5.0           2.5 persons
                                        Total            4.7           2.5 persons
Age (years)               2002                           28.2          22.0
                          2007                           29.9          21.8

No of visits to health    2002                          1.7            1.4
care facilities (days)
                          2007                          1.4            1.1
Medical expenditure Ja$   2002†                         1,144          2,946
                          2007††                        1,477          4,711
†Ja $40.97 = US $1.00
††Ja $80.47 = US $1.00




                                              25
Table 1.3. Health status by self-reported illness, and gender 2007

Characteristic                   Category               Health status (%)          Total no.
                                             Good         Fair            Poor
Self-reported dysfunction        0              89.1            8.7          2.2     5569
                                 ≥1             42.8           36.8         20.4      976
                                 Total         5381             845         319      6545

Gender                           Males          85.4            10.4        4.2      3195
                                 Females        79.2            15.3        5.5      3370
                                 Total no.      5397            848         320      6565




                                                26
Table 1.4. Self-reported diagnosed recurring illness by gender and years 2002 and 2007

Yea      Sex                   Self-reported diagnosed recurring illness (%)                Tota
 r                                                                                          l no.
                  Cold   Diarrhoe    Asthma Diabete      Hyper-    Arthriti Othe      No
                         a                  s            tension   s        r

200   Males       22.9   3.1         11.4      9.3       12.9      7.6         20.1   12.   125
2                                                                                     5     2

      Females     17.8   2.4         8.3       13.2      27.6      6.3         16.6   7.7   181
                                                                                            1

      Total no.   610    83          294       356       661       209         553    297   306
                                                                                            3

200   Males       17.2   2.7         11.7      7.7       14.4      6.0         25.4   14.   402
7                                                                                     9

      Females     13.4   2.7         8.0       15.4      24.8      5.4         22.1   8.2   597

      Total no    149    27          95        123       206       56          234    109   999




                                              27
Table 1.5. Medical care-seeking behaviour by gender, 2002, 2007
                                                 2002                    2007
Medical care-seeking behaviour
                                          Male          Female    Male          Female
Yes                                       60.7            66.0
                                                                  62.3           67.6

No                                       39.3            34.0     37.7           32.4

Total no.                                1266            1813     406            599




                                             28
Table 1.6. Health insurance coverage by area of residence 2007
                                           Area of residence

Health Insurance                                   Other
                                  Urban            towns         Rural   Total no.
  No coverage
                                   72.0            77.9          85.5      79.8

  Private coverage
                                   19.2            15.1           7.1      12.4

  Public coverage
                                    8.7             7.0           7.4      7.7

Total no.                          1939            1401          3177     6517




                                              29
Table 1.7. Logistic regression: Predictors of private health coverage in Jamaica
Characteristic                                              2002                       2007
                                                     OR              95% CI     OR             95% CI
Age                                                  1.00           0.98-1.02   1.01          1.01-1.02
Log consumption                                      1.90           1.12-3.23   1.00          1.00-1.00
Log income                                           1.71           1.02-2.87   1.00          1.00-1.00
Log medical expenditure                              0.99           0.81-1.21   1.00          1.00-1.00
Household head                                       4.61          0.21-99.16   1.03          0.86-1.23
Medical care seeking behaviour                       0.88           0.42-1.83   1.65          1.07-2.41
Sex
Male                                                 0.88           0.60-1.30   0.81          0.69-0.95
Marital status
Separated, divorced or widowed                       1.38           0.49-3.88   1.19          0.87-1.64
Married                                              3.91           2.47-6.20   1.84          1.52-2.22
†Never married                                       1.00                       1.00
Area of residence
Urban                                                2.24           1.23-4.09   1.30          1.08-1.57
Other towns                                          1.19           0.75-1.89   1.11          0.90-1.36
†Rural                                               1.00                       1.00
Education
Secondary                                            2.97           1.46-6.00   1.45          1.09-1.92
Tertiary                                             18.8           8.11-43.4
†Primary or below                                    1.00                       1.00
House tenure: owned                                  1.76           0.16-19.4
Social class
Middle                                               0.88           0.32-2.41   0.96          0.63-1.46
Upper                                                1.88           0.68-5.24   1.61          1.04-2.49
†Lower                                               1.00                       1.00
Social support                                       0.54           0.36-0.80   1.33          1.04-1.70
Health status
Good health                                          0.93           0.56-1.53   1.05          0.84-1.31
Durable goods index (excluding land)                 1.09           1.01-1.17
Physical environment                                 0.78           0.48-1.27
Crime index                                          1.01           0.99-1.03
Asset ownership (ie land or property)                0.79           0.51-1.22
Psychological condition
Negative affective conditions                        0.96           0.91-1.02
Log crowding                                         1.33           0.88-2.02   1.07          0.98-1.16
Social welfare                                                                  0.79          0.52-1.20
Time spent in health care facilities
Public                                               0.96           0.79-1.20   1.00          1.00-1.00
Private                                              1.43           0.02-85.3   1.00          1.00-1.00
Illness                                              4.01           0.44-36.4   1.14          0.90-1.43
Injury                                               0.68           0.36-1.75   1.12          0.57-2.20
N                                                                      25,007                     6,565
Chi2                                                                    451.3                     590.1
Nagelkerke R2                                                            0.45                      0.49
LR                                                                      776.4                   4,126.8




                                                30
                                                                            Chapter
                                                                                               2
      Hospital Healthcare Utilisation in middle-income developing country


Health is a crucible component in any discussion on development, and public-private hospital
health care utilisation accommodates this mandate of governments. The aim of the current study
is to examine factors that account for people’s hospital health care utilisation in Jamaica, and to
ascertain whether there is a difference between public hospital care utilisation and income
quintile and area of residence.The current findings revealed that 6 factors determine 35.6% of
the variability in visits to public hospital health care facilities utilisation in Jamaica. Two major
findings from this study are 1) health seeking behaviour and health insurance coverage are the
two most significant factors that determine public hospital health care facilities utilisation, and
that 2) the two aforementioned factors and positive affective conditions inversely correlate with
public hospital health care facility utilisation. In addition to the above, there is no statistical
difference between the utilisation of public hospital health care facilities and area of residence
while lower income quintile becomes the greater public hospital health care facilities utilisation
has been. The demands for public hospital health care facility utilisation in Jamaica were
primarily based on inaffordability and low perceived quality of patient care. The issue of low
quality of patient care speaks not to medical care, but to the customer service care offered to
clients. The greater percentage of Jamaicans who access private health care is not owing to
plethora of services, higher specialized doctors, more advanced medical equipment, or low, but
this is due to social environment – customer service and social interaction between staffers and
clients- and physical milieu – more than one person per bed sometimes, uncleansiless of the
facilities.



INTRODUCTION



Health is a crucible component in development. The health status of a people does not only mean

personal development; but also greater economic development for the nation as healthier people

                                                 31
are more likely to produce greater output than those who are ill, accounting for higher

productivity and efficiency. Illness or injury means in-voluntary absenteeism from the productive

process which accounts again for lowered production at the macro level. A substantial part of a

country’s Gross Domestic Product (GDP) per capita each year is loss to illnesses. The WHO has

forwarded that between 3 and 10 years of life is loss owing to illnesses [1,2], suggesting that

illness reduces not only output by quality of life. Hence, it is not important for observed length of

life (ie. life expectancy), but it is imperative to take into consideration loss years owing to illness

which means the measure of importance will be health life expectancy. And so, the public health

facility can accommodate this mandate of governments. While private health care facilities

supply a demand for health care, the average citizen in many countries is unable to afford the

medical expenditure of those facilities and so the public care facility is not only the access of the

average person is the bedrock upon which the health care system of the society relies.


       Public-private hospital health care utilisation in Jamaica for over the last 11-years (1996

to 2006) has been narrowing, suggesting that economic wellbeing of population has been falling

as the economic cost of survivability has been increasing and this explain the narrowing gap

seeing in the hospital health care facility utilisation (Figure 2.1). It is noted in the data that there

is decline in medical care seeking behaviour of Jamaicans in 2006 from 70% to 66% in 2007 (In

Table 2.2). Although there is an increasing demand of public hospital health care facilities

utilisation by those who seek medical care (Table 2.1), within the context of an increase in self-

reported illness (by 3.3%) coupled with the dialectic of reduction in medical care seeking

behaviour, and decline in public health utilisation (including clinics, Table 2.1), there is still a

positive sign as there was increase in health insurance coverage (from 21.2% in 2007 over 18.4%

in 2006).
                                                  32
       In 2007 inflation increased by 194.7% over 2006 and accounts for this narrowed gap

between public and private utilisation of health care in Jamaica. The exponential increase in

inflation (194.7%) has accounted for higher cost of living of Jamaicans and has rationalized the

decline in private health utilisation and the switching to public health care utilisation (Table 2.3).

Furthermore, this goes to the core of the drastic reduction in the bed occupancy at public hospital

health care facilities in 2004 over 2003 (by 33.7%), suggesting that the poor’s medical care

seeking behaviours are significantly affected in tough times. This is further accounted for in the

fact that data on private facilities utilisation for those in the poorest quintile fell by 36.1% in

2007 over 1991 and 37.1% for those in the poor quintile over the same period, while there was

an increase in public facilities utilisation for those in the poorest quintile (by 29.8%) and by

53.6% for those in poor quintile for the same period.

       Inflation is not the only economic impediment that is affecting health care utilisation in

Jamaica, as looking at the data on remittances which accounted for the single largest foreign

exchange receipt in the nation, this fell by 7.7% in 2007 over 2006 (Figure 2). The poor and the

poorest were the most affected by the decline in remittances as rate was 22.1% and 16.9%

respectively. Despite the reduction in remittances in Jamaica, 41.8% of Jamaican received

monies this way, which means that a 7.7% decline of those people whom received remittance

affect some 206,522 Jamaicans which include the most vulnerable such as the poor, children,

unemployable elderly and youths. When inflation is coupled with reduction in remittances, given

that remittance substantially contribute to the economic income for the poor and the poorest

quintile more than the other upper quintiles, this mean that health and health seeking behaviour

in the poor-to-the-poorest people will take a back seat to consumption expenditure on food and

non-alcoholic beverages [3].

                                                 33
       Comparatively there has been a marginal increase in private health care facilities

utilisation by 6.5% of those in the wealthiest quintile, a substantial increase (by 31%) for those in

the wealth quintile (quintile 4), and a mild decline by 0.47% for those in quintile 3 (middle

quintile). Nevertheless, there is a 3.9% increase in public health care facilities utilisation for

those in the wealthiest quintile, while the middle to wealth quintiles showed increases. Therefore,

emerging from these findings is a particular social profile of people who attend public health

care facilities in Jamaica as in excess of 62% of those in middle-to-wealthiest quintiles attended

private health care facilities compared to 66% and more of those in the poor-to-poorest quintile

(Table 2.3).

       In 2007, 50.7% of those in the poorest quintile indicated that they were unable to afford

to seek health care for ill/injury compared to 36.7% of quintile 2, 34.4% in quintile 3, 21.4% in

quintile and 7.1% of those in the wealthiest quintile. Adults sometimes may not attend medical

facilities for care, but they will take their children because they are protective of them. This is

revealing about affordability as in 2007, 51.7% of those in the poorest quintile indicated that they

sought medical care for their children (0-17 years), 52.7% in quintile 2, 61.2% in quintile 3,

61.8% in quintile 4 and 67.6% in the wealthiest quintile. Is in-affordability an issue in medical

care utilisation for those in the poorest to poor quintiles?

       The mean annual amount spent on ‘food and beverage’ in 2002 by those in the poorest

quintile was 50.4 per cent compared to 38.1 per cent of those in the wealthiest quintile. The mean

annual amount expended on the same in 2006 rose by 3.6 per cent for those in the former

quintiles compared to reduction of 0.1 per cent for those in the latter group [3]. Medical

expenditure which is a constituent of non-consumption expenditure was 2.2% for those in the

poorest quintile (in 2006) compared to 13.5% of wealthiest quintile. The economic well-being of

                                                  34
the poor and the poorest in the population has become even more graved as this is reflected in the

inflation rate as it increased by 3 times for 2007 over 2006 [4]. While the down turn the United

States economy in particular the Jamaica economy has more than one-half since 2006 (growth in

GDP at Constant (1996) prices in 2006 2.5 per cent and 1.2 per cent in 2007), those in the

poorest quintiles are hard hit by this economic recession, explaining the rationale for the

switching to home care or more public care.

       All the aforementioned arguments omit area of residence, suggesting that this is the same

across geographical boundaries. Poverty has been decline since 1991 from 44.6%, when inflation

rate was at the highest in the history of the nation (80.2%), to 9.9% in 2007. However, rural

poverty which was 71.3% in 2007 saw an 8.5% increase over 2006 (65.7%) within the economic

environment of a drastic increase in inflation, cost of living and prices of non-consumption items

such as medical care. When we take into consideration the reduction of remittance by 8.7% in

2007 over 2006 (42.3%) and fact that 67% of the elderly (people age 60+ years) dwell in rural

zones, remittance represents not only an income but economic living. Is this accounting for any

of the narrowing of the gap between public-private hospital health care facility utilisation? And

what are the factors which explain public hospital care facilities utilisation in Jamaica? This is

the first study in the English speaking Caribbean and in particular Jamaica to seek to examine

conditions that explain public hospital health care facility utilisation. Hence, the aim of the

current study is to examine factors that account for choice of public hospital care facilities

utilisation and to ascertain whether there is a difference between public hospital care utilisation

and income quintile and area of residence.




                                                35
MATERIALS AND M ETHODOLOGY

Data source

The current study extracted a sub-sample of 1,936 respondents from a national survey. The sub-

sample constitutes those respondents who indicated having visited public and private hospital

health care facilities for medical treatment owing to ill-health. The sample is taken from a larger

cross-sectional survey, which was conducted between June and October 2002. It was a nationally

representative stratified probability survey of 25,018 respondents. The sample (N=25,018 or

6,976 households out of a planned 9,656 households) was drawn, using a 2-stage stratified

random sampling technique, involving a Primary Sampling Unit (PSU) and a selection of

dwelling from the primary units. The PSU is an Enumeration District (ED), which constitutes a

minimum of 100 dwellings in rural areas and 150 in urban zones. An ED is an independent

geographic unit that shares a common boundary. This means that the country was grouped into

strata of equal size based on dwellings (EDs). Based on the PSU, a listing of all the dwellings

was made and this became the sampling frame from which a Master Sample of dwellings were

compiled and which provides the frame for the labour force. The survey adopted was the same

design as that of the labour force.


       The national survey was a joint collaboration between the Planning Institute of Jamaica

and the Statistical Institute of Jamaica. The data were collected by a comprehensive self-

administered questionnaire, which was primarily completed by heads of households on all

household members in Jamaica. The questionnaire was adopted from the World Bank’s Living

Standards Measurement Study (LSMS) household surveys and was modified by the Statistical

Institute of Jamaica with a narrower focus and reflects policy impacts. The instrument assessed:

(i) general health of all household members; (ii) social welfare; (iii) housing quality; (iv)
                                                36
household expenditure and consumption; (v) poverty and coping strategies, (vi) crime and

victimization, (vii) education, (viii) physical environment, (ix) anthropometrics measurement and

Immunization data for all children 0-59 months old, (x) stock of durable goods, and (xi)

demographic characteristics.


       Data were stored and retrieved in SPSS 15.0 for Windows.               The current study is

explanatory in nature. Descriptive statistics were forwarded to provide background information

on the sampled population.       Following the provision of the aforementioned demographic

characteristics of the sub-sample, chi-square analyses were used to test statistical association

between some variables; t-test statistics and analysis of variance (ie ANOVA) were also use to

examine the association between a metric dependent variable and either a dichotomous variable

or non-dichotomous variable respectively. Logistic regression was used to examine the statistical

association between a single dichotomous dependent variable and a number of metric or other

variables (Empirical Model). In order to test the association between a single dichotomous

dependent variable and a number of explanatory factors simultaneously, the best technique to use

was logistic regression.


Empirical Model


Given a plethora of factors that simultaneously affect health care visits, the use of bivariate

analyses will not capture this reality. Therefore, in order to capture those factors that influence

visits to public hospital health care facility, we used a logistic regression instead. The regression

model examines several factors that might affect visits to public health care facilities.




                                                 37
       The data source was from the Jamaica Survey of Living Conditions of 2002 on health,

consumption, social programme, physical environment, education, public-private hospitalisation

utilisation, and crime and victimization. The rationales for the use of 2002 data were (1) it was

the second largest national representative survey that was conducted in the history of data

collection by the Statistical Institute of Jamaica and the Planning Institute of Jamaica to assess

policy impacts (25,018 respondents), and (2) it was inclusive of issues on crime and

victimization, and physical environment that were not in the post-2002 survey, nor the preceding

years. Although there are more recent data (2004 to 2007), these have excluded many of the

factors that are present in the 2002 data (that is physical milieu, crime, victimization and mental

health), and wanting to establish factors that influence health care, we needed more possible

factors that less as well as crime and victimization as these are crucible issues that have been

facing the country increasingly since 2002.


       Ergo, the 2002 consist of more possible factors that determine people’s decision to visit

public hospital health care facilities utilisation compared to private hospital health care facilities

utilisation. Explanatory factors include psychological factors conditions self-reported health

insurance coverage; area of residence; educational level; and other variables.             The basic

specification for the model was:


       VPHCF i = ƒ (αjiDEM i, βjiPSYi , ƏP mci , πSS i , γjiHSBi, εi)                             (1)



       Where VPHCF i is visits to public or private hospital health care facilities of person i is a

function of demographic vector factors, DEM i; psychological factors of person i, PSYi, medical

expenditure, P mc; social support of individual i, SS i; health seeking behaviour of person i, HSBi;


                                                  38
εi is the residual term. Αji, βji, γji, are coefficient vectors for person i of variables j and Əi, π,

are coefficient of vector for person i. VPHCF i is a binary variable, where 1= self-reported visits

for public hospital health care facilities for medical care and 0=self-reported visits to private

hospital health care facilities. [I am not so clear on this sentence].


Measure


Public Hospital Health Care Utilisation variable measures the total number of self-reported cases

of visit to either public hospital health care facilities or private hospital health care facilities in

the last 4-weeks (whereby the survey period is used as the reference point). Public Hospital

Health utilisation was dummied to read 1=visits to public hospital health care facilities, and

0=private hospitals health care facilities.


Income Quintile Categorization. This variable measures the per capita population income

quintile that each individual is categories. There are 5 categories, from the poorest to the

wealthiest income quintile. For the purpose of the regression analysis, the variable was

measured as:

        1= Middle Quintile,     0=otherwise

        1=Two Wealthiest Quintiles, 0=otherwise

        The referent group is the two poorest income quintiles

Crowding. This is the total number of persons living in a room with a particular household.
                    , where represents each person in the household and r is is the number of
rooms excluding kitchen, bathroom and verandah.

Age: This is a continuous variable in years, ranging from 15 to 99 years.




                                                  39
Positive Affective Psychological Condition: Number of responses with regards to being

optimistic about the future and life generally.


Negative Affective Psychological Condition: Number of responses from a person on having loss

a breadwinner and/or family member, loss of property being made redundant, failure to meet

household and other obligations.


Private Health Insurance Coverage (or Health Insurance Coverage) proxy Health Seeking

Behaviour is a dummy variable which speaks to 1 if self-reported ownership of private health

insurance coverage and 0 if did not report ownership of private health insurance coverage.


Health Seeking Behaviour. Visits to health care practitioners. This is a binary variable where 1 =

self-reported seeking medical care and 0 = not reporting seeking medical care


R ESULTS

The sub-sample for the current study was 1,936 respondents of which 39.4% were males

(N=762) and 60.6% females (N=1,174), suggesting that females are 1.5 times more likely to seek

medical care from public or private hospitals compared to males. The findings (indicated in

Table 2.4) revealed that marginally more Jamaicans who visited hospital facilities for medical

care went to public facilities (53%, N=1,021). In addition to the aforementioned issues, 56%

(N=1,086) of the sample reported health care insurance coverage compared to 44% (N=850) who

did not. The mean age of the sample was 44 years (SD=27.5 years).               Some 45% of the

population were never married (N=671), 36% married (N=532), and 20% were divorced,

separated or widowed. Furthermore, Table 2.4 reveals that two-thirds of the population dwelt in

rural Jamaica, 22% (N=424) in Other Towns and 12% Kingston Metropolitan area (N=223).


                                                  40
        On the matter of the psychological state of Jamaicans, this was classified into two main

conditions - positive and negative psychological conditions. The mean negative psychological

conditions of population was 4.9 (out of 16, SD=3.3), suggesting that the negative psychological

conditions of the population was low. On the other hand, the mean value for the positive

affective psychological condition of the population was 3.2 (out of 6, SD = 2.4) indicating that

positive affective conditions of the population was moderate (Table 2.4).


        The examination between public-private hospital health care facility utilisation and area

of residence found no statistical correlation between the two aforementioned variables – χ 2(2)

=0.385, ρ-value=0.825 > 0.05 – (Table 2.5). The no correlation between the two conditions

indicates that Jamaicans, irrespective of their places of abode attended public-private hospital

health care facilities for care of ill-health. (Table 2.5)



        A cross tabulation between visits to health care facilities and per capita population

income quintile showed a statistical association - χ 2(4)=157.024, p<.001.          The    findings

revealed that people in the poorest income quintile was 2.4 times more likely to visit public

health care facilities compared to those in the wealthiest per capita income quintile; people in the

poorest income quintile was 1.5 times more likely to visit public facilities compared to those in

the second wealthiest quintile. However, the findings revealed that those in the second poorest

income quintile indicate no statistical difference themselves and those in the middle income

quintile - quintile 3 (Table 2.6). Nevertheless, people in the poorest income quintile were 1.3

times more likely to visit public facilities compared to those in the middle income quintile. There

is a substantial difference between those who visit public health institutions, who are in the

poorest income quintiles (73.8%, N=251) and those in the second poorest income quintile
                                                   41
(58.4%, N=208). Embedded in the aforementioned finding is the increase in switching from

public to private hospital health care facilities the more income quintile shifts to the wealthiest

category (Table 2.6). The aforementioned findings raise concern about the extent of public-

private hospital health care expenditure



       Of the sample (N=1,707), 912 people visited private hospital health care facilities and

reported that they spent on average $2,977.41 (SD=$4,053.01) compared to $1,376.12

(SD=$2,547.93, N=1,019) for a visit to a public hospital care facility, suggesting that those who

attend private hospital health care institutions spent about 2.2 times more than those who visit the

public hospital health care facilities.    Using t-test analysis, there is a difference between

expenditure on public hospital health care and private hospital health care – t 10.5 [1929] = ρvalue

< 0.001.



       Using analysis of variance (ANOVA), generally, it was found that a statistical association

exists between negative psychological conditions and per capita income quintile (F statistic [4,

1926] =28.793, ρ-value< 0.001). (Tables 2.7.1 – 2.7.2). Further investigation of the negative

affective conditions by per capita quintile revealed that there is no difference between the

negative affective psychological conditions of those in three bottom quintiles (quintiles 1 to 3),

p> 0.05 (Table 2.7.2). In addition to the aforementioned issue, there is no difference between the

negative psychological state of people in quintiles 3 and 4 (ρ-value>0.05) and quintiles 1, 2 and

3, indicating that negative affective conditions can be classified into 3 groups (1) high for those

in quintiles 1, 2 and 3; (2) moderate for quintile 4 and (3) low for those in quintile 5. However

those classified in quintile 5 has the lowest negative affective conditions compared to those in

                                                42
the other quintiles (ρ-value<0.001). Embedded in this finding is that as people move to the

wealthiest quintile, they experience less negative trauma such as the loss of breadwinner, owing

to abandonment, death or incarceration, crop failure, redundancy, loss of remittances, inability to

meet household expenses, and less hopeless about the future.



       There is statistical association between positive affective psychological conditions and

per capita income quintile - F statistic [4, 1492] =12.366, ρ-value< 0.001. (Table 2.8.1). Further

examination of the two aforementioned variables revealed that there is no statistical difference

between the positive affective psychological conditions for those in quintiles 1 and 2; and

between quintile 2 and quintiles 3 and 4. Hence the statistical difference in positive affective

conditions is between those who are classified into two poorest quintiles and those in the wealthy

quintiles (Table 2.8.2).

       Overall, there are statistical differences among health care expenditure of rural, urban and

periurban residences in Jamaica – F-statistic [2, 1928] = 4.902, ρvalue < 0.001. Rural area

dwellers spent on an average $2,009.98 (SD=$2,999.88, N=1286) per visit on medical care

compared to peri-urban residents who spent $2,593.13 (SD=$4,587.67, N=423) and $1,963.68

was spent by urban dwellers (SD=$3,188.31, N=222). Further examination revealed that there is

a difference between the medical expenditure made by rural residence and those in other towns –

p value <0.05.     The former on an average spent $583.17 less than those in other towns.

However, there are no statistical differences between medical expenditure of urban residents and

that of rural dwellers (ρvalue >0.05) and other towns (ρvalue >0.05).




                                                43
Empirical Results

The regression analytic model was established in order to simultaneously examine a number of

explanatory variables’ impact on those who attend public hospital health care facilities for ill-

health. Table 2.6 and Table 2.7 provide information on empirical model (Eq (1)) and in the

process answers the suitability of the model (Table 2.6), while Table 2.7 answers to the question

of which of the variables are factors and their importance. Before embarking on the report of the

regression model which contains all the predisposed variables and which those that are statistical

significant (ie pvalue<0.05), we will examine the ‘goodness’ of fit of the data in regard to the

model.


         Table 2.6 reports a ‘classification of visits to hospital health facilities owing to ill-health’

and contained examination of observed compared to predicted classification of the dependent

variable (that is visits to hospital health care facilities in due to negative health). Of the 1,051

respondents that were used to establish the model (using the principle of parsimony, that is only

those variables that have a pvalue < 0.05 will be used in the final model), 73% (N=767) were

correctly classified: 71.6% (N=374) of those who visit private hospital health care facilities for

care owing to illnesses or injuries and 74.3% (N=393) of those who visited public hospital health

care institutions for treatment of dysfunctions or injuries. Therefore, the data is a ‘good’ fit for

the model (ie. 73% were correctly classified).

         Table 2.10 contained the answers the empirical model (Eq. (1))

         VPHCF i = ƒ (αjiDEM i, βjiPSYi , ƏP mc , πSS i , γjiHSB i, εi)                              (1)

which shows that 35.6% of the variability in visits to health facilities for care are affected by a

number of factors- Chi-square (24) = 326.58, p-value < 0.001, -2Log likelihood = 1130.37. Of all

the demographic variables contained in the current study, only total expenditure was found to be
                                                    44
a factor of visits to public hospital health care facilities for ill-health (Wald statistic=4.458;

OR=1.00: 1.00, 1.00). The cost of medical care was directly related to reason for patients’ visits

to public hospital health care facilities for treatment against ill-health (Wald statistic=13.959;

OR=1.00: 1.00, 1.00) likewise was the positive statistical relationship between social support and

visits to health care facilities (Wald statistic=13.419; OR=1.741: 1.29, 2.34).           A direct

association was observed between negative affective psychological conditions and visits to

public hospital health care facilities. This suggested that more the patients/individuals are

impacted upon by the loss of a breadwinner, crop failure, redundancy, loss of remittances.

        On the other hand, people who have access to private health insurance coverage (Wald

statistic=89.35; OR=0.134: 0.089, 0.204), visited a health practitioners for non-ill checks (Wald

statistic=72.07; OR=0.494: 0.419, 0.581), and a positive affective psychological conditions

(Wald statistic=4.74; OR=0.931: 0.874, 0.993) are more likely not to attend public hospital

health care facilities. These issues are all preventative and optimistic measures which are directly

related with switching away from public to private hospital health care facilities. Embedded in

these findings (based on Table 5.2) is the fact that optimistic in the study are those in the middle

to the upper class. This study has shown that there is no distinction between the positive affective

psychological conditions of those patients who are classified in the middle to the wealthiest

class, but there is a difference between the aforementioned group and those in the poor classes

(ie. quintiles 1 to 2 – poorest to poor classes).

        Therefore, in addressing the issue of using self-reported health (subjective health or

wellbeing) to evaluate health (or wellbeing), it is imperative to note that there is an old

cosmology that forwards that subjective assessment of health (self-reported health) is not a good

measurement to apply to health or wellbeing. In this section of the study that discourse will not

                                                    45
be examined as it will be done in the discussion; however, we must briefly compare and contrast

self-reported visits to public facilities data collected by the Planning Institute of Jamaica and the

Statistical Institute of Jamaica (in Jamaica Survey of Living Conditions, JSLC) and actual data

collected by the Ministry of Health Jamaica for the period of 1996 and 2004.

       Using actual visits to public facilities (in Ministry of Health, Jamaica Annual Report) and

that of self-reported visits to the same institutions, the data revealed that generally the statistics

as collected by the Planning Institute of Jamaica and the Statistical Institute of Jamaica (in

Jamaica Survey of Living Conditions, JSLC) reveals health status and conditions of Jamaicans.

Based on Table 2.9, in 1997, the actual visits to public facilities were 33.1% as reported by the

Ministry of Health and the self-reported figure for the same period was 32.1% (in JSLC). The

difference between the actual and the subjective visits was 1%, which has no statistical

difference. Some eight years post 1997 (2004), another comparison was made to assess whether

the self-reported data is still good to use to proxy not only perception but reality of hospital

health care facility utilisation in Jamaica. The figures were 52.9% for actual visits and 46.8% for

subjective visits. This indicates that in 2004 Jamaica marginally report lower visits to facilities

(6.1%) than the data published by the Ministry of Health. Despite the under reporting of health

visits to public facilities in 2004 in Jamaica, there is no statistical difference between the year

and the figures by the aforementioned institutions – χ 2(4) =157.024, p<0.05


Discussion



In view of life expectancy for both genders in Jamaica (71.3 for males and 77.1 for females) (5),

this study indicates that health status of the populace are high as life expectancy means living or

denying the odds of disease causing pathogens. In order for a populace to defy the odds of
                                                 46
morality or to delay it, the following life expectancy precursors must be considered; namely:

healthy lifestyle behaviour or levels of health seeking behaviour, and hospital health care facility

must meet universal health standard. The foregoing suggests that health seeking behavior and

hospital health care facility utilisation plays a crucial role in embracing such reality. In 2007,

Jamaicans sought less medical care for ill-health by 4% over 2006 (70%) They reported more

health conditions over the same period (15.5% in 2007 and 12.2% in 2006). Although this is

suggesting that they are using more home (or herbal) remedy, It leaves concern about health care

facilities utilisation and factors that may be Influential.


        Data on health care facilities utilisation in Jamaica have been reported on and so this

paper is seminal. Over the last 2 decades (ending 2007), Jamaicans preference for private

hospital health care facility utilisation has been lower, narrowing towards public facility

utilisation. Within the global economic climate which is accounting for the lowered remittances

[3], people must spend more for increased consumption goods while at the same time,

maintaining good health. The World Health Organization (WHO), in recognizing the role of

income on health, postulated that the unfinished agenda for health, poverty remains the main

item [6], thus suggesting that poverty means increased hunger, malnutrition and by extension ill-

health. This study evidences that there is a correlation between public-private hospital health care

facility utilisation and per capita income quintiles which is in keeping with the literature [6-17].

The data showed that 74% of those in the poorest quintile used public facilities compared to

31.3% of those in the wealthiest quintile.         Embedded in the hospital health care facility

utilisations are socio-demographic characteristic (social standing) of demanders. Some 2.8 (≈3)

more people of the poorest quintile attended public facilities than private facilities, and that 2.4

more of the poorest than the wealthiest people attended the former than the latter facilities.
                                                   47
       The typological of hospital health care facility utilisation in the nation is a reflection of

inability (ability) and than inflation (increase prices) wills substantially lower the poorest

demand for medical care. It is well established in the literature that income affects health, and

lower income direct correlates with poor health [7], which was reinforced in a study conducted

by Powell, Bourne and Waller [8] who found that the those in the lower subjective social class

reported the least health status. Those in the poorest income quintile are more concerned and able

to primarily have difficulty purchasing the necessary nutrients from the required foods groups,

and this accounts for their high consumption of public facilities, owing to low cost medical

services. This study found that the cost of medical care strongly correlated with public hospital

health care facility utilisation, and further explains this potency as it was revealed that the more

people spending, the more they will attend public facility. An individual who spends more has

less income to save as well as use for medical expenditure that account for increased utilisation

of private facility with movement along the rung of per capita income quintile.


       With less income coupled with more spent on consumption items, health seeking medical

behaviour becomes less. Within this reality, the negative correlation between health seeking

behaviour and public hospital health care facility utilisations expected as public facility demand

is strongly correlated with income or affordability of health care. Private facility consumption

depends on one’s ability to pay the cost for the care, and it is this which bars the poorest from

highly accessing this facilities. This study has revealed that public hospital health care facility

utilisations substantially demanded by the poorest and those who are experiencing negative

affective conditions and positive affective psychological conditions.




                                                48
       Studies have shown that one psychological state affects his/her health [18-21]. This was

further refined into negative and positive affective conditions [18, 21, 22]. Being positive

directly correlated to health as people who entertain positive affective conditions are more likely

to view like a more optimistic manner and this enhance their health status. In seeking to unearth

‘why some people are happier’ Lyubomirsky [21] approached this study from the perspective of

positive psychology. She noted that, to comprehend disparity in self-reported happiness between

individuals, “one must understand the cognitive and motivational process that serves to maintain,

and even enhance happiness and transient mood’ [21]. Using positive psychology, Lyubomirsky

identified comfortable income, robust health, supportive marriage, and lack of tragedy or trauma

in the lives of people as factors that distinguish happy from unhappy people, which was

discovered in an earlier study by Diener, Suh, Lucas and Smith [23]. In an even earlier study by

Diener, Horwitz and Emmon [24], they were able to add value to the discourse of income and

subjective well-being. They found that the affluent (those earning in excess of US 10-million,

annually) self-reported well-being (personal happiness of the wealthy affluent) was marginally

more than that of the lowly wealthy.


       Studies revealed that positive moods and emotions are associated with well-being [20] as

the individual is able to think, feel and act in ways that foster resource building and involvement

with particular goal materialization [21].     This situation is later internalized, causing the

individual to be self-confident from which follows a series of positive attitudes that guide further

actions [25]. Positive mood is not limited to active responses by individual, but a study showed

that “counting one’s blessings,” “committing acts of kindness”, recognizing and using signature

strengths, “remembering oneself at one’s best”, and “working on personal goals” all positively


                                                49
influence well-being [25, 26]. Happiness is not a mood that does not change with time or

situation; hence, happy people can experience negative moods [27, 28].


        This takes the study to the next area, psychological conditions and per capital income

quintile. Those with negative psychological conditions are from the lower class (poor), and

studies have shown that there is a correlation between health and psychological conditions. Now,

additional issues have emerged from this study as poor are negative and attend public facility

more than those at the greater per capita income quintile. On the other hand, those who are more

likely to report positive affective psychological conditions are greater for those at the highest

level of the income quintile, the findings also show that those who attend private facility are

experience greater positive conditions. It follows that public facilities in Jamaica service and

service quality are more in keeping with particular psychological state and subjective social

class. Hence, private facilities are not only more expensive but the service that it affects is in

keeping with the high social standings of its clients, and the reverse is equally the case for public

facilities staffers and their clients.


CONCLUSION
     Health seeking behaviour (ownership of private health insurance coverage and visited a

health practitioners for non-ill checks) is the most important factor that determines visits to

public health facilities or private health facilities for care for illnesses (or injuries). Following the

value of health seeking behaviour is the cost of medical care; reinforcing the reality for financial

inability among people is it lower class, middle class or upper class will see a switching from

private to public facilities for ill-treatment. In continuing this discourse, social support is directly

related to visits to public hospital health care facilities and so offers some explaining for the large


                                                   50
number of people visiting the said institutions to support the patients who visit for treatment of

negative health conditions. Again the positive association that exists between expenditure and

visits to public facilities further reinforces the point that the more people spent which is the less

income they have for saving and further speaks about the poor, they will be less likely to visit

private hospital health care facilities. The poor who are less hopeful about the future (unlike

those in the middle class) are more optimistic because of financial stability and are ergo able to

access private hospital health care because of expenditure of private health care does intimate

better health care, which they are willing to pay for.


        In sum, the demands for public hospital health care facility utilisation in Jamaica are

primarily based on in affordability and low perceived quality of patient care. The issue of low

quality of patient care speaks to not medical care, but to the customer service care offered to

client. The greater percentage of Jamaicans who access private health care is not owing to

plethora of services, higher specialized doctors, more advanced medical equipment, or low, but

this is due to social environment – customer service and social interaction between staffers and

clients- and physical milieu – more than one person per bed sometimes, uncleansiless of the

facilities. These issues accommodate for the lowly particular persons visiting public and private

facilities for medical care.


Acknowledgement


The researcher would like to extend sincere gratitude to staff of the documentation centre at the

Sir Author Lewis Institute of Social and Economic Studies, Faculty of Social Sciences,

University of the West Indies, Mona, Jamaica for making available the dataset from which this

study was based.
                                                 51
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                                               53
Figure 1: Public-Private Health Care Utilisation in Jamaica (in %), 1996-2002, 2004-2007
Source: Taken from Jamaica Survey of Living Conditions, various issues




                                              54
Figure 2: Remittances By Income Quintiles and Jamaica (in Percent): 2001-2007
Source: Extracted from the Jamaica Survey of Living Conditions, 2007




                                            55
Table 2.1 Discharge, Average Length of Stay, Bed Occupancy and Visits to Public Hospital
Health Care Facilities, 1996-2004
Year          Discharge       Average            Bed Occupancy Visits to Public Facility

                              Length of Stay         Rate

1996          145,656         5.7                   56.1                546,933

1997          153,101         5.8                   57.3                598,004

1998          158,851         5.5                   58.0                634,792

1999          163,714         5.1                   52.2                654746

2000          173,700         4.9                   74.9                643,101

2001          171,963         6.0                   84.6                667,321

2002          173,614         6.9                   80.2                695,239

2003          179,322         6.4                   84.5                746,844

2004          182,053         6.8                   56.0                775,727

2005           NI             NI                     NI                    NI

2006           NI             NI                     NI                    NI

2007           NI             NI                     NI                    NI

Source: Ministry of Health, Jamaica, Planning and Evaluation Branch, various issues

NI No information available




                                               56
Table 2.2 Inflation, Public-Private Health Care Service Utilisation, Incidence of Poverty, Illness and Prevalence of Population with
Health Insurance (in per cent), 1988-2007

Year                  Inflation       Public              Private                      Prevalence         Illness           Health               Seeking
                      Mean
                                      Utilisation         Utilisation        of poverty                         Insurance                 Medical Care Days of
                                                                                                                Coverage                               Illness

1988                8.8               NI                  NI                 NI                  NI                 NI                    NI           NI
1989               17.2               42.0                54.0               30.5                16.8               8.2                   54.6         11.4
1990               29.8               39.4                60.6               28.4                18.3               9.0                   38.6         10.1
1991               80.2               35.6                57.7               44.6                13.7               8.6                   47.7         10.2
1992               40.2               28.5                63.4               33.9                10.6               9.0                   50.9         10.8
1993               30.1               30.9                63.8               24.4                12.0               10.1                  51.8         10.4
1994               26.8               28.8                66.7               22.8                12.9               8.8                   51.4         10.4
1995               25.6               27.2                66.4               27.5                9.8                9.7                   58.9         10.7
1996               15.8               31.8                63.6               26.1                10.7               9.8                   54.9         10.0
1997               9.2                32.1                58.8               19.9                9.7                12.6                  59.6         9.9
1998               7.9                37.9                57.3               15.9                8.8                12.1                  60.8         11.0
1999               6.8                37.9                57.1               16.9                10.1               12.1                  68.4         11.0
2000               6.1                40.8                53.6               18.9                14.2               14.0                  60.7         9.0
2001               8.8                38.7                54.8               16.9                13.4               13.9                  63.5         10.0
2002               7.2                57.8                42.7               19.7                12.6               13.5                  64.1         10.0
2003               13.8               NI                  NI                 NI                  NI                 NI                    NI           NI
2004               13.7               46.3                46.4               16.9                11.4               19.2                  65.1         10.0
2005               12.6               NI                  NI                 NI                  NI                 NI                    NI           NI
2006               5.7                41.3                52.8               14.3                12.2               18.4                  70.0          9.8
2007               16.8               40.5                51.9               9.9                 15.5               21.2                  66.0         9.9
Source: Bank of Jamaica, Statistical Digest, Jamaica Survey of Living Conditions, Economic and Social Survey of Jamaica, various issues
Note: Inflation is measured point-to-point at the end of each year (December to December), based on Consumer Price Index (CPI)
NI – No Information Available



                                                                                     57
Table 2.4 Demographic Characteristic of Sampled Population, n=1,936


                                              N                 Percent

Sex
        Male                                  762               39.4
        Female                                1174              60.6
Income Quintile Categorization
        Two Poorest Quintiles                 696               36.0
        Middle Quintile                       376               19.4
        Two Wealthiest Quintiles              864               44.6
Marital Status
        Married                               532               35.5
        Never married                         671               44.8
        Divorced                              20                1.3
        Separated                             25                1.7
        Widowed                               250               16.7
Visitors to hospital health care facilities
        Private hospital                      915               47.3
        Public hospital                       1021              52.7
Private Health Insurance Coverage
         No                                   1086              56.1
         Yes                                  850               43.9
 Area of residence
        Rural areas                           1289              66.6
        Other Towns                           424               21.9
        Kingston Metropolitan area            223               11.5
Educational Level
        Primary and below                     563               39.4
        Secondary or post-secondary           813               56.9
        Tertiary                              53                3.7

Age (Mean ± SD)                                          44.0 ± 27.5
Crowding (Mean ± SD)                                     1.7 ± 1.3
Negative Affective Psychological condition (Mean ± SD)   4.9 ± 3.3
Positive affective Psychological condition (Mean ± SD)   3.2 ± 2.4



                                                  58
Table 2.5 Public Hospital Health Care Facility Utilisation by Area of Residence (in percentage),
n =1,936

                                             Area of Residence

 Variable
                               Rural areas    Other towns        Urban areas     Total



Hospital        Private               46.9             48.6             47.1             47.3
Utilisation


                 Public               53.1             51.4             52.9             52.7



Total
                                      1289             424               223          1936

χ 2(2) =0.385, ρ-value=0.825 > 0.05




                                                59
Table 2.6 Public Hospital Health Care Facility Utilisation By Per Capita Population Income
Quintile (in per cent), N=1,936

                                            Per Capita Population Quintile


 Variable                         Poorest                               Wealthiest
                                  20%         2.00   3.00      4.00     20%          Total



Hospital        Private           26.2        41.6   41.2      51.7     68.8         47.3
Utilisation



                Public            73.8        58.4   58.8      48.3     31.3         52.7



Total
                                  340         356    376       416      448          1936

χ 2(4) =157.024, p < 0.001




                                              60
Table 2.7.1 Descriptive Statistics of Negative Affective Psychological Conditions and Per capita
Income Quintile
                                                                                               95% Confidence Interval
 Income Quintile                                                Std.            Std.            Lower
                                   N            Mean          Deviation         Error           Bound    Upper Bound
 1.00=Poorest 20%                   338             5.8              2.9          0.16              5.5             6.1
 2.00                               355             5.7              3.2          0.17              5.3             6.0
 3.00                               375             5.2              3.3          0.17              4.8             5.5
 4.00                               415             4.7              3.1          0.15              4.4             5.0
 5.00=Wealthiest 20%                448             3.7              3.4          0.16              3.4             4.0
 Total                             1931             4.9              3.3          0.07              4.8             5.1
F statistic [4, 1926] =28.793, ρ-value< 0.001




Table 2.7.2 Multiple Comparison of Negative Affective Psychological Condition by Per Capita
Income Quintile
 Per Capita Population   Per Capita
 Quintile                Population Quintile                                      95% Confidence Interval

                                                Std. Error       Sig.          Upper Bound       Lower Bound
 1.00=Poorest 20%                        2.00          0.24             0.98           -0.53              0.79
                                         3.00         0.24              0.07           -0.03                1.27
                                         4.00         0.23              0.00            0.45                1.73
                                         5.00         0.23              0.00            1.47                2.72
 2.00                                    1.00         0.24              0.98           -0.79                0.53
                                         3.00         0.24              0.23           -0.16                1.13
                                         4.00         0.23              0.00            0.33                1.58
                                         5.00         0.23              0.00            1.35                2.58
 3.00                                    1.00         0.24              0.07           -1.27                0.03
                                         2.00         0.24              0.23           -1.13                0.16
                                         4.00         0.23              0.24           -0.15                1.09
                                         5.00         0.22              0.00            0.87              2.08
 4.00                                    1.00         0.23              0.00           -1.73             -0.45
                                         2.00         0.23              0.00           -1.58             -0.33
                                         3.00         0.23              0.24           -1.09                0.15
                                         5.00         0.22              0.00            0.41                1.60
 5.00=Wealthiest 20%                     1.00         0.23              0.00           -2.72             -1.47
                                         2.00         0.23              0.00           -2.58             -1.35
                                         3.00         0.22              0.00           -2.08             -0.87
                                         4.00         0.22              0.00           -1.60             -0.41




                                                          61
Table 2.8.1 Descriptive Statistics of Total Positive Affective Psychological Conditions and Per
Capita Income Quintile
 Per Capita Income Quintile                                         Std.                        95% Confidence Interval
                                         N           Mean         Deviation      Std. Error      Lower
                                                                                                 Bound    Upper Bound
 1.00=Poorest                                243          2.42          2.66           0.17         2.08           2.75
 2.00                                        273          2.81          2.51           0.15         2.51           3.10
 3.00                                        278          3.22          2.30           0.14         2.95           3.49
 4.00                                        313          3.28          2.40           0.14         3.02           3.55
 5.00=Wealthiest                             386          3.69          2.22           0.11         3.47           3.92
 Total                                    1493            3.15          2.44           0.06         3.03           3.27
F statistic [4, 1492] =12.366, p< 0.001


Table 2.8.2 Multiple Comparisons of Positive Affective Conditions by Per Capita Income
Quintile
 Per Capita Population   Per Capita Population
 Quintile                Quintile                                                 95% Confidence Interval

                                                   Std. Error      Sig.        Upper Bound       Lower Bound
 1.00=Poorest 20%                         2.00             0.21       0.35             -0.97              0.19
                                          3.00            0.21        0.00              -1.38              -0.23
                                          4.00            0.21        0.00              -1.43              -0.31
                                          5.00            0.20        0.00              -1.82              -0.74
 2.00                                     1.00            0.21        0.35              -0.19               0.97
                                          3.00            0.20        0.25              -0.98               0.14
                                          4.00            0.20        0.11              -1.02               0.06
                                          5.00            0.19        0.00              -1.41              -0.37
 3.00                                     1.00            0.21        0.00               0.23               1.38
                                          2.00            0.20        0.25              -0.14               0.98
                                          4.00            0.20        1.00              -0.60               0.48
                                          5.00            0.19        0.09              -0.99               0.04
 4.00                                     1.00            0.21        0.00               0.31               1.43
                                          2.00            0.20        0.11              -0.06               1.02
                                          3.00            0.20        1.00              -0.48               0.60
                                          5.00            0.18        0.16              -0.91               0.09
 5.00=Wealthiest 20%                      1.00            0.20        0.00               0.74               1.82
                                          2.00            0.19        0.00               0.37               1.41
                                          3.00            0.19        0.09              -0.04               0.99
                                          4.00            0.18        0.16              -0.09               0.91




                                                            62
Table 2.10 Logistic Regression: Predictors of Public Hospital Health Care facility utilisation in
Jamaica

                                  Std.        Wald         OR          95% CI
  Explanatory variables           Error      Statistic
 Retirement Income                   0.4             2.4    0.54     0.249 - 1.181
 Household Head                      0.7             0.3    0.69     0.166 - 2.886
 Cost Health Care                    0.0       14.0***      1.00     1.000 - 1.000
 Health Insurance                    0.2       89.4***      0.13     0.089 - 0.204
 Other Towns                         0.2             0.9    1.20     0.818 - 1.765
 Urban areas                         0.4           0.01     1.03     0.514 - 2.079
 †Rural area                                                1.00

  Social support                      0.2       13.4***       1.74 1.294 - 2.343
  Crowding                            0.1             1.2     1.13 0.910 - 1.394
  Crime Index                       0.01              2.7     1.02 0.996 - 1.048
  Landownership                       0.2             1.7     0.80 0.568 - 1.120
  Environment                         0.2             1.9     0.75     0.502 -1.132
  Gender                              0.2             0.0     1.01 0.728 - 1.402
  Negative Affective                  0.1          7.1**      1.07 1.019 - 1.129
  Positive Affective                0.03              4.7     0.93 0.874 - 0.993
  Number of males in house            0.1             0.9     1.09 0.913 - 1.293
  Number of females in house          0.1             1.8     1.14 0.944 - 1.369
  Number of children in house         0.1           0.02      1.01 0.868 - 1.178
  Assets owned                      0.04              1.5     0.96 0.894 - 1.026
  Age                                 0.0             0.7     0.99 0.988 - 1.005
  Total Expenditure                   0.0           4.5*      1.00 1.000 - 1.000
  Health Seeking Behaviour            0.1       72.1***       0.49     0.419 -0.581
Model Chi-square (df = 21) = 326.58, p-value < 0.001
-2Log likelihood = 1130.37
Nagelkerke R-square = 0.356
Overall correct classification = 73.0%
Correct classification of cases of public utilisation =74.3%
Correct classification of cases of not public utilisation (private) = 71.6%
Hosmer and Lemeshow Test of goodness of fit, χ2(df = 8) = 5.395, 0.715
†Reference group
*p < 0.05, **p < 0.01, ***p < 0.001




                                                 63
Table 2.11 Public Hospital Facility Visits (using the JSLC and Ministry of Health Jamaica) By
1997 and 2004

                                          Public Facilities in Jamaica

Year                        Actual Visits, MOH1             Self-reported Visits, JSLC
                            %                               %
1997                        33.1                            32.1




2004                        52.9*                           46.8

Source: Ministry of Health Jamaica and the Jamaica Survey of Living Conditions (JSLC)
χ 2(df = 4) = 0.083, p > 0.05
1
 The Percentages of Actual visits were computed by author
*Preliminary data were used to calculate this percentage




                                             64
Table 2.3 Hospital Health Care Utilisation (Using Jamaica Survey of Living Conditions Data)
By Income Quintile (%): 1991-2007
             1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

         2002     2004     2006     2007

Public
Quintile
1=Poorest         57.8     48.8     57.5     54.1     49.4     54.8     44.5     59.1     61.0     55.7     67.6
      73.4        70.9     71.0     75.0
2                 43.3     41.8     36.9     34.9     25.3     42.7     39.9     49.0     46.3     44.3     53.5
         57.5     53.6     51.1     66.5
3                 29.0     28.8     29.3     17.0     22.7     32.8     37.3     40.7     37.5     41.3     32.1
         58.6     57.3     50.6     22.1
4                 35.8     27.1     20.6     25.6     21.7     29.5     26.3     35.1     37.7     44.6     35.3
         46.5     36.7     27.5     27.0
5=Wealthiest 20.6          12.3     16.5     15.7     16.8     11.9     12.4     17.2     15.4     12.8     24.4
      30.9 27.6            21.7     21.4


Private
Quintile
1=Poorest         34.4     46.3     32.3     41.2     47.1     40.4     49.1     35.5     34.7     38.7     29.3
      22.8        26.8     24.3     22.0
2                 52.9     48.4     58.7     57.0     66.3     54.1     51.1     45.0     50.3     53.8     38.7
         37.5     35.7     42.3     33.3
3                 64.5     65.9     62.2     77.0     69.7     62.5     51.8     56.6     59.8     48.8     62.9
         37.4     35.7     42.9     64.2
4                 53.1     65.4     74.2     72.2     68.0     63.8     62.5     58.3     57.1     48.8     59.1
         46.3     55.6     65.4     69.6
5=Wealthiest 73.8          78.1     82.5     81.5     80.0     84.6     80.0     78.4     75.4     78.4     66.5
      52.5 65.1            73.9     78.6
Source: Jamaica Survey of Living Conditions, various issues (a joint publication of the Planning Institute of
Jamaica and the Statistical Institute of Jamaica)




                                                         65
                                                                        Chapter
                                                                                           3
                   Health Inequality in Jamaica, 1988-2007




The mortality for men is not only greater than that of women as indicated by the life expectancy
but of the five leading cause of death in the nation (malignant neoplasms; cerebrovascular
disease; heart disease; diabetes mellitus and homicides), the rates for men were greater in four
(malignant neoplasms; cerebrovascular; heart disease and homicides). Despite these realities,
men seek less medical care than the women and stay longer in hospitals for curative care. This
study examines medical seeking behaviour, self-reported ill-health, and gender differential in
medical seeking health care and self-reported ill-health. Over a 2-decade period, 1988 to 2007,
only a small percentage of Jamaicans reported ill-health (between 9 to 19 %) and 15.5% in
2007, which is an increase of 3.3% over the previous year. Despite this low figure, increasingly
more men sought medical care over the study period (41.1%) compared to women (29%).
Nevertheless, health care seeking behaviour is still gender bias – 68.1% of women and 62.8% of
men who reported health conditions. For men, more of medical care seeking behaviour is
explained by ill-health (r-squared=35.4%) than women (r-squared 8.8%). This study is one of
the first to examine and provide some explanation on gender differentials in health care
behaviour and self-reported illness/injury in Jamaica. We found that while more men who report
ill-health have been seeking medical care, the gap between the sexes in regard health seeking
behaviour has been narrowing.




Introduction

Globally, in 1950-1955, life expectancy for women was 47.9 years compared to 45.2 years for

men. One-half of a century later, the disparity has increased to 4.2 years (68.1 years for women
                                              66
and 63.9 years for men). In the Caribbean, in the same aforementioned period, life expectancy

for women was 53.5 years and 50.8 years for men and 50 years later the disparity has increased

to 5.5 years (70.9 years for women and 65.4 years for men). Life expectancy which is an

indicator of mortality and to some extent morbidity is also proxy for health status of people.

Although there is some morbidity that is not life threatening, it is established that healthy life is

not equivalent to longer life. Hence, the World Health Organization developed DALE (disability

adjusted life expectancy) to discount life expectancy by lost time due to illness. This showed that

developing countries lost 9 years of life expectancy owing to unhealthy years.


           There has always been a health differential between the sexes in Jamaica[1] Dating

back as far as 1880, which was the first time that life expectancy data was recorded for men and

women in the island, women were outliving men. The Demographic Statistics for Jamaica

showed that for 1880 and 1882 women lived approximately 3 years more than men and 122

years later (2002-2004), they outlived them by 6 years, which is an additional 3 years. Globally,

women live longer than men by 8 years which is 2 years more than that of the life expectancy

gender differential in Jamaica. They are not only living longer, but enjoying greater quality of

life [2] A study of 3,009 older people done in 2007 in Jamaica [3] revealed that elderly women had

a higher quality of life (3.3 ± 2.2) than men (2.8 ± 1.8; p value = 0.001), which concurred with

the earlier work done by the WHO in 1998. But, studies that have examined well-being have

shown that men experienced a greater economic wellbeing than women[4], despite not having a

higher subjective wellbeing. What is explaining this health differential between the sexes?


       Life expectancy which is calculated using mortality data indicate that men are

experiencing particular pathogen causing diseases which are accounting for the greater increase

                                                 67
in mortality and lower life expectancy than women. An epidemiological profile of selected health

conditions and services in Jamaica for 1990-2002 was conducted by the Health Promotion and

Protection Division, Ministry of Health in 2005 which revealed that malignant neoplasm was the

leading cause of death in Jamaica. It was 39% greater for men than women. The second leading

cause of death, cerebrovascular disease, was 14% higher for men than women; heart diseases rate

was 71.2 per 100,000 for men and 66.1 per 100,000 for women, and diabetes mellitus was

greater for women than men. The statistics revealed that mortality caused by diabetes mellitus

was 64% higher for women than men.


       Jamaica is not unique in regard to i) women outliving men, ii) particular morality is

greater for men than women, and ii) some of the leading causes and death are gender specific[2]

The issue of higher mortality differential between the sexes at older ages begins with boys

suffering more illnesses and injuries than girls [5] The World Health Organization (WHO) offered

a potent finding that age-and sex differential in mortality dates back to 1955[2] This indicates that

higher mortality in the world’s population tend to favour men, and justifies the longer life that

they live compared to men.


       In demography, life expectancy is used to measure health. But this approach fails to

capture health as one can be alive but enjoy optimum health – living with varying levels of

morbidity. There is an argument that morbidity is accounted for in mortality, and this so.

However, some dysfunctions are not death causing, and so quality of life (health) will be lower

with these health conditions. It is owing to this reality that the World Health Organization

(WHO) introduced what is known as healthy life expectancy which discounts life expectancy by

morbidity.

                                                 68
       Healthy Life Expectancy

       One of the drawbacks to the use of life expectancy is its absence to capture ‘hale’ years

of life. Traditionally when life expectancy is measured, it uses mortality data to predetermine

the number of years of life yet to be lived by an individual, assuming that he/she subscribes to

the same mortality patterns of the group. The emphasis of this approach is on length of life and

not on the quality of those years lived. Hence changes in life expectancy are primary due to

mortality movements, and imply changes in external conditions of the socio-biological

environment. These changes include the components of public health, the physical milieu, and

technological/medical advancement. With all the aforementioned conditions that have improved

over the last century, increased life expectancy in the world is not surprising to scholars. One

way of evaluating population ageing in the world or in any geopolitical space is ‘life

expectancy’. Today, it should come as no surprise to people that many developing nations have

been experiencing increased gains in additional years of life for members with its population in

comparison to 20th century.


       Associated with ageing are high probability of increased dysfunctions and the

unavoidable degeneration of the body. This explains why it is germane to analyze healthy life

expectancy and not merely life expectancy. Healthy life expectancy is defined as the number

of years that an individual is expected to live in ‘good’ health. Technological advancement is

able to prolong life, but it is not able to remove morbidity and its deterioration in quality of lived

years of the individual. Thus, while life expectancy in the Caribbean is increasing and that this is

in keeping with the rest of the world, there is a simultaneous increase in chronic diseases and

resurgence of infectious disease. This reality highlights the disparity between quantity of years

lived and the quality of those lived years because of sociopsychological conditions- such as
                                                 69
loneliness, bereavement, social support (or the lack of), low self-esteem, and low self-

actualization and so on.


        In evaluating health or wellbeing, we must seek to examine more than just the number of

years that an individual is likely to survive as we should be concerned about the quality of those

years. Even though, life expectancy is an indicator of health, the new focus is on healthy life

expectancy. Based on the Healthy People 2010, the new thrust is on increasing quality of years

of life. In attempting to capture ‘quality of years lived’, in 1999, the WHO introduced an

approach that allows us to evaluate this, by the ‘disability adjusted life expectancy’ (DALE)[6]

DALE does not only use length of years to indicate health and wellbeing status of an individual

or a nation, but incorporate the number of years lived without disabilities.


        DALE is a modification of the traditional ‘life expectancy’ approach in assessing health.

It uses the number of years lived as its principal component. This is referred to as ‘full health’.

In addition, the number of years of ill-health is weighted based on severity as another component

in the equation. This is then subtracted from the expected overall life expectancy to give what is

referred to as years of hale life. Embedded in this approach is the adjustment of years lived in

‘ill-health’.


        Having arrived at ‘healthy life expectancy’, the WHO has found that poorer countries lost

more from their ‘traditional life expectancy’ than developed nations. The reasons forwarded by

the WHO are the plethora of dysfunctions and the devastating effects of some tropical diseases

like malaria that tend to strike children and young adults. The institution found that these

accounted for a 14 percent reduction in life expectancy for poorer countries and 9 percent for

more developed nations[6] This is in keeping with a more holistic approach to the measure of
                                                 70
health and wellbeing with which this study seeks to capture. By using the biopsychosocial

model in the evaluation of wellbeing of aged Jamaicans, we will begin to understand factors that

are likely to influence the quality of lived years of the elderly, and not be satisfied with the

increased length of life of the populace. Looking at the life expectancy data for Jamaica, the

figure is 74.1 years for both sexes[6] but by using healthy life expectancy it is 65.1 years[6] Here

life expectancy has been increasing at a faster rate than ‘healthy life expectancy’. Therefore,

Jamaicans are expected to spend some 9 years of their life in ‘poor health’.


       In summary, the use of life expectancy to measure health is inadequate and so morbidity

must be taken into consideration. When life expectancy is discounted by morbidity, it provides

an account of the healthy life expectancy of an individual. Hence, the use of life expectancy to

indicate health for men and women is equally insufficient in health analysis. It is evident from

statistics on life expectancy and particular diseases causing mortality that men are experiencing a

lower health status, and what accounts for this reality? Within the context of the aforementioned

issues, and the fact that medical health care seeking has increased from 54.6% in 1989 to 66.0%

in 2007 and that there is a decline of 5.7% over 2006 (Table 3.1), is this offering some

explanation the gender differential in health status? Although less Jamaicans are seeking medical

care of those who reported illnesses, 27.1% more Jamaicans reported dysfunctions (Table 3.1),

suggesting that there is greater health differential between the sexes. Hence, for this study,

medical seeking behaviour, self-reported ill-health, and gender differential in medical seeking

health care and self-reported ill-health will be examine to provide a better understanding of the

healthy life expectancy of the sexes in Jamaica.




                                                   71
Materials and Method


The current research used secondary data. The data constitute statistics from the Planning

Institute of Jamaica and the Statistical Institute of Jamaica (in Jamaica Survey of Living

Conditions, JSLC) and Ministry of Health Jamaica (MOH). The data were extracted from the

JSLC on medical care seeking behaviour, self-reported illness (or ill-health) and the gender

composition of those who reported ill-health. The Ministry of Health’s Annual Report provided

data on actual percentage of Jamaicans who visited public hospitals, which was contrasted by the

JSLC’s self-reported visits to public hospitals in order to further examine the gender differentials

on subjective ill-health.


       This study used 19 years of published data extracted from the JSLC (1988-2007). The

JSLC was born out of the World Bank’s Living Standard Survey. The JSLC began in 1988 when

the Planning Institute of Jamaica (PIOJ) in collaboration with the Statistical Institute of Jamaica

(STATIN) adopted with some modifications of the World Bank's Living Standards Measurement

Study (LSMS) household surveys. The JSLC has its focus on policy implications of government

programmes, and so each year a different module is included, evaluating a particular programme.

The JSLC is a self-administered questionnaire where respondents are asked to recall detailed

information on particular activities. The questionnaire covers demographic variables, health,

immunization of children 0 to 59 months, education, daily expenses, non-food consumption

expenditure, housing conditions, inventory of durable goods, and social assistance. Interviewers

are trained to collect the data, which is in preparation of the household members. The survey is

usually conducted between April and July annually. Furthermore, the instrument is posted on the




                                                72
World Bank’s site to provide information on the typologies of question and the

(http://www.worldbank.org/html/prdph/lsms/country/jm/docs/JAM04.pdf).


       Ministry of Health is the body which is constituted by statutes to regulate all health

institutions in the country. The Ministry of Health (MOH) collects statistics on health, health

services, health utilization, health related matters, and carry out health mandate of the

government. MOH has decentralized its operations. The island is sub-divided in four regions

(South-East; North-East; Western, and Southern), which emerged owing to the passage of the

National Health Service Act of 1997. Each region operates as a semi-autonomous regional body

under the general directs of the central Ministry of Health, which is subject to the directions of

the Minister of Health. The central Ministry of Health collates all the data sent it by the four

health authorities in country. Therefore, data revealed in the Annual Reported of the Ministry of

Health, Jamaica, reflect actual accounts of the health matters in the country.


       Scatter diagrams and best fitted lines were used to examine correlations between different

variables, and percentages were also utilized to evaluate events over two decade (1988-2007).


Measure


Gender is being male or female.


Gender differential is the disparity between self-reported ill-health of male or female.


Medical Care Seeking Behaviour denotes the proportion of self-reported cases of visits for

seeking medical care of those who indicated ill-health.




                                                 73
Self-reported Illness is the percentage of people who have reported cases of dysfunctions (ill-

health or health conditions) as indicated by a respondent in a 4-week reference period.


Poverty is measured using the poverty line. The poverty line estimate is particular attainable

consumption expenditure in excess of a minimum necessary level of expenditure on a

representative bundle of necessary goods and services valued at germane prices. (JSLC 2008)


Results


Some scholars may want to believe that the use of subjective data on health (self-reported ill-

health) cannot be used to proxy health as it is not a good estimate of actual health status. In

order to remove this myth, the researcher will examine the actual figures provided by the

Ministry of Health on visits to public health care facilities and those garnered by the Jamaica

Survey of Living Conditions (JSLC). The JSLC is an annual probability sampled survey which

collects data from Jamaicans based on their recollection of events (self-reported). Based on

Table 3.4, self-reported health as indicated by the JSLC is a good proxy of visits. The data

revealed that in 1997, the difference between Jamaicans recall of events and those actually

happened as recorded by the Ministry of Health was marginally different (1%). Some 7 years

later (2004), the difference between same phenomena was 6.1% suggesting that subjective

assessment of health is a good proxy for actual health. It is within this context, that the researcher

will examine self-reported health data from JSLC to understanding health differential between

the sexes in Jamaica.


          During the periods of the greatest double digits inflation in history of Jamaica (early

1990s) (Table 3.2) in particular inflationary rates that were in excess of 25% (1990-1995),


                                                 74
Jamaicans reported the lowest percentages in ill-health (health conditions). Moreover, in 1991

when inflation was at it peaks, the prevalence of poverty stood at its highest (44.6%), and the

data showed that self-reported illnesses were 13.7%. This figure was the fifth highest self-

reported ill-health in an 18 year period (1989-2007). In the unprecedented inflation of 1991

(80.2%), less men sought medical care (12.0%) over 1990 (16.35) compared to 15.0% in 1991

and 20.3% in 1990. In 1990, it was the first time in the history the of nation that inflation rose to

in excess of 20% and self-reported illness reached its maximum of 18.3%, and medical care

seeking behaviour was at its lowest (38.6%).


       In addition, in 1990, both sexes sought the most medical care (Table 3.3). Two years

later (1992), inflation rate fell by 49.9% (to 40.2%) over 1991 which explains the rationale for

the 24.0% decrease in prevalence of poverty; self-reported ill-health declined by 22.6%,

ownership of health insurance increased so to were people seeking medical care and the private

health care utilization. The irony here is that 17.5% less men reported accessing medical care for

their ill-health and 24.7% less women. This indicates that more of those people who did not

report ill-health visited private health care facilities for medical care. In 1993, inflation declined

further by 25.1%; poverty saw a reduction of 28.0%; self-reported health conditions increased by

13.2%; health insurance coverage increased by 12.2%; number of people seeking medical care

increased by 1.8%. In that same period, the number of women who sought care was 3.8 times

more (19.5%) than men (5.1%). Hence, high inflation was reducing visits for medical care and

another matter which emerged from the data during that period, that those who attending public

hospitals began reducing their visits while private hospital users, increased utilization (Table

3.2). There is a paradox post-2005 as inflation increased by an unprecedented 194.7% in 2007

over 2006 and this explains a corresponding decline in the number of persons who sought
                                                 75
medical care (by 5.7%). Nevertheless, the number of men who visited health care facilities

increased in the period by 21.2% and the number of women was 1.24 times more than men.


        The data show that in the last 17 years, women place more emphasis on their health than

men. Between 1988 and 2007, it was only on one occasion that men have indicated having

sought more medical care than women (in 1997) (Table 3.3). The difference between men

seeking medical care and that of women was 0.7%. If health seeking behaviour is a proxy for

preventative care, then it would appear that they were more health conscious. This is the not the

case as in the same period, then spent more days receiving care (mean of 11 days) compared to

10 for women. Hence, this increased in health seeking behaviour was owing to curative and

preventative care. Nevertheless, over the studied period, severity of care for both sexes has been

reality the same. Using mean number of days men received care for illness/injury, the difference

is minute, suggesting that severity of illness between the sexes in Jamaica is the same.


        Another interesting finding that emerged from the data is the narrowing of the gap

between public health care utilization and private health care utilization in the nations,

suggesting that costing of living is accounting for more visits to public care facilities. Embedded

in those findings is the affordability in people’s decision to seek medical care. This indicates that

there are some other conditions that are interfacing with men’s and women’s decision to visit

health care facilities for care outside of prices (inflation).


Results: Bivariate Analyses


Percentage of People Seeking Medical Care by Percentage of People reporting Illness




                                                   76
On examination of Figure 3.1, it was revealed that a negative correlation exists between number

of people who sought medical care and percentage of people who reported ill-health. This

indicates that as more people report health conditions, less of them are likely to seek medical

care. Furthermore, 16.3% of the variability in people seeking medical care can be explained by

illness, suggesting that ill-health is not a good reason for Jamaicans visiting health care

practitioners. On further investigation of people seeking medical care and self-reported

illness/injury, data (Tables 3.2,3.3) revealed that on the occasion when the highest percentage of

illnesses were reported, the least number of person sought care for those conditions. This irony

was equally the case for men (16.3%) as well as women (20.3%) (Table 3.3).


Percentage of People Seeking Medical Care by Prevalence of Poverty


On examination of a scatter diagram; it was observed that there is a negative correlation between

the percentage of people seeking medical care and prevalence of poverty. The best fit line

revealed that 57.6% of why people seek health care in Jamaica is determined by poverty (Figure

3.2). Hence, people are highly likely to visit health care facilities in periods of low poverty and

vice versa. This indicates that medical care is not simply about ill-health, it is equally determined

by affordability, suggesting that people will switch to home care in periods of increased poverty.

Irrespective of this knowledge, is there is sex disparity in regard to seeking medical care and

reporting illness?


       Percentage of Men Seeking Medical Care by Percentage of Men reporting Illness


Generally 16.3% of why Jamaicans visit health care facilities in search of care is owing to their

health conditions. However, for men, 35.4% of why they sought medical care was due to ill-


                                                 77
health as 35.4% of the variability in men seeking medical care can be explained by medical care.

On decomposing the data, when the least percentage of men sought medical care assistance

(37.9%), the most percentage of them reported illness (16.3%) (Table 3.3). Furthermore, when

the lowest percentage of men reported ill-health (health conditions/injuries) (7.4%), this was in

60% of those seeking more medical care. However, in 1999 and 2004, low self-reported illness

was correlated with relatively high health seeking behaviour.


Percentage of Women Seeking Medical Care by Percentage of Women reporting Illness


Health (medical) care seeking behaviour of women is lowly correlated with self-reported illness

(injury) (Figure 3.3). The scatter plot revealed that generally, the more women reported health

conditions the less likely they are to seeking medical care. Some 8.8% of the variability in

medial care seeking behaviour of this cohort can be explained by a change in self-reported health

conditions. Self-reported illness of women accounted for 54% less of the explanatory reason for

seeking medical care compared to that of the both sexes (16.3%), suggesting that women’s health

care behaviour is driven by other factors than ill-health. There are some similarities between

health care seeking behaviour and self-reported illness of both sexes as when women reported

the least percentage of health care seeking behaviour, this was corresponding to the most

reported health conditions (Table 3.3). Furthermore, when the least percentage of ill-health was

reported, this earmarked 59th percentage of the highest seeking medical care behaviour of

women. These were also the case for men.


Deconstruction the Self-Reported Health Status of Jamaicans by Gender, 1989-2006




                                               78
Over the last 2 decades (1988-to-2007), a small proportion of Jamaicans have reported illness (or

dysfunction) (Table 3.5). This has been has high as 168 per 1,000 (in 1989) to a low of 88 per

1,000 (in 1997), and the figure was 155 per 1,000 in 2006 (Table 3.5). On deconstruction the

population self-reported health status, it was revealed that women continue to report more health

conditions than men. In 1989, there 123 women (or women) who reported health conditions to

100 men (or men), and in 2004, the ratio was as high as 153 women per 100 men. This indicates

that 53% more women reported health conditions than men in the latter year and there was an

increase of 30% more women reporting dysfunctions over the 2 decades. Over the studied

period, in 1992, the disparity in self-reported health conditions between men and women was

very close of which there were 114 women to 100 men as it relates to self-reported health

conditions. On the other hand, over the last decade (1997-to-2006), the disparity was 136 or 153

women per 100 men, and in the last 2 years the value has been relatively stable (136 or 137

women per 100 men).


       Percentage of People Seeking Medical Care by Percentage with Health Insurance


       Health Insurance is one indicator of people’s intent to access care. On examination of the

data (Table 3.2), only a small percentage of Jamaicans in 2007 had health insurance (21.1%).

This meant that more people who will become ill would need to meet their medical expenses out

of savings, current income and assistance from social support agent(s). Table 3.2 revealed in

8.6% of Jamaica had health insurance coverage during the period when the inflation rate was at

its peak (80%) and when it fell to 40.2%, health insurance coverage increased by only 0.4%.

Further investigation of health seeking behaviour and health insurance coverage showed that the

ownership of health insurance was positively related to health seeking behaviour. A bivariate

                                               79
correlation between the two aforementioned factors revealed that 56.1% of the variability in

people seeking medical care was as a result of ownership of health insurance (Figure 3.5).



Ownership of Health Insurance and Prevalence of Poverty



Poverty does not only mean ones inability to purchase consumption items, but also non-

consumption items such as health insurance. On examining a scatter diagram with a best fit line

to establish any correlation between the two aforementioned variables, it was observed that a

moderately strong correlation existed (R-squared = 0.597) – Figure 3.5. This means that 60% of

the variability in ownership of health insurance can be accounted for by prevalence of poverty,

suggesting that poor is less likely to have health insurance coverage.

Discussion

                                                                              [8]
In the conclusion of the health chapter in one of the JSLC’s reports                it reads “Gender

differentials with respect to self-reported illness and health seeking behaviours need to be

investigated.” This is the rationale for this study, to provide an assessment of differences in

subjective health and medical seeking behaviour of men and women in Jamaica.

           Globally, regionally and in particular Jamaica, women seek more health care than men[1,
2, 4-7]
          This is not alarming as it commences from at childhood. In 1998, one health organization

wrote that girls are less likely to be injured and have broken bones compared to boys[2], which

continue during the life span. So when the mortality rates show a higher rate for men than

women [2, 6], this is just a continuation of early socialization. Health, therefore, is gender bias.

One of the rationales for the emphasis on health care by women is reason for male’s abstinence,


                                                 80
the culture. Within many cultures, men are not to display any form of weakness which includes

ill-health. This culturalization has embedded in boys and avoidance of speak of illness/injury and

the image of ill-health is negative and is primarily feministic in nature. This is not limited to

Jamaica or African descendent societies as it is equally the case in European geopolitical zones

such as Norway[9]

          Many cultures view (image) of health is the absence of diseases and this is sometimes

linked to cure of the gods or moral rationale, suggesting that ill-health is a weakened biological

state. Men who are culturalized to be strong and macho must now balance ill-health within a

plural culture. The 21 st century has seen the exponential increase in life expectancy of men

compared to women in nineteenth and earlier centuries, but what about high mortality for this

group. There has always been feminization of life expectancy in Jamaica since 1880 (Table 3.1)

and the disparity in life expectancy has double from 1880-1882 to 2002-2004 from 3 years to 6

years respectively. Life expectancy which is an indicator of health does not only speak of longer

life, there are also some cultural changes that account for this increased life span, the social

milieu.

          Despite the advancement in medical technology, men continue to outnumber women in

particular mortality rates.   These include heart disease and neoplasm to name a few non-

communicable diseases. Heart disease and neoplasms are caused through either lifestyle

behaviour or heredity, and the former explains more of heart disease than the latter. Globally, the

fact that women outlived men by 8 years and in Jamaica by 6 years, lifestyle behaviour

undoubtedly is explaining the higher morbidity in heart diseases of men.

          Life style behaviour is expressed in health seeking behaviour, the purchase of health


                                                81
insurance, preventative care and not curative care. Jamaica women continue to seek more care

than men, and this concurs with the finding so other studies[2, 3, 10, 11] Women do not take their

health for granted as society labels them with the nurturing role of children as well as ascribe

them softer tasks. This means that health and ill-health are interpreted and viewed from within

the perspective of personal experiences, and expectations. It is through the socialization process

which is carried out by mothers (women) that ill-health and health will be defined which

accounts for ones expectation and some percentage of how the world is viewed and interpreted

by people. In a qualitative study that was done in Nairobi slums, the authors found a strong

correlation between severity of illness and health seeking behaviour of children[12] These

children do not seek care of themselves, but are taken for medical care by their mothers. Another

study on street children (ages 5 to 13 years), who take themselves, like the Nairobi study

attended health care institutions for care dependent on i) severity of illness and ii) if it stops their

economic livelihood[13] Eight percentage of the sampled population of the latter study (in

Pakistan) were boys (men). This speaks to the image of health as viewed by men, and when care

is sought by them.

       Ill-health, therefore, based on the image of health seen through the lens of men is weak,

breaches machoism and borders on the fringes of feminism. Within the homophobis world,

despite the gradual reduction of the degree in some societies, men (or boys) do not want to be

labeled weak, homosexual or effeminate. Hence, there is dialectic here as men want to live which

means that they must address ill-health and at the same time they must appear to be macho. Men

are less likely to both report ill-healths as well as seek medical care because of its image and

social labels that they may ascribe to them by society. Women also play a part in this process as

they grown their boys to be strong, ‘tough’, and that they should not show weakness. Ill-health
                                                  82
is a weakness (or negative health), and so women on seeing men visiting health practitioners

especially if this is frequent construe this as weak, but his is not ascribe to a female for doing the

same thing.

       Medical care seeking behaviour is, therefore, construed as indicating ill-health (curative

care) and not preventative care for men. Chevannes[14] wanting to explain how men are as they

are, opined that early socialization played a critical role in shaping men’s masculinity, image of

self and interpretation of the world around them. The image of health as viewed as far back as

prehistoric society is that of sickness, a curse, a plague, a weakness and a state of biological

incapacitation. Men who are culturalized to be strong cannot afford to be seen as weak or

incapacitate by their peers or the opposite sex as the society removes the acclaim of greater,

power and prestige from any such male. This means that men must now report and display less

signs of ill-health (weakness), and the only time that illness must be shown is in times of severity

which is close to death.

       Jamaican men displaying low medical care seeking behaviour as cultural underpinnings,

and so does their unhealthy lifestyle practices. Unhealthy lifestyle is undoubtedly explaining

high mortality of men than women. This dates back to prehistoric society, when men must

hunters, heroes, warriors and fierce to defend themselves, their tribes and women. Such events

meant that they would take more risk than women, and this has continued during the centuries.

Although vast amount of information are available on health and health treatment, men continue

to indulge in risky behaviour which accounts for their high morbidity and mortality in some

conditions.   The literature speaks to 80% of injuries and between 30-40% of cases with

cardiovascular conditions and diabetes mellitus could have been prevented by lifestyle

practices[5] This explains much of the health conditions and increased in reported ill-health and
                                                 83
medical care seeking behaviour. What is the role of education in health differential in the sexes?

        Education which is well established has directly correlated with better health[15-22] does

not remove early culturalization by family, peer groups and religious affiliations. The general

education level of the Jamaican populace has been improving since the last 3-decade, but this

does mean the remove of the gender bias health image or stigma of weakness associated with

illness. In 1989, 54.6% of Jamaicans sought care for ill-health and in 2007 that figure has

increased by 9.9% (to 60%). In the same period that rate of increase for women was 29.0%

compared to 41.1% for men. Nevertheless, in 2007, for every 100 men that sought care for ill-

health, 108 women sought medical care. Although, we cannot divorce health from the social

milieu, more men are seeking medical care for illness and this accounts for the faded difference

between the mean numbers of days spent for care in both sexes. The 21 st century has aided men

in their recognition for the need to seek medical care for ill-health, in spite of traditional

cosmologies [23, 24]

        In contemporary societies, illness for men is not tied to health conditions such as

neoplasm, heart disease, hypertension, mellitus diabetes and stroke, but is synonymous to

sexuality which is a legacy of their socialization[14, 23-29] A medical doctor ascribes to the 21 st

century, gender roles that are tied to sex (biological category). This means that being male is

linked to being the stronger sex, fertile, and sexual prowess. Society has not removed from its

men that gender stereotype, and so the image of health for them is substantially tied to sexuality.

Men, therefore, do not see themselves as ill, unless they are impotent. Culturally, because

impotency and infertility are a curse, men will not openly speak about those matters or/and other

heath conditions. Again, male means strength, sexual potency, and these are all at the other end

of the pendulum of ill-health. This explains the reason for the lower purchase of individual health
                                                84
insurance as this symbolizes weakness or preparation of some negative conditions. In spite of

this reality, over the last one-half of a decade, there has been an increase in health insurance

coverage and health seeking behaviour of both sexes. As of 2007, 2.1% more women had health

insurance coverage than men (20.1%), which was more than the national average of 21.1%.

Again this speaks to the differences in image of health held sexes and how their decision is based

on this view. Health insurance is a component of lifestyle practices justify the advantages that

women enjoy compared in men concerning health status. This is also reinforced in the fact (in

2007) that for every 133 women who indicated that they were unable to afford to seek medical

care 100 men[1], showing that men are naturally, owing to their culturalization, unwilling to seek

medical care and this is evident in their lifestyle practices, purchase of health insurance,

reporting ill-health and visits to health care institution for preventative and curative care[1, 5]

        According to one scholars income buys health[30], which has some merit. The merit to this

argument is linked to the fact that income affords one the ability and option to purchase better

foods, medical care, a particularly good physical environment that are all positively correlated

with good health[3, 15-18] There is a negative side to affluence and income, as it afford particular

lifestyle that retard good health. Income affords one the lifestyle to purchase cigar, tobacco,

speedy cars, and in the process remove the disadvantage of low income or poverty. In a study

done by a group Caribbean scholars of 1,338 Jamaicans (ages 15 to 99 years), they found that

greatest subjective psychosocial wellbeing was had by the middle class followed by the wealth

and lastly by the poor[31]

        Embedded in the income and health debate, is the difficulty of the poor in seeking

medical care (curative and preventative care). This study has shown that there is a moderately

strong correlation between seeking medical care and prevalence of poverty, suggesting that poor
                                                   85
men are even less likely to seek care than those in the middle to upper class. When poverty is

coalescing with the cultural biases and image of health, men are likely to suffer more as they

must balance ill-health which is a weakness with in affordability. The issue of affordability is

seen in the percentage of those in the poorest quintile with health insurance in 2007 (6.6%)

compared to 12% in quintile 2, 18% in quintile 3, 22.7% in quintile 4 and 43.4% of those in the

wealthiest quintile. Embedded in this disparity is the poor’s inability to plan for the eventuality

of ill-health coupled with deplorable reality of the physical environment. This physical

environment is such to account for ill-health[32], and when poor nutrition is added to this situation

the poor will become even more ill.

Concluding Comments

In summary, illness is still seen and interpreted by Jamaicans as punishment and negative health,

and this explains their low self-reported health conditions and health care seeking behaviour.

Men who are product of the society must abide within the image of its dictates, which justifies

their unwillingness to seek medical care, report illness, purchase health insurance coverage and

create an image of weakness. In spite of this reality, men have become more involved that

women in seeking medical care over the last 17 years. This means that the society is becoming

increasingly more cognizant that ill-health is more than negative health or is simply equivalent to

weakness, female or less macho men. Although men are substantially driven by health conditions

to seek medical care than women, they are becoming more involved in health care treatment.




Recommendation

Further efforts are needed to eliminate more of the barriers of the negative image of health and

                                                 86
the use of medical services for ill-health in Jamaica. Medical practitioners, health care workers,

social workers and researchers must integrate the image of men in their treatment, and create an

atmosphere which is conducive to health care for men. A single prevalence study is needed to

ascertain the influence of each of the identified variables in this study and others in order to

understand the role of poverty, health insurance, ill-health, on the health seeking behaviour of

Jamaicans, the media, education as well as confounding variables such as gender, age,

religiosity, area of residence and subjective social class. In addition, a study is necessary to

ascertain whether the increased in self-reported health is owing to unemployment, and how much

of ill-health is accounted for by psychological conditions.




                                                87
References
   1. Planning Institute of Jamaica (PIOJ), and the Statistical Institute of Jamaica (STATIN),
       1990-2008. Jamaica Survey of Living Conditions, 1989-2007. Kingston: PIOJ, STATIN.
   2. World Health Organization, (WHO), 1998. The World Health Report 1998: Life in the
       21st Century, A vision for all. Geneva: WHO.
   3. Bourne, PA., 2008. Medical Sociology: Modelling Well-being for elderly People in
      Jamaica. West Indian Med J, 57(6):596-04
   4. Rudkin, L., 1993. Gender differences in economic wellbeing among the elderly of Java.
      Demography, 30:209-226.
   5. The Health Promotion and Protection Division, Ministry of Health Jamaica (MOH),
       2005. Epidemiology Profile of Selected Health Conditions and Services in Jamaica,
       1990-2002. MOH.
   6. WHO, 2000. WHO Issues New Healthy Life Expectancy Rankings: Japan Number One
       in New ‘Healthy Life’ System. WHO.
   7. Statistical Institute of Jamaica, (STATIN), 2007. Demographic Statistics, 2006.
       STATIN.
   8. WHO, 2003. Healthy life expectancy. Washington DC: WHO.
   9. Planning Institute of Jamaica (PIOJ), and the Statistical Institute of Jamaica (STATIN).
       2000. Jamaica Survey of Living Conditions, 1999. PIOJ, STATIN.
   10. Kaasa, K. 1998. Loneliness in old age: Psychosocial and health predictors. Norwegian
       Journal of Epidemiology, 8:195-201.
   11. Hutchinson, G., Simeon, DT., Bain, BC., Wyatt, GE., Tucker, MB., and E LeFranc, 2004.
       Social and health determinants of wellbeing and life satisfaction in Jamaica. International
       Journal of Social Psychiatry, 50 (1):43-53.
   12. Hambleton, IR., Clarke, K., Broome, Hl., Fraser, HS., Brathwaite, F., and AJ.
       Hennis, 2005. Historical and current predictors of self-reported health status
       among elderly persons in Barbados. Revista Panamericana de salud Pύblic,
       17(5-6):342-353.
   13. Taff, N., and G. Chepngeno, 2005. Determinants of health care seeking for childhood
       illness in Nairobi slums. Tropical Medicine and International Health, 10:240-245.
   14. Ali, M., and A. de Muynck, 2002. Illness incidence and health seeking behaviour among
       street children in Rawlpindi and Islamabad, Pakistan – a qualitative study. Child Care,
       Health & Development, 31:525-532.
   15. Chevannes, B., 2001. Learning to be a man: Culture, socialization and gender identity in
       five Caribbean communities. The University of the West Indies Press.
   16. Bourne, P., 2007. Using the biopsychosocial model to evaluate the wellbeing of the
       Jamaican elderly. West Indian Medical J, 56: (suppl 3), 39-40.
   17. Bourne, PA., 2008. Health Determinants: Using Secondary Data to Model Predictors of
       Well-being of Jamaicans. West Indian Medical J, 57(5):476-481.
   18. Longest BB, Jr, 2002. Health Policymaking in the United States, 3rd ed. Health
       Administration Press.
   19. Brannon, L., and J. Feist, 2007. Health psychology. An introduction to behavior and
       health 6th ed. Thomson Wadsworth.
   20. Grossman, M., 1972. The demand for health- a theoretical and empirical

                                               88
    investigation. National Bureau of Economic Research.
21. Smith, JP., and R. Kington, 1997. Demographic and economic correlates of health in old
    age. Demography; 34:159-170.
22. Ross, CE., and J. Mirowsky, 1999. Refining the association between education and
    health: The effects of quantity, credential, and selectivity. Demography; 36:445-460.
23. Freedman, VA., and LG. Martin, 1999. The role of education in explaining and
    forecasting trends in functional limitations among older Americans. Demography,
    36:461-473.
24. Meryn, S., 2004. Gender Quo Vadis: 21 the first female century: The Journal of Men’s
    health & gender, 1: 3-5.
25. Spector, RE., 2004. Cultural diversity in health and illness, 6th ed. New Jersey.
26. Barrow, Christine. 1998. Caribbean Gender Ideologies: Introduction and Overview. In
    Caribbean Portraits: essays on Gender Ideologies and Identities, Ed., Christine, B, Ian
    Randle Publishers, pp: xi-xxxviii.
27. Chevannes, B., 1999. What we sow and what we reap – problems in the cultivation of
    male identity in Jamaica. Grace, Kennedy Foundation.
28. Brown, J., and B. Chevannes,1998. Why man stay so – ties the Heifer and loose the bull:
    an examination of gender socialization in the Caribbean. University of the West Indies.
29. Bailey, W., (ed), 1998. Gender and the family in the Caribbean. Institute of Social and
    Economic Research.
30. Marmot, M., 2003.The influence of Income on Health: Views of an Epidemiologist:
    Does money really matter? Or is it a maker for something else? Health Affairs, 21:31-46.
31. Powell, LA., Bourne, P., and L. Waller, 2007. Probing Jamaica’s Political Culture: Main
    Trends in the July-August 2006 Leadership and Governance Survey, volume 1. Centre
    for Leadership and Governance, Department of Government, the University of the West
    Indies.
32. Pacione, M., 2006. Urban environmental quality and human wellbeing –a social
    geographical perspective. Landscape and Urban Planning, 65:19-30.




                                          89
Table 3.1: Life Expectancy at Birth of Jamaicans by Sex: 1880-2004

                           Average Expected Years of Life at Birth
Period:
                           Man                        Woman

1880-1882                  37.02                      39.80

1890-1892                  36.74                      38.30

1910-1912                  39.04                      41.41

1920-1922                  35.89                      38.20

1945-1947                  51.25                      54.58

1950-1952                  55.73                      58.89

1959-1961                  62.65                      66.63

1969-1970                  66.70                      70.20

1979-1981                  69.03                      72.37

1989-1991                  69.97                      72.64

1999-2001                  70.94                      75.58

2002-2004                  71.26                      77.07
Sources: Demographic Statistics (1972-2006) in Bourne, P. Determinants of well-being of the
Jamaican Elderly. Unpublished thesis, The University of the West Indies, Mona Campus; 2007.




                                             90
Table 3.2: Inflation, Public-Private Health Care Service Utilization, Incidence of Poverty, Illness and Prevalence of Population with
Health Insurance (in per cent), 1988-2007

Year                  Inflation       Public              Private                      Prevalence         Illness           Health               Seeking
                      Mean
                                      Utilization         Utilization        of poverty                         Insurance                 Medical Care Days of
                                                                                                                Coverage                               Illness


1988                8.8               NI                  NI                 NI                  NI                 NI                    NI           NI
1989               17.2               42.0                54.0               30.5                16.8               8.2                   54.6         11.4
1990               29.8               39.4                60.6               28.4                18.3               9.0                   38.6         10.1
1991               80.2               35.6                57.7               44.6                13.7               8.6                   47.7         10.2
1992               40.2               28.5                63.4               33.9                10.6               9.0                   50.9         10.8
1993               30.1               30.9                63.8               24.4                12.0               10.1                  51.8         10.4
1994               26.8               28.8                66.7               22.8                12.9               8.8                   51.4         10.4
1995               25.6               27.2                66.4               27.5                9.8                9.7                   58.9         10.7
1996               15.8               31.8                63.6               26.1                10.7               9.8                   54.9         10.0
1997               9.2                32.1                58.8               19.9                9.7                12.6                  59.6         9.9
1998               7.9                37.9                57.3               15.9                8.8                12.1                  60.8         11.0
1999               6.8                37.9                57.1               16.9                10.1               12.1                  68.4         11.0
2000               6.1                40.8                53.6               18.9                14.2               14.0                  60.7         9.0
2001               8.8                38.7                54.8               16.9                13.4               13.9                  63.5         10.0
2002               7.2                57.8                42.7               19.7                12.6               13.5                  64.1         10.0
2003               13.8               NI                  NI                 NI                  NI                 NI                    NI           NI
2004               13.7               46.3                46.4               16.9                11.4               19.2                  65.1         10.0
2005               12.6               NI                  NI                 NI                  NI                 NI                    NI           NI
2006               5.7                41.3                52.8               14.3                12.2               18.4                  70.0          9.8
2007               16.8               40.5                51.9               9.9                 15.5               21.2                  66.0         9.9
Source: Bank of Jamaica, Statistical Digest, Jamaica Survey of Living Conditions, Economic and Social Survey of Jamaica, various issues
Note: Inflation is measured point-to-point at the end of each year (December to December), based on Consumer Price Index (CPI)

NI – No Information Available

                                                                                     91
 Table 3.3: Seeking Medical Care, Self-reported illness, and Gender composition of those who
report illness and Seek Medical Care in Jamaica (in percentage), 1988-2007

                                                        Reporting Reporting      Mean    Mean
                                  Seeking    Seeking     Illness-  Illness-      Days    Days
          Seeking      Health     Medical    Medical       Men     Women           Of      Of
          Medical    Insurance     Care -     Care -                            Illness Illness
  Year     Care      Coverage      Men       Women                               Men Women
  1988      NI           NI          NI        NI           NI         NI          NI      NI
  1989     54.6          8.2        44.7      52.8         15.0       18.5       10.6    11.1
  1990     38.6          9.0        37.9      39.2         16.3       20.3       10.2    10.2
  1991     47.7          8.6        48.5      47.4         12.1       15.0       10.0    10.3
  1992     50.9          9.0        49.0      52.5          9.9       11.3       10.7    10.9
  1993     51.8         10.1        48.0      54.7         10.4       13.5       10.7    10.1
  1994     51.4          8.8        49.0      53.4         11.6       14.3       10.3    10.4
  1995     58.9          9.7        59.0      58.9          8.3       11.3       10.6    10.7
  1996     54.9          9.8        50.5      58.5          9.7       11.8       10.0    11.0
  1997     59.6         12.6        60.0      59.3          8.5       10.9       11.0    10.0
  1998     60.8         12.1        57.8      62.8          7.4       10.1       11.0    11.0
  1999     68.4         12.1        64.2      71.1          8.1       12.2       11.0    11.0
  2000     60.7         14.0        57.4      63.2         12.4       16.8        9.0     9.0
  2001     63.5         13.9        56.3      68.2         10.8       15.9         9       10
  2002     64.1         13.5        62.1      65.3         10.4       14.6       10.0    10.0
  2003      NI           NI          NI        NI           NI         NI          NI      NI
  2004     65.1         19.2        64.2      65.7          8.9       13.6        11.0   10.0
  2005      NI           NI          NI        NI           NI         NI          NI      NI
  2006     70.0         18.4        71.7      68.8         10.3       14.1        9.7    10.0
  2007     66.0         21.2        62.8      68.1         13.1       17.8       10.6     9.3

 Source: Jamaica Survey of Living Conditions, various issues

 NI - No Information was available




                                              92
Table 3.4: Public Health Care Visits (using the JSLC, data) and Actual Health Care Visits (using
                                               Ministry of Health Jamaica, data), 1997 and 2004

                                       Public Health Care Visits in Jamaica

Year                           Actual Visits, MOH1             Self-reported Visits, JSLC
                               %                               %
1997                           33.1                            32.1



2004                           52.9*                           46.8

Source: Ministry of Health Jamaica and the Jamaica Survey of Living Conditions (JSLC)

1
    The Percentages of Actual visits were computed by author

*Preliminary data were used to calculate this percentage




                                                93
 Table 3.5: Self-reported Health Status per 1,000 by Population, Men and Women; Sex-Ratio of
Self-reported Health Status, and Female to Male Ratio of Self-reported Health Status, 1989-2006
 Year         Self-reported Health Status per
                           1,000                   Male-to-Female       Female-to-Male ratio
                                                     ratio of Self-    of Self-reported Health
           Population          Men      Women      reported Health              Status
                                                         Status


    1989            168           150           185                      81                  123
    1990            183           163           203                      80                  125
    1991            137           121           150                      81                  124
    1992            106            99           113                      88                  114
    1993            120           104           135                      77                  130
    1994            129           116           143                      81                  123
    1995             98            83           113                      73                  136
    1996            107            97           118                      82                  122
    1997             97            85           109                      78                  128
    1998             88            74           101                      73                  136
    1999            101            81           122                      66                  151
    2000            142           124           168                      74                  135
    2001            134           108           159                      68                  147
    2002            126           104           146                      71                  140
    2003              -             -             -                       -                    -
    2004            114            89           136                      65                  153
    2005              -             -             -                       -                    -
    2006            122           103           141                      73                  137
    2007            155           131           178                      74                  136
Computed by Paul Andrew Bourne from Jamaica Survey of Living Conditions from various years




                                                    94
                          70.00




                          60.00
   Seeking Medical Care




                          50.00




                          40.00



                                                                                   R Sq Linear = 0.163



                          30.00


                                  8.00   10.00   12.00         14.00      16.00   18.00           20.00
                                                         Illness/Injury

Figure 3.1: Percentage of Men Seeking Medical Care by Percentage of Men reporting Illness




                                                          95
                          70.00




                          60.00
   Seeking Medical Care




                          50.00




                          40.00



                                                                         R Sq Linear = 0.576



                          30.00


                                  10.00   20.00            30.00           40.00
                                          Prevalence of Poverty

Figure 3.2: Percentage of People Seeking Medical Care by Prevalence of Poverty




                                                  96
                                          70.00
   Health Care Seeking Behaviour of Men




                                          60.00




                                          50.00




                                                                                 R Sq Linear = 0.354
                                          40.00




                                                  7.50   10.00         12.50           15.00           17.50
                                                         Self-reported Health of Men

Figure 3.3: Percentage of Men Seeking Medical Care by Percentage of Men reporting Illness




                                                                 97
                                           70.00
  Health Care Seeking Behaviour of Women




                                           60.00




                                           50.00




                                           40.00                                                    R Sq Linear = 0.088




                                                   10.00   12.00     14.00        16.00   18.00    20.00           22.00
                                                                   Self-reported Health of Women

Figure 3.4: Percentage of Women Seeking Medical Care by Percentage of Women reporting
Illness




                                                                             98
                          70.00




                          60.00
   Seeking Medical Care




                          50.00




                          40.00



                                                                            R Sq Linear = 0.561



                          30.00


                                  9.00   12.00           15.00      18.00           21.00
                                                 Health Insurance

Figure 3.5: Percentage of people Seeking Medical Care by Percentage with Health Insurance




                                                    99
                   21.00




                   18.00
Health Insurance




                   15.00




                   12.00




                                                                                       R Sq Linear = 0.597
                    9.00




                                   10.00             20.00             30.00               40.00
                                                     Prevalence of Poverty


                     Figure 3.6: Ownership of Health Insurance and Prevalence of Poverty




                                                             100
                                                                          Chapter
                                                                                             4
        Inflation, Public Health Care and Utilization in Jamaica


The current study examines whether public and private health care utilization switching occurs
in periods of inflation, and secondly to investigate the role of inflation on illness/injury,
prevalence of health insurance coverage, cost of health care in both public as well as private
health care. Over the past 2 decades [1988-2007] there has been a narrowing of public and
private health care utilization in Jamaica. On examination of aforementioned issues, we found
that inflation accounted for some of this lowered gap. Another interesting finding is the direct
association between inflation and injury/illness, and inflation is inversely correlated with
prevalence of health insurance coverage. Jamaicans have a preference for the utilization for
private health care than public health care services. Despite this preference, persistent increases
in the inflation rate, economic recession in America, lowered remittances, increasing costing on
‘food and beverage’ and ‘meats and poultry’, increased fuel bills are causing a substitution to
public health care utilization.




Introduction



Inflation is the persistent upward movement in general prices. It results in lowered standard of

living (wellbeing), increased cost of living and is equally synonymous with socio-economic

challenges such as readjustment of consumption spending, saving patterns, lowered nutritional

intakes, reduced real wage rates, and income-wealth redistribution. During the last 2 decades

(1988-2007), inflation in Jamaica has been moderate (average inflation was 19.6 (SD=17.1) and

reached a maximum in 1991 of 80.2 per cent which was a 169.13 per cent increase over 1990. A

                                               101
year later (1992), inflation fell substantially by 49.9 per cent (to 40.2 per cent). Since 1993 to

1999, it has been falling; however, this pattern was broken when in 2001, inflation rate increased

by 44.3 per cent over 2000 (Table 4.1). Between 2000 and 2007, average inflation was 11.8 per

cent, compared 32.3 per cent between 1988 and 1995 suggesting that socio-economic difficulties

on people during those periods have lessen but still remained a reality. Much of the economic

gains that have been attained during 2000 to 2007 have been eroded over 2006 to 2007 as

general price level in Jamaica increased by 194.7%. This coupled with the economic downturn

in the American means a number of challenges for Jamaicans. Many studies that have sought to

examine inflation have done so from the perspective of production, economic growth, monetary

policy, real wages, interest rates, retards competitiveness, and lower socio-economic activities1-6.

We have seen none that examine inflation and public and private health care utilization, inflation

and illness/injury from a Caribbean perspective or even in particular Jamaica. No one can deny

the association between inflation and increased prices and inflation and unemployment because

these are well established in the literature7-17, but what about inflation and health care utilization?


       Among the many challenges with which a populace must tackle is the increasing cost of

medical care. In periods when there is inflation, the cost of health care is one of the many cost

that rise. Is the substitution of health care utilization (public and private health care visits) as a

result of inflationary changes? During the period of the 1990s, Jamaica saw inflation as high as

80.2 per cent and despite this being the high in 2 decades (Table4.1), average inflation for that

period was 27.4 per cent compared to 10.6 per cent between 2000 and 2007. In spite the high

inflation in the 1990s, private health utilization cost - which is more than public health care

service - had a greater demand (Table 4.1). However, in the last seven years (2000 to 2007),

there has been a convergence of public and private health care utilization even though inflation
                                                 102
has been lower than in the 1990s (Table 4.1). Does inflation accounts for a proportion of this

pattern (Table 4.1)?     This study aims to contribute to the literature by investigating the

aforementioned issues. Utilizing statistical data for 2 decades, this study will examine inflation’s

role in accounting for public and private health care utilizations of Jamaicans as well as public

and private health care utilizing switching owing to inflation. Secondly, the study will examine

the correlation between inflation and illness/injury.


Method and Measure

The research design used secondary data taken from the last 2 decades of the Jamaica Survey of

Living Conditions (JSLC) to examine whether a correlation exists between inflation and public

and private health care utilization and secondly to investing if there is a substitution from private

health care utilization to public health care utilization in inflationary periods. The data were

taken from publications of the JSLC from 1988 to 2007. The JSLC began in 1988 when the

Planning Institute of Jamaica (PIOJ) in collaboration with the Statistical Institute of Jamaica

(STATIN) adopted with some modifications the World Bank's Living Standards Measurement

Study (LSMS) household surveys. The JSLC has its focus on policy implications of government

programmes, and so each year a different module is included, evaluating a particular programme.

The JSLC is a self-administered instrument (questionnaire) where respondents are asked to recall

detailed information on particular activities. The questionnaire covers demographic variables,

health, immunization of children 0 to 59 months, education, daily expenses, non-food

consumption expenditure, housing conditions, inventory of durable goods, and social assistance.

Interviewers are trained to collect the data, which is in preparation of the household members.

The survey is usually conducted between April and July annually. Furthermore, the instrument


                                                103
is posted on the World Bank’s site to provide information on the typologies of question and the

(http://www.worldbank.org/html/prdph/lsms/country/jm/docs/JAM04.pdf).


      The current study extracted data on the percentage on public and private health care

utilization, mean cost for visits to public and private health care facilities in the last 4-week of

the survey period, and health insurance coverage from the JSLC. Information was extracted on

annual inflation rate from 1988 to 2007. Scatter diagrams (graphical plots) were on variations of

public and private health care utilization by inflation, mean cost of care for visits as well as other

graphic presentations were used to assess whether any statistical association exists between the

dependent variable and the independent variable; and some of the graphs were only interpreted.

In the current study, number of hypotheses was tested to provide explanation for the narrowing

of the public and private health care utilization in Jamaica over the last 2 decades.


Measure


Health Insurance Coverage: This variable is conceptualized as self-reported ownership of health

insurance coverage by members of the population. For the purpose of the study, the variable is

measured as a percentage of the general population.


Inflation: This is measured as the per cent increase in prices from December to December of

each year.


Health Service Utilization: This denotes an individual demand and use of health care resources

and services and indicates the way customers (patients) interaction with health care providers.

Therefore health service utilization (utilization of health care services) is a proxy of health status



                                                 104
of a population and use of health care services. Health care service utilizations are provided by

public, private or public-private facilities.


Public Health Care utilization: This is the percentage of the total population of individuals who

reported having visited public health care institutions owing to illness/injury over the 4-week

period of the survey.


Private Health Care utilization: This is the percentage of the total population of individuals who

reported having visited private health care institutions owing to illness/injury over the 4-week

period of the survey.


Results: Bivariate Analysis

Hypothesis 1: There is direct statistical association between inflation and public health care
utilization

The statistical correlation between public health care utilization and inflation (Figure 4.2) is

curvilinear. There is a positive association between the two aforementioned variables, when

inflation is less than 40%, and changes to positive after an inflation rate of approximately 50%.

However, when inflation increases from 40 per cent to 80 per cent, there is a significant increase

in demand for public health care facilities for care.




                                                 105
            60.00




            50.00
   public




            40.00




            30.00



                                                                                R Sq Quadratic =0.357



            20.00


                    0.00        20.00            40.00                60.00   80.00            100.00
                                                          Inflation


Figure 4.2: Inflation By Public Health Care Utilization




                                                          106
Hypothesis 2: There is an indirect association exits between inflation and private health care
utilization

Generally the correlation between private health care utilization and inflation (Figure 4.3) is
curvilinear. A particular inflation rate (40 per cent) and beyond, people reduce their demand for
private health facility and below this rate, the demand for private health care was positively
related to inflation rates.




             70.00




             65.00




             60.00
   private




             55.00




             50.00




             45.00
                                                                                  R Sq Quadratic =0.454



             40.00


                     0.00         20.00             40.00               60.00   80.00            100.00
                                                            Inflation
Figure 4.3: Inflation by Private Utilization Care




                                                       107
Hypothesis 3: A strong statistical correlation exists between cost of medical care for services
offered by public health care facilities and private health care facilities.



Based on Figure 4.4, there is a strong positive statistical association between cost of medical care

for public health care and that of private health care (R-squared = 0.741). This means that 74.1%

of the variance in cost of public health care services is owing to a 1% change in the cost of

private health care services in Jamaica.



                 600.00




                 500.00




                 400.00
   COST_PUBLIC




                 300.00




                 200.00




                 100.00
                                                                                  R Sq Linear = 0.741



                   0.00


                          250.00   500.00          750.00          1000.00         1250.00
                                               COST_PRIVATE

Figure 4.4: Cost of Medical care for Public and private health Care




                                                108
Hypothesis 4: There exists an inverse statistical correlation between public health care and
private health care utilization

On examination of the data (Figure 4.4), there is a strong inverse statistical correlation between

public and private health care utilization of Jamaicans (R-squared = - 0.89). Based on Figure 4.5,

89% of the change in public health care utilization of Jamaicans is due to a 1% change in private

health care utilization. Continuing, the rate of change between public and private health care

switching is constant (or linear).



            60.00




            50.00
   public




            40.00




            30.00



                                                                                 R Sq Linear = 0.89



            20.00


                    40.00   45.00       50.00          55.00       60.00        65.00          70.00
                                                      private


Figure 4.5: Public and private health Care Utilization




                                                109
Hypothesis 5: There is a direct association between utilization of public health care facilities and
self-reported illness/injury


Based on Figure 4.6, the correlation between self-reported illness (or self-reported injury) is a
positive one. The statistical association is a weak one (R=0.2520, and that only 6.4% of the
variability in public health care utilization by Jamaicans can be explained by self-reported illness
(or self-reported injuries). Here, self-reported illness is a weak predictor (R-squared = 0.064) of
the rationale for public health care utilization in Jamaica. With a weak R-squared between the
two aforementioned variables, illness/injury is not a good explanatory for public health care
utilization in Jamaica as significantly more people attend public health care utilization (57.8 per
cent) when 12.6 per cent of the population reported illness/injury compared to 39.4 per cent
when 18.3 per cent of the populace indicated suffering from illness/injury.


            60.00




            50.00
   public




            40.00




            30.00



                                                                                R Sq Linear = 0.064



            20.00


                    8.00   10.00       12.00          14.00       16.00        18.00           20.00
                                               illness_injury

Figure 4.6: Visits to Public Health Care Facilities and the Number of Reported Illness/Injury




                                                110
Hypothesis 6: Increases in inflation rates reduces the ownership of health insurance coverage




On examination of the correlation between health insurance coverage and inflation it was

revealed that a non-linear relationship existed. The findings revealed that people purchase less

health insurance in periods of high inflation (Figure 4.7) except when inflation increases beyond

60 per cent, suggesting that less is spent on health seeking behaviour (proxied by the purchase of

health insurance coverage) in period of high inflation.



                      20.00




                      18.00




                      16.00
   Health Insurance




                      14.00




                      12.00




                      10.00
                                                                           R Sq Quadratic =0.477



                       8.00


                              0.00   20.00   40.00               60.00   80.00            100.00
                                                     Inflation

Figure 4.7: Health Insurance Coverage and Inflation




                                                     111
Hypothesis 7: There is a strong correlation between incidence of poverty and inflation


The data revealed a strong statistical correlation between incidence of poverty and inflation (R

squared 0.777) (Figure 4.9). This means that 77.7 per cent of ‘incidence of poverty’ can be

explained by a one per cent change in the inflation rate. In addition, inflation is not only

synonymous with increased prices but increased incidence of poverty, suggesting that in periods

of persistently high inflation, more people will become poorer. The association between the two

aforementioned variables is a linear one.




                       50.00




                       40.00
   INCIDENCE_POVERTY




                       30.00




                       20.00



                                                                                             R Sq Linear = 0.777



                       10.00


                               0.00          20.00           40.00               60.00   80.00             100.00
                                                                     inflation




                        Figure 4.8: Incidence of Poverty and Inflation, 1988-2007




                                                               112
Hypothesis 8: There is a strong positive correlation between public health care utilization and
incidence of Poverty

The findings (in Figure 4.9) have disproved the hypothesis as there is weak negative correlations

between public health care utilization and incidence of poverty (R2= 0.236). The relationship is

a curvilinear one, indicating that as the incidence of poverty increase people switch from visiting

public health facilities. Furthermore, only 23.6 per cent of public health care utilization is

explained by a 1% change in ‘incidence of poverty’. Embedded in this finding is the role of

switching from health care to home care in periods of increased poverty, suggesting that when

the people become poorer (or increases in poverty rates), people will be highly likely to spend

more for public health care utilization. Nevertheless, when incidence of poverty increases

beyond approximately 35%, people begin to switch to the services of public health care facilities.


            60.00




            50.00
   public




            40.00




            30.00



                                                                                R Sq Quadratic =0.236



            20.00


                    10.00        20.00               30.00              40.00                   50.00
                                           Incidence of Poverty

Figure 4.9: Public Health Care Utilization and Incidence of Poverty


                                               113
Hypothesis 9: There is a weak statistical correlation between private health care utilization and
incidence of poverty


The correlation between private health care institution and incidence of poverty is a moderate

one (R=0.56) (Figure 4.10). The relationship between the two variables is a non-linear one,

indicating that the rate of change is not constant over the event, as there is a positive association

between the aforementioned variables up to poverty rates of approximately 32% and beyond this

is private health care utilization begins to fall at an increasing rate. Furthermore, for every 1

percentage change in incidence of poverty, private health care utilization increases by 31.6

percentage points.




                                                114
             70.00




             65.00




             60.00
   private




             55.00




             50.00




             45.00
                                                                                R Sq Quadratic =0.316



             40.00


                     10.00     20.00                30.00               40.00                   50.00
                                         Incidence of Poverty

Figure 4.10: Private Health Care Utilization and Incidence of poverty




                                              115
Hypothesis 10: There is a positive correlation between illness/injury and inflation



Based on Figure 4.11, there is a weak positive correlation between illness/injury and inflation.

The data revealed that 4.4 per cent of illness/injury is explained by a 1 per cent change in

inflation suggesting that in period of high and persistent increases in inflation, more people will

become ill or injured (self-reported illness or injury).


                    20.00




                    18.00




                    16.00
   illness_injury




                    14.00




                    12.00




                    10.00
                                                                                  R Sq Linear = 0.044



                     8.00


                            0.00   20.00        40.00               60.00     80.00             100.00
                                                        inflation

Figure 4.11: Illness/Injury and Inflation




                                                 116
Hypothesis 11: There is a negative correlation between cost of Public and private health Care

Cost and Inflation



The findings revealed that in period of low inflation the cost of expenditure on private health

care is higher than expenditure on public health care utilization, suggesting that switching occurs

in those periods. The reverse is the case in periods of high inflation (Figure 4.11). There is a

remarkable disparity between expenditure on private health care and public health care in periods

of low and high inflation. The data revealed that in periods of low inflation the rate of

substitution for private health care utilization is substantial; however in periods of persistently

high inflation the rate of substitution is smaller than substitution rate in low inflationary periods.



                                                                                           COST_PRIVATE
                                                                                           COST_PUBLIC



   1,250




   1,000




     750




     500




     250




           0


                  6.10    7.20    8.80    9.20   13.70     15.30   16.80   25.60   29.80       40.20

                                                    Inflation



           Figure 4.12: Cost of Public and private health Care Cost and Inflation
                                                  117
Hypothesis 12: There is a positive correlation between home remedy and inflation



The relationship between seeking medical care and inflation is twofold. Firstly, when inflation

rates range from 0 and 60 per cent, there was an inverse correlation with seeking medical care.

However, when inflation increases beyond 60 per cent, it positively associated with seeking

medical care. Secondly, 68.9 per cent of people seeking medical care can be explained by

inflation. The findings show (Figure 4.13) is a strong association between seeking medical care

and inflation (R squared=0.689). The data were better fitted by a curvilinear diagram than a

linear one (Figure 4.13), and this explain why we did not use the linear valuation in any

interpretation for this examination. If the inflation rate of 80.2 per cent for 1991 is taken as an

outlier, the linear relationship between the two variables will be a strong moderate one,

indicating that 56.5 per cent of the medical health care seeking behaviour of Jamaicans can be

explains by a 1 per cent change in inflation rate.


                          70.00




                          65.00
   Seeking Medical Care




                          60.00




                          55.00




                          50.00
                                                                        R Sq Linear = 0.565
                                                                                          R Sq Quadratic =0.689



                          45.00


                                  0.00   20.00   40.00               60.00             80.00             100.00
                                                         Inflation


                                                   118
Figure 4.13: Seeking Medical Care By Inflation




                                                 119
Hypothesis 13: There is a correlation between People Seeking Medical Care and Incidence of

Poverty



The relationship between people ‘seeking medical care’ and ‘incidence of poverty’ is a

curvilinear one. This correlation is a strong negative one (R = 0.871). This finding revealed that

the more ‘incidence of poverty’ increases, the less likely it is that Jamaicans will demand

medical care whether public or private. Furthermore, there is a minimum percentage of

‘incidence of poverty’ beyond which people begin to demand more medical care suggesting that

reduction in ‘incidence of poverty’ explains 75.9 per cent of the reason for people seeking

medical care.


                          70.00




                          65.00
   Seeking Medical Care




                          60.00




                          55.00

                                                                                            R Sq Quadratic =0.759



                          50.00




                          45.00


                                  10.00           20.00            30.00            40.00                    50.00
                                                          Incidence of Poverty

                            Figure 4.14: Seeking Medical Care and Incidence of Poverty

                                                                 120
Hypothesis 14: There is a positive correlation between health seeking behaviour and health

insurance coverage



There is a strong statistical correlation between people ‘seeking medical care’ and ‘health

insurance coverage’ (Figure 4.15). The relationship between the two aforementioned variables is

curvilinear as people will seek more medical care with the ownership of more health insurance

coverage.                         The demand for health insurance optimizes at 18.4 per cent, after which the

population begins to seek less medical care. This means that health insurance coverage is a good

predictor of people willingness to demand (or seek) health care in Jamaica.




                          70.00




                          65.00
   Seeking Medical Care




                          60.00




                          55.00




                          50.00

                                                                                            R Sq Quadratic =0.751



                          45.00


                                   8.00       10.00      12.00           14.00      16.00   18.00           20.00
                                                                 Health Insurance

Figure 4.15: Seeking Medical Care and Health Insurance



                                                                   121
Limitation of study

The current study is affected by a number of limitations which influence its findings. The use of

secondary data limits the investigators to analyzing issues within the dataset. In addition to the

aforementioned issue, owing to the number of data points were limited to bivariate correlations

as the data did not lend itself to multivariate analysis. This limitation means that we do not

simple a single markers of factors that simultaneously determine public and private health care

utilization. Furthermore, Jamaica does not have statistics on depression rates in the period of the

study and so we were unable to disaggregate illness/injury in order to establish whether the

increase in illness/injury in inflationary periods is owing to psychological or physiological

symptoms.

Discussion

Generally, Jamaicans have a preference for private health care service utilization than public

health care service utilization (Table 4.1). For the past 2 decades [1988 to 2007], Jamaicans have

an ostensibly preference for private health care service. During 1991 to 1998, the minimum self-

reported usage of private health care facilities was 60 per cent with the highest being 67 per cent

in 1994. However over the last decade [1998 to 2007], this preference has been declining

indicating that a switch has been occurring to public health care service utilization. In order to

understand the substitution of private for public health care utilization within the context of

reduced ‘incidence of poverty’, the phenomenon of inflation must be taken into account within

this discourse that seek to explain the rationale for the switching of public and private health care

utilization.

        Inflation in the last one half decade [2003 to 2007] has been increasing unlike the first

from 1988 to 1992 when the rate was as high as 80.2 per cent. It is well established in literature

                                                122
that inflation retards production, reduce real wages and lower standard of living1-17. It also

creates socio-economic challenges such as reduced real wage, unemployment, increased prices,

lowered rate of ownership of health insurance, declining health seeking behaviour, increased

poverty, and then there is the issue of in affordability of goods and services. With the increase in

prices (costs) owing to inflation, there will be many challenges for customers, clients and

patients but it must be noted here that some inflation is good in an economy. This study is not

examining some inflation but persistent inflation. Again inflation retard Gross Domestic Product,

increased costing of goods and services in an economy, which speaks to the economic challenges

of poor and other people with that society.

       Despite the high inflationary period of the early 1990s, Jamaicans demand for private

health care utilization was higher than that of public health care utilization. The current study has

revealed a strong negative statistical correlation between ‘seeking medical care’ and inflation as

well as a strong association between ‘seeking medical care’ and ‘incidence of poverty’. This is

coupled with the reality that private health care costing is greater than that of public health care,

which accounted for the high utilization of private health care in the 1990s. The problem arises,

when inflation becomes persistent as people’s real wage will be lowered, unemployment rises, a

downturn in the world economy in particular America, and this provide all the economic

challenges to borne on health utilization. A group of scholars10 have found a positive correlation

between unemployment and low income11-12 and suicides18-21 and when this is juxtaposed with

these research findings, persistent inflation and the economic recession in America coupled with

the higher prices of ‘food and non-alcoholic beverages’ as well as fuel in addition to the time lag

between price reduction in the global economy and Jamaica, these economic challenges are

accounted for the substitution of public and private health care utilization. Another explanation

                                                123
for the increase in private health care utilization in periods of high inflation is owing to the

positive correlation between inflation and illness, suggesting that although costs are increasing

there is the need for medical care. There is a critical finding that emerged when we examine the

statistics as periods of high inflation ‘seeking medical care’ is lower indicating that people resort

to more home remedy. This is evident as in 1991when inflation was the highest (80.2 per cent);

seeking medical care was at its lowest (47.7 per cent) in the 2 decades of statistics reviewed for

this study.

       The current study showed that inflation is positively correlated with ‘incidence of

poverty’ which concurs with the literature1 and within the context that inflation is directly

associated with lowered real wages coupled with increased pricing of goods, and a marginal

increase in seeking medical care. Noting this it should not be surprising that there is a switching

from private health care utilization to public health care utilization. This study revealed a strong

indirect correlation between private health care service utilization and public health care service

utilization, with a fixed income or lowered real wages and increased prices of ‘food and

beverage’, inflation which is expressed through prices changes all commodities is explain

reduced health care seeking behaviour as well as the purchase of health insurance as decision to

buy ‘food’ is filled first before health care is sought by people.

       This study showed that when inflation is low the demand for private health care facilities

is significantly greater than public health care services. It was also found that when inflation rises

beyond a certain percentage (40 per cent), there is a substitution of private health care services

with public health care service utilization. Embedded in this finding is the strong preference for

private health care services of Jamaicans as they do not merely substitute private for public

health care services when inflation is low or it is not sustained over a year. Thus, what this study

                                                 124
has highlighted is the fact that if inflation continues to rise over many years people will switch

from private to public health care services. Inflation does not only influence the cost of private

health care services as this equally affects the cost of medical care of public health care services.

Costing is an important ingredient in decision to utilize public and private health care services

and what emerged from this study is fact that poverty increases when inflation increases and this

suggest that substitution has to do with affordability. This fact is supported by the statistics from

the Jamaica Survey of Living Conditions that have showed that over the last one half decade, the

poorest Jamaicans have increased their spending on ‘food and beverage’ from 50 per cent to

approximately 54 per cent and lower their demand for health care. On the other hand, the

wealthy and wealthiest Jamaicans have increased their spending on ‘food and beverage’ but their

expenditure was less than 40 per cent. It follows that with the increase spending on ‘basic food’

within the context of persistent rise in inflation, this accommodates for the substitution of private

health care facilities to public health care services.

        Health care seeking behaviour falls in periods of persistently high inflation. Using health

insurance as a proxy for health care seeking behaviour, over the last one half decade health

insurance coverage has been falling and this support the private-public health care substitution.

There is an important finding here as there is a positive correlation between inflation and

illness/injury and this justifies the increase in public health care utilization as in periods of

inflation this means increased prices even in medical care. Inflation mean higher prices and

people will have less disposable income to spend on health care as they must spend more for

consumption goods (food and beverage). One scholar argued that the Jamaican economy

underperformed in comparison to other Latin American and Caribbean societies in the 1990s1,

and inflation did not only reached a record 80.2 per cent in 1991, but private health care

                                                  125
utilization was the highest in that period. There is a crucible fact in this high private health care

utilization as demographic compositions of those who access this facility are private middle-to-

upper class individuals. This is inferred from the statistic of the Planning Institute of Jamaica and

the Statistical Institute of Jamaica (Jamaica Survey of Living Conditions) (Table 4.5) that

showed that approximately 68 per cent of consumption of the poorest Jamaicans is spent on food,

beverage and household expenses, with a maximum of 7 per cent spent on health care. Hence, in

period of persistently inflation, the wealth and the wealthiest suffer from more injuries as the

current study revealed a positive correlation between private health care service demand and

inflation, and it was found that direct association exists between injury/illness and inflation. The

poor on the other hand in periods of high inflation will resort more to spending on ‘food and non-

alcoholic beverage’ than on health care, which justify reduce public in periods of persistently

high inflation. For the poor in periods of low inflation they will attend to health care more than in

periods of high inflation and this is equally the case for the middle class. Furthermore, in periods

of low inflation the addition amount that is available to the individual coupled with lower cost of

health care explains the influx of people attending public health care because they are able to

afford their natural preference for private health care services that becomes difficult in times of

exorbitantly high prices.

       Given that increased cost of medical care is not only synonymous with private health care

utilization, and Jamaican preference for private health care utilization is evident as the rate of

substitution in periods of low inflation for the services of private health care facilities is such that

it is wider than in periods of persistently high inflation. Embedded in this reality is the society

low appetite for utilizing public health care services. One of the rationale for public health care

utilization not been overtaken by public health care utilization is the fact that private health care

                                                  126
costing has been reducing and this as well as the composition of the those who attend former

facilities accounts for the reduction but not the total substitution. Private health care facilities

provide a product in a different milieu and the service quality is different from that provided by

public health care facilities; hence, the substitution from attending public health care facilities is

substantial in the periods of low inflation but in high inflationary periods, the rate of substitution

away from private health care facilities is lower than that of substitution rate in periods of low

inflation. The performance of public and private health care services was never assessed in this

study, but it can be extrapolated from the findings that Jamaicans are dissatisfied with the

services offered by public health care facilities and this is borne out from the high substitution

rate in period of low inflation, suggesting that if they were able to afford it in period of inflation

they would have maintain utilizing the services of private health care facilities.

       The poorest in any society is the most affected in periods of inflation (persistent or

otherwise) and this is also reflected in the health seeking behaviour statistics. In 1991, when

inflation was 80.2 per cent statistics revealed that Jamaicans seek the least health care in 2

decades (47.7 per cent – Table 4.6). On the contrary, when inflation was at the least in the 2

decade period (5.7 per cent), Jamaicans sought medical care the most in the period (70.0 per

cent). One of the findings of this study is the strong correlation between medical care seeking

behaviour and inflation and within the context that inflation affects the poorest the most, the

findings revealed that in the period of the highest inflation, incidence of poverty stood at its peak

and medical care demand was at its least. Public health care demand Jamaica is substantially a

poor people phenomenon and this is embedded in the statistics as periods of high inflation (40 to

81 per cent) which corresponds to high incidence of poverty, public health care utilization was at

its least will private health care utilization was between 1.6 to 2.2 times more than that of visits

                                                 127
to public health care facilities. Another issue that emerged from this finding is the stressed level

of those in the middle to upper classes in period of high inflation and how they resort to medical

care to address their psychological state. The poor, on the other hand, because they are unable to

afford medical care compared to the middle class or the affluent resort to home care and

violence. Using Anthony Harriott’s work22, we found that the rates of violent crimes (per 100,

000) in Jamaica increased from 1988 to 1990, and over 1990 to 1992 during the period in which

inflation and incidence of poverty were high and health seeking behaviour of the poor was low.

       Costing of health care services23 is not the only deterrent to the utilization of public

health care services in Jamaica as the operation of public health facilities is a part of the rationale

for the switching in periods of affordability. A study conducted in Jamaica using a mixed

methodology (survey of 1,017 respondents and a focus group) revealed that loudness of staffers,

embarrassments, aggressive behaviour, physical layout of the public facilities including the

cleanliness of the facilities were among some of the reasons given for dissatisfaction with public

health utilization23, and these were concurred by the World Development Report24. The World

Development Report identified a number of factors – credibility of public health staffers,

unprofessional treatment of patients, abuse, corruption- that we will title switching factors that

account for the substitution of private to public health care utilization. Those are some of the

reasons why Jamaicans prefer utilizing private health care services as the treatment of the staffers

is highly professional, respectful and accommodating unlike the aforementioned issues that are

synonymous with public health care. Another deterrent factor that emerged from Bailey and her

colleagues’ work was transportation cost. This speaks to the accessible of health care for some

residents who dwell in rural areas coupled with their economic state of poverty.



                                                 128
       What accommodates for the narrowing of the gap between public and private health care

utilization in Jamaica within the context of an economy has been experiencing lowered rates of

‘incidence of poverty’ and lower rate of inflation than in the 1990s? In September 2001,

American experienced the 9/11 and the year proceeding that remittances was 26.6 per cent and

this was the lowest in six years [2001 to 2006] (Table 4.3). Now with the downturn in the world

economy in particular the American economy, this explains why Jamaicans have received lower

remittances in 2007 (Table 4.4). Remittance is a source of income for many Jamaicans, and the

downturn in the American economy is negatively impacting on the amount that is received by

Jamaicans. For 2007, the per cent of Jamaicans receiving remittances fell (Tables 4.3 & 4.4).

Remittance normally is an income subsidy for countless Jamaicans and this accounts for lowered

expenditure on health care and other goods (or services). Although inflation rates are not

generally comparable to that of the 1990s, again the recession in the American economy is

resulting in lowered income for many Jamaicans and inflation for 2007 over 2006 has increased

by 289.04 per cent. It should be noted here that less Jamaicans in 2007 utilize both public and

private health care services, suggesting more people were resorting to home remedy. This is

supported by the statistics which revealed that 66 per cent of Jamaicans seek medical care

compared to 70 per cent in 2006. The lowered economic growth in the United States coupled

with the increases in global food prices, the rise in prices of foods; beverage and fuel are forcing

Jamaicans to substitute utilizing private health care services for home care.

       Recently (2007), the Jamaica removes the cost associated with medical care from all

public health care institutions and this would not be captured in the 2007 public health care

utilization but this would be catering to a few people who wanted to attend those institutions but

were not able to afford it. Primarily those who attend private health care institutions are those of

                                                129
the middle-to-upper class who still do not have a preference for public health care services

(visits). The lowering of the cost of public health care means a lowering of health care cost of

private health care services. This adjustment in prices accommodate for the lowering of

substitution away from private health care to public health care despite the reality of economic

recession in the world economy. Based on Table 4.2, annual inflation on ‘food and non-alcoholic

beverage’ has increased by 24.7 per cent in 2007; cost of medical care has increased by 3.4%

coupled within the context of massive general annual rate of 16.8 per cent inflation, the

challenges of survivability is becoming increasing more difficult and so more people are

resorting to traditional care (home remedy). Jamaica Survey of Living Conditions (2006) had

that 28.5 per cent of Jamaicans indicated that they used home remedy as it was the preferred way

to go compared to 16.8 per cent in 2004; and in 2006, 22.2 per cent reported that they were

unable to afford medical costing compared to 19.6 per cent in 2004.

       In a national survey that was conducted in 2006, Powell, Bourne, & Waller25, using

probability sample of technique drew a sample of 1,338 Jamaicans (respondents), when the

respondents were asked “How would you describe your present economic situation ad that of

your family?”, 69% indicated at most average and this figure 19% indicated bad and very bad.

Another question that was asked is “Does you salary and the total of your family’s salary allow

you to satisfactorily cover your needs?” only 38.1% of Jamaicans said that their salary was able

to cover their needs. In the same study, when the respondents were asked “[Do you] feel secure

about the state [your] health?”, out of a maximum 10 points, those who classify themselves as in

the lower class had a score of 5.8, the middle class had a score of 6.7and the upper class, 6.6.




                                                130
Concluding Remarks



This paper has presented an exploration of public and private health care utilization in Jamaica

and in the process provides an understanding of the role of inflation on health seeking behaviour

as well as an explanation for the narrowing of the gap between the two aforementioned

utilizations. While inflation accounts for a low percentage of the explanation for the switching,

when it is persistent it results in increased unemployment, cost of living, downturn in the

economic, forfeiture in the payment of debts, and the increased in deprivation of the poor. This

research is advantageous to policy makers, medical practitioners and other scholars as we

provide information on this critical matter, but there are many areas that we were unable to

examine given that we used secondary data. It would be interesting to see whether suicides

increase in periods of persistently high inflation or depression increases in periods of inflation

but data on the matter are not consistent over the period. Nevertheless, we provide pertinent

information within the context of the available data for 2 decades (1988to 2007). We will now

conclude on the important issues of the study.

       There is an increasing concern in the world about economic recession, lowered real

wages, redundancies, increased prices, declining consumer demand for good (or services) and

poverty, but in our quest to stimulate economic growth because of its influence on all aspects of

socio-economic development, and now there is a study that has examined the relationship

between inflation and public and private health care utilization. Using two decades of statistics,

the findings of showed that persistently increased inflation results in substitution from private to

public health care utilization, and that in periods of low inflation (single digit), the rate of

substitution for private health care utilization away from public health care services is

                                                 131
significantly greater. One of the fundamental aspects to development is people, and people are

primarily concerned about their survivability which explains a critical aspect to this study. One

of the crucible finding of the current research is the positive correlation between inflation and

illness/injury. Within the context that persistent inflation over the last one half decade in Jamaica

(2003 to 2007) coupled with the increased prices in ‘food and non-alcoholic beverages’,

Jamaican are resorting to home care. Jamaicans have a preference for private health care

utilization, and within the context of the economic recession in America that influences the

survivability of tourism industry, remittances and the economic opportunities of countless

Jamaicans, people are resorting more to home care instead of substantially substituting private

for public health care utilization. In keeping with the natural instinct to survive with the

aforementioned issues, Jamaicans have taken the decision to fulfill their basic physiological

needs (food, shelter, and health) and with the persistent increase in those commodities, they have

taken decisions to spend on food, shelter, clothing and less on health care except if their ill-health

depends on their state of survivability. Food and beverage fulfill fundamental needs, and in case

of increased prices this will automatically mean they will spend more on those commodities, and

reduce their spending on health care.

       Powell, Bourne & Waller’s work25 when the respondents were asked “[Do you] feel

secure about affording necessities” out of a maximum score of 10, those who classified

themselves as lower class indicated 5.2, the middle class and the upper class indicated 6.7.

Poverty (poor) means deprivation from resources – income, health insurance coverage,

schooling, poor sanitation and drinking water, and nutrition - and this account for them

demanding less health care in period of high inflation. They will be unable to afford ‘food and

beverage’ and this they would prefer to purchase as against demand medical care and so explain

                                                 132
their low access to health care which accounts for more home care in this cohort. Therefore, poor

who equally prefers private health care services in Jamaica is unable to afford this, and their state

accounts for lowered public health care utilization in high inflationary period. In inflationary

periods the middle class to the wealthiest class demand more health care which is in keeping

with the psychological stressors of the time. Accordingly, the narrowing of the gap between

public and private health care in Jamaica is owing to (i) persistent increases in the inflation rates,

(ii) increase prices for consumption and non-consumption goods – including foods, fuel and

transportation costs, (iii) the downturn in the American economy and (iv) increases in

illness/injury within the aforementioned context. With education, despite the challenges of

economic shortfalls in the nation, people realize the importance of seeking medical care and in

order to accomplish this reality, the substitution of private for public health care utilization is in

keeping with health consciousness and increases costing of foods and non-consumption

commodities.

       Poverty in Jamaica is synonymous with rural residents (Table 4.7) and although there has

been a substantial decline in the prevalence of poverty over the years and more so in 2007 (Table

4.1), it increased in rural Jamaica. With the downturn in the America economy which is having

an inverse effect on remittances, increases in prices of food and non-alcoholic beverages coupled

with the increased poverty in rural Jamaica, a part of the decline in public and private health care

utilization and expenditure (Table 4.5) is owing to economic difficulties faced by rural residents.

Some Jamaicans continue to purport that among the difficulties for not seeking health care is

affordability – in 2007 over 2006, real mean public expenditure on public health care (in 1990 $)

decline by 40.5% while in the same period mean amount spent on drug fell by 20.8%- and this

reality is compounded for the elderly populace. Unlike the working age population, a small

                                                 133
proportion of the elderly are employed in addition to increased prices, downturn in the

Americans, lowered remittances and increased poverty in rural Jamaica, the elderly who

constitute of approximately 47 per cent of the rural Jamaica (Table 4.8) are having to facing the

economic challenges of the time. Within the context of those realities, the lowering of health

care seeking in Jamaica is due to elderly residents’ withdrawal from seeking health care

accounted for the lowered public and private health care utilization and expenditure.

       This research has many unresolved questions that are felt for further studies. One, we

know that there is a correlation between illness/injury but we are not cognizant whether or not

this is owing to physical or psychological conditions. Two, the study assumes that males and

females are similar experiences and with the context of studies that have shown that there is

disparity between the socio-economic conditions of the sexes, the research is needed to clarity

any similarities (or dissimilarities). Third, poverty is synonymous with rural areas and so any

study that seeks to understand Jamaicans experiences must disaggregate this by area of residence

and age cohorts. Fourthly, Jamaica is an island that is interdependent on the global economies

and so it would be interesting to inco-operatate this on public and private health care utilization

in Jamaica.


ACKNOWLEDGEMENT
The author would like to thank the Data Bank in the Sir Arthur Lewis Institute of Social and
Economic Studies, the University of the West Indies, Mona, Jamaica for making the dataset
(Jamaica Survey of Living Conditions, 2002) available. It was used for the current study.



Disclosure Statement
The authors have no conflicts of interest to report.



                                                134
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22. Harriott A. The Jamaican Crime Problem: Some Policy Considerations. In Harriott A,
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25. Powell LA, Bourne P, Waller L. Probing Jamaica’s Political Culture: Main Trends in the
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    Indies; 2007




                                         136
Table 4.1: Inflation, Public and private health Care Service Utilization, Incidence of Poverty,
Illness and Prevalence of Population with Health Insurance (in per cent), 1988-2007


Year             Inflation    Public        Private       Prevalence     Illness      Health
                              Utilization   Utilization   of poverty                Insurance

1988            8.8          NI              NI         NI              NI            NI
1989           17.2          38.0            54.0       30.5            16.8          8.2
1990           29.8          39.4            60.6       28.4            18.3          9.0
1991           80.2          35.6            57.7       44.6            13.7          8.6
1992           40.2          28.5            63.4       33.9            10.6          9.0
1993           30.1          30.9            63.8       24.4            12.0          10.1
1994           26.8          28.8            66.7       22.8            12.9          8.8
1995           25.6          27.2            66.4       27.5            9.8           9.7
1996           15.8          31.8            63.6       26.1            10.7          9.8
1997           9.2           32.1            58.8       19.9            9.7           12.6
1998           7.9           37.9            57.3       15.9            8.8           12.1
1999           6.8           37.9            57.1       16.9            10.1          12.1
2000           6.1           40.8            53.6       18.9            14.2          14.0
2001           8.8           38.7            54.8       16.9            13.4          13.9
2002           7.2           57.8            42.7       19.7            12.6          13.5
2003           13.8          NI              NI         NI              NI            NI
2004           13.7          46.3            46.4       16.9            11.4          19.2
2005           12.6          NI              NI         NI              NI            NI
2006           5.7           41.3            52.8       14.3            12.2          18.4
2007           16.8          40.5            51.9       9.9             15.5          21.2
Source: Bank of Jamaica, Statistical Digest, Jamaica Survey of Living   Conditions, Economic
and Social survey of Jamaica, various issues

Note: Inflation is measured point-to-point at the end of each year (December to December),
based on Consumer Price Index (CPI)

NI No Information available




                                              137
Table 4.2

Annual Inflation in Food and Non-Alcoholic beverages and Health Care Cost, 2003-2007


                     Food and Non-Alcoholic beverage             Health Care Cost

2002                         7.8                                 5.2

2003                         10.0                                9.7

2004                         13.7                                6.4

2005                         11.7                                7.5

2006                         5.0                                 9.7

2007                         24.7                                3.4


Source: Planning Institute of Jamaica, Economic and Social Survey of Jamaica, various issues

Note: Inflation is measure using point-to-point at the end of the year (December to December).




                                             138
Table 4.3
Percentage of Households Receiving Remittances By Region, 2001-2005

                                                          YEAR

Region                      2001*         2002*       2003*           2004*   2005*   2006   2007



KMA                         28.7          22.2        27.9            30.2    38.4    50.4   41.5

Other Towns                 34.2          27.9        32.7            38.9    43.3    45.0   48.6

Rural Area                  41.6          28.9        33.0            32.1    36.9    42.3   38.6

Jamaica                     35.8          26.6        31.5            32.9    38.7    45.3   41.8

Source: Jamaica Survey of Living Conditions, 2006
*Revised Figures 2001-2005




                                                             139
Table 4.4
Percentage of Households Receiving Remittances By Quintile, 2001-2005

                                                            YEAR

Quintile                   2001*         2002*           2003*          2004*   2005*   2006   2007


Poorest                    26.0          19.8            20.6           22.5    21.2    30.4   26.0

2                          35.3          25.9            28.1           29.6    38.8    40.3   33.0

3                          44.0          28.4            35.7           34.7    38.9    41.5   44.2

4                          35.8          29.9            37.0           35.2    42.1    47.4   46.6

5                          35.0          26.4            32.1           35.7    43.4    54.9   48.6

Jamaica                    35.8          26.6            31.6           32.9    38.7    45.3   41.8


Source: Jamaica Survey of Living Conditions, 2006-2007
*Revised figures 2000-2005




                                                                140
Table 4.5
Mean Patient Expenditure ($) on Health Care in Public and Private Facilities in the Four-Week Reference Period, JSLC 1993-2004,
2006
Year                                      Visits                                                    Drugs
                    Private                             Public                         Private                    Public
              Nominal             Real           Nominal        Real            Nominal       Real         Nominal       Real
                                   (1990$)                     (1990$)                     (1990$)                      (1990$)
1993          298                  85            115           33            331           94            131            37
1994          461                  109           91            21            417           98            163            38
1995          496                  99            130           26            509           101           234            47
1996          598                  104           148           26            685           119           176            31
1997          693                  95            283           39            946           129           575            78
1998          832                  106           315           40            1050          134           316            40
1999          1301                 154           339           40            1196          142           401            47
2000          1081                 120           309           34            1241          138           468            52
2001          1103                 115           546           57            1698          177           742            77
2002          1339                 132           464           46            1501          148           541            56
2004          2278                 191           489           41            2181          183           843            71
2006          1406                 101           860           62            2212          158           1174           84
2007          1679.5               114.2         539.9         36.9          2573.1        174           929.7          66.5

Source: Jamaica Survey of Living Conditions 2002, 2006 and 2007




                                                             141
Table 4.6
Purchased medication and Seeking Medical Care (Per Cent), 19-2006


              1991   1992     1993   1994   1995   1996   1997    1998   1999   2000   2001   2002   2004   2006   2007


Per cent purchased medication

Public        NI     8.9             21.4          19.1   22.0    19.7   18.5   20.8   20.0   26.5   19.1   15.9   13.7

Private       NI     58.5            75.6          78.0   74.3    76.6   77.0   73.3   76.9   68.0   74.3   76.6   80.3

Seeking
medical care 47.7    50.9     51.8   51.4   58.9   54.9   59.6    60.8   68.4   60.7   63.5   64.1   65.1   70.0   66



Source: Jamaica Survey of Living Conditions 2002, 2006 and 2007

NI No Information Available




                                                                 142
Table 4.7
Distribution of Poverty By Region (Per cent), 1997-2007


                                    1997   1998   1999    2000    2001   2002   2003   2004   2005   2006   2007


Region

KMA                                 13.6   12.5   18.2    17.2    14.7   15.8   12.8   26.3   20.3   21.2   19.9

Other Town                          13.1   15.1   12.5    16.0    13.7   15.7   13.2   9.9    9.3    13.1   8.9

Rural Area                          73.3   72.5   69.3    66.8    71.6   68.5   74.0   64.7   70.2   65.7   71.3



Source: Jamaica Survey of Living Conditions 2002, 2006 and 2007




                                                                 143
Table 4.8

Distribution of Elderly Population (ages 60 years and older) By Region (Per Cent), 1997-2007

Year                 KMA                   Other Towns                   Rural Area

1997                 27.2                  18.5                          54.3

1998                 18.4                  16.1                          65.6

1999                 26.6                  18.0                          55.4

2000                 28.4                  19.0                          52.6

2001                 25.4                  19.4                          55.2

2002                 27.0                  14.7                          58.3

2003                 25.0                  13.8                          61.2

2004                 29.1                  21.5                          49.4

2005                 30.9                  21.4                          47.7

2006                 30.8                  22.7                          46.5

2007                 32.5                  20.9                          46.6

Source: Jamaica Survey of Living Conditions, 2007




                                                               144
         60.00




         50.00
public




         40.00




         30.00



                                                                          R Sq Linear = 0.175



         20.00


                 10.00       20.00         30.00         40.00        50.00              60.00
                                             crime_murder

           Figure 4.17: Public Health Utilization and Crimes (Homicides per 100,000)




                                                   145
                                                                       Chapter
                                                                                         5
     Public Health Behaviour-Change Intervention Model for Jamaicans:
                 Charting the Way Forward in Public Health


               Paul A. Bourne, Donovan A. McGrowder, Desmalee Holder-Nevins




Health education and health promotion are driven based on understanding lifestyle practices of
a population. The aims of the study are to construct health care demand and health promotion
models which are appropriate to the Jamaican population, and to determine the predictors of
health care demand. Majority of the respondents did not have private health insurance coverage
(88.2%); 53.6% had a partner; and 35.2% were poor; 50.4% had at most primary level
education. The predictors of health care demand are: health care demand in previous period (t-
1) (OR = 0.049); illness (OR = 10.338); injury (OR = 2.370); social class (middle class: OR =
1.135; wealthy: OR = 1.394); per capita consumption (OR = 1.099); union status (OR = 0.845);
gender (OR = 2.221); private health insurance coverage (OR = 1.942); age (OR = 1.022) and
educational attainment of respondents (OR = 1.315). This study can be used to model critical
health promotion emphasis in Jamaica, and any other country with similar socio-demographic
and political characteristics.




Introduction



Health, wellbeing and wellness are multidimensional issues and the concept of health according

to the World Health Organization (WHO) is multifaceted. Health is defined as the state of

                                             146
complete physical, mental and social wellbeing, and not merely being the absence of disease or

infirmity (WHO, 1948). Health status is an indicator of wellbeing, which is a state of happiness

where an individual has positive feeling status and experiences life satisfaction (Diener, 1984;

Diener et al., 1985).




       It is from the definitions of health that public heath behavioural change programmes are

designed and fashioned in seeking to create ‘good’ health for people within different cultures.

There are many theories that are applied to individual health behaviour-change (Glanz et al.,

2002; Elder, 2001). King and colleagues argued that these are not relevant for populations in

‘traditional communal cultures’ (King et al., 1995). In addition to the limitation offered by King

et al’s work, they omitted the dominance of Western psychological theories that are from

developed countries and the fact that they are based on an understanding of the individual’s

cognitive process than about intervention. These theories are not for developing nations that have

unique characteristics and culture. Cohen and colleagues developed a model for behaviour

change using structural modeling which addresses physical structures, social structures, cultural

and media messages (Cohen et al., 2000). Though Cohen and colleagues’ work had broad

parameters for public health behaviour-change interventions, there are two critical limitations to

their study. These were established for developed nations and despite modifications for

developing countries; they are not model developed specifically from data for developing nations

or Caribbean and in particular Jamaica. Second, the model omits to emphasize personality and

cognition in public health behaviour-change intervention. Elder (2001) noted that health

communication and learning theories are culturally specific, and are relevant for developing

countries.
                                               147
       The biomedical model which has long been argued by the WHO (1948) and Engel (1960)

as early as in the 1940s and the 1960s respectively are regarded as too narrow and have been

replaced by a broader construct, the biopsychosocial model developed in the United States by

Engel (1978) and Longest (2005). Among the plethora of theories or models that are used in

Caribbean health education strategies such as Health Belief Model; Theory of Reasoned Actions;

Theory of Planned Behaviour; Transtheorical Model and Stages of Change, the Precaution

Adoption Process Model (with some modifications), none of which are indigenous to the

Jamaica and other countries in the Caribbean. According to Glanz and colleagues, while it is

reasonable to assume that a theory such as Health Belief Model is applicable to different

cultures, it also is important to realize that constructs may have to be adapted to make them more

relevant to the target culture (Glanz et al., 2002). Modification of these theories may not be the

most appropriate approach to the care of patient. Therefore there is the need to develop a health

care model that is indigenous to Jamaica and other countries in the Caribbean.




       Health education and health promotion are driven based on understanding lifestyle

practices of the population, and so it is important to investigate the determinants of health care

demand in Jamaica. Therefore the authors recognized the need for a discourse on health

assessment in addition to the image of health and how this influence health education and health

promotion. Understanding this concept should provide an insight into the use of practices by

health educators and health promoters in the Jamaica and by extension the Caribbean. The aims

of this paper construct health care demand and health promotion models which are appropriate to

the Jamaican population, and to determine the predictors of health care demand.
                                               148
Methods



The current research extracted a sub-sample of 16,619 respondents (66%) from the survey the

Jamaica Survey of Living Status (JSLC, 2002), based on those who indicated having sought

medical care in Jamaica. The decision to study only those who sought medical care was based

primarily on the lack of literature on health demand in the island. The sub-sample was taken

from a nationally cross-sectional survey of 25,018 respondents from the 14 parishes in Jamaica.

The survey used stratified random probability sampling technique to drawn the 25,018

respondents. The non-response rate for the survey was 29.7% accounted for by 20.5% who did

not response to particular questions, 9.0% who did not participated in the survey and another

0.2% who was rejected due to data cleaning. The study was conducted between June and

October, 2002. The study was commissioned by the Planning Institute of Jamaica (PIOJ) and the

Statistical Institute of Jamaica (STATIN). The two organizations are responsible for planning,

data collection and policy guideline for the country. The researchers selected this survey because

it was the second largest sample size for the survey in its history (since 1988 to 1998), and in that

year, the survey had questions on crime and victimization, and the physical environment unlike

previous years. Moreover, in order to establish a model that is comprehensive, the researcher

used this dataset as health status is a multidimensional tenet and so requires more variables than

less in order to examine this model. However, for the current work, the researcher used

descriptive statistics to provide background information on demographic characteristics of the

sub-sample population.



                                                149
       The JSLC was born out of the World Bank’s Living Standard Survey. The JSLC began in

1988 when the PIOJ in collaboration with STATIN adopted with some modifications of the

World Bank's Living Standards Measurement Study (LSMS) household surveys. The JSLC has

its focus on policy implications of government programmes, and so each year a different module

is included, evaluating a particular programme. The JSLC is a self-administered questionnaire

where respondents are asked to recall detailed information on particular activities. The

questionnaire covers demographic variables, health, immunization of children 0 to 59 months,

education, daily expenses, non-food consumption expenditure, housing conditions, inventory of

durable goods, and social assistance. Interviewers are trained to collect the data, which is in

preparation of the household members.




       All statistical analyses were performed using SPSS 16.0 software for Widows.

Descriptive statistics such as frequency, mean and standard deviation were used to provide

background information on the sample. A single hypothesis was tested, which is ‘health demand

of Jamaicans is a function of demographic, social, psychological and economic variables.’ The

initial variables that were selected for this study are based on literature as well as factors

identified in the theoretical framework. The enter method in logistic regression was used to test

the hypothesis in order to determine those factors that influence health status of rural residents.

Logistic regression was used as dependent variable is a binary one. Categorical variables were

coded using the ‘dummy coding’ scheme.




                                               150
        Results were presented using un-standardized B-coefficients, Wald statistics, odds ratio

and confidence interval (95% CI). The predictive power of the model was tested using Omnibus

Test of Model and Hosmer and Lemeshow (2000) was used to examine goodness of fit of the

model. The correlation matrix was examined in order to ascertain whether autocorrelation (or

multi-collinearity) existed between variables. Based on Cohen and Holliday (1982) correlation

can be low (weak), from 0 to 0.39; moderate, 0.4 to 0.69, and strong, 0.7 to 1.0. This was used to

exclude (or allow) a variable in the model. Wald statistics was used to determine the magnitude

(or contribution) of each statistically significant variables in comparison with the others, and the

odds ratio (OR) for interpreting each significant variables.




        The current study will test the hypothesis in Equation 1 (ie Model 1) to determine those

factors that are significant (p < 0.05), as those are the only ones that will constitute the final

model


HD i = f(ED i , U i , I i , C i , X i , HS i , J i , SS i , lnA) ……………………………………..[1]


The health care demand (health care seeking behavior) of person i, HD i , is a function of ED i , the

educational level of person i; Ui , the union status of person i; Ii , the duration of or severity of the

illness of person i; C i , the consumption per capita of person i; Xi, the sex of the person i; HS i ,

the ownership of life insurance of person i; J i , injuries or dysfunction of person i; SS i , the social

class (proxy by poverty quintile) of person i and A i , the age of person i, with lnA i being the log

age.


The model for health education in Jamaica is based on the inverse for Health Demand Model and

is given as:
                                                  151
                 …………………………………[2]

Where HP i is the health education of person i, which is an inverse function of HD i and some

error term, ε.

HP = 1/ f(Ei, Ui, Ii, Ci, Xi, HSi, Ji, SSi, lnA, ε) ………………………………………..[3]




Results



The sample comprised of 16,619 respondents (48.6% men and 51.4% women); with a mean age

of 39.75 years (SD = 19.05 years); majority did not have private health insurance coverage

(88.2%); 53.6% had a partner; and 35.2% were poor; 50.4% had at most primary level education

(Table 5.1).




In examining predictors of health care demands of Jamaicans, ten variables were found to be

significant. These were health care demand in previous period (t-1) (OR = 0.049); illness (OR =

10.338); injury (OR = 2.370); social class (middle class: OR = 1.135; wealthy: OR = 1.394); per

capita consumption (OR = 1.099); union status (OR = 0.845); gender (OR = 2.221); private

health insurance coverage (OR = 1.942); age (OR = 1.022) and educational attainment of

respondents (OR = 1.315) (Table 5.2). The model [Eqn (1)] had a statistical significant predictive

power (χ2 = 1,249.19 p = 0.001; Hosmer and Lemeshow goodness of fit χ2 = 5.606, p = 0.691).

In addition, it was revealed that overall 70.0% (n = 9,810) of the data were correctly classified:

52.7% (n = 3,358) of those who indicated health demand and 84.4% (n = 6,452) of those who

indicated no health care demand (Table 5.2).

                                               152
       The final health care demand model of Jamaicans, the health care demand (equation 1)

constitute education, union status (ie marital status), duration of illness or severity, consumption,

gender, injury, subjective social class, and age of respondents. Illnesses contributed the most (i.e.

Wald statistic = 969.89; OR = 10.338) to health demand. Interpreting the OR for illnesses

revealed that individuals who are ill for a longer period of time will be 10 times more likely to

demand health care compared to those who are ill for a shorter period. Furthermore, a positive β

value of 2.336 indicates that as persons move from no illnesses to illnesses, they will seek more

health-care. Given that the logit is positive for illnesses, being ill increases the odds of seeking

health care.



       The constant in the current study indicates preventative care, and it showed that

Jamaicans were 4.4% likely to demand health care owing to rationale of some socio-

demographic characteristics or non-disease condition. With regard to injuries, an individual who

has injuries is 2 times more likely to seek health care compared to someone who does not have

injuries (OR = 2.370). Furthermore, a positive β value of 0.863 indicates that with the increasing

number of injuries, the sampled population sought more health care (or health care behaviour

increases). A p value 0.001 with a positive logit for injuries suggests that being injured increases

the odds of seeking health care.




       There is a significant relationship between gender and health care behaviour (p = 0.001;

Table 5.2). Based on the Wald statistic, (418.53), gender is the second most important factor in

predicting health care behaviour. In addition, a positive β value of 0.793 indicates that females

sought more health care in comparison to males. Furthermore, a positive logit in relation to
                                                153
gender suggests that being female increases the odds of seeking health care. Females are likely to

demand health twice as much as their male counterparts.




Discussion



The demand for health is an important and critical in health education and health promotion, as it

provides a platform upon which health practitioners can plan. There are a number of studies that

have examined health promotion in various Caribbean countries. This study can be used as

model for health care demand and health promotion in Jamaica, and any other country with

similar socio-demographic and political characteristics. The predictors of health care demand

using the proposed model were illness, injury, social class, consumption, union status, gender,

private health insurance, age and educational attainment of the respondents.




       Health care demand or health seeking behaviour in terms of illness refers to those

activities undertaken by individuals in response to symptom experience (Tones, 2004) and is

influenced by a large number of factors apart from knowledge and awareness (Lurie et al., 1995).

This behaviour among different populations, particularly in the rural communities in which most

Jamaicans resides, is a complex outcome of many factors operating at individual, family and

community level including their bio-social profile, their past experiences with the health

services, influences at the community level, availability of alternative health care providers

including indigenous practitioners and last but not the least their perceptions regarding efficiency

and quality of the services. Belief systems prevalent in the communities i.e. how people


                                                154
conceptualize the etiology of health problem and how symptoms are perceived is an important

factor in deciding the first step of treatment seeking (Lurie et al., 1995). In 1993, a behaviour risk

factor survey was conducted in Jamaica to provide a baseline for tracking health-related

behaviours in the adult population (Figueroa et al., 1999). The survey included self-reported data

on health-seeking behaviour, chronic diseases, substance abuse, injuries and violence and

reproductive health. The survey found a high prevalence of self-reported hypertension (with 62%

not on treatment), of heavy alcohol use by men, of possible obesity among women and of

sexually transmitted disease.




       The type of symptoms experienced for the illness and the number of days of illness are

major determinants of health seeking behaviour and choice of care provider. Lack of resources

obstructs adequate treatment, and favour illness recrudescence and prolongation, which

ultimately increases the cost of health care. Long duration or concentration of illness episodes in

a household especially in rural Jamaica can lead to selling of available assets and other coping

strategies (e.g. borrowing money), pushing the household into the vulnerability spiral. Illness can

end up being extremely costly for the poor (Corbett, 1989). In general, the economic burden of

illness can have a double impact on poor households. Firstly, it can have an impact on health if

individuals see themselves forced to interrupt treatment because of lack of financial resources,

leading to increased vulnerability in terms of health. And secondly, when coping strategies lead

to a process of impoverishment, a household is placed in a position of vulnerability in terms of

material survival.




                                                 155
       Social class was the third predictor of health care demands of Jamaicans. Direct and

indirect treatment costs are among the most commonly mentioned obstacles to adequate health-

seeking behaviour of the poor for obtaining prompt and adequate treatment, treatment

compliance and access to preventive measures (Worrall et al., 2002). In April 2007, the Ministry

of Health in Jamaica established free health care for all Jamaicans. However, even if direct costs

are affordable, or if medical services are free, indirect costs (for transport, special food) can limit

access to treatment or lead patients to interrupt therapies (Abel-Smith and Rawal, 1982).

Treatment costs are not only an obstacle for adequate health-seeking of the poor; they also

signify a higher burden for the poorer households compared to the more affluent. Even if the

poor spend less or equal amounts on coping with illness, the percentage of the monthly or annual

income is higher among the poor. More wealthy Jamaicans more likely opt for private service.

Health care in the private sector, which is easily accessible is seen as delivering better quality

services, is much more expensive and is largely supported by direct out-of-pocket payments and

private health insurance (Gumber and Berman, 1995). Therefore financial barriers affect care-

seeking behaviour, with the wealthy most likely to use specialist facilities while the poor

typically use more non-specialist primary care facilities (Falkingham, 2004).




       Education appears to have a positive association with seeking health care. Lawson found

that for both men and women there is a gradual increase in the demand for health care upon

completion of some primary education through to university education (Lawson, 2004). Men and

women who possess a university education are more likely to demand formal health care,

relative to those with no education. There also appears a quite distinct trend away from

government hospital facilities to those privately provided, as the educational attainment of adults
                                                 156
increases, potentially supporting Li’s hypothesis for Bolivia, that the educated transfer away

from government health care because they regard its quality as inferior (Li, 1996). However,

other studies have found that higher educated people are healthier and are therefore less likely to

consume health care (Grossman and Kaestner, 1997; Hammond, 2002). If a higher educated

person has a health-impairment, an individual is more likely to seek medical assistance sooner.

Higher educated people are also more informed and more assertive about the opportunities and

the possibilities to obtain medical help, which also increases the chance of health care use (Groot

and Maassen van den Brink, 2006). There have been reports that lower levels of education

appear to be associated with underreporting of illness by patients (Mackenbach et al., 1996).




        In this study age was the eighth predictor of health care demand. In a study done in

Georgia by Gotsadze and colleagues, increased income, age and perceived seriousness of the

illness were all statistically significant factors increasing the probability of seeking health care

(Gotsadze et al., 2005). They found that individuals (aged 66 years and older) were three times

more likely to seek care than the youngest group (aged 0 - 3 years). In addition, patients who

perceived their illness to be moderately serious had higher odds of seeking care and, to a lesser

degree, so did those who perceived their illness to be very serious. The richest were almost five

times more likely to seek care than the poorest quintile (Gotsadze et al., 2005). Other qualitative

research in Georgia suggests that financial considerations, the perceived professionalism of a

provider and the geographic location of the provider are the three main criteria influencing

patients’ choice (Belli et al., 2004).




                                                157
       Financial resource availability plays an important role in health care decisions and

demands. The resources regarding health care decision-making could be health insurance or

monetary resources. Health insurance is important for access to health care and being uninsured

significantly reduces access to health services and substantially increases health problems.

Uninsured persons with poor health status are much more likely than their insured counterparts

to report that they or a family member did not receive doctor’s care or prescription medicines

(Families USA, 2000). Shi reported that income was the most significant predictor of lack of

health insurance coverage (Shi, 2001). Low-income adults tended to have lower health status and

uninsured adults tended to have problems accessing health care services (Wyn and Solis, 2001).

Mead et al. noted that low-income adults were less likely to have health insurance, while they

were more likely to have health care access problems, chronic illness and lower overall health

status than their richer counterparts (Mead et al., 2001). In Jamaica, Life of Jamaica and Blue

Cross Jamaica Limited are the only total health insurance companies catering to the widest cross-

section of Jamaica’s population. These companies offer a wide range of health insurance

products to best suit the needs of clients from individuals, students, executives, associations and

companies. The purchase of private health coverage among Jamaican residents is based on work

situation and to a lesser extent on the premise that an individual is likely to be ill. This can be

seen as coverage against the risk of becoming ill.




       In order to develop this model the authors assumed that health education and health

promotion must not emphasize the strengths found in the health care demand model, but must

health emphasize on low health demand and vice versa. Embedded in the health promotion and

health education model, the health care promotion model is the inverse relationship between
                                               158
health care demand and health promotion. Given that duration or severity of illness accounted for

42%, indicates that the longer individuals are ill and the more severe the ailment is, they are 42%

more likely to demand health care, and health promotion must be concerned with those who are

less likely to attend than more likely. Hence, health promotion should target those who are less

likely to demand health care in order to encourage or create healthy behaviour as against those

persons who displays good health behaviour. The results from this study should serve as a guide

and provide health practitioners with information on the areas of emphasis in health education

and promotion which need to be addressed, using probability from the health demand model.




Conclusion


The predictors of health care demand using the proposed model were illness, injury, social class,

consumption, union status, gender, private health insurance, age and educational attainment of

the respondents. In addition, embedded in the health care promotion and health education model

is the fact that the health promotion model is the inverse relationship between health care

demand and health promotion. Illness contributed the most to the health care demand model,

indicating that the longer individuals are ill and the more severe the ailment is, they are more

likely to demand health care. Therefore, health promotion must be concerned with those who are

less likely to seek health care. Hence, health promotion should target those who are less likely to

demand health care in order to encourage or create healthy behaviour as against those persons

who displays good health behaviour. The results from this study should serve as a guide and

provide health practitioners with information on the areas of emphasis in health education and

promotion which need to be addressed, using the health care demand model.

                                               159
Disclosure Statement



The authors have no conflicts of interest to report.




                                                160
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Table 5.1: Socio-demographic characteristics of sample


  Particular                     Frequency                      Percentage (%)

  Gender:
      Male                       8078                           48.6
      Female                     8541                           51.4

  Educational Level:
         Primary                 7294                           50.4
         Secondary               6045                           41.7
         Tertiary                1142                           7.9

  Health Insurance:
         Yes                     1919                           11.8
         No                      14292                          88.2

  Union Status:
        With a partner           8544                           53.6
        Without a partner        7395                           46.4

  Social Status:
            Poor                 5844                           35.2
           Middle                6762                           40.7
            Rich                 4013                           24.1

   Age (Mean ± SD)                  39.75 years ± 19.05 years




                                             164
Table 5.2: Logistic regression of health demand and some explanatory variables




  Particular                Coefficient S.E         Wald      Odds ratio

  Illnesses                 2.336        0.075      969.894   10.338***

  Injuries                  0.863        0.181      22.655    2.370***



  Social class

  †Poor                                                       1.0

  Middle                    0.127        0.056      5.128     1.135*

  Wealth                    0.332        0.050      44.601    1.394***



  Per capita                0.094        0.030      10.117    1.099***
  Consumption

  Union status              -0.169       0.040      18.024    0.845***

  Gender                    0.793        0.039      418.533   2.2210***

  Health insurance          0.664        0.064      106.383   1.942***

  Log age                   0.022        0.001      359.375   1.022***

  Education                 0.274        0.085      10.332    1.315***

  Constant                  - 3.024      0.319      89.691    0.049***


Nagelkerke R-squared 28.4%
Overall correct classification = 70%
Correct classification of cases of reported health demand = 52.7%
Correct classification of cases of no health demand = 84.4%
†Reference group
*p < 0.05, **p < 0.01, ***p < 0.001


                                              165
                                                                            Chapter
                                                                                               6
Biosocial determinants of health and health seeking behaviour of male youths
                                 in Jamaica


The current study aims to provide an understanding of the health of young males (ages 15-25
years) which has been primarily lacking in the Caribbean, and in particular Jamaica. This study
utilised secondary cross-sectional dataset for 2007 taken from the Jamaica Survey of Living
Conditions (JSLC). The questionnaire was modelled from the World Bank’s Living Standards
Measurement Study (LSMS) household survey. The current study extracted 607 respondents (15-
25 years) from a sample of 6,783 respondents. SPSS for Windows 16.0 (SPSS Inc; Chicago, IL,
USA) was used to store, retrieve and analyse the data. A p-value of < 0.05 (two-tailed) was used
to indicate statistical significance. Four variables emerged as statistical significant correlates of
health status: self-reported illness, OR = 16950, 95% CI = 46.4-6187362.9; self-reported injury,
OR = 114643.2, 95% CI = 100.2-131124116.9; crowding, OR = 0.2, 95% CI = 0.09-0.59 and
head of household, OR = 0.001, 95% CI = 0.0-0.2. None of the identified variables emerged as
significant correlates of the good self-rated health status of male youths - Model χ2= 16.284(8),
P < 0.061. Of the variables identified, 1 emerged as a correlate of poor self-rated health status –
self-reported illness – OR = 42.2, 95% CI = 2.6-693.2. Poverty is substantially a rural
phenomenon. Almost 30% of rural respondents were classified as being in the poorest 20%,
compared to 17.3% of those in semi-urban and 8.7% in urban areas. At the same time, no
statistical association existed between area of residence and self-reported health status, self-
reported injury, self-reported diagnosed health conditions and poor health status. The current
study does not concur with the established finding that poverty is more common among the
chronically ill than among those who are not chronically ill. However, it supports the literature
that current illness is primarily a response to males’ willingness to utilize health care. The
implications for these findings are far-reaching, and public health practitioners now have a


                                                166
platform upon which they can fashion interventions, health education and future research on this
vulnerable age cohort in Jamaica.



Introduction


The Caribbean continues to grapple with an alarming crime problem. Scholars have found that

much of it is perpetrated by young males (≤ 30 years of age) [1-8]. Statistics showed that 67.6%

of those arrested for major crimes in Jamaica (i.e. murder, shooting, robbery, house-breaking,

rape and carnal abuse) were 16-30 years old, and that young males accounted for more than 80%

of the numbers (Table 6.1). Bearing this out, for the period 1999-2002, statistics in Jamaica

revealed that more than 50% of those treated for gunshot wounds in the Accident and Emergency

departments at public hospitals were 10-29 years of age, with the figures being relatively the

same for males and females. A recently conducted study by Powell, Bourne and Waller [9] found

that 44% of Jamaicans indicated that ‘crime and violence’ was their most pressing problem, a

phenomenon which extends to many other Caribbean nations such as Barbados, Trinidad and

Tobago, and Guyana [1-8]. The social realities of the crime problem in the region justify (1)

studies on crime and violence, and (2) an inquiry into the fear of crime and victimization.


       Understandably, there are many studies on crime, violence and the fear of crime and

victimization in the Caribbean, but there are also other concerns such as substance abuse, sexual

practices, the survivability of young people, and male marginalization. Those issues have been

studied and re-studied [10-18], sometimes yearly, but in addition to crime which is a narrowed

aspect of the broad social issues that continue to challenge Caribbean peoples, there is a need to

expand research into this social problem. Studies continue to examine crimes, violence and the

                                               167
fear of crime and victimization, and rightfully so in the region and little attention if any has been

placed on men’s health, and in particular young men’s health in the region, except in the area of

reproductive health and sexual practices [19-21].


       Recently an entire book entitled “Health Issues in the Caribbean” [18] covered topics

such as child health, reproductive health, the elderly, chronic non-communicable diseases,

disability, health care-delivery and health issues in the Caribbean, reinforcing the claim of the

lack of research on men’s health and young males’ health. A study by Bourne [21] examined

“Demographic shifts in the health conditions of adolescents 10-19 years, Jamaica”, which

provided pertinent information on this cohort, but again men’s health or young males’ health was

not the emphasis; neither did it capture most of those who attested for crimes, nor those who are

influenced by crime and violence. With crime being such a dominant social problem and the fact

that it is perpetrated by mostly young males, there is the tendency to become overindulgent in

this discourse. However, research conducted by Powell, Bourne and Waller [9] found that only

18% of Jamaicans indicated that they have been victims of crime and violence in the last 12

months. Clearly, there is an obvious need to expand the research, from crime, violence, fear and

victimization to health status, health conditions and health care-seeking behaviour among the

youth. Apart from being the perpetrators of crime and violence, how is their (1) health status, (2)

health care-seeking behaviour and (3) how are the health conditions among young males (15 –

25 years)?


       The current study aims to provide an understanding of the health of young males (ages

15-25 years) which has been primarily lacking in the Caribbean, in particular in Jamaica. What is

influencing their health care seeking behaviour? Those questions cannot be answered from the

                                                168
perspective of a general study on health or the health care-seeking behaviour of Jamaicans [22],

as without disaggregating the results, pertinent information is lost on the this cohort because it

would be general findings on the populace.


Materials and Methods
Study population

This study utilised secondary cross-sectional dataset for 2007 taken from the Jamaica Survey of

Living Conditions (JSLC). The JSLC is a joint publication from the Planning Institute of Jamaica

(PIOJ) and the Statistical Institute of Jamaica (STATIN) for analysis [23-25]. The JSLC began in

1988 to collect data on the living conditions of Jamaicans in order to measure government

policies. These cross-sectional surveys were conducted between May and October of each year

across the 14 parishes of Jamaica. The current study extracted 607 respondents (15-25 years)

from a sample of 6,783 respondents [26, 27]. The JSLC used a stratified random probability

sampling technique to draw the original sample of respondents. The non-response rates were

26.2%. The JSLC survey used a complex design with multiple stratifications to ensure that it

represented the population, marital status, area of residence and social class. The sample was

weighted to reflect the population of Jamaica [23-25].


Study instrument


The JSLC used an administered questionnaire where respondents were asked to recall detailed

information on particular activities. The questionnaire was modelled using the World Bank’s

Living Standards Measurement Study (LSMS) household survey [23]. There are some

modifications to the LSMS, as JSLC is more focused on policy impacts. The questionnaire

covers demographic variables, health, immunization of children 0–59 months, education, daily
                                               169
expenses, non-food consumption expenditure, housing conditions, inventory of durable goods

and social assistance. Interviewers are trained to collect the data from household members.


Statistical methods

Descriptive statistics were used to analyse the socio-demographic characteristics of the samples.

Chi-square analyses were used to examine the association between non-metric variables for area

of residence, and gender of respondents. T-test statistics and Analysis of Variance were used to

evaluate metric and either a dichotomous or non-dichotomous variable respectively. Logistic

regression analyses examined 1) the association between good health status and some socio-

demographic, economic and biological variables, as well as 2) a correlation between self-

reported health conditions (illnesses, dysfunctions or ailments) and some socio-demographic,

economic and biological variables. SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA) was

used to store, retrieve and analyse the data. A p-value of < 0.05 (two-tailed) was used to indicate

statistical significance.


        The only selection criterion for this study was based on the males being 15-25 years old.

For the model, the selection criteria were based on 1) the literature review; 2) low correlations,

and 3) non-response rate. The correlation matrix was examined in order to ascertain if

autocorrelation and/or multicollinearity existed between variables. Based on Cohen & Holliday

[28, 29] correlation can be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-

1.0. This was used to exclude (or allow) a variable in the model. Where collinearity existed (r >

0.7), the variables were entered independently into the model to help determine which one

should be retained in the final model construction. This was used to exclude or include some

variables, and was based on the variables’ contribution to the predictive power of the model and

                                               170
its goodness of fit. Such an approach was utilised in order to reduce multicollinearity and/or

autocorrelation between or among the independent variables [30-36]. Forward stepwise logistic

regression technique was used to determine the magnitude (or contribution) of each statistically

significant variable in comparison with the others, and the Odds Ratios (OR) aided the

interpretation of each significant variable. To derive accurate tests of statistical significance, the

researcher used SUDDAN statistical software (Research Triangle Institute, Research Triangle

Park, NC), and this adjusted for the survey’s complex sampling design.


Measure

Age is a continuous variable which is the number of years alive since birth (using last birthday):

from 15 to 25 years.


Self-reported illness (or self-reported dysfunction): The question was asked: “Have you had an

illness such as influenza, asthma, et cetera in the past 4-week period?”


Health conditions (i.e. parent-reported illness or parent-reported dysfunction): The question was

asked: “Is this a diagnosed recurring illness?” The answering options are: Yes, Influenza; Yes,

Diarrhoea; Yes, Asthma; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No.


Self-rated health status: “How is your health in general?” And the options were: Very good;

Good; Fair; Poor and Very Poor.


Medical care-seeking behaviour was taken from the question ‘Has a health care practitioner,

healer or pharmacist been visited in the last 4 weeks?’ with there being two options: Yes or No.


Self-rated health status: “How is your health in general?” And the options were: Very good;

Good; Fair; Poor and Very poor. For this study the construct was categorized into 3 groups – (i)
                                                 171
Good; (ii) Fair, and (iii) Poor. A binary variable was later created from this variable (1=good and

fair 0=otherwise).


Social hierarchy: This variable was measured based on income quintile: The upper classes were

those in the wealthy quintiles (quintiles 4 and 5), the middle class was quintile 3, and the poor

were those in lower quintiles (quintiles 1 and 2).


Crowding: This is the total number of people in the household divided by the number of rooms,

excluding verandah, kitchen and bathroom.


Area of residence is a non-binary variable. 1= urban,             0=otherwise

                                               1=semi-urban,      0=otherwise
                                               Reference group is rural area

Results

The sample was 607 respondents (15-25 years): dwelling in urban areas, 33.9%; semi-urban,

20.9%; rural, 45.2%; injured in last 4-week period, 2.4%; self-evaluated illness (in last 4-week

period), 3.3%; health care-seeking behaviour, 68.0%;          primary or below education, 64.5%;

tertiary level education, 6.5%; household heads, 8.9%; very good self-rated health status, 49.2%;

good self-rated health status, 44.9%; moderate self-rated health status, 4.6%; poor self-rated

health status, 1.0%; and very poor health status, 0.3%. Of those who reported an illness, 75.5%

stated the typology of health conditions: influenza, 6.7%; diarrhoea, 13.3%; asthma, 6.7%;

hypertension, 6.7%; and other (unspecified), 66.7%. The mean age of the sample was 19.6 years

(SD = 3.2 years). The median length of illness (in days) was 5 (range = 1, 28) and the number of

visits to a health care practitioner was 1 time (range = 1, 3).



                                                 172
       Of the 8.9% of those who were heads of households, 29.6% were 24 years old; 18.5%

were 22 years old; 14.8% were 23 years old; 13.0% were 25 years old; 11.1% were 19 years old;

3.7% were 21, 20 and 17 years old; and .9% were 16 years old. Furthermore, a statistical

difference existed between those who were and those who were not heads of households (F

statistic = 48.6, P < 0.0001): mean age of those who were household heads was 22.4 years (SD =

2.3 years) compared to those who were not household heads (19.4 years ± 3.1 years).

       There is a statistical difference between the mean length of illness (in days) and area of

residence (F statistic = 5.706, P = 0.011). Those in urban areas recorded the greater length of

illness, 14.4 days (SD = 10.3 days); semi-urban areas, 6.5 days (SD = 5.8 days) and rural

respondents, 4.7 days (SD = 2.5 days). Furthermore, a statistical difference was found between

total annual household expenditure among the social classes (F-statistic = 64.870, P < 0.0001.

The mean annual household expenditure for the sample was USD 8,994.04 (SD = USD

6716.46).   However, those young males in households of the poorest 20% had an annual

household expenditure which was 3.3 times less than those in the households of the wealthiest

20% (USD 15,152.73 ± 10,179.74), with the following annual household expenditures being

poor, USD 6,311.23 ± 2,645.72; middle class households, USD 8,522.97 ± 3,656.07; and

wealthy, USD 10,812.74 ± 5,573.11.

       No statistical difference was found between the annual household expenditure of those

with illness (USD 11,091.18 ± 13,898.55) and those who did not report an illness (USD 9,001.44

± 6,433.52) – t-test = 1.320, P = 0.187.

       A statistical association existed between household heads and areas of residence (χ2 =

6.864, P = 0.032). Almost 7% of those who dwelled in rural areas were household heads



                                              173
compared to 6.3% of those living in semi-urban areas and 13.1% of those who resided in urban

areas.

          There is no significant statistical association between (1) self-reported illness and social

standing (χ2 = 1.315, P = 0.859), (2) self-reported injury and social standing (χ2 = 2.640, P =

0.620), and (3) self-reported diagnosed health conditions and social standing (χ2 = 12.80, P =

0.687).

          Table 6.2 highlights information on the demographic characteristics of the sample by area

of residence. A significant statistical association was found between social standing and area of

residence (χ2 = 83.5, P < 0.0001). Almost 30% of rural respondents were in the poorest 20%

compared to 17.3% of those in semi-urban areas and 8.7% in urban areas.

Multivariate analyses

          Table 6.3 shows information on factors that are correlated with the health care-seeking

behaviour of the sample. Using stepwise logistic regression, 4 variables emerged as statistical

significant correlates of health status: self-reported illness, OR = 16950, 95% CI = 46.4-

6187362.9; self-reported injury, OR = 114643.2, 95% CI = 100.2-131124116.9; crowding, OR =

0.2, 95% CI = 0.09-0.59 and head of household, OR = 0.001, 95% CI = 0.0-0.2. The model

explains 86.4% of the variability in the health care-seeking behaviour of respondents, and it is a

good fit for the data - Model χ2= 123.91(8), P < 0.0001, Hosmer and Lemeshow goodness of fit

χ2=37.781, P = 0.778. Concurrently, 55.1% of the variability in health care-seeking behaviour is

accounted for by self-reported illness, and self-reported injury accounted for 26.2%.

          Table 6.4 highlights the possible factors of self-rated good health status of the sample.

None of the identified variables emerged as significant correlates of good self-rated health status

of male youths - Model χ2= 16.284(8), P < 0.061. Almost 95% of the respondents were used to

                                                  174
established the model, and it was found that the model is a good fit for the data - Hosmer and

Lemeshow goodness of fit χ2=5.301 (8), P = 0.725.

       Table 6.5 examines possible factors that are correlated with poor self-reported health

status of respondents. Ninety-three percent of the sample was used to establish the model. Of the

variables identified, 1 emerged as a correlate of poor self-rated health status – self-reported

illness – OR = 42.2, 95% CI = 2.6-693.2. The model was a good fit for the data - Model χ2=

123.91(8), P < 0.0001; Hosmer and Lemeshow goodness of fit χ2=1.206 (8), P =0.997.

Discussion
The current study found that 94 out of every 100 young males (ages 15-25 years) reported at

least good health status. Only 1.3% of the sample indicated poor health status. However, 3.3%

indicated having had an illness in the last 4 weeks and 2.4% were injured in the same period of

time. Almost 1 in every 100 young males had reported an acute condition compared to 2 in

every 100 who reported a chronic condition. Of those who reported an illness (n=15), 66.7%

indicated other and 6.7% said hypertension. The prevalence of hypertension among young males

was 2 in every 1,000 persons. Sixty-eight percent of the sample had visited a health care

practitioner in the last 4 weeks; 83% purchased the prescribed medication; 15% had health

insurance coverage; 58.8% visited a public hospital for treatment compared to 5.9% who used a

private hospital, 11.8% who utilised public health care centres and 29.4% who attended a private

health care centre. Concurrently, the median length of time in illness was 5 days, with the

median number of visits to a health care practitioner being 1. In addition to the afore-mentioned

findings, 27% of poor health status can be explained by self-reported illness, and 47.6% of the

variability in health care-seeking behaviour can be accounted for by illness and 22.6% by injury.

Young males who had indicated that they were heads of household were 99.9% less likely to

                                              175
seek health care, and those respondents with more people per room were 76.5% less likely to

seek medical care.


       It is empirically established that health status is determined by medical, social,

environmental and psychological factors [19,20,37-49], but for this study self-reported illness

was the only factor that emerged as being significant, when correlated with health status. Using

data for elderly Barbadians, Hambleton et al. [43] found that 88% of the variability in self-

reported health status could be explained by current diseases, and the current study found 27% of

the variability in poor health being explained by current self-reported illness. Hambleton and

colleague’s study [43] did not examine the health care-seeking behaviour of the sample, but this

research found that 55.1% of the explanatory power of seeking medical care was accounted for

by current self-reported illness. Embedded in this study is the image of health of young males

and when they seek medical care. This is not atypical, as a qualitative study conducted by Ali &

de Muynck [50] in Pakistan found that illness and its severity are responsible for male street

children being willing to utilize medical care facilities. Clearly, in Jamaica as well as other non-

Caribbean nations [51-54], males’ image of illness is fundamentally based on illness, injury or

the severity of illness and injury, and not health from the perspective of wellbeing. It is this

narrow definition of health that influences the decision of young males to seek medical care, and

not preventative health. The current findings show that there is a very good explanation for

young males’ health care seeking behaviour in Jamaica (R2 = 86.4%), which suggests that health

is the absence of diseases, and this is what impacts on their demand for health care.


       Health care-seeking behaviour among the current sample is 68 out of every 100 males

compared to 66 per 100 of the general populace and 68 out of every 100 for females.

                                                176
Concurrently, the rate for young males seeking medical care is 5% more than the rate for males

in the general population and the same as for females in the population. It should also be noted

here that the rate of those with illness in this sample is less than 4% which is 4.7 times less than

the national average (i.e. 15.5% reported illness). Despite the substantially lowered rate of self-

reported illness, young males sought more medical care than males in the wider population. This

finding highlights a myth that young males, like their older counterparts, avoid seeking medical

care, but the issue which emerged from this research is that they seek both curative and

preventative care. Current self-reported illness and injury accounted for 81.3% of the variability

in the health care-seeking behaviour of young males, supporting the culture which stipulates that

men are strong and should not show weakness, and that illness is a sign of weakness. Hence, the

illnesses which influence health care-seeking behaviour cannot be mild, such as the common

cold or diarrhoea, but more like asthma, hypertension and injuries such as gunshots, knife

wounds and other severe conditions that are life-threatening, and therefore require immediate

medical attention.


       Although only 3.3% of the sample reported an illness and the prevalent rate for each self-

reported diagnosed health condition was low (influenza, 0.2%; diarrhoea, 0.3%; asthma, 0.2;

hypertension, 0.2%; and other (unspecified), 1.6%), on disaggregating those who reported a

health condition, it becomes clear that this is a public health problem. Two-thirds of those who

indicated a health condition claimed ‘other’, indicating a preponderance of some health

conditions and sexually transmitted diseases. A recently conducted study of Jamaicans by Wilks

et al. [13] found that only 24.3% of young males (ages 15-24 years) claimed that they had not

had sex in the last 12 months; 48.7% had more than one sexual partner; 75.7% had sexual

intercourse at least once per week; 0.9% claimed that they have had a STI in the last year; 65.8%
                                                177
used condoms; and 8.4% had not used a contraceptive method in the last 12 months. Based on

Wilks et al.’s study, there are other health conditions apart from sexually transmitted diseases

which are dominant in the unspecified health conditions stated in the current sample. Statistics

from the Ministry of Health (Jamaica) showed that 15% of males aged 10-29 years (in 2006) had

AIDS, which rules out AIDS as the explanation of the unspecified conditions of the current

study. Furthermore, examination of the statistics from the Ministry of Health (Jamaica) found

that in 2006 36.0% of young males (ages 10-29 years) utilised public health care facilities for

bites, 54.3% for gunshot wounds, and 43.1% for accidental lacerations. Those conditions may

account for the unspecified health conditions identified by the current study, but there is no

finality of this fact without a study of this cohort’s health, health conditions and injuries. Clearly

crime, violence and victimization are having an influence on the health of young males, and what

about the fear of crime and victimization on those who are not assaulted, but have witnessed the

events and are fearful from reading and watching the wired media?


       Concurrently, when the unspecified health conditions were disaggregated by area of

residence, most of the reported cases were in urban areas, followed by rural and semi-urban

areas. Currently, research cannot account for most of the unspecified health conditions, which

denotes that public health intervention programmes are designed without consideration being

given to this unknown event. But what is known is that almost 71% of the sample utilised public

health care facilities and they constituted mostly rural respondents, which denotes that public

health care facilities hold some of the clues to the condition of young males. However, the

hypertensive cases were in semi-urban areas, and this offers public health policy makers some

pertinent information upon which policies can be framed for young males.


                                                 178
       Poverty is empirically found as being associated with poor health status and a group of

scholars went as far as to declare that money can buy health [38]. Although Marmot [39] did

argue that poverty is associated with poor milieu, nutrition, opportunities and choices, more so

than the affluent, using the United States, he showed that there was a weak relationship between

Gross National Product and overall health. Embedded in Marmot’s finding was the fact that

other factors are more of an explanation of poverty and poor health and/or ill-health such as

education, material deprivation, social environment, racial inequality and occupational hierarchy.

He also pointed out that there was a weak statistical association between average income and life

expectancy in rich countries, but that within those nations there is a close association between an

individual’s income and his life expectancy and mortality. Clearly poverty is an influence on

illness, and with ill-health also affecting poverty, this could be influenced more by other social

conditions than money (or income). One of the issues which could make the difference between

Marmot’s work and this one is how health is measured in each study. Health had a narrow

definition in Marmot’s study as it was operationalized by life expectancy or mortality.


       However what emerged from these findings are (1) poverty is not associated with poor

health status; (2) poor young males do not seek less health care than the affluent; (3) rural

poverty (i.e. those in the poorest 20%) was 3.4 times more than urban poverty and 1.7 times

more than semi-urban poverty; (4) there was no significant statistical association between self-

reported illness or injury and area of residence; (5) there was no significant statistical

relationship between health care-seeking behaviour and area of residence; (6) urban young males

were 5.5 times more educated at the tertiary level than rural young males; (7) urban young males

had the greatest length of illness (in days); and (8) illness, injury or self-reported diagnosed

health conditions are not statistically related to social standing. The current work disproves the
                                               179
findings of Van Agt et al. [56], which showed that chronic illness is more prevalent among the

poor. However, this study concurs with Van Agt et al.’s work [56], in that material deprivation

was highest among the poor, but disproves the finding that the higher prevalence of material

deprivation was among the chronically ill people. Instead, this study found that those who were

chronically ill were more likely to be in the wealthy social hierarchy than in the poor social

hierarchies.


       According to Barillas et al. [57], there was a paradox in the national survey data collected

on Guatemalans, as the poor reported less illness than the non-poor, and individuals in rural areas

reported less illness than those in urban areas. It appears from the current study that no statistical

association between area of residence and self-reported illness and self-reported diagnosed health

conditions and social class would also be a paradox, but it is not the case as these are the realities

in those nations and not the empirical findings of international health literature. Hence, it is good

to use international empirical findings from one country and create intervention programmes or

public health education for another geopolitical area.


       In the current study 14.6% of rural young males claimed that they had an illness in the

last 4 weeks, but Bourne [58] conducted a study on rural men in Jamaican and found that 17%

reported an illness. This finding highlights the deceptive nature of using the national average for

public health planning, as what holds for men in one geo-political zone is not the same for young

males within the same area. This is also true as Bourne [58] found that (1) 61.2% of rural men

visited a health care practitioner, (2) 2.4% had tertiary level education, (3) 4.9% had health

coverage, and (4) good health status is a function of social, economic and biological factors.

However, in this research health care utilization was greater for young rural males (73.3%);

                                                 180
tertiary level respondents were about the same for rural men and young rural males (2.3%);

health coverage was significantly more for young rural males (14.5%) than rural men, and no

factor explains the good health status of young males in Jamaica compared to rural men. Hence,

even among people who live in the same geopolitical zones there are inequalities, suggesting that

public health practitioners cannot use national average or sub-national areas of one group to plan

for another.


       Crime is substantially concentrated in urban zones, and given that it is perpetrated more

by young males, and the fact that more of them are victims of crime, it follows therefore that the

longer time spent in illness could be due to gunshot wounds and other injuries, more so than in

the case of rural and semi-urban young males. Illness is not only physical but a psychological

condition. This was established long ago by the WHO, and clearly the psychosocial challenges

of life account for the health care-seeking and length of illness. The current findings revealed

that those young males who are household heads are almost 100% more likely to seek health care

as they need to provide for a household, which indicates that health care switching is occurring

among this cohort in favour of their families. The health care switching which occurs denotes

that many young males who are heads of households will forego health care treatment in

response to the demands of providing for a household. Not only do they forego health care but

they also forego preventative care, and this delay could account for premature death. The

socioeconomic challenge of the household also accounts for other risky lifestyles, and may hold

answers to their involvement in criminality and violence. While this study does not examine

whether there is a statistical association between a household head and crime, it can be

extrapolated from the findings that the areas in which young males recorded the most heads of

household status (urban areas, 13.1%; semi-urban, 6.3%; rural, 6.9% - χ2 = 6.86, P = 0.032), are
                                               181
precisely those areas with the greatest crime and violence. One might ask if this is coincidental

or circumstantial, but it needs to be examined as a possible explanation for high crime as well as

the psychological state of young males in urban areas.


Conclusion

It is well established in health research that health is a function of social, psychological,

environmental and biological factors. Some studies have gone so far as to state the proportion of

the explanatory variability of each of the afore-mentioned factors, but the current investigation

revealed that none of the factors that have been identified in previous studies were correlated

with good health status of young males. However, illness emerged as the single factor that

accounts for poor health. Less than 2% of the sample indicated poor health, less than 8% injury

or illness, but more young males visited medical practitioners in the last 4-week period than

males in Jamaica and equally the same as females in the nation. Unlike other studies done on

health care-seeking behaviour around the world, none found an explanatory power as high as this

research (R2 = 86.4%). Furthermore, illness or injury is primarily responsible for young males’

health care seeking behaviour (81% of the explanatory power, i.e. R2), suggesting that the image

of health is still the opposite of illness and psychosocial wellbeing. A pertinent finding of the

current study is the fact that young males who are heads of households are less likely to seek

medical care, which denotes that this group is practicing health care switching for family

survivability and protection.


       In sum, the self-rated health status of young males (ages 15 to 25 years) in Jamaica is

good with a small percentage reporting having had an illness or injury in the last 4-week period.

The low percentage of those with illness corresponds to 68% health care utilization in the same
                                               182
period, suggesting that young males are seeking medical care but that illness and injury is the

principal influence on health care-seeking behaviour. The implications for these findings are far

reaching, and public health practitioners now have a platform upon which can be fashioned

interventions, health education and future research on this vulnerable age cohort in Jamaica.




                                               183
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                                         187
Table 6.1: Arrested for Major Crimes By Age Group, 2005
                                        Age Group of Persons Arrested for Major Crimes for 2005
                Murder                   Shooting               Robbery                 Breaking                      Rape C/Abuse
Age Group       Male       Female Total Male        Female Total Male        Female Total Male Female Total Male Male                Total
12-15                  6       1       7        4        0       4      10         0     10     54         0     54     12      11     98
16-20              157         6    163      167         1     168     183         0    183    122         3    125     66      43    748
21-25              235         8    243      239         1     240     214         1    215    129         3    132     68      52    950
26-30              160         2    162      137         0     137     120         1    121    105         3    108     73      27    628
31-35               85         1      86       74        0      74      71         1     72     93         2     95     48      26    401
36-40               54         3      57       40        1      41      36         1     37     69         0     69     23      19    246
41-45               15         0      15       12        0      12      13         0     13     44         1     45     18      12    115
46-50                  7       1       8        2        0       2       1         0      1     18         0     18     12       5     46
51-55                  5       1       6        0        0       0       2         0      2      2         0     2       3       2     15
56-60                  1       0       1        1        0       1       6         0      6      1         1     2       1       1     12
61& Over               0       0       0        2        0       2       2         0      2      3         1     4       2       0     10
Unknown             40         0      40       86        0      86      23         0     23     11         0     11     10       0    170
 Total            765        23    788      764           3     767      681         4   685    651        14   665    336     198   3439
Source: Compiled by author from data supplied by the Statistics Unit of the Jamaica Constabulary Force (JCF)




                                                                188
Table 6.2. Demographic characteristic by area of residence, n = 607

                                                    Area of residence                       P
Characteristic                      Urban             Semi-urban         Rural
Social standing                                                                        χ2 = 83., P < 0.0001
   Poorest 20%                          18 (8.7)            22 (17.3)      82 (29.9)
   Poor                                28 (13.6)            27 (21.3)      75 (27.4)
   Middle                              42 (20.4)            26 (20.5)      49 (17.9)
   Wealthy                             48 (23.3)            24 (18.9)      48 (17.5)
   Wealthiest 20%                      70 (34.0)            28 (22.0)       20 (7.3)
Self-reported Injury                                                                   χ2 = 0.920, P = 0.631
    Yes                                 4 (2.0)               2 (1.6)        8 (3.1)
    No                               193 (98.0)            122 (98.4)     254 (96.9)
Marital status                                                                         χ2 = 4.345, P = 0.361
   Married                              1 (0.5)                0 (0.0)       1 (0.4)
   Never married                     194 (99.5)           124 (100.0)     253 (98.4)
   Divorced                             0 (0.0)                0 (0.0)       0 (0.0)
   Separated                            0 (0.0)                0 (0.0)       3 (1.2)
   Widowed                              0 (0.0)                0 (0.0)       0 (0.0)
Self-evaluated illness                                                                 χ2 = 2.689, P = 0.261
   Yes                                  4 (2.0)               3 (2.5)      12 (14.6)
   No                                196 (98.0)            118 (97.5)     250 (95.4)
Self-reported diagnosed                                                                χ2 = 11.000,P = 0.202
illness
   Influenza                              0 (0.0)            1 (33.3)        0 (0.0)
   Diarrhoea                              0 (0.0)             0 (0.0)       2 (22.2)
   Asthma                                       -                   -              -
   Diabetes mellitus                      0 (0.0)             0 (0.0)       1 (11.1)
   Hypertension                           0 (0.0)            1 (33.4)        0 (0.0)
   Arthritis                                    -                   -              -
   Other                               3 (100.0)             1 (33.3)       6 (66.7)
Health insurance coverage                                                              χ2 = 0.156,P = 0.925
   Yes                                31 (15.8)             18 (14.8)      37 (14.5)
    No                               165 (84.2)            104 (85.2)     218 (85.5)
Health care-seeking                                                                    χ2 = 4.243,P = 0.120
behaviour
   Yes                                  5 (83.3)             1 (25.0)      11 (73.3)
    No                                  1 (16.7)             3 (75.0)       4 (26.7)
Self-rated health status                                                               χ2 = 7.647, P = 0.467
Very good                              92 (46.2)            60 (48.4)     134 (51.9)
Good                                   91 (45.7)            61 (49.2)     109 (42.2)
Moderate                                13 (6.5)              3 (2.4)       11 (4.3)
Poor                                     3 (1.5)              0 (0.0)        3 (1.2)
Very poor                                0 (0.0)              0 (0.0)        1 (0.4)




                                                    189
Table 6.3. Stepwise logistic regression: Health care-seeking behaviour by explanatory variables

                                            Std.                                    95.0% C.I.
 Explanatory variable        Coefficient    Error          P   Odds ratio                                 R2
                                                                            Lower         Upper         change
 Self-reported illness                                                                                   0.476
                                   9.738       3.0     0.001     16950.1      46.4         6187362.9
 (1=yes)

 Self-reported injury                                                                                    0.226
                                  11.650       3.6     0.001    114643.2     100.2       131124116.9
 (1=yes)

 Crowding                         -1.448       0.5     0.002          0.2      0.1                0.6    0.093

 Head of household                                                                                       0.069
                                  -7.017       2.9     0.014          0.0      0.0                0.2
 (1=yes)

Model χ2= 123.91(8), P < 0.0001
Hosmer and Lemeshow goodness of fit χ2=37.781, P = 0.778
Nagelkerke R2 =0.864
-2LL = 1525.53
n = 575 (94.7%)
†Reference group




                                                     190
Table 6.4. Stepwise logistic regression: Self-rated good health status by variables
                                                  Std.               Odds           95.0% C.I.
 Variable                        Coefficient      Error    P         ratio
                                                                                Lower      Upper
 Self-reported illness (1=yes)         -0.656        1.1     0.6          0.5        0.1       4.7

 Self-reported injury (1=yes)             -0.010          1.1   1.0     1.0        0.1        8.2

 Age                                      -0.067          0.1   0.2     0.9        0.8        1.1

 Crowding                                 -0.029          0.1   0.7     1.0        0.8        1.1

 Consumption per person                    0.000          0.0   0.5     1.0        1.0        1.0

 Head of household (1=yes)                 1.340          1.1   0.2     3.8        0.5       32.4

 Urban area                               -0.590          0.4   0.2     0.6        0.3        1.2
  Semi-urban                               0.719          0.7   0.3     2.1        0.6        7.4
 †Rural                                                                 1.0

 Health care-seeking (1=yes)              -1.301          1.3   0.3     0.3        0.0        3.3
Model χ2= 16.284(8), P < 0.061
Hosmer and Lemeshow goodness of fit χ2=5.301 (8), P = 0.725
n = 575 (94.7%)
†Reference group




                                                    191
Table 6.5. Stepwise logistic regression: Self-rated poor health status by variables

                                                     Std.               Odds          95.0% C.I.
 Variable                          Coefficient       Error      P       ratio
                                                                                  Lower      Upper
 Self-reported illness (1=yes)            3.744           1.4   0.009     42.2       2.6      693.2

 Self-reported injury (1=yes)           -18.278      7389.8     0.998       0.0        0.0         0.0

 Age                                      0.250           0.1   0.083       1.3        1.0         1.7

 Crowding                                 0.191           0.1   0.104       1.2        1.0         1.5

 Consumption per person                   0.000           0.0   0.541       1.0        1.0         1.0

 Head of household (1=yes)              -16.380      4875.4     0.997       0.0        0.0         0.0

 Urban area                               0.501         0.9     0.581       1.7        0.3         9.8
 Semi-urban                             -16.497      3274.2     0.996       0.0        0.0         0.0
 †Rural                                                                     1.0

 Health care-seeking (1=yes)            -18.849      7842.7     0.998       0.0        0.0         0.0

 Health insurance (1=yes)               -16.130      3686.1     0.997       0.0        0.0         0.0
Model χ2= 123.91(8), P < 0.0001
Hosmer and Lemeshow goodness of fit χ2=1.206 (8), P =0.997
Nagelkerke R2 =0.271
-2LL = 55.913
n = 565 (93.1%)
†Reference group




                                                    192
                                                                       Chapter
                                                                                         7
Socio-demographic determinants of Health care-seeking behaviour,
 self-reported illness and Self-evaluated Health status in Jamaica


The objectives of this study were to examine self-rated health status and health care-seeking
behaviour of Jamaicans; and to ascertain the socio-economic determinants of health care-
seeking behaviour as well as good health status. A cross-sectional descriptive study of 1,006
respondents who answered the question on health-seeking behaviour was used, and this was
extracted from a larger nationally representative probability sampling survey of 6,783
Jamaicans. Descriptive statistics were used to provide background information on the
demographic characteristics of the sample, chi-square was used to examine correlation between
two non-metric variables and logistic regressions were employed to establish the predictors of
health care-seeking behaviour and good self-rated health status. Of the sample, 40.5% was men
and 59.5% women, with a mean age of 41.8 years (SD=27.6 years). Forty-four percent of the
sample reported at least good health, 97% claimed that they have had some form of dysfunction;
6% reported being injured due to accidents, and only 11% indicated that their illness was not
diagnosed by a health practitioner. Of those who indicated being diagnosed with a recurring
ailment, 5.6% had arthritis, 20.5% hypertension, 12.4% diabetes mellitus, 9.5% asthma and
14.9% cold. Only 65.4% of the sample sought health care. In the multivariate analyses, health-
care seeking behaviour of Jamaicans can be explained by age of respondents (OR=1.031,
95%CI=1.014, 1.049); area of residence (other towns OR=0.5, 95%CI=0.278, 0.902); log
consumption (OR=3.605 95%CI=1.814, 7.167); marital status (married OR=0.468
95%CI=0.260, 0.843; divorced, separated or widowed, OR=0.383, 95% CI 0.163, 0.903) and
social class (Upper class OR=0.319, 95%CI=0.106, 0.958). The health status of those who seek
health care can be predicted duration of the individuals to carry out their normal activities
(OR=0.594, 95%CI=0.413, 0.855); age of respondents (OR=0.967, 95%CI=0.949, 0.986) and
area of residence (urban area OR=2.415, 1.195, 4.881; other towns OR=2.514, 1.162, 5.442).
Self-rated health status was found to be a significant statistical predictor of self-reported
dysfunction - good self-rated health status with reference to poor self-rated health status
(OR=0.271, 95%CI=0.081, 0.915). This relationship disappears when socio-demographic
                                             193
characteristics were included. The findings of this study suggests that health service
professionals need to increase awareness about the benefits of purchasing prescribed
medication, and that this must be more so for rural and urban residents.



Introduction

The issue of health care-seeking (or medical-care) behaviour is crucible to all society. All nations

rely on its human capital in the creation and pursuit of growth and/or development. The human

capital is able to accomplish those desired objectives outlined by the society only on the

fundamental premise that the people are in good health. Health is more than the absence of

diseases and it includes social, psychological and economic wellbeing. Embedded in good health

is not the least disease, as this is more in keeping with poor health. While poor and good health

appears to be on the opposite end of a continuum, for this paper good health denotes the life

satisfaction and general acceptance with the happenings of life. On the other hand, poor health

speaks to the people’s perception of a low quality of life or life satisfaction. Hence, this is in

keeping with WHO’s conceptualization of health in the Preamble to its Constitution in 1946

(WHO, 1948) which stated, health is not merely the absence of diseases or infirmity but it is the

state of complete physical, social and psychological wellbeing.


       In spite of a discussion which began in the 1940s, stating that health was a composite

function that includes biological, social, psychological, environmental and economic factors

(WHO, 1948), Engel (1960, 1977a,1977b,1978, 1980) re-ash this in the late 1950s and was able

to use this in the treatment of mentally ill-patient. Prior to Engel conceptual model (ie

Biopsychosocial model), health was conceptualized, treated and viewed from a biomedical

perspective. This meant that health-care seeking behaviour was primarily based on diseases (or

disease causing pathogens) and not based on preventative care. WHO and Engel recognized this
                                                194
uni-dimensional approach to the view and treatment of health, and expanded on this conceptual

framework. Despite the contribution of the aforementioned names, health care-seeking behaviour

in Western societies is still fundamentally driven by negative health (illness, or poor health) and

not in keeping with the broader framework offered by the World Health Organization.


       Health policy makers in the Caribbean continue to rely on biomedical approach in the

collecting of information upon which they evaluate the health of the society. This is evident in

how data are collected from the populace on health. Since 1988, Jamaica has been collecting

statistics on illness from the general populace. The data were to be used to aid and assess

government policies as well as to guide future programmes. The use of dysfunctions (or

illnesses) to measure health is not accepting the multi-dimensions to humans; in recognition that

health is more than diseases. It was not until 2007, that the Planning Institute of Jamaica and the

Statistical Institute of Jamaica that are responsible for the collection of the data began collecting

data on self-reported health status. Those agents were involved in the collating of data on

dysfunction and health-care seeking behaviour that were limited to traditional view of health (ie

visits to health care institutions and health care practitioners; bought medication). This means

that all policies were based on the narrow definition of illness on a construct that has broader

negative view framework about health, which accounts for Jamaicans (Jamaica Survey of Living

Conditions, 1989-2008; Ministry of Health, 2005) and by extension Caribbean peoples’

willingness to visit doctors (Shaw et al., 1999).


       An extensive review of health literature revealed that no study was that examine factors

that account for health care-seeking behaviour of Jamaicans, as well as the sociodemographic

correlates of the health status of those who sought traditional medical care. The importance of

                                                195
why people seek medical care is undoubtedly critical in health policy planning, and it is within

this limitation that this study is timely and needed. While studies outside of Jamaica have

established different determinants of health-care seeking behaviour (Grover et al., 2006; Vu

2008; Williams et al. 2006; Stekelenburg 2005), this cannot be assumed to apply within the

Jamaicans context as the culture, socio-demographic and economic characteristics are different

and as such calls for an examination of this phenomenon. Hence, the objectives of this study

were to examine self-rated health status and health care-seeking behaviour of Jamaicans; and to

ascertain the socio-economic predictors of health care-seekers as well as to determine factors that

account for good health status of those who sought health care in order aid public health policy

makers and primary care physicians.


Method

The current study extracted a sample of 1,006 respondents based on those who indicated having

sought health care in the 4-week period of the survey. The sample was drawn from a large

nationally representative cross-sectional descriptive survey of 6,783 Jamaicans (Statistical

Institute of Jamaica 2007). The survey was drawn using stratified random sampling. This design

was a two-stage stratified random sampling design where there was a Primary Sampling Unit

(PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District

(ED), which constitutes of a minimum of 100 dwellings in rural areas and 150 in urban areas.

An ED is an independent geographic unit that shares a common boundary. This means that the

country was grouped into strata of equal size based on dwellings (EDs). Based on the PSUs, a

listing of all the dwellings was made, and this became the sampling frame from which a Master

Sample of dwelling was compiled, which in turn provided the sampling frame for the labour

force. One third of the 2007 Labour Force Survey (ie LFS) was selected for the survey (JSLC
                                               196
2007 – ie Statistical Institute of Jamaica 2007). The sample was weighted to reflect the

population of the nation.


       This study used JSLC (2007) which was conducted by the Statistical Institute of Jamaica

(STATIN) and the Planning Institute of Jamaica (PIOJ) between May and August 2007. The

researchers chose this survey based on the fact that it is the latest survey on the national

population and that it has data on self-rated health status of Jamaicans. A self-administered

questionnaire was used to collect the data which were stored and analyzed using SPSS for

Windows 16.0 (SPSS Inc; Chicago, IL, USA). The questionnaire was modelled from the World

Bank’s Living Standards Measurement Study (LSMS) household survey.                  There are some

modifications to the LSMS, as JSLC is more focused on policy impacts. The questionnaire

covered areas such as socio-demographic, economic and health variables. The non-response rate

for the survey was 26.2%.


       Descriptive statistics, such as mean, standard deviation (SD), frequency and percentage

were used to analyze the socio-demographic characteristics of the sample. Chi-square was used

to examine the association between non-metric variables, and an Analysis of Variance

(ANOVA) was used to test the relationships between metric and non-dichotomous categorical

variables. Logistic regression examined the relationship between the dependent variable and

some predisposed independent (explanatory) variables, because the dependent variable was a

binary one (self-reported health status: 1 if reported good health status and 0 if poor health).


       The results were presented using unstandardized B-coefficients, Wald statistics, Odds

ratio and confidence interval (95% CI). The predictive power of the model was tested using the

Omnibus Test of Model and Hosmer and Lemeshow (2000) to examine goodness of fit of the
                                                 197
model. The correlation matrix was examined in order to ascertain if autocorrelation (or

multicollinearity) existed between variables. Based on Cohen and Holliday (1982), correlation

can be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. This was used to

exclude (or allow) a variable in the model. Wald statistics were used to determine the magnitude

(or contribution) of each statistically significant variable in comparison with the others, and the

Odds Ratio (OR) for the interpreting of each significant variable.


        Multivariate regression framework (Asnani, 2008; Bourne, 2008a, 2008b) was utilized to

assess the relative importance of various demographic, socio-economic characteristics, physical

environment and psychological characteristics of the health status of Jamaicans as well as health

care-seeking behaviour. Econometric analyses were also employed by other scholars in other

societies (Grossman, 1972; Hambleton et al., 2005; Smith & Kington, 1997). This approach

allowed for the analysis of a number of variables simultaneously. Secondly, the dependent

variable is a binary dichotomous one and this statistic technique has been utilized in the past to

do similar studies. Having identified the determinants of health status from previous studies,

using logistic regression techniques, final models were built for women in general as well as for

each of the geographical sub-regions (rural, peri-urban and urban areas), using only those

predictors that independently predict the outcome. A p-value of 0.05 was used to test the

significance level.


Model


The use of multivariate analysis in the study of health and subjective wellbeing (ie self-reported

health or happiness) is well established (Grossman, 1972; Smith & Kington, 1997; Di Tella et

al., 1998; Blanchflower & Oswald, 2004) equally in Jamaica and Barbados (Bourne, 2008a,
                                               198
2008b; Bourne & McGrowder, 2009; Hutchinson et al., 2005). The current study will employ

multivariate analyses in the study of health and health care-seeking behaviour of Jamaicans. The

use of this approach is better than bivariate analyses as many variables can be tested

simultaneously for their impact (if any) on a dependent variable.

          Scholars like Grossman (1972), Smith & Kingston (1997), Hambleton et al. (2005),

Kashdan (2004), Yi & Vaupel (2002), the World Health Organization pilot work a 100-question

quality of life survey (WHOQOL) (Orley, 1995) and Diener (1984, 2000) have both used and

argued that self-reported health status can be used to evaluate health status instead of objective

health status measurement. Other scholars, on the other hand, employed self-reported health

conditions to operationalize health of individual (Bourne & McGrowder, 2009). Embedded in

the works of those researchers is the similarity of self-reported health status and self-reported

dysfunction in assessing health.

          The current study will examine whether self-rated health status and self-reported

dysfunctions are correlated variables (Equation [1]) as well as to model general self-reported

illness (Equation [2]), health care-seeking behaviour of Jamaicans (Equation [3]) and to evaluate

the predictors of self-rated health status of Jamaicans (Equation [4]).

          It = f (H t )                                                                              [1]

          where It is self-reported dysfunction (illness) is a function of current self-rated health

          status, H t.

It =f (A i , G i ,HH i , AR i , H t , lnLI i, lnC, lnD i , ED i, MR i , S i , HIi , lnY, ε i )         [2]

          where It (ie self-reported illness in current time t) is a function of age of respondents, A i ;

          sex of individual i, G i ; household head of individual i, HH i ; area of residence, AR i ;

          current self-reported health status of individual i, H t ; logged length of illness, LIi ; logged

                                                               199
          consumption per person per household member, lnC; logged duration of time that

          individual I was unable to carry out normal activities, lnD i ; Education level of individual

          i, ED i ; marital status of person i, MR i ; social class of person i, S i ; health insurance

          coverage of person i, HI i ; logged income, lnY; and an error term (ie. residual error).

M t =f(A i , G i ,HH i , AR i , H t , lnLIi, lnC, lnD i , ED i, MR i , S i , HI i , ε i )              [3]

          where M t is the health care-seeking behaviour in current time t, is a function of age of

          respondents, and the other variables were previously stated.


H t =f (A i , G i ,HH i , AR i , M t , lnLIi, lnC, lnD i , ED i, MR i , S i , HIi , It , J t , ε i )
[4]

          where H t is self-rated health status of time period t (ie current); It is self-reported illness

          in current time period t; J t is self-reported injured suffered in the last 4 weeks, and the

          other variables were previously stated.

Measure

Self-rated health status: “How is your health in general?” And the options were very good; good;

fair; poor and very poor. For this study the construct was categorized into 3 groups – (i) good;

(ii) fair, and (iii) poor. A binary variable was later created from this variable (1=good and fair

0=otherwise).


Self-reported illness (or self-reported dysfunction): The question was asked: “Is this a diagnosed

recurring illness?” The answering options are: Yes, Cold; Yes, Diarrhoea; Yes, Asthma; Yes,

Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. A binary variable was later

created from this construct (1=yes, 0=otherwise) in order to use in the logistic regression.


Income. Total expenditure was used to operationalize income.


                                                                200
Social class: This variable was measured based on the income quintiles: The upper classes were

those in the wealthy quintiles (quintiles 4 and 5); middle class was quintile 3 and poor those in

lower quintiles (quintiles 1 and 2).


Health care-seeking behaviour. This is a dichotomous variable which came from the question

“Has a doctor, nurse, pharmacist, midwife, healer or any other health practitioner been visited?”

with the option (yes or no).


Age is a continuous variable in years.


Age group is classified into 7 groups: children (ages less than 15 years); young adults (ages 15 to

30 years); other adults (ages 31 to 59 years); young-old (or young elderly, ages 60 to 74 years);

old-old (or old elderly, ages 75 to 84 years); and oldest-old (or oldest elderly, ages 85 years and

older).


Results


The sample was 1,006 respondents (40.5% men and 59.5% women), with a mean age of 41.8

years (SD=27.6 years). Forty-four percent of the sample reported at least good health, 20% at

least poor health and 36% indicated fair self-rated health status. However, 97% of the

respondents claimed that they have had some dysfunctions; 6% reported being injured due to

accidents, and only 11% indicated that their illness was not diagnosed by a health practitioner.

Of those who indicated being diagnosed with a recurring ailment, 5.6% had arthritis, 20.5%

hypertension, 12.4% diabetes mellitus, 9.5% asthma and 14.9% cold. Of those who reported an

injury in the last 4 weeks, 19.2% indicated that it was owing to motor vehicle, 46.2% domestic

accident and 11.5% mentioned industrial accident (Table 7.1). Forty-five percent dwelled in

                                               201
urban areas or other towns and 55% in rural areas. Majority of the sample indicated that they

have had no formal education (71%) compared to 1.2% tertiary, 15.1% basic or elementary

education, 8.7% primary or preparatory schooling and 4.0% secondary level schooling.

Substantially, more Jamaicans do not have health insurance coverage (75.3%); 11.5% private

health insurance; 7.6% NI Gold and 5.7% other public health insurance coverage. Two thirds of

the sample sought health care and a little over one-half of the respondents were heads of

households (Table 7.1).

       One half of the sample had annual incomes of US $5,936.77 (US $1.00 = Ja. $80.47) and

50th percentile of health expenditure was US $9.94. Forty-seven percent of the respondents were

never married (includes common-law), 35.8% were married with 3.0% divorced, 1.8% separated

and 12.0 widowed. The median length of an illness was 7 days and a median of 2 days were

calculated as the length of time in which an individual was ill (Table 7.1). Some 54.8% of the

sample resided in rural areas, 18.6% in other towns and 26.6% in urban areas.

       Of the sample, 63.8% responded to the visits to public health care facilities and 64.1% on

visits to private health care facilities. Of those who responded to each, 49% attended public

health care facilities compared to 57.3% to private health care facilities.

       Thirty-eight percent of the sample were classified as poor, 21.0% were in the middle

class and 41% as wealthy. Furthermore, 19% were below the poverty line and 21% were in the

wealthiest category. Approximately one-half of the rural residents were poor compared to 27% in

other towns and 17% in urban areas (χ2 (4) =132.664, p < 0.001, n=1002). A cross tabulation

between self-rated health status and social standing revealed no statistical correlation between

the two variables (χ2 (8) =14.139, p=0.078, n=1002) (Table 7.2).



                                                 202
       An examination of health expenditure, injured in the last 4 weeks, and self-reported

dysfunction by area of residence revealed no statistical association (0.088, 0.841, 0.848

respectfully) (Table 7.3). However there were statistical relationships between self-rated health

status (p < 0.001), purchase of medication (p=0.020) and health insurance coverage (p<0.001) by

area of residence. More women (98.3%) than men (94.8%) reported illness (χ2 (1) =9.885, p =

0.002, n=1001).

       Based on Table 7.3, rural respondents reported the highest poor self-rated health status

(22.8%) compared to those in other towns (10.2%) and urban dwellers (10.8%). Similarly, rural

residents reported a lower coverage of health insurance than those in other towns or urban areas.

       An examination of head of household by health care-seeking behaviour revealed no

statistical correlation (χ2 (1) =2.010, p=0.088) (Table 7.4). Furthermore, those who sought health

care had a greater mean consumption per capita (US $2,168.09 ± US $1,852.36) compared to

those who did not seek health care (US $1,847.39 ± US $1,625.14) [ t= -2.834, p=0.005].

       A cross tabulation between health care-seeking behaviour and sex of respondents

revealed no statistical correlation between the two variables (χ2 (1) =3.182, p=0.074) (Table 7.5).

Table 7.5 showed that 68% of women sought health care compared to 62% of men.

       On examination, no statistical correlation existed between health care seeking behaviour

and self-reported illness of sample (χ2 (1) =2.052, p=0.105). When this cross tabulation was

controlled for sex, there was no difference between men (χ2 (1) = 1.876, ρ value = 0.171, n= 406)

and women (χ2 (1) = 0.712, ρ value = 0.399, n= 596) (Table 7.6).

       Based on Figure 1, there was no statistical difference between self-reported health status

of men and women (χ2 (2) =5.618, p=0.060) (Figure 7.1). Figure 7.2 showed that 55.8% of the

respondents who indicated that they did not seek health care self-reported good health compared

                                               203
to 37.6% of those who said “yes” they visited a traditional medical care facility or practitioner.

Twenty-four percent of those who sought medical care reported poor health compared to 13%

who mentioned “no” to seeking health care in the past 4-weeks; whereas more people who

sought medical care indicated fair health status than those who reported poor health status (χ2 (2)

=33.298, p < 0.001).

         All the old-old and the oldest-old reported an illness compared to 97% of young-old,

90.7% of young adults and children (98.1%) (Figure 7.3).

         A statistical relation was found between self-reported illness and age cohort of

respondents (χ2 (35) = 453.697, p < 0.001, n=992). The association was a moderately strong one

(contingency coefficient = 0.560). Figure 7.4 showed that 37% of children had cold, 20%

asthma, 21% unspecified compared to 13% of young adults who had cold, 15% asthma and 40%

unspecified and this change after 31 years. The three leading causes of morbidity for other aged-

adults were unspecified (28%), hypertension (25%) and diabetes mellitus (15%). Hypertension

was significantly more for those older than 60 years, with rate being the highest for the oldest-

elderly (Figure 4). Based on Figure 4, 31% of young elderly reported hypertension compared to

44% of old-old and 47% of oldest-old whereas for diabetes mellitus, the most number of cases

were reported by young-old (26%), then old-old (17%), oldest-old (17%) and other aged-adults

(15%).


         The cross tabulation between visits to public health care facilities and area of residents

revealed a statistical correlation (χ2 (2) = 18.332, p < 0.001, n=641) as well as a relationship

between private health care facilities and area of residents (χ2 (2) = 22.147, p < 0.001, n=644).

Based on Figure 7.5, most of the rural residents attended public health care facilities (56.9%)

while most of the other town residents visited private health care facilities.
                                                 204
       An examination of visits to health care facilities and social class revealed a statistical

correlation: public (χ2 (2) = 35.874, p < 0.001, n=641) and private (χ2 (2) = 37.025, p < 0.001,

n=644). The poor were more likely to attend public health care facilities (63.3%) compared to

the middle class (52.5%) and the wealthy (36.6%), indicating that the rich were substantially

probable to visit private health agencies (68.8%) compared to the poor (41.7%) and those in the

middle class (57.9%) (Figure 7.6).


Results: Multivariate Analyses

Using logistic regression analyses, self-rated health status was found to be a significant statistical

predictor of self-reported dysfunction (Table 7.7): good self-rated health status with reference to

poor self-rated health status (OR=0.271, 95%CI=0.081, 0.915).

      The model had statistically significant predictive power (Model χ2=12.183, p=0.002;

Hosmer and Lemeshow goodness of fit χ2=0.000, P = 1.00) and correctly classified 96.9% of the

sample (correctly classified 100% of those who indicated self-reported dysfunctions and 0% of

those who do not have dysfunctions. The logistic regression model can be expressed as: Log

(probability of self-reported dysfunction/probability of not having dysfunction = 4.195 – 1.304

(1=Good Self-rated Health Status, 0=otherwise). Furthermore, the odds of reporting a

dysfunction for those who indicated good health status was 82.9%which is less likely than the

odds of reporting a dysfunction for those with poor health status (Table 7.7).

Predictors of Self-reported dysfunction. From the sample, three factors were found to be

predictors of self-reported illness: logged consumption (OR=0.088, 95%CI= 0.008, 0.961);

social class of the individual (upper class – OR=76.024, 95%CI=1.846, 3130.54); and age of

respondents (OR=1.095, 95%CI=1.024, 1.171) (Table 7.8).


                                                 205
       Table 7.8 revealed that self-reported dysfunction model had a significant predictive

power (Model χ2=27.515, p=0.001; Hosmer and Lemeshow goodness of fit χ2=1.450, P = 0.93),

and correctly classified 99.7% of the sample (correctly classified 95.8% of those who indicated

self-reported dysfunctions and 0% of those who do not have dysfunctions.

       The findings revealed that when the demographic variables were included with self-rated

healthy status, the latter was no longer significant (Table 7.8).

Predictors of Health Care-Seeking Behaviour.              Based on Table 7.9, from the logistic

regression, 5 variables are statistically significant predictors: Age of respondents (OR=1.031,

95%CI=1.014, 1.049); Area of residence (Other towns with reference to rural area – OR=0.5,

95%CI=0.278, 0.902); logged consumption (OR=3.605, 95%CI=1.814, 7.167); marital status

(married – OR=0.468, 95%CI=0.260, 0.843; divorced, separated or widowed – OR=0.383,

0.163, 0.903) and social class (Upper class – OR=0.319, 95%CI=0.106, 0.958).

       Health Care-Seeking Behaviour Model had statistically significant predictive power

(Model χ2=49.628, p=0.001; Hosmer and Lemeshow goodness of fit χ2=13.900, P = 0.84), and

correctly classified 77.8% of the sample (correctly classified 97.8% of those who sought health

care and 13.3% of those who did not seek health care (Table 7.9). The logistic regression model

can be expressed as: Log (probability of seeking health care/probability of not seeking health

care = -14.059 + 0.031 (Age in years) – 0.692 (1=Other Town, 0=Rural area) + 1.282(logged

consumption) – [0.759(1=if married, 0=single) + 0.959(1=if divorced, 0=single)] -1.144(1=if

upper class, 0=otherwise) (Table 7.9).

Predictors of Self-rated Health Status

Health status of those who seek health care can be predicted by 3 factors. These are logged

duration of the individuals to carry out their normal activities (OR=0.594, 95%CI=0.413, 0.855);

                                                 206
age of respondents (OR=0.967, 95%CI=0.949, 0.986) and area of residence (urban area –

OR=2.415, 1.195, 4.881; other towns – OR=2.514, 1.162, 5.442).


       The Health Status Model was a statistically predictive one (Model χ2=59.824, p=0.001;

Hosmer and Lemeshow goodness of fit χ2=4.324, P = 0.827), and correctly classified 77.2% the

sample (correctly classified 34.5% of those who reported good health status and 93.0% of those

who do not (Table 7.10). The logistic regression model can be expressed as: Log (probability of

self-reported good health status/probability of not reporting good health = 1.219 – 0.520 (logged

duration unable to work) + [0.882(1=Urban area, 0=otherwise) + 0.922(1=other town,

0=otherwise)] – 0.033(Age) (Table 7.10).

Discussion and Conclusion

Two thirds of the sample mentioned that they sought medical care in the last 4-week, while

marginally more individuals who indicated having sought health care, reported fair health status

than those who claimed good health status. Interestingly, 9 out of 10 respondents reported an

illness with 89 out of every 100 opined that their illness was diagnosed by a health care

practitioner. Rural residents were 2.4 times more likely to report poor health status than other

town dwellers; whereas urban residents were one-half less likely to evaluate their health as poor.

A critical finding of this study is that 51 out of every 100 rural residents were poor, while the

ratio was 27 out of 100 in other towns and 17 out of 100 in urban areas. In spite of the high

report of illness and that 5 out of 19 respondents had diagnosed chronic recurring illness (ie

diabetes mellitus, arthritis, asthma, and hypertension); only 6 out of 10 respondents purchased

the prescribed medication. The study revealed that good health status was negatively correlated

with self-reported dysfunctions. However, when the socio-demographic variables were

introduced within the model, health status dissipated as a factor of self-reported dysfunctions. Of
                                               207
the socio-demographic variables chosen to be tested in the self-reported dysfunction model,

consumption, social class and age of respondents were found to be determinants. Whereas, this is

so for the abovementioned variables the determinants of health care-seeking behaviour of

Jamaicans were age, area of residents, consumption, marital status, and social class; with

duration of time unable to work, area of residents and age of respondents.


       Many theories (or models) have been developed to explain health care-seeking behaviour

of people and these are widely used by Caribbean public health policy makers in planning health

demands and needs of societies. The disadvantages in using those theories (Health Belief Model;

Theory of Reasoned Actions; Theory of Planned Behaviour; Transtheoretical Model and Stages

of Change; Precaution Adoption Process Model) are that they were not developed from data

collected from the populace. These theories are atypical to Caribbean or in particular Jamaica.

They are germane the context that the culture is different along with other indigenous

characteristics. The use of health care-seeking models which are not biased to the culture means

that we mis-prescribed solutions which are for the targeted population. According to Glanz et

al. (2002), while it is reasonable to assume that a theory such as Health Belief Model is

applicable to different cultures, it also is important to realize that constructs may have to be

adapted to make them more relevant to the target culture.          Those modifications may be

applicable with some generalizability to developing nations, but this does not suggests its

comprehensive understanding of Caribbean peoples or Jamaicans.


       Although the Health Belief Model did not emerge from data in Jamaica or the wider

Caribbean, it has some merits which we examine in this study. This conceptual model is a

framework for health behaviour. The Health Belief Model (HBM) was developed in the 1950s

                                               208
by some social psychologists in the United States Public Health Service. It was designed to

account for the failure of people to become involve in preventative and detection disease

programmes (Hochbaum, 1958; Rosenstock, 1960); and then it evolved to peoples’s response to

symptoms (Kirscht, 1974) with a later expansion that entails individuals’ behaviour in response

diagnosed dysfunctions (Becker, 1974). Hence, embedded in the HBM are preventative actions,

illness behaviour, and sick-role behaviour, suggesting that dysfunction is the primary focus of

this model. This work does not concur with the HBM as it was found that health status was not

correlated with health care-seeking behaviour of Jamaicans. However, marital status, area of

residence and social class (ie upper class with reference to poor) were found to be negative

determinants of health care-seeking behaviour while age and consumption were positive

determinants.


       The current study revealed that consumption was the most significant predictor of health

care-seeking behaviour of Jamaicans followed by age of respondents. It was found that those

who are able to spend more on consumer expenditure are 4 times more likely to seek medical

care, which concurred with Brow et al (2008). Biological ageing means a greater likelihood for

people to seek health care and this concurs with other studies (Bourne, McGrowder & Nevins, in

print; Bourne 2009; Brown et al 2008; Erber 2005; Brannon & Fiest 2004; Costa 2002; Buzina

1999; CAJANUS 1999; Anthony 1999) as the reasons are linked to increased biological

conditions. According to Morrison (2000), there is a shift from infectious communicable diseases

to chronic non-communicable diseases as a rationale for the longevity of the Anglophone

Caribbean populace. This research concurs with Morrison. The findings on in this study revealed

that as people age, the typology of diseases change from cold, diarrhoea and asthma to diabetes


                                              209
mellitus, hypertension and arthritis. The probability of the first three illnesses resulting in

mortality is lower than the latter three morbidities.


       In Jamaica, statistics showed that among the 10 leading cause of mortality for males 5

years and older were external causes, cerebrovascular diseases, diabetes mellitus, ischaemic heart

diseases, malignant neoplasm and hypertension, while for females 5 years and older the diseases

were diabetes mellitus, cerebrovascular diseases, hypertension, ischaemic heart disease, external

cause and heart diseases (Statistical Institute of Jamaica, 2008). On the other hand, the leading

mortality among males and females under 5 years were disorders relating to gestation and fetal

growth, respiratory distress, other respiratory conditions, other congenital malformations, and

perinatal conditions. Hence, the study concurs with the statistics and Morrison’s claim that the

typology of diseases shift with the ageing of an individual. In addition another study found that

the sixth leading causing of mortality for elderly in Barbados, Trinidad and Tobago, St. Lucia,

Montserrat, Guyana, Dominica, Barbados and Bahamas were fundamentally the same. They

were respiratory infections, cerebrovascular diseases, hypertension, diabetes, malignant

neoplasm, and diabetes mellitus, which reinforced the primary finding that 39 out of every 100

Jamaican reported being diagnosed with diabetes mellitus, hypertension and arthritis and the

unspecified group was 23 out of 100 respondents. Among the diseases in the unspecified

category would be malignant neoplasm. In 2007, statistics indicated that 8.5% of the Jamaica’s

population was less than 5 years; 9.7% was 5 to 9 years; 10.3% was 10 to 14 years, meaning that

28.6% of the population was children. With approximately 71% of Jamaica’s population ages 15

years and older, this explains the high probability of chronic diseases accounting for more deaths

than communicable and illnesses affecting children.


                                                 210
       The current findings indicate that health status is not determined by health care-seeking

behaviour, self-reported illness or length of illness. This speaks volume about the culture of

unwillingness to visit traditional medical practitioners, and adds more information to the

discussion, of illness and mortality. Jamaicans are unlikely to visit health care practitioners

owing to their perspective of illness, severity of illness and the likely of the dysfunction to cause

mortality. When illness is equated to mortality, the probability of seeking medical care will be

high. A part of the reason for this health care-seeking behaviour is embedded in the culture as

illness is viewed as weakness.      Another explanation for this low probability is Jamaicans

involvement of non-traditional medical care behaviours. To address ill-health, Jamaicans visit

spiritual advisers (ie unknown as obeah men). These individuals perform the similar functions

like the traditional health practitioners except surgeries. The data used for this study excluded

non-traditional medical healers, and so underestimate the coverage of medical care-seeking

behaviour of sample.


       There is a finding which appears paradoxical as people who resided in urban areas do not

exhibit greater or lower health care-seeking behaviour than those who dwelled in rural areas.

Rural residents were more likely to seek medical care than other town dwellers while more

consumption was positively correlated with seeking more health care. Interestingly, the wealthy

spent more on consumption than the poor, yet the poor sought more medical care-seeking

behaviour than upper class Jamaicans. Although rural residents were more likely to be poorer

than other Jamaicans and that they were more likely to spend less than urban and other town

dwellers. Embedded in this finding, is the fact that it is not higher social class that determines

health seeking behaviour but money. Those in the upper class may have access to more financial

resources, but rural residents have greater social network which avails them of extended
                                                211
economic resources. Rural residents have more children, and the culture within those areas is

such that the wider community is willing to aid each other for consumption including medical

care. Another issue that is not on the surface of the finding is the fact that they (ie rural residents)

were attending more to medical care than other town dwellers and there is no evidence of

statistical difference in health-seeking behaviour between rural versus urban residents, owing to

disproportionally more of them in the country than other dwellers. The wide primary health care

coverage which is inexpensive means that Jamaicans even if they are poor can access health care.

Where the difference will be is in access to private health care service, and this is basic

fundamentally on one’s ability to afford it and not on wanting to access the service.


       The inverse correlation between self-reported dysfunction and consumption, showed a

positive association between health care-seeking behaviour and consumption within the context

that those in the upper class have a greater degree of reporting illness, means that there is a

cultural bias that explains Jamaicans unwillingness to seek health care. This study did not

initially examine lifestyle behaviour of Jamaicans, but given that consumption expenditures are

constituted of meal and non-consumption expenditures, consumption being a negative predictor

of self-reported illness, suggests that Jamaicans were involved in relatively good decisions that

are lowering illness. The positive relations between health care-seeking behaviour and

consumption are indicators of preventative lifestyle practices. This is so, because in 2006, 70%

of Jamaicans who reported ill-health sought medical care (Planning Institute of Jamaica and

Statistical Institute of Jamaica, 2008) compared to 66% in this study (in 2007), within the context

that inflation increased by 194.7% over 2006 (in 2007), this means that increased consumption

expenditure does not necessarily mean more meal consumed or non-consumption as this change


                                                  212
is owing to price increases. With more Jamaicans attended private health care facilities, inflation

would increase costing of the offered services.


       The current study found that rural residents had the least self-evaluated health status, and

thereby justify more of them seeking medical care than upper class Jamaicans. Another

explanation for more rural residents attended health care institution is owing to greater

percentage of them in older ages than in other geographic zones. Age is a negative determinant

of health status and positively correlated with self-reported dysfunction, and accounts for more

rural residents demanding more health care than other people in Jamaica.


       This study refined the finding of Grover el’s work (2006). They found that significantly

more urban area dwellers take self medication compared to rural area residents (see also, Sudha

et al, 2003). In this study, it was revealed that that more urban dwellers purchased over the

counter medication than rural residents; but that it was other town dwellers (ie semi-urban) that

significantly used self medication than rural area people. This means that self treatment was

more an urban and/or semi-urban phenomenon than a rural area reality. On the other hand, rural

area residents significantly purchased more prescribed medication than other town dwellers,

while urban area settlers bought more prescribed medicine compared to rural and semi-urban

residents. This study went further than Grover et al. (2006) and Sudha (2003), when it examined

those who did not buy by area of residence. The current work revealed that significantly more

rural area residents did not purchase medication than residents in other geographic areas.


       Interestingly health care-seeking did not differ significantly between the sexes, which

concur with a study by Williams et al (2006). One of the explanations for this non-significance



                                                  213
can be accounted for based on the non-significant disparity in health status and self-reported

dysfunctions of males and females.


Conclusion


Jamaica is comprised of peoples of different ethnic; socialization; social class; geographic zones

and culturalization, and this accounts for a difference in belief system and health behaviour.

This disparity must be taken into consideration when designing public health programmes.

Hence, the wholesale utilization of any health model that is developed outside of the society or

even medication of such a theory is not necessarily applicable to the nation.


       The current study revealed that health behaviour is a function of socio-demographic

variables. Poverty which is synonymous to rural areas influences people choice in visits to

health care-seeking facilities. An interesting consideration of rural residence with those than

residents of other geographic zones is the culture and its influence on health care-seeking

behaviour and other such decisions. Home remedy and non-traditional healers (ie obeah men) is

a substitute product that is used more by rural dwellers than others, because of the retention of

the African tradition. While urban and other town residents were exposed to this culture and

socialization, their higher level of education, access to more information and financial resources

account for re-socialization and new re-adaptation to traditional medical care utilization.

Therefore, when health literacy and public health programmes are fashioned in Jamaica or other

developing societies, health care-seeking behaviour model must not only be modified but must

utilize data from those nations to address the health needs of the geo-political zones and not

some model developed for developed societies with some modifications. The findings of this



                                               214
study suggests that health service professionals need to increase awareness about the benefits of

purchasing prescribed medication, and that this must be more so for rural and urban residents.




                                               215
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                                               218
Table 7.1: Demographic Characteristic of Sample
Variable                                                Number (n)    Percentage
Sex:
    Men                                                       407             40.5
    Women                                                     599             59.5
Self-rated Health Status
    Very good                                                 122             12.2
    Good                                                      317             31.6
    Fair                                                      361             36.0
    Poor                                                      173             17.3
    Very poor                                                  29              2.9
Health Care-Seeking Behaviour
    No                                                        348             34.6
    Yes                                                       658             65.4
Household Head
    No                                                        559             55.6
    Yes                                                       447             44.4
Marital Status
    Married                                                   265             35.8
    Never married (includes common-law)                       351             47.4
    Divorced                                                   22              3.0
    Separated                                                  13              1.8
    Widowed                                                    89             12.0
Injured in last 4-week
    No                                                        942             93.7
    Yes                                                        63              6.3
Diagnosed Recurring Illness
    Cold                                                      148             14.9
    Diarrhoea                                                  27              2.7
    Asthma                                                     94              9.5
    Diabetes mellitus                                         123             12.4
    Hypertension                                              204             20.5
    Arthritis                                                  56              5.6
    Other                                                     232             23.4
    No                                                        109             11.0
Health Insurance
    Private                                                   115             11.5
    NI Gold                                                    76              7.6
    Other Public                                               57              5.7
    No                                                        756             75.3
Self-reported dysfunction
    None                                                       31              3.1
  Have                                                        971             96.9
Annual Income Median                                          US $5, 936.77




                                                  219
Table 7.2: Self-rated Health Status by Social Standing

                                         Social Standing


Health         Poorest      Poor          Middle         Upper          Upper
Status:                                   class          Middle         Class         Total
                                                         class

Good            70 (37.0)    78 (41.9)       94 (44.8)     103 (50.0)     94 (44.5)    439 (43.8)
Fair            66 (34.9)    71 (38.2)       73 (34.8)      68 (33.0)     83 (39.3)    361 (36.0)
Poor              53 (28)    37 (19.9)       43 (20.5)      35 (17.0)     34 (16.1)    202 (20.2)
Total             189         186             210            206           211           1002
χ (8)=14.139, p=0.078, n=1002
 2




                                              220
Table 7.3: Health care, injured, self-rated health status, buy medication, and health insurance
coverage by area of residence

                                                       Area of residence
                                                                                          pvalue
              Variable

                                         Urban           Other towns         Rural

Injured in last 4-week                                                                      0.841
      No                                 252 (94.4)          174 (93.0)      516 (93.6)
      Yes                                  15 (5.6)            13 (7.0)        35 (6.4)
Self-rated Health status                                                                  < 0.001
       Very good                           26 (9.7)           34 (18.3)       62 (11.3)
       Good                              104 (38.8)           60 (32.3)      153 (27.9)
       Fair                              104 (38.8)           70 (37.6)      187 (34.2)
       Poor                               29 (10.8)           19 (10.2)      125 (22.8)
       Very poor                            5 (1.9)             3 (1.6)        21 (3.8)
Buy Medicine in last 4-weeks                                                                0.020
      Buy, Prescribed                    173 (66.0)          109 (60.6)      331 (62.9)
      Buy, partial prescribed               6 (2.4)             2 (1.1)        10 (1.9)
      Buy, prescribed over counter          9 (3.4)            12 (6.7)         8 (1.5)
      Buy, over counter medicine           14 (5.3)            11 (6.2)        27 (5.1)
      Did not buy, prescribed               9 (3.4)             1 (0.6)        22 (4.2)
      None prescribed required            51 (19.5)           45 (25.0)      128 (24.4)
Health Insurance Coverage                                                                 < 0.001
      Yes, Private                        50 (18.7)           29 (15.5)        36 (6.6)
      Yes, Government                      21 (7.9)            16 (8.6)        39 (7.1)
      Yes, Other Public                    14 (5.2)            11 (5.9)        32 (5.8)
      No                                 182 (68.2)          131 (70.2)      443 (80.5)
Self-reported dysfunction                                                                   0.848
     None                                    8 (3.0)            7 (3.7)       16 (2.9)
     Yes                                  257 (97.0)        180 (96.3)     534 (97.1)
Annual Income Mean (SD)               US $10,249.43       US $8,241.77   US $6,361.03     < 0.001
                                     (US $8,613.98)     (US $6,570.46) (US $4,849.60)
Social class                                                                              < 0.001
    Poor                                  44 (16.4)           51 (27.3)      282 (51.2)
    Middle                                49 (18.4)           42 (22.4)      120 (21.8)
     Upper                               174 (65.2)           94 (50.3)      149 (27.0)
Public facilities                                                                         < 0.001
    No                                   109 (59.2)           71 (61.2)      147 (43.1)
    Yes                                   75 (40.8)           45 (38.8)      194 (56.9)
Private facilities                                                                        < 0.001
   No                                     63 (33.9)           37 (31.6)      175 (51.3)
   Yes                                   123 (66.1)           80 (68.4)      166 (48.7)
                                             221
Table 7.4: Head of Household by Health Care-Seeking Behaviour

                                          Health Care-Seeking Behaviour
                                                                                pvalue

                                                 No             Seek Care

Head of Household:                                                              0.088

No                                               204 (58.6)        355 (54.0)

Yes                                              144 (41.4)        303 (46.0)
Total                                             348             658
χ2(1)=2.010, p=0.088, n=1006




                                           222
Table 7.5: Health Care-Seeking Behaviour by Sex of Respondents

                                                          Sex

                                                                              pvalue
                                                 Man             Woman


Health Care-Seeking Behaviour:                                                   0.074


Did not seek care                                154 (37.8)      194 (32.4)

Seek health care                                 253 (62.2)      405 (67.6)
Total                                             407             599
χ2 (1) =3.182, p=0.074, n=1006




                                           223
Table 7.6: Health Care-Seeking Behaviour by Illness, Controlled for Sex

                             Self-reported Illness        Self-reported Illness

                             Yes            No            Yes                     No

                                    Male1                         Female2

Health           No          149 (38.9)     5 (23.8)      191 (32.6)              2 (20.0
Care-seeking
behaviour

                 Yes         236 (61.3)     16 (76.2)     395 (67.4)              8 (80.0)


Total                        385            21            586                     10

χ (1) = 1.876, ρ value = 0.171, n= 406
1 2

 χ (1) = 0.712, ρ value = 0.399, n= 596
2 2




                                              224
Figure 7.1: Self-rated Health Status by Sex of respondents (n=1,002)




                                              225
Figure 7.2: Self-evaluated health status and health care-seeking behaviour




                                              226
Figure 7.3: Self-reported illness by age group of respondents




                                              227
Figure 7.4: Self-reported diagnosed recurring Illness by Age cohort of respondents




                                              228
Figure 7.5: Percentage of persons who visited public or private health care facilities




                                                229
Figure 7.6: Percentage of visits to public or private health facilities by social class




                                                 230
Table 7.7: Logistic Regression: Self-rated Health Status as predictor of Self-reported
Dysfunctions
                                                         95.0% C.I.
 Dependent variable:
 Self-reported
 Dysfunction                                     Odds      Lower,
                        Coefficient Std Error    ratio     Upper
 Good Health Status       -1.304       0.620     0.271 0.081, 0.915*

 Fair Health Status                 0.065           0.736        1.067   0.252, 4.513
 †Poor health status
                                    4.195           0.582       66.333        -
 Constant
χ2 (2) =12.183, p = 0.002; n = 998
-2 Log likelihood = 264.094
Hosmer and Lemeshow goodness of fit χ2=0.000, P = 1.00.
Nagelkerke R2 =0.050
Overall correct classification = 96.9%
Correct classification of cases of self-reported dysfunctions =100.0%
Correct classification of cases of no dysfunctions =0.0%
†Reference group
*p < 0.05, **p < 0.01, ***p < 0.001




                                                        231
Table 7.8: Logistic Regression: Predictors of Self-reported Dysfunctions
                                                                                      95.0% C.I.
 Variable                               Coefficient   Std. Error    Odds Ratio       Lower, Upper
   Urban area                              -0.090         0.861              0.914    0.169, 4.939
   Other town                              -0.438         0.923              0.645    0.106, 3.941
   †Rural area

   Health insurance                         0.966         0.933              2.626   0.422, 16.343
   Log consumption                         -2.430         1.219              0.088   0.008, 0.961*
   Log health expenditure                  -0.495         0.291              0.610    0.345, 1.079
   Log length of illness                   -0.031         0.336              0.970    0.501, 1.875
   Log duration unable to work             -0.369         0.367              0.691    0.337, 1.419

   Secondary or Tertiary                               8847.72      68054095
                                           20.338                                     0.000, 0.000
                                                             3             4
   †No formal education

   Married                                 -0.174         1.025              0.840    0.113, 6.267
   Divorced, separated,
                                           -1.545         1.326              0.213    0.016, 2.871
   widowed
   †Never Married

   Middle class                             2.080         1.108              8.005   0.912, 70.228
   Upper class                                                                               1.846,
                                            4.331         1.897         76.024
                                                                                         3130.54*
   †Poor

   Head household                          -1.404         0.913              0.246    0.041, 1.471
   Sex (1=Man)                             -0.959         0.725              0.383    0.093, 1.586
   Age                                                                                       1.024,
                                            0.091         0.034              1.095
                                                                                          1.171**
   Good health status                       0.046        0.989               1.047    0.151, 7.274
   Fair health status                       0.670        0.923               1.955   0.320, 11.927
   Logged income                            0.420        0.638               1.521    0.435, 5.317
   Constant                                26.315       14.148                   -        -
χ2 =27.515, p < 0.001
-2 Log likelihood = 74.212
Hosmer and Lemeshow goodness of fit χ2=1.450, P = 0.993
Nagelkerke R2 =0.303
Overall correct classification = 99.7%
Correct classification of cases of self-reported dysfunction =95.8%
Correct classification of cases of without self-reported dysfunction =0.0%
†Reference group
*p < 0.05, **p < 0.01, ***p < 0.001
                                                        232
Table 7.9: Logistic regression: Variables Explaining Health Care-Seeking Behaviour
                                                                    95.0% C.I.
 Variable                                         Std.    Odds
                                    Coefficient Error ratio        Lower, Upper
   Age                                  0.031      0.009 1.031 1.014, 1.049***
   Sex (1=Man)                         -0.357      0.273 0.700        0.410, 1.195
   Head Household                      -0.407      0.284 0.665        0.381, 1.161

     Urban Area                                 0.061         0.329   1.063      0.558, 2.025
     Other Town                                -0.692         0.300   0.500     0.278, 0.902*
     †Rural

     Good Health Status                        -0.563         0.355   0.569       0.284, 1.142
     Dummy Health Insurance                     0.434         0.303   1.543       0.852, 2.794
     Fair Health Status                        -0.054         0.319   0.948       0.507, 1.771
     Log Length of illness                     -0.077         0.110   0.926       0.746, 1.149
     Log Consumption                            1.282         0.351   3.605   1.814, 7.167***
     Log Duration unable to work                0.102         0.131   1.108       0.856, 1.432

     Secondary or Tertiary                     0.467          0.660   1.596      0.437, 5.821
     †No formal education

     Married                                   -0.759         0.300   0.468     0.260, 0.843*
     Divorced, separated or
                                               -0.959         0.437   0.383     0.163, 0.903*
     widowed
     †Never married

     Middle class                              -0.483         0.425   0.617      0.268, 1.419
     Upper class                               -1.144         0.562   0.319     0.106, 0.958*
     †Poor

     Constant                                 -14.059         3.952   0.000          -
χ (16) =49.628, p < 0.001
 2

-2 Log likelihood = 439.317
Hosmer and Lemeshow goodness of fit χ2=13.900, P = 0.84
Nagelkerke R2 =0.050
Overall correct classification = 77.8%
Correct classification of cases of seeking health care =97.4%
Correct classification of cases of not seeking health care =13.3%
†Reference group
*p < 0.05, **p < 0.01, ***p < 0.001




                                                        233
Table 7.10: Logistic Regression: Variables of Good Self-rated Health Status
 Variable                                      Std.     Odds         95.0% C.I
                                Coefficient Error        ratio     Lower, Upper
  Log Consumption                     0.105    0.463       1.111      0.448, 2.753
  Log Health Expenditure              0.105    0.170       1.110      0.796, 1.550
  Log Length of illness             -0.078     0.148       0.925      0.692, 1.236

   Log Duration unable to                     -0.520      0.185     0.594   0.413, 0.855**
   work

   Secondary or Tertiary                       0.633      0.675     1.884     0.502, 7.073
   †No formal education

   Married                                    -0.037      0.347     0.963     0.488, 1.900
   Divorced, separated or
                                               0.112      0.500     1.118     0.420, 2.978
   widowed
   †Never married

   Middle Class                               -0.358      0.485     0.699     0.270, 1.808
   Upper class                                -0.765      0.678     0.465     0.123, 1.760
   †Poor

   Self-reported illness                      -1.804      1.108     0.165     0.019, 1.446
   Self-report injury                         -1.073      0.887     0.342     0.060, 1.943
   Health Insurance                            0.454      0.324     1.575     0.834, 2.973

   Urban Area                                  0.882      0.359     2.415    1.195, 4.881*
   Other Town                                  0.922      0.394     2.514    1.162, 5.442*
   †Rural area

   Age                                        -0.033      0.010     0.967   0.949, 0.986**
   Sex (1=Man)                                 0.181      0.336     1.199      0.620, 2.317
   Head Household                              0.130      0.332     1.139      0.594, 2.184
   Constant                                    1.219      5.534     3.385          -
χ2 =59.568, p < 0.001
-2 Log likelihood = 303.022
Hosmer and Lemeshow goodness of fit χ2=4.324, p = 0.827
Nagelkerke R2 =0.254
Overall correct classification = 77.2%
Correct classification of cases of good self-rated health =34.5%
Correct classification of cases of not seeking health care =93.0%
†Reference group
*p < 0.05, **p < 0.01, ***p < 0.001



                                                        234
                                                                         Chapter
                                                                                           8
   Knowledge, attitude and practices of adults of the reproductive
    years on reproductive health matters, with emphasis on HIV
               infected people in a Caribbean society




                  Paul A. Bourne, Neva South-Bourne, Cynthia G. Francis




South and Southeast Asia represent the largest number of new HIV infections, while Sub-
Saharan Africa represents the highest rate of new infections, followed by Latin America and the
Caribbean. Yet no study that has emerged in the Caribbean has comprehensively examined
young people’s sexual and reproductive health attitudes, knowledge and practices, comparing
the result with those who are HIV infected. The present study examines core issues of sexual and
reproductive health among youths, particularly with respect to HIV. Almost 34% of the sample
had been tested for HIV, and 16.9% had done this in the past 12 months. Only 0.2% of the
sample knew that they were HIV positive and 4% had positive HIV test results when they did the
test. Of those with a positive HIV test result, 58.1% were females. Approximately 16% of those
with HIV have had an STI infection in the past, and 61% were actively practicing religion. The
mean age of first sexual relations for the sample was 15.4 years (SD = 3.2 years), and 15.6 years
for those infected with HIV. Four variables emerged as statistically significant factors of
Jamaicans’ willingness to do an HIV test in the future. The findings of this research are far-
reaching and can be used to guide public health policy formulation.




                                              235
Introduction
        Human immunodeficiency virus (HIV) is the second leading cause of death in the world

[1-3], the first in the Caribbean (among 15-49 year olds) [4] and the second in Jamaica [5]. The

risk of contracting HIV is considered higher in low-income countries, and lowers in

industrialized countries. Factors that contribute to such discrepancies in sexual and reproductive

risk all over the world are “weak and uneven distribution of health services, the concentration of

poverty among certain population groups and geographic areas, gender inequalities and harmful

social practices” [1].


        This paper focuses on adults in the reproductive ages of 15-49 years. This represents

almost 3 billion people in the world, and almost half of all new HIV infections [6]. In Jamaica,

youth represents 20% of the population of 2.6 million. The public health dilemma of HIV among

youths has led to commitments made by various nations (189 states) of the world at the United

Nations General Assembly Special Session (UNGASS) on HIV and AIDS in New York in 2001.

Such commitment was made via           the signing of a Declaration of Commitment which

encapsulates promises to acknowledge the role and contributions of young people in addressing

all aspects of HIV and AIDS, recognizing the full involvement and participation of the youth, in

designing, planning, implementing and evaluating programmes relating to responses to the

epidemic; reducing the prevalence of HIV among youths within the range of 15-24 years of age

by 25% by 2010, ensuring that 90% of youths have access to information and services that would

reduce their vulnerability to HIV infection, ensuring access to information through primary and

secondary school curricula on matters of safe and secure environment, strengthening sexual and

reproductive health programmes, and so on [6].


                                               236
       Under the CARICOM-PANCAP, the strategic objectives for national HIV responses are

“to prevent the sexual transmission of HIV, to decrease the vulnerability to sexual transmission

of HIV; to establish comprehensive, gender-sensitive, and targeted prevention programmes for

children (9-14 years old) and the youth (15-24 years old), to achieve universal access to targeted

prevention interventions among the most at-risk populations (such as MSM, SW, drug users,

prisoners, and migrant populations), to provide services for the prevention of mother-to-child

transmission of HIV to all pregnant women and their families; to strengthen prevention efforts

among PLHIV as part of comprehensive care; and to reduce vulnerability to HIV through early

identification and treatment of other sexually transmitted infections (STIs)” [7]. The achievement

of these objectives can also be hindered by policies (for example, legislation against men having

sex with men (MSM), the capacity to address legal constraints that hinder access to services, the

lack of integration of HIV policies and programmes into national development plans and [7]

programmes, the lack of political support and incongruities between policies and legislations.


       Within the context of the high HIV incidence and prevalence rates among people in

developing nations, and in particular the Caribbean, and more so among young people, the

attitude toward consistent condom usage is problematic and needs to be examined in developing

countries. Hence, we wanted to elucidate information as to whether there are differences in the

knowledge, attitude and practices of adults in their reproductive years regarding their

reproductive health issues, compared with those who have HIV in a Caribbean society, as well as

to model factors which account for their willingness to do an HIV test in the future. No study

emerged in the Caribbean that has comprehensively examined adults in their reproductive ages

(15-49 years) on their sexual and reproductive health attitudes, knowledge and practices, and

compares the result with those who are HIV-infected youth, as well as factors which explain
                                               237
people’s willingness to do an HIV test in the future. The present study examines core issues of

sexual and reproductive health among youths, particularly with respect to those who are HIV-

infected in Jamaica in order to provide a comprehensive understanding of people’s perceptions,

which will be used to fashion public health intervention programmes.


Methods
Sample

The study population comprised people aged 15-49 years who resided in Jamaica at the time of

the survey in 2004 (May-August). The population data for this research were collected by Hope

Enterprises Limited8 on behalf of the Jamaican Ministry of Health. A multi-staged sampling

design was used to collect the data. Each of the 14 parishes in Jamaica was stratified into

constituencies, with each constituency stratified into three areas – rural areas, parish capitals

(urban areas) and main towns (semi-urban areas). The areas which comprised a constituency

were then stratified into primary sampling units (PSUs) or enumeration districts (EDs).


       A random sample of each PSU was then selected, based on probability proportional to

size (PPS). Seventy-two EDs were selected for the study – 23 EDs in urban areas, 25EDs in

semi-urban areas, and 24 EDs in rural areas. Twenty-five households were systematically chosen

from each ED, and cluster sampling was carried out, where all the people living in the household

of the designated areas were interviewed for the survey.


Data sources

A questionnaire was used to collect the data from respondents. It was a 154-item instrument. The

questions were demographic characteristics, sexual history (including number and type of

partners, and having sexual relations with commercial sex workers), condom usage, STIs and

                                               238
health issues, knowledge of HIV/AIDS (including “Have you ever had an HIV test?”, “Did you

go back for the results yourself or were you contacted by a health worker?, and “Would you be

willing to do an HIV test [in the future]?”). The interviewers were trained for a 5-day period, of

which 2 days were devoted to field practices [8]. Interviewers were assigned to a team composed

of two females, two males and a supervisor. Oral consent was sought and given before the actual

interview would commence. Interviewees were informed of confidentiality and their right to stop

the interview at any time, if they should so desire. No names, addresses or other personal

information were collected from respondents in order to ensure anonymity and confidentiality.

The instrument used in the survey utilized indicator measures and definitions consistent with

UNAIDS and the USAID Priority Prevention Indicator [8].


Statistical analyses


Data were entered, stored and retrieved using SPSS for Windows, Version 16.0 SPSS Inc;

Chicago, IL, USA). Descriptive statistics were performed on particular sociodemographic

characteristics of the sample. Statistical analyses used were an independent sample t-test,

ANOVA, and Pearson’s Product Moment correlation. Multivariate logistic regressions were

fitted using one outcome measure: self-reported, confirmed positive HIV test results. We

analyzed correlation matrices to examine multicollinearity. Where collinearity existed (r > 0.7),

variables were entered independently into the model to determine those that should be retained

during the final model construction [9]. A p-value < 0.05 (two-tailed) was used to establish

statistical significance.




                                               239
Analytic Model


For this study, the analytic model used is one that can accommodate multiple independent

variables on a single binary dependent variable (positive HIV test result, which was confirmed

by an agent of the state). Using logistic regression, this paper tested variables identified in the

literature as being associated with having a positive HIV test result (Equation [1]):


HIV ti = f(A i , X i , ED i , Ei , MS i , C i , SIi , N i , AS i , L i , CUi , K i , F i , P i , Ti ,STIi, R i , Q i )…………Eqn

[1]


         where HIV ti denotes currently having a positive HIV test result for individual i, A i is age

of individual i, ED i represents educational level of individual i, U i, means employment status of

individual i, SS i is social class of individual i, AR i indicates area of residence of individual i, P i

denotes currently having sexual relations with a commercial sex worker for individual i, MS i is

marital status of individual i, Ci means length of time dwelling in community for individual i,

SLi is age of first sexual intercourse of individual i, S i represents type of sexual practice of

individual i, N i is number of sexual partners of individual i, R i denotes actively practicing

religion of individual i, K i is having had an STI of individual i, Wi represents crowding in

household of individual i, Q i denotes frequency of condom usage of the individual i, and the

parameter ε i is the model’s error term.


         Using logistic regression to test the hypothesis (Equation [1]), we now know that marital

status, employment status, age of respondents, and other variables are associated with those who

are currently HIV positive individuals, and can write equation [2].




                                                            240
      HIVti = f(Ai, MS i , U i , Pi , ε i )         Eqn

[2]




                                              241
Measurement


Crowding is the total number of persons who dwell in a room (excluding kitchen, bathroom and

verandah).


Contraceptive method is any device or approach that is used to prevent pregnancy. These

methods include tubal ligation, vasectomy, implant (Norplant), injection, emergency

contraceptive protection, pill, condom, foaming tablets, creams, jellies, diaphragm, abstinence,

withdrawal, the rhythm method, calendar or Billings.


Non-steady sexual partner denotes sexual relations that are casual, with someone with whom the

individual is not having a common law sexual union, a visiting relationship or to whom the

individual is not legally married.


Education is taken from the question, ‘How many years did you attend school?’ This is coded as

junior high or below (0 – 9 years), secondary (10-12 years) and tertiary (13+ years).


Shared facility is taken from ‘Are these [sanitary conveniences] shared with another household?

The options are shared, not shared or not stated. This was coded as 1 = shared and 0 = otherwise.


New HIV infection was measured using “Did you go back for the results yourself or were you

contacted by a health worker?” If the individual indicated that he/she was contacted by a health

worker, this was used to indicate a positive HIV result.


Old HIV infected people were measured using self-reported information on the “Do you know

the result of the test [HIV]”, and whether this was positive or negative (1= know positive status,

0 = otherwise).


                                                242
Knowledge in this study was measured using the following issues: Have you heard of HIV?,

Have you heard of a disease called AIDS?, Do you think that a healthy looking person can be

infected with HIV, the virus that causes AIDS?


Attitude for this research was measured using the following issues: If a member of your family

became sick with HIV, the virus that causes AIDS, would you be willing to care for him or her in

your household?, Have you ever had an HIV test?, Would you be willing to do an HIV test [in

the future]?


Practice was evaluated from the following questions: Have you ever had sexual intercourse? By

‘sexual intercourse’ I mean vaginal sex (penis in vagina) or anal sex (penis in bottom). At what

age did you first have sex?, With how many persons have you had sex during the last 3 months?,

Frequency of condom usage with recent, next recent or next most recent partners (in the last 12

months), what did you do to avoid getting pregnant?, ‘the last time you had sex, did you or this

partner do anything to delay or avoid getting pregnant?, and ‘have you ever had sex with

someone whom you paid for sex, that is, a commercial partner?’


Results
Table 8.1 presents the sociodemographic characteristics of the sample. The sample consisted of

1,800 respondents (of which males accounted for 48.8%).


       No significant statistical association existed between the gender of a respondent and

his/her positive HIV test result (χ2 = 0.900, P = 0.343). There was a statistical difference

between the mean age at first sexual relations for males (14.1 years (SD = 3.2 years) and of

females (16.7 years (SD = 2.7)) – t-test = 16.416, P < 0.0001. The mean age of females in the


                                              243
sample was 28.7 years (SD = 11.3 years) compared to that for males (27.8 years, SD = 10.9

years) – t-test = 1.656, P = 0.098.


       The mean age of first sexual relations for the sample was 15.4 years (SD = 3.2 years), and

a significant statistical difference was found for the mean age of first sexual intercourse among

the union statuses (F-statistic = 31.96, P < 0.0001): The mean age of first sexual relations for

married people was 16.2 years (SD = 3.1 years); partner who stays overnight (15.2 years

(SD=3.0 years)), sees partner occasionally (14.2 years (SD = 3.0 years)), and single (15.6 years

(SD = 3.3 years)). Furthermore, the mean age of first sexual relations was greater for those who

are actively practicing religion (15.9 years (SD = 3.4 years)) compared to those who are not

actively practicing religion (14.8 years (SD = 2.9 years)) – t-test = -6.768, P < 0.0001. Likewise,

there was a significant statistical difference for the mean age of first sexual relations and

typology of sexual acts performed – F-statistic = 4.273, P = 0.005. The mean age at first sexual

relations for those who have had anal sex was 14.0 years (SD = 0.0) compared to those who

practice vaginal sex (15.4 years, SD = 3.2) or those who did both (vaginal and anal sex), 12.9

years, SD = 4.17 years.


       A statistical correlation existed between the age at first sexual relations and number of

sexual partners in the last 12 months (r = - 0.246, P < 0.0001).


       When the sample was asked “Do you think this partner [current] has other partner(s)?”

35% indicated yes; “Do you sometimes feel embarrassed to buy a condom?” only 12% remarked

yes, and “To what extent do you usually have a condom on you?” every time, 20.4%; most

times, 14.2%; sometimes, 13.2%; rarely, 13.6% and never, 38.7%. When asked “Can you always

find your favourite brand [condom] when you need one in a hurry?” 89.7% said yes. “If that
                                                244
brand [condom] is not available would you take another brand or you would rather do without?,

only 1.6% indicated that they would rather go without using a condom, and 22.5% stated that

their partner would be upset if he/she found that they had a condom ready available.


         The sociodemographic characteristics of those with positive HIV test results were

examined in Table 8.1. Almost 6% of those with HIV had sexual relations with a commercial sex

worker. Of those who have had sexual relations with a sex worker, 75% indicated that they had

used a condom. Approximately 16% of those with HIV had contracted an STI infection in the

past, all of them knew that they had the HIV virus, and 61% were actively practicing religion.

Twenty-nine percent of the HIV-infected individuals had given birth in the last 2 years or were at

least 6 months pregnant. Twenty-seven percent of the sample indicated that they always used a

condom; most times, 13.4%; occasionally, 25.4%; and never, 34.3%, with their most recent

partner.


         When respondents were asked why they did not used a condom on the last sexual

relations, most of them indicated that they knew the person well (44.7%), 2.6% said they both

had HIV, 2.6% indicated that a condom was not available, 5.3% mentioned that the other partner

objected to its usage, 7.9% mentioned that they used other contraceptive methods, 5.3% said that

they did not need to, 7.9% said no special reason and 13.2% indicated that they had not thought

of it.


         Table 8.2 presents information on knowledge of the sample and those who are infected

with HIV. Among those who are infected with the HIV virus, almost 73% indicated that they had

at most a slight chance of contracting the virus compared to 86.6% of the sample. Only 8.1% of



                                               245
the infected respondents stated that there was a good probability of their contracting the virus

compared to 6.1% of the sample.


       Seventy-eight percent of the sample indicated a willingness to do a HIV test, and when

asked “What is your reason for not being willing to do the test?” the majority did not want to

know their status, 59.9%; no need to know (because not sexually active), 15.3%, and 9.9%

mentioned that they know that they do not have the virus.


       Figure 8.1 shows that more people who are HIV positive indicated that they had never

used a condom with their recent partner as well as the next most recent partner.


       Table 8.3 shows the variables which explain those who are willing to do an HIV test in

the future. Using logistic regression analyses, four variables emerged as statistically significant

factors of Jamaicans’ willingness to do an HIV test in the future. The model had statistically

significant predictive power (model χ2 = 31.86, p = 0.032; Hosmer and Lemeshow goodness of

fit χ2 = 5.17, P = 0.74), and correctly classified 87.0% of the sample.


Discussion


Approximately 33.2 million people in the world are living with HIV or AIDS, of which 30.8

million are adults and 15.5 million are women, while 2.0 million are children [10]. In the

Caribbean, HIV prevalence represents 1% in 2007, with 15,000-17,000 newly infected cases and

approximately 11,000 deaths, with a higher infection rate among men than women (ratio = 2:1)

[7]. However, young women are more likely to become infected with the virus because the tissue

lining of the genital tract is not fully developed; hence their thinner mucus membranes are less

protective than older women [11]. In other words, the transmission of HIV from male to female
                                                246
is two to ten times more likely than female to male [12]. As a result, the people of Thailand and

Uganda, for example, blame women for the transmission of the virus; while in East Africa, the

word STI is referred to as a disease of women [11].


       In the case of Jamaica, approximately 1.3% of the adult population is infected, with two-

thirds not knowing their status [8]. Of the number of infected adults, 4,447 [13] (out of the

27,000 infected cases) started antiretroviral treatment – thus representing 69% coverage of the

estimated 7,000 persons who require antiretroviral therapy [13]. This also includes 400

antiretroviral-treated children out of those who are infected [13]. This is tantamount to bringing

Jamaica closer to providing universal coverage with respect to HIV treatment [14].


       Adolescent females (age 10-19) have a two and a half times higher risk of HIV infection

than boys of the same age group. This is owing to social factors such as young girls having

sexual intercourse with older men [15].


       In terms of sexual orientation, the HIV/AIDS endemic has plagued humanity for more

than 20 years; and infection rates continue to grow. Persistent behavioural, social and cultural

factors continue to fuel the HIV epidemic [16]; coupled with the fear that friendship with an HIV

positive person would cause self-stigmatization [17]. Owing to the stigma and discrimination,

people living with HIV (PLHIV) tend not to disclose their HIV status, hence the potential spread

of the infection [7]. Other factors include fear of rejection, side effects of HIV drugs, uncertain

life span, disclosure of transmission and the impact of loss [18]. The authors also noted that

without support it becomes extremely difficult for adolescents and youth, the most vulnerable

group (15-24 year olds), to adhere to treatment.



                                               247
       Research has shown that more than 25% individuals are not cognizant of the status of

their sexual partner and that 40% do not use condoms [7]. With regard to Jamaica, approximately

eight out of every hundred persons (7.9% of the population) engages in risky sexual activity [8].

Research has shown significant relationships between age, relationship status and condom use

[8]. In addition, condom usage was not prevalent among main partners, especially where

multiple partnerships existed (75.1% males aged 25-49; 70.1% females aged 15-24), thus

resulting in approximately 25-30% of individuals who expose themselves as well as their

partners to HIV and other STIs [8].


       STI case rates (per 100,000 persons) reported a steady increase over the period 2006

(637.77 ), 2007 (787.17 ) and 2008 (850.43) for infections such as Pelvic Inflammatory Disease,

Herpes, Chancroid, Bacterial Vaginosis, Trichomoniasis, Candidiasis, Opthalmia Neonatorum

and Congenital Syphilis [13]. Where cost of and access to condoms poses a challenge, youths

become more vulnerable to HIV. This results in 90% of girls (10-15 year olds) refraining from

using male condoms, while both cost and concerns regarding the female condom become a

barrier [19]. In some instances, access to contraceptives (especially male condoms) is more

favourable to males than females, as the latter encounter barriers such as being shunned or

chastised [20].


       In terms of sexual orientation and status, approximately 10% of HIV cases in the

Caribbean represent men having sex with men (MSM); while commercial sex workers (CSW)

vary from 9% to 31% [7,21]. While there are persons who are ignorant of their HIV status

because of non-testing [22, 23], there still remain those who lack knowledge about HIV. The

literature pointed out that where there is a lack of knowledge, combined with early sexual

                                              248
activity, these factors put youths at risk of not only unintended pregnancy, but STIs and HIV [5].

This resulted in 86% of 1,000 participants who were surveyed not considering themselves

personally responsible for being pregnant and/or contracting STIs and HIV [5]. Despite

widespread information on HIV, the global community still lags behind in prevention efforts. For

instance, in 24 Sub-Saharan countries approximately two-thirds of young women lack adequate

knowledge of HIV transmission [6]; also fewer than one in five people at risk for infection

globally have access to basic prevention services [6]. Sexual relation is mainly the medium

through which most people contract HIV/AIDS [24], indicating that the lack of knowledge (or

low) is affecting the risky sexual behaviour.


       It is imperative to note that a lack of knowledge regarding HIV is fuelled in part by

poverty, which makes it difficult for persons to learn about HIV or to purchase condoms or

antiretroviral drugs [25]. However, the adoption of voluntary counselling and testing (VCT) is

seen as a way to remedy a lack of knowledge regarding the infection and thus facilitates safer

behaviour [26]. Nonetheless, although there has been evidence of success regarding VCT, the

issue of confidentiality is often expressed. Adults and young people, who refrain from VCT,

claim that they fear being identified at a testing site, with the possibility of having a health care

provider who knows them and may share their information with someone else [26]. It is also

recognized that many young people, especially adolescents, do not have independent access to

HIV prevention services, despite the fact that the age of sexual debut is earlier than the age of

legal majority [27].


       Another way of curtailing the spread of HIV among adolescents and reducing new

infection was the recommendation for routine, opt-out HIV screening without separate written

                                                249
consent or prevention counselling for persons within the age range of 13 to 65 years [28].

Jamaica’s prevention and service strategies for young females (15-24 years of age) encompasses

components such as legislation, policy, programmes, service availability, participation and rights

[19]. The social reality is that the age at first sexual intercourse of Jamaicans was 15.4 years and

15.6 years for those with HIV, which indicates that intervention measures must be instituted with

urgency to address this public health concern.


       Among the realities that emerged from this study is the inconsistency with which

Jamaicans used a condom, and this was also the case with HIV patients. A study by Wilks et al

[22] found that 24.4% of Jamaicans (ages 15-74 years) had more than one partner, 48.4% had

sex once per week, while those with secondary education were more likely to have more partners

compared to those with at most primary level education, while those with tertiary education were

the least likely to have multiple partners.


       It is also evident that parental consent is deemed to be the greatest legal barrier to

minors/adolescents being able to access HIV testing on their own. In cases where parental

consent is not required under state law or policy, an increased number of minors visit test sites

and receive antibody tests. The literature recommended therefore that the desire of minors to

receive HIV testing without parental consent should be treated as a right of the said minors [29].


Conclusion

       In summary, HIV is not a homosexual virus but a heterosexual phenomenon. While more

Jamaicans between the ages of 15-49 years who were diagnosed with HIV were in visiting

relationships, marginally less of them were married or in common-law unions.


                                                 250
References
  1. Population Action International. A Measure of Survival. Calculating Women’s Sexual
      and Reproductive Risk. Washington DC: Population Action International; 2007
  2. World Health Organization (WHO). World health statistics, 2009. Geneva: WHO; 2009.
  3. Rawlins J, Crawford T. Women’s Health in the English-Speaking Caribbean: The Case of
      Trinidad and Tobago. Journal of Social and Economic Studies; 2006; 55:1-31.
  4. Camera B, Lee R, Gatwood J, et al. The Caribbean HIV/AIDS epidemic epidemiological
      status: Success stories—a summary. CAREC Surveillance Report (CSR), 2003; 23:1–16.
  5. Thomas T. Youth Reproductive and Sexual Health in Jamaica. Washington DC.,
      Advocates for Youth;2006
  6. Audelo S Revisiting the United Nations General Assembly Special Session on HIV and
      AIDS. Washington D.C.: Advocates for Youth; 2006.
  7. CARICOM-PANCAP. Caribbean Regional Strategic Framework on HIV and AIDS
      2008-2012. Pan Caribbean Partnership Against HIV/AIDS: Scaling up the Caribbean’s
      Response. CARICOM-PANCAP; 2008.
  8. Hope Enterprise Limited. HIV/AIDS Knowledge, Attitudes and Behaviour Survey, 2008.
      Kingston: Jamaica, Ministry of Health, National HIV/STI Programme; 2008.
  9. Polit DF. Data analysis and statistics for nursing research. Stamford: Appleton & Lange
      Publisher; 1996.
  10. UNAIDS/WHO. Worldwide HIV and AIDS Statistics. UNAIDS/WHO; 2007.
  11. Stine GJ. AIDS update 2005. San Francisco: Benjamin/Cummings Publishers; 2005.
  12. World Health Organization. Women’s Health. In: Stine GJ. AIDS update 2005. San
      Francisco: Benjamin/Cummings Publishers; 2005.
  13. National HIV/STI Programme. Annual Report 2008. Ministry of Health, National
      HIV/STI Programme; 2008.
  14. UNICEF          (n.d.).     Children        and       HIV/AIDS.        Accessed      at
      http://www.unicef.org/jamaica/hiv_aids.html on April 16, 2010.
  15. Jamaica, Ministry of Health. Facts and Figures. HIV/AIDS Epidemic Update 2004.
      Kingston, Jamaica, Ministry of Health, National HIV/STD Prevention and Control
      Programme; 2004.
  16. Jamaica, Ministry of Health. Jamaica’s HIV/AIDS Response 2006-2007. `Ministry of
      Health, National HIV/STI Control Programme; 2007.
  17. Phillips D. Youth HIV/AIDS in the Caribbean: Teenage Sexuality in Montserrat. Journal
      of Social and Economic Studies; 2006; 55:2006:32-54.
  18. Henry-Reid L, Weiner L, Garcia A. Caring for Youth with HIV. Quarterly Journal on
      HIV Prevention, Treatment and Politics; 2009.
  19. International Planned Parenthood Federation (IPPF). The role of religious and
      conservative groups in the United States. IPPF’s Opposition Manual 2006.
  20. Crawford TV, McGrowder DA, Crawford A. Access to Contraception by Minors in
      Jamaica: A Public Health Concern. North AmJ. Med Sci. 2009; 1:247-255.

                                           251
21. Duncan J, Gebre Y, Grant Y, et al. HIV prevalence and related behaviors among sex
    workers in Jamaica. Sexually Trans Dis 2010;
22. Wilks R, Younger N, Tulloch-Reid M, et al. Jamaica health and lifestyle survey 2007-8.
    Kingston: Tropical Medicine Research Institute, University of the West Indies, Mona;
    2008.
23. Nnedu ON, McCorvey S, Campbell-Forrester S, et al. Factors influencing condom use
    among sexually transmitted infection clinic patients in Montego Bay, Jamaica. The Open
    Reproductive Science J 2008;1:45-50
24. Steiner MJ, Cates W. Are condoms the answer to rising rates of non-HIV sexually
    transmitted infections? Yes. BMJ 2008; 336:184.
25. Population Action International. Fact Sheet. How Reproductive Health Services and
    Supplies are Key to HIV/AIDS Prevention. Washington DC: Population Action
    International; 2004.
26. McCauley AP. Equitable Access to HIV Counselling and Testing for Youth in
    Developing Countries: A Review of Current Practice. Washington DC: Population
    Council Inc; 2004.
27. World Health Organization. Guidance on Provider-Initiated HIV Testing and Counselling
    in Health Facilities. Geneva: World Health Organization; 2007.
28. Hahn EK. Incorporating the CDC Recommendations for Adolescent HIV Screening into
    Practice. Journal of Nurse Practitioners 2009; 5:265-273.
29. Meehan TM, Klein WC. The Impact of Parental Consent on the HIV Testing of Minors.
    Am J public Health 1997;87:1338-1341




                                         252
Table 8.1. Sociodemographic characteristics of sample and HIV infected people
Characteristic                                                              Sample       HIV infected people
                                                                               n (%)                   n (%)
Sex
 Male                                                                    878 (48.8)                 31 (41.9)
 Female                                                                  920 (51.2)                 43 (58.1)
Education
 Primary or below                                                           51 (2.8)                  3 (4.0)
 Secondary                                                              1546 (85.9)                 57 (70.3)
 Tertiary                                                                203 (11.3)                 19 (25.7)
Employment status
 Employed: Full time                                                     626 (34.8)                 31 (41.9)
               Part time                                                 201 (11.1)                   5 (6.8)
 Unemployed                                                              563 (31.3)                 26 (35.1)
 Student                                                                 410 (22.8)                 12 (16.2)
Union status
  Married/common-law                                                     561 (31.2)                 25 (33.8)
 Visiting                                                                619 (34.4)                 26 (35.1)
 Single                                                                  619 (34.4)                 23 (31.1)
Ever had sexual relations
 Vaginal                                                                1543 (85.7)                  69(93.2)
 Anal                                                                        1 (0.1)                   1 (1.1)
 Both                                                                       14 (0.8)                   1 (1.4)
 No                                                                      242 (13.5)                    3 (4.0)
Number of sexual partners in
   Last 4 weeks median (range)                                              1 (0,17)                   1 (0,4)
   Last 3 months median (range)                                             1 (0,30)                 1 (0,13)
   Last 12 months median (range)                                           1 (0,100)                 1 (0,30)
   More than 12 months median (range)                                       1 (0,24)                  1 (0,3)
Condom usage on first sexual relations (with current
partner)
   Yes                                                                  1042 (57.9)                 52 (70.3)
   No                                                                    454 (25.2)                 13 (17.5)
   Non-response                                                          304 (16.9)                  9 (12.2)
Condom usage (last time had sexual relations)
   Yes                                                                   718 (39.9)                 29 (39.2)
   No                                                                    792 (44.0)                 40 (54.0)
   Non-response                                                          290 (16.1)                   5 (6.8)
Sexual relations with a commercial partner (ever had)
  Yes                                                                       89 (5.6)                  66 (5.7)
  No                                                                    1498 (94.4)                   4 (94.3)
Length of time living in community median (range)                  7.5 years (0, 40)         5.0 years (0, 30)
Age mean (SD)                                                28.3 years (11.1 years)   32.3 years (10.4 years)
Age of first sexual relations mean (SD)                       15.4 years (3.2 years)    15.6 years (3.5 years)




                                                 253
Table 8.2. Knowledge, attitude and practices of sample and of HIV infected sample
Characteristic                                                                Sample     HIV infected
Heard about HIV
  Yes                                                                      1798 (99.9)     74 (100.0)
  No                                                                           1 (0.1)         0 (0.0)
Heard about AIDS
  Yes                                                                      1796 (99.8)     74 (100.0)
  No                                                                           3 (0.2)         0 (0.0)
Methods of protection from HIV or AIDS
 Have one sexual partner                                                    574 (32.0)      16 (21.9)
 Use a condom                                                               756 (42.3)      42 (57.5)
 Use a condom sometimes                                                        7 (0.4)              -
 Use a condom at all times                                                  339 (18.9)       8 (11.0)
 Abstain                                                                      95 (5.3)        6 (8.2)
 No sex with strangers                                                         4 (0.2)              -
 No blood transfusion                                                          0 (0.0)              -
 Avoid homo/bisexuals                                                          0 (0.0)              -
 Other                                                                         3 (0.2)        1 (1.4)
 Nothing                                                                      13 (0.7)
Protective measure from contracting HIV/AIDS
 Yes                                                                       1516 (84.5)      62 (83.8)
 No                                                                         279 (15.5)      12 (16.2)
Have you spoken about safe sex with current partner
 Yes                                                                        995 (58.6)      57 (79.2)
  No                                                                        702 (41.4)      15 (20.8)
Can healthy people contract HIV/AIDS virus?
 Yes                                                                       1732 (96.9)      71 (95.9)
 No                                                                           41 (2.3)        2 (2.7)
 Don’t know                                                                   15 (0.8)        1 (1.4)
Knowledge of someone with HIV or who died from AIDS
  Yes, close relative or friend                                             284 (15.8)      13 (17.6)
  Yes, not a close friend or relative                                       437 (24.3)      20 (27.0)
  At a workshop                                                                8 (0.5)        1 (1.3)
  No                                                                        979 (54.4)      40 (54.1)
  Not sure                                                                    90 (5.0)              -
Would you care for a family member with AIDS?
  Yes                                                                      1404 (78.2)      58 (78.4)
  No                                                                         117 (6.5)        6 (8.1)
  Don’t know                                                                275 (15.3)      10 (13.5)
Chance of contracting HIV
 None                                                                       887 (52.8)      30 (40.6)
 Little                                                                     569 (33.9)      24 (32.4)
 Moderate                                                                    116 (6.9)       8 (10.8)
 Good                                                                        108 (6.4)        6 (8.1)
 Don’t know                                                                          -        6 (8.1)




                                                  254
Figure 8.1: Frequency of condom usage with recent, next recent and next most recent partner for
Sample and HIV infected sample




                                             255
Table 8.3. Logistic regression analyses: Variables of willing to do HIV test in the future

                                                                                          Wald                       CI (95%)
 Variable                                                              β Coefficient     statistic    Odds ratio

            Married                                                              -0.83        3.96           0.44*   0.19 - 0.99
            Visiting unions                                                      -0.69        2.70            0.50   0.22 - 1.14
            Single (reference group)                                                                          1.00

            Practice vaginal sexual acts                                                                                   0.06
                                                                                 -0.51        0.18            0.60
                                                                                                                           6.12
            Practice anal sexual acts                                            22.22        0.00   4476198076.84        0.00 -
            No sexual relations (reference group)                                                             1.00

            Full time employed                                                   -0.15        0.17            0.86   0.42 - 1.76
            Part time employed                                                   -0.61        0.83            0.54   0.15 - 2.02
            Student                                                               1.16        4.46           3.20*   1.09 - 9.42
            Unemployed (reference group)                                                                      1.00

            Tertiary                                                             -0.79        0.66            0.45   0.07 - 3.05
            Secondary                                                            -1.32        1.97            0.27   0.04 - 1.69
            Primary or below (reference group)                                                                1.00

            Age                                                                   0.04        4.15           1.04*   1.00 - 1.08
            Sexual relations with commercial worker                               0.27        0.13            1.30   0.31 - 5.55
            No. of sexual partner in last 12 months                               0.01        0.03            1.01   0.93 - 1.09
            Used condom on first sexual relation (with current
                                                                                  1.06        6.48          2.90**   1.28 - 6.58
            partner)
            Only one time had sexual relations with person                       -0.27        0.15            0.76   0.19 - 3.04
            Had STI                                                              -0.58        1.60            0.56   0.23 - 1.37
            Actively practicing religion                                         -0.36        1.27            0.70   0.38 - 1.30
            Age at first sexual relations                                        -0.05        1.13            0.95   0.86 - 1.05


            At least most time used a condom                                     -0.67        2.49            0.51   0.22 - 1.18
                                                                                 -0.04        0.01            0.96   0.42 - 2.19
            Moderate condom usage

            Never used condom                                                                                 1.00


            Male                                                                  0.37        1.00            1.44   0.70 – 2.96
            Constant
                                                                                 -0.30        0.03            0.74
Model χ2 (19) = 31.86, P = 0.032
-2 Log likelihood = 316.37
Nagelkerke r-squared = 0.127
Hosmer and Lemeshow (df = 8) = 5.17, P = 0.74
Overall correct classification = 87.0%
*P < 0.05, **P < 0.01, ***P < 0.001

                                                                 256
                                                                          Chapter
                                                                                             9
 Perception, attitude and practices of women towards pelvic examination and
                            Pap Smear in Jamaica




 Paul A. Bourne, Christopher A.D. Charles, Cynthia G. Francis, Neva South-Bourne, and
                                    Racquel Peters




Studies have shown that women’s ability to access contraceptive methods depend on their socio-
economic, educational, professional status, and the health and well-being of their families and
themselves. Therefore, the embarking of the Governments of the Caribbean on important
initiatives relating to gynaecological matters is very important and timely. This study aims to
examine the perception, attitude and practice of Jamaican women towards the matter of pelvic
examination. The findings revealed that older women are more likely to have done a Pelvic
examination compared to younger women (χ2= 675.29, P < 0.001). Age, number of pregnancies
that resulted in miscarriages, number of pregnancies that resulted in induced abortion, age of
first sexual intercourse, number of years of schooling, area of residence and socio-economic
class are statistically significant factors of Pelvic examinations in Jamaica. Therefore, the model
had significant predictive power where (χ2= 1022.79, P < 0.001). The multidimensional nature
of the variables, which emerged in the current study, indicate that a multisectoral approach
should be used to address low pelvic and Pap smear examination among Jamaican women.




INTRODUCTION


                                               257
        Research has shown that owing to women’s unique reproductive capacities, their ability to

access contraceptives impacts on their socio-economic, educational, professional status, health status

and overall well-being [1]. The use of some contraceptive methods (for example, the intrauterine

device, IUD) could result in gynaecological concern (pelvic inflammatory disease). As a result,

women’s attitude towards such contraceptive result in myths and misconceptions such as the IUD (a)

inhibits pregnancy; (b) increases the risk of ectopic pregnancy; and (c) is an abortificient [2].

However, it is imperative to note that there is slight risk of Pelvic Inflammatory Disease (PID) that is

associated with women who use IUD [3]. Sexually active women in their reproductive years are

mostly at risk, more so than those under 25 years old. This is because the cervix of teenagers and

young women are not as fully mature as those who are older. Pelvic Inflammatory Disease usually

occurs when bacteria moves from a woman’s vagina or cervix into her reproductive organs. It is

mostly associated with gonorrhea and Chlamydia [3]. It is not easily detected, and so a lack of early

treatment could result in damage to the reproductive organs (for example, scar tissue to the fallopian

tubes) [3].


        Using a probability sample of 2,848 Jamaicans aged 15-74 years, Wilks and colleagues

found that 93.3% of females aged 15-74 years old have had sexual relations, 8.4% reporting

having 2+ sexual partners, 41% had sexual intercourse once per week, 82.9% had been pregnant.

compared with 76.4% of females aged 15-24 years who indicated having had sexual intercourse

and 40.6% reported having sex once per week. Despite the sexual practices of women in

Jamaica, only 18% of those aged 15-74 years old had done a Papanicolaou smear (Pap smear)

examination in the last 12 months, 18.0% between 1-2 years ago and 29.2% more than 3+ years

ago, with 71.3% of females aged 15-24 years had never done a Pap smear examination [4].

Furthermore, Wilks et al. also found that 41.2% of women aged 15-74 years had used a condom

                                                  258
during their last sexual intercourse [4]. Of which, 37.4% were aged 15-24 years, 33.2% aged 25-

34 years, 25.2% aged 35-44 years, 18..7% aged 45-54 years, and 6.4% aged 55-64 years old.

Thus inconsistent condom use which is among highly sexed females could result in PID and

other disease causing pathogens.


       Another reality noted by the World Health Organization (WHO) is that cancers are

diagnosed more frequently in the developing world than the developed nations; and that cervical

cancer is the second most prevalent of cancers among women [5]. In 2002, statistics on Jamaica

revealed that malignant neoplasms (or cancers) were the leading cause of mortality [6], and that

in 2007; cervical cancer was among the 5th leading cause of morality among Jamaican women

[7]. Inspite of South and Southeast Asia recording the largest number of new HIV infections, and

Sub-Saharan Africa represents the highest rate of new infections, followed by Latin America and

the Caribbean [8,9] as well as “more than 340 million new cases of the common bacterial and

protozoal STIs (syphilis, gonorrhoea, Chlamydia, genital infections, trichomoniasis) occur every

year throughout the world in men and women ages 15-49” [8], Jamaican females exhibit a low

willingness to have a Pap smear or pelvic examination done even though they are highly sexed

individuals, most have been pregnant at least once, and few of them consistently use a condom

[4].


       According to Berer, “Human beings are sexual by nature. If nothing else, one thing seems

certain-people will never stop having sex or wanting to have sex” [10]. Despite the negative

realities associated with risky sexual behaviour, people will continue their involvement and

engagement in sexual practices, and sometimes these will be risky. One of the ironies which

emerged from the examination of data on reproductive health is a high knowledge of method of

                                              259
contraception, access to contraception and sexually transmitted infections, in particular HIV.

However, there is low consistent usage of contraception, and in particular condom [4, 11]. Thus,

Pap smear and pelvic examination should be apart of the routine health care of women because

they detect cancers, sexually transmitted infections and abnormalities that can result in cancer of

the cervix, yet some women delay this exercise owing to fear of the unknown.


       Previous studies which have examined reproductive health in the Jamaica have provided

information on contraceptive use and knowledge, fertility, Pap smear examination, sexual

activity [4, 11], factors associated with cervical cancer screening [12-19], and Pap smear of older

women [20], but little is known about the perception, attitude and practice of Jamaican women

towards the matter of pelvic examination; and the factors which influence their attitude towards

the practices (or not). Fletcher provides insight into the practice and perception of women in

Jamaica [21]. He found that 9 out of every 10 women who died from cervical cancer was never

screened, and continued that “Most women have heard of the Pap smear but believe its purpose is

to detect rather than prevent cervical cancer” [21]. Jamaican females are not atypical as another

study conducted on older women in Latin American and Caribbean Cities found that Pap smear

examination was also low (from 21% in Bridgetown to 45% in Mexico City) [20].


       Within the context of a sexually active female, particularly those who used condoms

inconsistently during sexual activities, ignoring gynaecological consultation could result in late

detection of abnormal cells relating to viral and bacterial infections that severely affect the female

reproductive organ, cases in point are infections associated with HPV/cervical cancer and PID. With

regard to the former, the literature postulated that throughout the World, there were approximately

470,606 cases of cervical cancer and 233,372 deaths arising from malignant neoplasm of the cervix


                                                 260
uteri (in 2000) [22]. In the Caribbean, however, there was an incidence rate of 35.78 and mortality

rate of 16.84 (per 100,000) in the said year.22 In Jamaica, cases of cervical cancer in the 2000s

represent 43.4 per 100,000 populations [23]. This paper examines, therefore, the perception,

attitude and practice of Jamaican women towards the matter of Pelvic examination. Assessments

are made based on factors such as age, area of residence (urban versus rural) and socio-economic

class.


Methods and measure

This study used the 2002 Reproductive Health Survey (RHS) data on women ages 15-49 years.

The sample was 7,168 women and represents a response rate of 91.8%. Stratified random

sampling was used to design the sampling frame from which the sample was drawn. Using the

2001 Census sector (or sampling frame), a three stage sampling design was used. Stage 1 was the

use of selection frame of 659 enumeration areas (or enumeration districts, EDs). This was

calculated based on probability proportion to size. The health sector of Jamaica is classified into

four health regions: South East Region - Kingston, St. Andrew, St. Thomas and St. Catherine;

North East Region - Portland, St. Mary and St. Ann; Western Region - Trelawny, St. James,

Hanover and Westmoreland; and Southern Region - St. Elizabeth, Manchester and Clarendon.

The 2001 Census showed that region 1 comprised 46.5% of Jamaica compared to Region 2,

14.1%; Region 3, 17.6% and Region 4, 21.8% [11].


         Stage 2 saw the clustering of households into primary sampling units (PSU). This

constitutes an ED comprising 80 households. The previous sampling frame was updated between

January and May 2002. The new sampling frame forms the basis upon which the sampling size



                                               261
was computed for the interviewers to use. Stage 3 was the final selection of one eligible female

and male and this was done by the interviewer on visiting the household.


       The Statistical Institute of Jamaica (STATIN) provided the interviewers and supervisors,

McFarlane Consultants, who were trained to carry out the survey. The instrument was a 35-item

questionnaire of 35 pages. This was administered during the period October 26, 2002 to May 9,

2003 [11].


       The data was weighted in order to accurately represent the population of women ages 15-

49 year old.


Statistical methods


The Statistical Packages for the Social Sciences (SPSS) for Windows, Version 16.0 (SPSS Inc;

Chicago, IL, USA) was used to analyse the data. Frequencies and means were computed on the

sociodemographic characteristics, health conditions, pregnancy, papanicolaou (Pap) smear,

pelvic examination and reasons for contraceptive choices. The researchers also performed χ2

tests to compare associations in particular sociodemographic variables, contraception, pregnancy,

and Pelvic examination. Stepwise multiple logistic regressions were used to analyze factors that

explain gynaecological examination, in the last 12-months and Pap smear test done in the last 12-

months. Where collinearity existed (r > 0.7), variables were entered independently into the model

to determine those that should be retained during the final model construction [24]. To derive

accurate tests of statistical significance, we used SUDDAN statistical software (Research

Triangle Institute, Research Triangle Park, NC), and this was adjusted for the survey’s complex

sampling design.


                                              262
Measures

Crowding is the total number of persons who dwell in a room (excluding kitchen, bathroom and

verandah). Age is the number of years a person is alive up to his/her last birth day (in years).

Contraceptive method comes from the question “Are you and your partner currently using a

method of contraception …”, and if your answer is yes “Which method of contraceptive do you

use?” Age at which participants began using contraception was taken from “How old were you

when you first used contraception? Area of residence is measured from “In which area do you

reside?” The options were rural, semi-urban and urban. Currently having sex is measured from

“Have you had sexual intercourse in the last 30 days?” Education is measured from the question

“How many years did you attend school?” Marital status is measured from the following

question “Are you legally married now?”, “Are you living with a common-law partner (now; that

is, are you living as man and wife now with a partner to whom you are not legally married?”,

“Do you have a visiting partner, that is, a more or less steady partner with whom you have sexual

relations?” and “Are you currently single?” Age at first sexual intercourse is measured from “At

what age did you have your first intercourse?” Pelvic examination is taken from “Have you ever

had a pelvic examination?” Pelvic examination is measured from the question “Have you ever

had a gynaecologic examination? Pap smear is measured using the question “A Pap Smear is a

test for cancer of the cervix which is done during a pelvic examination by a doctor or nurse.”

And, “How long has it been since your last Pap Smear, if ever? Pregnancy was assessed by “Are

you pregnant now?” Religiosity was evaluated from the question “With what frequency do you

attend religious services? The options range from at least once per week to only on special

occasions (such as weddings, funerals, christening, et cetera). Subjective social class is measured

from “In which class do you belong?” The options are lower, middle or upper social hierarchy.

                                               263
Results

Table 9.1 presents information on the sociodemographic, pregnancy and health conditions of

sample. The sample was 7,168 women (ages 15 – 49 years). Most women indicated that they

had used at least one contraceptive method in the past (88.1%) and 63.8% claimed that they are

currently using a contraceptive method. Fifty-five percentage of the sample dwelled in rural

areas, 29.0% in semi-urban and 16.0% in urban zones. One-half of the sample indicated that their

first sexual intercourse occurred at 17 years (Range = 29: 7, 36), with 25% starting at 15 years

and 75% stated less than or equal to 19 years.


       One half of the sample indicated that their first menarche began at 13 years old (range: 8

– 21 years old) while one quarter of the sample reported having that their first menarche at 12

years and three-quarter indicated that they began at 14 years old.


       Table 9.2 examines information on Pelvic examinations and the reasons why some

women have not done the test. Of the 7, 168 women surveyed, more than a half of them (56.9%)

have not done a Pelvic examination, and 57.1% have not responded to the question on the last

time one was done. Only 18.0% of the women have done an examination within the last 12

months. The data also showed that 37.3% have never done a papanicolaou test whilst 35.4%

have done tests within the last 2 years. Most women have not had a non-menstrual discharge

(83.0%) in the last 12 months. For the question pertaining to reasons for not having done a Pelvic

examination, 35.9% of the sample cited “never thought of it”; 15.9% claimed “healthy and has

no sign of gynaecological problems; 11.8% claimed that it was “not recommended by doctor

and 10.6% indicated that they “did not need to go”.


                                                 264
       Table 9.3 shows information on Pelvic examinations by particular sociodemographic

variables and contraception usage. There is a significant statistical difference among the mean

age of women for those who have (or have not) done a Pelvic examination. Cross-tabulation

provides a more detailed break-down of the percentage of women in particular age cohort who

have done the examination15-19 years, 11.5%; 20-24 years, 29.9%; 25-29 years, 41.8%; 30-34

years, 51.1%; 35-39 years, 53.7%; 40-44 years, 55.6%; and 45-49 years, 57.4% (χ2 = 675.287, P-

value< 0.0001). The findings highlight that older women are more likely to have done a Pelvic

examination compared to younger women.


       Figure 9.1 presents information on particular demographic characteristic of study

population that had never done a pelvic examination or Pap smear. Based on the Figure, rural

women in the reproductive ages were less likely to have done Pap smear or pelvic examinations

as well as those ages 15-19 years, in a sexual union, and of the middle socio-economic stratum of

society.

       Using logistic regression analyses, eight variables emerged as statistically significant

factors of Pelvic examination in Jamaica (Table 9.4). The factors are age, number of pregnancy

that resulted in live births, number of pregnancy that resulted in miscarriages, number of

pregnancy that resulted in induced abortion, age at first sexual intercourse, number of years of

schooling, area of residence and socioeconomic class. Furthermore, the model had statistically

significant predictive power (model χ2 (DF = 8) = 1022.79, P< 0.0001; Hosmer and Lemeshow

goodness of fit χ2 = 4.52, P = 0.912), and correctly classify 68.3% of the sample.


       Table 9.5 examines factors that account for Pelvic examination of women in the last 12-

months. From the logistic regression analyses, six factors explain why women (ages 15-49 years)

                                               265
have had a Pelvic examination in the past 12 months. The model had statistically significant

predictive power model (χ2 (DF = 7) = 131.81, P< 0.0001; Hosmer and Lemeshow goodness of

fit χ2 = 3.06, P = 0.912), and correctly classify 64.0% of the sample.


       Papanicolaou smear (Pap smear) examination (in the last 12 months) is explained by six

factors. The six factors account for 25% of the variability in Pap smear examination (Table 9.6).

The findings revealed that urban women are more likely to have had a Pap smear examination in

the last 12 months compared to rural women (ages 15-49 years). The model had a statistically

significant predictive power (model χ2 (DF = 7) = 182.2, P< 0.0001; Hosmer and Lemeshow

goodness of fit χ2 = 6.31, P = 0.713), and correctly classify 76.6% of the sample.


           Table 9.7 presents information on factors which account for why the study population

has never done a Pap smear examination. Using logistic regression analyses, four variables

emerged as statistically significant factors of why women of the reproductive ages have never

done a Pap smear examination (Table 9.7): education (years of schooling, OR = 0.85, 95% CI =

0.75 – 0.95), age of menarche (OR = 1.25, 95% CI = 1.05 – 1.48), number of pregnancies which

resulted in live birth(s) (OR = 0.84, 95% CI = 0.72 – 0.99), and never done a pelvic examination

(OR = 6.07, 95% CI = 3.26 – 11.32).

       Using logistic regression analyses, four variables emerged as statistically significant

predictors of why the study population had never done a pelvic examination (Table 9.8): social

class, number of pregnancies which resulted in live birth(s), number of pregnancies that resulted

in miscarriages, and never done a Pap smear examination. The model had statistical significant

predictive power (model Model χ2 (DF = 7) = 71.80, P< 0.0001), and almost 73% of the sample

was correctly classified (Table 9.8).

                                                266
DISCUSSION

The findings of this research have stated the various reasons why the subjects refrained from pelvic

examination. This form of examination is used to detect early onset of abnormal cells in the cervix

uteri, which could result in cervical cancer for example. Schools of thought have argued on the

subject matter, and it is noted that (a) lower educational attainment was negatively associated with

accessing cervical screening, particularly in Barbados and Trinidad and Tobago;25 (b) unemployment

did not facilitate pap smear because of cost; and (b) women from larger household size were less

likely to have a Pap smear done [25].


       In the current study the prevalence of Pap smear examination was greater than that in a

research conducted in Latin American and the Caribbean Cities found that Pap smear examination

from 21% in Bridgetown to 45% in Mexico City [20]. In this study, it was revealed that 21 out of

every 100 females in the reproductive ages had done a Pap smear in the last 12 months, and that

18 out of every 100 had done a pelvic examination. The rationales forwarded by the study

population for not having done a pelvic examination included never thought of it (36%); healthy

(16%) and does not need to do one (11%). The findings in the current research showed some

dissimilarity between factors which account for having done a Pap smear or pelvic examination

and having never done either. A critical finding which emerged from this work is a female who

is of the reproductive age who had never done a Pap smear will 6.1 times more likely not do a

pelvic examination, and one who had never done a pelvic examination is 5.2 times more likely

not do a Pap smear. Although females in the study were sexually active, most have been

pregnant, the age at first sexual intercourse was in the adolescence years, and some had multiple


                                                267
partners, not doing a Pap smear or pelvic examination means that they will be ignorant of their

pelvic inflamatory disease status.


       In relation to PID, it is evidenced that “about 20% of affected women become infertile,

20% develop chronic pelvic pain, and 10% of those who conceive have an ectopic pregnancy”

[26]. Where sexually transmitted infections such as Chlamydia and gonococcal infections are

untreated, these could result in PID, ectopic pregnancy, infertility and neonatal infection [27].

From a sample of 767 family planning clients in Kingston Jamaica, who were screened for

agents of gonorrhea, Chlamydia and trichomoniasis and syphilis, detections were found mainly

in persons who mostly under the age of 25 years old had multiple partners in the past year. That

study, (in making reference to the World Health Organization’s risk inclusive algorithm), noted

that for cervical infection, this was “least accurate (a positive predictive value of 14%). The

weighted-risk algorithm was least accurate (a positive predictive value of 23%), while the

interview-alone and the rapid risk assessment were slightly less accurate (positive predictive

values of 20%)” [27].


       The use of contraception, particularly condoms, has the capacity to not only reduce fertility

rate (from 3 children per women in 1993 to 2.5 children per woman in 2002 in Jamaica) but also

incidences of PID and HPV and other related STI bacteria/virus. Overall contraceptive prevalence in

Jamaica is 85.3%, with condom (72.7%) being the most utilized among women aged 15-49 [4].

According to Wilks and colleagues, 1 in every 2 female aged 15-24 years have been pregnant

compared with 22 out of every 25 aged 25-34 years old, 48 out of every 50 aged, and 49 out of every

50 aged 45-54 years [4]. Those statistics revealed that there is a disparity between prevalence of

condom use and consistent condom use. Simply put, inconsistent condom use is great among


                                               268
Jamaican women, suggesting that they are exposed to HIV/AIDS and other sexually transmitted

infections (STIs).


        It is anticipated that in meeting the Millennium Development Goal 5 (to combat HIV/AIDS)

under the ambit of the National 2030 Plan [28], as well as meeting the 1994 programme of action of

the International Conference on Population and Development 6 (to reduce the spread of HIV

infection and minimize its impact) [29], HIV prevalence will be 0.8% between ages 15-24 year olds

in 2010, requiring approximately US$10.64 million in annual resources for Jamaica [30]. It is

imperative to note that HIV prevalence rate between 15-49 year olds is 1.2 (in 2004) [31]. Earlier it

was argued that cost was an impediment to accessing gynecological service, particularly Pap smear.

Scholars have pointed out that “poverty makes it difficult to learn about HIV/AIDS or to purchase

condoms or drugs” [32]. Other barriers are perception of risk, as well as power relations between

men and women [33], for example, out of 3,151 cases, 1.4% women (15-49 year olds) expressed fear

of side effects from contraceptive methods, while 0.7 claimed that their partner did not support the

use of contraception [11].


        Part of the goals of the ICPD is to reduce unmet need for family planning service, including

contraceptives by 2015. This involves changes in attitudes that prevent women and girls from

exercising their RH rights [34], as well as being knowledgeable about proper and ideal RH care such

as gynaecological consultations. This study realized, however (based on Table 9.2) that despite cost

and education, attitude towards Pelvic examination influences RH care and treatment. One insightful

scholar posited that “quality of care can only be achieved where quality has been defined by both the

users and providers of services and where women are actively involved….” [35].


        Previous studies which have examined factors that account for increased cancer screening

utilization in Latin American and Caribbean countries have highlighted that these include health
                                                269
insurance, marital status, frequency of doctor visits, high education, high income, high functional

and sexual partner approval [12-19]. However, while these provide some understanding of Pap

smear or pelvic examination along with the additional factors which emerged from this study, we

now know issues which account for females’ unwillingness to do such medical examination as a

part of a comprehensive physical medical check-up. This research found that the number of

pregnancies which resulted in live birth(s), having never done a Pap smear or pelvic

examination, and age of menarche positive influence female unwillingness to include Pap smear

or pelvic examination as a part of medical check-up. Within the context that most females in

Jamaica (ages 15-74 years) have been pregnant and that 51% have been pregnant at least 3 times

[2], coupled with their risky sexual behaviour and inconsistent contraceptive usage [4, 11] it

follows that a low willingness to do cervical screening could not be stopped by merely

understanding why females do Pap smear or pelvic examinations.


       The high prevalence of cervical cancers in Jamaica can be explained by the findings of

the current study, and any policy which is geared towards cervical cancers reduction must

understand why women do not do Pap smears or pelvic examinations; in order to address this

public health problem. As part of a policy objective the various Governments of the Caribbean

(including Jamaica) have embarked upon very important initiatives relating to gynaecological

matters. Based on the current findings, a public intervention programme is needed to directly address

perception, attitude and practices of women in rural areas, middle class, with post-secondary level

education and in a sexual union as they are least likely to do a pelvic or Pap smear examination. We

also know that the issues which associate with those who had never done a Pap smear and pelvic

examination must be included in the public health intervention programme.



                                                270
Conclusion

The rationales provided for the low decisions in pelvic examination were being healthy, not

recommended by medical practitioner, and never thought of it among other reasons. The factors

associated with ever ‘done pelvic examination and having done a pelvic examination’ in the last

12 months are somewhat different, and the multidimensional nature of the variables indicate that

a multisectoral approach should be used in addressing low pelvic and Pap smear examination

among Jamaican women. In addition, we are recommending that the age for Pap smear

examination be lowered to 15 or 20 years old instead of the Jamaican Ministry of Health’s figure

of 25-54 years because of early sexual initiation of females (in their adolescence years),

inconsistent condom usage, early median age at first menarche and the frequency of sexual

activities among those 15-24 years old.



Conflict of interest
The authors have no conflict of interest to report.



Acknowledgement
The researchers would like to note that while this study used secondary data from the Jamaica
Survey of Living Conditions, none of the errors in this paper should be ascribed to the Planning
Institute of Jamaica or the Statistical Institute of Jamaica, but to the researchers.




                                                271
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                                          273
Table 9.1. Sociodemographic characteristic of sample, n = 7,168
Characteristic                                                           n(%)
Religiosity
  At least once a week                                                      2707 (37.8)
  At least once a month                                                     1368 (19.1)
  Less than once a month                                                     861 (12.0)
  Only on special occasions (weddings, funerals, christening)               1631 (22.8)
  Does not attend at all                                                      524 (7.3)
  No response                                                                  77 (1.1)
Marital status
  Legally married                                                           1542 (21.5)
  Common-law                                                                1733 (24.2)
  Visiting                                                                  1959 (27.3)
  Not currently in union                                                    1934 (27.0)
Currently pregnant
  Yes                                                                         288 (4.4)
  No                                                                        6219 (94.6)
Ever been pregnant
  Yes                                                                       5301 (84.3)
  No                                                                         985 (15.7)
Forced to have sex
  Yes                                                                        747 (11.4)
  No                                                                        5707 (86.8)
Health conditions
  Diabetes                                                                   284 (12.2)
  Anemia                                                                     438 (18.8)
  Heart disease                                                                94 (4.0)
  Pelvic inflammatory disease                                                 125 (5.4)
  Urinary tract infection                                                    800 (34.3)
  Asthma                                                                     587 (25.0)
  Hepatitis B                                                                   6 (0.3)
Area of residence
  Urban                                                                     1144 (16.0)
  Semi-urban                                                                2079 (29.0)
  Rural                                                                     3945 (55.0)
Socioeconomic class
  Lower                                                                     1705 (23.8)
  Middle                                                                    3079 (43.0)
  Upper                                                                     2384 (33.2)
No. of pregnancies that resulted in live births median (range)               2.0 (0, 14)
Years of schooling mean (SD)                                      13.0 years (3.0 years)
Age mean (SD)                                                     31.3 years (9.3 years)

                                             274
Table 9.2. Gynaecological examination of studied population

Characteristic                                                      n(%)
Pelvic examination
    Yes                                                       3074 (42.9)
   No                                                         4079 (56.9)
Last Pelvic examination
  < 12 months                                                 1287 (18.0)
   1 – 2 years                                                 731 (10.2)
   2 – 3 years                                                  343 (4.8)
   3+ years                                                     631 (8.8)
  Don’t remember                                                 82 (1.1)
  Did not answer                                              4094 (57.1)
Last Papanicolaou Test (pap smear)
  < 12 months                                                 1496 (20.9)
   1 – 2 years                                                1042 (14.5)
   2 – 3 years                                                  641 (8.9)
   3+ years                                                   1257 (17.5)
  Don’t remember                                                 56 (0.8)
  Never                                                       2676 (37.3)
In last 12 months non-menstrual vaginal discharge
  Yes                                                         1112 (15.5)
  No                                                          5944 (83.0)
  Not sure                                                      106 (1.5)
Reason for not having done a pelvic examination
  Does not need to go                                          436 (10.6)
  Healthy and has no sign of gynaecological problems           649 (15.9)
  No time                                                        66 (1.6)
  Forget to go                                                   33 (0.8)
  Does not like the process                                      57 (1.4)
  Difficult to get an appointment                                11 (0.3)
  Does not like the environment                                   6 (0.1)
  Long waiting time                                               5 (0.1)
  Not recommended by doctor                                    485 (11.8)
  Embarrassed to do the test                                     22 (0.5)
  Never thought of it                                         1471 (35.9)
  Sexually inactive                                             127 (3.1)
  Virgin                                                        188 (4.6)
  Other                                                         302 (7.4)
  Do not remember                                               236 (5.8)




                                             275
Table 9.3. Pelvic examination by sociodemographic characteristics
                                                                           Pelvic examination                                   (P-value)
Characteristic                                           Yes                       No           Do not remember
                                                        n (%)                    n (%)               n (%)                         χ2 = 4.998 (0.544)
Currently pregnant
   Yes                                                     120 (41.7)             168 (58.3)              0 (0.0)
   No                                                     2885 (46.4)            3319 (53.4)             11 (0.2)
   Not sure                                                 26 (38.2)              42 (61.8)              0 (0.0)
Ever been pregnant                                                                                                           χ2 = 36.984 (< 0.0001)
   Yes                                                    2542 (48.0)            2749 (51.9)               8 (0.2)
   No                                                      369 (37.5)             611 (62.2)               3 (0.3)
Socioeconomic status                                                                                                         χ2 = 539.181 (< 0.0001)
   Lower class                                             435 (25.5)            1268 (74.4)               1 (0.1)
   Middle class                                           1195 (38.8)            1877 (61.0)               5 (0.2)
   Upper class                                            1444 (60.6)             934 (39.2)               5 (0.2)
Marital status                                                                                                               χ2 = 507.152 (< 0.0001)
   Married                                                 908 (58.9)             631 (40.9)               2 (0.1)
   Common-law                                              723 (41.8)            1008 (58.2)               0 (0.0)
   Visiting                                                772 (39.4)            1183 (60.4)               4 (0.2)
   Previously in union                                     607 (48.5)             640 (51.2)               4 (0.3)
   Never in union                                            64 (9.4)             617 (90.5)               1 (0.1)
Contraception (currently)                                                                                                      χ2 = 12.276 (= 0.002)
   Yes                                                    1802 (44.7)            2219 (55.1)               6 (0.1)
   No                                                     1125 (49.3)            1153 (50.5)               4 (0.2)
Contraception (ever)                                                                                                         χ2 = 268.595 (< 0.0001)
   Yes                                                    2931 (46.8)            3373 (53.4)             10 (0.2)
   No                                                     1431 (16.8)             706 (83.1)              1 (9.1)
Area of residence                                                                                                            χ2 = 353.787 (< 0.0001)
   Urban                                                   668 (58.5)             469 (41.1)               5 (0.4)
   Semi-urban                                             1095 (52.7)             983 (47.3)               1 (0.1)
   Rural                                                  1311 (33.2)            2627 (66.6)               5 (0.1)
Age mean (SD)                                               34.3 (8.1)             29.0 (9.5)           30.6 (8.9)   F-statistic = 308.754 (< 0.0001)



                                                                         276
Table 9.4. Logistic regression: Explanatory variables of every done pelvic examination, n = 5,388


 Explanatory variable                                                                   CI (95%)
                                                    Coefficient          Odds ratio

 Age                                                          0.07               1.07     1.06 - 1.08

 No. of pregnancy (live births)                              -0.23               0.80     0.77 - 0.83

 No. of pregnancy (miscarriages)                              0.16               1.17     1.05 - 1.30

 No. of induced abortion                                      0.41               1.51     1.11 - 2.05

 Age at first sexual intercourse                             -0.00               1.00     0.99 - 1.00

 No. of years in school                                       0.08               1.08     1.06 - 1.11

 Urban area (reference group)                                                    1.00
 Rural                                                       -0.54               0.58     0.52 - 0.66

 Lower class (reference group)                                                   1.00
 Middle class                                                 0.44               1.56     1.34 - 1.81
 Upper class                                                  1.18               3.26     2.74 - 3.87
-2Log likelihood = 6437.09
R2 = 0.23
Model χ2 (df = 8) = 1022.79
P< 0.0001
Overall correct classification = 68.3%
Correct classification of cases that had Pelvic examination = 63.9%
Correct classification of cases that did not have Pelvic examination = 72.3%




                                                             276
Table 9.5. Logistic regression: Explanatory variables of those who had a pelvic examination in last 12
months, n = 5,388


 Explanatory variable
                                                        β Coefficient          Odds ratio       95% (CI)

 Age                                                                16.72               0.98     0.97 - 0.99

 No. of pregnancy (live births)                                      6.92               0.92     0.87 - 0.98

 Currently pregnant                                                  4.05               1.49     1.01 - 2.19

 No. of years in school                                             15.87               1.06     1.03 - 1.08

 Urban                                                                                  1.00
 Semi urban                                                         11.55               0.68     0.55 - 0.85
 Rural                                                              40.57               0.50     0.40 - 0.61

 In union                                                            5.96               1.30     1.05 - 1.60
-2Log likelihood = 3341.13
R2 = 0.27
Model χ2 (DF = 8) = 131.81
P< 0.0001
Overall correct classification = 64.0%
Correct classification of cases that had Pelvic examination in the last 12-month = 68.8%
Correct classification of cases that did not have Pelvic examination in last 12-month = 87.4%




                                                             277
Table 9.6. Logistic regression: Explanatory variables of those who had a Pap smear in last 12 months, n =
5,388


 Explanatory variable
                                                      β Coefficient          Odds ratio       95% (CI)

 No. of pregnancy (live births)                                    -0.04               0.96    0.92 - 1.00

 No. of pregnancy (miscarriages)                                   0.12                1.12    1.01 - 1.25

 Age                                                               0.06                1.06    1.04 - 1.08

 Urban (reference group)                                                               1.00
 Rural                                                             -0.24               0.79    0.69 - 0.90


 Currently using contraception                                     0.17                1.18    1.03 - 1.35

 Lower class (reference group)                                                         1.00
 Middle class                                                      0.25                1.29    1.08 - 1.54

 Upper class                                                       0.69                1.99    1.65 - 2.40
-2Log likelihood = 5683.51
R2 = 0.25
Model χ2 (DF = 7) = 182.2
P< 0.0001
Overall correct classification = 76.6%
Correct classification of cases that had Pap smear in the last 12-month = 81.2%
Correct classification of cases that did not have Pap smear in last 12-month = 99.7%




                                                             278
Table 9.7: Logistic regression: Explanatory variables of those who had not done Pap smear, n = 5,388


 Explanatory variable
                                                                    β Coefficient      Odds ratio   95% (CI)

 Years of schooling                                                           -0.17       0.85**     0.75 -0.95

 Age of menarche                                                               0.22         1.25*   1.05 - 1.48

 Number of pregnancy that resulted in live births                             -0.18         0.84*   0.72 - 0.99

 Never done pelvic examination                                                 1.80      6.07***    3.26 -11.32
-2Log likelihood = 310.03
R2 = 0.249
Model χ2 (DF = 7) = 59.30
P< 0.0001
Overall correct classification = 73.7%
Correct classification of cases that had Pap smear in the last 12-month = 63.3%
Correct classification of cases that did not have Pap smear in last 12-month = 92.6%




                                                             279
Table 9.8: Logistic regression: Explanatory variables of those who had never done a pelvic examination n
= 5,388


 Explanatory variable                                                                     Odds
                                                                       β Coefficient      ratio      95% (CI)

 Middle class                                                                     -0.60     0.55*     0.30 - 0.99
 Upper class                                                                      -1.24    0.29**     0.14 - 0.61
 Lower class (reference group)                                                                1.00

 Number of pregnancy that resulted in live births                                  0.19    1.21**     1.06 - 1.39

 Number of pregnancy that resulted in miscarriages                                -0.47    0.62**     0.44 - 0.88

 Never done Pap smear examination                                                  1.65   5.22***     2.81 - 9.70
-2Log likelihood = 364.80
R2 = 0.272
Model χ2 (DF = 7) = 71.80
P< 0.0001
Overall correct classification = 72.6%
Correct classification of cases that had Pap smear in the last 12-month = 73.5%
Correct classification of cases that did not have Pap smear in last 12-month = 95.7%




                                                             280
                                                                                   Chapter
                                                                                                10
       Impact of poverty, not seeking medical care,
  unemployment, inflation, self-reported illness, and health
           insurance on mortality in Jamaica
An extensive review of the literature revealed that no study exists that has examined poverty, not seeking
medical care, inflation, self-reported illness, and mortality in Jamaica. The current study will bridge the
gap by providing an investigation of exactly these things. The average percentage of Jamaicans not
seeking medical care over the last 2 decades was 41.9%; and that figure has been steadily declining over
the last 5 years. In 1990, the percentage of Jamaicans who did not seek medical care was 61.4%, and this
fell to 52.3% in 1991; 49.1% in 1992 and 48.2% the proceeding year. In the early 1990s (1990-1994),
the percentage of Jamaicans not seeking medical care despite illness was close to 50%, but in the latter
part of the decade, the figure was in the region of 30% and was as low as 31.6% in 1999. In 2006, the
percentage of Jamaicans not seeking medical care despite being ill was 30%, which increased by 4% the
following year. Concomitantly, poverty fell by 3.1 times over the 2 decades to 9.9% in 2007, while
inflation increased by 1.9 times. Self-reported illness was 15.5% in 2007 with mortality averaging 15,776
per year over the 2 decades. There is a significant statistical correlation between not seeking medical-
care and prevalence of poverty (r = 0.759, p< 0.05), as well as a statistical correlation between not
seeking medical care and unemployment; but the association is a non-linear one. The relationship
between mortality and unemployment was an uncertain one, with there being no clear linear or non-
linear correlation. The findings revealed that there is a strong direct association between not seeking
medical care and inflation rate (r = 0.752). A strong negative statistical correlation was found between
mortality and prevalence of poverty (r=0.717). There is a non-linear statistical association between not
seeking medical care and illness/injury. Conclusions: Not seeking medical care is not a good indicator of
premature mortality; but when the percentage of those not seeking medical care is above 55%, then
premature mortality becomes evident. While this study cannot confirm a clear rate of premature
mortality, there are some indications that this occurs beyond a certain level of not seeking care for
illness.



Introduction



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Health (medical) care-seeking behaviour of people is not only an indicator of their willingness to preserve

life but it is crucial to personal, societal and national development. The health of an individual affects all

areas of his/her life and extends to the family, community, society and the nation. The cost of ill health is

not only borne by the individual, but the entire society. Ill health means less time on the job, lowered

production and productivity, reduced Gross Domestic Product and savings, high health care expenditure,

and the switching of expenditure from education and other social development to health care, and all this

can further increase poverty for an individual or his/her family. Health therefore holds a key to social and

economic development. Hence, long life must be supported by a healthy individual or population. It is this

interrelationship among health, life expectancy, and social and economic development that accounts for a

demand in health care services.

       Life expectancy is computed from mortality data, and so healthy life expectancy means the

delaying of mortality. Mortality statistics provide an insight into morbidity patterns as well as the health

of a person or a population. These statistics also provide a basis upon which we can estimate the burden of

premature deaths [1, 2]; lifestyle practices; and health care-seeking behaviour [3]. The Caribbean is

experiencing a health transition which accounts for a reduction in fertility and mortality, and the changing

pattern of diseases from communicable to non-communicable diseases as the leading cause of death [2,

4]. The Caribbean is not atypical in regards to the aforementioned pattern [1] it is argued that 80% of

chronic disease deaths occur in low-to-middle income countries, and that this has a serious influence on

the causes of premature mortality.

       Statistics from the Planning Institute of Jamaica and the Statistical Institute of Jamaica published

in the Jamaica Survey of Living Conditions [5] revealed that in 2007, 15.5% of Jamaicans reported an

illness/injury compared with 9.7% in 1997. Of the 15.5% of Jamaicans who reported health conditions,

66% of them sought medical care. Of those who sought health care, 40.5% went to public facilities
                                                     282
compared to 51.9% who attended private health care facilities. The typologies of diseases were asthma

(8.7%), diabetes mellitus (12%), hypertension (22.4%), and arthritis (8.8%). Concomitantly, 33.9% of

Jamaicans who did not seek care reported that they were unable to afford it; 30.2% mentioned that they

preferred home remedies and 6.0% remarked that they had no time. According to Fraser [6], the

prevalence of hypertension in the Caribbean was 28% and 55% for those over 25 years and 40 years

respectively. This explains Fraser’s call for an aggressive management drive to address the prevention of

those health conditions, which was equally echoed by other scholars [7, 8].


       Morrison [9] titled an article “Diabetes and hypertension: Twin Trouble” in which he established

that diabetes mellitus and hypertension have now become two problems for Jamaicans as well as in the

Caribbean in general. This situation was equally collaborated by Callender [10] at the 6th International

Diabetes and Hypertension Conference, which was held in Jamaica in March 2000. They found that there

is a positive association between diabetic and hypertensive patients – 50% of individuals with diabetes

had a history of hypertension [10]. Prior to those scholars’ work, Eldemire [11] found that 34.8% of new

cases of diabetes and 39.6% of hypertension were associated with senior citizens (i.e., ages 60 and over).

A national study of 958 Jamaicans found that 18% of women had hypertension compared to 8% of men;

4.8% of women with diabetes compared to 3.3% of men [4]; and an earlier study by Forrester et al [8] had

found that 19.3% of African-Jamaican females reported hypertension compared to 13.0% of African-

Jamaican males.

       When the WHO [1] found that some deaths are premature, they also learned that the reasons for

this lie in care-seeking behaviour; time of treatment; identification of illness; poverty; inaccessibility;

unhealthy lifestyle practices; and physical inactivity. According to the WHO [1], one-half of all chronic

diseases occur prematurely in people who are below the age of 70 years compared to one-quarter of those


                                                   283
younger than 60 years. The organisation also reported that 80% of premature heart disease, stroke and

diabetes mellitus could have been prevented from happening. Can premature deaths be prevented?

       Embedded in WHO publication is the relationship between poverty and illness, poverty and

chronic diseases and poverty and premature death. Marmot [12] explained that income is positively

associated with better health, and that poverty means poor nutrition, inadequate physical milieu, and poor

water and food supply which account for increased ill-health in this cohort. Like Marmot [12], Sen

[13,14] argued that poverty denotes reduced capability as this retards choices, freedom, educational

access, proper nutrition, and therefore explains not only chronic diseases but also employability, health

insurance coverage, and medical care-seeking behaviour. Statistics from the Planning Institute of Jamaica

and the Statistical Institute of Jamaica [5] revealed that those below the poverty line sought the least

medical care: 51.7% for those below the poverty line, 52.7% for those just above the poverty line, 61.2%

for those in the middle income categorization, 61.8% in the wealthy income category and 67.6% of those

in the wealthiest income cohort. Concomitantly, the poorest income category had the highest reported

illness (85.4%) compared to 85.1%; 79.6%: 67.5%; and 74.3% for poor, middle class, wealthy and

extremely wealthy income categories respectively [5].

       The poor not only seek less medical care – and this offers some more explanation for their

increased probability of contracting chronic illness and other mortality causing morbidities – but they are

also the least likely group to purchase health insurance coverage. Poverty means low access to material

and other social resources. In 2007, statistics on Jamaica revealed that 2.2% of those below the poverty

line had health insurance coverage compared to 10.1% of those just above the poverty line; 15.9% of the

middle class; 20.9% of the wealthy and 37.7% of the wealthiest income category [5]. This finding

highlights the reality of the poor: that in order for them to access health care, they must supply the

substantial funds out of pocket, or the state must pay for it. With the probability that people living in
                                                   284
poverty are the least likely to find out-of-pocket money to utilize on health care, premature mortality

indeed will be greater for this cohort than other income cohorts.

       Poverty therefore erodes the good health status of a populace and further deepens individual and

national poverty while creating a public health concern for the society. Inflation is a persistent upward

movement in prices. It erodes the socio-economic choices of people within a society. Inflation increases

the prices of goods and services and a part of this consequence is the cost of health care. In 2007, the

annual rate of inflation on food and non-alcoholic beverages was 24.7% compared to 3.4% on health care

cost (Table 10.1), while it was 16.8% for the nation. The rate of the increase of inflation for 2007 over

2006 was 194.7%. With increases in food prices comes the upward price movement in other goods and

services and this removes the willingness of people to prioritise the pursuit of medical care over food. The

information above highlights the interconnectedness between poverty, unemployment, ill-health, not

seeking medical care, health insurance coverage and mortality. In spite of this reality, extensive review of

the literature has not found a study that has examined the aforementioned variables in a single research.

The current study will bridge the gap by providing an investigation of poverty, not seeking medical care,

illness, health insurance coverage, inflation and mortality in Jamaica.

       Using two decades’ worth of data (1988-2007), the current work will examine 10 hypotheses and

provide an extensive account for mortality, not seeking medical care, illness, health insurance coverage

and unemployment patterns in Jamaica in an attempt to provide research literature for future public health

planning and a better understanding of mortality and premature mortality in Jamaica. The hypotheses are

1) there is a statistical correlation between not seeking medical care and poverty; 2) there is a statistical

association between not seeking medical care and unemployment; 3) there is a statistical association

between poverty and unemployment; 4) there is a statistical relationship between poverty and inflation; 5)

there is a statistical association between not seeking medical care and illness; 6) there is a statistical
                                                    285
association between not seeking medical care and health insurance coverage; 7) there is a statistical

association between mortality and poverty; 8) there is a statistical relationship between mortality and

unemployment, 9) there is a statistical relationship between mortality and not seeking medical care, and

10) there is a significant statistical association between not seeking medical care and inflation.

      The aim of this study was to examine the impact of poverty, not seeking medical care,

unemployment, inflation, self-reported illness, and health insurance coverage on mortality in Jamaica in

order to provide public health practitioners and health promotion specialists with research findings on

those matters in Jamaica.


       The current findings revealed significant statistical correlation between not seeking medical-care

and 1) prevalence of poverty (r = 0.759, p< 0.05); 2) unemployment; 3) inflation (r = 0.752); 4) illness; 5)

health insurance coverage; and mortality. There is a positive correlation between the prevalence of

poverty and unemployment (r = 0.69), with 48% of poverty able to be explained by unemployment. A

strong positive statistical correlation was found between poverty and inflation (r = 0.856), as 73.2% of

poverty can be explained by inflation. A strong negative statistical correlation was found between

mortality and prevalence of poverty (r=0.717), with 51.4% of the variance in mortality being explained by

poverty. The relationship between mortality and unemployment was an uncertain one, with there being no

clear linear or non-linear correlations. Linear associations were found between most of the

aforementioned variables; however, non-linear correlations were found between 1) mortality and not

seeking-medical care; 2) mortality and unemployment; 3) not seeking medical care and health insurance

coverage; not seeking medical care and illness; and 4) not seeking medical care and unemployment.




                                                     286
Materials and Methods

Using two decades’ worth of data (1988-2007), the current study used three sets of secondary data

published by the 1) Planning Institute of Jamaica and the Statistical Institute of Jamaica (Jamaica Survey

of Living Conditions); 2) the Statistical Institute of Jamaica (Demographic Statistics); and 3) the Bank of

Jamaica (Economic Report). The years selected for this paper were selected due to the availability of data

on health care-seeking behaviour and illness.

       Health care-seeking behaviour, poverty and illness data were taken from the Jamaica Survey of

Living Conditions. The Jamaica Survey of Living Conditions (JSLC) is conducted jointly by the Planning

Institute of Jamaica and the Statistical Institute of Jamaica. Its purpose is to collect data on living

standards of Jamaicans. The JSLC used a detailed questionnaire to collect data from respondents between

April and October each year. A self-administered questionnaire was used to collect the data which were

stored and analyzed using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA). The questionnaire was

modelled from the World Bank’s Living Standards Measurement Study (LSMS) household survey. There

are some modifications to the LSMS, as the JSLC is more focused on policy impacts. The questionnaire

covered areas such as socio-demographic, economic and health variables. The non-response rate for the

survey was 26.2%.

       The survey was drawn using stratified random sampling. This design was a two-stage stratified

random sampling design where there was a Primary Sampling Unit (PSU) and a selection of dwellings

from the primary units. The PSU is an Enumeration District (ED), which constitutes a minimum of 100

dwellings in rural areas and 150 in urban areas. An ED is an independent geographic unit that shares a

common boundary. This means that the country was grouped into strata of equal size based on dwellings

(EDs). Based on the PSUs, a listing of all the dwellings was made, and this became the sampling frame

from which a Master Sample of dwelling was compiled, which in turn provided the sampling frame for
                                                   287
the labour force. One third of the Labour Force Survey (i.e., LFS) was selected for the survey. The sample

was weighted to reflect the population of the nation. Furthermore, the instrument is posted on the World

Bank’s       site     to     provide      information      on     the      typologies     of     question

(http://www.worldbank.org/html/prdph/lsms/country/jm/docs/JAM04.pdf).


         Unemployment data were taken from the publication of the Labour Force Survey of Jamaica

(conducted by the STATIN).

         Mortality data were taken from the publication of the demographic statistics. Although a medical

certificate of death is used to indicate mortality, data from the Registrar General Department (RGD) were

cleaned, modified and validated by the Statistical Institute of Jamaica [15]. Using a study that was

conducted in 1999 which showed that there was an under-registration of deaths in RGD’s figures, the

STATIN developed a methodology that accounted for complete mortality.

         For the period 1998-2001, STATIN subtracted the number of deaths as reported by the police

(deaths from external causes) from the RGD’s record on external deaths. The difference was added to the

mortality data set. Secondly, on investigation of the infant mortality (ages below 1 year), STATIN found

that 80.25 per cent of the deaths occurred in the year in question and 19.75 percent occurred in the

previous year. This was taken into consideration with the RGD’s figures in order to account for all deaths

occurring in the year in question. For a more detailed explanation of this methodology, readers can

consult Demographic Statistics [15].

         Information is not available on those who are ill but not seeking medical care. As a result, this

information was computed by subtracting the percentage reported as seeking medical care each year from

100.




                                                    288
       The aforementioned data will be used to provide background information on the study.

Descriptive statistics and percentages will be presented on mortality, seeking medical care for the

population, and males and females.


      Scatter diagrams were used to examine correlations between the particular dependent and

independent variables. For the current study, a number of hypotheses were tested to provide a better

understanding of the correlation among mortality, poverty, unemployment, self-reported illness, health

insurance and inflation in Jamaica. Four hypotheses will be tested in this study: (1) there is a statistical

correlation between not seeking medical care and poverty; (2) there is a statistical association between not

seeking medical care and unemployment; (3) there is a statistical association between poverty and

unemployment; (4) there is a statistical relationship between poverty and inflation; (5) there is a statistical

association between not seeking medical care and illness; (6) there is a statistical association between not

seeking medical care and health insurance coverage; (7) there is a statistical association between mortality

and poverty; (8) there is a statistical relationship between mortality and unemployment, (9) there is a

statistical relationship between mortality and not seeking medical care, and (10) there is a significant

statistical association between not seeking medical care and inflation.


Measures

Inflation: This is measured as the per cent increase in prices from December to December of each year.




Not seeking medical care: This variable is the difference between those who reported seeking medical

care owing to illness/injury (which is expressed as a per cent) and 100 per cent.




                                                     289
Medical care-seeking behaviour: This is the total number of people who reported seeking medical care

(i.e., health care practitioner, healer, pharmacist, nurse, etc.) (expressed in per cent).


Poverty is categorized into two major headings: (1) absolute and (2) relative poverty [13]. Absolute

poverty denotes the lack of particular social necessities caused by “limited material resource” in which to

function – affordability of meeting basic needs, such as adequate nutrition, clothing and housing. Relative

poverty, on the other hand, speaks to the individuals’ low financial resources (money or income) or other

material resources relative to other people. The Senate says that “relative poverty is defined not in terms

of a lack of sufficient resources to meet basic needs, but rather as lacking the resources required to

participate in the lifestyle and consumption patterns enjoyed by others in the society” [16].




The Senate Community Affairs Reference Committee (SCARC) ascribes Professor Ronald Henderson the

developer of the “poverty line”. “…he developed his ‘poverty line’ which was originally set equal to the

minimum wage plus child endowment in Melbourne in 1966” [16]. Within this measurement approach,

poverty becomes a relative phenomenon instead of an absolutism technique. The SCARC [16] says that

“the aggregate money value of the poverty gap indicates the minimum financial cost of raising all poor

families to the poverty line” [16]. The concept of the poverty line is used in Jamaica to evaluate poverty.

In 2007, the poverty line for a household of five was $302,696.07 compared to $281,009.93 in 2006 [5].




                                                       290
Results

On average, over the studied period, the percentage of Jamaicans not seeking medical care was 41.9%.

The number of Jamaicans not seeking medical care has been steadily declining, which indicates that

health care seekers have been increasing over the past two decades (Figure 10.1; Table 10.2). In 1990, the

highest percentage of Jamaicans who did not seek medical care was 61.4% and this fell to 52.3% in 1991;

49.1% in 1992 and 48.2% the proceeding year. The percentages in the early 1990s (1990-1994) show that

the percentage of Jamaicans not seeking medical care was close to 50% and in the latter part of the

decade, the figure was in the region of 30%, and as low as 31.6% in 1999. In 2006, the percentage of

Jamaicans not seeking medical care despite being ill was 30% and this increased by 4% the following

year.

        Figure 1 showed that not seeking medical care (which is derived by subtracting medical care-

seeking behaviour from 100%) can be fitted with a straight line. Furthermore, not seeking medical care

has been steadily declining. However, mortality is best fitted with a non-linear curve. It was found that

mortality was falling up until 1990 where it reached the minimum, and then began rising at an increasing

rate up until 2002, and finally an ever-growing decline set in post-2005 (Fig. 2).

        Based on the findings (Table 10.2), Jamaicans have a preference for private health care utilization.

During the 1990s (1994-1995), the disparity between private and public health care utilization was

approximately 40%, and the divide has continued to narrow after that period. In 2007, the disparity was

11%, which represents a 28% narrowing of the gap between both utilizations.


        Concomitantly, during the latter part of the 1980s to early 1990s, inflation began mounting so

much so that it peaked at 80.2% in 1991(Table 10.2). While inflation was rising, there were fluctuations

between poverty and self-reported illness/injury. Continuing, when inflation was at its highest (80.2%),


                                                    291
poverty was also at its peak (44.6%), unemployment was close to the peak (15.3%) (Table 10.3) and so

was the percentage of those not seeking medical care (52.3%). Inflation increased by 194% in 2007 over

2006 and during that period, health insurance coverage was at its highest (21.2%); medical care-seeking

behaviour fell by 4%, self-reported illness increased by 3% (to 15.5%) and 4% more Jamaicans did not

seek medical care.


       Table 10.3 revealed that average mortality over the two-decade period was 15,966 people. In

1999, that figure was 18,200 people with a low of 13,200 people in 1992. Correspondingly, over the two

decades there was one occasion where men sought more medical care than women (2006), with the

general trend in the data being that men are less likely to report illness/injury. In 2007, the findings

revealed that the mean number of days spent in medical care by men was marginally more (10.6 days)

compared to women (9.3 days); but that generally the difference is minimal (Table 10.3).


Not seeking medical care

There is a significant statistical correlation between not seeking medical care and prevalence of poverty

(r=0.759, p<0.05). The association therefore is a strong positive one, where 57.6% of the variance in not

seeking medical care can be explained by 1% change poverty (Fig. 3).

       There is a statistical correlation between not seeking medical care and unemployment; but the

association is a non-linear one (Fig. 4). The findings revealed that there is a direct correlation between

not seeking medical care and unemployment between 7.5% and 15% after which it begins to fall. At 15%

of unemployment (not clear) not seeking medical care is at its maximum. Then post that rate, the rate of

not seeking medical care falls precipitously.




                                                   292
       The findings revealed that there is a strong direct association between not seeking medical care

and inflation rate (r=0.752). Continuing, 56.5% of the variance in not seeking medical care can be

explained by a 1% change in inflation rate.


       There is a non-linear statistical association between not seeking medical care and illness/injury

(Fig. 5). The findings revealed that when the rate of illness/injury is more than 9% and less than 14%, the

rate of not seeking medical care falls at a decreasing rate, and after 15% the rate rises significantly.

       Figure 6 revealed a statistical association between not seeking medical care and health insurance

coverage, but the relationship is a non-linear one. It was found that between 8 to 18%, the correlation is an

inverse one and after 18% it becomes a direct one. Hence, the more people have health insurance

coverage, the less likely it is that they will not seek medical care and this correlation reverses beyond 18%

of coverage.

       There is a statistical relationship between mortality and not seeking medical care. Based on Figure

6, the correlation is best fitted with a non-linear curve than a linear one. Hence, the association does not

have the same gradient throughout the curve. It follows that after 35% of not seeking medical care, the

rate of change in mortality was decreasing and after 55% of not seeking medical care, the rate begins to

mount at an increasing rate.


Poverty, Unemployment, Inflation and Mortality

There is a positive correlation between prevalence of poverty and unemployment (r=0.69), with 48% of

poverty being able to be explained by unemployment (Fig. 7).

       A strong positive statistical correlation was found between poverty and inflation (r=0.856), as

73.2% of poverty can be explained by inflation (Fig. 8).



                                                      293
       A strong negative statistical correlation was found between mortality and prevalence of poverty

(r=0.717), with 51.4% of the variance in mortality being able to be explained by poverty.

       The relationship between mortality and unemployment was an uncertain one, with there being no

clear linear or non-linear correlation (Fig. 10).



Discussion



Murray [18] found that there is a clear interrelation between poverty and health. She noted that financial

inadequacy prevents an individual from accessing (food and good nutrition, potable water, proper

sanitation, medicinal care, preventative care, adequate housing, knowledge of health practices) and

attendance (of particular educational institutions among other things), which was in agreement with

Marmot and Sen’s perspectives. Marmot [12] opined that poverty reduced an individual’s socio-economic

and political choices and like Sen [13], he saw this phenomenon as a retardation of human capabilities.

They believed that poverty accounted for much of the low educational outcome as well as accounting for

poor nutrition, low water quality, and poor physical environment, which is not surprising when the poor

experience increased health conditions. Marmot [12] argued that money can buy health, as those who

have it are able to afford medical care treatment, able to purchase particular goods, able to create a good

physical milieu and by extension, able to experience a better health status than the poor. This argument is

not entirely correct as income cannot actually buy health, as health is not a commodity that can be

purchased. However, income can buy the treatment which is a precursor to better health status, and this is

what the wealthy has over the poor. Easterlin [17] argued that material resources have the capacity to

improve one’s choices, comfort level, state of happiness and leisure, and not that money can buy actual

health or happiness.
                                                    294
       Poverty undoubtedly incapacitates those who live with it, which explains why the WHO [1]

argued that some of the mortality in this group will be premature. The current study found that there is a

strong direct correlation between not seeking medical care and poverty. With 57% of the reason

Jamaicans do not seek medical care being accounted for by poverty, it follows that some of the

morbidities that require medical care will be attended to with home remedies and non-medical healers, or

nothing at all, and by extension will result in premature deaths. This concurs with Murray’s work which

showed that poverty also leads to increased dangers to health; working environments of poorer people

often hold more environmental risks for illness and disability, and other environmental factors, such as

lack of access to clean water disproportionately affect poor families [18].


       The studies clearly show a relationship between persistent and prolonged poverty and health and

even mortality [18-20]. If poverty is an undisputable a primary cause of malnutrition [21], then access to

money plays a pivotal role in well-being. In order to grasp the severity of the issue of money, we need to

be brought into the recognition of poverty and health status. According to Bloom and Canning [22], “ill-

health” significantly affects poor people. This postulate further goes on to explain the higher probability

(5 times) of mortality of the poor than the rich [23].


       A survey conducted by Diener, Sandvik, Seidlitz and Diener [24], stated that the correlation

between income and subjective well-being was small in most countries. According to Diener [25],

“…there is a mixed pattern of evidence regarding the effects of income on SWB [subjective well-being]”.

Benzeval, Judge and Shouls’s [26] study concurred with Diener in that income is associated with health

status. Benzeval et al went further as their research revealed that a strong negative correlation exists

between increasing income and poor health. Furthermore, from a study, it was found that people from the

bottom 25 percent of the income distribution self-reported poorer subjective health by 2.4 times than

                                                     295
people in the fifth quintile [26].


        The poor, like the wealthy or middle class, also want long life and a life full of satisfaction, but the

reality is, in order for them to spend money on education and health care, they must first cover food and

non-alcoholic beverage costs. In 2007, inflation on non-alcoholic beverages was 24.7%, which means that

the poor must now face the additional cost of survivability before venturing into health care treatment. In

2003 and 2006, health care costs were close to double digits, and in the latter year, the price increase was

greater than that for food and non-alcoholic beverages. With the poor experiencing material and income

inadequacies, inflation not only creates an economic hardship but a treatment care hindrance. This study

revealed that there is a strong positive statistical relationship between not seeking medical care and

inflation, which means that when inflation increased by 194% in 2007 over 2006, many poor Jamaicans

delayed medical care treatment to their very detriment. It should be noted here that during the

aforementioned period, the percentage of Jamaicans reporting health conditions increased to 15.5% (from

12.2% in 2006), suggesting that many poor people were not being treated for some of the chronic diseases

that they were experiencing on a daily basis.

        One of the ways many people afford health care is with health insurance coverage. Health

insurance coverage reduces out-of-pocket payment, and makes medical care more affordable for countless

non-wealthy people. To address the exponential increase in prices that took place in 2007 over 2006,

many Jamaicans purchased health insurance as the percentage of people holding health insurance

coverage stood at 21.2%, the highest in the nation’s 20 year history. Concomitantly, only 2.2% of those in

the poorest income categorization were holders of health insurance coverage, and 10.1% of those were

just above the poverty line, suggesting that health care treatment would be an out-of-pocket payment for

those individuals. With the typologies of diseases reported by Jamaicans being hypertension, diabetes


                                                      296
mellitus, asthma, and arthritis, health insurance coverage increases the probability of medical care

utilization and non-out-of-pocket expenditures on medication and health care treatment. The current

research revealed that health insurance coverage is positively correlated with not seeking medical care.

However, the association is not a linear one. The association between not seeking medical care and health

insurance coverage is negatively related up to 15% and above 18% of Jamaicans holding health insurance

coverage, the association changes to a positive one. Embedded in this finding is the fact that buying more

health insurance coverage does not indicate a willingness to seek medical care treatment.

       The WHO [1] opined that poverty is associated with increased chronic diseases and premature

death, and their opinion is cemented by this work. The findings herein revealed that poverty is positively

correlated with lowered medical care-seeking behaviour, and it was also found that there is a negative

relationship between mortality and poverty. This denotes that more poverty does not equate to increased

death; instead the converse is true. The study showed that when mortality is high, poverty is less than

18% and that when poverty increased beyond 20%, mortality begins to decline and reaches its lowest

level when poverty is in excess of 40%. If poverty is not directly correlated with mortality, then is it

possible that there are premature deaths of the poor?

       Studies on mortality have shown that there is a high correlation between patterns of death and

health and/or life expectancy [27, 28], indicating that unattended health conditions could cause death.

According to Kimmel [29], 80% of deaths of people over 65 years are attributed to cardiovascular

diseases, blindness, hearing impairment, diabetes, heart conditions, high blood pressure, arthritis, and

rheumatism. While this study was on Jamaicans and not of a particular age cohort, the poor reported the

greatest percentage of health conditions and within the context of their lack of ability to afford health care

and their low response to seek medical treatment compared to other social classes, there should be some

cases of premature mortality associated with low health care-seeking behaviour.
                                                     297
       An interesting finding of the current study was observed as an association was found between

mortality and not seeking medical care, and that it was a non-linear one. On disaggregating the data, it

was found that there were some extraordinary events which occurred that can justify the peak and tough

in the data. In 1988, hurricane Gilbert ravished Jamaica which would account more health care from

physical and mental illness which would have occurred during and after the natural disaster. Another

extraordinary event which occurred was structural adjustment in the late 1980s which resulted in lowered

income, unemployment and people inability to afford many things including health care. This therefore

can explain the high non-health care-seeking between 1990 and 1991. Extraordinary ordinary events

included the malaria outbreak which occurred in Jamaica in 2006. Using available statistics on health care

seeking behaviour for 2004 and 2006, in 2006 health care seeking behaviour increased by 4.9% which

indicates people’s response to the disease outbreak. In 2006, the malaria outbreak accounted for the

increased mortality that is recorded in the data for 2007. But why was there not an increase in health care-

seeking behaviour in 2007? The answer lies in another extraordinary event, which was inflation. In 2007

over 2006, inflation rose by over 190%, which accounts for the reduction in seeking medical care. Food

and non-alcoholic beverages prices increased by 24.7% and health care costs increased by 3.4% which

would account for the switching from formal care to non-formal care (i.e. home remedy). This means that

the poor would have become poorer and those among them who are sick would be unable to afford

medical care. The increased non-seekers of medical care with some of them have chronic illness would

explain the increased mortality in 2007. Some of the deaths would be premature as they would have

resulted from people inability to seek medical care and be treated for the particular health conditions that

would have caused deaths. There is a direct correlation between poverty and not seeking medical care,

indicating that poverty coupled with the aforementioned extraordinary events resulted in more Jamaicans

not seeking medical care and justifies poverty role in mortality. In examining mortality and not seeking
                                                    298
medical care data for Jamaica, sex must be included within the discourse. Statistics for Jamaica in 2005

showed that there were 117 males who died for every 100 females, and this increased in 1998 to 115

males for every 100 females [15]. The current study provides some explanations for the disparity in

mortality data for the sexes. Males’ reluctance in seeking medical care accounts for a part of the mortality

disparity between the sexes. Their unwillingness in seeking medical care explains the rationale for them

spending more time receiving care and also results in premature mortality. Premature mortality is another

of the explanations of the mortality disparity in between the sexes.



Conclusions

Not seeking medical care is influenced by inflation, poverty and unemployment. With the low probability

that the impoverished are likely to be holders of health insurance coverage in Jamaica, their out-of-pocket

payment for health care treatment will be higher resulting in a higher likelihood of them not seeking

medical care to their detriment. Not seeking medical care is not a good indicator of premature mortality;

but when the percentage of those not seeking medical care is above 55%, then premature mortality

becomes evident. While this study cannot confirm a clear rate of premature mortality, there are some

indications that this occurs beyond a certain level of not seeking care for illness.

Acknowledgement

The author would like to extend sincere gratitude to Ms. Neva South-Bourne who offered invaluable

assistance in editing the final draft of this manuscript.




                                                      299
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                                            302
Table 10.1. Annual Inflation in Food and Non-Alcoholic beverages and Health Care Cost, 2003-
2007

                      Food and Non-Alcoholic beverage             Health Care Cost

2002                         7.8                                  5.2

2003                         10.0                                 9.7

2004                         13.7                                 6.4

2005                         11.7                                 7.5

2006                         5.0                                  9.7

2007                         24.7                                 3.4


Source: Planning Institute of Jamaica, Economic and Social Survey of Jamaica, various issues

Note: Inflation is measure using point-to-point at the end of the year (December to December).




                                              303
Table 10.2. Inflation, Public-Private Health Care Service Utilization, Incidence of Poverty, Illness and Prevalence of Population with
Health Insurance (in per cent), 1988-2007

Year                  Inflation       Public              Private                      Prevalence         Illness           Health               Seeking
                      Mean
                                      Utilization         Utilization        of poverty                         Insurance                 Medical Care Days of
                                                                                                                Coverage                               Illness


1988                8.8               NI                  NI                 NI                  NI                 NI                    NI           NI
1989               17.2               42.0                54.0               30.5                16.8               8.2                   54.6         11.4
1990               29.8               39.4                60.6               28.4                18.3               9.0                   38.6         10.1
1991               80.2               35.6                57.7               44.6                13.7               8.6                   47.7         10.2
1992               40.2               28.5                63.4               33.9                10.6               9.0                   50.9         10.8
1993               30.1               30.9                63.8               24.4                12.0               10.1                  51.8         10.4
1994               26.8               28.8                66.7               22.8                12.9               8.8                   51.4         10.4
1995               25.6               27.2                66.4               27.5                9.8                9.7                   58.9         10.7
1996               15.8               31.8                63.6               26.1                10.7               9.8                   54.9         10.0
1997               9.2                32.1                58.8               19.9                9.7                12.6                  59.6         9.9
1998               7.9                37.9                57.3               15.9                8.8                12.1                  60.8         11.0
1999               6.8                37.9                57.1               16.9                10.1               12.1                  68.4         11.0
2000               6.1                40.8                53.6               18.9                14.2               14.0                  60.7         9.0
2001               8.8                38.7                54.8               16.9                13.4               13.9                  63.5         10.0
2002               7.2                57.8                42.7               19.7                12.6               13.5                  64.1         10.0
2003               13.8               NI                  NI                 19.1                NI                 NI                    NI           NI
2004               13.7               46.3                46.4               16.9                11.4               19.2                  65.1         10.0
2005               12.6               NI                  NI                 14.8                NI                 NI                    NI           NI
2006               5.7                41.3                52.8               14.3                12.2               18.4                  70.0          9.8
2007               16.8               40.5                51.9               9.9                 15.5               21.2                  66.0         9.9
Source: Bank of Jamaica, Statistical Digest, Jamaica Survey of Living Conditions, Economic and Social Survey of Jamaica, various issues
Note: Inflation is measured point-to-point at the end of each year (December to December), based on Consumer Price Index (CPI)

NI – No Information Available

                                                                                     304
Table 10.3. Seeking Medical Care, Self-reported illness, and Gender composition of those who
report illness and Seek Medical Care in Jamaica (in percentage), 1988-2007

                                                                   Reporting Reporting   Mean    Mean
                                           Seeking       Seeking    Illness-  Illness-    Days    Days
                                           Medical       Medical      Men     Women         Of      Of
                                            Care -        Care -                         Illness Illness
  Year     Mortality     Unemployed         Men          Women                            Men Women
  1988     12,167.0         26.8              NI           NI         NI        NI          NI      NI
  1989     16400.0          18.0             44.7         52.8       15.0      18.5       10.6    11.1
  1990     14900.0          15.3             37.9         39.2       16.3      20.3       10.2    10.2
  1991     13300.0          15.3             48.5         47.4       12.1      15.0       10.0    10.3
  1992     13200.0           9.4             49.0         52.5       9.9       11.3       10.7    10.9
  1993     13900.0           9.5             48.0         54.7       10.4      13.5       10.7    10.1
  1994     13500.0          10.9             49.0         53.4       11.6      14.3       10.3    10.4
  1995     15400.0           9.6             59.0         58.9       8.3       11.3       10.6    10.7
  1996     15800.0          10.8             50.5         58.5       9.7       11.8       10.0    11.0
  1997     15100.0          10.6             60.0         59.3       8.5       10.9       11.0    10.0
  1998     17000.0          10.0             57.8         62.8       7.4       10.1       11.0    11.0
  1999     18200.0          10.0             64.2         71.1       8.1       12.2       11.0    11.0
  2000     17400.0          10.2             57.4         63.2       12.4      16.8        9.0     9.0
  2001     17800.0          10.3             56.3         68.2       10.8      15.9         9       10
  2002     17000.0          10.6             62.1         65.3       10.4      14.6       10.0    10.0
  2003     16699.0          17.6              NI           NI         NI        NI          NI      NI
  2004     16900.0           7.9             64.2         65.7       8.9       13.6        11.0   10.0
  2005     17552.0                            NI           NI         NI        NI          NI      NI
  2006     16300.0             7.0           71.7         68.8       10.3      14.1        9.7    10.0
  2007     17000.0             6.2           62.8         68.1       13.1      17.8       10.6     9.3

 Source: Jamaica Survey of Living Conditions, various issues

 NI - No Information was available




                                                     305
                                   70.00
    Not seek medical care (in %)




                                   60.00




                                   50.00




                                   40.00




                                   30.00

                                           1985.00   1990.00   1995.00          2000.00    2005.00   2010.00
                                                                         Year



         Figure 10.1. Not seeking medical care (in %) by Year



There is a linear pattern in percent of Jamaicans not seeking medical care (Figure 10.1)




                                                                  306
                           19000.00
                                                                                                   R Sq Linear = 0.43



                           18000.00
   Mortality (in people)




                           17000.00



                           16000.00



                           15000.00



                           14000.00
                                                                                                     R Sq Cubic =0.745



                           13000.00

                                      1985.00       1990.00          1995.00           2000.00   2005.00          2010.00
                                                                                Year


                       Figure 10.2. Annual Mortality (No. of people) in Years



Based on Figure 10.2, the annual number of Jamaicans who die is best fitted with a non-linear diagram.




                                                                       307
                                    70.00
   Not seeking medical care (in%)




                                    60.00




                                    50.00




                                    40.00


                                                                                         R Sq Linear = 0.576



                                    30.00

                                            10.00      20.00            30.00               40.00
                                                    Prevalence of Poverty (in %)


Figure 10.3. Not Seeking Medical Care (in %) by Prevalence of poverty rate (in %)


There is a linear association between not seeking medical care (in %) and prevalence of poverty (in %) in Jamaica

(Figure 10.3). Furthermore, 58% of the variability in not seeking medical care (in %) can be explained by a 1%

change in prevalence of poverty (in %).




                                                            308
                                      70.00
    Not seeking medical care (in %)




                                      60.00




                                      50.00




                                      40.00


                                                                                                                    R Sq Cubic =0.581



                                      30.00

                                                        7.50            10.00             12.50            15.00             17.50
                                                                        Unemployment rate (in %)




                                              Figure 10.4. Not Seeking Medical Care (in %) by Unemployment rate (in %)



The statistical correlation between not seeking medical care (in %) and unemployment rate (in %) is not a linear one.
Based on Figure 10.4, it is best fitted with a non-linear cure.




                                                                                 309
                                               70.00


             Not seeking medical care (in %)


                                               60.00




                                               50.00




                                               40.00


                                                                                                                            R Sq Cubic =0.365



                                               30.00

                                                         8.00          10.00         12.00          14.00          16.00   18.00         20.00
                                                                                          Illness/Injury (in %)



                                               Figure 10.5. Not Seeking Medical Care (in %) by Illness/Injury (in %)




Figure 10.5 revealed that statistical correlation between not seeking medical care (in %) and illness/injury (in %) is a
non-linear one.




                                                                                      310
                                             19000.00


                                                                                                   R Sq Cubic =0.794
                                             18000.00
                  Mortality (No of people)



                                             17000.00



                                             16000.00



                                             15000.00



                                             14000.00


                                                                                                              R Sq Linear = 0.559
                                             13000.00

                                                        30.00         40.00              50.00              60.00                   70.00
                                                                         Not seeking medical care (in %)


                                       Figure 10.6. Mortality (No of people) by Not Seeking Medical Care (in %)



Based on Figure 10.6, the association between mortality (number of people that died) and not seeking medical care
(in %) can be best fitted with a non-linear curve.




                                                                        311
                               40.00
Prevalence of Poverty (in %)




                               30.00




                               20.00




                               10.00                                                      R Sq Linear = 0.48




                                       7.50   10.00            12.50              15.00             17.50
                                              Unemployment rate (in %)
     Figure 10.7 Prevalence of poverty rate (in %) and Unemployment rate (in %)




                                                      312
Not seeking medical care (in %)   70.00




                                  60.00




                                  50.00




                                  40.00
                                                                                                           R Sq Quadratic =0.693




                                  30.00

                                               9.00              12.00               15.00             18.00              21.00
                                                               Health Insurance Coverage (in %)



                              Figure 10.8. Not Seeking Medical Care (in %) by Health Insurance Coverage (in %)



                              A non-linear relationship existed between not seeking medical care (in %) and health insurance coverage (in
                              %) (Figure 10.8).




                                                                               313
                           19000.00



                           18000.00
Mortality (No of people)




                           17000.00



                           16000.00



                           15000.00
                                                                                                                R Sq Linear = 0.514



                           14000.00



                           13000.00

                                                  10.00                 20.00                30.00                 40.00
                                                                 Prevalence of Poverty (in %)



                            Figure 10.9. Mortality (No. of people) by Prevalence of Poverty (in %)



                            Mortality (annual number of deaths) and prevalence of poverty (in %) is a linear one (Figure 10.9).




                                                                           314
                           19000.00



                           18000.00
Mortality (No of people)




                           17000.00



                           16000.00



                           15000.00



                           14000.00



                           13000.00

                                                   7.50            10.00             12.50            15.00            17.50
                                                                    Unemployment rate (in %)


                           Figure 9.10. Mortality (No. of people) by Unemployment rate (in %)




                           There is no clear pattern between mortality (number of people who die, annual) and unemployment
                           rate (in %) in Jamaica (Figure 9.10).




                                                                  315
                               40.00
Prevalence of Poverty (in %)




                               30.00




                               20.00




                               10.00                                                                                 R Sq Linear = 0.732




                                        0.00              20.00             40.00              60.00             80.00             100.00
                                                                           Inflation rate (in %)

                                Figure 10.11. Prevalence of poverty rate (in %) by Inflation rate (in %)



                                       A strong statistical association existed between prevalence of poverty (in %) and inflation rate (in %)
                                       – R2 = 0.732 (Figure 10.11).




                                                                                316
                                     70.00



   Not seeking medical care (in %)

                                     60.00




                                     50.00




                                     40.00




                                     30.00

                                             0.00   20.00          40.00         60.00    80.00     100.00
                                                                  Inflation rate (in %)

Figure 10.12. Not Seeking Medical care (in %) by Inflation rate (in %)



   There is a linear statistical correlation between not seeking medical care ( in %) and inflation rate
   (in %) in Jamaica. Fifty-seven percent of the variance in not seeking medical care (in %) can be
   explained by a 1% change in inflation rate (Figure 10.12)




                                                            317
                                                                          Chapter
                                                                                       11
   Retesting and refining theories on the association between illness, chronic
               illness and poverty: Are there other disparities?




Poverty is well established as being associated with illness and chronic illness. Studies which
have examined this phenomenon have done so using objective indices such as life expectancy,
infant mortality and general morality. This study (1) examined subjective indices such as self-
reported illness and self-reported health, (2) re-tested the theories that chronic illnesses are
more likely to be greater in number among the poor and that illnesses are positively correlated
with poverty, and (3) evaluated other social characteristics that account for the poverty-illness
theory. Those in the two wealthy social hierarchies were 18% less likely to report chronic
illnesses compared to those in the two poor social hierarchies. Males were 69% less likely to
report chronic illness compared to females as well as 56% less likely to indicate an illness. When
the chronic illnesses were disaggregated by sex of respondents, the prevalence rate of females
with hypertension was 2.2 times more than hypertensive males; 3.2 times more than male
arthritic patients, and 3.0 times more than male diabetics. Forty-five percent of those with
chronic illnesses were married. While poverty has declined in Jamaica since the 1990s, the
health disparity between the poor and the upper social hierarchy continues to this day. The
information provided in this research has far-reaching implications, and may be used to guide
policies, frame interventions and provide a focus for future research in Jamaica.



Introduction
Empirically there are many studies which have found and established a statistical association

between poverty and illness [1-8]. Some research has shown that those in the lower

socioeconomic status are less healthy than those in the wealthy socioeconomic groups [9, 10]. A

study by Van Agt et al. [8] found that poverty was greater among chronically ill people than the

                                               318
non-chronically ill, and the WHO [4] concurred with Van Agt et al. [8] when it opined that 80%

of chronic illnesses were in low and middle income countries. Poverty is not only associated with

illness and ill-health, but also higher rates of mortality. According to the WHO [4], 60% of

global mortality is caused by chronic illness, and this should be understood within the context

that four-fifths of chronic dysfunctions are in low-to-middle income countries. The rationales

given for the poverty and illness theory are (1) money (insufficient financial resources); (2)

medical expenditure; and (3) other types of socio-political incapacity [3, 8, 11]. Sen [11]

encapsulated this well when he opined that high levels of unemployment in the economy are

associated with higher levels of capabilities, pointing to money and other incapacities of those

who are likely to be unemployed in the society. The poor are therefore more likely to be

unemployed, to be ill, to suffer from more chronic illnesses, to have insufficient money, low

levels of educational attainment, to experience a greater percentage of infant and other mortality

and to live in an inadequate physical environment, compared to those in the wealthy social

hierarchies.


       Using objective indices such as infant mortality and life expectancy to measure the health

of a population, studies in Latin America and the Caribbean concur with the aforementioned

research. Cass et al. [12] found that infant mortality in Peru for those in the poorest quintile (i.e.

poorest 20%) was almost 5 times more than that for those in the wealthiest quintile (i.e.

wealthiest 20%). Another study revealed that out-of-pocket medical expenditure accounts for

some people becoming poor and that a greater percentage of these people do not have health

insurance coverage [2]. One study highlighted the fact that life expectancy between the poorest

20% and the wealthiest 20% was 6.3 years and this was 14.3 years for disability-free life

expectancy [13]. The relationship between poverty and illness is longstanding, and the Director
                                                 319
of the Pan American Health Organization in 2001 wrote that it is still evident in contemporary

societies [14]. He however went further to state that poverty affects mental as well as physical

health, and concurs with the literature that those in the lower socioeconomic status have greater

levels of illnesses (i.e. psychopathology).


       It has been clearly understood and well-established for centuries that poverty is

associated with illness, and that it affects those individuals by constricting their capacity, which

further affects their health. The poor have less access to money and other resources than the

wealthy, and are also deprived of a good health outcome in the future. A study by Mayer et al.

[15] provided evidence that there is a strong relationship between health and future economic

growth, suggesting that current poverty contracts future health and economic prosperity. Mayer

et al.’s work provides pertinent insight into the retardation of poverty, but also gives an

understanding of how poverty affects health, production, productivity and how it poses a present

and future problem for public health policy makers. How is this of concern to public health

policy makers in Jamaica?


       A recent study conducted by Bourne [16] found that (1) moderate and direct correlation

between the prevalence of poverty (in %) and unemployment (R2 = 0.48); (2) direct association

existed between not seeking medical care (in %) and prevalence of poverty (in %) – R2 = 0.58;

(3) a strong statistical relationship between prevalence of poverty and mortality – R2 = 0.51; and

(4) a non-linear relationship between not seeking medical care and illness. From Bourne’s

findings, the challenges for public health specialists as well as policy makers are a reality in

Jamaica, as in other nations. If poverty is associated with unemployment and not seeking medical

care, and not seeking medical care is related to illness, it appears to be a non-issue to re-test the

                                                320
established theory of poverty and illness and poverty and chronic illness in Jamaica, but this is

not the case as there is self-reported illness may not give the same result as diagnosed illnesses.


       None of the aforementioned studies that have examined poverty and illness have used

self-reported data to test the poverty and illness, and poverty and chronic illness phenomena. The

aims of the current study are to investigate (1) poverty and self-reported illness, (2) poverty and

self-reported chronic illness, and (3) other socio-demographic characteristics, in order to provide

an understanding of existing disparities as well as to concur with, or refute, current theories.


Methods
Study population

The current study used a secondary cross-sectional dataset from the Jamaica Survey of Living

Conditions (JSLC). The JSLC was provided by the Planning Institute of Jamaica (PIOJ) and the

Statistical Institute of Jamaica (STATIN) for analysis [17-19]. These two organizations are

responsible for planning, data collection and formulating policy guidelines for Jamaica. The

cross-sectional survey was conducted between May and August 2002 in the 14 parishes across

Jamaica and included 25,018 people of all ages [20]. The JSLC used stratified random

probability sampling technique to draw the original sample of respondents, with a non-response

rate of 26.2%. The sample was weighted to reflect the population.


Study instrument


The JSLC used an administered questionnaire where respondents were asked to recall detailed

information on particular activities. The questionnaire was modelled on the World Bank’s Living

Standards Measurement Study (LSMS) household survey. The questionnaire covered


                                                 321
demographic variables, health, education, daily expenses, non-food consumption expenditure

and other variables. Interviewers were trained to collect the data from household members.


Statistical methods

Descriptive statistics were used to provide socio-demographic characteristics of the sample. Chi-

square analyses were used to examine the association between non-metric variables. Analysis of

variance was used to test the statistical significance of a metric and non-dichotomous variable.

Logistic regression analyses examined 1) the relationship between good health status and some

socio-demographic, economic and biological variables; as well as 2) a correlation between

medical care-seeking behaviour and some socio-demographic, economic and biological

variables. The statistical package SPSS 16.0 was used for the analysis. A p-value less than 5%

(2-tailed) was used to indicate statistical significance.


        The correlation matrix was examined in order to ascertain if autocorrelation and/or

multicollinearity existed between variables. Based on Cohen and Holliday [21] correlation can

be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. Any variable that had

at least moderate (r > 0.6) was re-examined in order to address multicollinearity and/or

autocorrelation between or among the independent variables [22-28]. Another approach in

addressing collinearity (r > 0.6) was to independently enter variables in the model to determine

which one should be retained during the final model construction. The method of retaining or

excluding a variable from the model was based on the variables’ contribution to the predictive

power of the model and its goodness of fit. Wald statistics were used to determine the magnitude

(or contribution) of each statistically significant variable in comparison with the others, and the

Odds Ratio (OR) for the interpreting of each significant variable.

                                                  322
Measures

Self-reported illness status is a dummy variable, where 1 = reporting an ailment or dysfunction or

illness in the last 4 weeks, which was the survey period; 0 if there were no self-reported ailments,

injuries or illnesses [29-31]. While self-reported ill-health is not an ideal indicator of actual

health conditions because people may under-report, it is still an accurate proxy of ill-health and

mortality [32, 33]. Health status is a binary measure where 1=good to excellent health; 0=

otherwise which is determined from “Generally, how do you feel about your health?” Answers

for this question are on a Likert scale, ranging from excellent to poor. Medical care-seeking

behaviour was taken from the question “Has a health care practitioner, healer, or pharmacist been

visited in the last 4 weeks?” with there being two options: Yes or No. Medical care-seeking

behaviour therefore was coded as a binary measure where 1=Yes and 0= otherwise. Crowding is

the total number of individuals in the household divided by the number of rooms (excluding

kitchen, verandah and bathroom).


Sex: This is a binary variable where 1= male and 0 = otherwise.


Age is a continuous variable which is the number of years alive since birth (using last birthday).




                             where ki represents the frequency with which an individual

                             witnessed or experienced a crime, where i denotes 0, 1 and 2, in

which 0 indicates not witnessing or experiencing a crime, 1 means witnessing 1 to 2, and 2

symbolizes seeing 3 or more crimes. Tj denotes the degree of the different typologies of crime

                                                323
witnessed or experienced by an individual (where j = 1…4, which 1 = valuables stolen, 2 =

attacked with or without a weapon, 3 = threatened with a gun, and 4 = sexually assaulted or

raped. The summation of the frequency of crime by the degree of the incident ranges from 0 to a

maximum of 51.

Result

       The sample was 25,018 respondents: males, 49.3%; rural residents, 61%; semi-urban

residents, 25.6%; married, 16.2%; never married, 67.3%; divorced, 0.8%; separated, 1.2%;

widowed, 5.6%; self-reported illness, 12.5%; self-reported injury, 1.2%; health care seekers in

the last 4-week period, 63.9%; level of education primary or below, 20.9; secondary level

education, 73.1%, and the mean age of the sample was 28.8 years (SD = 22.0 years). The mean

number of people per room was 2.0 (SD = 1.4), and the mean number of crimes experienced

(including family members) was 2.1 (SD = 8.0).

       Table 11.1 presents information on demographic characteristics of the sample by area of

residence for 2002. There was a significant statistical association between social hierarchy and

area of residence – χ2 = 1739.98, P < 0.0001. Poverty (i.e. poorest 20%) was substantially a rural

phenomenon (74.9%) compared to semi-urban poverty (17.2%) and urban poverty (7.9%) - χ2 =

1739.98, P < 0.0001. Almost 14% of rural residents reported having an illness in the last 4 weeks

compared to semi-urban residents (10.9%) and urban residents (10.9%) - χ2 = 36.861, P <

0.0001. However, for 2002, no significant statistical relationship existed between self-reported

diagnosed health conditions and area of residents - χ2 = 12.62, P = 0.397.

       The mean age of the sample was 28.8 years (± 22.0 years), with there being a statistical

difference between the mean ages of respondents based on their area of residence – F-statistic [2,

24991] = 7.28, P < 0.0001: the mean age of rural residents was 29.1 years (± 22.6 years); that of
                                               324
semi-urban residents was 27.9 years (± 21.0) and the mean age of urban dwellers was 29.1 years

(± 21.0 years). Concurringly, the mean number of visits to health care practitioners in the last 4-

week period was 1.7 (± 1.4). There was a significant statistical difference between the mean

number of visits to health care practitioners and area of residence (F-statistic = 5.48, P = 0.004:

the mean number of visits by rural residents was 1.6 (± 1.2) compared and 2.0 (± 2.5) for urban

dwellers, but non between rural and semi-urban dwellers (1.6 ± 1.2). However, there was no

significant difference between mean medical expenditure and area of residence (mean public

health care expenditure was USD 9.05 ± USD 25.65 – F-statistic [2, 1126] = 0.577, P = 0.562;

and mean private health care expenditure was USD 24.40 ± USD 37.13 – F-statistic [2,935] =

0.577, P = 0.220).

       There was a significant statistical difference between crime and victimization and area of

residence - F-statistic [2, 24958] =28.604, P < 0.0001. The mean number of crimes and incidents

of victimization experienced by people in rural residents was 1.8 ± 7.7 compared to semi-urban

residents, 2.3 ± 8.0; and urban dwellers, 2.9 ± 9.3.

       Table 11.2 examines visits to health care facilities, health insurance coverage, educational

level and crime by social hierarchy.

       When self-reported illness and social hierarchy was disaggregated by area of residence,

the significant statistical relationship was explained by rural areas (χ2 = 30.92, P < 0.0001) and

not semi-urban (χ2 = 8.84, P = 0.065) and urban areas (χ2 = 1.74, P = 0.789).

       Table 11.3 presents information on self-reported injury, normally go if ill/injured, why

didn’t seek care for current illness, length of illness and number of visits to health practitioner by

social hierarchy. A statistical relationship existed between each of the variables (P < 0.0001). A

statistical difference existed between the mean length of the illness among the social hierarchy –

                                                 325
F statistic = 2.536, P = 0.038. This difference was accounted for by the poorest 20% and the

wealthy (P = 0.049) and the poorest 20% and the wealthiest 20% (P = 0.049). Likewise the

statistical difference between the mean number of visits made to medical practitioner(s) and

social hierarchy were accounted for by the poorest 20% and wealthy (P = 0.011) and the poorest

20% and wealthiest 20%.

       The prevalence of chronic illness was 104 out of every 10,000 respondents. On

disaggregating the overall prevalence of chronic illness into the different typology of conditions

it was found that 5 out of every 10,000 respondents had diabetes mellitus; 50 out of every 10,000

had hypertension; 28 per 10,000 had arthritis; and other chronic illnesses (unspecified) accounted

for 21 per 10,000.

       Chronic illness was more a female phenomenon than for males- χ2 = 6.56, P = 0.013. The

prevalence rate of females with chronic illness was 144 per 10,000 compared to 62 per 10,000

for males. Furthermore, the prevalence rates of those with particular chronic illnesses by sex was

as follows: diabetes mellitus 2 per 10,000 for males and 7 per 10,000 for females; hypertension

32 per 10,000 for males and 69 per 10,000 for females; arthritis 13 per 10,000 for males and 42

per 10,000 for females and other chronic conditions, 15 per 10,000 for males and 27 per 10,000

for females. Seventy-two percent of those who indicated that they had a chronic illness sought

medical care in the last 4-week period, compared to 78.9% not suffering from a chronic illness

who sought medical attention - χ2 = 0.030, P = 0.562. Likewise no statistical association existed

between health insurance coverage and chronic illness - χ2 = 0.048, P = 0.649. Concurringly,

there was a significant statistical association between marital status and individuals with chronic

illness - χ2 = 12.708, P = 0.013. Of those who indicated that they had chronic illness, 44.9%

were married; 29.1% were never married; 0.4% divorced; 1.2% separated and 24.4% widowed.

                                               326
Multivariate analyses

Table 11.4 provides information on particular variables and their correlation (or not) with self-

reported illness. Of the 17 variables identified from the literature and available for this study, 5

emerged as being statistically significant correlates of self-reported illness of Jamaicans (i.e.

social hierarchy, medical expenditure, sex, age and income) - Model χ2 (17) =56.45, P < 0.001.

The statistically significant correlates accounted for 14.8% of the variability in self-reported

illness.



Table 11.5 examines social hierarchy and sex and their influence (or not) on self-reported

chronic illness. One sex emerged as being a statistically significant correlate of self-reported

chronic illness in Jamaica - Model χ2 (3) =6.42, P < 0.001.

Discussion

The current study revealed that 13 out of every 100 Jamaicans reported an illness in the 4-week

surveyed period. Concurringly, those in the two wealthy social hierarchies were 18% less likely

to report chronic illnesses compared to those in the two poor social hierarchies, and the former

group was 64% less likely to report an illness compared to the latter group. Males were 69% less

likely to report chronic illness compared to females, as well as 56% less likely to indicate an

illness. The prevalence rate of those with chronic illness was 104 per 10,000 respondents –

diabetes, 5 per 10,000; hypertension, 50 per 10,000; arthritis, 28 per 10,000 and other chronic

conditions, 21 per 10,000. When the chronic illnesses were disaggregated by sex of respondents,

the prevalence rate of females with hypertension was 2.2 times more than hypertensive males;

3.2 times more than male arthritic patients, and 3.0 times more than male diabetics. Poverty was

                                                327
substantially a rural phenomenon (75%), and almost 14% of rural residents indicated an illness

compared to semi-urban (11%) and urban dwellers (11%). The disparity did not cease there as

rural residents had the least percentage of people with tertiary level education, and the least per

capita consumption, which was 57.4% of consumption per capita of urban residents and 69.0%

of that consumption per capita of semi-urban people. On the contrary, those in the poorest 20%

self-reported fewer injuries (owing to work and care accidents, poisoning, and burns) than those

in the wealthiest 20%.

       For centuries, using objective indices such as life expectancy, infant mortality and

general mortality, it has been well established that poverty is associated with illness, and those

with more chronic illnesses are more likely to be poor. The current study, using self-reported

illnesses, has concurred with the literature that the poor report more illnesses and are more likely

to have more chronic illness than those in the upper class. This study, however, found that there

is no significant statistical correlation between self-reported illness or chronic illness of those in

the poor social hierarchies and those in the middle class. The current research does not concur

with the literature that married people are healthier than other marital cohorts [34-38] as the

findings showed no statistical association between marital status and self-reported illness.

However, the findings revealed that almost 45% of those with chronic illnesses were married

compared to those who were never married, widowed, separated or divorced.

       Lillard and Panis [39] contradicted many of the traditional findings, for instance that

married people are healthier and report less health conditions than non-married people. They

found that healthier men are less likely to be married; and secondly, that healthier married men

enter into unions later in life and that they do postpone remarriage. Conversely, Lillard and Panis

[39] revealed that it is unhealthy men who enter marriage at an early age, which suggests that

                                                 328
these men do so because of health reasons [39]. This then would support the current research of

married people indicating more chronic illnesses than non-married people. Concurringly, married

people do not report more illnesses, but do report more chronic illnesses than non-married people

in this study.


        An interesting finding that emerged from this study is the low statistical relationship

between self-reported illness and self-reported injury (i.e. contingency coefficient = 0.11).

Furthermore 4.4% of those who indicated that they were ill had an injury in the last 4 weeks, and

of those who had an injury, 46.2% claimed they were ill. This denotes that few people

considered illness and injury and vice versa. Illnesses therefore is in keeping with acute and

chronic health conditions, and less so with injuries caused by accidents, burns, poisoning and

other such events.


        Marmot [3] asked the question “Does money matter for health? If so, why?” It is the lack

of money (i.e. insufficient money) that accounts for the inability of the poor to access (1) higher

level education; (2) greater and better, or the best, health care treatment; (3) a better physical

milieu; (4) lower levels of infant mortality; (5) better material conditions; (6) clean water and

nutrition; and (7) social position. It follows that poverty incapacitates the individual and this

extends into the future if he/she is not assisted by external sources. Does money really make a

difference in Jamaica? The answer is a resounding yes. Those in the poorest 20% spent on

average almost 3 times less than those in the wealthiest 20%, and the second poor spent 2 times

less than those in the wealthiest 20% on medical expenditure. Concurringly, 76 out of every 100

of those in the poorest 20% normally utilize public health facilities (including hospitals)

compared to 28 out of every 100 of those in the wealthiest 20%.

                                               329
Poverty therefore retards people’s health care choices, expenditure on medication, and by

extension healthy life expectancy. The current study found that 35 out of every 100 respondents

in the poorest 20% indicated that the reason why they have not visited a health care practitioner

was owing to insufficient funds, compared to 9 out of every 100 of those in the wealthiest 20%.

Furthermore, findings from the present research showed that people who spend more on medical

expenditure are 39% less likely to report an illness, suggesting that the poor are more likely to be

living with their health conditions without seeking medical care, compared to the wealthy. This

matter of insufficient financial resources hampers the healthy life expectancy of the poor, as well

as explaining the greater infant and general mortality among them than those in the upper class.

According to Grossman [40], Smith and Kington [41], there is a positive statistical association

between income and health, and income and demand for health, which further unfolds the

complexity of poverty and health. Corbett [42] argued that Edwin Chadwick, in the 1840s,

believed “that the primary cause of pauperism and misery was not poverty or rampant capitalism,

but filth.” This study is not arguing that the main cause of pauperism is ill-health, but it does

substantiate an association between poverty and illness and poverty and chronic illness. This

finding is contrary to the belief of Edwin Chadwick; insufficient money does account for some

amount of illness, and illness can lead to poverty and future constraints on capabilities, limiting

opportunities for the creation of a better life for themselves.


       If those in the poorest 20% group experienced illnesses and visited medical practitioners

more than those in the upper class, it follows that poverty explains (1) most of the prevalence of

illness, (2) the severity of the illness, and (3) more chronic illnesses. Money therefore does

matter in health, and offers an explanation of how chronic illness can result in poverty, and how

pauperism leads to increased morbidity and premature mortality. An understanding of poverty in
                                                 330
Jamaica as well as a comprehensive knowledge of the relationship between poverty and illness as

well as the other health inequalities, will aid physicians in understanding the reasons for the

disproportionately greater number of poor visiting them and having particular chronic illnesses.

Health is also a social phenomenon, and so physicians need training in the roles of social

determinants and their influence on health, as these are outside of the clinical laboratory, but

provide an understanding of those on the social margins of the health care system. Given that

illness is influenced by exposure to pathogens, the socio-physical milieu of the poor, coupled

with their incapacitation because of money, provides some insights into their plight. It is critical

to understand this group and where they live, as Kiefer said, and to see poverty “not as a simple

economic condition, but as a state of demoralization, where people lack all or most of the

minimum ingredients we accept as the basis of a decent life” [43] and we can also add the

justifications of their encounter with illness and particular health conditions such as tuberculosis,

HIV/AIDS, diarrhoea, respiratory tract infections, arthritis and malaria.


       Another issue is nutritional deficiency, as some people hold the belief that so long as they

have something to eat, or a ‘full tummy’, it is enough to prevent illness. The image of a ‘full

tummy’ is embedded in those in the lower socioeconomic class and not the upper class. It

follows therefore that households in lower socioeconomic group find it difficult to address

material, food and opportunity deprivation within the context of a social setting to pay special

attention to the nutritional value in food intake. Households in low-income groups are

substantially found in rural areas in Jamaica where a ‘full tummy’ is important and not the

nutritional intake of the food groups. According to Foster [44] “…a better-off individual who is

generally healthy may be more readily able to identify when he or she is ill than a poor

individual with low caloric intake.” Within Foster’s perspective lies the underlying fact that
                                                331
reported illnesses among those in the lower socioeconomic group may be understated figures, as

their image of ill-health is hampered by nutritional deficiency. Diet and nutrition are important

ingredients in good health [45], but do residents of low-income rural areas as well as low-income

urban areas know that a deficient intake of calcium, iron, magnesium, zinc, folate, vitamin A,

vitamin B 6 and vitamin C is responsible for some of their illnesses? And another aspect to this

discussion is their image of health, illness and the role that these play in influencing the collected

survey data on health, health conditions and health outcome from those in the lower

socioeconomic group.


Conclusion

For centuries researchers have been using objective indices such as life expectancy, infant

mortality and the general mortality of a population or sub-population to measure health, and

these have been used to establish a statistical association with poverty. Other scholars and

institutions have found a significant statistical relationship between diagnosed illness and

poverty, but this research has established that self-reported illness and self-reported diagnosed

health conditions can be used instead of the objective indices of the past. While those people in

poor social hierarchies were more likely to report more illnesses and self-reported chronic

illnesses than those in the wealthy group, there is no difference between those in the poor group

and the middle class.

       Those with chronic illnesses are not only more likely to be poor, they are married,

females, rural residents, less educated at the tertiary level, more likely to visit public hospitals,

most likely to have hypertension, and there is less probability that they will utilize health care

facilities than the upper class. In summary, subjective indices such as self-reported illness or self-


                                                 332
reported diagnosed health conditions can be used to measure health as the traditional infant

mortality, general mortality and life expectancy. Poverty indeed still continues to influence ill-

health, and those with chronic illnesses are more likely to be poor than in the upper class, but

other demographic characteristics provide more information on the poor and those with chronic

illnesses.

        In summary, much investment has been made in health and this clearly has not reduced

the inequalities and disparities between and among the different social groups in Jamaica. It

means that merely mobilizing greater domestic resources for health will not address the

inequalities, as using national health aggregates do not provide a detailed understanding of the

disparities between and among groups. While poverty has declined in Jamaica since the 1990s,

the health disparity between the poor and the upper social hierarchy has continued to this day.

The information provided in this research has far-reaching implications, and can be used to guide

policies, frame interventions and provide a focus for future research in Jamaica.

The way forward

Subjective indices such as self-reported illness and self-reported chronic illness can be used to

measure ill-health and replace infant and general mortality in the study of health. The use of

national statistics does not provide a comprehensive understanding of the health disparity and

inequalities between and among the social groups in a society. In order to address some of the

health inequalities and disparities in society, programmes are needed that will address issues in

rural areas, gender, income inequalities, and the health disparities between public and private

health care services offered to the public. Another area which must be addressed is that of the

nutritional deficiencies between and among the social hierarchies and area of residences. A

national dietary survey is needed in order to provide evidence for policy intervention as well as
                                               333
the role of identified social problems and their influence on mental health. Concurringly, future

research is needed to examine the harmful effects of mental health on the accumulation of

people’s negative life events, and their effects on the crime problem in the Caribbean. Another

issue which must be investigated is the quality of care offered to the poor from the perspective of

the individual (i.e. a survey research). This would provide pertinent information as to whether

those people who are poor perceived themselves to be receiving the worst health, and to devise a

method that will objectively assess, service and deliver to the social group in order to address

this, if it is contributing to the lower health outcomes. Researchers need to treat poverty as an

illness and not a cause of illness, which would allow for a new shift in the study of poverty, and

this thereby could provide more answers to health practitioners and policy makers.

Conflict of interest
The author has no conflict of interest to report.




                                                    334
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                                          337
Table 11.1: Demographic characteristic of sample, 2002
                                                                           2002
                                                                     Area of residence                      P
Characteristic                                 Urban                  Semi-urban       Urban
                                               n (%)                  n (%)            n (%)
Sex                                                                                                     < 0.0001
 Male                                              7727(50.7)             3062(47.9)    1543(46.0)
 Female                                            7524(49.3)             3337(52.1)    1814(54.0)
Marital status                                                                                           < 0.0001
 Married                                           2460(25.5)             1115(26.9)     475(21.0)
 Never married                                     6436(66.6)             2758(66.5)    1619(71.6)
 Divorced                                             56(0.6)                41(1.0)       26(1.2)
 Separated                                           104(1.1)                49(1.2)       32(1.4)
 Widowed                                             610(6.3)               187(4.5)      108(4.8)
Self-reported diagnosed illness                                                                             0.397
 Acute conditions
  Influenza                                                 1(0.5)            0(0.0)           0(0.0)
  Diarrhoea                                                 4(2.1)            5(8.9)           0(0.0)
  Respiratory                                               6(3.1)            2(3.6)           1(3.1)
 Chronic conditions
  Diabetes mellitus                                     10(5.2)               1(1.8)         1(3.1)
  Hypertension                                         82(42.9)             29(51.8)       15(46.9)
  Arthritis                                            48(25.1)             13(23.2)        8(25.0)
  Other                                                40(20.9)              6(10.7)        7(21.9)
Health care-seeking behaviour                                                                               0.816
 Yes                                               1302(63.8)              436(63.4)     228(65.3)
  No                                                740(36.2)              252(36.6)     121(34.7)
Self-reported illness                                                                                    < 0.0001
  Yes                                              1987(13.5)              669(10.9)     354(10.9)
   No                                             12713(86.5)             5488(89.1)    2902(89.1)
Health insurance                                                                                         < 0.0001
   Yes                                              1036(7.0)             1023(16.5)     612(18.7)
    No                                            13714(93.0)             5178(83.5)    2654(81.3)
Social hierarchy                                                                                         < 0.0001
 Poorest 20%                                       3724(24.4)              858(13.4)     393(11.7)
 Poor                                              3574(23.4)              968(15.1)     414(12.3)
 Middle                                            3169(20.8)             1217(19.0)     598(17.8)
 Wealthy                                           2774(18.2)             1427(22.3)     822(24.5)
 Wealthiest 20%                                    2017(13.2)             1929(30.1)    1130(33.7)
Per capita consumption mean ±                      1181±1340              1771±1605     2129±2434
SD (in USD)
†USD 1.00 = Ja. $ 80.47 at the time of the survey) (2007)
††USD 1.00 = Ja. $50.97 (in 2002)



                                                              338
Table 11.2. Particular variable by social hierarchy, 2002
                                                                                         Social hierarchy                             P
Characteristic                                              Poorest       Poor            Middle          Wealthy      Wealthiest
                                                            20%                                                        20%
Sex                                                                                                                                    0.002
 Male                                                        2454(49.3)     2345(47.3)      2440(49.0)    2482(49.4)    2611(51.4)
 Female                                                      2520(50.7)     2609(52.7)      2542(51.0)    2540(50.6)    2464(48.6)
Marital status                                                                                                                       < 0.0001
 Married                                                      569(21.1)      656(22.3)       742(23.3)     860(25.4)    1223(31.7)
 Never married                                               1926(71.3)     2094(71.2)      2229(69.9)    2303(67.9)    2261(58.7)
 Divorced                                                       14(0.5)         5(0.2)         16(0.5)       26(0.8)       62(1.6)
 Separated                                                      30(1.1)        21(0.7)         30(0.9)       31(0.9)       73(1.9)
 Widowed                                                       162(6.0)       164(5.6)        173(5.4)      172(5.1)      234(6.1)
Visits to health care institutions (for last visit)
  Public hospitals                                            166(49.3)      135(38.5)       164(42.7)     175(42.1)     137(30.6)   < 0.0001
  Private hospitals                                             14(4.2)        29(8.3)         19(5.0)       40(9.7)      52(11.7)   < 0.0001
  Public health care centre                                   107(31.7)      102(29.1)        75(19.6)      64(15.5)       34(7.6)   < 0.0001
  Private health care centre                                   76(22.6)      120(34.1)       137(35.6)     176(42.2)     258(57.2)   < 0.0001
Health insurance ownership                                                                                                           < 0.0001
  Yes                                                           84(1.7)       172(3.6)        270(5.6)     655(13.5)    1490(30.7)
   No                                                        4745(98.3)     4651(96.4)      4574(94.4)    4204(86.5)    3370(69.3)
Educational level                                                                                                                    < 0.0001
  Primary and below                                            609(24.6)   588(22.0)   628(22.7)   604(20.1)   568(16.5)
  Secondary                                                  1837(74.3)   2048(76.5)  2114(75.3)  2249(75.0) 2292(66.4)
  Tertiary                                                        25(1.0)     41(1.5)     57(2.0)   146(4.9)   591(17.1)
Crime and victimization index mean ± SD                         2.4±10.2     1.5±4.9     2.0±7.2     2.2±8.5     2.4±8.2
Age mean ± SD                                                 25.5±22.7    26.8±22.2   28.3±21.9   29.6±21.3  33.8±20.9              < 0.0001
Crowding mean ± SD                                               3.0±1.8     2.3±1.3     2.0±1.2     1.6±0.9     1.2±0.8             < 0.0001
Total medical expenditure mean ± SD (in USD)†               15.22±28.91 21.67±37.99 22.54±42.87 33.11±70.35 45.53±79.52              < 0.0001
†USD 1.00 = Jamaican $50.97




                                                                      339
Table 11.3. Self-reported injury, normally go if ill/injured, why didn’t seek care for current
illness, length of illness and number of visits to health practitioner by social hierarchy, 2002

                                                   Social hierarchy                                P
Characteristic             Poorest      Poor         Middle        Wealthy        Wealthiest
                           20%                                                    20%
Self-reported injury                                                                               < 0.0001
  No                       4811(99.1) 4815(99.1) 4801(98.9) 4806(98.7) 4797(98.2)
  Yes                         46(0.9)    43(0.9)    54(1.1)    61(1.3)    87(1.8)

Normal go it ill/injury                                                                            < 0.0001
  Public hospital       2252(46.4) 2004(41.3) 1786(36.8) 1449(29.7) 1049(21.5)
  Public health centre 1474(30.3) 1124(23.2) 854(17.6) 605(12.4)      315(6.5)
  Private hospital      1123(23.1) 1713(35.3) 2202(45.4) 2799(57.4) 3498(71.6)
  Pharmacy                   2(0.0)     0(0.0)     1(0.0)     3(0.1)     3(0.1)
  Other                      7(0.1)     8(0.2)   12(0.2)    17(0.3)    10(0.4)
Why didn’t seek care                                                                               < 0.0001
for current illness
  Could not afford it     72(35.1)   61(26.3)   47(21.3)   23(11.2)    19(8.6)
  Was not ill enough      59(28.8)   92(39.7) 111(50.2) 105(51.2)     97(43.9)
  Use home remedy         50(24.4)   43(18.5)   35(15.8)   47(22.9)   61(27.6)
  Did not have the time      2(1.0)     2(0.9)   10(4.5)      6(2.9)   14(6.3)
  Other (unspecified)     22(10.7)   34(14.7)    18(8.1)   24(11.7)   30(13.6)
Length of illness (in    11.5±10.4 10.8±10.0 10.4±10.9      9.8±9.7    9.9±9.7                         0.038
days) mean ± SD
Number of visits to        6.1±8.8    5.5±8.6    4.9±7.7    4.6±6.3    4.8±7.7                         0.007
health practitioner
mean ± SD




                                                 340
Table 11.4. Logistic regression: Self-reported illness by particular variables
                                            Std.                               Odds        95.0% C.I.
 Variable                  Coefficient     error       Wald          P         ratio
                                                      statistic                          Lower    Upper
Injury                            -0.20       0.32         0.40       0.53        0.82     0.44     1.52
 Health care-seeking               0.57       0.43         1.81       0.18        1.78     0.77     4.09
 Middle                           -0.80       0.51         2.49       0.12        0.45     0.17     1.21
 Two Wealthy quintiles            -1.03       0.51         4.02       0.04        0.36     0.13     0.98
 †Two poor quintiles                                                              1.00
 Logged medical
                                  -0.49       0.14       12.00        0.00        0.61     0.47         0.81
expenditure
 Durable goods                     0.01       0.07         0.01       0.91        1.01     0.88         1.16
 Separated, divorced or
                                   0.27       0.64         0.18       0.67        1.31     0.38         4.57
widowed
 Married                           0.08       0.42         0.03       0.86        1.08     0.47         2.47
†Never married                                                                    1.00
 Physical environment             -0.43       0.33         1.74       0.19        0.65     0.34      1.23
 Semi-urban                       -0.01       0.37         0.00       0.99        0.99     0.48      2.07
 Urban                             0.96       0.77         1.58       0.21        2.62     0.59     11.72
†Rural                                                                            1.00
 Secondary                        -0.33       0.44         0.55       0.46        0.72     0.31         1.71
 Tertiary                         -0.90       0.87         1.07       0.30        0.41     0.08         2.23
†Primary or below                                                                 1.00
 Sex                               0.81       0.32         6.54       0.01        0.44     0.24         0.83
 Crowding                         -0.15       0.16         0.88       0.35        0.86     0.63         1.18
 Age                               0.03       0.01         5.51       0.02        1.03     1.01         1.05
 Total expenditure                 0.00       0.00         3.54       0.06        1.00     1.00         1.00
Model χ2 =56.45, P < 0.001
-2 Log likelihood = 368.58
Nagelkerke R2 =0.148
Hosmer and Lemeshow goodness of fit χ2= 6.53, P = 0.59
Overall correct classification =97.1%
Correct classification of cases of self-rated illness =100.0%
Correct classification of cases of not self-rated ill =54.9%
†Reference group




                                                         341
Table 11.5. Logistic regression: Self-reported chronic illness by some variable

                                                     Std.                          Odds       95.0% C.I.
 Variable                          Coefficient       error       Wald       P      ratio
                                                                statistic                   Lower    Upper
 Middle                                    -0.34        0.66         0.26   0.61     0.72     0.20     2.62

 Two wealthy quintiles                     -0.33        0.58        0.31    0.58     0.72     0.23         2.26
 †Two poor quintiles                                                                 1.00

 Sex                                       -1.16        0.49        5.75    0.02     0.31     0.12         0.81
Model χ2 =6.42, P < 0.001
-2 Log likelihood = 368.58
Nagelkerke R2 =0.06
Hosmer and Lemeshow goodness of fit χ2= 1.34, P = 0.854
Overall correct classification =93.2%
Correct classification of cases of self-rated illness =100.0%
Correct classification of cases of not self-rated ill =49.9%
†Reference group




                                                         342
                                                                            Chapter
                                                                                          12
     Health, lifestyle and health care utilization among health professionals




         Paul A. Bourne, Lilleth V. Glenn, Hazel Laws, and Christopher A.D. Charles3




Health care workers are responsible for the execution of the health policy of a nation, yet little if
any empirical evidence is there on health, lifestyle, health choices, health conditions of health
care workers in Hanover. The current study examines health, lifestyle and health behaviour
among health professional in Hanover. The current study has a sample of 212 respondents. A
26-item questionnaire was used to collect the data. Data from the questionnaires were coded and
entered into a micro-computer and analysis done using SPSS for Widows Version 15.0 software.
The Chi-square test was used to test association between non-metric variables. A p-value < 0.05
(two-tailed) was selected to indicate statistical significance. Five percentage points of
respondents had diabetes mellitus (2.8% of those with diabetes mellitus were males compared to
19.8% females); 22.6% had hypertension (25.5% were female and 12.8% were males); 0.5%
breast cancer; 0.5% stomach cancer; 1.9% enlarged heart; 0.5% ischemic heart disease. Forty-
three percentage points of the sample was overweight, 33.5% obese and 24.1% had a normal
weight. Over 15% of nurses and doctors were obese compared to 38% of ancillary staffers.
Twenty percentage points of respondents consume alcohol on a regular basis; 15.6% do no
regular physical exercise, 42.4% add sweetening to their hot beverages, and 4.7% were smokers.
There is a need for public health practitioners to formulate a health intervention programme that
will target people in Hanover, but also specific groups such as doctors, nurses, administrative,
ancillary staffers and technical staffers.


                                                343
Introduction



This study is an examination of the lifestyle, health choices and the resultant health condition of

healthcare workers in Hanover. Empirically, it is well established that poverty and illness are

positive associated with each other and that 80% of all chronic illnesses were in low-to-middle

income countries [1-17]. Sen [2] encapsulated this well when he forwarded that low levels of

unemployment in the economy is associated with higher levels of capabilities, suggesting that

poverty predisposed people to illnesses and further poverty. WHO [1] stated that 60% of global

mortality is caused by chronic illness and four-fifths of chronic dysfunctions are in low-to-

middle income countries, which goes further than Sen’s finding to show that poverty does not

only predisposed people to illnesses but that it accounts for premature mortality.


       Exposure during childhood to relative poverty and relative income equality has serious

negative effects on wellbeing across the life course [3]. In India a relationship was found

between poverty and health where improved health conditions tied to improved incomes

increased the nutritional status of poor households. Policy improvements are necessary because

many household are located at the bottom of the wellbeing index. The living standards of low

income households could be improved by improving health services [4]. The existence of

persistent poverty exits in the rural parts of south Eastern United States has had a negative

impact of the health of residents. This situation prompted the federal government through

legislation and the work of federal and state agencies working with local communities to create

appropriate health polices to address the problems [5].



                                                344
       The lack of resources available for use by the government to tackle poverty in a

significant way negatively impacts health in Jamaica. There is a significant statistical correlation

between poverty and illnesses in Jamaica [8, 10]. A study by Bourne [8] found (1) a positive

correlation between not seeking medical care and poverty (r2 = 0.58); (2) a positive correlation

between poverty and unemployment (r2 = 0.48); (3) an inverse correlation between mortality and

poverty (r2 = 0.51), which does not substantiate the findings of the literature of the correlation

between premature mortality and poverty. Despite Bourne’s findings, national poverty in

Jamaica was 9.9% in 2007, but this was 15.3% in rural areas compared to 6.2% in urban and

4.0% in peri-urban area [18]. Another statistical fact is that rural residents indicated the highest

percentage points of illness (17.3%) compared to urban (14.1%) and peri-urban residents

(13.9%).


       Although premature mortality was empirically not found using the data for Jamaica, the

positive correlation between poverty and illness still is present and cannot be overlooked as there

are public health challenges owing to this reality. Jamaica is an English-speaking Caribbean

nation (or a developing country). In 2007, it had a population of 2,682,120 people (49.3% males,

in 2007); 75% black and 13% mixed; a growth rate of 0.47; 10.9% elderly population (i.e. 60+

year old); a crude death rate of 6.4 per 1,000; crude birth rate of 17.0 per 1,000 [19]; income

inequality of 0.4 (Gini coefficient); and 71.3% of the poor lived in rural areas [18]. The country

is geographical divided into 14 parishes and three counties (Cornwall, Middlesex and Surrey).

Cornwall covers the Western belt which includes parishes such as Westmoreland, Hanover, St.

James, St. Elizabeth. Middlesex constitutes the middle proportions of the island with parishes

such as Clarendon and St. Catherine. Surrey comprises the Eastern region with parishes such as


                                                345
Kingston, St. Thomas and Portland. Cities accounts for 27.3% of the population, peri-urban

30.2% and rural areas, 42.5% in 2007.


        With 43 out of every 100 Jamaicans resided in rural areas and those areas have 15.3% of

the poverty, public health policy makers must be concerned about health and health behaviour

among rural residents. Hanover has the smallest percent of the nation’s population (2.6% -

69,660, in 2007), with one urban centre (i.e. Lucea) [19]. The capital of Hanover, Lucea, is home

to about 5,951 people. The parish of Hanover therefore is substantially rural, and the people rely

on tourism, agriculture and seasonal employment for their economic livelihood. Although

Hanover is rural and shares many of the economic challenges of rural zones, little if no

information is available about health, lifestyle practices and health care seeking behaviour of the

residents. Since public health agencies relies on research information to make inform decision

that can effectively aid in improving the health of a population, then it follows that pertinent

information is needed on residents of Hanover in order to enhance public health capability on the

parish. Most if not all the health information on Hanover is from the Ministry of Health which

only produce standard curative statistics (i.e. health service utilization; mortality; health care

expenditure; health care resources; morbidity) [20-22]. The current study will fill the gap by

extending health information on people in the parish by examining health, lifestyle and health

behaviour among health professional in Hanover in order to understanding choices, decision and

health among its residents, with the purpose of aiding policy formulation and health intervention

programmes for the parish. The next section deals with the method and measure used in this

study




                                               346
Methods and measure
Sample, sampling methods and setting

We selected a representative sample of people from Hanover’s health Institutions, which had

sufficient numbers to represent the people of the parish. The Ministry of Health, Jamaica (MoHJ)

sub-divided the country into 4 regional administrative authorities (RHAs): the South-East

(SERHA); South (SRHA); North-East (NERHA), and Western (WRHA). The NERHA covers

four parishes – Hanover, Westmoreland, St. James and Trelawny. Another classification of the

island is statistical one based on Enumeration Districts (EDs). The Planning Institute of Jamaica

(PIOJ) and Statistical Institute of Jamaica (STATIN) used Primary Sampling Units (PSUs) as its

sampling frame from which it design surveys of the national population [18]. A PSU is an ED or

a composition of EDs, usually consisting of 100 dwellings in a rural area and 150 dwellings in

urban areas [18]. STATIN further refined required dwellings by stating that up to 400

households constituted a PSU [19]. The EDs are independent geographical units which share

common boundaries with contiguous EDs. In keeping with a sampling error of ± 3% and a

confidence interval of 95%, the calculated population for selection was 280 respondents. In

another survey, the researchers used 36 persons per ED to calculate a representative sample of

the nation [23, 24]. Hanover has 4 PSUs, which means that using 36 persons per ED the sample

should be 144. Hence, based on previous surveys, the current study is sufficient to generalize on

the parish because it is has representative sample size [23, 24]. The current sample of 212

respondents represents 0.3% of the population of the parish of Hanover (in 2007; N = 69,660).

For this study, the sample was stratified by area of work, area of residence, and a Kish Random

Selection Method of sub-sampling was used to select the actual respondents thereby facilitating

independence of response [25]. On occasions when an individual was selected and he could not

                                              347
participate, no other person was used to replace the individual. In cases where the selected

person was not available a minimum of three call-back visits would be made to that person’s

place of work. The response rate was 75.7%, of which 1.3% of the data were lost during data

cleaning. This is in keeping with surveys conducted by PIOJ and STATIN [18], and Wilks et al.

[24]. For the survey study 77.8% of the sample was female, which is similar to one by Wilks et

al. [21] in which the female sample was 75.9%. Next is an outline of the reliability checks

conducted on the questionnaire used in the study.


       Questionnaire reliability


       Test-retest reliability of the questionnaire was conducted for a month (i.e. February 2008)

prior to the main study. The instrument was vetted by academics from the University of the West

Indies, Mona, Jamaica. Then 20 respondents who were non-participants (i.e. health professional

in Westmoreland Health Services) in the main study were interviewed on two separate occasions

in about 7 days apart. The reliabilities were determined by the percentage of agreement.

Modifications were made to the final instrument based on the recommendations, queries and

issues raised by the participants in order to attain clarity and conciseness of questions.


       A 26-item questionnaire was used to collect the data. The instrument was sub-divided

into general demographic profile of the sample; family history; health-seeking behaviour;

perception on prostate examination and choice of method in prostate examination. Below are the

components of the measures used.




                                                 348
Measure

       Regional Health Authorities. Decentralization of public health care the shifted the central

government (i.e. Ministry of Health) into four semi-autonomous regional bodies: South-East,

North-East, Western, and Southern.


       Standardized instruments were used to record participant’s weight (in kilometers) and

height (in squared metres). The body mass index (BMI) is the weight in kg divided by height in

m2. In this study we used the classification of the World Health Organisation. BMI was classified

as normal, overweight and obese.


       Normal BMI is defined as 18.5 kg/m2 to 24.99 kg/m,2 .Overweight BMI is defined as

25.00 kg/m2 to 29.00 kg/m2 and obese BMI is defined as ≥ 30.00 kg/m2. Risky behaviour denotes

bad health choices such as smoking, alcohol consumption, infrequent exercise, poor dietary habit

and food choices. The participants’ health status was measured using BMI categorization.


       Technical staffers include trained personnel such as dental nurses, health educators,

nutritionists and public health inspectors, contact investigators, pharmacists, and lab technicians.


The technical support staff comprises community health-aides, psychiatric aides, ward assistants,

porters, mosquito spray men and community peer educators. Administrative staffers constitute

administrator, parish manager, personnel officer, and matron. The administrative support staff

comprises accountants, security personnel, medical records officers, secretaries, drivers,

telephone operators; cashiers and clerks. The ancillary staffers are cleaners, cooks and gardeners.

The data analysis procedures are addressed in the next section.




                                                349
Data analysis

         The data were double entered using SPSS, verified and cleaned. Data was stored,

retrieved and analyzed, using SPSS for Windows (16.0). Percentages were used to provide

background information on demographic characteristics on sample, knowledge of prostate and

self-reported information on prostate. Chi-square tests were utilized to examine whether

statistical associations existed between non-metric dependent and independent variables. A p-

value of 5% (i.e. 95% confidence interval) will be used to determine statistical associations

between variables.




Ethics


         This study sought and was granted ethical approval by the University of the West Indies,

Mona, Ethics Committee. All participants gave written consent, and they were informed of

procedures and the choice of withdrawal at any time convenient to them if they so desire. The

data received from the participants is reported below.


Results

         A sample of 212 respondents was interviewed for this study: females, 77.8%; blacks,

90%; single, 46.7%; tertiary level education, 39.6%; full-time employed, 86.9%; religious,

97.6%; nurses and doctors comprised of 22.3% of the sample (Table 12.1). Forty-seven

percentage points of the sample were Seventh Day Adventist and Pentecostal members; 42.5%

were overweight, 33.5% obese and 24.1% had a normal weight.



                                               350
       Table 12.2 presents information on particular self-reported diagnosed health conditions.

Five percentage points of respondents had diabetes mellitus; 22.6% had hypertension; 0.5%

breast cancer; 0.5% stomach cancer; 1.9% enlarged heart and 0.5% ischemic heart disease.

       Table 12.3 shows information on the lifestyle behaviour of respondents. Twenty

percentage points of respondents consumed alcohol on a regular basis; 15.6% do no regular

physical exercise, 42.4% add sweetening to their hot beverages, and 4.7% were smokers.

       Table 12.4 highlights information on BMI categorisation by occupation of respondents. A

significant statistical relationship exists between BMI categorisation and occupation of sample

(P < 0.01). Over 15% of nurses and doctors were obese compared to 38% of ancillary staffers.

       Table 12.5 presents information on physical activity (in duration of time) by occupation.

Of the 178 respondents who indicated that they do some form of physical activity per week over

the survey period, 52.3% spent at least one hour on the activity. Of the different typology of

occupation, technical support staff had the lowest percentage points of engagement for at least

one hour (20.0%); with administrative support staff recorded the greatest engagement of 1 hour

or more in physical activity (63.5%).

       On disaggregating the aforementioned demographic, health and lifestyle characteristic of

the sample, 2.8% of those with diabetes mellitus were males compared to 19.8% females. Of the

diagnosed diabetic, most of them were ancillary staffers (36.4%); 45.5% were 40 to 49 years old;

and 36.4% were 31 to 45 years old - χ = 10.577, P < 0.005.

       Of the 22.6% of the sample who had hypertension, 25.5% were female and 12.8% were

males. The highest percentage points of the sample that had hypertension were 31 to 45 years

old (47.9%), 27.1% were at least 45 years old, and 6.3% were unable to recall the age when they

were first diagnosed with hypertension. When occupation of respondents was disaggregated by

                                              351
diagnosed hypertensive cases technical staffers recorded the high percentage points of cases

(33.9%) followed by ancillary staffers (32.4%); nurse and doctors (22.2%); administrative

staffers (20.0%) and administrative support staff (13.8%) – χ = 15.375, P < 0.0001.

Concurrently, a statistical relationship existed between overweight respondents and hypertensive

respondents. (P < 0.0001).

       No significant statistical association was found between BMI categorisation and gender

of respondents (χ = 3.793, P = 0.150). However, a significant relationship existed between BMI

categorisation and self-reported diagnosed health condition (P < 0.0001).

       Disaggregated the smoker cohort revealed that 57.1% consumed between 1 to 9 cigarettes

per day, and that males were more likely to be smokers (57.1%) than females (42.9%).

       Significant more males regularly consume alcohol (12.6%) than females (9.1%) (P <

0.0001). However, females (47.9%) than males (13.3%) indicated that they were regularly

engaged in physical activities (or exercise). The age cohort that indicated the most in physical

activity was 60+ year olds (67.1%). Of the 60+ year olds who are engaged in regular physical

activities, 13.7% indicated that they do so every day over the survey period. The percentage

points of other age cohorts and engagement in physical activities were 50 to 59 years (59.1%);

30 to 39 years (56.5%) and 40 to 49 years (54.5%).

       Of those who indicated sitting watching television or reading as their leisure activities

(34.2%), 65% did this on a daily basis and 18% between 4 to 6 times per week. Furthermore,

43% of overweight respondents were engaged in sitting and watching television or reading as

their leisure activities compared to 32% of those who were obese. Concurrently 17% of females

were engaged in sitting and watching television or reading as their leisure activities compared to



                                               352
8.3% of males. Forty-three percentage points of overweight respondents were 40 – 49 years old

and 24.1% of the obese were in this age cohort.

       Of the respondents who indicated being on a special dietary programme (23%), 35.4%

were on low salt; 25.2% vegetarians; 16.8% weight loss; 12.4% low fat; 4.0% weight gain and

6.2% were on low cholesterol programme. Health care workers in Hanover prefer to consume

fried meats, and this was mostly higher among those younger than 20 years (50%) followed by

those 50-59 years (49%) and those 40-49 years (31.3%) as well as the those 20-29 years (31.3%).

Ancillary workers were most likely to consume fried meats (68%) compared to any other

occupational group. Twenty five percentage points of the same had fruit juice (17.5% had it 2-3

times daily; 11.3% had it occasionally), and 49.5% had soda (57.1% had it occasionally; 14.3%

daily and 1.9% 6 days per week). Twenty-nine percentage points had vegetables daily, 23% 2-3

times per week and 0.5% never had vegetables.

       On general health care-seeking behaviour, 28% of female respondents indicated having

visited a health care provider in the last 6 months for breast examination. There was no

significant statistical association between breast examination and occupation (P> 0.05); BMI

(P>0.05) and health conditions (P>0.05). Majority of the females had done a pap smear (75%).

Those who indicated that they had not done a pap smear, the highest was among administrative

staff (42.9%) followed by other technical staff (35.7%); administrative support staff (31.2%) and

the least by technical support staff (12.2%). Forty percentage points of female have not done a

breast examination compared to 62.9% of males who had never had a rectal examination. A

significant relationship existed between rectal examination and occupational type (P < 0.0001).

The percentage points of males who had never done a rectal examination by occupational type

can be disaggregated as technical support staff, 87.5%; administrative support staff, 60.1%; other

                                               353
technical staff, 55.6%. Furthermore, the highest number of males who had not done a rectal

examination was among those 50 to 59 years old (69.2%). The findings outlined above are

discussed in the next section



Discussion



This study examined the lifestyle, health and the use of health care services of some health care

workers. Generally, the health status of people who are employed to health institutions in

Hanover is good, but went this was disaggregated into occupational types more information was

revealed that indicated worrying sign for health care in the future. Using BMI categorisation to

measure health status, the findings revealed that 34% of employees were classified as having

normal weight, 43% overweight and 24% obese. Apart from the afore-mentioned findings, 5%

had diabetes mellitus, 23% hypertension, 0.5% breast cancer, 0.5% stomach cancer, 1.9%

enlarged heart and 0.5% ischemic heart disease. Concurrently, 22% of men had done a rectal

examination for cancer, 60% of women had done a breast examination, 77% indicated that they

eat every and/or anything, 42% added sweetening to their hot beverage, 5% were smokers and

16% do no physical activities and 34% indicated that their leisure time was spent sitting

watching television and/or reading. The disaggregation of BMI by occupation revealed that most

doctors and nurses were at least overweight (73%); other technical staff (54%); technical support

staff (71%) and those in the ancillary categorisation were the most in the at least overweight

category (79%).

       In 2007, statistics from the PIOJ and STATIN [18] revealed that 12% of Jamaicans had

diabetes mellitus and 22% had hypertension. On disaggregating the figures, 8% of males had
                                              354
diabetes mellitus compared to 14% of females, and 16% of males had hypertension compared to

27% of females. In the current study diabetes mellitus disparity between the sexes was 7.1 times

(males, 2.8%; females, 19.8%) which was 4.3 times more than the national disparity. With

respect to hypertension, there was no difference between the percentage of those with diabetes in

the country and health workers in Hanover. There are no available statistics on diabetes mellitus

and hypertension by occupational type, and the current study provided this valuable information.

The findings showed that hypertension was not greater among females than males; but it was

also highest among those 31-45 years and among technical support as well as ancillary staffers.

Both technical support and ancillary staffers are among the poor, which concurs with the

literature that poverty is associated with more illness as is the cases in this research [1, 11-17].

       A variable for which there is no national data is BMI. Despite no national statistics for

comparison, the current work highlighted that there was no statistical association between gender

and BMI categorisation, however one existed for BMI and self-reported diagnosed health

condition. Low socioeconomic status is empirically established as having more people with

illness, but the current study goes further to show that they were more likely to be obese than

those who are more likely to be in the middle-to-upper class. The current work revealed that 34%

of those in the technical support staff and 38% of those in ancillary staff category were obese and

these people are in the low socioeconomic status compared to 16% of doctors and nurse who are

middle-to-upper class individuals. Despite this finding, it can be extrapolated from the data that

doctors and nurses schedule and lifestyle is accounts for a significant percent of them being at

least overweight. This is implications for the future of the health sectors as overweight and

obesity are associated with increased risk of morbidities and mortalities.



                                                 355
       The sedentary lifestyle of health care professionals in Hanover is such that this is a public

health problem which will become worse in the future if the problem is not addressed. The health

behaviour of the sample is also a cause for concern as although their lifestyle is a sedentary one,

they make unhealthier lifestyle choices than healthy ones. It is clear from the findings that

education, knowledge of health and health care are not influencing the decision of health care

providers in Hanover. The findings concur with a study which showed that non-communicable

diseases are largely apart of the lifestyle of Jamaicans, and that 50% of deaths were owing to

non-communicable diseases such as heart, stroke, diabetes mellitus, cancers and obesity [26]. A

study in BMJ [27] found that four-fifths of those with stroke had high blood pressure when they

were taken to hospital for treatment post stroke, and that two-thirds of them had a history of

hypertension.    A later study by Woo et al. [28] found that untreated hypertension was

significantly a risk of hemorrhagic stroke (i.e. OR = 3.5, 95% CI = 2.3, 5.2; P < 0.0001) and that

treated hypertension was significantly lower in causing hemorrhagic (OR=1.4, 95% CI=1.0 to

1.9; P=0.03). The World Health Organisation revealed that obesity was associated with health

problems such as respiratory difficulties, chronic musculoskeletal problems, skin problems and

infertility [29], indicating the pending public health challenge in the health sectors in Hanover. In

2000, the Jamaica Lifestyle Survey revealed that 8% of Jamaican had diabetes mellitus 96.1% of

males and 9.1% of females) and that the most cases were among the elderly (i.e. 60+ years) [26],

which reiterate the health problem challenge that Hanover faces and speaks to the role of culture

and low socioeconomic status influencing the healthy lifestyle choices of Hanover residents.

Morrison [30] in an article entitled ‘Diabetes and hypertension: Twin Trouble’ showed that

diabetes mellitus and hypertension have now become two problems for Jamaicans and in the

wider Caribbean. This finding was also corroborated by Callender [31] who found that there is a

                                                356
positive association between diabetic and hypertensive patients (i.e. 50% of individuals with

diabetes had a history of hypertension), which emphasizes the public health problem of

unhealthy health workers in Hanover.

       When the sedentary lifestyle, unhealthy lifestyle choices, and low socioeconomic status

are coupled with the fact that the sample is relatively old (i.e. mean age was 41.0 years), with the

increased risk of morbidities and disabilities associated with ageing, this is make worse by the

unhealthy diet, alcohol consumption, lack of exercise and sedentary lifestyle. While the

prevalence of smokers among residents and health workers in Hanover (4.7%) are lower than the

national figures (17.7%), the percentage points of male smokers in this was 2.3 times more than

the prevalence in the national (24.8%) and this was 5.9 times more females in this sample

compared to the national figures (7.1%).



       The fact that the majority in each category of health care workers are obese is a worrying

finding. Instructing patients to take care of their health in an environment where healthcare

workers are overweight including nurses and doctors may cause patients to ignore the

information they receive from health care staff. There is an area for future research. Another

programme of research should be an examination of the reasons why some health care workers

particularly the professionals who have years of education, knowledge, experience and training

do not buffer themselves from unhealthy lifestyle practices. Once the reasons for the poor

lifestyle choices of health care workers that affect their health are understood, further research is

necessary on the content and procedures that are required for a strategic and effective national

health literacy communication programme. This should programme should be cognizant of the

fact that education and knowledge about health does not automatically influence the educated

                                                357
knowledge holders’ behavior in a positive way. There is also need for research on how

mindfulness training and wellness programme for health care workers on the job including

doctors and nurses would influence the choices they make about their health.



Conclusion



The current study has revealed pertinent information on the perception of health care workers

about healthy lifestyle, health choices and general perception of residents in Hanover on their

health. The study also highlights that overweight, obesity, hypertension, diabetes and sedentary

lifestyle among health care workers in Hanover is a public health challenge and cannot be

allowed to continue in its present form. Most of the studies in the literature deal with stress, burn

out and mental health of doctors and nurses. This is one of the first studies to move beyond the

mental health issues of doctors and nurses to look at the health of health care workers. The

Jamaican culture undoubtedly is dominant in this study as this can be measured using eating

habits and choices, prostate care examination, and prevalence of non-communicable diseases.

The level of health education is greater among health workers than non-health care workers

which indicate that the pending health problems in Hanover would have been understated by the

current study. There is a need for public health practitioners to formulate a health intervention

programme that will target people in Hanover, but also specific groups such as doctors, nurses,

administrative, ancillary staffers and technical staffers in health care institution in the parish of

Hanover. The findings of this study should not be generalized to the country since the data was

collected in only one parish.



                                                358
       In sum, hypertension is a major health problem among adults in the Caribbean, Jamaica

and in particular health workers in Hanover. Smoking, obesity, overweight, high cholesterol,

sedentary lifestyle, unhealthy lifestyle practices and low socioeconomic status increased the risk

of cardiovascular diseases in health workers in Hanover and this is further complicated by

hypertension, diabetes and unhealthy choices. The reported findings of this work highlight the

challenges which lie ahead for public health specialists to trained public health workers on

healthy lifestyle choices. Clearly education and knowledge of health do not leading to better and

healthier choices by health care workers in Hanover, and this could be a general social dilemma

as the general populace may be left to use home remedy if premature mortality were to befall

those high risk health workers in Hanover. Then, there is reality of an increase burden of health

care workers in Hanover on the health care services in the future, which would increase health

care expenditure for the country.

Competing interests

The authors declare that they have no competing interests to report.




                                               359
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                                             361
Table 12.1. Demographic characteristics of sample
Characteristics                                     n               %
Gender
  Male                                                   47               22.2
  Female                                                165               77.8
Ethnicity
  Black                                                 191               90.0
  Burmese                                                 1                0.5
  Indian                                                  5                2.4
  Mixed                                                  15                7.1
Marital status
  Single                                                 99               46.7
  Married                                                80               37.7
  Common-law                                             14                6.6
  Widowed                                                 4                1.9
  Divorced or separated                                   5                7.1
Education
  Primary or below                                       11                5.2
  Secondary                                             116               55.0
  Tertiary                                               84               39.8
Employment status
  Employed                                              184               86.8
  Unemployed                                             25               11.8
  Not stated                                              3                1.4
Religious
  Yes                                                   207               97.6
   No                                                     5                2.4
BMI categorization
  Normal                                                 71               33.5
  Overweight                                             90               42.5
  Obese                                                  51               24.0
Occupation
   Nurses and doctors                                    45                22.3
   Other technical staffers                              28                13.9
   Technical support staff                               56                27.7
   Administrative staffers                               10                 5.0
   Administrative support staff                          29                14.4
   Ancillary                                             34                16.8
Age Mean (SD)                                                 41.0 years (11.8)




                                             362
Table 12.2. Self-reported diagnosed chronic health conditions

Characteristics                                                   n     %
Diabetes mellitus
   Yes                                                           11    5.2
    No                                                          200   94.3
Hypertension
   Yes                                                           48   22.6
    No                                                          164   77.4
Cancer
 Breast
   Yes                                                            1    0.5
    No                                                          211   99.5
 Stomach
   Yes                                                            1    0.5
    No                                                          211   99.5
Enlarged heart
   Yes                                                            4    1.9
    No                                                          208   98.1
Ischemic heart disease
   Yes                                                            1    0.5
    No                                                          211   99.5




                                              363
Table 12.3. Lifestyle behaviour

Characteristics                                         n         %
Smoking behaviour
  Smoke                                                      10        4.7
  Do not smoke                                              202       95.3
Regular alcohol consumption
  Yes                                                        43       20.3
   No                                                       169       79.7
Physical activity (i.e. exercise)
   None                                                      33       15.6
   1 – 2 times a week                                       100       46.7
   4 – 6 time a week                                         61       29.0
   7 times a week                                            18        8.7
Dietary habits
  Special dieting                                            49       23.0
  Eat anything                                              163       77.0
Adding sweetening to hot beverage
  Yes                                                        90       42.4
   No                                                       122       57.6
Breast examination
  Monthly                                                    69       42.0
  Rarely                                                     31       18.4
  Never                                                      65       39.6
Leisure time activity
  Sitting watching TV/reading                                73       34.2
  Cycling                                                    30       14.2
  Gardening or farming                                       56       26.4
  Playing indoor games (chess, scrabble, domino, etc)        35       16.5
  Regular physical activity (i.e. exercise)                  18        8.7
Rectal examination
  Yes                                                        10        4.7
   No                                                        30       14.2
   Did not answer                                           172       81.1
How do you prepare or eat meat
  Eat no meat                                                29       13.7
  Fried                                                      71       33.3
  Stewed                                                     55       26.0
  Baked                                                      12        5.9
  Jerked                                                     45       21.1




                                             364
Table 12.4. BMI categorisation by occupation

                                                              BMI categorisation

Occupation                                           Normal       Overweight       Obese

                                                       %               %            %

Nurses/doctors                                             33.3            57.1         15.6

Other technical staff                                      46.4            42.9         10.7

Technical support staff                                    28.6            37.5         33.9

Administrative staff                                       50.0            40.0         10.0

Administrative support staff                               41.4            44.8         13.8

Ancillary staff                                            20.6            41.2         38.2

P < 0.01




                                               365
Table 12.5. Physical activity (in duration of time per day) by occupation
                                      Physical activity (in duration of time)
Characteristic          < 15       15 – 29      30 – 44         45 – 59             >1hr Total
                      minutes minutes minutes                       minut
                                                                      es
                          n (%)        n (%)       n (%)            n (%)          n (%)    n (%)
Occupation
Nurses/doctors         6 (16.2)     7 (18.9)     3 (8.1)           1 (2.7)      20 (54.1)       37
                                                                                            (20.8)
Other      technical    3 (13.0)    6 (26.1)    3 (13.0)          1 (4.3)        6 (43.5)       19
staff                                                                                       (10.7)
Technical support       4 (40.0)    3 (30.0)    1 (10.0)          0 (0.0)        2 (20.0)       10
staff                                                                                        (5.6)
Administrative          5 (18.5)    6 (22.2)     2 (7.4)          0 (0.0)       14 (51.9)       27
staff                                                                                       (15.2)
Administrative           5 (9.6)    7 (13.5)     2 (3.8)          5 (9.6)       33 (63.5)       52
support staff                                                                               (29.2)
Ancillary staff          2 (6.1)     2 (6.1)    9 (27.3)          2 (6.1)       18 (54.5)       33
                                                                                            (18.5)
Total, n                     25          31            20              9              93      178




                                                 366
                                                                        Chapter
                                                                                      13
  Health literacy and health seeking behaviour among older men in a
                         middle-income nation




 Paul A. Bourne, Chloe Morris, Christopher A.D. Charles, Denise Eldemire-Shearer, and
                             Maureen D. Kerr-Campbell



Health literacy is a measure of the patient’s ability to read, comprehend and act on medical
instructions. This research article examines health literacy and health-seeking behaviours
among elderly men in Jamaica, in order to inform health policy. In this study, 56.9% of urban
and 44.5% of rural residents were health literate. Only 34.0% of participants purchased
medications prescribed by the medical doctor and 19.8% were currently smoking. Despite the
reported good self-related health status (74.4%) and high cognitive functionality (94.1%) of the
older men, only 7.9% sought medical care outside of experiencing illnesses. Thirty-seven percent
of rural participants sought medical care when they were ill compared with 31.9% of their urban
counterparts. Thirty-four percent of the participants took the medication as prescribed by the
medical doctor; 43% self-reported being diagnosed with cancers such as prostate and colorectal
in the last 6 months, 9.6% with hypertension, 5.3% with heart disease, 5.3% with benign
prostatic hyperplasia, 5.3% with diabetes mellitus, and 3.8% with kidney/bladder problems.
Approximately 14% and 24% of the participants indicated that they were unaware of the signs
and symptoms of hypertension and diabetes mellitus, respectively. The elderly men displayed low
health literacy and poor health-seeking behaviour. These findings can be used to guide in the
formulation of health policies and intervention programmes for elderly men in Jamaica.




                                              367
Introduction


Jamaica is a developing Caribbean country that is situated in the Northern part of the Caribbean

and spans an area of 4,411 square miles. It has a population of approximately 2,692,358 million

people; comprised of 1,326,907 males and 1,265,451 females.1 Eldemire2 noted that the elderly

in Jamaica represent 10% of the population, and that they are for the most part mentally

competent and physically independent. With a calculated life expectancy of 75.5 years,3 the

burden on the healthcare system can be expected to increase. The epidemiologic transition in the

Caribbean over the last 40 years has produced an epidemic of lifestyle-related chronic non-

communicable diseases.4 Among these are obesity, diabetes mellitus, and hypertension, along

with such complications as stroke, heart disease, and amputations.4 Cardiovascular disease is by

far the leading cause of death at older ages in developing countries, although the impact of

communicable diseases remains considerable.5 One comprehensive analysis attributes nearly 46

percent of all deaths among females aged 60 and over in developing countries in the early 1990s

to cardiovascular disease, while the corresponding figure for older males is 42 percent.5

       Health literacy is defined as the extent to which people have the capacity to obtain basic

health information, which they are able to process and understand so that they can make

informed decisions about their health.6 Health literacy is crucial if patients are to benefit from

healthcare. People who cannot read or understand the words used to describe health problems,

diagnostic tests, medications and directions for care may experience confusion in negotiating the

healthcare system, and are significantly handicapped in the tasks of self-care or caring for family

members.7 A significant statistical relationship was found between functional health literacy and

the quantitative components of general functional literacy, prose and document.8

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       Just under 36% of the variance in the scores of the general context numeracy, and 26% of

the variance in the scores of health context numeracy among independently functioning older

adults, were predicted by the education they attained, their prose health literacy and their anxiety

about mathematics.9 An interpretation of the foregoing findings also reveals that shame about an

individual’s condition is a troubling emotion that prevents low literate patients from interacting

effectively with healthcare providers.10 Therefore, a person’s level of education, race and

available resources are crucial factors that help to determine the approaches to take in obtaining

health information. The importance of these variables is evident in the findings - after controlling

for the demographic factors that influence health, elderly participants without a high school

diploma had lower self-related health status, and worse physical and mental health, compared to

participants with a high school diploma. These differences were reduced by 22%-41% when

health literacy was taken into account. In addition, blacks, other minority groups, immigrants and

people with fewer resources had lower health literacy skills than members of the dominant racial

group and people with more resources.11

       In a study by Baler et al.12 functional health literacy was lower among community-

dwelling elderly persons after controlling for differences in their health status, their visual acuity,

scores on the mini mental state examination and the frequency with which they read newspapers.

Functional literacy among elderly men with prostate cancer is fostered across the life course and

supported by cultural resources. These men developed critical open-mindedness from supportive

home and elementary school environments, and because they had an interest in being educated

and a history of reading at home.13

       Health literacy is also important for dealing with a range of chronic diseases. Participants

with inadequate health literacy compared to those with adequate health literacy knew much less

                                                 369
about their chronic diseases. This finding suggests that health literacy independently influences

the knowledge of disease,14 but does not always influence healthy lifestyle practices. This

anomaly is evident in the finding that people were more likely to completely abstain from the use

of alcohol, to have never smoked in their lives, and to be sedentary when they had inadequate

health literacy compared to people who had adequate health literacy.15 Despite this anomaly, in

general health literacy directly influences health outcomes. Therefore, the health status of the

elderly should be improved and the cost of emergency room services reduced with an effective

health literacy strategy.16

        The demographic assessment for health literacy is useful for broadening the scope of

research with health literacy data from national surveys.17 It is important to create a set of

indicators to measure health literacy from these surveys. The developed health literacy index

could be used as a composite scale, which would record the health capabilities and competence

of residents in groups and communities, as well as the populations of entire countries. These

variables would be linked to socio-economic and health outcomes. In addition, this index could

also quantify the outcomes of health prevention and promotion activities.18

        Health literacy influences the lifestyle practices of men (55 years and older) and impacts

their health. Furthermore, there is inadequate functional and health literacy among the elderly.

Therefore, it is very important to understand the level of health literacy among these older men.

There is a lot of information on the health literacy of the elderly in developed countries.19-21

However, on the other hand there is a dearth of information on the elderly, especially males, in

developing countries such as Jamaica. To bridge this knowledge gap, this study examined the

health literacy of older men and their health-seeking behaviours in Jamaica. To the best of the



                                               370
author’s knowledge, this study is the first of its kind in Jamaica and the English-speaking

Caribbean.




Methods
This study used primary cross-sectional survey data on men 55 years and over from the parish of

St. Catherine in 2007. The survey was submitted and approved by the University of the West

Indies Medical Faculty’s Ethics Committee. A stratified multistage probability sampling

technique was used to draw the sample (2,000 participants). The only inclusion-exclusion

criterion for this study was men 55+ years who were residing in the parish of St. Catherine. A

132-item questionnaire was used to collect the data. The instrument was sub-divided into general

demographic profile of the sample, good self-related health status (past and current), health-

seeking behaviour, retirement status, social and functional status. The overall response rate for

the survey was 99% (n = 1,983). Data was stored, retrieved and analyzed, using SPSS for

Windows 16.0 (SPSS Inc; Chicago, IL, USA).

       The Statistical Institute of Jamaica (STATIN) maintains a list of enumeration districts

(EDs) or census tracts. The parish of St. Catherine is divided into a number of electoral

constituencies made up of a number of EDs. The one hundred and sixty-two (162) enumeration

districts in the parish of St. Catherine provided the sampling frame. The sample size was

determined with the help of STATIN. The enumeration districts were listed and single-stage

cluster sampling was used to select the sample. The enumeration districts were numbered

sequentially and selection of clusters was arrived at by calculating a sampling interval. From a


                                              371
randomly selected starting point, forty (40) enumeration districts (clusters) were subsequently

selected with the probability of selection being proportional to population size. Under the advice

of STATIN and having utilized the C-survey computer software, it was decided that 50 older

men in each enumeration district would be interviewed, yielding a sample size of 2,000.

       The parish of St. Catherine had approximately 233,052 males (preliminary census data

2001), of whom 33,674 were 55 years and older. STATIN maintains maps with enumeration

districts or census tracts which included the selected EDs and access routes, and had references

to the selected site of a starting point household within each ED. The starting point was

determined by randomly selecting a household with a man 55 years and older, and included the

list of persons in the ED.

       Where the selected household did not have an older man, the adjacent household was

canvassed. Where households had a man 55+ years and he was not at home, a ‘call-back’ form

was left indicating a proposed time when the interviewer would return, which would not be

longer than two days after the initial visit. In households where there was more than one man 55

years and older, then all were included in the survey.

       The sampling frame was the parish of St Catherine. This parish was chosen because

previous data and surveys by STATIN suggested that the demographic characteristics of this

parish are similar to Jamaica as a whole.



Measure

Social network was defined as self-reported involvement in church, civic organizations, social

clubs and community groups. This is a binary variable, where 1 = social network, 0 = otherwise.

The variable ‘happiness’ was measured based on people’s self-reporting on their level of high

                                                372
satisfaction. It is a Likert scale question, which ranges from always to rarely happy. The variable

‘health status’ was measured, using people’s self-rating of their overall health condition, which

ranges from excellent to poor. The question was ‘How would you rate your health today?’ The

response choices were (1) excellent, (2) good, (3) fair and (4) poor. In terms of education, the

question for this variable was “What is the highest level of education you have attained? The

response choices were (1) no formal education, (2) basic school, (3) primary school/all age, (4)

secondary/high/technical school, (5) vocational (i.e. apprenticeship/trade), (6) diploma, (7)

undergraduate degree, (8) post-graduate degree. With regard to physical exercise, the question

for this variable was “Do you take time out of your regular schedule for physical exercise?” The

response choices were (1) yes, or (2) no. The question on type of physical exercise was also

posed. On the matter of childhood illness, the first question for this variable was ‘Were you

seriously ill as a child?’ The response choices were (1) yes, or (2) no. The second question was

‘Were you frequently ill as a child?’ The response choices were (1) yes, or (2) no. If the response

to either question was yes, this was coded as poor childhood health status, and if the response

was no in both cases it was coded as good health status in childhood. Age group was categorized

into three sub-groups. These were (1) ages 55 to 64 years, (2) ages 65 to 74 years and (3) ages 75

years and older (i.e. 75 years and older).

       Functional status is the summation of activities for daily living (ADL), as well as the

instrumental activities of daily living (IADL). Performance of ADL was used to describe and

monitor the improvement in the functional status of a person compared with his or her baseline

level of functioning overtime. There are systems such as the Katz ADL tool that seek to quantify

these functions and obtain a numerical value. These systems are useful for the prioritizing of care

and resources. These should be seen as rough guidelines for the assessment of a patient’s ability

                                               373
to care for himself. There were 14 items including daily activities, household chores, shopping,

cooking and paying bills. The reliability of the items was very high, α = 0.801. In scoring the

Katz ADL, independence on a given function is given a score of 1, and being dependent is given

a score of 0. Total scores range from 0-14 with lower scores indicating high dependence and

higher scores indicating greater independence.

       The IADL was used to assess the participants’ accomplishment of activities necessary for

their continued independent residence in the community. The IADL is more sensitive to subtle

functional deficiencies than the ADL. In addition, the IADL differentiates among task

performance, including the amount of help needed to accomplish each task. Since only men were

used as participants in the study, the University of Wollongong’s modified IADL functional

ability scale was used to assess the IADL of men. Consequently the domains of food preparation,

laundry and housekeeping were omitted in this study (with regard to the IADL for older men).

       In scoring the IADL, this reflects the number of areas of impairment - that is the number

of skills/domains in which subjects are dependent. Scores range from 0-5. Higher scores thus

indicate greater impairment and dependence. Cohen and Holliday stated that correlation can be

low/weak (0-0.39); moderate (0.4-0.69), or strong (0.7-1).22 High dependence ranges from 0 to

5.5; moderate dependence is from 5.6 to 9.7 and low dependence (i.e. independence) ranges from

9.8 to 14. Independence means without supervision, direction or active personal assistance. The

performance on the functions can be further classified and analyzed using the format below. The

classification recognizes combinations of independence/dependence with respect to particular

functions reflecting the different degrees of levels of capability regarding ADL. The

classification outlined below was used to further describe the functional status of men (regarding

ADL). Cohen and Holliday22 correlation coefficients were used in the present study to exclude

                                                 374
(or allow) a variable. Variables having a high correlation or a non-response rate in excess of 20%

were excluded.


Statistical analyses
For the current study, descriptive statistics were employed to provide background information on

the sample. In addition, chi-square was used to examine non-metric variables. A P-value < 0.05

(two-tailed) was used to establish statistical significance.



Results

Demographic characteristics of sample

Of the sampled participants (n = 2,000), 74.2% indicated that they had good health during their

childhood; 74.4% reported good current self-related health status; 51.0% lived in rural areas;

3.5% were mostly satisfied with life; 10.4% had moderate to high functional dependence; 89.6%

had low functional dependence; 21.9% were ages 75 years and older; 35.6% were ages 64.5 to

74 years and 42.6% reported ages 55 to 64 years. Fifty one percent of the participants resided in

rural areas; 93% had at most primary level education; 88% were heads of households; 25.6%

were employed; 44.7% were married; single, 34.3%; separated, 5.6%; common-law unions,

6.8%; widowed, 8.6%; and 41.2% owned their homes. In addition 94.1% had high cognitive

functionality; 43.1% reported that they were depressed; 67.3% reported that they did some kind

of physical exercise; 4.5% mentioned that they were happy most of the time and 71.5% claimed

occasional happiness. Half of the participants indicated that they spent J$ 100 (US $1.45)

monthly for medical expenditure; 34% of the participants bought their prescribed medication;

17.1% reported that they had been hospitalized since their sixth birthday and 65.8% reported that

they took no medication. Of those who mentioned that they were ill during their childhood
                                                 375
(17.5%, n = 350), 34.9% of the instances were in relation to measles or chicken pox; 26.3%

asthma; 10.0% pneumonic fever; 8.9% polio; 6.6% accident; 4.6% jaundice; 1.7% hernia, and

5.1% gastroenteritis. Twenty four percent of elderly men indicated that they were rarely happy;

40.5% said sometimes; 31.0% mentioned often, and only 4.5% reported always. Furthermore,

8.6% indicated that they were experiencing prostate cancer (self-reported); 19.8% were current

smokers; and 48.2% had smoked in the past.

       In response to the question ‘reasons for not seeking medical care’, 40.7% did not

respond; 27.2% stated that they did not have a reason; 8.9% claimed financial constraints; 10.3%

used home remedies; 10.4% indicated that they were not ill enough and 0.8% indicated that they

did not like traditional doctors. In addition, in response to the question “What can be done to

avoid medical complications?” 24.3% indicated that they had stopped drinking; 24.8% claimed

that they took medication; 2.8% indicated diet; 2.0% mentioned exercise; 1.0% indicated stress

avoidance and 45.4% did not respond to the question.

       Fifty-four percent of participants’ mothers died from other types of cancer, hypertension

or diabetes mellitus, while 45% of their fathers died from the same health conditions. Almost

30% of respondents did not know the mortality of their fathers and 16% did not know the cause

of death of their mothers (Figure 1). In examining the health status, health literacy and other

sociodemographic characteristics by area of residence, a significant statistical association was

found between healthcare-seeking behaviour and area of residence [χ2 (df = 1) = 6.40, P =

0.011]. Thirty-seven percent (37.3%) of rural participants sought medical care when they were

ill, as compared to 31.9% of their urban counterparts (Table 13.1). Based on the definition of

health literacy, 56.9% of urban and 44.5% of rural residents were health literate. Only 34.0% of

the participants purchased medication and 19.8% are currently smoking (Table 13.1).

                                              376
       In examining the information on lifestyle practices, typology of retirement planning,

health advice and last visits to a medical doctor, there was a significant statistical association

between the last visit to a medical doctor and the area of residence (P < 0.05). Urban residents

were more likely to have visited a medical doctor in the last 35 months than rural residents. The

latter, however, visited more in 36 years and beyond (Table 13.2). Almost 14 percent (13.7%) of

the participants indicated that they were unaware of the signs and symptoms of hypertension, and

24% were unaware of the signs and symptoms of diabetes mellitus (Table 13.3).

       Forty-three percent of the participants self-reported being diagnosed with cancers such as

prostate and colorectal in the last 6 months; 9.6% with hypertension; 5.3% with heart disease,

5.3% with benign prostatic hyperplasia; 5.3% with diabetes mellitus; 3.8% with kidney/bladder

problems and 27.4% did not specify the health condition(s). Fifty-two percent of participants

sought medical attention immediately on the onset of illness; 42.3% stated 2-7 days after the

onset of the illness; 34.2% took the medication as prescribed by their medical doctor; 25.4%

knew the type of medication consumed; 57.9% of respondents indicated that good health denotes

the absence of illness; as well as a state of mind (7.4%).



Discussion


This study demonstrated that health literacy is a very basic problem among the participants.

Compared with the research by Montalto and Spiegler (2001)23 in which only 15% of the rural

population studied experienced health literacy deficits, this research identified nearly half of the

participants (48.2%) with health literacy difficulties. Therefore, it is possible that some of the

participants were unable to read words commonly used in healthcare. Words such as allergic,

                                                377
diagnosis, and inflammatory are extremely common and are critical to the effective self-

management of many health problems. Inability to understand these common words can lead to

detrimental health outcomes. Furthermore, patients are frequently given forms to complete

asking if they have any allergies. Misreading this word could be life-threatening to the person

who has a drug or treatment allergy, but fails to share that information as a result of an inability

to recognize the printed word. Likewise, the word inflammatory is a common term used with

many healthcare problems. Anti-inflammatory medications are prescribed for treatment of many

conditions, and a lack of understanding of the word may lead to drug misuse.24

       The majority of the participants indicated good self-related health status and most

experienced good health status during their childhood; just over half of the participants viewed

good health status as the absence of disease. In addition, the study also found that the majority of

the elderly men had high cognitive functionality and low functional independence. However, the

narrow understanding of health status held by these men ignored their quality of life, given their

reported low functional independence. It is possible that the majority of these men were in

intimate partner relationships, and received social support from their partner, friends and

relatives. Studies have shown that family, friends, and home healthcare workers may act as

“surrogate readers” for individuals with inadequate literacy, and thus may mitigate the negative

effects of inadequate literacy on patients’ understanding of their medications and self-care

instructions.25 In contrast, individuals who are in average or above-average health may rely more

on their own reading abilities to decipher medical instructions, and this may put them at risk for

preventable hospitalizations.25

       The problems of low health literacy may be especially acute for those who live in rural

areas. Rural areas are characterized by residents with lower levels of education, higher rates of

                                                378
unemployment, lower salaries, and lack of health insurance.26,27 The low level of health literacy

becomes more evident with the significant statistical association found between healthcare-

seeking behaviour and area of residence, where more rural residents sought medical care, such as

visits to primary health centres, hospitals or private medical institutions when they are ill, as

compared with urban residents. However, more urban residents visited their medical doctors in

the last 12 months, or 12 - 35 months compared with their rural counterparts. The finding that

more rural residents sought medical care compared with urban residents is interesting, and could

be due to the availability of more primary health centres in rural St. Catherine compared with

hospitals and other health centres in urban areas; rural men being more concerned about their

health related problems, and the possibly higher prevalence of chronic disease. Our findings are

not in accordance with other studies that have found that urban residents are more likely to seek

medical care sooner than rural residents.28,29 This is because rural residents are culturally likely

to delay seeking healthcare until a condition has become advanced or urgent, or until multiple

chronic conditions exist. They then experience a relative shortage of healthcare sites and choices,

a need to travel greater distances to reach healthcare, problems of transportation, and, very

probably, an explanation of a complicated treatment regimen to act upon.29,30

       The majority of residents in both residential locations did not seek medical attention

when they were ill, and had not visited the doctor in the last 12 months. In addition, just over half

of the participants sought medical attention immediately on the onset of illness, while

approximately two-fifths stated 2-7 days after the onset of the illness. There may be practical or

economic hindrances to accessing medical services. Furthermore, there may be other factors such

as long hours of travelling time, waiting time, costs, and work or family obligations. Healthcare

in Jamaica is free to all citizens and legal residents at government hospitals and clinics. This

                                                379
includes prescription drugs. However one of the drawbacks to free healthcare is long lines with

no appointments accepted by the physicians. In addition, some rural and urban public hospital

and clinics are without their full cadre of medical doctors and other healthcare practitioners. The

long lines and waiting times experienced by some of the participants at these public healthcare

institutions may have contributed to their dissatisfaction with the quality of healthcare offered by

the state, and deterred them from readily visiting their institutions for medical care. This may be

one of the factors which account for the low health-seeking behaviour of the participants in this

study, a fact that is consistent with reports from other developing countries.31

       Cultural factors influence a person's decision to seek medical help. Strong social ties and

cultural traditions could provide a reservoir of lay knowledge that can be used instead of

professional services.32 In this study one-tenth of the respondents used home remedies.

Furthermore, employment may be a contributing factor to the low health-seeking behaviour of

the participants in the study, as only one-quarter of the participants were employed and almost

nine-tenths were heads of their households. These factors may reduce the amount of resources

available to these men in order for them to seek medical care when they become ill. The

literature suggests that people with fewer resources have lower health literacy than people with

more resources.11

       One of the most obvious and critical areas where low literacy skills can have a direct

effect on a person’s health is the failure to understand and comply with the use of prescription

drugs.33,34 While medication non-compliance among patients occurs at all age levels, the reasons

for non-compliance differ across the life course. The problem is more evident among elderly

patients since they are more likely to use medication and take several drugs simultaneously; they

may become confused or misunderstand the proper dosage, fail to comply intentionally because

                                                380
of the cost or side effects, or otherwise fail to follow instructions because of an increased

sensitivity to drug effects.33 In this study approximately one-third of the participants were taking

medication for different chronic diseases including cancer, hypertension, diabetes mellitus and

cardiovascular conditions. Of these patients, non-compliance was approximately one-third and

only one-quarter knew the name, or had any information on the medications they were taking.

Low health literacy lowers health-seeking behaviours and medication compliance. This leads to

adverse health outcomes.35

       The combination of low literacy and chronic illness is particularly common among the

elderly.36 In 1999, cardiovascular diseases were the leading cause of hospital admissions among

persons 60 and older in Jamaica, followed by diabetes mellitus. The leading cause of hospital

deaths in 1999 was cardiovascular disease, followed by diseases of the respiratory system. In

2000, persons aged 60 years and older accounted for 9.7 % of the population. The main non-

communicable diseases affecting the elderly were hypertension, arthritis, overweight, and

diabetes mellitus.37 Furthermore, inadequate health literacy (e.g. the inability to read and

comprehend basic health-related materials such as prescription bottles and appointment slips) is

associated with less knowledge among patients with chronic diseases, worse self-management

skills, and lower use of preventive services.38,39 The low health literacy among the men in this

study may contribute to less control of their chronic diseases, as people with inadequate health

literacy are less knowledgeable about their chronic diseases than people with high health

literacy.14 Another key finding is the low awareness among the participants of the symptoms of

diabetes and hypertension, two of the main non-communicable diseases among the elderly in

Jamaica. Only about one-seventh to one-quarter of the elderly men knew the symptoms of

diabetes mellitus and hypertension.

                                                381
       The lack of awareness of the symptoms of these two diseases may be explained by the

finding that the majority of the participants only received primary school education. This finding

corroborates the finding in the literature, which postulates that level of education predicted 36%

and 26% respectively of the variance in general context numeracy and health context numeracy

among older adults. Moreover, people who completed high school have greater health literacy

than people who did not complete high school.9 Furthermore, low health literacy in elderly men

may affect the management of their diseases and overall quality of life. Nurss (1998)40 compared

the health literacy of people with hypertension and diabetes to knowledge about the disease, and

found that only around half of those with inadequate health literacy knew the important clinical

signs required for disease self-management. Glycemic control was worse for people with

diabetes and health literacy problems according to Schillinger et al.41 (2002). The combination of

inadequate health literacy and chronic illnesses, such as diabetes mellitus, reduces the likelihood

that people will participate in their care to the extent needed for effective disease management.42

       People of low health literacy are neither unintelligent nor unmotivated.43 Although

reticent to ask for assistance because of shame and embarrassment,40 those who struggle with

literacy do have the ability to learn, if appropriate explanations are given or if patient education

materials are presented at their level. Approximately one-third of the participants reported that

they did not receive any advice from the medical doctor on their last visit to the hospital and/or

clinic, while the others got advice on a number of diseases such as prostate cancer, hypertension

and diabetes mellitus. A number of factors may have contributed to this finding, which could

include the time that the medical doctor spent with the participant; whether the patient asked the

medical doctor for advice, and the patient’s ability to communicate with the medical doctor. In

medical care settings, a patient’s oral language skills are related to his or her ability to describe

                                                382
symptoms and can subsequently affect the practitioner’s ability to diagnose. For example, studies

have indicated that a physician’s assessment of a patient’s health history, or the test of a patient

for dementia, may be affected by the patient’s literacy status.44 Furthermore, the patient’s oral

comprehension abilities may curtail his or her dialogue with the physician, or ability to

comprehend oral instructions. This may contribute to delayed diagnosis of many of the medical

conditions indicated by the participants in this study. In this study just over one quarter of the

participants were diagnosed with cancers such as prostate and colorectal in the past 1 - 6 months.

Approximately one-tenth were diagnosed with hypertension and one-twentieth with heart disease

and diabetes mellitus.

       Morbidity and mortality data over the last three decades highlighted prostate cancer as

the most commonly diagnosed malignancy in Jamaican males. The cancer registry in Jamaica

records incident cases of cancer in the parishes of Kingston and St. Andrew where approximately

27% of the island’s population of 2.5 million residents lives.45 Information on cancer-related

deaths showed prostate cancer to be the leading cancer site among males (30.3%) and the leading

cause of cancer mortality in Jamaica (16.5% of total cancer deaths).46 Bennett and associates

(1998)47 assessed the relationship of literacy, race, and stage of presentation among patients

diagnosed with prostate cancer. The focus of the study was 212 low-income men from two

prostate cancer clinics. The authors report that men with literacy levels below sixth grade were

more likely to present with advanced-stage prostate cancer. They conclude that low literacy may

be an overlooked but significant barrier to the diagnosis of early-stage prostate cancer among

low-income white and black men. They suggest that the development of culturally sensitive,

low-literacy educational materials may improve patient awareness of prostate cancer and the

frequency of diagnosis at early stages. Thus it is possible that the low health literacy rate

                                                383
contributes to the high number of participants diagnosed with cancer during the past 1 – 6

months, in particular prostate cancer. However, there is no information in this study or in the

literature as to the number of men in Jamaica diagnosed with advanced prostate cancer.

        Health literate individuals have the knowledge and ability to make healthy choices and

adopt healthy lifestyles. They employ health skills, which are a subset of their life skills. The

elderly have the greatest health literacy needs due to their high prevalence of chronic diseases,

yet they are disproportionately represented among the health illiterate.48,49 Preventive strategies

can in fact improve the health of the ageing population by bringing benefits to major health

conditions like obesity, diabetes mellitus, cardiovascular disease and osteoporosis. Such

measures can help both to improve the quality of life of older people as well as contribute to the

control of health care costs. In this study approximately two-thirds of the participants reported

being engaged in some form of physical activity; however some were also engaged in adverse

lifestyle practices, as nearly one-fifth of the participants were currently smokers. Evidence from

other research confirms this, by showing that the benefit of moderate exercise can help in

improving arthritis-related and other chronic illnesses such as diabetes mellitus and heart

diseases.51 Indeed, physical activity plays a central role in the prevention and management of

chronic disease,52 and physical inactivity is identified as a leading cause of disability among

older adults.53 Even failure to walk for exercise, for example, is shown to be an important risk

factor for illness in old age.54

        This article has made a significant contribution to the literature by highlighting and

linking health literacy, health-seeking behaviour and lifestyle practices among men in Jamaica.

This subject area is under-researched in Jamaica and the wider English-Speaking Caribbean.

However, there are some limitations to this study. Women 55+ years were not a part of our

                                               384
sample, and so the study cannot be generalized about Jamaicans. However, it is generalizable for

men 55 years and older. In addition, there is the possibility of social desirability bias, where the

participants in the study told the interviewers what they wanted to hear to get their approval.



Conclusion

This study demonstrated that health literacy is a very basic problem among elderly men in

Jamaica. The majority of the participants reported good current health status and high cognitive

functionality but low functional independence. A significant number of the respondents did not

take their prescribed medication, nor did they seek medical attention despite having reported

prostate cancer and a number of other chronic diseases. These findings point to low health

literacy among elderly men, which requires urgent programmatic attention from the Ministry of

Health to reduce adverse health outcomes in the country. This study also provides invaluable

information on men that is far-reaching and can be used to guide in the formulation of health

policies and intervention programmes.




Disclosure

The authors have no competing interests to report.




                                                385
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43. Cheatham JB. TUTOR: A Collaborative Approach to Literacy Instruction. Syracuse, NY:

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49. Health Literacy: A Prescription to End Confusion, National Academy of Sciences;2003.

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54. Strawbridge W, Cohen R, Shema S et al. Successful dying: predictors and associated

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                                              391
Figure 1: Causes of mortality of the parent(s) of participants




                                                392
Table 13.1: The examination of variables such as health status and health literacy by area of
        residence


                                         Area of residence

Variable
                                      Urban             Rural             Total
                                                                                           P

Health status                                                                               0.190
  Excellent                           160 (17.5)         197(20.5)       357 (19.0)
  Good                                523 (57.1)         515 (53.7)     1038 (55.4)
  Fair                                233 (25.4)         247 (25.8)      480 (25.6)
Health literacy                                                                            *0.001
  Physically well/Not sickness        119 (56.9)            65(44.5)     184 (51.8)
  Healthy lifestyle                    39 (18.7)           24 (16.4)      63 (17.7)
  Self-care                            30 (14.3)           32 (21.9)      62 (17.5)
  Religious activity                     9 (4.3)           15 (10.3)       24 (6.8)
  Psychological conditions               7 (3.4)              4 (2.8)      11 (3.1)
  Physical functioning                   5 (2.4)              6 (4.1)      11 (3.1)
Education                                                                                  *0.006
  No formal education                 103 (10.5)           97 (9.5)      200 (10.0)
  Primary and infant                  828 (84.4)         833 (81.7)     1661 (83.1)
  Secondary                             33 (3.4)           69 (6.8)       102 (5.1)
  Tertiary                              17 (1.7)           20 (2.0)        37 (1.9)
Types of medication taken                                                                  *0.001
  No medication                       873 (89.0)         824(80.9)      1697 (84.9)
  Injection                             62 (6.3)         112 (11.0)       174 (8.7)
  Tablets                               24 (2.4)           45 (4.4)        69 (3.5)
  Liquid                                14 (1.4)           28 (2.7)        42 (2.1)
Purchased medication                                                                        0.078
  Yes                                 318 (32.4)         362 (35.5)      680 (34.0)
  No                                  663 (67.6)         657 (64.5)     1320 (66.0)
Smoking behavior                                                                            0.251
Never smoked                          319 (32.5)         322 (31.6)      641 (32.1)
In the past                           483 (49.2)         481 (47.2)      964 (48.2)
Smoking now                           179 (18.2)         216 (21.2)      395 (19.8)
Health care-seeking                                                                        *0.011
behaviour (when ill)
    Yes                               313 (31.9)         380 (37.3)      693 (34.7)
   No                                 668 (68.1)         639 (62.7)     1307 (65.4)
*P < 0.05; χ (df = 1) = 6.40, P = 0.011
             2




                                               393
Table 13.2: The examination of variables such as medication compliance, visit to the medical
doctor by area of residence

                                           Area of residence

Variable
                                         Urban           Rural        Total
                                                                                    P

Lifestyle practices                                                                     0.498
 Smoking                                   88 (14.9)     108 (17.4)    196 (16.2)
 Prostate cancer                            48 (8.1)       56 (9.0)     104 (8.6)
 Diet                                     143 (24.3)     154 (24.8)    297 (24.6)
 Physical activity                        310 (52.6)     302 (48.7)    612 (50.6)
Type of retirement plan                                                                 0.133
 Finance                                     8 (2.0)       19 (4.5)      27 (3.3)
 Social arrangement                         14 (3.5)       14 (3.3)      28 (3.4)
 Health care                              379 (94.5)     391 (92.2)    770 (93.3)
Taking medication                                                                       0.225
 No medication                            662 (67.5)     653 (64.1) 1315 (65.8)
 Cancer                                   253 (25.8)     283 (27.8) 536 (26.8)
 Hypertension                               54 (5.5)       58 (5.7)   112 (5.6)
 heart problems                              9 (0.9)       15 (1.5)    24 (1.2)
 Benign prostatic hyperplasia                3 (0.3)        8 (0.8)    11 (0.6)
 Diabetes mellitus                           0 (0.0)        2 (0.2)     2 (0.1)
Advice (or none) given                                                                  0.601
 None                                     582 (59.3)     583 (57.2) 1165 (58.3)
 Smoking                                    53 (5.4)       45 (4.4)    98 (4.9)
 Prostate cancer                            47 (4.8)       53 (5.2)   100 (5.0)
 Diet                                     165 (16.8)     190 (18.6) 355 (17.8)
 Physical exercise                        134 (13.7)     148 (14.5) 282 (14.1)
Last visit to doctor                                                                *0.021
 Less than 12 months                      148 (32.0)     141 (31.2)    289 (31.6)
 12 to 35 months                          196 (42.4)     160 (35.4)    356 (38.9)
 36 and beyond months                     118 (25.5)     151 (33.4)    269 (29.4)

*P < 0.05




                                            394
Table 13.3: Awareness of signs and symptoms of hypertension and diabetes mellitus

                              Hypertension                                Diabetes mellitus

Signs and symptoms                 n         %     Signs and symptoms          n          %

-                                  -           - Frequent urination          232        11.6

Headache                        259        13.0 Excessive thirst             311        15.6

Dizziness                       272        13.6 Excessive hunger             187         9.4

Blurred vision                  538        26.9 Unusual weight loss          132         6.6

Nausea                          195          9.8 Increased fatigue           252        12.6

Swollen feet                    217        10.9 Irritability                 106         5.3

Shortness of breath             245        12.3 Blurred vision               301        15.1

Unaware                         274        13.7 Unaware                      479        24.0




                                             395
Table 13.4: Self-reported diagnosed health conditions


Details                 1-6          7-12          2-5         6-10        11-20             21-30 years     30+
                        months       months        years       years       years                             years

                          n (%)         n (%)       n (%)       n (%)          n (%)               n (%)        n (%)

Cancer                  180 (43.3)    105 (41.5)   36 (17.1)    12 (7.5)       3 (2.9)             1 (1.6)       1 (4.8)
Hypertension              40 (9.6)     27 (10.7)   32 (15.2)   33 (20.6)           -              8 (13.1)       2 (9.5)
Heart disease             22 (5.3)      24 (9.5)    10 (4.7)   25 (15.6)     17 (16.7)             6 (9.8)       1 (4.8)
Benign                    22 (5.3)      18 (7.1)   29 (13.7)   29 (18.1)     24 (23.5)           13 (21.3)      7 (33.3)
prostatic hyperplasia
Diabetes mellitus         22 (5.3)      18 (7.1)   33 (15.6)    12 (7.5)      25(24.5)           14 (23.0)      5 (23.8)
Kidney/Bladder            16 (3.8)     60 (23.7)   71 (33.6)   49 (30.6)      33(32.4)           19( 31.2)      5 (23.8)
diseases
Unspecified             114 (27.4)       1 (0.4)       (0.0)       (0.0)           (0.0)             (0.0)           (0.0)
Total                         416           253         211         160                102             61              21




                                                    396
Table 13.5: Heath related issues that affected the elderly men


Characteristic                                                     n     %
Seeking medical advice (outside of illness)
  Yes                                                             157    7.9
   No                                                            1843   92.2
Meaning of good health
  Physically well (not sick)                                     567    57.9
  Healthy diet                                                   190    19.4
  Self-care                                                       96     9.8
  Religious activity                                              40     4.1
  State of mind                                                   73     7.4
  Physical functioning                                            14     1.4
Factors responsible for one’s ill-health or good health
  Diet                                                            327   16.4
  Exercise                                                        134    6.7
  Sleep                                                            75    3.8
  Age                                                              44    2.2
  Religion                                                         22    1.1
  No chronic disease                                               13    0.7
  Regular checkup                                                  29    1.5
  Family life                                                      12    0.6
  Self-care                                                        20    1.0
  Financial status                                                  6    0.3
  Mobility                                                          5    0.3
  Non-response                                                   1313   65.7
Took medication as prescribed by medical
practitioner
  Yes                                                             685   34.2
   No                                                            1315   65.8
Name and nature of taken medication
  Known                                                           509   25.4
  Unknown                                                        1491   74.6
Length of time before seeking medical attention
  Immediately                                                    1041   52.1
  2- 7 days                                                       825   41.3
  1 – 3 weeks                                                      36    1.8
  4 weeks – 1 month                                                17    0.9
  Don’t go                                                         14    0.7
  Wait                                                             67    3.4




                                              397
                                                                        Chapter
                                                                                      14
 Healthcare providers in St. Catherine, Jamaica: Older men’s satisfaction (or
            dissatisfaction) with their healthcare service delivery




Paul A. Bourne, Chloe Morris, Christopher A.D. Charles , Maureen D. Kerr-Campbell, and
                               Denise Eldemire-Shearer


Patient satisfaction and quality of life are becoming increasingly important in the more
traditional clinic outcomes in the monitoring and evaluation of healthcare delivery. This study
explored patient’s self-rated health and patient satisfaction with healthcare providers; and
examined whether health care providers are barrier to patient care. The sample consisted of
2,000 men who are 55+ years in the parish of St. Catherine, Jamaica. A 132-item questionnaire
was used to collect the data. Descriptive statistics was used to provide information about their
satisfaction with the healthcare system. Seventy-four percent of the sample indicated good self-
rated health status (excellent, 19.0%). Forty-seven percentage of the sample sought advice from
a health care provider in the last 12 months; 14.1% understood the advice of the clinician,
community health aide (19.9%), pharmacist (15.4%), nurse (2.1%) and nurse aide (4.6%). The
respondents indicated that community health aides contributed more to improving their health
(43.4%) when compared with nurses (34.8%), clinicians (17.5%), and herbalists (3.7%).
Furthermore, 31.7% indicated that their medical doctors were hospitable and 4.2% were
knowledgeable. Negative self-rated health, perceived lack of knowledge among medical doctors,
lack of understanding of advice from healthcare providers, are just some of the factors
associated with dissatisfaction of patients with chronic conditions. These findings provide a
framework and foundation from which further studies on effective intervention aimed at
improving healthcare provider-patient relationship and service can be conducted.




                                              398
Introduction

Jamaica is a developing country that has an estimated population of 2.7 million people, 49% are

males, and almost 11% are 60+ years.1 Life expectancy at birth in Jamaica for males between

1879 and 1882 was 37.02 years 39.80 years for females, and between 2002 and 2004 males’ life

expectancy had arisen to 71.26 years 77.07 years for females. This is clearly an indictor of

demographic ageing.1 Despite on average female living longer than males, males continue to

report less illness than their female counterparts.2,3 The increased in life expectancy for both

sexes are owing to (1) improved in sanitary, (2) water quality, (3) public health intervention and

(4) the deliverables of the health sector. Inspite of the afore-mentioned conditions which account

for increased life expectancy for the sexes, in particular males, their healthcare utilization has

always been less than females. Thus, healthcare utilization is a feminized phenomenon,3 and this

is equally so among middle-to-older aged males.

       Jamaica's health system is well supported to meet the needs of the medical services of its

society. The health sector of Jamaica is organized into privately and publicly owned institutions.

Most of the health care institutions offering ambulatory care are privately owned. Private sector

health services are provided through an extensive network of professionals offering specialist

services, and by family doctors throughout the island. A number of non-governmental

organizations provide health services for a nominal fee.2

       The hospitals and the preventive institutions are mainly in the public sector. “The health

system offers primary, secondary, and tertiary care. Ambulatory care at the community level is

delivered through a network of 343 health centers. Secondary and tertiary cares are offered via

23 government hospitals and the teaching hospital of the University of the West Indies, with a

combined capacity of 4,802 beds”.3 Hospitals are classified into categories        A, B, C, and
                                               399
Specialist, depending on the level of complexity of the services offered. There are three Type A

hospitals; four Type B hospitals; eleven Type C and 6 Specialist. The Type A hospitals, are

mostly situated in urban areas. They provide inpatient and outpatient services and support

referrals from Types B and C facilities (found in rural areas), of which there are currently 11 in

the country. There are currently six Specialist hospitals3. Approximately 38% of the population

utilizes the public sector for ambulatory care, 57% use the private sector, and 5% use both

sectors.3

       Modern healthcare systems in Jamaica are seeking to adopt a more client-oriented

approach to the delivery of healthcare. With this paradigm shift, patient satisfaction and quality

of life are becoming increasingly important in the more traditional clinic outcomes in the

monitoring and evaluation of healthcare delivery.4 Furthermore, patient acceptance and

satisfaction with care has only recently received attention in the medical literature.5 Satisfaction

of users of health services is a quality-of-care indicator employed to evaluate healthcare and to

identify, from the user perspective, aspects of services that can be improved; it also serves as a

method to conduct comparative analyses of healthcare programs.6,7 Satisfaction is multifaceted

and reflects the experiences, expectations and preferences of users with regard to different

components of the care process, such as access, facilities available, interpersonal relationships

and technical quality. Satisfaction is influenced by user characteristics such as gender, age,

socio-economic status, and co-morbidity, and by the health outcomes achieved by care-

principally expectation fulfillment.8,9 In addition, satisfaction has an effect on user behavior.

Specifically, individuals who are satisfied with healthcare are more likely to comply with

treatment regimens and are more willing to continue visiting the same doctor in the same

institution.10,11 Patient satisfaction is usually assessed from questions designed to measure

                                                400
satisfaction with services provided at healthcare facilities and by all categories of staff (clinicians

, nursing personnel and allied health staff).12

       In both developed and developing countries the incidence and prevalence of chronic

diseases is showing a steady increase and Jamaica is facing a growing demand for care of

patients with chronic conditions as well.13 The Jamaican Government has been undertaking

various steps to reform the health system of Jamaica. Some of them include decentralization,

integration of services, setting some quality assurance standards, better cost sharing,

improvement of the efficiency, fostering public-private partnerships, and equity. The leading

agenda behind these reforms is that the healthcare services provided should better match the

current demands of the Jamaican people and to make the available resources much more

efficient. The ultimate goal of health services in Jamaica is to improve and maintain the health

and functional capacity of the population served.13

       With the increase in demand from patients who value doctors who are patient-centered,

together with the rise of consumerism in medicine, health service research on doctor-patient

relationship has become an important area of interest for both medical researchers and

administrators alike. The satisfaction of patients in developing countries such as Jamaica is an

unexplored area. Josephs and Nichols examined patient satisfaction and quality of life among

persons attending chronic disease clinics in South Trinidad.14 They found that approximately

two-thirds of participants gave health and support staff a rating of good to excellent; fifty-three

and a half per cent and 58% gave a poor to fair rating for the length of the waiting time and

explanation offered when there was a significant delay in the starting times of clinics

respectively.14



                                                  401
       Gender comparative studies reveal that men are less likely to go to the doctor for physical

and mental health problems and to have an usual place of healthcare.15,16 It is with this

understanding that the authors sought to explored patients self-related health and patient

satisfaction among middle-aged and older men in the parish of St. Catherine, Jamaica. The study

also determine what, if any, correlation exists between self-related health, employment, life

satisfaction, also between income of respondents and their satisfaction with healthcare providers.

Methods
Sample

The current study is based on the dataset taken from a survey that was conducted in 2007 on

older men (ages 55 years and over) in St. Catherine, Jamaica. A representative sample of St.

Catherine was drawn. Stratified multistage probability sampling technique was used to draw a

sample of 2,000 respondents.

       The Statistical Institute of Jamaica (STATIN) maintains a list of enumeration districts

(ED) or census tracts. The parish of St. Catherine was chosen as previous data and surveys by

STATIN suggest that it has a mix of demographic characteristics (urban, rural and age-

composition) which is similar to Jamaica. The parish of St. Catherine is divided into a number of

electoral constituencies made up of a number of enumeration districts (ED). The enumeration

districts in the parish of St. Catherine provided the sampling frame and the sample size was

determined with the help of STATIN. The one hundred and sixty-two (162) enumeration districts

in the parish of St. Catherine provided the sampling frame. The enumeration districts were listed

and numbered sequentially and selection of clusters was arrived at by the use of a sampling

interval. Forty (40) enumeration districts (clusters) were subsequently selected with the

probability of selection being proportional to population size. Using STATIN and the C-Survey

                                               402
computer software, it was determined that 50 older men in each enumeration district would be

interviewed yielding a sample size of 2,000.

       The parish of St. Catherine had approximately 233,052 males, (preliminary census data

2001) of which number 33,674 males were 55+ years. STATIN maintains maps with

enumeration districts or census tracts which include the selected EDs and access routes and have

references to the selected site of a starting point household within each ED. The starting point

was determined by randomly selecting a household with a man 55 years and over from the list of

persons in the ED. The requisite number of interviews for each ED was completed.

       Where the selected household was found to be subsequently devoid of an older man (due

to out-migration or death), an adjacent household was canvassed. Where households had a man

55 years and older as a resident and he was not at home, the interviewer returned within two

days. In households where there was more than one man 55 years old and over, then all were

included in the survey.



Instrument

A 132-item questionnaire was used to collect the data. The instrument was sub-

divided into general demographic profile of the sample; past and current health

status, health-seeking behaviour, retirement status, social and functional status. The

overall response rate for the survey was 99% (n = 1,983). Data were stored,

retrieved and analyzed, using SPSS for Windows, version 16.0 (SPSS Inc; Chicago,

IL, USA). The questionnaire was subsequently developed to collect data for the Doctor of

Philosophy in Public Health for one of the authors. It was pre-tested at the Health Centre at the

                                               403
Department of Community Health & Psychiatry, the University of the West Indies (Mona) to test

for clarity and appropriateness and necessary adjustments made. The final instrument was

formatted into a booklet for easier handling.

Measure

The variable happiness was measured based on people self-report of their happiness. It is a Likert

scale question, which ranges from always to rarely happy. The variable self reported health status

was measured using people self-rate of their overall health status, which ranges from excellent to

poor. The question that was asked was ‘how would you rate your health today?’ (1) Excellent:

(2) good; (3) fair and (4) poor. In evaluating education the question that asked was ‘what is your

highest level of education attained?’ The options were (1) no formal education; (2) basic school;

(3) primary school/all age; (4) secondary/high/technical school; (5) vocational (i.e.

apprenticeship/trade); (6) diploma; (7) undergraduate degree; and (8) post-graduate degree. In

assessing childhood illness the question that was asked was ‘were you seriously ill as a child? (1)

Yes, (2) no. In addition ‘were you frequently ill as a child? (1) Yes, (2) no. If the response to

either question was yes, this was coded as poor childhood health status and if the response was

no in both cases it was coded a good health status in childhood. Age group was categorized into

three sub-groups. These were (1) ages 55 to 64 years; (2) ages 65 to 74 years; and (3) ages 75

years and older (i.e. 75 years and older).

       The variable is measured based on people self-report on their happiness.17-19 This

operationalization is based on a basic indicator proposed by Diener (2000),20 including a more

emotional component referring to happiness. The question that was asked was ‘taking all things

together, how happy would you say you are?’ It is a Likert scale question, which ranges from

high to low happiness. In terms of life satisfaction, Diener (2000)20 had proposed that happiness

                                                404
includes emotional components and a more cognitive component referring to life satisfaction

taking into consideration the question, ‘all things considered, how satisfied are you with your life

as a whole nowadays?’ The variable heath status is measured using people self-rate of their

overall health status according to Kahneman and Riis,21 which ranges from excellent to poor

health status. The variable used in this study for health status is a binary one, whether or not the

person had good-to-excellent or poor health status.

       Patient satisfaction was assessed from questions designed to measure satisfaction with

services provided, facilities and staff. Participants were asked to rate quality of healthcare

services, condition of the facility, and performance of categories of staff (i.e. medical doctors,

nursing personnel, community aides) using the Likert scale excellent, very good, good,

moderate, poor, very poor. The questionnaire covered aspects such as have the respondent sought

medical advice in the last 12 months; how soon did the respondent sought medical care after the

onset of illness; did the respondents understand the advice given by the health care provider; how

knowledgeable was the healthcare provider; and the extent to which the healthcare provider

contributed to improving their health.

       For the current study, descriptive statistics was employed to provide background

information on the sample, and chi-square was used to examine non-metric variables. A P-value

less than 5% (2-tailed) was used to indicate statistical significance, and there were two exclusion

criteria. One, in the event a variable has more than 20% of the cases missing; and two,

collinearity. In addressing collinearity (r > 0.6) the aim was to independently enter variables in

the model to determine which one should be retained during the final model construction. To

retain or exclude a variable from the model, this was based on the variables’ contribution to the

predictive power of the model and its goodness of fit.

                                                405
Results

Table 14.1 presents the socio-demographic characteristic of the sample. The sample had 2,000

men ages 55 years and older. Of those who were employed, majority were tradesmen (46.8%)

compared with professionals (administrators, 24.0% and technical, 14.3%), clerks (7.6%) and

service workers (7.3%). Seventy-four percentage of the sample indicated at least good self-rated

health status (excellent, 19.0%). Almost 46% of the sample had visited a medical doctor in the

last 5 years (31.6% in less than 12 months; 39% in 12 to 35 months and 29.4% in 36 months and

over). Only 34.7% of the sample indicated that they visit a medical doctor at the onset of an

illness. Most of those who sought medical care on the onset of an illness did so immediately after

(52%); 41.2% indicated between 2 to 7 days; 1.8% indicated 1 to 3 week; 0.9% indicated 4

weeks; and 0.7% reported that they did not sought medical care.

       Eighteen percentage of the respondents experienced illness during childhood. The

medical conditions were measles (34.5%); asthma (26%); pneumonia (9.9%); polio (8.7%);

accident (6.5%); jaundice (4.5%); hernia (1.7%), and others (3.1%). Currently the medical

conditions experienced by the respondents in the study were prostate and other cancers (16.8%);

kidney diseases (12.7%); hypertension (9.2%); heart disease (5.3%); benign prostatic hyperplasia

(7.2%); diabetes mellitus (6.5%); arthritis (1.0%), asthma (0.3%) and others (5.9%). Only 34.5%

of the sample had ever done a prostate cancer screening examination.

       Majority of the sample indicated that they were rarely depressed (56.9%); 2.5% were

always depressed and 34.3% were depressed sometimes. Only 24% of the sample was rarely

happy and 4.5% indicated always. Almost 33% of the sample was rarely satisfied with life, 3.5%

indicated always and 33.7% said sometimes.



                                               406
       Forty-seven percentage of the sample sought advice from a medical provider in the last

12 months; 14.1% understood the advice of the medical doctor, community health aide (19.9%),

pharmacist (15.4%), nurse (2.1%) and nurse aide (4.6%). In terms of the level of satisfaction

received from healthcare providers, respondents gave a rating of very good for community health

aides and pharmacist while good was highest among nurses and medical doctors, who also

received a rating of very poor (Figure 1). The respondents indicated that community health aides

contributed more to improving their health status (43.4%) compared with the other healthcare

providers (nurses, 34.8%; medical doctors, 17.5%; herbalists, 3.7%). Six percentages of the

respondents indicated that they did not like their medical doctor; 31.7% indicated that their

medical doctors was hospitable; 4.2% reported they were knowledgeable; 36.4% reported that

the waiting time was too long to see the doctor; and 17.5% indicated that the physical milieu was

poor. The utilization of the health care system of the respondents were health centers 32.1%;

private medical doctors 31.0%; public hospitals 18.8%; private hospitals 17.3%; herbalists 0.1%

and visiting health institutions abroad 0.1%.

       Respondents who were happier and have greater life satisfaction were more likely to visit

a doctor in the last 12 months (Table 14.2). Males who resided in rural areas were more

dissatisfied with life (35.9%) than urban males (29.8%). Males who owned their homes (31.3%)

had greater self-rated health status compared to those who did not (9.9%). There was moderate

self-rated health status for those who do not own their homes (36.2%) and those who own their

homes (11.4%; χ2 = 222.7, P < 0.0001).

       There was a significant statistical association between self-rated health status and

employment category (χ2 = 81.0, P < 0.0001). Respondents who were employed had greater self-

rated (excellent self-related health status) (28.3%) than those who were unemployed (8.3%), and

                                                407
retired (18.2%). Furthermore, respondents who had secondary or post-secondary (42.1%) and

tertiary level education (35.1%) had higher self-related health status than those without formal

education (17.7%) and primary education (17.3%; χ2 = 88.4, P < 0.0001).

       There was a significant statistical relationship between satisfaction and the income

category of respondents (Table 14.3). Respondents in the low income category were more

satisfied with the service of medical doctors and nurse aides, while respondents in the high

income category were more satisfied with community aides and pharmacists.



Discussion



The study found that approximately one-half of the respondents visited a medical doctor in the

last five years compared with less than one-third in the last 12 months and approximately two-

fifths in the last 12 to 35 months. Furthermore, the respondents aged from 55 and over indicated

that they had mostly non-communicable diseases such as prostate and other cancers, kidney

diseases, hypertension, heart disease, benign prostatic hyperplasia, diabetes mellitus and arthritis.

The level of satisfaction with healthcare providers and the overall quality of service was fair

although there were aspects of the healthcare service that was rated from moderate to poor.

These findings are consistent with those of other studies that patients with chronic conditions

were dissatisfied more frequently.22 The fact that these were patients with chronic conditions

who were dissatisfied deserves attention because they use the health services continually and

their satisfaction can influence their contribution to disease management, which is important for

better control of their conditions. Patients with chronic conditions receive long-term care and this

should be reliable, periodic, continuous, and coordinated among different healthcare providers.
                                                408
        In this study, the majority of the men indicated that they were rarely depressed,

approximately one third depressed sometimes; and almost the same number rarely satisfied with

life or sometimes satisfied with life. These results are lower compared to values obtained for

self-related health status. Approximately three-quarters of the respondents indicated at least good

self-related health status with approximately one-fifth indicated excellent health. Interestingly

the study showed that men who were happier and had greater life satisfaction were more likely to

visit a medical doctor in the last 12 months. Studies have showed that patients with negative self-

rated health were at greater risk for dissatisfaction with healthcare.23 Most of the men in this

study had chronic non-communicable diseases, and a chronic condition is related to negative

self-rated health and, at the same time, patients with such conditions tend to perceive healthcare

as unsatisfactory.24,25 Positive self-rated health is related to better functional and physical

states.26 Therefore, keeping the disease under control in a patient with a chronic condition is

crucial for a positive perception of self-rated health. Given that in this study approximately three-

quarters of the respondents indicated at least good self-related health status with approximately

one-fifth indicated excellent health, it is possible that there are other factors other than their self-

related health status that may have contributed to the low level of satisfaction among the

respondents. It is possible that there were respondents in this study that had more than one

illness. Therefore these respondents were likely to express their dissatisfaction with the quality of

care they received from the healthcare providers. Poor subjective quality of life among persons

with multiple illnesses has been found in a number of studies and suggests the need for regular

monitoring and evaluation of those persons at increased risk for poorer perceived health. These

become more important when it is realized that subjective quality of life can predict morbidity

and mortality.27-29

                                                  409
       Some characteristics of the organizational dimension can cause dissatisfaction among

patients. The main problems are related to cleanliness of the facilities and administrative

procedures. Nearly one-fifth of the respondents indicated that the physical milieu was poor and

just over one-third indicated that the waiting time to see a medical doctor was too long. The

registration of patients, the opening of a clinical chart for a newcomer, the receipt of medication

from the pharmacists is procedures that may take longer than expected and sometimes cause

dissatisfaction. Furthermore, healthcare in Jamaica is free to all citizens and legal residents at

government hospitals and clinics. This includes prescription drugs. However one of the

drawbacks to free healthcare is long lines with no appointments accepted by the physicians. In

addition, some rural public hospital and clinics are without their full cadre of medical doctors

and other healthcare providers. In this study, one third of the respondents visited health centers

and just under one-fifth visited public hospitals. The long lines and waiting times experience by

some of the respondents at these public healthcare institutions may have contributed to their

dissatisfaction with the quality of healthcare offered by the state. This is consistent with reports

from other developing countries.30 Therefore attention to both elements, the doctor-patient

relationship and organizational arrangements are essential to improve patient satisfaction. The

improvement of the medical doctor-patient relationship depends heavily on the attitude of the

medical doctors whereas the improvement of organizational arrangements is the responsibility of

managers. In this study, we found a positive statistical association between life satisfaction and

frequency of doctor’s visits, indicating that low satisfaction with healthcare providers will see

lower visits in health care among patients.

       Informing patients on different aspects of their health and about the care they need are

very important for those with chronic non-communicable conditions. Also, treating patients as

                                                410
co-participants in the process of decision-making has been repeatedly emphasized as an

important patient right.31 When patients are well-informed and participate in treatment decisions,

their anxiety decreases and their therapeutic adherence improves, thus increasing the chances of

getting better health outcomes.32 Nevertheless, this critical component of communication is badly

neglected by medical doctors.33 In a study by Doubova et al. one of the most frequent

prescription errors in ambulatory patients over 60 years of age with non-malignant pain

syndrome was that family doctors failed to provide instructions to the patients about how to take

the prescribed drugs, and did not inform them of possible adverse effects.34 From our

perspective, close doctor-patient communication and relationship is the backbone of care; this

allows the medical doctor to better know the condition of the patient and to place treatment in a

context that permits comprehensive disease management. Nevertheless, we found that

approximately one-half of the respondents visited a medical doctor in the last 12 months and

approximately one-eight understood the advice offered; this was slightly higher among

community health aids and pharmacists. It is important to note that many patients did not

understand the advice given by the nurses or nurse aides. It is likely that the medical doctors used

medical jargons and technical medical terms and abbreviations which are like a foreign language

to many patients; given the context of this study where only 6% of the respondents have post-

secondary or tertiary education.

       Patients have two needs which correspond with the demands of treatment: the need for

medical information and instruction, and the need for emotional support and reassurance.35 These

needs correspond with instrumental behaviour for giving information and advice, and affective

behaviour for showing concern and giving emotional support.36 It may be hypothesized that at

first patients’ uncertainty and anxiety require doctors’ emotional support and attention, whereas,

                                                411
in subsequent visits, patients might need more medical-technical information to apply the new

treatment methods effectively. Adequate management of the condition is likely to require doctors

to respond to these changing needs accordingly, by shifting their communication style.37 It is

likely that because the medical doctors fail to give the necessary information to the patients, this

may account for just 4.2% of them deemed knowledgeable by the respondents. This may affect

their subsequent visits to the doctor. Furthermore, it is reported that men in Jamaica have not

fully accessed and utilized the health care services provided by the government.38 In a study by

Figueroa et al. conducted in Jamaica, significantly more men (86/463 or 18.6%) than women

(40/927 or 4.3%); (P < 0.0001) had never had their blood pressure taken by a health

professional.38 In this study, just over one-half of the respondents reported that they seek medical

care on the onset of the illness while just over two-fifths indicated that they do so between two to

seven days.

       Medical doctors many times do not give adequate consideration to the patient's feelings

or desires regarding the illness or condition being treated. The patient, on the other hand, often

assumes a doctor may know things about his condition when he actually does not. In a study by

Jackson et al. involving 500 patients who were seen by 38 primary care clinicians for physical

symptoms, aspects of patient doctor communication such as receiving an explanation of the

symptom cause, likely duration, and lack of unmet expectations were found to be the key

predictors of patient satisfaction.39 In this study most of the respondents reported that the

community aides contributed more to improvement in their health and therefore greater

satisfaction compared to nurses and medical doctors. Furthermore, just over three-tenths

indicated that their medical doctors were hospitable. It has been shown that doctor's attitude

towards his patients, his ability to elicit and respect the patients' concerns, the provision of

                                                412
appropriate information, the demonstration of empathy and the development of patient trust are

the key determinants of good compliance with medical treatments in patients.40,41

       The potency, evaluative and activity images a patient has in his mind about his doctor are

positively related to age and length of time seeing the doctor. These images are negatively

related to the educational level of the patient. More educated patients do not have as desirable an

image of the doctor as do less educated patients. Older patients and those who have been coming

to the doctor longer apparently have more desirable images of the doctor than younger or newer

patients. Conversely, older patients may have more trouble understanding the doctor than do

younger ones. Furthermore older patients may feel they do not get enough opportunities to

communicate with the doctor especially in the public healthcare system in Jamaica where

healthcare centers and hospitals especially in rural areas are overcrowded and medical doctors

are unable to spend adequate time with the patients. In addition older patients like the men in this

study with the majority being over 65 years and having less than secondary education may

present more communication related problems to the doctor than do younger patients. Older

patients understand less and are prone to frustration because of this. Sometimes these older

patients seldom feel they get enough chances to talk to the doctor and tell him what they want

although they think well of him mentally.

       According to Wilks et al.’s42 work males in Jamaica with tertiary education were the least

likely to smoke cigarettes, marijuana and take illegal drugs, but were more susceptible to

alcoholic consumption. They further argued that post-secondary level males were the most

probable to use seat belt as front seat passengers in a motor vehicle. Lifestyle practices of

educated males contributed to their better health than those in other educated cohorts.42 Well-

educated patients present some specific negatives to the doctor in the process of building a

                                                413
relationship. Presumably because of their training and reasoning ability, these more-educated

patients can detect flaws and cover-ups in the doctor's communication with them. They may not

feel he is "leveling" with them.43 More educated patients may be able to detect flaws in doctor-

patient communication situations than less educated patients can. This could contribute to their

dissatisfaction. Because of this, the doctor's image suffers and this increases their dissatisfaction.

In this study, approximately 6% of patients have post secondary or tertiary education. However

an interesting finding is that respondents in the high income category were more satisfied with

community aides and pharmacists compared with medical doctors and nurses. A high percentage

of these persons are skilled professionals and like those who are more educated are likely to

expect more from medical doctors compared with their low income counterparts, hence likely

their dissatisfaction.

        Approximately one-third of the respondents visited private practices of medical doctors

while just over one-sixth visited private hospitals. Medical doctors, when dealing with patients in

the office, will build a stronger, more credible relationship with their patients. Most of these

private patients will more likely see the same doctor on subsequent visits, so the background

information on the patient is very important in the relationship-building process since the doctor

may gain credibility over the years and is perceived to communicate better. Patients also think

more favorably of the medical doctor the longer they come to see him. How often they see him is

unimportant in the relationship and hence the higher level of satisfaction.6 Patients who visit

their medical doctors in private practice or those who visit private hospital will more likely spend

more time compared with those who visit public health institutions. With more time, it is likely

that the doctor may communicate better with the patient, laying a firm foundation, on which a

significant, meaningful, and mutually satisfactory relationship can be built. Therefore that it is

                                                 414
likely in this study that more private patients have a greater overall satisfaction than public

patients.

        One of the main shortcomings of this research has been the lack of direct measurement of

the variables instead of collecting data as was done (questionnaire). Perhaps some actual

measurements of time spent with the clinician and communication effectiveness would yield

some satisfactory results. Despite this limitation, it has been established that surveys and

interviews can be utilized in order to ascertain information from patients on (a) healthcare

delivery; (b) attitudes of healthcare professionals; (c) areas that need improvement in the health

sectors and (d) how to use these responses to improvement patient and quality care.44,          45,46.


Patients are more than lower-level animals in experiments, and so in order to effectively improve

their health outcomes, their views are critical in the health care feedback and implement process.

A study by Vingerhoets, Wensing and Grol47 found that even after the provision of patients’

evaluation of care to General Providers, there was no change in their evaluation of the care

received and therefore may challenge using patients’ views in improving quality care. However

the researchers47 opined that using patients’ evaluation in the feedback process of quality care is
                                                        48
but one component, which supports Wilcock et al.’s           viewpoint that patients’ views should be

incorporated in health care modernization. If the health care industry, in particular healthcare

workers, seek to deliver on its mandate of improving health care and quality care, quality

improvements must be done within the context of incorporating patients’ intelligence, patients’

satisfaction and dissatisfaction in health care deliverables.




                                                 415
Conclusion

There are some interpersonal and organizational situations that reveal dissatisfaction among

patients with chronic conditions receiving care from both public and private healthcare

institutions in Jamaica. Negative self-rated health, perceived lack of knowledge among medical

doctors, lack of understanding of advice from healthcare providers, are just some of the factors

associated with dissatisfaction of patients with chronic conditions, and these are among

explaining rationale for low patient health care utilization (barriers to patient care). Based on the

findings, there is the need for improved communication between healthcare providers and

patients. Strategies employed should incorporate the ideas or health beliefs of the patient and go

beyond the mere receipt of instructions. Responding to the satisfaction of patients with chronic

conditions is important to improve the quality of the services that are most important to them.

Therefore, better management of patients with chronic conditions by medical doctors is

desirable, as are institutional changes that enable doctors to provide more consultation time.

Consultancy time must be about reaching the patient, and not using medical jargons which

satisfied the medical profession but fail to reach the patient. The outcome of medicine is the

improvement of health and not the utilization jargons, more healthcare providers and health care

facilities that are distant from having a change in unhealthy practices of patients as well as

lowing health conditions. This study has shown that patient intelligence is not been addressed as

the patients have spoken that their concerns are not been effectively reached by healthcare

professionals who have neglected their views, attitude, and knowledge in patient care. Therefore,

conducting research in this area may help clinicians, educators and health service administrators

to better understand the doctor-patient relationship and communication that is unique in our

culture and social settings. This may provide a framework and foundation from which further

                                                416
studies on effective intervention aimed at improving doctor patient relationship can be

conducted. This is a particularly important issue for medical doctors and other health care

providers.

Disclosure

The authors declare no conflict of interest.

Ethical approval
Ethical approval was sought and given by the University of the West Indies Medical Faculty’s
Ethics Committee.




                                               417
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                                              422
Table 14.1. The sociodemographic characteristic of sample
Characteristic                                                n     %
Area of residence
     Urban                                                   981   49.0
     Rural                                                  1019   51.0
Age cohort
     55 – 59 years                                           469   23.4
     60 – 64 years                                           413   20.6
     65 – 69 years                                           374   18.7
     70 -74 years                                            345   17.3
     75 – 79 years                                           189    9.5
     80+ years                                               210   10.5
Marital status
     Single                                                  686   34.3
     Married                                                 894   44.7
     Separated                                               112    5.6
     Common law                                              136    6.8
     Widowed                                                 172    8.6
Head of household
     Self                                                   1763   88.2
     Partner                                                 122    6.1
     Child                                                    63    3.1
     Sibling                                                  36    1.8
     Other                                                    16    0.8
House tenure
     Own                                                     824   41.2
     Other (rent, lease)                                    1176   58.8
Employment status
     Employed                                                524   26.2
     Unemployed                                              412   20.6
     Retired                                                1064   53.2
Monthly income1
     < $1000 - $4999                                         206   39.4
     $5000 - $9999                                           143   27.3
     $10000 - $14999                                          90   17.2
     $15000 - $19999                                          47    9.0
      > $20000                                                37    7.1
Educational level
    No formal                                                200   10.0
    Infant (basic)                                          1104   55.2
    Primary                                                  557   27.8
    Secondary                                                 79    3.9
    Post-secondary                                            37    1.9
    Tertiary                                                  23    1.2
1
 Income is in Jamaican dollars




                                                 423
Figure 1. Satisfaction with service delivery of healthcare providers




                                               424
Table 14.2. Health-care seeking behaviour by life satisfaction and happiness

                                                        Life satisfaction1

Characteristic                       Rarely         Sometimes        Most times      Always

                                     n (%)             n (%)            n (%)        n (%)

Doctor’s visit

 Less than 12 months                  89 (29.3)         81 (26.3)       102 (39.1)    17 (41.5)

 12 – 35 months                      123 (40.5)        143 (46.4)        82 (31.4)     8 (19.5)

 36+ months                           92 (30.2)         84 (27.3)        77 (29.5)    16 (39.0)

                                                           Happiness2

Doctor’s visit

 Less than 12 months                  60.(28.3)         96 (27.1)       114 (38.5)    19 (36.5)

 12 – 35 months                       96 (45.3)        151 (42.7)        96 (32.4)    13 (25.0)

 36+ months                           56 (26.4)        107 (30.2)        86 (29.1)    20 (38.5)



χ = 23.4, P < 0.0001
1 2



χ = 19.3, P = 0.004
2 2




                                              425
Table 14.3. Satisfied patients with explanation of particular healthcare provider by income group

                                                                    Income group                                               P

Characteristic             < $1000 - $4999      $5000 - $9999     $10000 - $14999        $15000 - $19999   > $20000


                                         %                   %                      %                 %                 %

Satisfied patient with:

Community health aide                   1.9                 4.2                12.2                  8.5              16.2   <0.0001

Nurse                                   5.8                 2.1                 2.2                  0.0               0.0     0.091

Medical doctor                         21.4                11.9                11.1                  0.0               0.0   <0.0001

Pharmacist                              7.3                 7.7                15.6                 21.3              18.7     0.007




                                                                  426
                                                                   Chapter
                                                                                 15
         Public-private healthcare utilization differentials in Jamaica



     Paul A. Bourne1*, Denise Eldemire-Shearer1, Tomlin J. Paul1, Janet
                  LaGrenade1, Christopher AD Charles2




To assess trends in the use of private and public healthcare services among Jamaicans
over a 15-year period (1991-2007). Statistics on the use of healthcare services were
obtained from the Jamaica Survey of Living Conditions (JSLC) published by the
Statistical Institute of Jamaica (STATIN) and Planning Institute of Jamaica (PIOJ) for
the fifteen year period 1993 to 2007. Use of services were represented in quintiles and
compared for private and public facilities. For this study, differentials in the use of
services were measured as the difference in percentage use between public and private,
which was compared by quintiles over the one and one-half decades. The variability in
the differentials by quintiles was assessed using appropriate statistical methods. An
inverse utilization pattern of public and private services by economic well-being is
illustrated in this study. The public-private differential is exaggerated for the wealthiest
quintile. There is a widening of the differences in utilization between public and private
centres as level of quintiles increases (P < 0.001). Internal and external economic
conditions influence the use of private and public healthcare services in Jamaica.
Although the percentage increase in the cost of public healthcare is more than the
percentage increase in the cost of private healthcare, the actual cost to use the public
healthcare system is still much cheaper than using the private system.
                                            427
INTRODUCTION




Access to healthcare, and utilization of healthcare, is increasingly being identified as one

of the most pressing challenges to the healthcare system and by extension policy in many

developing as well as some developed nations.1-2 The discussion is taking place against

the background of increasing recognition of health as a human right, an important

component of development and one of the main dimensions and factors in overall

wellbeing.1 Regional health priority, according to PAHO and Caricom, needs to reduce

health inequalities.3 While many factors contribute to the development of health

inequalities, it is recognized that healthcare is one of the factors.1 Studies have also

shown a relationship between socioeconomic differences and the use of healthcare

services in several countries.4


       Despite the higher cost of private healthcare in India for gynecological disorders,

urological diseases, heart diseases, tuberculosis and diarrheal disorders, 58% of the

patients in a national sample used private healthcare.5 The public-private mix in Australia

facilitates high as well as fair access to medical services compared to other OECD

countries. However, Australians with higher income are more likely to see specialists

compared to their low income counterparts who see general practitioners. There is

concern that Australians with different incomes do not get the same mix of medical

services.6 In Western Australia, policies favoring private health insurance (PHI) modified

patients’ behavior by decreasing the move away from the private sector. Policy reforms

                                            428
have generated demand for healthcare because the increase use of PHI was partly a

function of the demand of patients who were patients in public hospitals.7


       The Norwegian public health system with expensive private care and free public

healthcare is very revealing. One study found that the physicians working in dual practice

create lower overall provision of healthcare because dual practice crowds out the

provision of public services. Regarding solutions, the health authority can offer higher

wages. Also a ban on dual practice works best if public and private care is relatively close

substitutes and competition from the private sector is weak. However, a pure national

health system is not good when the private sector is strong because a mixed system with

dual practice is better.8 The interests of the various stakeholders should be taken into

account.


       In 2005 a majority of Canadian doctors voted in support of private insurance to

cover necessary medical services that cannot be offered in a timely manner by the public

system. The doctors have been accused of acting in their own self interests and their have

been calls to support medicare and the public interests.9


       In low middle income developing countries, the quality of private healthcare for

the poor can enhanced by improving quality, prevent non-exploitative prices and wide

access. These strategies can be buttressed by free service for targeted groups, community

education and accreditation programs. The effectiveness of these additional strategies is a

function of the capabilities of stakeholders and the context in which they operate.10




                                            429
       Sinclair showed convincing status differential in use of health centers and private

general practice services between communities in Kingston, Jamaica.11 The extent of the

differentials in use of public/private healthcare service is affected by the relative price of

healthcare, which in turn is dependent on the actual price and economic enabling factors

such as health insurance.11 A positive correlation has been illustrated between health

insurance coverage and utilization of private practitioner services in a low income

Jamaican suburb, supporting the finding that public/private differentials are sensitive to

affordability of healthcare services.12 Also, the public-private differential in Jamaica is

not just affected by socio-economic status, government policy and resource insufficiency

but also traditional spiritual beliefs, which influences healthcare utilization particularly in

rural Jamaica.13 However, the research evidence on the success of public-private

partnerships to improve welfare and health services for a broad range of health problems

in developing countries is mixed.14


       Therefore, the comparative effectiveness research for public private health

systems is needed. However, care should be taken that data driven method does not

ignore expert driven medicine and dehumanize patients. Therefore, the best methods

should be identified that takes the patients into account during the interpretation,

circulation and implementation of data for policy purposes.15 This paper examines the

changes in the utilization of public and private healthcare services in Jamaica between

1988 and 2008 and the change in other economic variables such as value of the dollar,

cost of care and private health insurance coverage.



                                             430
AIMS AND OBJECTIVES


Aim: To describe the relationship between use of public and private health service

utilization over a fifteen-year period.


Specific Objectives:


    1.   To determine the changes in use of public and private healthcare service

         utilization

    2. To describe the effect of having private health insurance coverage on

         public/private healthcare utilization

    3. To determine the relationship between economic variables and public/private

         healthcare utilization




DESIGN AND METHODS


The researchers utilized historical comparative analysis methodology for the current

study. This methodology allowed for the use of secondary data (statistical and review of

literature). The statistical data was provided by the Jamaica Survey of Living Conditions

(JSLC). The Survey is an adaptation of the World Bank’s Living Standard Measurement

Study (LSMS) household survey, with some modifications as the JSLC focuses and

emphasizes policy impacts. Since, 1988, the Statistical Institute of Jamaica in

collaboration with the Planning Institute of Jamaica has been conducting annual studies

of living conditions of Jamaicans. The survey design is that of a multi-topic household

survey including a section on health, consumption, education, house, anthropometric
                                                 431
measurements and immunization data for all children 0-59 months, and demographic

variables.


       The survey is carried out with a self-administered questionnaire by trained

interviewers to responsible household members. Participants are asked to recall specific

and detailed consumption patterns over the last 30 days of the survey period as well as

their healthcare expenditure. The basic structure of the questionnaire has remained the

same over the years with inclusive of social safety net, crime and victimization, physical

environment, remittances and other components as modules at different survey periods.




The data on utilization of health services is published annually as part of the Jamaica

Survey of Living Conditions (JSLC). Data for this study was obtained from the JSLC for

the periods 1988 to 2007.


       The data for use of the public and private services is organized by quintiles of

economic wellbeing. The quintiles were formed from sample household members after

arranging them in ascending order of their per capita household consumption. The per

capita consumption is arrived at by dividing the total household consumption by the

number of household members. All members of the household are assumed to have the

same per capital consumption. The number of questionnaires analyzed per year averaged

1,800. Data were also extracted on purchase of pharmaceuticals and the related costs per

prescription over the period. Information was extracted from the Ministry of Health’s

annual report on the number of visits to public facilities. Differences between public and

                                           432
private services were computed (Public-Private) by quintile. Graphical plots were made

of the quintile variation in public private use by year and the graphs interpreted.


The levels of poverty and consumption were extracted as were the reported costs of care

in the two sectors and the levels of health insurance coverage.


RESULTS



Over 50% of persons reporting ill-health (Figure 15.1) seek healthcare, and the

percentage is steadily increasing (Figure 15.1). The use of private healthcare facilities

have declined over the period studied (Figure 15.2) and in 2002 the use of public

healthcare facilities actually exceeded the use of private healthcare utilization even as the

rate of unemployment declined (Figure 15.1). In the same period, the prevalence of

poverty increased by 16.6% (from 16.9 to 19.7%) and this was following 11/9, reduction

in labour force, decline in the number of people seeking medical care and bed occupancy

in public hospitals as well as mean expenditure on medication, and fall in real Gross

Domestic Product at constant prices by 0.1% for the same period, indicating the external

economy plays a role on healthcare utilization and the type of utilization and production

in Jamaica (Table 15.1-15.5)


       A cross-tabulation between per capita income quintiles and mean public-private

healthcare service utilization reveal that relation exists between the two aforementioned

variables - χ2 (df = 4) =35.68, p-value = 0.005 (Table 15.2). Further examination of

Table 15.2 shows that as income quintile changes from poorest to wealthiest, people use


                                            433
substantially less public healthcare facilities compared to private healthcare services. The

poorest Jamaicans use 14% more public healthcare services compared to the wealthiest

who utilize 62.2% more private facilities for healthcare. This is in keeping with the fact

that public healthcare costs expressed in terms of 1990 prices have increased by 105%

and that of private by 85.4%, Jamaicans still prefer private than public healthcare services

despite the narrowing of the gap over the researched period.


        The use of public-private healthcare utilization, purchasing of medication and

hospitalization as shown in Tables 15.2- 15.4 have shown marginal increases over the

studied period. Public healthcare demand by Jamaicans increased from 38.0% (in 1989)

to 40.5% (in 2007) and in 2002 following the downturn in tourist expenditure and

increased incidence of poverty saw the highest percentage of utilization of those facilities

(57.8%). Private healthcare demand, on the other hand, has declined from 54.0% (in

1989) to 51.9% (in 2007) and peak at 66.7% in 1994. Based on Figure 15.2, the wide

disparity that existed between public and private healthcare utilization in the 1990s has

narrowed post 2000, indicating that Jamaicans have a revealed preference for private

healthcare facilities.


        There were marginal fluctuations in the public-private purchase of medication.

The data revealed that during the downturn in tourism owing to the 9/11 in the United

States, Jamaicans who attended public healthcare facilities bought 6.5% more medication

(from 20.0% in 2000 to 26.5% in 2001) and those who visited private healthcare facilities

purchased 8.9% less medication (from 76.9% in 2000 to 68.0% in 2001). While we do

not have the data for discharge from private healthcare facilities in Jamaica, the discharge
                                            434
from public facilities increased by 1% (Table 15.1) coupled with a longer stay in those

institutions (by approximately 1%; Table 15.1).


        The data on expenditure indicate the increasing cost of healthcare and medication

in actual and real terms. Owing to the fact that the cost of medication increased by 394%

in the private sector and 437% in the public healthcare sector though the cost is

substantially lower. Yet there is little change in purchasing patterns of Jamaicans on

healthcare. Instead of a substantial decline in demand for private healthcare services

within the context of the trending up of inflation since 2006, there is a marginal reduction

in those who have reported an illness seeking medical care. Nevertheless, Jamaicans

have to stay longer in public facilities.


        Hospitalization rates increased in public healthcare facilities, and remained the

same in private ones. The Ministry of Health (MOH) data supports the self-reported data

of the Jamaica Survey of Living Conditions (JSLC). The JSLC survey reveals that public

healthcare utilization has increased and this was concurred by the MOH, which show that

public hospital discharges increased by 16.1% in 2004 over 2003 and by 118.9% between

1997 and 2004 (Table 15.1).          Comparatively, using MOH statistics, actual public

hospitals visits between 1997 and 2004 increased by 29.7% (Table 15.1) and the SLC

statistics for the same period increased by 26.5%. This indicates that the SLC data (on

self-reported conditions) is a good proxy for actual conditions as the difference between

the actual (MOH) and the self-reported public healthcare visits (JSLC) is 3.2%.

Embedded in this finding is the quality of the JSLC data to not only reflect the views of

Jamaicans but also their actual experiences.
                                            435
       Using actual visits to public hospitals (in Ministry of Health, Jamaica Annual

Report) and that of self-reported visits to the same institutions, the data revealed that

generally the statistics as collected by the Planning Institute of Jamaica and the Statistical

Institute of Jamaica (in Jamaica Survey of Living Conditions, JSLC) are very good not as

a proxy for health but it reveals health status and conditions of Jamaicans. Based on

Table 15.1, in 1997, the actual visits to public hospitals were 33.1% as reported by the

Ministry of Health and the self-reported figure for the same period was 32.1% (in JSLC).

The difference between the actual and the reported visits was 1%, so there is no statistical

difference. Some eight years post 1997 (2004), another comparison was made to assess

whether the self-reported data is still good to use to proxy not only perception but reality

of hospital utilization in Jamaica. The figures were 52.9% for actual visits and 46.8% for

reported visits. This indicates that in 2004 Jamaica marginally reported lower visits to

hospitals (6.1%) than the data published by the Ministry of Health. Despite the under

reporting of health visits to public hospitals in 2004 for Jamaica, there is no statistical

difference between the year and the figures by the aforementioned institutions – χ 2(4)

=157.024, P-value<0.05.


       Table 15.6 shows the public-private healthcare utilization of the income quintiles

of Jamaicans between the period1991-2007. Clearly from the table, there is a healthcare

utilization differential between the wealthy and the poor in regards to public-private

healthcare visits. Public healthcare utilization is primarily driven by low socioeconomic

resources and verse is true about private healthcare utilization. Table 15.6 shows that as



                                             436
Jamaicans change from the lower to a higher social standing, they switch from public to

private healthcare utilization.


DISCUSSION


This research examined the association between the utilization of private and public

healthcare services in Jamaica from the period 1993-2007. Just fewer than 50 % of the

persons who are ill seek medical care. This low health seeking behavior is partly related

to the public-private differentials in healthcare utilization. Within this context, the use of

the public health service has increased over the use of the private health service in 2002.

There has been greater use of the public health service which is reflected in the increase

public hospital discharges and longer stay in these hospitals because of several

interrelated internal and external economic factors.


       These factors are rising unemployment (Table 15.4) exacerbated by the reduction

in the size of the labor force, increasing poverty, the fall in GDP (Table 15.5) which

partly resulted from the economic decline of the United States economy after the

September 11, 2001 terrorist attacks in that country. The current study revealed that not

clear here experienced not only an economic downturn in 2002 over 2001, but also

reduced healthcare seeking behaviour of Jamaicans, increased prevalence of poverty,

switching from private to public healthcare utilization, and when the economy expanded

in 2004 over 2002, poverty fell, healthcare seeking behaviour increased, and even those

in the poorest 20% switch from public to private healthcare utilization. Despite the

increase in the use of the public healthcare over the private healthcare system, there has


                                             437
been an overall decline in the amount of public patients seeking medical care which is

reflected in the reduction in the use of public hospital beds and the average amount of

money spent purchasing medication.


       The income differential among socio-economic hierarchies influences the mean

public-private utilization of healthcare because as income increases the use of private

healthcare service increases. Therefore, the wealthier Jamaicans with greater income

utilize 62.2% more private health facilities than the poorest Jamaicans who use 14% more

of the public healthcare facilities. The differences between public and private healthcare

utilization in Jamaica is clearly more than merely (differences is about income

differential that is explain what is now a public-private healthcare utilization differential)

this section not clear. Following the economic downturn in the Jamaican economy in

2002 over 2001, though those in the wealthiest 20% prefer to utilize private healthcare

facilities for treatment, there was an increase of more than 6% of them switching to

public healthcare facilities, and this was reverse when the economy recovered in 2004.

While there has been a greater percentage increase in public healthcare cost compared to

private healthcare cost at 1990 prices, the cost of using public healthcare is much less

although the cost differentials between the public and private health sectors is narrowing

over time.


       Jamaicans’ demand for public healthcare increased by 2.5% between 1989-2007

while the demand for private healthcare declined by 3% during the same period. These

negligible shifts in private-public demand which closed the huge gap of the 1990s,

occurred because of an increase in poverty following a downturn in tourism revenue in
                                             438
Jamaica’s post 9/11 tourist dependent economy. There were other changes such as the

6.5% increase in the purchase of medication by users of the public health system between

2000-2001 compared to the purchase of medication by private users which declined by

8.9%. The decline in private use does not obscure the fact that Jamaicans have a

preference for private healthcare services because of the significant reduction in the gap

between public-private utilization of the healthcare services alluded to earlier. This

increase demand of private healthcare has occurred despite the 394% increase in the cost

of medication in the private sector and the steep rise in inflation since 2006. The harsh

economic climate has increased and stabilized the rate of hospitalization in public and

private facilities respectively.


        A qualitative study by Ali and de Muynck, 16 in Pakistan, found that illness and its

severity are responsible for male street children’s willingness to utilize medical care

facilities. Previous studies in developing nations have shown that healthcare seeking

behaviour and utilization are influenced by illness, severity, and if illness is likely to

result in the separation from ones job.17-20 Statistics from the Planning Institute of

Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN)21 revealed that health

care utilization is a feminized phenomenon, which is similar to other developing

countries such as Pakistan, India, South Africa and other African nations.16-20


        WHO opined that 80% of chronic illnesses were in low and middle income

countries and that 60% of global mortality is caused by chronic illness, 22 suggesting that

illness interfaces with poverty and other socio-economic challenges. According to

Marmot, poverty is associated with illness.23 It is not only related to illness, but poor
                                            439
milieu, low nutritional intake, and the type of healthcare sought. Thus, healthcare

utilization to public facilities is sought by many people in developing countries because

of inafffordability, poverty and low material resources. A study by Peters et al.24 found

that the poor in developing nations demand less healthcare services than those in better-

off-countries, which is concurred by the current study. Jamaica is among the developing

nations, and so the current study provides pertinent information on healthcare utilization

disparities among people that exist in a similar society. The current work showed that the

average amount spent for public healthcare utilization costs were substantially lower than

that for private healthcare utilization costs, which is an indication of those who attend

each facility.


        The official data of public hospital visits recorded by the MOH and the JSLC

approximates the self reported data of public hospitals visits. Therefore, the official data

provides useful insights into the public-private differentials in sources of healthcare

utilization among Jamaicans. The public-private healthcare utilization differential in

Jamaica highlights people’s preference for private healthcare services, and provides an

understanding of Jamaicans dissatisfaction with the service delivery of public healthcare

facilities. It can be extrapolated from the current study that service quality including

interpersonal relationships among patients and healthcare providers, along with waiting

time, insufficient time spent between healthcare providers and patients, ambiance, bed

space, and crowding in public healthcare facilities are among some of the reasons why

people prefer public healthcare facilities.



                                              440
Conclusion



Internal and external economic conditions influence the use of private and public

healthcare services in Jamaica. Although the percentage increase in the cost of public

healthcare is more than the percentage increase in the cost of private healthcare, the

actual cost to use the public healthcare system is still much cheaper than using the private

system. Economic wellbeing influences the use of healthcare services where higher

income households tend to use the private system. Despite the harsh economic

conditions, there is a strong preference for the private system because the gap in the

public-private differential has decreased significantly. However, these same economic

conditions have led to a marginal increase in public use and a marginal decrease in

private use. The increase in public use is evident in greater public hospitals visits, greater

bed occupancy and longer stay in public hospitals. The official data on healthcare

utilization approximates self reported healthcare utilization so the official data are useful

in understanding the public-private differentials in Jamaica




Disclosure


The authors have no competing interests to report.




                                             441
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   Kingston. B.A. Thesis, University of the West Indies, Jamaica, (1985) pp. 47



12. Paul TJ, Maharaj SR. The prevalence of health insurance in a Jamaican suburb

   and its correlations with service utilization. West Ind Med J. 1989; 38:238-240.




13. Von Eigen KA. Science and spirit: Healthcare utilization in rural Jamaica.


   Dissertation Abstracts International 1992; 53:868.


14. Barr DA. A research protocol to evaluate the effectiveness of public-private


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16. Ali M, de Muynck A: Illness incidence and health seeking behaviour among street

children in Pawalpindi and Islamabad, Pakistan – qualitative study. Child: Care, Health

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17. Taff N, Chepngeno G: Determinants of health care seeking for children illnesses in

Nairobi slums. Tropical Medicine and Int Health. 2005; 10:240-45.

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18. Sudha G, Nirupa C, Rajasakthivel M, Sivasusbramanian S, Sundaram V, Bhatt S,

Subramaniam K, Thiruvalluvan E, Matthew R, Renu G & Santha T: Factors influencing

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seeking behaviour and utilization of traditional healers in Kalabo, Zambia. Health Policy.

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Natal. Working Paper No. 116. Cape Town: Centre for Social Science Research,

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Jamaica Survey of Living Conditions, 1989-2007. Kingston, Jamaica: PIOJ & STATIN;

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Geneva: WHO;2005.


23. Marmot M. The influence of income on health: Views of an Epidemiologist. Does

money really matter? Or is it a marker for something else? Health Affairs 2002; 21:31-46.


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                                           444
Figure 15.1: Self-reported Illness and those Seeking Medical Care (in per cent), 1988-
2007




                                         445
Figure 15.2: Public-Private Healthcare Utilization of Jamaicans (in per cent), 1989-2007




                                                             446
Table 15.1: Discharge, Average Length of Stay and Bed Occupancy in Public Hospitals,
1996-2007




Year          Discharge     Average              Bed Occupancy        Visits

                            Length of Stay        Rate




1996          145,656       5.7                  56.1                 546,933

1997          153,101       5.8                  57.3                 598,004

1998          158,851       5.5                  58.0                 634,792

1999          163,714       5.1                  52.2                 654746

2000          173,700       4.9                  74.9                 643,101

2001          171,963       6.0                  84.6                 667,321

2002          173,614       6.9                  80.2                 695,239

2003          179,322       6.4                  84.5                 746,844

2004          182,053       6.8                  56.0                 775,727

2005

2006

2007

Source: Compiled by Paul A Bourne from Ministry of Health, Jamaica, Planning and
Evaluation Branch, various issues




                                        447
Table 15.2: Per Capita Income Quintile By Mean Percentage Public-Private Healthcare
Service Utilization, 1991-2000




                      Mean Public-Private Healthcare Service Utilization




Quintile              Public               Private               Marginal differential

                                                                 (in respect to public
                                                                 utilization)




1 (Poorest 20%)       54.5                 40.4                            14.0



2                     41.7                 53.5                            -11.8



3                     32.8                 62.4                            -29.7



4                     31.2                 62.7                            -31.7



5 (Wealthiest 20%)    16.5                 78.7                            -62.2



χ2 (df = 4) =35.68, P-value = 0.005




                                          448
Table 15.3: Purchase Medication, (in per cent), 1988-2007

   Year            Purchase Medication              Hospitalization of those                 Mean Expenditure on Healthcare Visits
                                                     Seeking Medical care            Nominal (in Ja.$)           Real (in 1990 Ja.$)
                   Public          Private          Private        Public            Private       Public        Public         Private
   1988            NA              NA               NA             NA                     NA            NA      NA              NA
   1989            18.6            90.5             NA             NA                  54.00          11.0       14             74
   1990            10.9            72.1             NA             NA                  72.10         10.90       11             72
   1991            NA              NA               NA             NA                  81.90         10.90       6              44
   1992            8.9             58.9             1.1            5.1                167.00         13.90       5              63
   1993            15.9            79.9             0.5            6.9                298.00         115.00      33             85
   1994            21.4            75.6             0.8            4.6                461.00         91.00       20             103
   1995            16.4            81.9             0.2            6.0                496.00         130.00      25             95
   1996            19.1            78.0             0.5            5.1                598.00         148.00      23             92
   1997            22.0            74.3             1.8            7.4                693.00         283.00      39             95
   1998            19.7            76.6             0.9            7.6                832.00         315.00      40             106
   1999            18.5            77.0             0.9            7.4               1301.00         339.00      40             154
   2000            20.8            73.3             0.4            7.6               1081.00         309.00      34             120
   2001            20.0            76.9             0.4            7.3               1103.00         546.00      57             115
   2002            26.5            68.0             0.3            7.7               1339.00         464.00      46             132
   2003            NA              NA               NA             NA                   NA            NA         NA             NA
   2004            19.1            74.3             0.7            7.1               2278.00         489.00      41             191
   2005            NA              NA               NA             NA                   NA             NA        NA             NA
   2006            15.9            76.4             0.8            6.2               1406.00         860.00     62              101
   2007            13.7            80.3             0.3            5.8               1679.50         539.90     36.9            114.2
   Source: Compiled by Paul A. Bourne from Jamaica Survey of Living Conditions 1998-2008
   NA Not Available




                                                                           449
Table 15.4: Particularized Labour Force Indicators by Sex, 1990-2007

                                                                           Year

                  1990    1991    1992    1993    1994    1995    1996     1997     1998    1999    2000    2001    2002    2003    2004    2005    2006    2007


Male:
Labour    Force   564.6   571.8   570.1   571.3   574.8   617.9   614.6    613.8    614.3   611.7   615.0   618.1   618.4   611.1   663.5   661.9   695.6   699.1
(000’s)
Employed          896.3   518.1   516.0   509.2   519.9   551.0   553.3    549.0    552.9   550.3   552.4   554.8   552.8   552.3   610.9   611.4   646.8   656.1
Labour Force
(000’s)

Unemployment      15.3    9.4     9.5     10.9    9.6     10.8    10.0     10.6     10.0    10.0    10.2    10.3    10.6    9.7     7.9     7.6     7.0     6.2
Rate (in %)



Female:
Labour    Force   494.0   500.7   504.8   511.7   515.8   532.2   528.2    520.0    514.2   507.4   490.3   486.7   506.1   487.7   531.3   529.1   557.5   562.2
(000’s)
Employed          513.1   389.6   389.7   397.1   403.2   412.4   406.5    397.9    400.7   393.6           384.7   401.6   402.3   444.3   445.6   476.9   480.8
Labour Force
(000’s)

Unemployment      9.1     22.2    22.8    22.4    21.8    22.5    23.1     23.5     22.1    22.4    22.3    21.0    20.7    17.6    16.4    15.8    14.5    14.5
Rate (in %)

Compiled by Paul A. Bourne from Economic and Social Survey (1990 – 2007)




                                                                                   450
Table 15.5: Inflation, Public-Private Healthcare Service Utilization, Incidence of Poverty, Illness and Prevalence of Population with
Health Insurance (in per cent), 1988-2007

Year                  Inflation       Public             Private            Prevalence         Illness          Health                Seeking      Rate of growth
                                      Utilization        Utilization        of poverty                        Insurance               Medical Care GDP (constant
                                                                                                              Coverage                             prices)

1988                8.8               *                  *                  *                  *                  *                   *                 2.9
1989               17.2               42.0               54.0               30.5               16.8               8.2                 54.6              6.5
1990               29.8               39.4               60.6               28.4               18.3               9.0                 38.6              5.7
1991               80.2               35.6               57.7               44.6               13.7               8.6                 47.7              0.4
1992               40.2               28.5               63.4               33.9               10.6               9.0                 50.9              1.4
1993               30.1               30.9               63.8               24.4               12.0               10.1                51.8              1.2
1994               26.8               28.8               66.7               22.8               12.9               8.8                 51.4              1.1
1995               25.6               27.2               66.4               27.5               9.8                9.7                 58.9              0.5
1996               15.8               31.8               63.6               26.1               10.7               9.8                 54.9              -1.3
1997               9.2                32.1               58.8               19.9               9.7                12.6                59.6              -2.0
1998               7.9                37.9               57.3               15.9               8.8                12.1                60.8              -0.5
1999               6.8                37.9               57.1               16.9               10.1               12.1                68.4              -0.4
2000               6.1                40.8               53.6               18.9               14.2               14.0                60.7              0.8
2001               8.8                38.7               54.8               16.9               13.4               13.9                63.5              1.5
2002               7.2                57.8               42.7               19.7               12.6               13.5                64.1              1.1
2003               13.8               *                  *                  *                  *                  *                   *                 2.3
2004               13.7               46.3               46.4               16.9               11.4               19.2                65.1              0.9
2005               12.6               *                  *                  *                  *                  *                   *                 1.4
2006               5.7                41.3               52.8               14.3               12.2               18.4                70.0              2.5
2007               16.8               40.5               51.9               9.9                15.5               21.2                66.0              1.2
Source: Compiled by Paul A Bourne from Statistical Digest, Jamaica Survey of Living Conditions, Economic and Social survey of Jamaica, various issues
Note: Inflation is measured point-to-point at the end of each year (December to December), based on Consumer Price Index (CPI)
*Missing



                                                                                   451
Table 15.6:
Hospital Healthcare Utilization by Income Quintile (in per cent), 1991-2007
              1991 1992 1993 1994 1995 1996 1997 1998 1999                                      2000     2001     2002     2004    2006     2007
Public
Quintile
1=Poorest     57.8 48.8 57.5 54.1 49.4 54.8 44.5 59.1 61.0                                      55.7     67.6     73.4     70.9    71.0     75.0
2             43.3 41.8 36.9 34.9 25.3 42.7 39.9 49.0 46.3                                      44.3     53.5     57.5     53.6    51.1     66.5
3             29.0 28.8 29.3 17.0 22.7 32.8 37.3 40.7 37.5                                      41.3     32.1     58.6     57.3    50.6     22.1
4             35.8 27.1 20.6 25.6 21.7 29.5 26.3 35.1 37.7                                      44.6     35.3     46.5     36.7    27.5     27.0
5=Wealthiest 20.6 12.3 16.5 15.7 16.8 11.9 12.4 17.2 15.4                                       12.8     24.4     30.9     27.6    21.7     21.4

Private
Quintile
1=Poorest        34.4     46.3     32.3    41.2     47.1     40.4     49.1     35.5    34.7     38.7     29.3     22.8     26.8    24.3     22.0
2                52.9     48.4     58.7    57.0     66.3     54.1     51.1     45.0    50.3     53.8     38.7     37.5     35.7    42.3     33.3
3                64.5     65.9     62.2    77.0     69.7     62.5     51.8     56.6    59.8     48.8     62.9     37.4     35.7    42.9     64.2
4                53.1     65.4     74.2    72.2     68.0     63.8     62.5     58.3    57.1     48.8     59.1     46.3     55.6    65.4     69.6
5=Wealthiest     73.8     78.1     82.5    81.5     80.0     84.6     80.0     78.4    75.4     78.4     66.5     52.5     65.1    73.9     78.6
Source: Compiled by Paul A Bourne from Jamaica Survey of Living Conditions, various issues (a joint publication of the Planning Institute of Jamaica and the
Statistical Institute of Jamaica)




                                                                             452
                                                                          Chapter
                                                                                       16
Health status and Medical Care-Seeking Behaviour of the poorest 20% in Jamaica



Data for the last 2-decades (1988-2007) in Jamaica have shown a gradual decline in the
percentage of people in the lowest 20th income quintile category. Despite this reality, statistics
from WHO for 2005 revealed that 80 percent of chronic diseases are in low-to-middle income
nations. Poverty is undoubtedly correlated with ill-health; but no study has ever been done in the
Caribbean in particular Jamaica that examines health status, health care-seeking behaviour,
health insurance coverage and the typology of illness influencing those people in the poorest
categorization. This study bridges the gap in the literature by evaluating how recurring illness
influences the poorest, health status and health care-seeking behaviour of this group as well as
ascertaining factors that account for their good health status and medical care-seeking
behaviour. The sample was 1,343 respondents (671 males and 672 females). Majority of the
sample did not have health insurance coverage (93.2%) compared to 5.6% with public coverage
and 1.2% private. A substantial percentage of the sample had at most basic schooling (71.5%);
17.5% primary or preparatory; 10.6% secondary and 0.4% tertiary. Only 14.7% of respondents
indicated that they had an illness. Of those who indicated an illness, 93.2% of them reported that
this was diagnosed by a medical practitioner. The self-reported diagnosed ailments were asthma,
11.9%; hypertension, 24.2%; arthritis, 7.7%; diabetes mellitus, 10.8%; diarrhoea, 1.5%;
influenza, 13.4% and 24.2% did not specify. Four variables emerged as statistically significant
correlates of good health status. These are age (OR = 0.956, 95% CI = 0.945 – 0.968); illness
(OR = 0.125, 95% CI = 0.085 – 0.185); male (OR = 1.543, 95% CI = 1.107 – 2.151) and per
capita consumption (OR = 1.152, 95% CI = 0.741 – 1.790). The model (good health status) had
statistically predictive power [χ2 (df = 10) = 354.269, p < 0.001]; Hosmer and Lemeshow
goodness of fit χ2= 6.086, P = 0.638, correctly classify 85.4% of the sample (correctly classified
96.1% of those who had good health status and 45.3% of those who had poor health status). The
model (i.e. independent variables) can explain 38% (Nagelkerke R2) of the variability in good
health status of the sample. The thrust to reducing poverty in developing countries in particular
Jamaica must be coupled with lifestyle behavioural modification programmes for the poorest

                                               453
20% along with multi-dimensional approach to health, perception of health and treatment
among this cohort.



Introduction

Poverty which incapacitates an individual (Sen, 1979) and accounts for some typology of chronic

illnesses (WHO, 2005) has drastically fallen in Jamaica from 19.9% in 1997 to 9.9% in 2007.

Despite the significant reduction in national poverty in Jamaica, in 2007, 15.3% of rural residents

were living in poverty compared to 6.2% of urban and 4.0% of semi-urban Jamaicans (Planning

Institute of Jamaica and Statistical Institute of Jamaica, 2008). Globally statistics on poverty for

2007 revealed that 5.3% of Jamaicans were in the poorest 20% compared to 10.6% for Japan and

5.4% for the United States (UNDP, 2007). Concomitantly, since the 1900s poverty has been

reducing in the world and in particular the Caribbean (Ahmed and Wiesmann, 2007; UNDP,

2006, 2007; World Bank, 2007), but it should be noted that this is synonymous with increased

chronic conditions.


       Statistics from the WHO (2005) revealed that 80% of deaths due to chronic diseases

occurred in low and middle income countries and in the next decade, these will increase by 17%,

suggesting that the burden of illnesses will erode the health expenditure of poor individuals,

families, communities and the developing nations in which they reside. Poverty is not only

associated with low education (Oxaal, 1997; Younger, 2002), poor milieu, low choices and

worse health (Marmot, 2002; WHO, 2005), but it is the equally correlated with the depletion of

valuable human capital. When poverty is coupled with social exclusion, it increases the risk of

more chronic diseases and which can result in complications and premature deaths.


                                                454
       Poverty constitutes the poor and poorest, and through extensive examination of the

Caribbean literature in particular Jamaica, the latter group is absent from the discourse as to what

explains their health status. Using health indicators such as child mortality, life expectancy and

under-nutrition for Jamaicans, it may appear that there is no need to examine the poorest health

status as those indicators are highly comparable to many developing nations. In Jamaica,

statistics revealed that in 1997, 11.0% of the poorest reported illness in the four-week period of

the survey and 2-decades later, the figure increased by 35% (to 15.0). In addition to the

aforementioned, there is no information on what determines current good health status of this

cohort. The 20th lowest income categorization (or poorest) in Jamaica (ie those who received 20

percent of the income) has been over looked in health statistics discourse. Within the perspective

that 80% of chronic diseases are in low-to-middle income countries, this is sufficient reason to

examine health status and medical care seeking behaviour of the poorest 20% as this will aid in

the planning process.


       The poverty discourse cannot be left to income inequality. Income inequality in Jamaica

is vast; according to Ventura (2004), 20 percent of the population accounts for 50 percent of the

national consumption. While it is undeniably the case that income mal-distribution and

deprivation account for health conditions, singly examining those phenomena do not account for

the rationale of predictors of health status of the poorest in any geographic area in the Caribbean

or in Jamaica.


       The current study will bridge the gap in the literature, by examining the socio-economic

and medical characteristics of the 20th lowest income categorization in Jamaica. In addition,

another objective is to examine variables that are correlated with the current health status of the
                                                455
poorest 20%. The model will provide socio-economic and biological correlates of current good

health status; their contribution to the overall model and assist in understanding estimators of the

health status of those in the poorest 20 percent categorization in Jamaica.


Methods

Sample and respondents

Data from the Jamaica Survey of Livings Conditions (JSLC) for 2007 commissioned by the

Planning Institute of Jamaica and the Statistical Institute of Jamaica were used to provide the

analyses for this study. These two organizations are responsible for planning, data collection and

policy guideline for Jamaica, and have been conducting the JSLC annually since 1989. The

cross-sectional survey was conducted between May and August 2007 from the 14 parishes across

Jamaica and included 6,782 people of all ages. The sample for this study was 1,343 respondents

who are classified as the poorest 20 percent in Jamaica (or the poorest).

       The JSLC used stratified random probability sampling technique to drawn the original

sample of respondents, with a non-response rate of 26.2%. The JSLC survey was based on a

complex design with multiple stratifications to ensure that it represents the population; marital

status; area of residence; and social class. The sample was weighted to reflect the population.

       The instrument used by the JSLC was an administered questionnaire where respondents

are asked to recall detailed information on particular activities. The questionnaire was modeled

from the World Bank’s Living Standards Measurement Study (LSMS) household survey. There

are some modifications to the LSMS, as JSLC is more focused on policy impacts. The

questionnaire covers demographic variables, health, immunization of children 0–59 months,

education, daily expenses, non-food consumption expenditure, housing conditions, inventory of

                                                456
durable goods and social assistance. Interviewers were trained to collect data from household

members. The sample for this study was 1,343 respondents who are classified as receiving 20th

percentile of the income in Jamaica (or the poorest 20%).

Statistical Analysis

Data was stored, retrieved and processed using SPSS for windows 16.0 and a 5 percent level of

significance was used to test significance (ie 95% confidence interval). Descriptive statistics

were used to provide background information on the sample; chi-square and F-statistic were used

for bivariate analyses and logistic regression was performed to determine the factors for the

model. Using logistic regression, the forward stepwise technique was used to estimate the

association coefficient of each significant independent variable on the dependent variable. Odds

Ratio (OR) was used to interpret each significant variable as well as the association coefficient.

         The predictive power of the model was tested using the Omnibus Test of Model and

Hosmer & Lemeshow (2000) to examine goodness of fit. The association matrix was examined

in order to ascertain whether auto-correlation (or multicollinearity) existed between variables.

Based on Cohen & Holliday (1982) association can be low (weak) - from 0 to 0.39; moderate –

0.4-0.69, and strong – 0.7-1.0 (Cohen, 1988; Cohen, et al., 2003). This was used to exclude (or

allow) a variable in the model. In addition, variables were excluded from the model if they had in

excess of 20% of the cases missing. Marital status was omitted from being tested in the model as

it had 40% of non-responses.

Models

Multivariate analyses have been used in the past to model health status (Grossman, 1972; Smith

and Kington 1997; Hambleton et al. 2005; Bourne, 2008a, 2008b; Bourne and McGrowder,


                                                457
2009; Bourne, 2009), and this approach is in keeping with the social determinants which has

been emphasized by the World Health Organization (2008) and others (Solar & Irwin, 2005;

Graham, 2004; Marmot, 2003; Kelly et al., 2007). The use of multivariate analysis captures

more variables, and so this study modified the works of aforementioned scholars. Importantly, a

fundamental difference of the current work and that of Grossman; Smith and Kington;

Hambleton et al; Bourne, and Bourne and McGrowder is that it is cohort-specific (ie it focused

on those in the 20th income quintile). The proposed model that this research seeks to evaluate is

displayed (Eqn 1):

        H t = f(Ai, I i , ED i , HI i ,ARi , X, HH i , C i ,ε i )                                 1


The variables identified in Eqn [1] were based on the literature. Using the principle of

parsimony, only those explanatory variables that are statistically significant (p < 0.05) were used

in the final model to predict good health status of poorest (i.e. those who received 20th percentile

of the income) in Jamaica. Hence, the predictive model of the current work is (Eqn 2).


        H t = f(Ai, I i , X, C i , ε i )                                                         2


        Current good health status of the poorest Jamaicans, H t , is a function of 4 explanatory

variables: where H t is current good health status of person i, if good or above; X i is the gender of

person i, 1 if male, 0 if female; age of respondent i, A i ; per capita consumption expenditure of

person i, C i ; and illness (1 if person I has one or more illness, 0 if no).




                                                              458
Measurement of variable


Selected variables from the JSLC were chosen to represent dependent and independent variables

for this study. Measurement of dependent and independent variables used in this research are

explained below.


Dependent variable


Self-rated health status: is measured using people’s self-rate of their overall health status

(Kahneman, & Riis, 2005), which ranges from excellent to poor health status. The question that

was asked in survey was “How is your health in general?” And the options were very good;

good; fair; poor and very poor. For the purpose of the model in this study, self-rated health was

coded as a binary variable (1= good and fair 0 = Otherwise) (Finnas, et al., 2008; Helasoja, et al.,

2006; Molarius et al., 2006; Leinsalu, 2002; Idler, & Benjamin, 1997; Idler & Kasl, 1995)


Independent variables


Age is a continuous variable which is the number of years alive since birth (using last birthday)


Age group is a non-binary measure: children (ages less than 15 years); young adults (ages 15 to

30 years); other-aged adults (ages 31 to 59 years); young elderly (ages 60 to 74 years); old

elderly (ages 75 to 84 years) and oldest elderly (ages 85 years and older).


Self-reported illness (or self-reported dysfunction): The question was asked: “Is this a diagnosed

recurring illness?” The answering options are: Yes, Influenza; Yes, Diarrhoea; Yes, Asthma;

Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. A binary variable was



                                                459
later created from this construct (1= yes, 0 = otherwise) in order to be applied in the logistic

regression.


Medical care-seeking behaviour was taken from the question ‘Has a health care practitioner, or

pharmacist being visited in the last 4 weeks?’ with there being two options Yes or No. Medical

care-seeking behaviour therefore was coded as a binary measure where 1= Yes and 0 =

otherwise.


Crowding is the total number of individuals in the household divided by the number of rooms

(excluding kitchen, verandah and bathroom). Age is a continuous variable in years.


Sex. This is a binary variable where 1= male and 0 = otherwise.


Results

Socio-demographic characteristic of the sample

The sample was 1,343 respondents: 671 males and 672 females. Majority of the sample did not

have health insurance coverage (93.2%, n = 1,201) compared to 5.6% (n = 72) with public

coverage and 1.2% private (n = 12). Fifty-eight percent of the sample answered the marital status

question (n = 773). Of those who indicated a marital status, seventy-three percent were never

married, 19.8% married, 5.6% widowed, 1.2% legally separated and 0.5% legally divorced.

Approximately 41% (n = 546) of the sample was children, 24.3% (n = 326) young adults; 22.8%

(306) other aged-adults; and 12.3% (n = 165) elderly (8.1% young elderly; 3.4% old elderly and

0.8% oldest elderly). For those who answered the education question, the response rate was

97.9% (n = 1,315). A substantial percentage of the valid sample (n = 1,315) had at most basic

schooling (71.5%) compared to 17.5% primary or preparatory; 10.6% secondary and 0.4%
                                              460
tertiary. Only 14.7% (n = 194) of the respondents indicated that they had an illness. Of those

who reported having an illness, 93.8% indicated that this was diagnosed by a medical

practitioner. The self-reported diagnosed illnesses were asthma, 11.9%, n=23; hypertension,

24.2%, n = 47; arthritis, 7.7%, n=15; diabetes mellitus, 10.8%, n = 21; influenza, 13.4%, n=26;

diarrhoea, 1.5%, n = 3 and unspecified condition, 24.2%, n=47. When the respondents were

asked about their health status, 96.8% responded (n = 1,300). The self-rated health status

responses were 32.9% indicated very good; 46.4% good; 13.0% fair; 6.6% poor and 1.1%

reported very poor. When the respondents were asked ‘has a doctor, nurse, pharmacist, midwife,

healer or other health practitioner been visited? 14.3% of the sample responded (n=192).

Marginally more of those who responded to having visited medical practitioner in the 4-week

period of the survey indicated “yes” (54.7%); 53.5% revealed that they purchased the prescribed

medication and 30.8% indicated that they did not buy the prescribed medicines. The median

amount spent on medical care was US $3.72 (1 US$ = Ja. 80.47); 25th percentile spent US $1.24;

the 50th percentile spent US $3.72 and the 75th percentile used US $12.43. In addition, the mean

per capita consumption per day for the sample was US $1.80 (SD = US $0.48).

       Table 16.1 showed bivariate relationships between variables. There was a statistical

association between the purchase of medication and area of residence Table 16.1; (p < 0.05).

Continuing, urban poor were the mostly likely to purchase the prescribed medication (66.3%)

compared to rural poor (62.9%) and semi-urban poor (61.0% ; p < 0.05. The least mean amount

spent for daily consumption per person was by rural respondents (US $1.77 ± US $0.48)

compared to urban (US $1.91 ± US $0.48) and US $2.07 ± US $0.48 by semi-urban respondents

(p< 0.05). Furthermore, the findings revealed a significant statistical difference between the


                                              461
mean number of persons per room in the different areas of residence: households in urban areas

have significantly more persons per room (7 ± 4) compared to rural areas (6 ± 3) and semi-urban

households (5 ± 2). However, there was no significant statistical difference in the amount spent

on medical care by the area of residence (p > 0.05; Table 16.1).

       Based on Table 16.2, there was a statistical association between self-rated health status

and self-reported illness (χ2 (df = 4) = 265.716, p < 0.001). Only 7.9% of those who revealed

that they had at least one illness indicated very good health status compared to 37.3% of those

who did not report an ailment. Twenty-four percent of those with at least one illness reported

poor health status compared to 3.5% of those who did not indicate a dysfunction. Furthermore,

there was a negative statistical association between self-rated health status and self-reported,

with the association being also a moderate one (contingency coefficient = 0.413 or 41.3%).

       Fifty-five percent of respondents indicated that they sought medical, and there was no

significant statistical difference between medical care-seeking behaviour and gender of

respondents (p = 0.250): 49.3% of males and 57.9% of females.


       Figure 16.1 displayed the percentage of sample that sought medical care by particular

self-reported diagnosed recurring illnesses. Of those who had asthma 59.1% sought medical care;

61.9% of those with diabetes mellitus; 56.5% of those with hypertension; 40% of those with

arthritis and 50% of those with unspecified conditions.


       When the respondents were asked ‘Why did they not seek care?’ the reasons included

could not afford it (33%); 35% reported that they were not ‘ill’ enough, 12% used home

remedy1% indicated that they did not have the time and 19% did not specify (Figure 16.2).


                                               462
         Table 16.3 showed a cross-tabulation between self-reported illnesses and age group of

respondents. Based on Table 16.3, there was a statistical association between self-reported illness

and age group (p < 0.001). Young adults were the least likely to report an illness (7.3%); and

children were more likely to report an illness than young adults. The findings revealed that as

people become older, they were more likely to report an illness. However, the old-elderly

reported more ailments than the other elderly. In fact the old-elderly who reported are the most

likely ones to indicate having a dysfunction: sixty-two percent of old elderly reported an illness

compared to 46% of oldest-elderly, 32% of young elderly.



         Table 16.4 displayed a cross tabulation between self-reported diagnosed recurring

illnesses and age group of respondents. The cross tabulation between self-reported diagnosed

recurring illnesses and age group revealed a statistical association (p < 0.001). The findings

revealed that as the sample becomes older, the typology of recurring illnesses change from

influenza, diarrhoea and asthma to diabetes mellitus, hypertension and arthritis. Forty-nine

percent of elderly had hypertension compared to 28% of other aged-adults and this was similar

for arthritis (8% of other aged-adults and 17% of elderly). Although no children reported having

hypertension and arthritis, approximately 2% had recurring diabetes mellitus. Diabetes mellitus

and hypertension were most prevalence amongst other adults, and arthritis among elderly (Table

16.4).




                                               463
Multivariate Analysis


Table 16.5 displayed selected independent and dependent variables. Using multiple logistic

regression technique, four variables emerged as statistically significant predictors of good health

status in this sample (Table 16.5): age (OR = 0.956, 95% CI = 0.945 – 0.968); illness (OR =

0.125, 95% CI = 0.085 – 0.185); gender (OR = 1.543, 95% CI = 1.107 – 2.151) and per capita

consumption (OR = 1.152, 95% CI = 0.741 – 1.790).


       The model (good health status) had statistically predictive power [χ2 (df = 10) = 354.269,

p < 0.001]; Hosmer and Lemeshow goodness of fit χ2= 6.086, P = 0.638, and correctly classify

85.4% of the sample (correctly classified 96.1% of those who had good health status and 45.3%

of those who had poor health status). The model (ie. independent variables) can explain 38%

(Nagelkerke R2) of the variability in good health status of sample. The logistic regression model

can be written as: Log (probability of good health status/probability of poor health status) =

2.075 – 0.045 (Age) – 2.077 (Illness) + 0.434 (Male) + 0.000 (per capita consumption).

       Having established those variables that are correlated with good health status of the

sample, forward stepwise multiple logistic regression technique was used to determine the

correlation coefficient of each significant variable. Table 16.6 displayed the significant statistical

correlates of good health status, and their correlation coefficient. Of the thirty-eight percentage

points of the independent variables that can be used to explain the dependent variable (ie good

health status), illness accounted for 22.8%; age 13.2%, consumption 1.4% and gender 0.6%

(Table 16.6).




                                                 464
Discussion

Infant mortality and life expectancy traditionally have been utilized to measure health status of a

population; but this does not comprehensively explain the influence of poverty on an individual,

family, community, or nation. Marmot (2002) argued that it is ignorant to perceive that there is

no significant statistical association between poverty and health as poverty accounts for low

quality housing, lack of sanitation, malnutrition, overcrowding, high infant mortality, chronic

illnesses, material deprivation and lack of quality medical care. All of these increase the

probability of lower standard of living and life expectancy. There is a paradox with poverty,

infant mortality and life expectancy as infant mortality in Jamaica for 2007 was 17 per 1000

(UNDP, 2007) while the life expectancy was 72 years and only 5.3% of population was in the

poorest 20%. Jamaica’s life expectancy is high incomparable with that of the many developed

nations such as United States (77.4 years) and that 5.4% of population in the United States were

classified as in the poorest 20%, yet Jamaica is a developing country and the former is a

developing nation. Economic indicators for each nation are vastly different; suggesting that

studies in the developed world should not be widely used to formulate policies nor guide public

health practices in the Caribbean or other developing nations like Jamaica.

       In the current study, 8 out of every 10 respondents in the poorest 20% of the Jamaican

population indicated at least good health status which is similar to rural Jamaicans (8 out of

every 10; Bourne and McGrowder, 2009) and higher than that of the Jamaicans who sought

medical care (5 out of every 10; Bourne, 2009b). Good health status does not mean that people

are not experiencing a dysfunction. This study revealed that 15 out of every 100 of the poorest

20% of the Jamaican reported an illness, which is same for the population of Jamaicans


                                               465
(Planning Institute of Jamaica and Statistical Institute of Jamaica, 2008). There is a statistical

significant association between health status and illness of the poorest 20%, and that 36 out of

every 100 respondents who reported an illness indicated at least good health compared to 28 out

of every 100 of respondents with poor health status. Furthermore, the general health status of

Jamaicans across the different social standings is high and offers minimal difference. According

to a cross-sectional probability survey of 1,338 Jamaicans, Powell, Bourne and Waller (2007)

found that those in the lower class indicated that their ‘state of health’ was 5.9 out of 10

compared to 6.5 for those in the upper class and 6.6 for those classified in the middle class.

While Powell et al.’s work did not deconstruct the health conditions of the different social

classes; this research offers information about this issue for those classified in the poorest 20% of

the Jamaican population.

       The WHO (2005) declared that chronic illnesses are associated with poverty, and this

study concurs with the current findings and a study by McCally et al. (1998). The findings of the

current research showed that 24 out of every 100 of the poorest 20% had hypertension, 11 out of

every 100 diabetes mellitus, 8 out of every 100 arthritis and 24 out of every 100 unspecified

conditions and 13 out of every 100 influenza. Comparatively, 22 out of every 100 of the

Jamaican population had hypertension, 9 out of every 100 had arthritis, 12 out of every 100

diabetes mellitus and 9 out of every 100 Jamaicans had asthma. The high rates for hypertension

and diabetes mellitus for the poorest 20% are reflecting their lifestyle practices. The inadequacy

to afford the proper nutrients and food are responsible for those numbers; but these will be

difficult to change as these people would be less likely to afford not only the correct foods but

seek adequate medical care. Of the 45 out of every 100 respondents who did not seek medical


                                                466
care, 33 out of every 100 was because of in-affordability and 35 out of every 100 were due to

‘not ill enough’. The issue of not being ill enough speaks to the poorest 20% unwillingness not

only to seek medical care for all illnesses but their perception about severity of illness and that

being use to measure and indicate when medical treatment should be sought. The number of

people seeking medical care in Jamaica for 2007 was 66 out of every 100, which is 11% more

than that for those in the poorest 20%. The poorest 20% are also not seeking medical care, but

only 53 out of every 100 purchased the prescribed medication compared to 66 out of every 100

of the general population (Statistical Institute of Jamaica, 2008). The poorest 20% of Jamaicans

spent a mean of US$3.72 on medical care which is 2.1 times more than their average per person

consumption per day, and their medical expenditure is 7.4 times less than that for the population

(US $27.58).

       The capacity of this group to recover from their current socio-economic status will be

difficult with assistance from government and other social networks as 89 out of every 100 of the

poorest 20% had at most primary level education. The severity of this social reality can be

further understood within the context that 65% of this group is less than 31 years and 41% less

than 15 years. Although since 2007 user fee for medical services have been reduced for

Jamaicans 18 years and younger, this does not take away the difficulty of the group to seek

health care, and nutrients deficiency. Only 10 out of every 100 children were ill and out of every

100 for young adults, which means that the issues for this group is not curative care but is

preventative care and the high cost for the society for curative care for this cohort when they

become old (ages 60 years and beyond). This research revealed that 49 out of every 100 elderly

in the poorest 20% reported hypertension which is 1.3 times more than that for the population 65


                                               467
years and beyond and 1.8 times more than that for the general population, suggesting the cost of

curative care for the elderly poorest 20% will be higher for the nation. It is not only the elderly

poorest 20% that has greater risk of particular pathogens in Jamaican than the general elderly

population or the general population, but this spread across the poorest 20% cohort.

       A study by Hambleton et al. (2005) on elderly Barbadians found that current disease

indicators (health conditions) accounted for 33.6% of the explanation of health status out of total

explanation of 38.2% (ie R2), indicating power of ill estimators. While the current study found

that current disease indicators accounted for 22.8% of the explained variation in health status,

this represented 60% of the variability compared to 52% in Hambleton et al’s work. In Jamaica,

with inflation having increased by 194.7% in 2007 over 2006 coupled with the global economic

downturn, this not only speak of the economic challenges of the poorest 20% but also reinforced

the economic burden of this cohort on the national budget. Nugent (2008) noted that between

0.02 to 6.77% of GDP in a country is estimated to be spent on chronic illnesses, and in United

States the figure is 5.0% of GDP. He continued that the treatment costs of diabetes mellitus in

developing countries are estimated to be 9% of the global total.

       Infectious diseases continue to be among the leading cause of premature mortality in

adults in the developing countries which emphasize the choices that are made by poor in order

for livelihood. This includes the poorest 20% in Jamaica who have not sought medical care

although they indicated that they have particular chronic illness. It should be noted that 60 out of

every 100 arthritic poorest 20% did not seek care, 44 out of every 100 hypertensive and 38 out of

every 100 diabetic which are causes of premature mortality and economic burden of futuristic

care and the challenges for public health in Jamaica. In an article published by CAJANUS, the


                                                468
prevalence rate of diabetes mellitus affecting Jamaicans is higher than in North American and

“many European countries”(Callender 2000). Diabetes Mellitus is not the only challenge faced

by patients, but McCarthy (2000) argued that between 30 to 60% of diabetics also suffer from

depression, which is a psychiatric illness.

       Poverty is considered to be the greatest cause of health inequalities between affluent and

poor countries (WHO 1998); but this study has shown that the poorest 20% of Jamaicans are

substantially been affected by not only poverty, low education and material deprivation but also

include health conditions, and their low responses to preventative as well as curative medical

care. Hence, the reality for the poorest 20% of Jamaicans is likely to be catastrophic in the future

and will account for high mortality and economic burden for the society (Nugent, 2008). This is

confirmed by a study conducted by McCally et al. (1998) which found that mortality rates for

those in the lower class higher than that for the other social classes (Marmot 1994; Marmot et al.

1984; Marmot et al. 1991). Another study presented to the United Nations by a Caribbean

scholar cited that poverty is correlated with risky sexual behaviour (Bernard, 2003) furthering

exposure to disease causing pathogens and accounting for some of the HIV/AIDS cases in the

Caribbean in particular Jamaica.

       Consumption was found to be positively correlated with good health status for the

poorest 20% which concurs with many other studies (Marmot, 2002; McCally et al. 1998;

Bourne and McGrowder 2009; Smith and Kington, 1997; Grossman, 1972). With the poorest

20% being incapacitated by economic and material deprivation, another critical aspect to this

study is what is embedded in their consumption pattern. Their consumption pattern will

constitute of mostly innutritious items such as fatty foods and starches, which add to the reasons


                                                469
for the higher hypertension in this cohort than that for the population of Jamaica. Another aspect

to this issue is the barrier to health care that the lack of income affords the poorest 20% from

purchasing prescribed medication and an explanation for lowered visits to medical practitioners

for preventative check-ups.

       Among the social determinants of health status of the poorest 20% of the Jamaicans is

gender. The findings indicate that men have a greater health status than women. They are 1.5

times likely to report a greater health status than females, suggesting that latter group will be

experiencing greater socio-economic hardships. Females have a high propensity than males to

contract particular conditions such as depression, osteoporosis and osteoarthritis (WHO, 2005;

Herzog, 1989). A study that was conducted by Schoen et al. (1998) on a group of adolescents

reveals something different from that which was reported by WHO. They found that males are

more likely than females to feel stressed ‘overwhelmed’ or ‘depressed’, and they attributed this

to limitedness of men’s social networks. Other research have agreed with Schoen et al that men

in general tend to be more stressed and less healthy than females, and further argued that men

can use denial, distraction, alcoholism and other social strategies to conceal their illness or

disabilities (Friedman, 1991; Kopp et al. 1998; Weidner and Collins, 1993; Sutkin and Good,

1987). Males, nevertheless, are more likely to have heart diseases, gout and hypertension than

women. World Health Organization attributes this biomedical condition to difference between

the genders based on hormonal differentiations, social networks and support, and cultural and

lifestyle practices of the sexes, this was concurred by Courtenay et al. (2002).




                                                470
Conclusion



The thrust to reducing poverty in developing countries in particular Jamaica must be coupled

with lifestyle behavioural modification programmes for the poorest 20% along with multi-

dimensional approach to health, perception of health and treatment among this cohort. While the

economic costs of treatment of chronic diseases are high, public health practitioners and

governments cannot allow the poorest 20% to become ill before retarding all possibilities of

futuristic delays in the seeking of medical care outside of curative measures.

Acknowledgement

The author would like to thank the Data Bank in Sir Arthur Lewis Institute of Social and
Economic Studies, the University of the West Indies, Mona, Jamaica for making the dataset
(Jamaica Survey of Living Conditions, 2002) available for use in this study. In addition, he
would like to extend gratitude to Mrs. Cynthia Francis, Librarian in the Documentation Centre,
Department of Community Health and Psychiatry, University of the West Indies, Mona, for her
contribution in sourcing materials for the literature review.




                                               471
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                                                   474
Table 16.1. Socio-demographic characteristics of sample
                                                                 Area of residence
Variable                                           Urban           Semi-Urban          Rural         pvalue
                                                   n (%)              n (%)            n (%)
Health insurance coverage                                                                            0.120
      No coverage                                 166 (90.2)          152 (91.6)      883 (94.0)
      Private                                        5 (2.7)             1 (0.6)        10 (1.1)
      Public                                        13 (7.1)            13 (7.8)        46 (4.9)
Self-reported illness                                                                                0.073
      No                                          164 (88.6)          144 (89.4)      811 (84.0)
      At least one                                 21 (11.4)           17 (10.6)      155 (16.0)
Educational level                                                                                    0.000
     No formal education                          107 (58.2)          103 (62.4)      512 (53.0)
     Basic                                         29 (15.8)           20 (12.1)      169 (17.5)
     Primary (or preparatory)                      22 (12.0)           30 (18.2)      178 (18.4)
     Secondary (or high)                           22 (12.0)            12 (7.3)      106 (11.0)
     Tertiary                                        4 (2.2)             0 (0.0)         1 (0.1)
Gender                                                                                               0.499
     Female                                        97 (52.2)           77 (46.1)      498 (50.3)
     Male                                          89 (47.8)           90 (53.9)      492 (49.7)
Health status                                                                                        0.001
     Very good                                     57 (31.0)           36 (21.7)      335 (35.3)
      Good                                         89 (48.4)          103 (62.0)      411 (43.3)
      Fair                                         25 (13.6)           20 (12.0)      124 (13.1)
      Poor                                          13 (7.1)             5 (3.0)        68 (7.2)
      Very poor                                      0 (0.0)             2 (1.2)        12 (1.3)
Medical Care-seeking behaviour                                                                       0.074
      Yes                                         190 (71.2)          119 (63.6)      349 (63.3)
      No                                           77 (28.2)           68 (36.4)      202 (36.4)
Did you purchase medication                                                                          0.021
      Yes, Prescribed                             175 (66.3)          111 (61.0)       331 (62.9)
      Yes, Partial prescription                        6 (2.3)            2 (1.1)        10 (1.9)
      Yes, Prescribed/Over the counter                 9 (3.4)          12 (6.6)           8 (1.5)
      Over the counter                               14 (5.3)           11 (6.0)         27 (5.1)
      Prescribed, but didn’t buy                       9 (3.4)            1 (0.5)        22 (4.2)
      Did not buy                                   51 (19.3)          45 (24.7)       128 (24.3)
†Medical cost Mean (SD)                           US $23.89       US $19.31 (US        US $14.98     0.236
                                                (US $93.10)              $25.63)     (US $38.45)
†Per person Daily Consumption                      US $1.91            US$ 2.07         US $1.77     < 0.001
Expenditure Mean (SD)                              (US 0.48)          (US $0.48       (US $0.48)
Crowding Mean (SD)                                   7.0 (4.2)          5.0 (1.8)        6.1 (2.9)   < 0.001
Number of visits to health care practitioner     1.45 (1.06)         1.42 (1.28)     1.39 (0.963)    0.788
Mean (SD)

†Ja $80.47 = US $1.00


                                                475
 Table 16.2. Health status by Self-reported Illness
                                                  Self-reported Illness


                                                             At least one        No           Total

  Health Status                                                  n (%)          n (%)         n (%)

 Very good                                                          15 (7.9)    412 (37.3)   427 (33.0)


  Good                                                            54 (28.4)     546 (49.4)   600 (46.3)


 Fair                                                             68 (35.8)      101 (9.1)   169 (13.1)


 Poor                                                             46 (24.2)       39 (3.5)     85 (6.6)


 Very poor                                                            7 (3.7)      7 (0.6)     14 (1.1)
 Total                                                                   190          1105        1295
χ2 (df =4) = 265.716, p < 0.001, n = 1, 295, contingency coefficient = 0.413




                                                              476
Figure 16.1: Percentage of sample that sought medical care by particular self-repored diagnosed
recurring illness




                                              477
Figure 16.2. Reason for not seeking medical care (in %)




                                             478
Table 16.3. Self-reported illness by age group

                                                                   Age group

  Self-reported                           Young          Other-aged                                       Oldest
 illness                 Children         adults           adults        Young old         Old Elderly    Elderly       Total
                          n (%)           n (%)            n (%)           n (%)             n (%)         n (%)        n (%)
    Yes
                           53 (10.0)          23 (7.3)      51 (16.8)          35 (32.4)      26 (61.9)    5 (45.5)   193 (14.7)

    No
                                                                                                                           1119
                         478 (90.0)      293 (92.7)        253 (83.2)          73 (67.6)      16 (38.1)    6 (54.5)
                                                                                                                          (85.3)
 Total                           531              316              304              108              42         11         1312

χ2 (df = 5) = 134.22, p < 0.001, n = 1, 312




                                                             479
Table 16.4. Self-reported diagnosed recurring illness by age group
                                                                Age group

                                                             Young         other aged-
                                             Children        adults          adults       Elderly     Total
 Self-reported diagnosed recurring illness    n (%)          n (%)            n (%)        n (%)      n (%)
   Influenza                                   19 (35.8)       2 (8.0)          3 (6.0)     2 (3.0)   26 (13.4)
    Diarrhoea
                                                 2 (3.8)       1 (4.0)          0 (0.0)     0 (0.0)     3 (1.5)
    Asthma
                                               12 (22.6)      3 (12.0)         5 (10.0)     3 (4.5)   23 (11.9)
    Diabetes mellitus
                                                 1 (1.9)       1 (4.0)        10 (20.0)    9 (13.6)   21 (10.8)
    Hypertension
                                                 0 (0.0)       1 (4.0)        14 (28.0)   32 (48.5)   47 (24.2)
    Arthritis
                                                 0 (0.0)       0 (0.0)          4 (8.0)   11 (16.7)    15 (7.7)
    Unspecified
                                               15 (28.3)     12 (48.0)        12 (24.0)    8 (12.1)   47 (24.2)
    Not diagnosed
                                                 4 (7.5)      5 (20.0)          2 (4.0)     1 (1.5)    12 (6.2)
 Total                                                  53            25            50          66            194

χ2 (df = 21) = 116.97, p < 0.001, n = 194




                                                   480
Table 16.5. Logistic Regression: Self-reported illness and socioeconomic variables on Good
Health status of Poorest 20% in Jamaica
                                                                       Wald       Odds
 Variable                                Coefficient     Std. Error   statistic   ratio     95.0% C.I.
  Age                                      -0.045           0.01       56.89      0.96    0.95 - 0.97***
  Illness                                  -2.077           0.20      107.82      0.13    0.09 -0.19***

    Basic or Primary                        -0.350          0.29       1.44       0.71     0.40 - 1.25
    Secondary or Tertiary                   -0.071          0.37       0.04       0.93     0.45 - 1.92
    †No formal education

    Dummy Health insurance                  -0.239          0.30       0.62       0.79     0.43 - 1.43

    Urban area                              -0.062          0.24       0.07       0.94     0.59 - 1.49
    Other town                               0.189          0.28       0.45       1.21     0.70 - 2.10
    †Rural

    Male                                    0.434           0.17       6.54       1.54    1.11 - 2.15*
    Per capita consumption                  0.000           0.00       10.40      1.00    1.00 - 1.00**
    Head Household                          0.142           0.23       0.40       1.15     0.74 - 1.79
    Constant                                2.075           0.40       27.19      7.96           -
χ2 (df = 10) = 354.269, p < 0.001, n = 1, 266
-2 Log likelihood = 950.084
Nagelkerke R2 =0.380
Hosmer and Lemeshow goodness of fit χ2= 6.086, P = 0.638
Overall correct classification = 85.4%
Correct classification of cases of Good Health Status = 96.1%
Correct classification of cases of Poor Health status = 45.3%
†Reference group
*P < 0.05, **P < 0.01, ***P < 0.001




                                                                481
Table 16.6. Model Summary of Estimator: Using Stepwise regression

                                    -2 Log          Nagelkerke R     R Square
Model                             likelihood          Square          change
Illness                                1103.449              0.228        0.228
Illness, Age                            971.206              0.360        0.132
Illness, Age, Consumption               955.988              0.374        0.014
Illness, Age, Consumption, Male         949.578              0.380        0.006




                                              482
                                                                          Chapter
                                                                                        17
 Disparities in self-rated health, health care utilization, illness, chronic illness
    and other socioeconomic characteristics of the Insured and Uninsured



This study examines self-rated health status, health care utilization, income distribution, and
health insurance status of Jamaicans, and the disparity by the insured and uninsured. It also
models self-rated health status, health care utilization, income distribution, and how these differ
between the insured and uninsured. Cross-sectional data from the 2007 Jamaica Survey of
Living Conditions (JSLC), conducted by the Planning Institute of Jamaica (PIOJ) and the
Statistical Institute of Jamaica (STATIN), were used to analyse the information for this study.
The JSLC is a modification of the World Bank’s Living Standard Household Survey, with a
sample of 6,783 respondents. Analytic models, using multiple logistic and linear regressions,
were used to determine factors which explain self-rated health status, health care utilization, and
income distribution. Disparities in self-rated health status, health care utilization, and income
distribution were examined by the insured and uninsured. Majority (61.1%) of those who
reported being diagnosed with a chronic condition were 60+ years old (diabetes mellitus,
59.3%; hypertension, 60.2%; arthritis, 67.9%) and 2.4% were children. The mean age of those
with chronic illness was 62.3 years (SD = 16.2), and this was 61.5 years (SD = 16.5) for the
uninsured and 63.8 years (SD = 15.8) for those with insurance coverage. Only 20.2% of
respondents had health insurance coverage (private, 12.4%; NI Gold, public, 5.3%; other
public, 2.4%). Most of the chronically ill were uninsured (67%). More people with chronic
illnesses who had health insurance coverage were elderly, (65.9%), compared to uninsured
chronically ill elderly (58.4%). Majority of health insurance was owned by those in the upper
class, (65%), and 19%, by those in the lower socioeconomic strata. Insured respondents were
1.5 times (Odds ratio, OR, 95% CI = 1.06 – 2.15) more likely to rate their health as moderate-
to-very good compared to the uninsured, and they were 1.9 times (95% CI = 1.31-2.64) to seek
more medical care, 1.6 times (95% CI = 1.02-2.42) more likely to report having chronic illness,
and more likely to have greater income (β = 0.094) than the uninsured. Illness is a strong
predictor of why Jamaicans seek medical care (R2 = 71.2% of 71.9%), and health insurance
coverage accounted for less than one-half percent of the variance in health care utilization.
However, health care utilization is a strong predictor of self-reported illness, but it was weaker
than illness explaining health care utilization (61.1% of 66.5%). Public health insurance was

                                               483
mostly had by those with chronic illnesses (76%) compared to 44% private health coverage and
38% had no coverage (χ2 = 42.62, P < 0.0001). With the health status of the insured being 1.5
times more than the uninsured, their health care utilization being 1.9 times more than the
uninsured and illness being a strong predictor of health care seeking, any reduction in the health
care budget in developing nations denotes that vulnerable groups (such as elderly, children and
the poor) will seek less care, and this will further increase the mortality among those cohorts.



Introduction

This study examines self-rated health status, health care utilization, income distribution, and

health insurance status of Jamaicans, and the disparity between the insured and uninsured. It also

models self-rated health status, health care utilization, income distribution, and how these differ

between the insured and uninsured. The current findings revealed that 20.2% of Jamaicans had

health insurance coverage (i.e. 2,140,316 Jamaicans are uninsured, using end of year population

for 2007), suggesting that a large percent of the population are having to use out of pocket

payment or government’s assistance to pay their medical bills.

       The health of individuals within a society goes beyond the individual to the

socioeconomic development, standard of living, production and productivity of the nation.

Individuals’ health is therefore the crux of human’s development, survivability and explains the

rationale as to why people seek medical care on the onset of ill-health. In seeking to preserve

life, people demand and utilize health care services. Western societies are structured that people

meet health care utilization with a combination of approaches. These approaches can be any

combination of out of pocket payment, health insurance coverage, government assistance and

families’ aid.

       In Latin America and the Caribbean, health care is substantially an out of pocket

expenditure aided by health insurance policy and government’s health care policy. Within the

                                               484
context of the realities in those nations, the health of the populace is primarily based on the

choices, decisions, responsibility and burden on the individual. Survival in developing nations

are distinct from Developed Western Nations as Latin America and Caribbean peoples’

willingness, frequency, and demand for health care as well as health choices are based on

affordability. Affordability of health care is assisted by health insurance coverage; as the

provisions of care offered by the governmental policies mean that the public health care system

will be required to meet the needs of many people. Those people will be mostly children, elderly

and other vulnerable groups.

       The public health care system in many societies often time involve long queues, long

waiting times, frustrated patients and poor people who are dependent on the service. In order to

circumvent the public health care system, people purchase health insurance policies as a means

of reducing futuristic health care cost as well as an avoidance of the utilization of public health

care. Uninsurance in any society means a dependency on the public health care system,

premature mortality and oftentimes public humiliation. The insured on the other hand are able to

circumvent many of the experiences of the poor, elderly, children and other vulnerable cohorts

who rely on public health care system. Insurance in developing nations, and in particular

Jamaica, is private system between the individual and a private insurance company. Because of

the nature of health insurance and insurance, people buy into a pool which is usually

accommodated through employment. Such a reality excludes retired elderly, unemployed,

unemployable, and children of those cohorts. In seeking to understand health care non-utilization

and high mortality in developing nations, insurance coverage (or lack of) becomes crucial in any

health discourse.


                                               485
       There is high proportion of uninsured in the United States and this is equally the reality in

many developing nations, particularly in Jamaica [1-6]. According to the World Health

Organization (WHO), 80% of chronic illnesses were in low and middle income countries, and

60% of global mortality is caused by chronic illnesses [7]. It can be extrapolated from the

WHO’s findings that

uninsurance is critical in answering some of the health disparities within and among groups and

the sexes in the society. The realities of the health inequalities between the poor and the wealthy

and the sexes in a society and those in the lower income strata having more illnesses and in

particular chronic conditions [7-12] is embedded in financial deprivation.

       The WHO stated that “In reality, low and middle income countries are at the centre of

both old and new public health challenges” [7]. The high risk of death in low income countries

is owing to food insecurity, low water quality, low sanitation coupled with in access to financial

resources [11, 13]. Poverty makes it insurmountable for poor people to respond to illness unless

health care services are free. Hence, the people who are poor will suffer even more so from

chronic diseases. The WHO captures this aptly “...People who are already poor are the most

likely to suffer financially from chronic diseases, which often deepens poverty and damage long

term economic prospects” [7]. This goes back to the inverse correlation between poverty and

higher level education, poverty and non-access to financial resources, and now poverty and

illness. According to the WHO [7], “In Jamaica 59% of people with chronic diseases

experienced financial difficulties because of their illnesses...” and emphasize the importance of

health insurance coverage and the public health care system for vulnerable groups.




                                               486
       Previous studies showed that health insurance coverage is associated with health care

utilization [1-6], and this provides some understanding of health care demand (or the lack of) in

developing countries. Studies have been conducted on the general health of the insured and/or

uninsured, health care utilization and other health related issues [1-6] have used a piecemeal

approach, which means that there is a gap in the literature that could provides more insight into

the insured and uninsured. While the current body of health literature provide pertinent

information on health and health care utilization and how these differ based on the insured and

uninsured, health choices are complex and requires more than piecemeal inquiry.



Materials and methods


Data methods

This study is based on data from the 2007 Jamaica Survey of Living Conditions (JSLC),

conducted by the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica

(STATIN). The JSLC is an annual and nationally representative cross-sectional survey that

collects information on consumption, education, health status, health conditions, health care

utilization, health insurance coverage, non-food consumption expenditure, housing conditions,

inventory of durable goods, social assistance, demographic characteristics and other issues [14].

The information is from the civilian and non-institutionalized population of Jamaica. It is a

modification of the World Bank’s Living Standards Measurement Study (LSMS) household

survey [15].

       Overall, the response rate for the 2007 JSLC was 73.8%. Over 1994 households of

individuals nationwide are included in the entire database of all ages [16]. A total of 620

                                              487
households were interviewed from urban areas, 439 from other towns and 935 from rural areas.

This sample represents 6,783 non-institutionalized civilians living in Jamaica at the time of the

survey. The JSLC used complex sampling design, and it is also weighted to reflect the

population of Jamaica.


Statistical analysis


Statistical analyses were performed using the Statistical Packages for the Social Sciences v 16.0

(SPSS Inc; Chicago, IL, USA) for Windows. Descriptive statistics such as mean, standard

deviation (SD), frequency and percentage were used to analyze the socio-demographic

characteristics of the sample. Chi-square was used to examine the association between non-

metric variables, and an Analysis of Variance (ANOVA) was used to test the equality of means

among non-dichotomous categorical variables. Means and frequency distribution were

considered significant at P < 0.05 using chi-square, independent sample t-test, and analysis of

variance f test, multiple logistic and linear regressions.


Analytic Models


Cross-sectional analyses of the 2007 JSLC were performed to compare within and between sub-

populations and frequencies. Logistic regression examined the relationship between the

dichotomous binary dependent variable and some predisposed independent (explanatory)

variables. A pvalue < 0.05 was selected to established statistical significance.


       Analytic models, using multiple logistic and linear regressions, were used to ascertain

factors which are associated with (1) self-rated health status, (2) health care utilization, (3) self-

                                                 488
reported illness, (4) self-reported diagnosed chronic illness, and income. For the regressions,

design or dummy variables were for all categorical variables (using the reference group listed

last). Overall model fit was determined using log likelihood ratio statistic, odds ration and r-

squared. Stepwise regressions were used to determine the contribution of each significant

variable. All confidence interval (CIs) for odds rations (ORs) were calculated at 95%.


Results

Demographic characteristic of sample

The sample was 6,783 respondents (48.7% males and 51.3% females). Children constituted

31.3%; other aged adults, 31.3%; young adults, 25.9%; and elderly, 11.9%. The elderly

comprised 7.7% young-old, 3.2% old-old and 1.0% oldest-old. Majority of the sample had no

formal education (61.8%); primary, 25.5%; secondary, 10.8% and tertiary, 2.0%. Two-thirds of

the sample sought health in the last 4-weeks; 69.2% were never married; 23.3% married; 1.7%

divorced; 0.9% separated and 4.9% were widowed respondents. Almost 15% reported an illness

in the last 4-weeks (43.3% had chronic conditions, 30.4% had acute conditions and 26.3% did

not specify the condition). Of those who reported an illness in the last 4- weeks, 87.9% provided

information on the typology of conditions: cold, 16.7%; diarrhea, 3.0%; asthma, 10.7%; diabetes

mellitus, 13.8%; hypertension, 23.1%; arthritis, 6.3%; and specified conditions, 26.3%. Marginal

more people were in the upper class (40.3%) compared to the lower socioeconomic strata

(39.8%). Only 20.2% of respondents had health insurance coverage (private, 12.4%; NI Gold,

public, 5.3%; other public, 2.4%). Majority of health insurance was owned by those in the upper

class (65%) and 19% by those in the lower socioeconomic strata.



                                               489
Bivariate analyses

       Sixty-one percent of those with chronic conditions were elderly compared to 16.6% of

those with other conditions (including acute ailments). Only 39% of those with chronic

conditions were non-elderly compared to 83.4% of those with other conditions – (χ2 = 187.32, P

< 0.0001).

       Thirty-three percent of those with chronic illnesses had health insurance coverage

compared to 17.8% of those with acute and other conditions - (χ2 = 26.65, P < 0.0001).

Furthermore examination of self-reported health conditions by health insurance status revealed

that diabetics recorded the greatest percent of health insurance coverage (43.9%) compared to

hypertensive, (28.2%); arthritic (25.5%); acute conditions’ patients (17.0%) and other health

conditions respondents (18.8%). Sixty-seven percent of respondents who reported being

diagnosed with chronic conditions sought medical care in the last 4-weeks compared to 60.4% of

those with acute and other conditions (χ2 = 4.12, P < 0.042). Those with primary or below

education were more likely to have chronic illnesses (45.0%) compared to secondary level

(6.1%) and tertiary level graduants (11.1%) - (χ2 = 23.50, P < 0.0001).     There      was       no

statistical association between typology of illness and social class - (χ2 = 0.63, P = 0.730): upper

class, 44.6%; middle class, 41.1% and lower class, 43.0%.

       This study found significant statistical association between health insurance status and (1)

educational level (χ2 = 45.06, P < 0.0001), (2) social class (χ2 = 441.50, P < 0.0001), and (3) age

cohort (χ2 = 83.13, P < 0.0001). Forty-two percent of those with at most primary level education

had health insurance coverage compared to 16.3% of secondary level and 42.2% of tertiary level

respondents. Thirty-three percent of upper class respondents had health insurance coverage


                                                490
compared to 16.7% of those in the middle class and 9.4% of those in the lower socioeconomic

strata. Almost 33% of the oldest-old had health insurance coverage compared to 15.1% of

children; 18.4% of young adults; 23.6% of other aged- adults; 28.6% of young-old and 24.9% of

old-old. A significant statistical association was found between health insurance status and area

of residence (χ2 = 138.80, P < 0.0001). Twenty-eight percent of urban dwellers had health

insurance coverage compared to 22.1% of semi-urban respondents and 14.5% of rural residents.

Furthermore, similarly a significant relationship existed between health care seeking behaviour

and health insurance status (χ2 = 33.61, P < 0.0001). Fourteen percent of those with health

insurance sought medical care in the last 4-weeks compared to 9.0% of those who did not have

health insurance coverage. Likewise a statistical association was found between health insurance

status and typology of illness (χ2 = 26.65, P < 0.0001). Fifty-eight percent of those with

insurance coverage had chronic illnesses compared to 38.3% of those without health insurance.

Concurringly, 42% of those with insurance coverage had acute or other conditions compared to

62% of those who did not have health insurance coverage. Further examination revealed that

other public health insurance was mostly had by those with chronic illnesses (76%) compared to

NI Gold (public, 65%) and 44% private health coverage (χ2 = 42.62, P < 0.0001). Private health

coverage was most had by those with non-chronic illnesses (56%) compared to 35% with NI

Gold (public) and 25% other public coverage.

       No significant statistical difference was found between the average medical expenditure

of those who had insurance coverage and non-insured (t = 0.365, P = 0.715) – mean average

medical expenditure of those without health insurance was USD 10.68 (SD = 33.94) and insured




                                               491
respondents’ mean average medical expenditure was USD 9.93 (SD = 18.07) - (Ja. $80.47 = US

$1.00 at the time of the survey).

       There was no significant statistical relationship between health care utilization (public-

private health care visits) and health conditions (acute or chronic illnesses) – χ2 = 0.001, P =

0.975. 49.2% of those who had chronic illnesses used public health care facilities compared to

49.3% of those with acute conditions.

       There is a statistical difference between the mean age of respondents with non-chronic

and chronic illnesses (t = - 23.1, P < 0.0001). The mean age of some with chronic illnesses was

62.3 years (SD = 16.2) compared to 29.3 years (SD = 26.1) for those with non-chronic illnesses.

Furthermore, the mean age of insured respondents with chronic illnesses was 63.8 years (SD =

15.8) compared to 32.5 years for those with non-chronic conditions. Concurringly, uninsured

chronically ill respondents’ mean age was 61.5 years (SD = 16.5) compared to 28.6 years (SD =

25.9) for those with non-chronic illnesses.

       Table17.1 examines information on crowding index, total annual food expenditure,

annual non-food expenditure, income, age, time in household, length of marriage, length of

illness and number of visits made to medical practitioner by health insurance status.

       Self-rated health status, health care seeking behaviour, illness, educational level, social

class, area of residence, and health conditions, health care utilization by health insurance status

are presented in Table 17.2.

       Table 17.3 presents information on age cohort of respondents by diagnosed health

conditions. A significant statistical association was found between the two variables χ2 = 436.8,

P < 0.0001.


                                               492
       Table 17.4 examines illness by age of respondents controlled for by health insurance

status. There existed a significant statistical relationship between illness and age of respondents,

but none between the uninsured and insured, P = 0.410.

       Table 17.5 presents information on the age cohort by diagnosed health conditions, and

diagnosed health conditions controlled by health status.

       There is a statistical difference between the mean age of respondents and the typology of

self-reported illnesses (F = 99.9, P < 0.0001). Those with cold, 19.2 years (SD = 23.9);

diarrhoea, 30.3 years (SD = 31.4); asthma, 22.9 years (SD = 22.1); diabetes mellitus, 60.9 years

(SD = 16.0); hypertension, 62.5 years (SD = 16.8); arthritis, 64.3 years (SD = 14.5), and other

conditions, 38.3 years (SD = 25.3).

Analytic Models

Nine variables account for (Table 17.6), 32.8% of the variance in moderate-to-very good self-

rated health status of Jamaicans The variables are medical expenditure, health insurance status,

area of residence, household head, age, crowding index, total food expenditure, health care

utilization and illness. Self-reported illnesses accounted for 62.2% of the explained variability of

moderate-to-very good health status.

       Table 17.7 shows information on the explanatory factors of self-reported illnesses. Seven

factors accounted for 66.5% of the variability in self-reported illnesses. Ninety-two percent of

the variability in self-reported illnesses was accounted for by health care utilization (health care

seeking behaviour).




                                                493
       Three variables emerged as statistically significant correlates of health care utilization.

They accounted for 71.9% of the variance in health care utilization. Most of the variability can

be explained by self-reported illnesses (71.2%, Table 17.8).

       Self-reported diagnosed chronic illnesses can be explained by 5 variables (gender, marital

status, health insurance status, age and length of illness), and they accounted for 27.7% of the

variance in self-reported diagnosed chronic illness (Table 17.9).

       Sixty-two percent of the variability in income can be explained by crowding index, social

class, household head, health insurance status, self-rated health status, health care utilization,

area of residence and marital status). Most of the variability in income can be explained by social

class (Table 17.10).

       Table 17.11 presents information on the explanatory variables which account for health

insurance coverage. Six variables emerged as significant determinants of health insurance

coverage (age, income, chronic illness, health care utilization, marital status and upper

socioeconomic class). The explanatory variables accounted for 19.4% of the variability in health

insurance coverage. Income was the most significant determinant of health insurance coverage

(explained 43% of the explained variance, 19.4%).



Discussion

The current study revealed that 15 out of every 100 Jamaicans reported having an illness in the

last 4-weeks, and 57% of those with an illness had chronic conditions. Sixty-one out of every

100 of those with chronic illnesses were 60+ years; 67% of the chronically ill sought medical

care when compared to 66% of the population. Most of the chronically ill respondents were


                                               494
uninsured (67%). The chronically ill had mostly primary level education, and there was no

statistical association between typology of illness and social class. Almost 2 in every 100

chronically ill Jamaicans were children (less than 19 years), and most of them were uninsured.

Nine percent more of the chronically ill who the other aged adult cohort did not have health

insurance coverage. Insured respondents were 1.5 times more likely to rate their health as

moderate-to-very good compared to the uninsured, and they were 1.9 times more likely to seek

more medical care, 1.6 times more likely to report having chronic illnesses, and more likely to

have greater income than the uninsured. Illness is a strong predictor of why Jamaicans seek

medical care (R2 = 71.2% of 71.9%), and health insurance coverage accounted for less than one-

half percent of the variance in health care utilization. However, health care utilization is a strong

predictor of self-reported illness, but it was weaker than illness explaining health care utilization

(61.1% of 66.5%). Public health insurance was most common among those with chronic illnesses

(76%) compared to 44% private health coverage and 38% had no coverage. Those in the upper

income strata’s income was significant more than those in the middle and lower socioeconomic

group, but chronic illnesses were statistically the same among the social classes.

       Health disparities in a nation are explained by socioeconomic determinants as well as

health insurance status. Previous research showed that health care utilization and health

disparities are enveloped in unequal access to insurance coverage and social differences [2, 4,

17-19]. The present paper revealed that health insurance coverage is mostly had by those in the

upper class, with less than 20 in every 100 insured being in the lower socioeconomic class.

Although this study found that those in the lower class does not have more chronic illness than




                                                495
those in the wealthy class, 86 out of every 100 uninsured respondents indicated that their health

status was poor.

       Health insurance coverage provides valuable economic relief for chronically ill

respondents as this allows them to access needed health care. Like Hafner-Eaton’s research [2],

this paper found that health insurance status was the third most powerful predictor of health care

utilization. Forty-nine to every 100 chronically ill persons use the public health care facilities.

This mean that health insurance coverage appeases the health care burden of its holder, but the

insured in Jamaica are mostly wealthy, older, chronically ill, married, and seek more medical

care than the uninsured. The uninsured ill are therefore less likely to demand health care, and this

economic burden of health care is either going to be the responsibility of the state, the individual

or the family. The difficulty here is that the uninsured are more likely to be in the lower-to-

middle class, of working age or children, experienced more acute illness, 38 out of every 100

chronically ill are in the lower class, these provide a comprehensive understanding of the insured

and uninsured that will allow for explanations in health disparities between the socioeconomic

strata and sexes. With 43 out of every 100 people in the lower socioeconomic strata self-reported

being diagnosed with chronic illness, health insurance coverage, public health system and other

policy intervention aid in their health, and health care utilization.

       Among the material deprivation of the poor is uninsurance. Those in the wealthy

socioeconomic group in Jamaica were 3.5 times more likely to be holder of health insurance

coverage than those in the lower socioeconomic strata. And Gertler and Sturm [3] identified that

health insurance cause a switching from public health to the private health system, which

indicates that a reduction in public health expenditure and health insurance will significantly


                                                 496
influence the health of the poor. This research showed that only 19% of those with health

insurance were in the lower class. Therefore issue of uninsurance creates futuristic challenges for

the poor in regard to their health and health care utilization. As on the onset of illness, those in

the lower income strata without health insurance must first think about their illness and weight

this against the cost of losing current income in order to provide for their families as well as

parents of ill children must also do the same. The public health care system will relieve the

burden of the poor, and while those with health insurance are more likely to utilize health care,

this is a futuristic product in enhancing a decision to utilize health care. But outside of those

issues, their choices (or lack), the cost of public health care, national insurance scheme and

general price index in the society further lowers their quality of life. Although the poor may be

dissatisfied with the public health care system (waiting time, crowding, discriminatory practices

by medical practitioners), better health for them without health coverage is through this very

system. It can be extrapolated therefore from the present data that there are unmet health needs

among some people in the lower socioeconomic strata. As those who do not have health

insurance, want to avoid the public health care system owing to dissatisfaction or

inafffordability, and will only seek health care when their symptoms are severe and sometimes

the complications from the delay make it difficult to be addressed on their visits. Among unmet

health needs of the poor will be medication. Even if they attend the public health care system and

are treated, the system does not have all the medications which is an indication that they are

expected to buy some. The challenge of the poor is to forego purchasing medication for food,

and this means their conditions would not have been rectified by the health care visitation.




                                                497
       By their very nature, the socioeconomic realities of the poor such as lower access to

education, proper nutrition, good physical milieu, poor sanitation and lower health coverage,

cripple their future health status, this accounts for high premature mortality and hinders health

care utilization. It is this lower health care utilization which accounts for their increase risk of

mortality as the other deprivations such as proper sanitation and nutrition exposes them to

disease causing pathogens which means that their inability to afford health insurance increased

their reliance on the public health care system. The present findings showed that the uninsured

are mostly poor and within the context of Lasser et al.’s work [20] that they receive worse access

to care, are less satisfied with the care they receive and medical services than the insured in the

US, this is an indication of further resistant of the poor from willingly demanding health care as

this rehashes their dissatisfaction and humiliation. Despite the dissatisfaction and humiliation,

their choices are substantially the public health care system, abstinence from care, risk of death,

and the burden of private health care. Apart of the rationales why those in the lower

socioeconomic strata have fewer health coverage than those in the wealthy income group are (1)

inafffordability, (2) type of employment (mostly part time, seasonal, low paid and uninsured

position) which makes it too difficult for them to be holders of health insurance and this retards

the switch from public-to-private health care utilization. Recently a study conducted by Bourne

and Eldemire-Shearer [21] found that 74% of those in the poorest income quintile utilized public

hospitals compared to 58% of those in the second poor quintile and 31% of those in the

wealthiest 20%. Then, if public health becomes privatized or become increasingly more

expensive for recipients, the socioeconomically disadvantaged population (poor, elderly, children

and other vulnerable groups) will become increasingly exposed to more agents that are likely to


                                                498
result in their deaths, increased utilization of home remedy as well as the widening of the health

outcome inequalities among the socioeconomic strata.

       Illness and particularly chronic condition can easily result in poverty, before mortality

sets in. With the World Health Organization (WHO) opined that 80% of chronic illnesses were in

low and middle income countries and that 60% of global mortality is caused by chronic illness

[7], leveling insurance coverage can reduce burden of care for those in the lower socioeconomic

strata. The importance of health insurance to health care utilization, health status, productivity,

production, socioeconomic development, life expectancy, poverty reduction strategy and health

intervention must include increase health insurance coverage of citizenry within a nation. The

economic cost of uninsured people in a society can be measured by the lost of production,

payment of sick time, mortality, lowered life expectancy and cost of care for children, orphanage

and elderly who become the responsibility of the state from the death of the poor. Therefore the

opportunity cost of reduced public health care budget is the economic cost of the aforementioned

issues, and goes to the explanation of premature mortality in a society.

       Particularly the chronically ill, they benefit from health insurance coverage not because

of the reduced cost of health care, but the increased health care utilization that result from health

coverage. From the findings of Hafner-Eaton’s work [2], the chronically ill in the United States

were 1.5 times more likely to seek medical care and while this is about the same for Jamaicans,

health insurance is responsible to their health care utilization and not the condition or illness.

According to Andrulis [22], “Any truly successful, long-term solution to the health problems of

the nation will require attention at many points, especially for low-income populations who have

suffered from chronic underservice if not outright neglect” Embedded in Andrulis’s work is the


                                                499
linkage between poverty, poor health care service delivery, differences in health outcomes

among the socioeconomic groups, higher mortality among particular social class, deep-seated

barriers in health care delivery and the perpetuation of those and how they can increase health

differences among the socioeconomic strata. The relationship between poverty and illness is well

established in the literature [7, 8, 23] as poverty means deprivation from proper nutrition, safe

drinking water, and those issues contribute to lower health, production, productivity, and more

illness in the future. Free public health care or lower public health care cost does not mean equal

opportunity to access, eliminate the barriers to equal opportunity, neither does it increase health

and wellness for the poor and remove lower health disparities among the socioeconomic groups.

However, lower-income, increase price indices, removal of government subsidy from public

health care, increased uninsurance, lower health care utilization, increase poverty, premature

mortality and lower life expectancy of the population and particular subpopulations.

       Increases in diseases (acute and chronic) are owing to lifestyle practices of people.

Lifestyle practices are voluntary lifestyle choices and practices [24]. The poor are less educated,

more likely to be unemployed, undernourished, deprived from financial resources, and their

voluntary actions will be about survival and not diet, nutrition, exercise and other healthy

lifestyle choice. Lifestyle choices such as diet, proper nutrition, and sanitation, safe drinking

water are costly, which oftentimes occurs because of poverty, some people can afford to make

these choices. It follows therefore that those in the lower socioeconomic strata’s voluntary action

will be unhealthy choices which are cheaper. Poverty therefore handicaps its people, and

predetermines unhealthy lifestyle choices, which further accounts for greater mortality, lower life

expectancy, health insurance coverage and private health care utilization.


                                               500
Conclusion


Poverty is among the social determinants of health, health care utilization, and health insurance

coverage in a society. While the current study does not support the literature that chronic

illnesses were greater among those in the lower socioeconomic strata, they were less likely to

have health insurance coverage compared to the upper class. Poverty denotes socioeconomic

deprivation of resources which appears in a society, and goes to the crux of health disparities

among the socioeconomic groups and sexes. Health care utilization is associated with health

insurance coverage as well as government’s assistance, and this embodies the challenges of those

in the vulnerable groups.

        Within the current global realities, many governments are seeking to reduce their public

financing of health care which would further shift the burden of health care to the individual, and

this will even increase premature mortality among those in the lower socioeconomic strata.

Governments in developing nations continue to invest in improving public health measures such

as safe drinking water, sanitation, mass immunization) and the training of medical personnel,

building clinics and hospitals and there is definite a need to include health insurance coverage to

their public health measure as this will increase access to health care utilization. Any increase in

health care utilization will be able to improve health outcome, reduce health disparities between

the socioeconomic groups and the sexes that will see improvements in the quality of life of the

poor.

        In summary, with the health status of the insured being 1.5 times more than the

uninsured, their health care utilization being 1.9 times more than the uninsured and illness being

a strong predictor of health care seeking, any reduction in the health care budget in developing

                                                501
nations denotes that vulnerable groups (such as elderly, children and poor) will seek less care,

and this will further increase the mortality among those cohorts.

Conflict of interest
The authors have no conflict of interest to report.



Disclaimer
The researchers would like to note that while this study used secondary data from the Jamaica
Survey of Living Conditions, none of the errors in this paper should be ascribed to the Planning
Institute of Jamaica or the Statistical Institute of Jamaica, but to the researchers.




                                                502
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                                        504
Table 17.1. Crowding, expenditure, income, age, and other characteristics by health insurance
status
                                             Health insurance status                     P
Characteristics                         Non-insured           Insured
                                         mean ± SD          mean ± SD
Crowding index                                4.9 ± 2.6              4.1±2.1   t = 10.32, < 0.0001
                                1
Total annual food expenditure         3476.09±2129.97 3948.12±2257.97         t = - 6.81, < 0.0001
Annual non-food expenditure1          3772.91±3332.50 6339.40±5597.60 t = - 21.33, < 0.0001
Income1                               7703.62±5620.94 12374.89±9713.00 t = - 22.75, < 0.0001
Age (in year)                                28.7±21.4           35.0 ±22.7   t = - 9.40, < 0.0001
Time in household (in years)                  11.7±1.6             11.8±1.3        t = - 1.62, 0.104
Length of marriage                           16.9±14.3            18.3±13.8        t = - 1.55, 0.122
Length of illness                            14.7±51.1            14.1±36.2      t = - 0.217, 0.828
No. of visits to medical practitioner          1.4±1.0               1.5±1.2     t = - 0.659, 0.511
1
Expenditures and income are quoted in USD (Ja. $80.47 = US $1.00 at the time of the survey)




                                                     505
Table 17.2. Health, health care seeking behaviour, illness and particular demographic characteristics by health insurance status
                                                                       Health insurance status                                             P
Characteristic                                                       Coverage                                 No coverage
                                        Private n (%)     Public, NI Gold n (%)       Other Public n (%)      n (%)
Health conditions                                                                                                                 χ2 = 42.62, P < 0.0001
    Acute and other                           53 (56.4)                    24 (34.8)             13 (24.5)       415 (61.7)
    Chronic                                   41 (43.6)                    45 (65.2)             40 (75.5)       258 (38.3)
Health care seeking behaviour                                                                                                     χ2 = 70.09, P < 0.0001
    No                                      724 (89.3)                    283 (81.3)            118 (75.2)      4735 (91.0)
    Yes                                       87 (10.7)                    63 (18.2)             39 (24.8)         468 (9.0)
Illness                                                                                                                           χ2 = 67.14, P < 0.0001
    No                                      699 (86.2)                    272 (78.6)            101 (64.3)      4453 (85.8)
    Yes                                     112 (13.8)                     74 (21.4)             56 (35.7)       736 (14.2)
Education level                                                                                                                   χ2 = 78.10, P < 0.0001
    Primary and below                       684 (84.4)                    318 (92.2)            144 (91.7)      4536 (87.4)
    Secondary                                  80 (9.9)                     23 (6.7)                9 (5.7)      577 (11.1)
    Tertiary                                   46 (5.7)                      4 (1.2)                4 (2.5)         74 (1.4)
Social class                                                                                                                     χ2 = 596.08, P < 0.0001
    Lower                                      78 (9.6)                   135 (39.0)             31 (19.7)      2345 (45.1)
    Middle                                  111 (13.7)                     80 (23.1)             27 (17.2)      1085 (20.9)
    Upper                                   622 (76.7)                    131 (37.9)             99 (63.1)      1773 (34.1)
Area of residence                                                                                                                χ2 = 190.29, P < 0.0001
    Urban                                   373 (46.0)                    106 (30.6)             63 (40.1)      1397 (26.8)
    Semi-urban                              212 (26.1)                     66 (19.1)             32 (20.4)      1091 (21.0)
    Rural                                   226 (27.9)                    174 (50.3)             62 (39.5)      2715 (52.2)
Self-rated health status                                                                                                          χ2 = 67.14, P < 0.0001
    Poor                                    699 (86.2)                    272 (78.6)            101 (64.3)      4453 (85.8)
    Moderate-to-excellent                   112 (13.8)                     74 (21.4)             56 (35.7)       736 (14.2)
Health care utilization                                                                                                           χ2 = 30.06, P < 0.0001
   Private                                    65 (79.3)                    29 (47.5)             18 (46.2)       215 (46.8)
   Public                                     17 (20.7)                    32 (52.5)             21 (53.8)       244 (53.2)



                                                                        506
Table 17.3. Age cohort by diagnosed illness

                                                               Diagnosed illness

                                 Acute condition                            Chronic condition
                                                                 Diabetes
                      Cold         Diarrhoea       Asthma        mellitus       Hypertension     Arthritis     Other        Total
 Age cohort
                      n (%)          n (%)         n (%)          n (%)            n (%)          n (%)        n (%)       n (%)


 Children            97 (65.1)       13 (48.1)     51 (53.7)         3 (2.4)          0 (0.0)       0 (0.0)    54 (23.1)   218 (24.5)


 Young adults         14 (94)          2 (7.4)     16 (16.8)         3 (2.4)          6 (2.9)       1 (1.8)    43 (18.4)     85 (9.6)




 Other-aged adults   22 (14.8)        6 (22.2)     18 (18.9)       44 (35.8)        76 (36.9)    17 (30.4)     85 (36.3)   268 (30.1)




 Young old             8 (5.4)         2 (7.4)       7 (7.4)       49 (39.8)        61 (29.6)    22 (39.3)     32 (13.7)   181 (20.3)




 Old Elderly           8 (5.4)        3 (11.1)       2 (2.1)       19 (15.4)        49 (23.8)    14 (25.0)      13 (5.6)   108 (12.1)




 Oldest Elderly        0 (0.0)         1 (3.7)       1 (1.1)         5 (4.1)         14 (6.8)       2 (3.6)      7 (3.0)     30 (3.4)
Total                     149                27          95               123              206            56        234          890




                                                           507
Table 17.4. Illness by age of respondents controlled for health insurance status
                                                                  Age of respondents
Characteristic                                              Uninsured             Insured
                                                            Mean ± SD            Mean ± SD
Illness
   Acute condition
     Cold                                                        18.8 ± 23.5       21.0 ± 26.3
     Diarrhoea                                                   28.4 ± 30.3       31.8 ± 13.5
     Asthma                                                      21.0 ± 21.7       29.4 ± 22.9
   Chronic condition
     Diabetes mellitus                                           58.7 ± 16.1       63.8 ± 15.4
     Hypertension                                                62.1 ± 17.3       63.6 ± 15.7
     Arthritis                                                   64.0 ± 13.3       65.0 ± 18.7
  Other condition                                                38.1 ± 25.0       39.2 ± 26.8
F statistic                                                73.1, P < 0.0001 23.3, P < 0.0001




                                              508
Table 17.5. Age cohort by diagnosed health condition, and health insurance status

                        Diagnosed health                   Diagnosed health condition
                            condition
Characteristic          Acute       Chronic         Acute     Chronic        Acute      Chronic
                                                       Uninsured                  Insured
                           n (%)         n (%)        n (%)      n (%)         n (%)       n (%)
Age cohort
Children              215 (42.6)        3 (0.8)   183 (44.1)       1 (0.4) 32 (35.6)     2 (1.6)
Young adults            75 (14.9)     10 (2.6)     58 (14.0)       6 (2.3) 17 (18.9)     4 (3.2)
Other aged-adults     131 (25.9)     137 (2.6)    110 (26.5) 100 (38.6) 21 (23.3) 37 (29.4)
Young-old                49 (9.7) 132 (34.3)         37 (8.9)    82 (31.7) 12 (13.3) 50 (39.7)
Old-old                  26 (5.1)    82 (21.3)       20 (4.8)    55 (21.2)   6 (6.7) 27 (21.4)
Oldest-old                9 (1.8)     21 (5.5)        7 (1.7)     15 (5.8)    2(2.2)     6 (4.8)
Total                        505           385           415          259        90         126
                      χ2 = 317.5, P < 0.0001      χ2 = 234.5, P < 0.0001 χ2 = 73.6, P < 0.0001




                                              509
Table 17.6. Logistic regression: Explanatory variables of self-rated moderate-to-very good
health
 Explanatory variable                     Coefficient    Std. error   Odds ratio   95.0% C.I.
                                                                                                  R2

 Average medical expenditure                   0.000          0.000       1.00*      1.00 -1.00   0.003

 Health insurance coverage (1= insured)        0.410          0.181       1.51*     1.06 - 2.15   0.005

 Urban                                         0.496          0.180      1.64**     1.15 - 2.34   0.007
 Other                                         0.462          0.197       1.59*     1.08 - 2.34   0.006
 †Rural                                                                     1.00

 Household head                                0.376          0.154       1.46*     1.08 - 1.97   0.004

 Age                                           -0.046         0.004     0.96***     0.95 - 0.96   0.081

 Crowding index                                -0.156         0.035     0.86***     0.80 - 0.92   0.010

 Total food expenditure                        0.000          0.000     1.00***     1.00 - 1.00   0.003

 Health care seeking (1=yes)                   -0.671         0.211      0.51**     0.34 - 0.77   0.005

 Illness                                       -1.418         0.212     0.24***     0.16 - 0.37   0.204
Model fit χ2 = 574.37, P < 0.0001
-2LL = 1477.76
Nagelkerke R2 = 0.328
†Reference group
***P < 0.0001, **P < 0.01, *P < 0.05




                                                        510
Table 17.7. Logistic regression: Explanatory variables of self-reported illness
                                                            Std
 Explanatory variable                       Coefficient     Error    Odds ratio   95.0% C.I.        R2


 Average medical expenditure                       0.000     0.000        1.00*       1.00 - 1.00   0.001

 Male                                             -0.467     0.137       0.63**       0.48 - 0.82   0.003

 Married                                           0.527     0.146      1.69***       1.27 - 2.25   0.002

 Age                                               0.031     0.004      1.03***       1.02 - 1.04   0.037

 Total food expenditure                            0.000     0.000       1.00**       1.00 -1.00    0.002

 Self-rated moderate-to-excellent health          -1.429     0.213      0.24***       0.16 -0.36    0.009

 Health care seeking (1=yes)                       5.835     0.262    342.11***   204.71 -571.72    0.611
Model fit χ2 = 2197.09, P < 0.0001
-2LL = 1730.41
Hosmer and Lemeshow goodness of fit χ2 = 4.53, P = 0.81
Nagelkerke R2 = 0.665
†Reference group
***P < 0.0001, **P < 0.01, *P < 0.05




                                                      511
Table 17.8. Logistic regression: Explanatory variables of health care seeking behaviour

                                                            Std         Odds
 Explanatory variable                      Coefficient     error        ratio                        R2
                                                                                   95.0% C.I.


 Health insurance coverage (1= insured)          0.620         0.179      1.86**       1.31 - 2.64   0.003

 Self-reported illness                           5.913         0.252   369.92***   225.74 - 606.17   0.712

 Self-rated moderate-to-excellent health        -0.680         0.198      0.51**       0.34 - 0.75   0.004

Model fit χ2 = 1997.86, P < 0.0001
-2LL = 1115.93
Hosmer and Lemeshow goodness of fit χ2 = 1.49, P = 0.48
Nagelkerke R2 = 0.719
†Reference group
***P < 0.0001, **P < 0.01, *P < 0.05




                                                         512
Table 17.9. Logistic regression: Explanatory variables of self-reported diagnosed chronic illness


 Explanatory variable                          Coefficient   Std error   Odds ratio    95.0% C.I.
                                                                                                      R2
 Male                                               -1.037       0.205       0.36***    0.24 - 0.53   0.048

 Married                                             0.425       0.199         1.53*    1.04 - 2.26   0.012
 †Never married                                                                 1.00

 Health insurance coverage (1= insured)              0.454       0.220         1.58*    1.02 - 2.42   0.008

 Age                                                 0.047       0.005       1.05***    1.04 - 1.06   0.201

 Logged Length of illness                            0.125       0.059         1.13*    1.01 - 1.27   0.008

Model fit χ2 = 136.32, P < 0.0001
-2LL = 673.09
Hosmer and Lemeshow goodness of fit χ2 = 15.96, P = 0.04
Nagelkerke R2 = 0.277
†Reference group
***P < 0.0001, **P < 0.01, *P < 0.05




                                                     513
Table 17.10. Multiple regression: Explanatory variables of income
                                          Unstandardized         Standardized
                                           Coefficients           Coefficients
 Explanatory variable                                                                                R2
                                           B       Std. Error        Beta           95% CI
 Constant                                 11.630        0.061                    11.511 - 11.750

 Crowding index                            0.206         0.008        0.625***     0.190 - 0.221   0.195

 Upper class                               1.265         0.052        0.649***     1.162 - 1.368   0.320

 Middle Class                              0.692         0.047        0.347***    0.599 - 0.784    0.133
 †Lower class

 Household head                           -0.181         0.038       -0.108***   -0.256 - -0.106   0.012

 Health insurance coverage (1= insured)    0.137         0.042         0.075**     0.054 - 0.220   0.007

 Self-rated good health status             0.165         0.040        0.094***    0.088 - 0.243    0.006

 Health care seeking (1=yes)               0.109         0.039         0.063**     0.033 - 0.185   0.003

 Urban                                     0.145         0.046         0.079**     0.055 - 0.235   0.002

 Other town                                0.130         0.049         0.063**     0.033 - 0.226   0.003
 †Rural area

 Married                                   0.075         0.038          0.044*     0.000 - 0.150   0.001
 †Never married


F = 144.15, P < 0.0001
R2 = 0.682
†Reference group
***P < 0.0001, **P < 0.01, *P < 0.05




                                                   514
Table 17.11. Logistic regression: Explanatory variables of health insurance status (1= insured)

 Explanatory variable          Coefficient   Std. error   Odds ratio   95.0% C.I.       R2

 Age                                 0.014       0.006         1.01*      1.00 - 1.03        0.040

 Income                              0.000       0.000       1.00***      1.00 - 1.00        0.082

 Chronic condition                   0.563       0.210         1.7**      1.16 - 2.65        0.013

 Health care seeking (1=yes)         0.463       0.211         1.59*      1.05 - 2.40        0.010

 Married                             0.647       0.192        1.91**      1.31 - 2.79        0.024
 †Never married

 Upper class                         0.841       0.227       3.46***      1.49 - 3.62        0.025
 †Lower class

Model fit χ2 = 95.7, P < 0.0001
-2LL = 686.09
Hosmer and Lemeshow goodness of fit χ2 = 5.08, P =0.75
Nagelkerke R2 = 0.194
†Reference group
***P < 0.0001, **P < 0.01, *P < 0.05




                                                      515
                                                                           Chapter
                                                                                         18
         Good Health Status of Rural Women in the Reproductive Ages


                            Paul A. Bourne and Joan Rhule



Women are traditionally over represented among the poor and therefore in the long run, have
less access to remuneration and health resources, including health insurance and social security
services. Women are disadvantaged on some fundamental economic indicators such as
unemployment and access to economic resources. In 2007 in Jamaica, for instance among the
124 500 unemployed persons in the labour force, 65.4 % were women (Planning Institute of
Jamaica, 2008). Thus, women's health and the control that they can exercise over resources are
key factors in achieving effectiveness, efficiency, and sustainability in health interventions. This
study examined the good health status of rural women in the reproductive ages of 15 to 49 years.
Having extensively reviewed the literature, this paper is the first study of its kind in Jamaica and
will provide pertinent information on this cohort for the purpose of public health planning.
Using logistic regression analyses, 6 variables emerged as statistically significant predictors of
current good health status of rural women (i.e. ages 15 to 49 years) in Jamaica. These are social
standing (two wealthiest quintile – OR=0.524, 95%CI: 0.350,0.785); marital status (separated,
divorced or widowed – OR=0.382, 95%CI: 0.147, 0.991); health insurance (OR=0.041, 95%CI:
0.024, 0.069); negative affective psychological conditions (OR=0.951, 95%CI:0.704, 1.284);
asset ownership (OR=1.089, 95%CI:1.015, 1.168) and age of respondents (OR=0.965,
95%CI:0.949, 0.982). Poverty is synonymous with rural area and women, and inspite of this
reality majority of rural women in Jamaica ages 15 to 49 years reported current good health
status. Wealth creates more access to financial and other resources, and makes a difference in
nutritional intake, water and food quality as well as an explanation for better environmental
conditions. In this study, wealth did not mean better health but that poor women had greater
health status than their wealthy counterparts. Another interesting finding was that good health is
inversely correlated with the ownership of health insurance coverage.

                                                516
Introduction



Many studies have shown that there is a statistical relationship between health status and poverty

(Murray, 2006; Marmot, 2002; Muller & Krawinkel, 2005; Bloom & Canning, 2003; Smith &

Waitzman, 1994), standard of living (Pacione, 2003; Bourne, 2007a, 2007b), and other socio-

economic determinants (Grossman, 1972; Smith & Kington 1997; Bourne, 2009; Bourne &

McGrowder, 2009; PAHO & WHO, 2007; Casas et al., 2001, Benzeval et al, 2001) . According

to Abel-Smith (1994), the influence of income on health decreases as the society shifts from

lower to higher levels of income. And this is in keeping with the findings that show an inverse

relationship between income of a country and levels of mortality, and the reverse is equally true

(Abel-Smith, 1994; Matsaganis, 1992). Other scholars have refined this association when they

opined that it is inequalities of income within a country that explains higher mortality and not

mere income (Cochrane et al, 1978). The use of mortality to assess health is primary because this

is easily measurable, unlike the use of morbidity which is a minimalist’s approach to the study of

health (Grossman, 1972); but the latter still does not capture quality life expectancy and so is the

former measure. The emphasis on income to provide explanation for health status without

incooperating sanitation, education and lifestyle practices (Bourne, 2007a, 2007b; Hambleton et

al, 2005), water and (Abel-Smith, 1994), health care does not provide the core rationale for the

health status of a population as the determinants of health covering, social, economic,

psychological, environmental, and biological conditions.


        In many societies across the world, poverty is rural and gender specific. Poverty is more

than just the lack of income (ie. low income) as it includes the lack of access to services,

                                                517
resources and skills, vulnerability, insecurity and powerlessness. There is another result of

poverty which has a multiple effect on the economy, and that is poor health conditions owing to

malnutrition, low water quality, non-access to primary health care and food insecurity.

According to the WHO (2005), 80% of chronic illnesses were in low and middle income

countries, suggesting that illness interfaces with poverty and vice versa. A study by Bourne &

McGrowder (2009), using 2-decade of data on unemployment, self-reported and health-care-

seeking behaviour of Jamaicans (from 1988-2007), found that there was a positive correlation

between poverty and unemployment; poverty and illness; and crime and unemployment.

Understanding poverty is an insight to examining ill-health. PAHO (2001; 5) stated that “The

relationship between poverty and ill health has been known for centuries…” and went further to

state that poverty is a significant cause of diseases, suggesting that any study of health in

developing countries must include this phenomenon.


       In Jamaica, poverty is substantially a rural and gender phenomenon. Statistics from the

Planning Institute of Jamaica and the Statistical Institute of Jamaica (PIOJ & STATIN, 2008)

revealed that in 1997, 19.9% of Jamaicans were poor. Of this figure, 73.3% was in rural areas;

13.1% in semi-urban zones and 13.6% in urban areas. One decade later (ie 2007), the prevalence

of poverty fell to 9.9% of which 71.3% was in rural areas, 8.9% in semi-urban and 19.9% in

urban zones. In the same year (ie 2007), 11.1% of persons living in female-headed households

were classified as poor compared to 8.6% of those residing in male-headed household. Poverty

is not only rural as there has been a rising in its levels in urban areas. The survey determined the

poverty line was US$ 1,070.32 per year (US $2.92 per day) for an individual and US$ 4045.29

per year for a family of five (US $2.22 per person per day). The Jamaica Survey of Living

                                                518
Conditions (2002) indicated that the wealthiest 20% of the population accounted for 45.9% of

national consumption while the poorest 20% accounted for only 6.1% of national consumption.

On average, the wealthiest 10% of the population consumed approximately 12.5 times more than

the poorest 10%. This is a mean per capita annual consumption expenditure of US$ 3963.53

compared to US$314.48. Jamaica is not atypical in having poor people or having to address the

predominance of this rural phenomenon. The World Bank (1996) estimated that in 1996, 38% of

the total population (or 25% including Haiti) in the Caribbean or more than seven million people

to be poor. In this study 46% of sample was poor (ie classified as in the two poorest income

quintile), and so poverty plays a critical role in this paper.


            According to Bourne (2008), in 1880 to 1882, life expectancy at birth for men was 37.02

years and 39.80 years for women with the gap between sexes widening to 5.81 years (71.26 for

men and 77.07 for women). Despite the high life expectancy of women in Jamaica which is

comparable to that of many developed nations (United Nations, 2002), people with lower

socioeconomic status have worse health in all adult age groups, including older ages (House et

al, 2005). Reduced capacity to generate income and the growing risk of illness increase the

vulnerability of the elderly to poverty, regardless of their original economic status, in developing

and industrialized countries (Lloyd-Sherlock, 2000). Poverty, therefore, is age, area and gender

specific.


            Women are traditionally overrepresented among the poor and therefore in the long run,

have less access to remuneration and health resources, including health insurance and social

security services. Women are disadvantaged on some fundamental economic indicators such as

unemployment and access to economic resources. In 2007 in Jamaica, for instance among the
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124 500 unemployed persons in the labour force, 65.4 % were women (Planning Institute of

Jamaica, 2008). Thus, women's health and the control that they can exercise over resources are

key factors in achieving effectiveness, efficiency, and sustainability in health interventions.


        According to Marmot (2002), poverty accounts for poor nutrition and physical milieu,

deprivation from material resources and further explains the higher levels of health conditions of

those that are therein. The WHO (2005) concurs with Marmot as it opined that poverty explains

chronic illness and premature death. Women are more likely to be poor, unemployed and have

lower material wealth compared to men. Like the WHO (2005), Marmot (2002) and Abel-Smith

(1997) showed the health challenges of being poor and by extension female. It therefore suggests

that study of health status and women must include not only poverty but other socio-

demographic variables.


        Poverty is substantially more than income poverty; it is the denial of choices and

opportunities for living a tolerable life (UNDP, 1997). Over the past two to three decades, our

understanding of poverty has broadened from a narrow focus on income and consumption to a

multi-dimensional notion of education, health, social and political participation, personal security

and freedom, and environmental quality. Hence, those socio-economic factors not only explain

poverty they influence health status for the individual, household, society, country and world.


        Health which is more than the absence of diseases (WHO, 1948) suggests that people

are multi-dimensional and any study of their health status must incorporate the environment

(Pacione, 2), income (Grossman, 1972; Smith & Kingston, 1997; Bourne, 2009). The WHO has

endorsed the evaluation of social determinants in any examination of health status (WHO, 2008;


                                                520
Kelly et al. 2007). It is the social determinants (ie non-biological factors) which produce the

inequality in income, health and regards health development. Hence, addressing those

determinants account for a percentage of health status (Hambleton et al. 2005). In a study of

elderly Barbadians, Hambleton et al. (2005) found that biological conditions accounted for

67.5% of health status of sample.       This indicates that the social determinants are equally

important in the examination of health status (they account for 32.5% of the explanatory power

of health status).


         Concomitantly, Hambleton et al.’s work reveals that there was a statistical causal

relationship between socioeconomic conditions and the health status of Barbadians.            The

findings reveal that 5.2% of the variation in reported health status was explained by the

traditional determinants of health.      Furthermore, when this was controlled for current

experiences, this percent fell to 3.2% (falling by 2%). When the current set of socioeconomic

conditions were used they accounted for some 4.1% of the variation in health status, while 7.1%

were due to lifestyle practices compared to 33.5% (out of 38.2%) that was as a result of current

diseases (see Hambleton et al. 2005). It holds that importance placed by medical practitioners on

the current illnesses – as an indicator of health status – is not unfounded as people place more

value on biomedical conditions as responsible for their current health status.


        Diener (1984, 2000) and others (Idler & Benyamini 1997; Idler & Kasl, 199) have

showed that wellbeing, happiness or health status is equally good to measure health or subjective

wellbeing. Economists like Grossman (1972) and Smith & Kington (1997) have used self-

reported health status in evaluating health of people. Hence, self-reported health status (health

status) is widely accepted in health literature as a measure of the state of one’s health. In this
                                                521
study, data were not collected on health status but on health conditions. The sample was asked to

state whether they have an illness or not, and if they do what were the typology of health

conditions. For this paper the researcher used good health status to indicate not reported a health

condition and poor health to indicate at least one reported health condition. Self-reported ill-

health is not an ideal indicator of actual health conditions because people may underreport;

however, it is still an accurate proxy of ill-health and mortality (Idler & Kasl, 1991; Idler &

Benyamini, 1997).


        The reason for the importance of health conditions (illness) is simply that a healthy

population holds the key to development. It is within this framework that a study of health is

required to examine the factors that determine health status of women in the reproductive years

of 15 to 49 years. It is clear from the review of the literature that health is influenced by income

and other social factors. A literature search revealed that no study existing in the Caribbean, in

particular Jamaica has sought to examine factors that determine the health status of rural women

in the reproductive ages of 15 to 49 years. This is the first research of its type in the Caribbean

and in particular Jamaica. It provides an insight into the factors that determine self-reported

health status of women in ages 15 to 49 years, and this can now be used to guide public health

policy. Hence, the purposes of this study are to (i) examine the good health status of women in

the reproductive ages, (ii) model socio-economic determinants of good health status of women in

the reproductive ages, and (iii) provide public health policy makers with research information on

this cohort for better policies design in the future.


Methods


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Participants and questionnaire


The current research extracted a sample of 3 450 respondents who indicated that they were rural

women ages 15 to 49 years. This sample was taken from a national cross-sectional survey from

the 14 parishes in Jamaica. The survey used a stratified random probability sampling technique

to drawn the original 25 018 respondents. The non-response rate for the survey was 29.7%. The

study used secondary cross-sectional data from the Statistical Institute of Jamaica (2003) (ie

Jamaica Survey of Living Conditions or JSLC). The JSLC was commissioned by the Planning

Institute of Jamaica and the Statistical Institute of Jamaica. These two organizations are

responsible for planning, data collection and policy guidelines for Jamaica.




The JSLC is a self-administered questionnaire, where respondents are asked to recall detailed

information on particular activities. This information was collected by trained interviewers from

the Statistical Institute of Jamaica. The questionnaire covers demographic variables, health,

immunization of children 0–59 months, education, daily expenses, non-food consumption

expenditure, housing conditions, inventory of durable goods and social assistance. Interviewers

are trained to collect the data from household members. The survey is conducted between April

and July annually.


Model


The multivariate model used in this study (a modification of Bourne and McGrowder’s health

status model) captures a multi-dimensional concept of health and health status. It is

fundamentally different from that of Bourne and McGrowder’s model (2009) as it is gender
                                               523
(women) and age specific (15 to 49 years), and a number of new variables were included such as

social standing; crime and pregnancy. Hence, the proposed model that this research seeks to

evaluate is displayed (Eqn (2)):


H t = f(lnP mc, ED i, R t, HI i, HT i, X i, CR i, (ΣNP i, PP i ), M i, F i, N i, Ai, ε i )   [1]




Where the current good health status of a rural resident, H t , is a function of 12 explanatory

variables, where H t is current good health status of person i, if good or above (ie no reported

health conditions in the 4 weeks leading up to the survey period to trained interviewers from the

Statistical Institute of Jamaica), 0 if poor (ie at least one health condition reported to trained

interviewers from the Statistical Institute of Jamaica); lnPmc is the logged cost of medical care of

person i; ED i is the educational level of person i, 1 if secondary, 1 if tertiary and the reference

group is primary and below; Rt is the retirement income of person i, 1 if receiving private and/or

government pension, 0 if otherwise; HI i is the health insurance coverage of person i, 1 if they

have a health insurance policy, 0 if otherwise; HT i is the house tenure of person i, 1 if rent, 0 if

squatted; Xi is the gender of person i, 1 if female, 0 if male; CRi is crowding in the household of

person i; (∑2 i=1 NPi, PPi ) NPi is the sum of all negative affective psychological conditions, and

PPi is the sum of all positive affective psychological conditions; M i is the number of males in

the household of person i and Fi is the number of females in the household of person i; Ai is the

age of the person i and N i is the number of children in the household of person i; LLi is the living

arrangements, where 1 = living with family members or relatives, and 0 = otherwise.




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            Variables were identified from the literature, using the principle of parsimony. Only those

explanatory variables that are statistically significant (p <0.05) were used in the final model to

predict current health status of Jamaican women in the reproductive ages of 15 to 49 years. Here,

the final model that accounted for self-reported good health of Jamaican women in the

reproductive years of 15 to 49 years is expressed in Eqn. [2].


H t = f(W i , MR i , HI i, NP i, , D i , Ai, ε i )                                         [2]


The current good health status of Jamaican women in the reproductive ages of 15 to 49 years, H t ,

is a function of social standing of individual i, W i ; marital status of individual i, MR i ; health

insurance of person i, HIi; NP i is negative affective psychological conditions of person i; D i is

total number of durable goods owned by individual i (excluding property and land) and Ai is the

age of the person i.


Measures

An explanation of some of the variables in the model is provided here. Health status is a dummy

variable, where 1 (good health) = not reporting an ailment or dysfunction or illness in the last

4 weeks, which was the survey period; 0 (poor health) if there were no self-reported ailments,

injuries or illnesses. While self-reported ill-health is not an ideal indicator of actual health

conditions because people may underreport, it is still an accurate proxy of ill-health and

mortality (Idler & Kasl, 1991; Idler & Benyamini, 1997). Social supports (or networks) denote

different social networks with which the individual is involved (1 = membership of and/or visits

to civic organizations or having friends who visit one’s home or with whom one is able to

network, 0 = otherwise). Psychological conditions determine the psychological state of an

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individual, and this is subdivided into positive and negative affective psychological conditions

(Diener, 2000; Harris & Lightsey, 2005) Positive affective psychological condition is the number

of responses with regard to being hopeful, optimistic about the future and life in general.

Negative affective psychological condition is the number of responses from a person on having

lost a bread