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HEALTH INSURANCE AND HEALTH

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HEALTH INSURANCE AND HEALTH Powered By Docstoc
					  Health Insurance
          &
       Health
                                                                                                                                      Jamaica: 2009
                                                                                                                                      2. Per cent of Each Sex


                       70.00


                                                                                                              Male                                                                        Female


                       65.00


                                                                                                                                                         70-74


                                                                                                                                                         60-64
Seeking Medical Care




                       60.00




                                                                                                                                                         50-54

                       55.00
                                                                                                                                                         40-44


                                                                                                                                                         30-34
                       50.00


                                                                                 R Sq Quadratic =0.751                                                   20-24


                       45.00
                                                                                                                                                         10-14

                               8.00   10.00   12.00         14.00        16.00   18.00           20.00

                                                      Health Insurance                                                                                   0-4
                                                                                                         12




                                                                                                                     10




                                                                                                                                                                                             10




                                                                                                                                                                                                   12
                                                                                                                          8




                                                                                                                                            2
                                                                                                                              6




                                                                                                                                  4




                                                                                                                                                     0



                                                                                                                                                               0




                                                                                                                                                                     2




                                                                                                                                                                              4




                                                                                                                                                                                  6




                                                                                                                                                                                      8




                                                                                                                                                                   Per cent




                                                                                 Paul Andrew Bourne
    Health Insurance
            &
         Health
 




                       i 

 
    Health Insurance
            &
         Health
 

 

 

 

 

        Paul Andrew Bourne
                    Director 
         Socio‐Medical Research Institute 
                         




                                             ii 

 
©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 Insurance & Health 

                                         
Includes index 
 
ISBN 
 
Bourne, Paul Andrew 
 
 
All rights reserved. Published, 2011 
 
Cover designed by Paul Andrew Bourne 




                                                iii 

 
Preface
The population of Jamaica was estimates to be 2,698,810 people (end of year, 2009), with about

49.3% males (sex ratio = 97.1) and 11% in the older age adulthood category (60+ years old).

There are two features about the Jamaican population that must be noted here 1) the feminization

at older ages and 2) a high rate of growth at older ages (80+ years) compared to other age

cohorts. There is evidence that showed that there is a strong statistical correlation between

people ‘seeking medical care’ and ‘health insurance coverage’ in Jamaica. However, the

relationship between the two aforementioned variables is curvilinear one as people will seek

more medical care with the ownership of more health insurance coverage, and this will fall after

more than 18% of Jamaicans purchasing health insurance coverage. Despite the fact that there is

direct association between health insurance and health care seeking behaviour, in 2007, only

21.2% of Jamaicans were holders of health insurance coverage (572,148 Jamaicans).

       With only 21 out of every 100 Jamaica being holders of health insurance in 2007, this

speaks to the high cost of individual health coverage and it justifies the public health care

utilization in this country and the switching from the public health care to the private health care

utilization with increased income and wealth (socioeconomic status). This volume

comprehensively examines health insurance and health among Jamaicans, using survey data for

2002 and 2007.

        Health Insurance and Health is but the commencement of those phenomena, and I hope
that this will foster more discussion in the future as well as guide research.


                                                                       Paul Andrew Bourne
                                                                                        Director
                                                                 Socio-Medical Research Institute
                                                                                    March 2011

                                                                                                  iv 

 
Acknowledgement
The writing of a book is a time consuming and a tedious process, which is assisted by many
people. A book is not a singulate effort and this must be recognized by the author(s), editor(s)
and/or publisher(s). Like many other authors, I am indebted to many people who contributed in
different ways to the completion of this book. These individuals are 1) Mrs. Evadney Bourne, 2)
Kimani Bourne, 3) Kerron Bourne, 4) Paul Andrew Bourne, Jnr, who stayed up with me on
countless nights, and longer on Saturdays and Sundays. Ms. Neva South-Bourne, whose tireless
efforts and endless patience in proofreading some of the chapters as well as Mrs. Cindi
Scholefield. I am also indebted to the Derek Gordon Databank, University of the West Indies,
Mona (Jamaica) that made the dataset available from which many of the chapters emerged. The
majority of the chapters are published works in different journals, and I am grateful for their
permission to use the materials in this book (North American Journal of Medical Sciences,
Health, Current Research Journal in Social Sciences, International Journal of Collaborative
Research on Internal Medicine and Public Health, HealthMed Journal, and Journal of Clinical
and Diagnostic Research; Journal of Applied Sciences Research). Finally, I would like to thank
all my co-authored who wrote different articles with me. Any errors of omission or commission
in this book should not be ascribed to anyone or organizations as these are of the author.




                                                                                              v 

 
Table of Contents
                                                     
Preface                                                                                                iv

Acknowledgement                                                                                         v

Chapter 1                                                                                               1 

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

Chapter 2                                                                                             31 

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

Chapter 3                                                                                             63 

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

Chapter 4                                                                                             87 

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

Chapter 5                                                                                            113 

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

Chapter 6                                                                                            137 

Health Inequality in Jamaica, 1988‐2007 

Chapter 7                                                                                            172 

Social determinants of self‐reported health across the Life Course 

Chapter 8                                                                                            194 

Sociomedical Public Health in Jamaica 

Chapter 9                                                                                            226 



                                                                                                       vi 

 
Modelling social determinants of self‐evaluated health of poor older people in a middle‐income 
developing nation 

Chapter 10                                                                                          252 

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

Chapter 11                                                                                          278 

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

Chapter 12                                                                                          304 

Variations  in  social  determinants  of  health  using  an  adolescence  population:  By  different 
measurements, dichotomization and non‐dichotomization of health 

Chapter 13                                                                                          331 

Childhood Health in Jamaica: changing patterns in health conditions of children 0‐14 years 
 

Chapter 14                                                                                          359 

The uninsured ill in a developing nation 
 
Chapter 15                                                                                          391 
 
Determinants of self‐rated private health insurance coverage in Jamaica 

Chapter 16                                                                                          415 

Difference  in  social  determinants  of  health  between  men  in  the  poor  and  the  wealthy  social 
strata in a Caribbean nation 

 

 
 

 

 

 
                                                                                                      vii 

 
 



Health Insurance
        &
 
     Health




                   viii 

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



                                    Paul Andrew Bourne

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. This study
used two secondary cross-sectional data from the Jamaica Survey of Living Conditions (JSLC).
The JSLC was commissioned by the PIOJ and the Statistical Institute of Jamaica (STATIN) in
1988. 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 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. The surveys used stratified random probability
sampling technique to draw the original sample of respondents. Descriptive statistics were used
to provide background information on the sample, and logistic regression was to determine
predictors of private health insurance coverage. 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.

                                                                                                  1 

 
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

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
                                                                                                    2 

 
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,

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;

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.


                                                                                                  3 

 
        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

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


                                                                                                  4 

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

that drives health insurance coverage; but something else.


       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 (see also, Cohen &

Cohen, 2003; Cohen, 1988). This was used to exclude (or allow) a variable in the model. In

                                                                                                   6 

 
addition, variables were excluded from the model 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(Ht, Ai, Gi, HHi, ARi, lnC, ∑Di, EDi, MRi, Si, HTi, lnY, CRi, MCt, SSi, Ti , CIi, Pi, Eni, HSB,
εi )                                                                                           (1)

       Where HIi is health insurance coverage of person i, Ht (ie self-rated current health status

       in time t) is a function of age of respondents, Ai ; sex of individual i, Gi; household head

       of individual i, HHi; area of residence, ARi; house tenure of individual i, HTi; logged


                                                                                                  7 

 
       consumption per person per household member, lnC; summation of durable goods and

       asset owned, ∑Di; Education level of individual i, EDi; marital status of person i, MRi;

       social class of person i, Si;; logged income, lnY; crowding of individual i, CRi; medical

       expenditure of individual i in time period t, MCt; social support of individual i, SSi; social

       assistance (ie welfare) individual i, Ti; crime index, CIi; physical environment of

       individual i, Eni, health care seeking behaviour and an error term (ie. residual error).

      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):

      HIt(Jamaicans,    2002)    =f(ARi,     lnC,   EDi,   MRi,      lnY,    SSi,    ∑Di,        HSB,   εi)

(2)

      HIt(Jamaicans,   2007)    =f(ARi,    lnC,   EDi, MRi,   lnY,    SSi,   Ai,    Gi,   S i,   HSB,   εi )
(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


                                                                                                          8 

 
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

“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

                                                                                                9 

 
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 (SD=1.4 days) to 1.4 days (SD=1.1 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 1.1).
       Based on Table 1.2, the mean annual income of respondents in 2002 was Ja $331,488.32

(SD = JA $304,040.77) and this increased by 108.6% in 2007: Ja $691,560.45 (SD = Ja

$128,742.65). 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 cross-tabulation between health status and self-reported illness revealed a significant
statistical correlations - χ2(df = 2) = 1,289.23, p < 0.001 (Table 1.3). Table 1.3 revealed that an
individual who reported poor health status was 9.3 times more likely to have an illness than those
stating a dysfunction. On the other hand, an individual who reported good health status was 2.0
more likely not to report an illness than those reporting at least one ailment. Based on Table 1.3,
more males (85.4%) reported good health status than females (79.2%) - (χ2(df = 2) = 44.666, p <
0.001) - and the converse was true for poor health status, with 5.5% of females compared to
4.2% of males.
       Based on Table 1.4, there was a change in pattern of 5-leading recurring illnesses in

Jamaica. 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


                                                                                                10 

 
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, n

= 3,063) and in 2007 (2 χ2(df = 1) = 40.916, p < 0.001, n= 999; 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.

        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 data revealed that 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 correlation 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 revealed 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,



                                                                                                11 

 
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

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



                                                                                                 12 

 
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,

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

                                                                                                13 

 
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

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

                                                                                                   14 

 
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

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).

                                                                                               15 

 
       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

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
                                                                                                 16 

 
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

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

                                                                                                 17 

 
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.

       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

                                                                                                 18 

 
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

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.

                                                                                                   19 

 
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

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           $440,451.50    $521,519.38   < 0.001
                                         Other towns $385,625.70        $276,644.12
                                         Rural           $284,810.20    $231,540.04
                                         Total           $331,488.32    $304,040.77
                               2007†† Urban              $865,674.20    $673,512.10   < 0.001
                                         Other towns $771,300.50        $597,582.65
                                         Rural           $551,633.70    $389,765.68
                                         Total           $691,560.45    $128,742.65
Crowding                       2002      Urban           2.0 persons    1.4 persons   > 0.05
                                         Other towns 2.0 persons        1.4 persons
                                         Rural           2.0 persons    1.4 persons
                                         Total           2 persons      1.4 persons
                               2007      Urban           4.3 persons    2.4 persons   < 0.001
                                         Other towns 4.6 persons        2.3 persons
                                         Rural           5.0 persons    2.5 persons
                                         Total           4.7 persons    2.5 persons
Age                            2002                      28.2 yrs       22.0 yrs
                               2007                      29.9 yrs       21.8 yrs

No of visits to health care    2002                    1.7 days         1.4 days
facilities
                               2007                    1.4 days         1.1 days
Medical expenditure            2002†                   $1,144.14        $2,946.02
                               2007††                  $1,477.07        $4,711.15
†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
                                                    Good       Fair            Poor
Self-reported dysfunction1               0             89.1          8.7           2.2    5569
                                         ≥1            42.8         36.8          20.4     976
                                         Total        5381          845            319    6545

Gender2                                  Male         85.4          10.4          4.2     3195
                                         Female       79.2          15.3          5.5     3370
                                         Total        5397          848           320     6565
1 2
    χ (df = 2) = 1,289.23, p < 0.001, c=0.405
2 2
    χ (df = 2) = 44.666, p < 0.001, c=0.082




                                                                                                 26 

 
Table 1.4. Self-reported diagnosed recurring illness by gender and years (2002, 2007)

Yea     Sex                   Self-reported diagnosed recurring illness (%)                   Tota
 r                                                                                             l
               Col    Diarrhoe   Asthm     Diabete   Hypertensio Arthriti     Othe      No
               d      a          a         s         n           s            r

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

      Femal    17.8 2.4          8.3       13.2      27.6           6.3       16.6      7.7   181
      e                                                                                       1

      Total    610    83         294       356       661            209       553       297   306
                                                                                              3

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

      Femal    13.4 2.7          8.0       15.4      24.8           5.4       22.1      8.2   597
      e

      Total    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
Yes1                                        60.7           66.0    62.3           67.6

No2                                         39.3           34.0    37.7           32.4

Total                                       1266           1813    406            599
1 2
 χ (df = 1) = 9.006, p = 0.003, n = 3,079
2 2
 χ (df = 1) = 3.004, p = 0.048, n= 1,005




                                                                                         28 

 
Table 1.6. Health insurance coverage by Area of Residence, 2007
                                          Area of Residence

     Health Insurance                            Other
                                       Urban     towns            Rural   Total
      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                               1939     1401             3177    6517
    χ2(df = 4) = 184.347, p < 0.001, n = 6,517




                                                                                  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    8.11-43.43***
†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          0.44-36.43   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
*P< 0.05, **P< 0.01, ***P< 0.001




                                                                                                   30 

 
                                      Chapter 2

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



                                         Paul A. Bourne
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 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

                                                                                                    31 

 
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

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


                                                                                                32 

 
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.

       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

                                                                                                 33 

 
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.


       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
                                                                                                34 

 
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

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

                                                                                              35 

 
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-

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

                                                                                                   36 

 
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.

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).


                                                                                              37 

 
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

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

                                                                                                 38 

 
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

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 =

                                                                                              39 

 
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.

       Table 2.1 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 2.2.

       Table 2.3 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.

       Table 2.4 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 2.5 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

                                                                                                 40 

 
Nine variables account for (Table 2.6), 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 2.7 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).

       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 2.8).

       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 2.9).

       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 2.10).

       Table 2.11 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


                                                                                                 41 

 
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

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



                                                                                                  42 

 
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

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

                                                                                                 43 

 
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

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

                                                                                                 44 

 
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.

       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

                                                                                                 45 

 
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

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.

                                                                                                  46 

 
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

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

                                                                                                47 

 
predetermines unhealthy lifestyle choices, which further accounts for greater mortality, lower life

expectancy, health insurance coverage and private health care utilization.

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.




                                                                                                 48 

 
       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

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.




                                                                                               49 

 
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        West Indies [distributors]; 2008.
                                                                                               50 

 
    17. Hayward RA, Shapiro MF, et al. Inequalities in health services among insured
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        disparities in the United States and Canada: Results of a Cross-National Population-
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        Australian J of Basic and Applied Scie 2009; 3:3067-3080.
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    23. Foster AD. Poverty and illness in low-income rural areas. The American Economic
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        8:179-182.




                                                                                          51 

 
Table 2.1. 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
Total annual food expenditure1        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
       1
Income                                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)




                                                                                                  52 

 
Table 2.2. 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)

                                                                                                                                     53 

 
Table 2.3. 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




                                                                                                                          54 

 
Table 2.4. 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




                                                                                                 55 

 
Table 2.5. 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
                       2                          2                      2
                      χ = 317.5, P < 0.0001      χ = 234.5, P < 0.0001 χ = 73.6, P < 0.0001




                                                                                            56 

 
Table 2.6. 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




                                                                                                            57 

 
Table 2.7. 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




                                                                                                             58 

 
Table 2.8. 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




                                                                                                           59 

 
Table 2.9. 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




                                                                                                             60 

 
Table 2.10. 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




                                                                                                             61 

 
Table 2.11. 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




                                                                                                        62 

 
                                    Chapter 3

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



                                       Paul A. Bourne


On examination of the literature in Latin America and the Caribbean, and in particular Jamaica,
no study could be found that investigated the health and health care-seeking behaviour of
uninsured people. This study bridges the gap in the literature, by evaluating uninsured
Jamaicans’ medical care-seeking behaviour and good health status. The study extracted a sample
of 5,203 uninsured respondents 15 years and older from a national probability cross-sectional
survey of 6,782 Jamaicans. Descriptive statistics were used to provide background information
on the sample; cross-tabulations evaluated bivariate analyses, and logistic regression was used to
model health and medical care-seeking behaviour. Good health of uninsured Jamaicans is
correlated -reported biological condition (OR =0.114, 95% CI = 0.090 -0 .145) followed by age
(OR =0.952, 95% CI = 0.946- 0.959); gender (OR = 1.501, 95% CI = 1.221–1.845);
consumption (OR = 1.000, 95% CI = 1.000–1.000); social class (upper class OR = 0.563, 95%
CI = 0.357–0.888); education (secondary and above OR = 0.622, 95%CI = 0.402–0.963), and
area of residence (other towns OR = 1.351, 95% CI = 1.026–1.778). Medical care-seeking
behaviour is associated with age (OR = 1.020, 95% CI = 1.006 – 1.033); poor health status (OR
= 2.303, 95% CI = 1.533–3.461), and marital status (married OR = 0.518, 95% CI = 0.325–
0.824). The findings are far reaching and provide an understanding of the uninsured, and the
information can be used to aid public health intervention and education programmes.


Introduction

Poverty is among the reasons for some people in developing nations not seeking medical care;

and it also explains premature death owing to low health care utilization. The World Health

Organization (WHO) [1] opined that 80% of chronic illnesses were in low and middle income

countries, suggesting that poverty interfaces with illness and creates other socio-economic

challenges. Poverty does not only impact on illness, it causes premature deaths, lower quality of

                                                                                               63 

 
life, lower life and healthy life expectancy, low development and other social ills such as crime,

high pregnancy rates, and social degradation of the community. According to Bourne &

Beckford [2], there is a positive correlation between poverty and unemployment; poverty and

illness; and crime and unemployment. Sen [3] encapsulated this well when he put forward the

idea that low levels of unemployment in the economy are associated with higher levels of

capabilities. The WHO [1] opined that 60% of global mortality is caused by chronic illness, and

within the context that four-fifths of chronic dysfunctions are in low-to-middle income countries,

health insurance coverage reduces the burden of out-of-pocket medical expenditure for the

individual and the family.


       Jamaica is among those countries classified as developing nations. Hence, the challenges

which were stated earlier also influence the quality of life of some people within the society. In

1988, Jamaica’s unemployment rate was 18.9% and 2 decades later (2007), it fell by 67.2% (to

6.2%) which indicates close to full-employment. [4] This significant reduction in unemployment

rates cannot be the only indicator used to evaluate the socio-economic status of Jamaica, or for a

hasty conclusion to be drawn that the quality of life of Jamaicans is better in 2007 compared to

1988. In 1988 the inflation rate in Jamaica was 8.8% and this increased by over 90%, suggesting

that the economic cost of living for Jamaicans was substantially higher than twenty years earlier.

It is important to note that the inflation rate in 2007 (16.8%) increased by 194.7% over 2006. A

national representative probability sample cross-sectional survey of 1,338 Jamaicans which was

conducted in 2007 revealed that 68.7% of respondents claimed that their current economic

situation was at most the same compared to 12 months ago, and of this figure 25% mentioned

that it was worse. [5] Furthermore, 62% of the sample indicated that their salaries were not able


                                                                                               64 

 
to satisfactorily cover their basic needs, and 71.9% claimed that they were concerned about the

likelihood of being unemployed in the next 12 months. Those realities, then, explain why in

2007, the number of Jamaicans seeking medical care fell to 66% over 70% in the previous year;

while the self-reported figures rose to an unprecedented 15.5%.


       In Jamaica, rural poverty is twice (15.3%) that of urban poverty (6.2%). [4] This may

create the impression that urban poverty is low and does not demand an examination. Poverty is

poverty and whether it occurs in rural, peri-urban and urban areas; its effect is the same. Hence,

when poverty is coupled with unemployment, chronic illnesses will require health care for either

preventive or curative measures which must lead to a financial commitment that can erode their

resources or that of their families. [5] In 2007, statistics on health in Jamaica showed that 50.8%

of people in the poorest income quintile (i.e. below the poverty line) indicated that they were

unable to afford to seek medical care, compared to 36.7% of those just above the poverty line

and 7.1% of those in the wealthiest income quintile. [4] It is private health insurance and social

security that facilitate access to medical care for the poor and do assist in reducing the financial

commitment of individuals and families for those with chronic or recurring illnesses. Twenty-

one of every 100 Jamaican in 2007 has health insurance coverage, suggesting that the majority of

people pay for medical care out of their pockets.


       Many studies have examined the insured and health care demand of the general populace

[6-10] but on reviewing the literature no study was found in Latin America and the Caribbean, in

particular Jamaica, that has investigated the uninsured in regards to their medical care-seeking

behaviour and health status. According to Call & Ziegenfuss, [7] health insurance is a significant

predictor of access to medical care services, and people who do not have access to health

                                                                                                 65 

 
insurance have less possibilities of accessing health care services. This was contradicted by

Bourne [11] who found that health insurance is not significant when correlated with the medical

care-seeking behaviour of Jamaicans or a predictor of the good health of Jamaicans [11] or

female Jamaicans. [12] Call & Ziegenfuss [7] added that rural residents are more restricted from

access to health insurance coverage than urban citizens, suggesting that medical care-seeking

behaviour would be lower for rural than urban residents. While Call & Ziegenfuss’ perspectives

provide us with basic information about the insured, it is inadequate for this cohort of people

based on the findings of Bourne [11], and Bourne & Rhule [12].


       For 2007, statistics revealed that 21.2% of Jamaicans had health insurance coverage and

66% sought medical care, indicating that most of the people who utilized medical care services

did not use health coverage. Within the context of the global economic downturn, increased job

redundancies and prices of commodities, the uninsured will be asked to pay more for medical

care. Apart from the increased odds of not utilizing health care services, little is known about the

uninsured in Latin American and the Caribbean, and in particular Jamaica. This study will

bridge the gap in the literature, by evaluating their health status, medical care-seeking behaviour,

and the medical conditions of uninsured Jamaicans in order to establish whether there are

differences in the three geographical regions, and to use the information for public health

intervention and policy formulation. The researcher used data from the 2007 Jamaica Survey of

Living Conditions to evaluate medical care-seeking behaviour, medical conditions, purchased

medication, and the health status of uninsured Jamaicans as well as building two models for good

health status and health care-seeking behaviour of this uninsured group.


Methods and materials
                                                                                                 66 

 
Data

The current study extracted a sample of 5,203 respondents 15 years of age and over from a

national probability cross-sectional survey (Jamaica Survey of Living Conditions, JSLC) of

6,782 Jamaicans [13-15]. 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 [16]. 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. [13-15]


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. There are some modifications to the

LSMS, as the JSLC is more focused on policy impacts. The questionnaire covers demographic

variables, health, and other issues. Interviewers were trained to collect the data from household

members. Data on 5, 203 individuals who indicated not having health insurance coverage was

used in data analysis.


Statistical methods

Descriptive statistics such as mean, standard deviation, frequency and percentage were used to

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

examine the association between non-metric variables for area of residence, and gender of

respondents. Logistic regression analyses examined 1) the relationship between good health


                                                                                              67 

 
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 for Windows version 16.0 (SPSS Inc;

Chicago, IL, USA) was used to analyze the data. A p-value less than 5% 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 [17] correlation can

be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. The 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. [18-24] 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.


Models


The current study will employ multivariate analyses in the study of the health status (Equation

[1]) and medical care seeking behaviour of Jamaicans (Equation [2]). 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.

Ht=f(Ai, Gi, HHi, ARi, lnC, EDi, MRi, Si, ∑MCt, SRIi, εi)                                  1

        Where Ht (i.e. self-rated good current health status in time t) is a function of age of

        respondents Ai; sex of individual i, Gi; household head of individual i, HHi; area of

                                                                                               68 

 
       residence, ARi; logged consumption per person per household member, lnC; Education

       level of individual i, EDi; marital status of person i, MRi; social class of person i, Si;

       summation of medical expenditure of individual i in time period t, MCt; self-reported

       illness, SRIi, and an error term (i.e. residual error).

MCSBi=f(PHt ,Ai, Gi, HHi, ARi, lnC, EDi, MRi, Si, CRi, εi)                            2

       Where MCSBi is medical care-seeking behaviour of individual i is a function of PHt (ie

       self-rated poor current health status in time t of individual i); age of respondents Ai; sex

       of individual i, Gi; household head of individual i, HHi; area of residence, ARi; logged

       consumption per person per household member, lnC; education level of individual i, EDi;

       marital status of person i, MRi; social class of person i, Si; logged consumption per

       person per household member i, lnC; crowding of person i, CRi; and an error term (i.e.

       residual error).

From Equation (1) was derived Equation (3) and Equation (4):

       Ht=f(Ai, lnC, SRIi, Si, EDi, ARi, Gi, εi)                                           3

      MCSBi=f(PHt ,Ai, MRi, εi)                                                                4

Measures

An explanation of some of the variables in the model is provided here. 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. [11, 12, 25] 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. [26, 27] 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

                                                                                                   69 

 
for this question were on a Likert scale matter ranging from excellent to poor. Age group was

classified as children (ages less than 15 years); young adults (ages 15 through 30 years); other

aged adults (ages 30 through 59 years); young-old (ages 60 through 74 years); old-old (ages 75

through 84 years) and oldest-old (ages 85+ years). 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.


Results

Socio-demographic characteristics of sample

The sample was 5,203 uninsured respondents (49.2% males and 50.8% females). Of the sample,

32.9% were children; 26.9% young adults; 30.0% other aged adults; 10.8% elderly.                The

majority of those sampled had good health status (82.9%); 73% were never married; 62.0%

visited medical care-seeking behaviour; 60.3% had at most no formal education; 52.2% lived in

rural areas; 21.0% in semi-urban areas and 26.8% in urban areas. Fifty-nine percent of the

sample purchased the prescribed medication, and 14.2% reported an illness. Of those who

reported ailments, 89.5% revealed that they were diagnosed by health care practitioners.

Approximately 17% indicated cold; 3.5% diarrhoea; 9.8% asthma; 19.7% hypertension; 5.5%

arthritis; 25.3% and unspecified dysfunctions. Forty-five percent of the sample were poor

(23.1% below the poverty line), 20.9% in the middle class, and 34.1% were classified as wealthy

(14.8% in the wealthiest group).

       A significant statistical correlation was found between medical care-seeking behaviour

and health status (χ2 (df = 2) =36.199, P < 0.001, n=752). Seventy-six percent (N= 160) of those


                                                                                                 70 

 
who reported poor health status sought medical care compared to 68.0% (n = 174) of those who

reported fair health status and 50.6% (n= 170) of those who indicated good health status.

       Table 3.1 revealed that significantly more rural residents were poor (58.7%) compared to

34.9% of semi-urban and 26.5% of urban dwellers. Only 21.2% of rural respondents were in the

upper class which was significantly lower than those in semi-urban areas (42.6%) and the

percentage is even greater in urban zones (52.5%).

       A cross-tabulation between health status and area of residence revealed a statistical

correlation (P<0.001). Further examination showed that substantially more rural respondents

indicated poor health status (6.3%) than semi-urban (3.3%) and urban (3.9%) (see Table 3.1).

Significantly more rural dwellers reported being diagnosed with a recurring illness (15.9%) than

semi-urban (11.8%) and urban respondents (12.7%). No significant statistical correlation was

found between medical care-seeing behaviour and area of residence (P= 0.375).

       Seventeen percent of females reported a recurring illness which was significantly more

than the 12% for males (Table 3.2). Of the diagnosed recurring illness, approximately twice as

many females reported diabetes mellitus (11.3%) and hypertension (24.6%) than males (6.1%)

and 12.6% respectively. While more males indicated cold (18.1%); diarrhoea (3.6%); asthma

(11.3%); arthritis (6.5%); and unspecified (27.5%) compared to females – cold (15.6%);

diarrhoea (3.4%); asthma (8.8%); arthritis (4.7%), and 23.7% unspecified ailments.

       A cross-tabulation between health status and self-reported illness found that there was a

significant statistical correlation (χ2 (df = 2) = 989.552, P < 0.001). The association was a

moderately strong one (contingency coefficient = 0.401). Further examination of the results

revealed that 89.4% (n=3,964) of those who reported no illness had good health status, and only

43.7% of respondents with an ailment indicated poor health status. Approximately 22% of

                                                                                             71 

 
individuals with at least one dysfunction had poor health status compared to 2.3% of those who

did not have an illness (Table 3.3).


       A significant statistical correlation existed between self-reported illness and age cohort

(χ2 (df = 5) = 407.365, P < 0.001, n = 5,189). The findings revealed that 12.4% children reported

at least one illness compared to 5.5% of young adults and following this age cohort self-reported

illness increased to 14.7% for other aged adults; 33.3% of young old; 49.7% of old-old and

51.2% of oldest-old.

Multivariate Analysis

      Table 3.4 examines variables that seek to explain the good health status of insured

Jamaicans. Good health statuses of uninsured Jamaicans are correlated with socio-demographic,

economic and biological factors. The correlates of good health status of uninsured Jamaicans are

statistically significant (χ2 (df = 15) =993.114, P < 0.001; -2 Log likelihood = 2554.359;

Nagelkerke R2 =0.390; Hosmer and Lemeshow goodness of fit χ2=11.159), and 84.6% of the data

were correctly classified: 94.9% of cases in good health status were correctly classified and

46.6% were cases with poor health status.

       Table 3.5 presents information on variables that determine (or not) the medical care-

seeking behaviour of uninsured Jamaicans. The correlates that explain medical care-seeking

behaviour of uninsured respondents are statistically significant χ2 (df = 14) = 47.79, P < 0.001; -2

Log likelihood = 648.32; Nagelkerke R2 =0.117; Hosmer and Lemeshow goodness of fit

χ2=4.480), and 67.5% of the data were correctly classified: 88.1% of data correctly classified

medical care-seeking behaviour and 30.0% of data otherwise.

Discussion

                                                                                                 72 

 
Caribbean societies, in particular Jamaica, have seen an increase in illnesses such as HIV/AIDS,

malignant neoplasm, diabetes mellitus, hypertension, ischaemic heart disease, and arthritis [28-

33] which require continued treatment. Although this is a reality, only 21.2% of Jamaicans had

health insurance coverage in 2007, indicating that the majority of people are without health

insurance coverage and many people will not be able to afford medical care.

       The current study found that approximately one-half of Jamaicans who do not have

health insurance were poor compared to 34.1% of the wealthy and 20.9% of those in the middle

class. Substantially more Jamaicans below the poverty line (23.1%) did not have health

insurance compared to 14.8% of those in the wealthiest 20%. In addition, 33% were children

compared to 11% who were older than 60 years. Although there is a preponderance of Jamaicans

who are poor and uninsured, this research found that there was no statistical difference between

medical care-seeking behaviour and social class; medical care-seeking behaviour and sex; and

health care-seeking behaviour and area of residence. Embedded in this finding is the dominance

of a non-medical care-seeking behaviour culture in Jamaica, and it is so fundamental that

education, social class and income are not able to retard the practice. This is captured in an

analysis of the study that had 44 out of every 100 respondents indicating that they were ill

enough to seek medical care compared to 34 out of every 100 in the population; and 18 out of

every 100 stated they preferred home remedies compared to 30 in 100 in the populace.

       Sixty-six out of every 100 Jamaicans sought medical care, comprising the poorest 20%-

to-wealthiest 20% in 2007. The current study revealed that 45 out of every 100 poor people were

not covered by health insurance compared to 17 out of 50 for the wealthy and 21 out of 100 for

the middle class. Concomitantly, 33 out of every 100 children (less than 15 years) and 60 out of

every 100 Jamaicans who had no formal education were not covered by health insurance. The

                                                                                             73 

 
rationale which accounts for the fact that there is no significant difference in medical care-

seeking behaviour among the social classes is embedded in the removal of user fees in the health

care system; and how this has narrowed the health care-seeking behaviour gap between the poor

and the wealthy.

       In 2007, the government of Jamaica introduced national health insurance coverage for

those who suffer from particular illnesses, as well as for those who are older than 60 years. This

social security coverage commissioned by the Jamaican government eliminates health insurance

for ‘unwell’ patients, suggesting that health is conceptualized as diseases, which is not in keeping

with an operationalization of health offered by the WHO. [34] According to the WHO, health

does not only mean the absence of disease, but it must include social, psychological and physical

wellbeing. The health insurance coverage offered by the government explains the low uninsured

group among the Jamaican elderly. Hence, this means that most of those who possess health

insurance would have private coverage; the high ‘unwell’ Jamaicans therefore justify the high

non-insured group in the nation. This paper examines the uninsured or the ‘unwell’.

       This analysis has found that good health status can be determined by age, consumption,

self-reported illness, social class, education, area of residence and gender of respondents, which

concurs with other studies. [35-39] While this study is the first of its type in Jamaica, its findings

are similar to other multivariate studies that have examined the health status of people. Using

data for elderly Barbadians, Hambleton et al.’s work [35] found that dysfunction accounted for

the most explanatory power in health status, which is confirmed by this analysis. The model that

was developed for the good health status of uninsured Jamaicans was based on the 7

aforementioned variables with a coefficient of determination of the current study being 39.0%

(Nagelkerke R2 =0.390). This predictive model seems weak; but Hambleton et al’s work on

                                                                                                   74 

 
elderly Barbadians had a coefficient of determination of 38.2%, indicating that the analysis of

this paper is relatively good in keeping with a non-Jamaican study of a similar nature.

       In spite of the similarities, there are some notable differences with other studies. Eight-

three out of every 100 uninsured Jamaicans reported at least good health status; 20 out of every

100 were hypertensive; 9 out of 100 diabetic and 6 out of 100 arthritic compared to the

percentage of respondents in the population with particular health conditions: hypertension, 22

out of every 100; diabetes mellitus, 12 out of every 100; and, arthritis, 9 out of every 100. It is

interesting to note that Jamaicans have a preference for private health care utilization [15] but

this is not the case for the uninsured. In 2007, 52 out of every 100 Jamaican visited private health

care services compared to 6 out of every 100 of the uninsured. The percentage of uninsured who

visited public health care facilities (34 out of every 100) was also lower than in the general

populace (41 out of every 100).

       The analysis of this study went further than that of other identified studies as it found that

uninsured Jamaicans who resided in rural areas reported a greater percentage of illnesses,

followed by urban, than other town residents. Marmot [35] opined that income influences health

as it provides access to more resources, medical services, and lower infant mortality. The

analysis of this work concurs with Marmot [35] and PAHO et al. [9] as consumption (which can

proxy income) is positively correlated with good health status. With this reality, there seems to

be a paradox, as those in the wealthy classes had lower good health status than those in the poor

classes.

       Income undoubtedly provides access to more resources, better physical conditions and

opens the way to better quality of water and food; it also offers individuals, societies or nations

the highest quality medical services which cannot be accessed by the poor. [35] There is another

                                                                                                  75 

 
side to this discourse in that the lifestyle practices of the wealthy help to erode the advantages of

income. According to Bourne, McGrowder & Holder-Nevins, [41] health behaviour which is a

function of one’s culture suggests that the wealthy will continue their involvement in parties and

other social arrangements which will involve the use of alcoholic beverages, smoking and other

risky lifestyle practices that reduce the advantage of income. While income can buy access to

better medical services, this paper highlights that it cannot buy good health. It is clear from the

current study that wealthy uninsured Jamaicans are using their income the wrong way in regards

to its negative impact on health. Insufficient money is associated with more illness; however,

this study has revealed that there is no statistical difference between the wealthy and the poor

seeking medical care. Although the wealthy substantially used private health care facilities and

the poor utilized public health facilities, [15] embedded in this analysis therefore is the fact that

the quality of primary level care in Jamaica is of a high standard.

       While there is no difference between the wealthy uninsured and the poor uninsured

seeking medical care, the study revealed that those with poor health status were 2.3 times more

likely to seek health care services than those in good health. The analysis of this work showed

that 22 out of every 100 uninsured Jamaicans who indicated at least one health condition

reported poor health status. Hence this study highlights the fact that there is a disparity between

respondents’ conceptualization of health status and that of illness, as 44% of uninsured ill

respondents indicated that they had good health status.

       The JSLC report revealed that the prevalence of recurrent (chronic) diseases is highest

among individuals 65 years and over. [41] According to PIOJ & STATIN [42] individuals 60-64

years were 1.5 times more likely to report an injury than children less than five years old, and the

figure was even higher for those 64 years and older (2.5 times more). It should be noted here that

                                                                                                  76 

 
this increase in self-reported cases of injuries/ailments does not represent an increase in the

incidence of cases as the JSLC for 2004 said that the proportion of recurring/chronic cases fell

from 49.2% in 2002 to 38.2% in 2004 [43]. Eldemire [44] 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).

Bourne, McGrowder, & Crawford [39] found that the poor health status of people 60 to 64 years

was 33.2% and this increased to 36.1% for elderly 65 to 69 years, 49.4% for elderly 70 to 74

years and 51.7% for those 75 years and older, emphasizing the positive correlation between

increased ailments and ageing of the Jamaican elderly.

       An analysis of the current study revealed that there is no significant difference among the

populations across the 3 geographical areas in Jamaica in regards to health care-seeking

behaviour, suggesting that the uninsured medical care-seeking behaviour is the same in the 3

geographical areas. This research concurs with the finding of a study by Call & Ziegenfuss [7]

meaning that the uninsured in Jamaica are not atypical as they are in keeping with those in

Minnesota, United States. Further, no significant correlation was found among urban, semi-

urban, rural and educational levels of uninsured Jamaicans which were similar to that of Call &

Ziegenfuss.

       Many studies have shown that married people (or those in unions) had greater health

status than those who were never married. [45-51] The current work disagreed with those

findings as it found that there was no significant statistical correlation between good health status

of married uninsured people, and those who were never married, or separated, divorced or

widowed. Analysis of this research revealed that those who were married were 48.2% less likely

to seek medical care than those who were never married. The answer to this lies in the lifestyle

practices of these people. Smith & Waitzman [49] offered the explanation that wives were able

                                                                                                  77 

 
to dissuade their husband from particular risky behaviours such as the use of alcohol and drugs,

and would ensure that they maintain a strict medical regimen coupled with proper eating habits.

[50,51] Koo, Rie & Park’s findings [48] revealed that being married was a ‘good’ cause for an

increase in psychological and subjective wellbeing in old age. This study is the first of its kind

in the Caribbean, in particular Jamaica, which models the health care-seeking behaviour of

uninsured respondents, and so there is nothing to compare it with. The coefficient of

determination for this model was 11.9%, which means that although it is low its validation will

need further research.

Limitation of study

A single cross-sectional study cannot be used to examine causality, as well as a snap shot survey

cannot effectively capture the continuous matter of the variables. The severity of illness was

excluded from the medical care-seeking behaviour model because of missing cases and this

could have been critical to the study.

Conclusion

The findings of this research are far reaching and provide an understanding of the uninsured, and

the information can be used to aid public health intervention and education programmes.




Conflict of interest

There is no conflict of interest to report.

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        predictive correlates of good health status of rural residents. J of Rural and Remote
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    51. Gore WR. Sex, marital status, and mortality. Am J of Sociology 1973;79:45-67.




                                                                                          81 

 
Table 3.1: Socio-demographic characteristics of sample
                                                      Area of residence                    P
Variable                                  Urban         Semi-urban           Rural
                                           n (%)            n (%)            n (%)
Sex                                                                                        0.284
  Male                                    662 (47.4)       544 (49.9)      1354 (49.9)
  Female                                  735 (52.6)       547 (50.1)      1361 (50.1)
Social class                                                                              < 0.001
  Poor                                    370 (26.5)       381 (34.9)      1594 (58.7)
  Middle                                  294 (21.0)       245 (22.5)       546 (20.1)
  Upper                                   733 (52.5)       465 (42.6)       575 (21.2)
Age group                                                                                  0.002
   Children                               418 (29.9)       334 (30.6)       961 (35.4)
   Young adults                           411 (29.4)      306 928.0)        646 (23.8)
   Other aged adults                      416 (29.8)       344 (31.5)       803 (29.6)
   Young old                                 93 (6.7)         72 (6.6)       199 (7.3)
   Old-old                                   48 (3.4)         27 (2.5)        82 (3.0)
   Oldest-old                                11 (0.8)          8 (0.7)        24 (0.9)
Health status                                                                             < 0.001
    Good                                1137 (81.7)        956 (87.6)      2202 (81.6)
    Fair                                  201 (14.4)          99 (9.1)      329 (12.2)
    Poor                                     54 (3.9)         36 (3.3)       169 (6.3)
Education                                                                                 < 0.001
   No formal                              841 (60.4)       687 (63.1)      1599 (59.1)
   Basic                                  174 (12.5)       118 (10.8)       362 (13.4)
   Primary/preparatory                    168 (12.1)       158 (14.5)       429 (15.8)
   Secondary/High                         166 (11.9)       111 (10.2)       300 (11.1)
   Tertiary                                  43 (3.1)         14 (1.3)        17 (0.6)
Marital status                                                                             0.012
   Married                                177 (18.3)       132 (17.5)       382 (21.9)
   Never married                          721 (74.5)       562 (74.6)      1245 (71.4)
   Divorced                                  18 (1.9)         17 (2.3)        15 (0.9)
   Separated                                  5 (0.5)          8 (1.1)        20 (1.1)
   Widowed                                   47 (4.9)         34 (4.5)        82 (4.7)
Self-reported illness                                                                      0.001
    Yes                                   176 (12.7)       128 (11.8)       432 (15.9)
     No                                 1215 (87.30        958 (88.2)      2280 (84.1)
Medical care-seeking behaviour                                                             0.375
     Yes                                  120 (66.3)        78 (59.5)        270 (60.9)
      No                                   61 (33.7)        53 (40.5)        173 (39.1)
Number of visits to medical            1.4 days (SD          1.4 days     1.4 days (SD     0.846
facilities                                     = 0.7)      (SD= 1.3)             = 1.0)




                                                                                               82 

 
Table 3.2: Sociodemographic characteristic by Sex
Variable                                                         Sex                     P
                                                       Male              Female
Self-reported illness                                                                   < 0.001
     Yes                                               298 (11.7)         438 (16.6)
      No                                              2256 (88.3)        2197 (83.4)
Diagnosed Self-reported illness                                                         < 0.001
      Cold                                              56 (18.1)          69 (15.6)
      Diarrhoea                                          11 (3.6)           15 (3.4)
      Asthma                                            35 (11.3)           39 (8.8)
      Diabetes mellitus                                  19 (6.1)          50 (11.3)
      Hypertension                                      39 (12.6)         109 (24.6)
      Arthritis                                          20 (6.5)           21 (4.7)
      Other (unspecified)                               85 (27.5)         105 (23.7)
      No                                                44 (14.2)           35 (7.9)
Medical care-seeking behaviour                                                           0.101
   Yes                                                 182 (58.5)         286 (64.4)
    No                                                 129 (41.5)         158 (35.6)
Purchase medication                                                                      0.251
   Prescribed medicine                                 170 (56.9)         259 (60.1)
   Partial prescription                                    3 (1.0)           13 (3.0)
   Prescribed/over the counter                             9 (3.0)           15 (3.5)
   Over counter                                           20 (6.7)           25 (5.8)
   Prescribed/did not buy                                  9 (3.0)           17 (3.9)
   None prescribed required                              88 (29.4)        102 (23.7)
Number of visits to medical facilities Mean (SD)    1.3 days (0.7)     1.4 days (1.1)    0.252




                                                                                             83 

 
Table 3.3. Health status by Self-reported dysfunction

                                        Self-reported Dysfunction

                                                        At least one
    Health Status                     No ailment          ailment         Total
                                        n (%)              n (%)          n (%)

      Good                               3964 (89.4)        320 (43.7)    4284 (82.9)


      Fair                                 372 (8.4)        255 (34.8)     627 (12.1)


      Poor                                 100 (2.3)        158 (21.6)      258 (5.0)

    Total                                      4436                 733           5169
χ2 (df = 2) =989.552, P < 0.001




                                                                                         84 

 
Table 3.4.         Ordinary Logistic Regression: Correlates of Good Health Status of Uninsured
Jamaicans
                                                                         Wald       Odds
    Variable                               Coefficient     Std Error    statistic   ratio       95.0% C.I.
     Age                                        -0.049         0.004      191.667     0.95   0.95 -0.96***
     Logged consumption per capita               0.000         0.000       11.692     1.00   1.00 - 1.00**
     Self reported illness                      -2.168         0.121      323.527     0.11   0.09 -0.15***

     Middle class                                 0.086         0.154      0.314     1.09      0.81 - 1.47
     Upper class                                 -0.575         0.233      6.107     0.56     0.36 - 0.89*
     †Lower class                                                                    1.00

     Married                                      0.138         0.129      1.154     1.15        0.89 -1.48
     Divorced/separated/widowed                  -0.217         0.192      1.277     0.81       0.55 - 1.17
     †Never married
                                                                                     1.00
     Primary schooling                          19.089     40192.970       0.000                0.00 -0.00
     Secondary and above                        -0.475         0.223       4.525     0.62     0.40 - 0.96*
     †No formal education                                                            1.00

     Urban area                                  -0.115         0.124      0.870     0.89       0.70 -1.14
     Other town                                   0.301         0.140      4.593     1.35      1.03 -1.78*
     †Rural area                                                                     1.00

     Man                                          0.406         0.105     14.872     1.50    1.22 -1.85***
     Household head                               0.097         0.113      0.741     1.10        0.88 -1.37
     Cost of public medical care                  0.000         0.000      0.040     1.00       1.00 - 1.00
     Cost of private medical care                 0.000         0.000      3.003     1.00        1.00 -1.00
χ2 (df = 15) =993.114, P < 0.001
-2 Log likelihood = 2554.359
Nagelkerke R2 =0.390
Hosmer and Lemeshow goodness of fit χ2=11.159, P = 0.693
Overall correct classification = 84.6%
Correct classification of cases of good health status = 94.9%
Correct classification of cases of poor health status = 46.6%
†Reference group
*P < 0.05, **P < 0.01, ***P < 0.001




                                                                                                              85 

 
Table 3.5. Ordinary Logistic Regression: Correlates of Medical Care-Seeking Behaviour of
Uninsured Jamaicans

    Variable                                                    Std.        Wald        Odds
                                             Coefficient        Error      statistic    ratio         95% C.I.
     Man                                            -0.282       0.205         1.894      0.76      0.51 - 1.13
     Age                                             0.019       0.007         8.213      1.02    1.01 - 1.03**

     Middle class                                     0.544      0.284          3.675    1.72       0.99 - 3.00
     Upper class                                      0.683      0.427          2.558    1.98       0.86 - 4.57
     †Lower                                                                              1.00

     Poor health                                      0.834      0.208         16.139    2.30    1.53 - 3.46***

     Urban area                                       0.070      0.248          0.079    1.07       0.66 - 1.75
     Other town                                      -0.243      0.260          0.877    0.78       0.47 - 1.31
     †Rural                                                                              1.00

     Crowding                                         0.111      0.067          2.749    1.12       0.98 - 1.27
     Per capita consumption                           0.000      0.000          0.017    1.00       1.00 - 1.00

     Secondary and above                              0.431      0.571          0.569    1.54       0.50 - 4.71
     †No formal education                                                                1.00

     Married                                         -0.659      0.237          7.720    0.52     0.33 -0 .82**
     Divorced, separated/widowed                     -0.453      0.332          1.864    0.62        0.33 - 1.22
     †Never married                                                                      1.00

     Head household                                  -0.210      0.218          0.933    0.81       0.53 - 1.24
χ2 (df = 14) = 47.79, P < 0.001
-2 Log likelihood = 648.32
Nagelkerke R2 =0.117
Hosmer and Lemeshow goodness of fit χ2=4.480, P = 0.811
Overall correct classification = 67.5%
Correct classification of cases of medical care-seeking behaviour = 88.1%
Correct classification of cases of no medical care-seeking behaviour = 30.0%
†Reference group
*P < 0.05, **P < 0.01, ***P < 0.001




                                                                                                           86 

 
                                     Chapter 4
    Variations in health, illness and health care-seeking behaviour of those in the
                   upper social hierarchies in a Caribbean society


                                      Paul Andrew Bourne


Little research exists in the Caribbean, and in particular Jamaica, on the upper class, and no study
emerged from a search of the literature examining health, illness, and health care-seeking
behaviour of this group. To provide pertinent information on the upper class in regards to their
general health status, illnesses, typology of illnesses, health care seeking behaviours and factors
which determine their (1) moderate-to-very good health status, (2) illness, and (3) health care
seeking behaviour in order to make available to policy specialists and public health practitioners
information on this group, to be used as a guide in their decision making policies. A sample of
2,734 respondents from the wealthiest 20% and second wealthy social hierarchies was extracted
from a cross-sectional survey of 6,783 respondents. An 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 on the World Bank’s Living Standards
Measurement Study (LSMS) household survey. The majority of the sample stated at least good
health status (83.3%), with 0.5% indicating very poor health status, and 15.3% who indicated an
illness in the last 4-week period. Four variables emerged as statistically correlated with
moderate-to-very good health status of those in the upper class (i.e. second wealthy and
wealthiest 20%) - Model fit χ2 = 57.54, P < 0.0001. The model explained 33.2% of the variance
in moderate-to-very good health status, and that the model is a good fit for the data. Three
variables emerged as statistically correlated with self-reported illness - Model fit χ2 = 1087.7, P
< 0.0001. The significant variables (i.e. health care-seeking behaviour, good health status, and
marital status) accounted for 72.4% of the variability in self-reported illness. Three variables
emerged as statistically significant correlates of health care-seekers - Model fit χ2 = 995.45, P <
0.0001. The statistically significant correlates (i.e. good health status, self-reported illness,
marital status) accounted for 76.4% of the variance in health care-seeking behaviour of the upper
class. Rural residents continue to have lower moderate-to-very good health status when
compared to the general population, and the second wealthy and the wealthiest 20% in Jamaica.
Although only 4 percent of the upper social hierarchy utilizes the public health care system, there
is still a demand for public health services for this group, and it must be taken into account as a
part of the general planning for the health care system of the country.

Introduction

Studies have long established health disparities between the poor and the wealthy classes, and

this is no different in Latin America and the Caribbean [1-17]. According to the World Health
                                                                                                 87 

 
Organization [7], 80% of chronic illnesses were in low and middle income countries, which

illustrate the dichotomy between illness and material deprivation. The dichotomy between illness

and poverty is not only limited to low-to-middle income nations, as a study in the Netherlands

found that those who were chronically ill were more likely to be poor [15], and this was also

found in other European nations [16,17]. The association between insufficient money and health

is not limited to illness, but the WHO [7] opined that 60% of global mortality is caused by

chronic illness, which raised another issue, the relationship between poverty and premature

mortality.


       Marmot [8] postulated that money makes a difference in health, infant mortality and

general morality. The association between income and health expands beyond the direct

relationship between income and access to good physical and social milieu, good nutrition and

access to high quality health care services, to the indirect association between income and health

through access to education, employment, material resources and occupational class. Clearly

there are inequalities in health between those in the upper class and those in the lower class [18,

19], but limited studies existed on the wealthy and the wealthiest 20% in nations. In keeping

with public health aims, many studies have been carried out on the poor; poverty and illness;

poverty and productivity; chronic illness, capabilities and poverty, but what about the second

wealthy and the wealthiest 20% in regard to their health, illness, health care-seeking behaviour

and factors which influence health, illness and health care-seeking behaviour?


       Public health is about improvements in the health conditions of all members of a society

and not just a particular group. Embedded in the mandate of public health is the access to

information which will guide policy formulation, intervention and health education programmes,

                                                                                                88 

 
and so information is equally needed on the affluent groups. Limited information, if any, exists

in the Caribbean on the health of the second wealthy and wealthiest 20% classes. While general

statistics indicate that the upper class has a greater health status and more access to material

resources than the poor class, the former group constitutes a percentage of the population and

must be studied like the poor class. The current study revealed that the prevalence rate of the

upper class utilizing public health care facilities (i.e. hospitals and health centres) was 4%,

suggesting that this group must be planned for, as they utilize and demand public health care

resources like other social classes. Concurringly, this research showed that 3% of those in the

wealthy social class had chronic illnesses, and that 1% had diabetes mellitus, which denotes that

public health must make available resources for this group. Within the context that the upper

social class utilizes public health care resources, it is surprising that no studies exist in Jamaica

that have examined health, illness, and the health care seeking-behaviour of this social group.


       The current study aims to provide pertinent information on the upper class in regards to

their general health status, illness, typology of illness, health care seeking behaviours and factors

which determine their (1) moderate-to-very good health status, (2) illness, and (3) health care

seeking behaviour, in order to make available to policy specialists and public health practitioners

information on this group, which will serve as a guide for their decision-making policies.


Methods and materials
Sample

A sample of 2,734 respondents from the wealthiest 20% and second wealthy social hierarchy

was extracted from a cross-sectional survey of 6,783 respondents: 50.5% in the wealthiest 20%

and 49.5% in the second wealthy group. The survey was carried out jointly by the Planning

                                                                                                  89 

 
Institute of Jamaica and the Statistical Institute of Jamaica [20]. The method of selection of the

sample from each survey was based solely on rural residence. The survey (Jamaica Survey of

Living Conditions) was begun in 1989, collecting data from Jamaicans in order to assess

government policies. Each year since 1989, the JSLC has added a new module in order to

examine that phenomenon which is critical within the nation. In 2002, the foci were on 1) social

safety net and 2) crime and victimization; while for 2007, there was no focus. The current sample

was extracted from the 2007 dataset.

       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

is composed of a minimum of 100 residences in rural areas and 150 in urban areas. An ED is an

independent geographical 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

dwellings was compiled, which in turn provided the sampling frame for the labour force. One

third of the Labour Force Survey (i.e., LFS) was selected for the JSLC [20]. The sample was

weighted to reflect the general population of the nation.

       The JSLC 2007 [20] was conducted in May and August of that year. An 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 on 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 variables, for example education, daily expenses (for

                                                                                                90 

 
the past 7-day period), food and other consumption expenditures, inventory of durable goods,

health variables, crime and victimization, social safety net, and anthropometry. The

questionnaire contains standardized items such as socio-demographic variables, excluding crime

and victimization, which were added in 2002 and later removed from the instrument, with the

exception of a few new modules each year. The non-response rate for the survey for 2007 was

27.7%. The non-response includes refusals and cases rejected in data cleaning.

Measures

Self-rated health status: is measured using people’s self-rating of their overall health status [21],

which ranges from excellent to poor. The question that was asked in the 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, 0 =

Otherwise) [21-28]. The binary good health status was used as the dependent variable.


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, Respiratory

diseases; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. A binary

variable was later created from this construct (1=no 0=otherwise) in order to be applied in the

logistic regression.


Age is a continuous variable which is the number of years alive since birth (using last birthday).


Age groups were classified as children, young adults, other adults, young-old (or young-elderly),

old-old, and oldest-old: children – 0 to 14 years; young adults – 15 to 30 years; other adults – 31

to 59 years; young-old – 60 to 74 years; old-old - 75 – 84 years and oldest-old – 85+ years.



                                                                                                  91 

 
Medical care-seeking behaviour was taken from the question ‘Has a health care practitioner 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.


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).


Statistical Analysis

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 t-test and an Analysis of Variance (ANOVA)

were used to test the relationships between metric and/or dichotomous and non-dichotomous

categorical variables. Box-plots were used to examine what was happening among age, self-

reported illness, and social hierarchy as well as age, typology of illness and social hierarchy (i.e.

poorest 20% and wealthiest 20%). Multiple logistic regression techniques were conducted to

identify parameters and their estimates. Stepwise logistic regression technique was used to

determine the contribution of each significant determinant to the model. A p-value less than 0.05

(two-tailed) was selected to indicate statistical significance (i.e. 95% confidence interval).

                                                                                                  92 

 
Results
Table 4.1 presents information on the socio-demographic characteristics of the sample. One

percent of the sample reported an injury. Of those who reported an injury, 67.9% stipulated the

injury experienced in the last 4weeks. Domestic accidents and incidents accounted for 47.3% of

the injuries experienced. Fifteen percent of the sample indicated an illness in the last 4 weeks. Of

those who reported an illness, 89.1% stipulated the typology of the health condition.


       When the respondents were asked if they had purchased the prescribed medication,

67.7% said yes. Of those who did not purchase the medication, 9.5% claimed they were unable

to afford it; 39.7% said they were not ill enough; 27.6% remarked that they used a home remedy;

5.2% indicated that they did not have the time and 18.1% stated other. Seventy-one percent of

the sample sought medical care in the last 4weeks, 32.5% had health insurance coverage (i.e.

23.7% private). The majority of the sample stated at least good health status (83.3%), with 0.5%

indicating very poor health status.


       Of the sample, only 10.6% indicated where the medical visit took place in the last

4weeks. Of those who responded (n=288), 27.4% indicated a public hospital, 61.8% said a

private health care centre and 12.5% remarked that it was a public health care centre. Twenty-

nine percent of those who responded to typology of medical facility used in the last 4weeks had

chronic conditions and attended a public facility. The prevalence rate of the upper class utilizing

public health care facilities (i.e. hospitals and health centres) was 4% (3% had a chronic illness;

of the 3%, 1% had diabetes mellitus).


       There was no significant statistical association between marital status and social

hierarchy (i.e. second wealthy or wealthiest 20%) – χ2 = 8.518, P = 0.744.
                                                                                                 93 

 
       Table 4.2 shows information on particular variables and social hierarchy. A significant

statistical relationship existed between area of residence and social hierarchy. Those in the

wealthiest 20% were more likely to be urban dwellers (48.6%) than those in the second wealthy

social group (36.9%) - χ2 = 57.002, P < 0.0001.


       Rural dwellers were more likely to be wealthy (59.1%) compared to semi-urban residents

(50.1%) and urban respondents (42.1%). Concurringly, urban settlers were more likely to be in

the wealthiest 20% (57.9%) compared to semi-urban (49.9%) and rural respondents (40.9%) – P

< 0.0001.


       There was a significant statistical association between educational level and social

hierarchy (χ2 = 30.53, P < 0.0001). Those in the wealthiest 20% were more likely to be educated

at the tertiary level (5.3%), as compared to those in the second wealthy social group (1.9%).

Likewise there was a statistical relationship between health insurance coverage and social

hierarchy (χ2 = 113.27, P < 0.0001). Forty-two percent of those in the wealthiest 20% had health

insurance coverage compared to 22.6% of those in the second wealthy social group.


       There were significant statistical differences between those in the wealthy and the

wealthiest 20% (1) age ( t = - 4.745, P < 0.001) – mean age of the wealthy 30.14 ± 21.1, and the

wealthiest 20% 33.9 ± 20.4; (2) crowding (t = 15.991, P < 0.0001 – mean household crowding

for those in the wealthy group was 4.2 ± 2.2 compared to 3.0 ± 1.6 for those in the wealthiest

20%, and (3) total expenditure (t = - 16.219, P < 0.0001) – mean total expenditure for those in

the wealthy group was USD 9,713.00 ± USD 5,327.88 and those in the wealthiest 20% was USD

14,915.29 ± USD 10,550.99. Furthermore, there was a significant statistical difference between

mean duration of illness of those in the second wealthy social group (23.8 days ± 96) and those
                                                                                             94 

 
in the wealthiest 20% (9.9 days ± 18.7) – t = 1.985, P = 0.048; but none between duration of

marriage and social hierarchy (wealthy, 16.7 years ± 14.6; wealthiest 20%, 17.3 ± 13.6) – t = -

0.593, P = 0.553.


Multivariate analyses


Table 4.3 shows information on particular variables that are correlated (or not) with self-reported

moderate-to-very good health status of the sample. Four variables emerged as statistically

correlated with moderate-to-very good health status of those in the upper class (i.e. second

wealthy and wealthiest 20%) - Model fit χ2 = 57.54, P < 0.0001. The model explained 33.2% of

the variance in moderate-to-very good health status, and the model is a good fit for the data

(Hosmer and Lemeshow goodness of fit χ2 = 2.87, P = 0.94, -2LL = 194.22). Eighty-one percent

of the data were correctly classified: 94.9% of those who had indicated moderate-to-very good

health status and 33.3% of those that were classified into poor and very poor health status.

        Table 4.4 presents information on variables that either correlated or did not correlate with

self-reported illness of the sample. Three variables emerged as statistically correlated with self-

reported illness - Model fit χ2 = 1087.7, P < 0.0001. The significant variables (i.e. health care-

seeking behaviour, good health status, and marital status) accounted for 72.4% of the variability

in self-reported illness. The model is a good fit for the data (Hosmer and Lemeshow goodness of

fit χ2 = 8.11, P = 0.42, -2LL = 649.69). Ninety-five percent of the data were correctly classified:

72.2% of those who were classified as having an illness and 99.6% of those who did not report

an illness.

        Table 4.5 displays variables that seek to explain the variability in self-reported health

care-seeking behaviour of the sample. Three variables emerged as statistically significant
                                                                                                 95 

 
correlates of health care-seekers - Model fit χ2 = 995.45, P < 0.0001. The statistically significant

correlates (i.e. good health status, self-reported illness, marital status) accounted for 76.4% of the

variance in health care-seeking behaviour of the upper class. The model was a good fit for the

data - Hosmer and Lemeshow goodness of fit χ2 = 3.64, P = 0.90. Ninety-five percent of the data

were correctly classified: 96.2% of those who had selected seeking medical care in the last 4

weeks and 95.3% of those who did not seek medical care.

Discussion

The present work revealed that 88 out of every 100 respondents in the upper class in Jamaica

indicated that their health status was at least good, with only 5 in every 1,000 experiencing very

poor health statuses. One in every 100 had an injury and 15 per 100 had an illness in the last 4-

week period. The prevalence rate of self-reported diagnosed acute health conditions was 36 per

1,000 and 96 per 1,000 for chronic conditions. Twenty-four per 1,000 had diabetes mellitus; 28

out of every 1,000 had hypertension and 7 per 1,000 reported having been diagnosed with

arthritis. Seventy-one percent sought medical care; there was no significant statistical association

between (1) self-reported injury and being second wealthy or in the wealthiest 20% as well as (2)

between self-reported illness and social hierarchy (i.e. second wealthy or wealthiest 20%). The

mean length of time experiencing the current illness (in days) was greater for those in the second

wealthy class, as compared to those in the wealthiest 20%. Although only 1% of the sample

reported an injury in the study, 47.3% of the injuries were owing to domestic accidents and

domestic incidents, and 21.1% were due to motor vehicle accidents. Four percent of the sample

utilized public health care facilities for their last medical visit, and 11.8% of the sample were

elderly (ages 60 years and beyond), 24.6% children (ages less than 15 years); 49.6% of those in

the wealthiest 20% dwelled in urban areas compared to 36.9% of those in the second wealthy
                                                                                                   96 

 
social group. Those in the wealthiest 20%, according to average total expenditure, were 1.5 times

more than those in the second wealthy class and they were 2.9 times more educated at the tertiary

level. Concurringly, rural upper class respondents had the lowest moderate-to-very good health

status; those with good health status were 48% less likely to seek medical care; those with

illnesses were 449 times more likely to seek medical care, and married upper class respondents

were 45% less likely to seek health care, while married wealthy residents were 2.3 times more

likely to report an illness.

        Marmot [8] asked the question “Does money matter for health? If so, why?” and opined

that it does in terms of access to good nutrition, material resources, lower infant mortality, health

care choices, and a good physical environment compared to those in the lower socioeconomic

group. Clearly there are differences in health outcomes between the social hierarchies [1-17], but

does money matter for health between the second wealthy and the wealthiest 20%? The current

study found that money does not matter for health between the wealthy and the wealthiest 20%.

Money does not matter for the general health status of the wealthy and the wealthiest 20%, but

also for self-reported injuries and illnesses (i.e. both acute and chronic conditions). Embedded in

this finding is the reality that there is a basic amount of money necessary, and any more than that

will not improve the health of the individual. This work showed that those in the wealthiest 20%

on average spent almost 2 times more than those in the second wealthy class, and are about 3

times more educated at the tertiary level, but this does not produce additional improvements in

health for the wealthiest 20%.

        The present paper found that a large health disparity occurred between upper class

respondents and geographic area of residents, which concurs with the findings of Vila et al.’s

work. Vila et al.’s research [9] used self-reported health status (i.e. fair-to-poor health status) and

                                                                                                    97 

 
found that lower socioeconomic class residents of Milwakee had the greatest fair-to-poor health

status with those in the upper class indicated the least fair-to-poor health status. Concurringly,

they also found that upper socioeconomic group had the greatest health in the city, which was

different in this research. In this study, upper socioeconomic group who resided in semi-urban

areas were the healthiest, and had lower total annual expenditure than those upper class

respondents who lived in urban areas. The huge health disparity was found between the upper

class rural and semi-urban dwellers, suggesting that lifestyle practices in semi-urban geographic

areas was greatest and was remarkably different from that of upper class rural respondents.

       However, the health disparity is among those who dwell in particular geographical areas,

and those who have health insurance coverage, and not between the wealthy and the wealthiest

20%. Rural upper class Jamaicans had the least moderate-to-very good health status. This health

disparity is substantial as upper class semi-urban residents were 4.8 times more likely to report

moderate-to-very good health status, and those who dwelled in urban areas were 4.3 times more

likely to report moderate-to-very good health status compared to those in the rural areas. Such

inequality in health emphasized that the lifestyle of rural residents is such that money does not

equate their health status with those of their other wealthy urban and semi-urban peers. This is

embedded in the present work as there is no significant statistical correlation between self-

reported illness and area of residence, or area of residence and health care seeking behaviour of

the upper class. It follows that it is not money and illness that separate the rural from the other

affluent respondents, but this must be therefore embedded in the cultural differences between

people. Another finding which emerged from the current research is the fact that married upper

class respondents reported more illness than those who were never married, yet the former group

sought less medical attention than the latter group. Although married upper class respondents

                                                                                                98 

 
reported more illness, there was no statistical correlation between marital status and moderate-to-

very good health status. A plethora of studies have examined the health status of married and

non-married respondents and the verdict is that the former group’s health status is greater [29-

35], which means that money removes this health disparity.

       According to Moore et al. [35], people who reside with a spouse have a different base of

support which aids in better health choices and justifies greater health status, as against those

without social support from a marital union. This was also found in earlier studies by Smith and

Waitzman [31] and Lillard and Panis [34]. Cohen and Wills [36] found that perceived support

from one’s spouse increased well-being, while Ganster et al. [37] reported that support from

supervisors, family members and friends was related to low health complaints. Another study

found that being married was a ‘good’ cause for an increase in psychological and subjective

well-being in old age [38]. Smith and Waitzman [31] offered the explanation that wives were

likely to dissuade their husbands from particular risky behaviours such as the use of alcohol and

drugs, and would ensure that they maintained a strict medical regimen coupled with proper

eating habits. On the contrary, this paper revealed that married affluent Jamaicans were more

likely to report illness, as compared to never-married wealthy respondents, but that this does not

translate into better health status for one group over the other.


       Using the relationship of the absence of illness to health of the wealthy-to-wealthiest 20%

of Jamaicans, this should denote that the wealthiest should be healthier than the second wealthy.

Clearly, there is a cognitive disparity between the image of health and illness. Illness is well

established to be a narrow approach to the conceptualization of health [39-46], and this is what

emerged as the case for the upper class. According to the WHO [39], health is social,

psychological and physical wellbeing and not merely the absence of illness. Clearly upper class
                                                                                                99 

 
respondents subscribe to this conceptualization as experiencing illness was correlated with low

moderate-to-very good health status, but illness was not a factor which determines the moderate-

to-very good health status of those in the upper class.


       Ferrer and Palmer’s work [14] revealed marginal health variabilities between those

people in the second wealthy and the wealthiest 20%, and using self-reported to measure health

status, this study found no statistical association between self-reported health and the two social

hierarchies. The present work goes further than Ferrer and Palmer’s research that used health

status and investigated general illness and particular health conditions and those in the second

wealthy and the wealthiest 20%. Ferrer and Palmer’s research did not examine illness or

particular typology of illness. Statistics revealed that 15.5% of Jamaicans reported an illness in

the last 4weeks in 2007 [47] compared to 15.3% of those in the upper class. Seemingly there is

no difference between self-reported illness in the population and those in the upper class, but

further examination of the diagnosed health conditions revealed some differences between the

population and the subpopulation. For the population, the prevalence rates for people with

asthma were 87 per 1,000; diabetes mellitus, 120 per 1,000; hypertension, 224 per 1,000 and

arthritis, 88 per 1,000 [47] compared to those in the upper class, being asthma, 12 per 1,000;

diabetes mellitus, 24 per 1,000; hypertension, 28 per 1,000 and arthritis, 7 per 1,000. The

findings of this study highlight that those in the affluent social hierarchy have a lower prevalence

of chronic illness than people in the general population of Jamaica, which concurs with the

literature that those in the lower socioeconomic group were more likely to experience more

chronic illness than the affluent. Although those in the wealthy-to-wealthiest 20% group in

Jamaica had a lower prevalence of chronic health conditions compared to the general population,

they had a prevalence rate of 37 per 1,000 for other health conditions.
                                                                                                100 

 
       The other conditions constitute ailments such as prostate and breast cancers, ischemic

heart disease, malignant neoplasm of the trachea, bronchus and other heart diseases. Statistics on

the mortality of males 5 years and older revealed that cerebro-vascular diseases, diabetes

mellitus, ischemic heart diseases, malignant neoplasm of the prostate, hypertensive disease,

chronic lower respiratory infections, other heart diseases and malignant neoplasm of the trachea

and HIV were among the 10 leading causes of death [48]. For females 5 years and older it was

about the same as the 10 leading causes of death for males, except for malignant neoplasm of the

prostate and malignant neoplasm of the trachea, these being replaced by malignant neoplasm of

the breast and pneumonia.


       Although the upper class clearly has lower prevalence rates of particular chronic

illnesses, compared to the general population, and more than those in the poorest 20% [47],

diabetes mellitus, hypertension and other health conditions are high among them and may

explain the levels of mortality among those therein. Chronic illnesses are linked to lifestyle

causes, and though they have lower rates of chronic illness than people in the lower

socioeconomic group, the reality among the upper class is that their lifestyle explains their

particular morbidity and mortality. A study by Wilks et al. [49] found that 64.3% of Jamaicans

were currently using alcohol (i.e. liquor, wine, beer or stout, and mixed alcoholic coolers), 13.5%

used marijuana, 14.5% smoked cigarettes, and the rates were even greater for males than

females. Concurringly, 71% of those in the upper class consumed alcohol (i.e. 84.3% of males

and 48.7% of females); 9.8% smoked cigarettes (i.e. 12.4% of males and 6.7% of females);

10.4% smoked marijuana (i.e. 16.9% of males and 2.2% of females) and 10.5% used illegal

drugs (17.1% of males and 2.7% of females) [49]. Furthermore, the percentage of upper class

males who consumed alcohol was more than for those males in the lower (76.1%) and the middle
                                                                                               101 

 
class (79.4%) [49]. Unhealthy lifestyle practices are therefore responsible for the composition of

illnesses which are experienced by the upper class and account for many of their ailments.

Furthermore, it is clear from the findings that among the upper socioeconomic class there are no

vulnerable groups, but what is equally evident is that socioeconomic status accounted for a major

role in determining the health status of upper class Jamaica as was found for all socioeconomic

classess in Blanc et al.’s work [11].


Conclusion

While poverty is associated with illness and illness is more related to poverty and lower health

status for the poor than for those in the upper class, the same is not true of the relationship

between the wealthy and the wealthiest 20% in Jamaica. It follows that money and wealth,

beyond a certain amount, does not add any further improvements to good health status. Income

and wealth beyond that which is accessible to the second wealthy in Jamaica do not provide

those beyond that with any greater health status. However, what emerged from the current work

is that the health disparity between the rural areas’ affluent people and others is vast, suggesting

that there are some underlying cultural conditions which exist among the rich of different

geographical areas, and which do not disappear because the individual is wealthy. Another

pertinent finding is that the wealthy spent more days in illness compared to the wealthiest 20%,

but this does not translate into lower moderate-to-very good health status. A part of the

justification for this non-health disparity is owing to their conceptualization of health compared

to the image of illness.


       There are affluent Jamaicans who utilize the public health care system, and many of them

have diabetes mellitus. Within the context of the utilization of the public health care system by
                                                                                                102 

 
the wealthy, although the percentage is very small, the current finding are important to public

health policy makers in understanding the service utilization of this group and their health, and

illness profile.


        In summary, money and wealth beyond that which is accessible by the second wealthy in

Jamaica will show no further disparity in moderate-to-very good health status. The paper

highlighted the fact that health insurance coverage is not a good measure of health care-seeking

behaviour and illness is not a good proxy for the health status of the upper class. However, the

health disparity which existed for the general society among the different areas of residents is the

same for the upper class. Rural residents continue to have lower moderate-to-very good health

status than the general population, and the second wealthy and the wealthiest 20% in Jamaica.

Although only 4 percent of the upper social hierarchy utilizes the public health care system, there

is still a demand for public health services for this group, and it must be taken into account as a

part of the general planning for the health care system of the country.


Conflict of interest


The author has no conflict to interest to report




                                                                                                103 

 
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                                                                                              106 

 
Table 4.1. Demographic characteristics of sample
Characteristics                                             Frequency                  %
Social hierarchy
  Second wealthy                                                 1352                49.5
  Wealthiest 20%                                                 1382                50.5
Sex
  Male                                                           1356                49.6
  Female                                                         1378                50.4
Area of residence
  Urban                                                          1184                43.3
  Semi-urban                                                      706                25.8
  Rural                                                           844                30.9
Injury
  Yes                                                              28                 1.1
  No                                                             2622                98.9
Self-reported typology of injury
  Motor vehicle accident                                            4                21.1
  Domestic accident                                                 7                36.8
  Industrial accident                                               5                26.3
  Domestic incident                                                 2                10.5
  Other (unspecified events)                                        1                 5.3
Self-reported illness
  Yes                                                             405                15.3
   No                                                            2237                84.7
Self-reported diagnosed illness
 Acute conditions
  Influenza                                                        56                15.5
  Diarrhoea                                                         8                 2.2
  Respiratory                                                      34                 9.4
Chronic condition
  Diabetes mellitus                                                66                18.3
  Hypertension                                                     76                21.1
  Arthritis                                                        19                 5.3
  Other                                                           102                28.3
Educational level
  Primary or below                                               2311                87.3
  Secondary                                                       241                  9.1
  Tertiary                                                         95                  3.6
Length of time married median (inn years)                               12 (Range = 1, 71)
Number of visits to medical practitioners in last 4-weeks                        1.4 (1.1)
mean (SD)
Length of illness median (in days)                                      5 (Range = 0,200)


                                                                                      107 

 
Table 4.2. Particular variables by social hierarchy
                                                         Social hierarchy                         P
                                                         Wealthy        Wealthiest 20%
Area of residence                                          n (%)                 n (%)   χ2 = 57.002, P < 0.0001
 Urban                                                 499 (36.9)           685 (49.6)
 Semi-urban                                            354 (26.2)           352 (25.5)
 Rural                                                 499 (36.9)           345 (25.0)
Sex                                                                                        χ2 = 0.074, P = 0.407
 Male                                                  667 (49.3)           689 (49.9)
 Female                                                685 (50.7)           693 (50.1)
Self-reported diagnosed health condition                                                   χ2 = 5.190, P = 0.520
 Acute conditions
    Influenza                                           32 (17.9)            24 (13.2)
    Diarrhoea                                             3 (1.7)              5 (2.7)
    Asthma                                               12 (6.7)            22 (12.2)
 Chronic conditions
    Diabetes mellitus                                   33 (18.4)            33 (18.1)
    Hypertension                                        38 (21.2)            38 (18.1)
    Arthritis                                             8 (4.5)             11 (6.0)
    Other (unspecified)                                 53 (29.0)            49 (26.9)
Health care-seeking behaviour                                                              χ2 = 1.272, P = 0.154
  Yes                                                  141 (68.4)           155 (73.5)
   No                                                   65 (31.6)            56 (26.5)
Self-reported illness                                                                      χ2 = 0.000, P = 0.520
  Yes                                                  200 (15.3)           205 (15.3)
   No                                                 1105 (84.7)          1132 (84.7)
Self-reported health status                                                                χ2 = 8.815, P = 0.066
  Very good                                            567 (43.2)           531 (40.0)
  Good                                                 536 (40.8)           565 (42.5)
  Fair                                                 157 (12.0)           185 (13.9)
  Poor                                                   42 (3.2)             45 (3.4)

                                                                                                            108 

 
    Very poor   11 (0.8)   3 (0.2)




                                     109 

 
Table 4.3. Logistic regression: Moderate-to-very good health status by particular variables

                                                                                Odds
                               Coefficient   Std. Error     Wald        P       ratio         95% CI
    Age                             -0.051        0.013     15.260     0.000      0.95         0.93, 0.98
    Male                            -0.351        0.387      0.822     0.365      0.70         0.33, 1.50
    Self-reported illness          -19.926   13414.774       0.000     0.999      0.00             0.000,

    Married                         -0.353        0.433       0.666    0.415       0.70        0.30, 1.64
    Divorced, separated or
                                    -0.383        0.549       0.487    0.485       0.68        0.23, 2.00
    widowed
    †Never married                                                                 1.00

    Health insurance                 0.997        0.408       5.976    0.015       2.71        1.22, 6.02
    Medical expenditure              0.000        0.000       4.712    0.030       1.00        1.00, 1.00

     Urban area                      1.474        0.439      11.258    0.001       4.37       1.85, 10.34
     Other town                      1.584        0.511       9.622    0.002       4.88       1.79, 13.26
    †Rural area                                                                    1.00

 Head of household               0.031        0.410     0.006          0.940       1.03        0.46, 2.30
  Per capita consumption         0.000        0.000     0.206          0.650       1.00        1.00, 1.00
            2
Model fit χ = 57.54, P < 0.0001
Hosmer and Lemeshow goodness of fit χ2 = 2.87, P = 0.94
-2LL = 194.22
Nagelkerke R2 =0.332
†Reference group




                                                                                                110 

 
Table 4.4. Logistic regression: Self-reported illness by particular variables


                                                    Std.      Wald               Odds     95.0% C.I.
    Variable                        Coefficient     Error    statistic   P       ratio

    Age                                    0.013     0.008      2.769    0.096     1.01           1.0, 1.03
    Male                                  -0.415     0.233      3.188    0.074     0.66          0.42, 1.04

    Married                                0.821     0.260      9.960    0.002     2.27          1.37, 3.79
    Divorced, separated or wid            -0.141     0.421      0.113    0.737     0.87          0.38, 1.98
    †Never married                                                                 1.00

    Health insurance                      -0.259     0.244      1.132    0.287     0.77          0.48, 1.24

    Urban area                            -0.347     0.257      1.832    0.176     0.71          0.43, 1.17
    Other town                            -0.219     0.294      0.551    0.458     0.80          0.45, 1.43
    †Rural area                                                                    1.00

    Head of household                      0.408     0.243      2.810    0.094     1.50          0.93, 2.42
    Per capita consumption                 0.000     0.000      0.595    0.440     1.00          1.00, 1.00

    Good health status                    -1.872     0.248    56.921     0.000     0.15        0.10, 0.25
    Health care-seekers                    6.080     0.417   212.549     0.000   437.11   193.02, 989.89

Model fit χ2 = 1087.7, P < 0.0001
Hosmer and Lemeshow goodness of fit χ2 = 8.11, P = 0.62
-2LL = 649.69
Nagelkerke R2 =0.724
†Reference group




                                                                                          111 

 
Table 4.5. Logistic regression: Self-reported health seeking behaviour by particular variable

                                                          Std.      Wald               Odds       95.0% C.I.
                                      Coefficient         Error    statistic    P      ratio

    Age                                      0.014         0.008      3.080    0.079     1.02         1.00, 1.03
    Male                                    -0.109         0.260      0.175    0.676     0.90         0.54, 1.49

     Married                                -0.601         0.295      4.151    0.042     0.55         0.31, 0.98
    Divorced, separated or wid              -0.291         0.445      0.429    0.513     0.75         0.31, 1.79
    † Never married                                                                      1.00

    Health insurance                         0.463         0.269      2.954    0.086     1.59         0.94, 2.69

    Urban area                               0.134         0.287      0.218    0.640     1.14         0.65, 2.01
    Other town                              -0.034         0.328      0.011    0.918     0.97         0.51, 1.84
    †Rural area                                                                          1.00

    Head of household                       -0.069         0.270     0.066     0.797     0.93         0.55, 1.58
    Per capita consumption                   0.000         0.000     0.042     0.837     1.00         1.00, 1.00
    Self-reported illness                    6.108         0.417   214.598     0.000   449.37   198.47, 1017.42

    Good health status                      -0.658         0.266      6.147    0.013     0.52         0.31, 0.87

Model fit χ2 = 995.45, P < 0.0001
Hosmer and Lemeshow goodness of fit χ2 = 3.64, P = 0.90
-2LL = 446.41
Nagelkerke R2 =0.764
†Reference group




                                                                                                    112 

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



                                    Paul Andrew Bourne


Health literature in the Caribbean, and in particular Jamaica, has continued to use objective
indices such as mortality and morbidity to examine children’s health. The current study uses
subjective indices such as parent-reported health conditions and health status to evaluate the
health of children instead of traditional objective indices. The study seeks 1) to examine the
health and health care-seeking behaviour of the sample from the parents’ viewpoints; and 2) to
compute the mean age of the sample with a particular illness and describe whether there is an
epidemiological shift in these conditions. Two nationally representative cross-sectional surveys
were used for this study (2002 and 2007). The sample for the current study is 3,062 respondents
aged less than 5 years. For 2002, the study extracted a sample of 2,448 under 5 year olds from
the national survey of 25,018 respondents, and 614 under 5 year olds were extracted from the
2007 survey of 6,728 respondents. Parents-reported information were used to measure issues on
children under 5 years old. In 2007, 43.4% of the sample had very good health status; 46.7%
good health status; 2.5% poor health and 0.3% very poor health status. Almost 15% of children
had illnesses in 2002, and 6% more had illnesses in 2007 over 2002. In 2002, the percentage of
the sample with particular chronic illnesses was: diabetes mellitus (0.6%); hypertension (0.3%)
and arthritis (0.3%). However, none was recorded in 2007. The mean age of children less than 5
years old with acute health conditions (i.e. diarrhoea, respiratory diseases and influenza)
increased over 2002. In 2007, 43.4% of children less than 5 years old had very good health
status; 46.7% good health status; 7.1% fair health status; 2.5% poor and 0.3% very poor health
status. The association between health status and parent-reported illness was - χ2 (df = 4) =
57.494, P < 0.001 – with the relationship being a weak one, correlation coefficient = 0.297. A
cross-tabulation between health status and parent-reported diagnosed illness found that a
significant statistical correlation existed between the two variables - χ2 (df = 16) = 26.621, P <
0.05, cc = 0.422, - with the association being a moderate one, correlation coefficient = 0.422. A
cross tabulation between health status and health care-seeking behaviour found a significant
statistical association between the two variables - χ2 (df = 4) = 10.513, P < 0.033 - with the
correlation being a weak one – correlation coefficient = 0.281.Rural children had the least health
status. The health disparity that existed between rural and urban less than 5 year olds showed that
this will not be removed simply because of the abolition of health care utilization fees.
                                                                                               113 

 
Introduction
In many contemporary nations, objective indices such as life expectancy, mortality and

diagnosed morbidity are still being widely used to measure the health of people, a society and/or

a nation [1-6]. The World Health Organisation (WHO) in the Preamble to its Constitution in the

1940s wrote that health is more important than disease, as it expands to the social, psychological

and physical wellbeing of an individual [7]; and lately that during the 21st century the emphasis

must be on healthy life expectancy [8,9]. In keeping with its opined emphasis, the WHO

formulated a mathematical approach that diminished life expectancy by the length and severity

of time spent in illness as the new thrust in measuring and examining health. Although healthy

life expectancy removes time spent in illness and severity of dysfunctions, it fundamentally rests

on mortality. The WHO therefore, instead of moving forward, has given some scholars, who are

inclined to use objective indices in measuring health, a guilty feeling about continuing this

practice.


       The Caribbean, and in particular Jamaica, continues to use mortality and morbidity to

measure the health of children or infants [1-6]. The use of mortality, morbidity and life

expectancy is the practice of Caribbean scholars, and is widely used in Jamaica by the: Ministry

of Health (MOHJ) [10]; Statistical Institute of Jamaica (STATIN) [11]; Planning Institute of

Jamaica (PIOJ) [12]; PIOJ and STATIN [13] as well as the Pan American Health Organization

(PAHO) [14] in measuring health. In spite of the conceptual definition opined by the WHO in

the Preamble to its Constitution in 1946, the health of children who are less than 5 years old in

Jamaica is still measured primarily by using mortality and morbidity statistics. Recently a book

entitled ‘Health Issues in the Caribbean’ [15] had a section on Child Health; however the

articles were on 1) nutrition and child health development [16] and 2) school achievement and
                                                                                              114 

 
behaviour in Jamaican children [17], indicating the void in health literature regarding health

conditions.


       An extensive review of health literature in the Caribbean region found no study that has

used national survey data to examine the health status of children less than 5 years of age. The

current study fills this gap in the literature by examining the health status of children less than 5

years of age using cross-sectional survey data which are based on the views of patients. The

objectives of this study are 1) to examine the health and health care-seeking behaviour of the

sample; and 2) to evaluate the mean age of the sample with a particular illness and to describe

whether there is an epidemiological shift in these conditions.


Materials and methods
Sample

The current study used two secondary nationally representative cross-sectional surveys (for 2002

and 2007) to carry out this work. The sub-samples are children less than 5 years old, and the only

criterion for selection was being less than 5 years old. The sample in the current study is 3,062

respondents of ages less than 5 years. For 2002, a sub-sample of 2,448 less than-5 year olds was

extracted from the national survey of 25,018 respondents in 2002, and information on 614 less

than-5 year olds was extracted from the 2007 survey. The survey (Jamaica Survey of Living

Conditions) began in 1989 to collect data from Jamaicans in order to assess government policies.

Since 1989, the JSLC has added a new module each year in order to examine that phenomenon,

which is critical within the nation [18, 19]. In 2002, the focus was on 1) social safety nets, and

2) crime and victimization, while for 2007, there was no focus.


Methods

                                                                                                 115 

 
Stratified random sampling technique was used to draw the sample for the JSLC. 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 comprises a minimum of 100 residences in rural areas and 150 in urban areas. An

ED is an independent geographical 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 dwellings was compiled, which in turn provided the sampling frame for the labour

force. One third of the Labour Force Survey (i.e. LFS) was selected for the JSLC [18, 19]. The

sample was weighted to reflect the population of the nation [18-20].


       The JSLC 2007 was conducted in May and August of that year; while the JSLC 2002 was

administered between July and October of that year. The researchers chose this survey based on

the fact that it is the latest survey on the national population, and that that it has data on the self-

reported health status of Jamaicans. An administered questionnaire was used to collect the data

from parents on children less than 5 years old, and the data were stored, retrieved and analyzed

using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA). The questionnaire was modelled

on 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 of socio-demographic variables – such as education; daily expenses

(for the past 7 days); food and other consumption expenditures; inventory of durable goods;

health variables; crime and victimization; social safety net and anthropometry. The non-response

rates for the 2002 and 2007 surveys were 26.2% and 27.7% respectively. The non-response

includes refusals and cases rejected in data cleaning.
                                                                                                    116 

 
Measures

Social class: This variable was measured based on the income quintiles: The upper classes were

those in the wealthy quintiles (quintiles 4 and 5); the middle class was quintile 3 and the poor

were the lower quintiles (quintiles 1 and 2).


Age is a continuous variable in years.


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, Cold; 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.


Parent-reported illness status. The question is ‘Have you had any illness other than due to injury

(for example a cold, diarrhoea, asthma, hypertension, diabetes or any other illness) in the past

four weeks? Here the options were Yes or No.


Statistical analysis

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 Analysis of Variance (ANOVA) was

used to test the relationships between metric and non-dichotomous categorical variables, whereas

an independent sample t-test was used to examine the statistical correlation between a metric

                                                                                              117 

 
variable and a dichotomous categorical variable. The level of significance used in this research

was 5% (i.e. 95% confidence interval).


Results
Demographic characteristic of sample

In 2002, the sex ratio was 98.8 males (less than 5 years old) to 100 females (less than 5 years

old), which shifted to 116.2 less than-5 year old males to 100 less than-5 year old females. The

sample over the 6 year period (2002 to 2007) revealed internal migrations to urban zones (Table

5.1): In 2002, 59.6% of respondents resided with their parents and/or guardians in rural areas,

which declined to 5.07%. The percentage of children less than 5 years of age whose parents

were in the poorest 20% fell to 25.4% in 2007 over 29.6% in 2002. In 2007 over 2002, 1.7 times

less children less than 5 years of age were taken to public hospitals, compared to 1.2 times less

taken to private hospitals (Table 5.1). Approximately 6% more children less than 5 years were

ill in 2007 over 2002. Based on Table 5.1, less than-5 year olds with particular chronic illnesses

had: diabetes mellitus (0.6%); hypertension (0.3%) and arthritis (0.3%). However, none was

recorded in 2007.


       There were some occasions on which the response rates were less than 50%: In 2002,

health care-seeking behaviour was 14.3%; parent-reported diagnosed health conditions, 14.2%;

and visits to health care institutions, 8.9% (Table 5.1). For 2007, the response rate for health

care-seeking behaviour was 20.2%; parent-reported diagnosed health conditions, 20.2%, and less

than 11% for cost of medical care.


Health conditions



                                                                                              118 

 
Based on Table 5.1, the percentage of less than-5 year olds with particular acute conditions saw a

decline in colds and asthmatic cases, as well as chronic conditions. Figure 5.1 revealed that in

2007 the mean age of children less than 5 years old with acute health conditions (i.e. diarrhoea,

respiratory diseases and influenza) increased over 2002. On the other hand, the mean age of

those with unspecified illnesses declined from 1.76 years (SD = 1.36 years) to 1.64 years (SD =

1.36 years). Concomitantly, the greatest mean age of the sample was 2.71 years (SD = 1.21

years) for asthmatics in 2007 and 2.59 years (1.24 years) in 2002. It should be noted here that

the mean age of a child less than 5 years of age in 2002 with diabetes mellitus was 1.50 years

(2.12 years).


Health status

In 2002, the JSLC did not collect data on the general health status of Jamaicans, although this

was done in 2007. Therefore, no figures were available for health status for 2002. In 2007,

43.4% of children less than 5 years old had very good health status; 46.7% good health status;

7.1% fair health status; 2.5% poor and 0.3% very poor health status. The response rate for the

health status question was 96.9%.


       Ninety-seven percent of the sample was used to examine the association between health

status and parent-reported illness - χ2 (df = 4) = 57.494, P < 0.001 – with the relationship being a

weak one, correlation coefficient = 0.297. Table 5.2 revealed that 24.2% of children less than 5

years of age who reported an illness had very good health status, compared to 2 times more of

those who did not report an illness. One percent of parents indicated that their children (of less

than 5 years) who had no illness had poor health status, compared to 5.6 times more of those

with illness who had poor health status.

                                                                                                119 

 
Health conditions, health status and medical care-seeking behaviour

A cross-tabulation between health status and parent-reported diagnosed illness found that a

significant statistical correlation existed between the two variables - χ2 (df = 16) = 26.621, P <

0.05, cc = 0.422, - with the association being a moderate one, correlation coefficient = 0.422

(Table 5.3). Based on Table 5.3, children less than 5 years old with asthma were less likely to

report very good health status (5.9%), compared to those with colds (30.5%); diarrhoea (22.2%);

and unspecified health conditions (22.7%).


       When health status by parent-reported illness (in %) was examined by gender, a

significant statistical relationship was found, P < 0.001: males - χ2 (df = 4) = 25.932, P < 0.05, cc

= 0.320, and females - χ2 (df = 4) = 39.675, P < 0.05, cc = 0.356. The health statuses of males

less than 5 years old in the very good and good categories were greater than those of females

(Figure 5.2). However, the females had greater health statuses in fair and poor health status than

males, with more males reporting very poor health status than females.


       Based on Figure 5.3, even after controlling health status and parent-reported illness (in

%) by area of residence, a significant statistical association was found: urban - χ2 (df = 3) =

10.358, P < 0.05, cc = 0.238; semi-urban - χ2 (df = 3) = 9.887, P = 0.021, cc = 0.273, and rural -

χ2 (df = 3) = 45.978, P < 0.001, cc = 0.365. Concomitantly, children less than 5 years of age were

the least likely to have very good health status (19.4%) compared to rural (25.8%) and semi-

urban children (25.9%). Furthermore, the respondents who resided in urban areas were 2.1 times

more likely to have parent-reported very poor health status, compared to rural respondents.

       In examining health status and reported illness (in %) by social classes, significant

statistical relationships were found, P < 0.05: poor-to-poorest classes - χ2 (df = 4) = 52.374, P =

                                                                                                 120 

 
0.021, cc = 0.393; middle class - χ2 (df = 3) = 8.821, P = 0.032, cc = 0.259, and wealthy class - χ2

(df = 3) = 10.691, P = 0.02, cc = 0.234. Based on Figure 5.4, middle class children who are less

than 5 years old had the greatest very good health status (37%) compared to the wealthy class

(26.8%) and the poor-to-poorest classes (16.1%). Fourteen percent of poor-to-poorest class

children who are less than 5 years old had at most poor health status compared to 0% of the

middle class and 4.9% of the wealthy class, while 1.8% of poor-to-poorest classes less than 5

years of age had very poor health status.

       When health status and parent-reported illness was examined by age, sex, social class,

and area of residence, the correlation was a weak one – correlation coefficient = 0.295, P <

0.001, n=583.


       A cross tabulation between health status and health care-seeking behaviour found a

significant statistical association between the two variables - χ2 (df = 4) = 10.513, P < 0.033 -

with the correlation being a weak one – correlation coefficient = 0.281. A child less than 5 years

old was 2.44 times more likely to be taken for medical care if he/she had at most poor health

status. On the other hand, a child who had very good health status was 1.97 times more likely not

to be taken to health care practitioners (Figure 5.5).

       In 2007, an examination of the health care-seeking behaviour and parent-reported illness

of the sample revealed no statistical correlation - χ2 (df = 1) = 0.430, P = 0.618. Sixty-two

percent of the sample, who was ill, was taken to health care practitioners, while 38.5% were not.

On the other hand, more were taken for medical care than in 2007 in the 4-week period of the

survey. No statistical correlation was noted for the aforementioned variables in 2002 - χ2 (df = 1)

= 1.188, P = 0.276. Of those who reported ill, 63.7% were taken to health care practitioners.

Discussion
                                                                                                121 

 
Infant mortality has been declining since the 1970s, and this has further decreased since 2004

[14]; this, as the literature shows, is not a good measure of health. The current study found that,

using general health status, children less than 5 years of age in Jamaica had good health. The

findings revealed that 90 out of every 100 less than-5 year olds had at least good health status,

with 44 out of every 100 having very good health status. In spite of the good health status of less

than-5 year olds in Jamaica in 2007, 20.8% of them had an illness in the 4-week period of the

survey, which is a 5.9% increase over 2002. It is interesting to note the shift in this study away

from specific chronic illnesses. In 2002, 30 out of every 1,000 less than-5 year olds in Jamaica

were diagnosed with hypertension and arthritis (i.e. parent-reported), with 60 out of 1,000 having

been parent-reported with diabetes mellitus. None such cases were found in 2007, suggesting

that in the case of the children who had those particular chronic illnesses, their parents had either

migrated with them or they had died. Concomitantly, the country is seeing a reduction in

children less than 5 years old with colds; however, marginal increases were seen in diarrhoea,

asthma and unspecified health conditions over the last 6 years. Although there were increased

reported cases of illness over the studied period, in 2007, 62 out of every 100 ill children were

taken to medical practitioners, and this fell from 64 in every 100 in 2002. One of the arguments

put forward by some people is that what retards or abates health care-seeking behaviour is

medical cost. With the abolition of health care user fees for children since 2007, the culture must

be playing a role in parents and/or guardians not taking children who are ill to medical care

facilities for treatment.


        Medical cost cannot be divorced from the expenditure that must be incurred in taking the

child to the health care facility. In 2007, 25 out of every 100 children less than 5 years of age had

parents and/or guardians who were less than the poverty line. Although this has declined by
                                                                                                 122 

 
4.2% since 2002, it nevertheless means that there are children whose parents are incapacitated by

other factors. Marmot [21] opined that the financial inability of the poor is what accounts for

their lowered health status, compared to other social classes. The current study concurs with the

findings of Marmot, as it was revealed that children less than 5 years of age from poor

households had the least health status. This means that poverty is not merely eroding the health

status of poor Jamaicans, but that equally it is decreasing the health status of poor children.


       Rural poverty in Jamaica is at least twice as great as urban poverty, and approximately 4

times more than semi-urban [13], which provides another explanation for the poor health status

of children less than 5 years of age. The current study found that 3.2% of those children dwelling

in urban zones recorded at most poor health status, compared to 13.6% of rural children,

suggesting that the health status of the latter group is 4.3 times worse than the former. This

means that poverty in rural zones is exponential, eroding the quality of life of children who are

less than 5 years old. Poverty in semi-urban areas was 4% which is 2.5 times less than that for

the nation; and those less than 5 years of age recorded the greatest health status, supporting

Marmot’s perspective that poverty erodes the health status of a people. Hence, the decline in

health care-seeking behaviour for this sample is embedded in the financial constraints of parents

and/or guardians as well as their geographical challenges. The terrain in rural zones in Jamaica is

such that medical care facilities are not easily accessible to residents compared to urban dwellers.

With this terrain constraint comes the additional financial burden of attending medical care

facilities at a location which is not in close proximity to the home of rural residents, and this

accounts for the vast health disparity between rural and urban children. As a result of the above,

the removal of health care utilization fees for children less than 18 years of age does not

correspond to an increased utilization of medical care services, or lowered numbers of unhealthy
                                                                                                  123 

 
children less than 5 years of age. If rural parents are plagued with financial and location

challenges, their children will not have been immunized or properly fed, and their nutritional

deficiency would explain the health disparity that exists between them and urban children who

have easier access to health care facilities.


        The removal of health care utilization fees is not synonymous with an increased

utilization of medical care for children less than 5 years old, as 46.5% of the sample attended

public hospitals for treatment in 2002, and after the abolition of user fees in April 2007

utilization fell by 1.7 times compared to 2002. In order to understand stand why there is a switch

from health care utilization to mere survival, we can examine the inflation rate. In 2007, the

inflation rate was 16.8% which is a 133% increase over 2002 (i.e. 7.2%), which translates into a

24.7% increase in the prices of food and non-alcoholic beverages, and a 3.4% increase in health

care costs [22]. Here the choice is between basic necessities and health care utilization, which

further erodes health care utilization in spite of the removal of user fees for children.


        Health status uses the individual self-rating of a person’s overall health status [23], which

ranges from excellent to poor. Health status therefore captures more of people’s health than

diagnosed illness, life expectancy, or mortality. However, how good a measure is it? Empirical

studies show that self-reported health is an indicator of general health. Schwarz & Strack [24]

cited that a person’s judgments are prone to systematic and non-systematic biases, suggesting

that it may not be a good measure of health. Diener, [25] however, argued that the subjective

index seemed to contain substantial amounts of valid variance, indicating that subjective

measures provide some validity in assessing health, a position with which Smith concurred [26].

Smith [26] argued that subjective indices do have good construct validity and that they are a

                                                                                                 124 

 
respectably powerful predictor of mortality risks [27], disability and morbidity [27], though these

properties vary somewhat with national or cultural contexts. Studies have examined self-reported

health and mortality, and have found a significant correlation between a subjective and an

objective measure [27-29]: life expectancy [30]; and disability [28]. Bourne [30] found that the

correlation between life expectancy and self-reported health status was a strong one (correlation

coefficient, R = 0.731); and that self-rated health accounted for 53% of the variance in life

expectancy. Hence, the issue of the validity of subjective and objective indices is good, with

Smith [26] opining that the construct validity between the two is a good one.


       The current research found that parent-reported illness and the health status of children

less than 5 years of age are significantly correlated. However, the statistical association was a

weak one (correlation coefficient = 0.297), suggesting that only 8% of the variance in health

status can be explained by parent-reported children’s illnesses. This is a critical finding which

reinforces the position that self-reported illnesses (or health conditions) only constitute a small

proportion of people’s health. Therefore, using illness to measure the health status of children

who are less than 5 years of age is not a good measure of their health, as illness only accounts for

8% of health status. However, based on Bourne‘s work [30], health status is equally as good a

measure of health as life expectancy. One of the positives for the using of health status instead

of life expectancy is its coverage in assessing more of people’s general health status by using

mortality or even morbidity data.


Conclusion
       In summary, the general health status of children who are less than 5 years old is good;

however, social and public health programmes are needed to improve the health status of the

                                                                                                125 

 
rural population, which will translate into increased health status for their children. The health

disparity that existed between rural and urban children less than 5 years of age showed that this

will not be removed simply because of the abolition of health care utilization fees. In keeping

with this reality, public health specialists need to take health care to residents in order to further

improve the health status of children who are less than 5 years old.




Conflict of interest
The author has no conflict of interest to report.

Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica
Survey of Living Conditions, 2007, none of the errors that are within this paper should be
ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica as they are not
there, but owing to the researcher.

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                                                                                              128 

 
Table 5.1. Socio-demographic characteristic of sample, 2002 and 2007

                                                                       2002                           2007
Variable                                                                n              %             n              %
Sex
   Male                                                              1216            49.7          330            53.7
   Female                                                            1231            50.3          284            46.7
Income quintile
   Poorest 20%                                                        725            29.6          156            25.4
   Poor                                                               554            22.6          140            22.8
   Middle                                                             474            19.4          126            20.5
   Wealthy                                                            402            16.4          117            19.1
   Wealthiest 20%                                                     293            12.0           75            12.2
Self-reported illness
   Yes                                                                345            14.9          125            20.8
    No                                                               1969            85.0          475            79.2
Visits to health care facilities (hospitals)
    Private, yes                                                       17             7.8             5            6.7
    Public, yes                                                       100            46.3           20            26.7
Area of residence
    Rural                                                            1460            59.6          311            50.7
    Semi-urban                                                        682            27.9          125            20.4
    Urban                                                             306            12.5          178            29.0
Health (or, medical) care-seeking behaviour
    Yes                                                               221            63.3           76            61.3
    No                                                                128            36.7           48            38.7
Health insurance coverage
    Yes, private                                                      211             9.0           66            11.1
    Yes, public                                                          *              *           33             5.5
    No                                                               2123            91.0          496            83.4
Self-reported diagnosed health conditions
   Acute
      Cold                                                            185            53.3           60            48.4
      Diarrhoea                                                        20             5.8             9            7.3
      Asthma                                                           46            13.3           17            13.7
   Chronic
      Diabetes mellitus                                                  2            0.6             0              0
      Hypertension                                                       1            0.3             0              0
      Arthritis                                                          1            0.3             0              0
   Other (unspecified)                                                 54            15.6           22            17.7
   Not diagnosed                                                       38            11.0           16            12.9
Number of visits to health care institutions                          1.53 (SD = 0.927)             1.43 (SD = 0.989)
Duration of illness Mean (SD)                                     8.51 days (6.952 days)        8.07 days (7.058 days)
Cost of medical care
    Public facilities Median (Range)in USD                                 2.36 (157.26)1                0.00 (64.62)2
                                                                                        1
    Private facilities Median (Range)in USD                              13.76 (117.95)                 10.56 (49.71)2
1
  USD1.00 = Ja. $50.87
 2
   USD1.00 = Ja. $80.47
*In 2002, all health insurance coverage was private and this was change in 2005 to include some public option




                                                                                                              129 

 
Table 5.2. Health status by self-reported illness
                                           Self-reported illness
        Health status                               Yes                   No
                                               n (%)                   n (%)
Very good                                   30 (24.2)              227 (48.3)
Good                                        61 (49.2)              217 (46.2)
Fair                                        23 (18.5)                19 (4.0)
Poor                                          9 (7.3)                 6 (1.3)
Very poor                                     1 (0.1)                 1 (0.2)
Total                                               124                  470


χ2 (df = 4) = 57.494, P < 0.001, cc = 0.297, n = 594




                                                                                130 

 
Table 5.3. Health status by self-reported diagnosed illness

                                               Self-reported diagnosed illness
    Health status         Cold        Diarrhoea        Asthma         Unspecified      No
    Very good
                         18 (30.5)        2 (22.2)         1 (5.9)          5 (22.7)   5 (31.3)


    Good                 31 (52.5)        5 (55.6)        4 (23.5)         11 (50.0)   8 (50.0)


    Fair                  7 (11.9)        2 (22.2)        8 (47.1)          3 (13.6)   3 (18.8)


    Poor                    2 (3.4)        0 (0.0)        4 (23.5)          3 (13.6)    0 (0.0)


    Very good               1 (1.7)        0 (0.0)         0 (0.0)           0 (0.0)    0 (0.0)

    Total                        59               9             17               22         16

χ2 (df = 16) = 26.621, P < 0.05, cc = 0.422,




                                                                                            131 

 
Figure 5.1. Mean age of health conditions of children less than 5 years old, 2002 and 2007




                                                                                             132 

 
Figure 5.2. Health status by Parent-reported illness (in %) examined by gender




                                                                                 133 

 
Figure 5.3. Health status by parent-reported illness (in %) examined by area of residence




                                                                                            134 

 
Figure 5.4. Health status by parent-reported illness (in %) examined by social classes




                                                                                         135 

 
Figure 5.5. Health status by health care-seeking behaviour




                                                             136 

 
                                    Chapter 6
                  Health Inequality in Jamaica, 1988-2007


                                       Paul A. Bourne


In Jamaica, mortality for men is not only greater than that of women as indicated by the life
expectancy but of the five leading causes of death (malignant neoplasms; cerebrovascular
disease; heart disease; diabetes mellitus and homicides), the rates for men were greater in four of
the five categories (malignant neoplasms; cerebrovascular; heart disease and homicides). Despite
these realities, men seek less medical care than women while staying longer in hospitals for
curative care. Hence, this study examines medical seeking behaviour, self-reported ill-health, and
sex differential in medical seeking health in nation. The current research used secondary data.
The data were extracted from the Jamaica Survey of Living Conditions (JSLC) on medical care
seeking behaviour, self-reported illness (or ill-health) and the sex composition of those who
reported ill-health. The JSLC was born out of the World Bank’s Living Standard Survey. Data
were also taken from the Ministry of Health’s Annual Report, which provided statistics on actual
percentage of Jamaicans who visited public hospitals. The current study used 19 years of
published data extracted from the JSLC (1988-2007). Scatter diagrams and best fitted lines were
used to examine correlations and trends. 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 sex 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 sex 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 increased to 4.2 years (68.1 years for women and

63.9 years for men). For the Caribbean, in the same aforementioned period, life expectancy was

53.5 years and 50.8 years for women and men respectively; and 50 years later, the disparity
                                                                                               137 

 
increased to 5.5 years (70.9 years for women and 65.4 years for men) which was greater than that

for the world. Life expectancy which is an indicator of mortality and morbidity are also a

measure for health status; and speaks to the quality of labour for the society. Although there are

some morbidity that are not life threatening, health literature showed that healthy life is not

equivalent to lived years. The World Health Organization being aware of this disparity

developed the DALE (ie disability adjusted life expectancy) to discount life expectancy for the

time lost due to illness. Based on this information, statistics revealed that developing countries

lost 9 years of life expectancy owing to unhealthy years (or illness); and this is still within the

cultural context of men’s unwillingness to seeking medical care. While this provides a general

framework for the rationale of the disparity in life expectancy of the sexes, it does not afford us a

comprehensive understanding of health inequality.


           There has always been a health differential between the sexes in Jamaica [1] Dating

back to 1880, which was the first time that life expectancy data was recorded for men and

women in the island, women outlived men. Statistics for Jamaica showed that for the period

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 sex differential

in Jamaica. Women are not only living longer than their male counterparts, but they are 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


                                                                                                 138 

 
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

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 indicated 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 sex 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.
                                                                                                139 

 
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.


       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

                                                                                            140 

 
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

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’.




                                                                                                  141 

 
       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

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 6.1), is this offering some

explanation the sex differential in health status? Although less Jamaicans are seeking medical

care of those who reported illnesses, 27.1% more Jamaicans reported dysfunctions (Table 6.1),
                                                                                                142 

 
suggesting that there is greater health differential between the sexes. Hence, for this study,

medical seeking behaviour, self-reported ill-health, and sex 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.


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 sex

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 sex 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
                                                                                                143 

 
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

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


Sex is the biological composition of being men or women.


Sex differential is the disparity between self-reported ill-health of male or female.



                                                                                                144 

 
Medical Care Seeking Behaviour denotes the proportion of self-reported cases of visits for

seeking medical care of those who indicated ill-health.


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 6.4, 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.


                                                                                                  145 

 
       During the periods of the greatest double digits inflation in history of Jamaica (early

1990s) (Table 6.2) in particular inflationary rates that were in excess of 25% (1990-1995),

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 6.3). 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
                                                                                                  146 

 
6.2). 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

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 6.3). 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



                                                                                                 147 

 
Percentage of People Seeking Medical Care by Percentage of People reporting Illness


On examination of Figure 6.1, 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 6,2 & 6.3) 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 6.3).


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

6.2). 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

                                                                                                 148 

 
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-

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 6.3). 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 6.3). 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 6.3). 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 Sex, 1989-2006
                                                                                             149 

 
Over the last 2 decades (1988-to-2007), a small proportion of Jamaicans have reported illness (or

dysfunction) (Table 6.5). 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 6.5). 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 6.2), 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 6.2 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

                                                                                             150 

 
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 6.5).



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 6.5. 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

In the conclusion of the health chapter in one of the JSLC’s reports [8] it reads “Sex 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 sex bias. One of

the rationales for the emphasis on health care by women is reason for male’s abstinence, the


                                                                                                    151 

 
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 6.1)

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


                                                                                               152 

 
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
                                                                                                    153 

 
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 medical
                                                                                                  154 

 
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 sex 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 21st

century, sex 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

sex 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
                                                                                                  155 

 
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
                                                                                                       156 

 
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
                                                 [32]
environment is such to account for ill-health       , 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

                                                                                               157 

 
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 sex, 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.

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
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    4. Rudkin, L., 1993. Sex 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
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    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. 

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    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
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        Health & Development, 31:525-532.
    15. Chevannes, B., 2001. Learning to be a man: Culture, socialization and sex 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  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
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    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. Sex Quo Vadis: 21 the first female century: The Journal of Men’s health & sex, 
        1: 3‐5. 
    25. Spector, RE., 2004. Cultural diversity in health and illness, 6th ed. New Jersey. 
    26. Barrow, Christine. 1998. Caribbean Sex Ideologies: Introduction and Overview. In
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        Publishers, pp: xi-xxxviii.
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        male identity in Jamaica. Grace, Kennedy Foundation.
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        an examination of sex socialization in the Caribbean. University of the West Indies.
    29. Bailey, W., (ed), 1998. Sex and the family in the Caribbean. Institute of Social and
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    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
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                                                                                                      159 

 
    160 

 
Table 6.1: 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.




                                                                                         161 

 
Table 6.2: 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

                                                                                                                                                                 162 

 
 Table 6.3: Seeking Medical Care, Self-reported illness, and Sex 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




                                                                                            163 

 
    Table 6.4: 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




                                                                                                164 

 
     Table 6.5: 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




                                                                                               165 

 
                           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 6.1: Percentage of Men Seeking Medical Care by Percentage of Men reporting Illness




                                                                                                           166 

 
                           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 6.2: Percentage of People Seeking Medical Care by Prevalence of Poverty




                                                                                               167 

 
                                           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 6.3: Percentage of Men Seeking Medical Care by Percentage of Men reporting Illness




                                                                                                                168 

 
                                             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 6.4: Percentage of Women Seeking Medical Care by Percentage of Women reporting
Illness




                                                                                                                             169 

 
                           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 6.5: Percentage of people Seeking Medical Care by Percentage with Health Insurance




                                                                                                   170 

 
                       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 6.6: Ownership of Health Insurance and Prevalence of Poverty




                                                                                                                 171 

 
                                     Chapter 7
    Social determinants of self-reported health across the Life Course


                                      Paul Andrew Bourne



The socio-psychological and economic factors produced inequalities in health and need to be
considered in health development. In spite of this, extensive review of health Caribbean revealed
that no study has examined health status over the life course of Jamaicans. With the value of
research to public health, this study is timely and will add value to understanding the elderly,
middle age and young adults in Jamaica. The aim of this study is to develop models that can be
used to examine (or evaluate) social determinants of health of Jamaicans across the life course,
elderly, middle age and young adults. The current study used dataset of 2002 Jamaica Survey of
Living Conditions (JSLC). It is a cross-sectional survey which used stratified random probability
sampling technique to collect data from respondents. Logistic regression analyses were used to
model the social determinants of health status of Jamaicans across the life course. Eleven
variables emerged as statistically significant predictors of current good health Status of Jamaicans
(p<0.05). The factors are retirement income (95%CI=0.49-0.96), logged medical expenditure
(95% CI =0.91-0.99), marital status (Separated or widowed or divorced: 95%CI=0.31-0.46;
married: 95%CI=0.50-0.67; Never married), health insurance (95%CI=0.029-0.046), area of
residence (other towns:, 95%CI=1.05-1.46; rural area:), education (secondary: 95%CI=1.17-1.58;
tertiary: 95%CI=1.47-2.82; primary or below: OR=1.00), social support (95%CI=0.75-0.96),
gender (95%CI=1.281-1.706), psychological affective conditions (negative affective:
95%CI=0.939-0.98; positive affective: 95%CI:1.05-1.11), number of males in household
(95%CI:1.07-1.24), number of children in household (95%CI=1.12-1.27) and previous health
status. There are disparities in the social determinants of health across the life course, which
emerged from the current findings. The findings are far reaching and can be used to aid policy
formulation and how social determinants of health are viewed in the future.


INTRODUCTION



Health is a multidimensional construct which goes beyond dysfunctions (illnesses, ailment or

injuries) [1-14]. Although World Health Organization (WHO) began this broaden conceptual

framework in the late 1940s [1], Engel [3] was the first to develop the biopsychosocial model that


                                                                                                172 

 
can be used to examine and treat health of mentally ill patient. Engel’s biopsychosocial model

was both in keeping with WHO’s perspective of health and again a conceptual model of health.

Both WHO and Engel’s works were considered by some scholar as too broad and as such difficult

to measure [15]; although this perspective has some merit, scholars have ventured into using

different proxy to evaluate the ideal conceptual definition forwarded by WHO for some time now.


       Psychologists have argued that the use of diseases to proxy health is unidirectional (or

negative) [2], and that the inclusion of social, economic and psychological conditions in health is

broader and more in keeping with the WHO’s definition of health than diseases. Diener was the

first psychologist to forward the use of happiness to proxy health (or wellbeing) of an individual

[16, 17]. Instead of debating along the traditional cosmology health, Diener took the discussion

into subjective wellbeing. He opined that happiness is a good proxy for subjective wellbeing of a

person, and embedded therein is wider scope for health than diseases. Unlike classical economists

who developed Gross Domestic Product per capita (GDP) to examine standard of living (or

objective wellbeing) of people as well this being an indicator of health status along with other

indicators such as life expectancy, Diener and others believe that people are the best judges of

their state. This is no longer a debate, as some economists have used happiness as a proxy of

health and wellbeing [18-20]; and they argued that it is a good measurement tool of the concept.


Theoretical Framework


       Whether the proxy of health (or wellbeing) is happiness, self-reported health status, self-

rated health conditions, life satisfaction or ill-being, it was not until in the 1970s that econometric

analyses were employed to the study of health. Grossman [9] used econometric to capture factors

that simultaneously determine health stock of a population. Grossman’s work transformed the
                                                                                                   173 

 
conceptual framework outlined by WHO and Engel to a theoretical framework for the study of

health. Using data for the world, Grossman established an econometric model that captures

determinants of health. The model read (Model 1):


       Ht = ƒ (Ht-1, Go, Bt, MCt, ED) ……………………………………………….. Model (1)


       where Ht – current health in time period t, stock of health (Ht-1) in previous period, Bt    –


smoking and excessive drinking, and good personal health behaviours (including exercise – Go),

MCt,- use of medical care, education of each family member (ED), and all sources of household

income (including current income).


       Grossman’s model was good at the time; however, one of the drawbacks to this model was

the fact that some crucible factors were omitted by the aforementioned model. Based on that

limitation, using literature, Smith and Kington [10] refined, expanded and modified Grossman’s

work as it omitted important variables such as price of other inputs and family background or

genetic endowment which are crucible to health status. They refined Grossman’s work to include

socioeconomic variables as well as some other factors [Model (2)].


       Ht = H* (Ht-1, Pmc, Po, ED, Et, Rt, At, Go) ………………………..…………… Model (2)


       Model (2) expresses current health status Ht as a function of stock of health (Ht-1), price of

medical care Pmc, the price of other inputs Po, education of each family member (ED), all sources

of household income (Et), family background or genetic endowments (Go), retirement related

income (Rt ), asset income (At).


       It is Grossman’s work that accounts for economists like Veenhoven’s [20] and Easterlin’s

[19] works that used econometric analysis to model factors that determine subjective wellbeing.
                                                                                                 174 

 
Like Veenhoven [20], Easterlin [19] and Smith and Kington [10], Hambleton et al. [6] used the

same theoretical framework developed by Grossman to examine determinants of health of elderly

(ages 65+ years) in Barbados. Hambleton et al.’s work refined the work of Grossman and added

some different factors such as geriatric depression index; past and current nutrition; crowding;

number of children living outside of household; and living alone. Unlike Grossman’s study, he

found that current disease conditions accounted for 67.2% of the explained variation in health

status of elderly Barbadians, with life style risks factors accounting for 14.2%, and social factors

18.6%. One of the additions to Grossman’s work based on Hambleton et al.’s study was actual

proportion of each factor on health status and life style risk factors.


       A study published in 2004, using life satisfaction and psychological wellbeing to proxy

wellbeing of 2,580 Jamaicans, Hutchinson et al. [21] employed the principles in econometric

analysis to examine social and health factors of Jamaicans. Other studies conducted by Bourne on

different groups and sub-groups of the Jamaican population have equally used the principles of

econometric analysis to determine factors that explain health, quality of life or wellbeing [5, 8, 22,

23]. Despite the contribution of Hutchinson et al’s and Bourne’s works to the understanding of

wellbeing, there is a gap in the literature on a theoretical framework explains good health status of

the life course of Jamaicans. The current study will model predictors of good health status of

Jamaicans as well as good health status of young adults, middle age adults and elderly in order to

provide a better understanding of the factors that influence each cohort.


METHODS


Participants and questionnaire



                                                                                                  175 

 
The current research used a nationally cross-sectional survey of 25,018 respondents from the 14

parishes in Jamaica. The survey used stratified random probability sampling technique to draw

the 25,018 respondents. The non-response rate for the survey was 29.7% with 20.5% who did not

respond to particular questions, 9.0% did not participated in the survey and another 0.2% was

rejected due to data cleaning. The study used 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). These two organizations are

responsible for planning, data collection and policy guideline for Jamaica.


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 from household members. The survey is conducted between April

and July annually.


Model


The multivariate model used in this study is a modification of those of Grossman and Smith &

Kington which captures the multi-dimensional concept of health, and health status. The present

study further refine the two aforementioned works and in the process adds some new factors such

as psychological conditions, crowding, house tenure, number of people per household and a

deconstruction of the numbers by particular characteristics i.e. males, females and children (ages

≤ 14 years). Another fundamental difference of the current research and those of Grossman, and

Smith and Kington is that it is area specific as it is focused on Jamaican residents.
                                                                                              176 

 
           The proposed model that this research seeks to evaluate is displayed below [Model (3)]:

Ht = f(Ht-1,Pmc, EDi, Rt, At, Qt, HHt, Ci, Eni, MSi, HIi, HTi, SSi, LLi,Xi, CRi, Di, Oi, Σ(NPi,PPi), Mi,Ni, FSi, Ai, Wi, εi)…..
Model (3)

           The current health status of a Jamaica, Ht, is a function of 23 explanation variables, where

Ht is current health status of person i, if good or above (i.e. no reported health conditions four

week leading up to the survey period), 0 if poor (i.e. reported at least one health condition); Ht-1 is

stock of   health for previous period; lnPmc is logged cost of medical care of person i; EDi is

educational level of person i, 1 if secondary, 1 if tertiary and the reference group is primary and

below; Rt is retirement income of person i, 1 if receiving private and/or government pension, 0 if

otherwise; HIi is health insurance coverage of person i, 1 if have a health insurance policy, 0 if

otherwise; HTi is house tenure of person i, 1 if rent, 0 if squatted; Xi is gender of person i, 1 if

female, 0 if male; CRi is crowding in the household of person i; Σ(NPi,PPi) NPi is the summation

of all negative affective psychological conditions and PPi is the summation of all positive

affective psychological conditions; Mi is number of male in household of person i and Fi is

number of female in household of person i; Ai is the age of the person i and Ni is number of

children in household of person i; LLi is living arrangement where 1= living with family

members or relative, and 0=otherwise and social standing (or social class), Wi.


Statistical analysis


Statistical analyses were performed using Statistical Packages for the Social Sciences (SPSS) for

Windows, Version 16.0 (SPSS Inc; Chicago, IL, USA). A single hypothesis was tested, which

was ‘health status of rural resident is a function of demographic, social, psychological and

economic variables.’ 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 if the dependent
                                                                                                                          177 

 
variable is a binary one; and linear multiple regression in the event the dependent variable was a

normally distributed metric variable . The final model was established based on those variables

that are statistically significant (ie. p < 0.05) – ie 95% confidence interval (CI), and all other

variables were removed from the final model (p>0.05). Continuing, categorical variables were

coded using the ‘dummy coding’ scheme.


       The predictive power of the model was tested using Omnibus Test of Model and Hosmer

and Lemeshow [24] 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. Cohen and Holliday [25] stated that correlation can be low/weak (0 to 0.39); moderate

(0.4-0.69), or strong (0.7-1.0). This was used in this study to exclude (or allow) a variable in the

model. Where collinearity existed (r > 0.7), variables were entered independently into the model

to determine those that should be retained during the final construction of the model. 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. Finally, 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 the

interpreting each significant variables.


Results: Modelling Current Good Health Status of Jamaicans, Elderly, Middle Age and

Young adults


Predictors of current Good Health Status of Jamaicans. Using logistic regression analyses, eleven

variables emerged as statistically significant predictors of current good health status of Jamaicans

(p<0.05, Model 4). The factors are retirement income, logged medical expenditure, marital status,
                                                                                                178 

 
health insurance, area of residence, education, social support, gender, psychological affective

conditions, number of males in household, number of children in household and previous health

status (Table 7.1).


       Ht = f(Ht-1, Rt, Pmc, EDi, MSi, HIi, SSi,ARi, Xi, Σ(NPi,PPi), Mi,Ni, εi)...……..... Model (4)

       The model [ie Model (4)] had statistically significant predictive power (χ2 (27) =1860.639,

p < 0.001; Hosmer and Lemeshow goodness of fit χ2=4.703, p = 0.789) and overall correctly

classified 85.7% of the sample (correct classified 98.3% of cases of good health status and

correctly classified 33.9% of cases of dysfunctions).

       There was a moderately strong statistical correlation between age, marital status,

education, retirement income, per capita income quintiles, property ownership, and so these were

omitted from the initial model (ie model 3). Based on that fact, three age groups were classified

(young adults – ages 15 to 29 years; middle age adults – ages 30 to 59 years; and elderly – ages

60+ years) and the initial model was once again tested. There were some modifications of the

initial model in keeping with the age group. For young adults the initial model was amended by

excluding retirement income, property ownership, divorced, separated or widowed, number of

children in household, and house tenure. The exclusion was based on the fact that more than 15%

of cases missing in some categories and a high correlation between variables.


Predictors of current Good Health Status of elderly Jamaicans. From the logistic regression

analyses that were used on the data, eight variables were found to be statistically significant in

predicting good health Status of elderly Jamaicans (P < 0.5) (Model 5). These factors were

education, marital status, health insurance, area of residence, gender, psychological conditions,




                                                                                                  179 

 
number of males in household, number of children in household and previous health status (Table

7.2).


        Ht = f(Ht-1, EDi, MSi, HIi, ,ARi, Xi, Σ(PPi), Mi,Ni, εi)...……………… …..... Model (5)

        The model had statistically significant predictive power (model χ2 (27) =595.026, P <

0.001; Hosmer and Lemeshow goodness of fit χ2=5.736, p = 0.677) and overall correctly

classified 75.5% of the sample (correctly classified 94.6% of cases of good or beyond health

status and correct classified 44.7% of cases of dysfunctions).



Predictors of current Good Health Status of middle age Jamaicans. Using logistic regression, six

variables emerged as statistical significant predictors of current good health status of middle age

Jamaican (p < 0.05) (Model 6).             These factors are logged medical expenditure, physical

environment, health insurance, gender of respondents, psychological condition, number of

children in household and previous health status (Table 7.3)

        Ht = f(Ht-1, Pmc, Eni, HIi, Xi, Σ(NPi),Ni, εi)..........................………..... Model (6)

        Based on table 7.3, the model had statistically significant predictive power (model χ2 (27)

=547.543, p < 0.001; Hosmer and Lemeshow goodness of fit χ2=4.318, p = 0.827) and overall

correctly classified 87.2% of the sample (correctly classified 98.3% of cases of good or beyond

health status and correct classified 28.2% of cases of dysfunctions).



Predictors of current Good Health Status of young adult in Jamaica. Using logistic regression, two

variables emerged as statistically significant predictors of current good health status of young

adults in Jamaica (p<0.05) (Model 7). These are health insurance coverage, psychological

condition, social class and previous health status (Table 7.4).
                                                                                                     180 

 
                Ht = f(Ht-1, Wi, HIi, Σ(NPi), εi)................................…………….....Model (7)

       From Table 7.3, the model had statistically significant predictive power (model χ2 (19)

=453.733, p < 0.001; Hosmer and Lemeshow goodness of fit χ2=5.185, p = 0.738) and overall

correctly classified 92.6% of the sample (correctly classified 99.0% of cases of good or beyond

health status and correct classified 28.2% of cases of dysfunctions).


Limitations to the Models


       Good Health Status of Jamaicans [ie Model (4)], elderly [ie Model (5)], middle age adults

[ie Model (6)], and young adults [ie Model (7) are derivatives of Model (3). Good Health

Status[ie Model (4) – Model (7)] cannot be distinguished and tested over different time periods,

person differential, and these are important components of good health.



       Ht = f(Ht-1, Rt, Pmc, EDi, MSi, HIi, SSi,ARi, Xi, Σ(NPi,PPi), Mi,Ni, εi)...………………………..... Model (4)


       Ht = f(Ht-1, EDi, MSi, HIi, ,ARi, Xi, Σ(PPi), Mi,Ni, εi)...………………………………………..... Model (5)


       Ht = f(Ht-1, Pmc, Eni, HIi, Xi, Σ(NPi),Ni, εi)....................................……………………………..... Model (6)

       Ht = f(Ht-1, Wi, HIi, Σ(NPi), εi).......................................................……………………….…….......Model (7)

       Ht = f(Ht-1,Pmc, EDi, Rt, At, Qt, HHt, Ci, Eni, MSi, HIi, HTi, SSi, LLi,Xi, CRi, Di, Oi,
       Σ(NPi,PPi), Mi,Ni, FSi, Ai, Wi,εi)……………………………………………………………………….. Model (3)




       The current work is a major departure from Grossman’s theoretical model as he assumed

that factors affecting good health Status over the life course are the same, this study disagreed

with this fundamental assumption. This study revealed that predictors of good health status are

not necessarily the same across the life course, and differently from that of the general populace.

Despite those critical findings, healthy time gained can increase good health status directly and
                                                                                                                        181 

 
indirectly but this cannot be examined by using a single cross-sectional study. Health does not

remain constant over any specified period, and to assume that this is captured in age is to assume

that good or bad health change over year (s). Health stock changes over short time intervals, and

so must be incorporated within any health model.


       People are different even across the same ethnicity, nationality, next of kin and

socialization. This was not accounted for in the Grossman’s or the current work, as this is one of

the assumptions. Neither Grossman’s study nor the current research recognized the importance of

differences in individuals owing to culture, socialization and genetic composition. Each

individual’s is different even if that person’s valuation for good health Status is the same as

someone else who share similar characteristics. Hence, a variable P representing the individual

should be introduced to this model in a parameter α (p). Secondly, the individual’s good (or bad)

health is different throughout the course of the year and so time is an important factor. Thus, the

researcher is proposing the inclusion of a time dependent parameter in the model. Therefore, the

general proposition for further studies is that the function should incorporate α (p, t) a parameter

depending on the individual and time.


       An unresolved assumption of this work which continues from Grossman’s model is that

people choose health stock so that desired health is equal to actual health. The current data cannot

test this difference in the aforementioned health status and so the researcher recommends that

future study to account for this disparity so we can identify factors of actual health and difference

between the two models.


Discussions



                                                                                                 182 

 
       This study has modelled current good status of Jamaicans. Defining health into two

categories (ie good – not reported an acute or illness; or poor – reported illness or ailment), this

study has found that using logistic regression health status can be modeled for Jamaicans. The

findings revealed that the probability of predicting good health status of Jamaicans was 0.789,

using eleven factors; and that approximately 86% of the data was correctly classified in this study.

Continuing, in Model (4) approximately 98% of those who had reported good health status were

correctly classified, suggesting that using logistic regression to examine good health status of the

Jamaican population with the eleven factors that emerged is both a good predictive model and a

good evaluate or current good health status of the Jamaican population. This is not the first study

to examine current good health status or quality of life in the Caribbean or even Jamaica [6, 21-

23, 26], but that none of those works have established a general and sub-models of good health

over the life course.


       In Hambleton et al’s work, the scholars identified the factors (ie historical, current, life

style, diseases) and how much of health they explain (R2=38.2%). However, they did not examine

the goodness of fit of the model or the correctness of fit of the data. Bourne’s works [12,13] were

similar to that of Hambleton et al’s study, as his study identified more factors (psychological

conditions; physical environment, number of children or males or females in household and social

support) and had a greater explanatory power (adjusted r square = 0.459) but again the goodness

of fit and correctness of fit of the data were omitted. Again this was the case in Hutchinson et al.’s

research.


       Like previous studies in the Caribbean that have examined health status [6, 21-23, 26],

those conducted by the WHO and other scholars [27-32] did not explore whether social

                                                                                                  183 

 
determinants of health vary across the life course. Because this was not done, we have assumed

that the social determinants are the same across the life. However, a study by Bourne and

Eldemire-Shearer [33] introduced into the health literature that social determinants differ across

social strata for men. Such a work brought into focus that there are disparities in the social

determinants of health across particular social characteristic and so researchers should not

arbitrarily assume that they are the same across the life course. While Bourne and Eldemire-

Shearer’s work [33] was only among men across different social strata in Jamaica (poor and

wealthy), the current study shows that there are also differences in social and psychological

determinants of health across the life course.


       The current study has concluded that the factors identified to determine good health status

for elderly, had the lowest goodness of fit (approximately 68%) while having the greatest

explanatory power (R2= 35%). The findings also revealed low explanatory powers for young

adults (R2=22.6%) and middle age adults (R2=23%), with latter having a greater goodness of fit

for the data as this is owing to having more variables to determine good health. Such a finding

highlights that we know more about the social determinants for the elderly than across other age

cohorts (middle-aged and young adults). And that using survey data for a population to ascertain

the social determinants of health is more about those for the elderly than across the life course of

a population.


       Another important finding is of the eleven factors that emerge to explain good health

status of Jamaicans, when age cohorts were examine it was found that young adults had the least

number of predictors (ie health insurance, social class and negative affective psychological

conditions). This suggests that young adult’s social background and health insurance are

                                                                                                184 

 
important factors that determine their good health status and less of other determinants that affect

the elderly and middle age adults. It should be noted that young adult is the only age cohort with

which social standing is a determinant of good health. Even though the good health status model

that emerged from this study is good, the low explanatory power indicates that young adults are

unique and further study is needed on this group in order to better understand those factors that

account for their good health. Furthermore, this work revealed that as people age, the social

determinants of health of the population are more in keeping with those of the elderly than at

younger ages. Hence, the social determinants identified by Grossman [9], Smith and Kington [10]

and purported by Abel-Smith [11] as well as the WHO [27] and affiliated researchers [28-32] are

more for the elderly population than the population across the life course.


Conclusions

There are disparities in the social determinants of health across the life course, which emerged

from the current findings. The findings are far reaching and can be used to aid policy formulation

and how we examine social determinants of health. Another issue which must be researched is

whether there are disparities in social determinants of health based on the conceptualization and

measurement of health status (using self-reported health, and health conditions).


Disclosures


The author reports no conflict of interest with this work.


Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica
Survey of Living Conditions (JSLC), none of the errors in this paper should be ascribed to the
Planning Institute of Jamaica (PIOJ) and/or the Statistical Institute of Jamaica (STATIN), but to
the researcher.
                                                                                                185 

 
Acknowledgement

The author thanks 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 (2002 JSLC) available
for use in this study, and the National Family Planning Board for commissioning the survey.




                                                                                         186 

 
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                                                                                          189 

 
Table 7.1: Good Health Status of Jamaicans by Some Explanatory Variables
                                                                                                      CI (95%)
                                                                      Wald
                                                                     statistic             Odds
    Variable                                                                       P       Ratio
                                          Coefficient   Std Error.                                  Lower    Upper
      Middle Quintile                           -0.03        0.10        0.09      0.764     0.97     0.81    1.17
      Two Wealthiest Quintiles                  -0.11        0.10        1.26      0.261     0.90     0.74    1.09
      Poorest-to-poor Quintiles*

      Retirement Income                         -0.38        0.17        4.88      0.027     0.68    0.49     0.96
      Household Head                             0.17        0.29        0.37      0.543     1.19    0.68     2.08
      Logged Medical Expenditure                -0.05        0.02        5.10      0.024     0.95    0.91     0.99
      Average Income                            0.00         0.00        1.56      0.212     1.00    1.00     1.00
      Average Consumption                       0.00         0.00        0.16      0.689     1.00    1.00     1.00
      Environment                               0.01         0.07        0.02      0.891     1.01    0.88     1.16
      Separated or Divorced or Widowed          -0.97        0.10       87.36      0.000     0.38    0.31     0.46
      Married                                   -0.55        0.08       53.05      0.000     0.58    0.50     0.67
      Never married*

      Health Insurance                          -3.31        0.12     776.64       0.000     0.04    0.03     0.05

      Other Towns                                0.21        0.08        6.64      0.010     1.24    1.05     1.46
      Urban Area                                -0.01        0.13        0.00      0.952     0.99    0.78     1.27
      Rural Area*

      House Tenure - Rent                       -1.08        0.88        1.48      0.224     0.34    0.06     1.93
      House Tenure - Owned                      -0.42        0.55        0.58      0.447     0.66    0.23     1.93
      House Tenure- Squatted*

      Secondary Education                       0.31         0.08       15.81      0.000     1.36    1.17     1.58
      Tertiary Education                        0.71         0.17       18.09      0.000     2.03    1.45     2.82
      Primary and below*

      Social Support                            -0.17        0.07        6.33      0.012     0.85    0.75     0.96
      Living Arrangement                        -0.06        0.13        0.20      0.659     0.95    0.73     1.22
      Crowding                                  -0.01        0.04        0.08      0.772     0.99    0.91     1.07
      Land ownership                            -0.07        0.07        0.90      0.342     0.93    0.81     1.08
      Gender                                    0.39         0.07       28.67      0.000     1.48    1.28     1.71
      Negative Affective                        -0.04        0.01       14.96      0.000     0.96    0.94     0.98
      Positive Affective                        0.07         0.01       26.26      0.000     1.08    1.05     1.11
      Number of males in household              0.14         0.04       13.36      0.000     1.15    1.07     1.24
      Number of females in household            0.06         0.04        2.36      0.124     1.06    0.98     1.14
      Number of children in household           0.17         0.03       29.16      0.000     1.19    1.12     1.27
      Constant                                  1.89         0.65        8.31      0.004     6.59
χ2 (27) =1860.639, p < 0.001; n = 8,274
-2 Log likelihood = 6331.085
Hosmer and Lemeshow goodness of fit χ2=4.703, p = 0.789.
Nagelkerke R2 =0.320
Overall correct classification = 85.7% (N=7,089)
Correct classification of cases of good or beyond health status =98.3% (N=6,539)
Correct classification of cases of dysfunctions =33.9% (N=550); *Reference group

                                                                                                                 190 

 
Table 7.2: Good Health Status of Elderly Jamaicans by Some Explanatory Variables
                                                    Std         Wald                   Odds
                                    Coefficient     Error      statistic       P       Ratio        CI (95%)
                                                                                                 Lower     Upper
    Middle Quintile                        -0.10      0.15          0.47       0.495      0.90      0.67     1.22
    Two Wealthiest Quintiles                0.12      0.17          0.47       0.491      1.12      0.81     1.56
    Poorest-to-poor quintiles

    Retirement Income                      -0.22      0.22          1.00       0.317      0.81      0.53        1.23
    Household Head                          0.89      0.65          1.86       0.172      2.44      0.68        8.76
    Logged Medical Expenditure             -0.06      0.04          2.16       0.142      0.95      0.88        1.02
    Average Income                          0.00      0.00          0.93       0.335      1.00      1.00        1.00
    Environment                            -0.16      0.12          1.80       0.180      0.86      0.68        1.08

    Separated or Divorced or
                                           -0.49      0.15         11.00       0.001      0.61      0.46        0.82
    Widowed
    Married                                -0.33      0.15          4.82       0.028      0.72      0.54        0.97
    Never married*
                                           -3.35      0.22       241.88        0.000      0.04      0.02        0.05
    Health Insurance

    Other Towns                             0.33      0.14          5.32       0.021      1.39      1.05        1.83
    Urban                                   0.40      0.21          3.48       0.062      1.49      0.98        2.27
    Rural areas*

    House tenure - rented                 -20.37   40192.9          0.00       1.000      0.00      0.00
    House tenure - owned                    1.22      1.24          0.96       0.327      3.38      0.30       38.60
    House tenure – squatted*

    Secondary Education                    -0.46      0.11         16.06       0.000      0.63      0.51        0.79
    Tertiary Education                      0.81      0.35          5.45       0.020      2.26      1.14        4.47
    Primary or below*

    Social support                         -0.08      0.11          0.47       0.495      0.93      0.75        1.15
    Living arrangement                      0.26      0.18          2.11       0.146      1.30      0.91        1.84
    Crowding                               -0.05      0.09          0.29       0.593      0.95      0.80        1.14
    Landownership                           0.17      0.13          1.72       0.190      1.19      0.92        1.54
    Gender                                  0.47      0.12         14.67       0.000      1.60      1.26        2.04
    Negative Affective                     -0.03      0.02          1.97       0.160      0.97      0.94        1.01
    Positive Affective                      0.07      0.02          9.26       0.002      1.07      1.03        1.12
    Number of male                          0.18      0.07          6.75       0.009      1.19      1.04        1.36
    Number of females                       0.05      0.07          0.49       0.485      1.05      0.91        1.21
    Number of children                      0.22      0.06         12.09       0.001      1.24      1.10        1.40
    Constant                               -1.32      1.44          0.83       0.362      0.27
χ2 (27) =595.026, p < 0.001; n = 2,002
-2 Log likelihood = 2,104.66
Hosmer and Lemeshow goodness of fit χ2=5.736, p = 0.677.
Nagelkerke R2 =0.347
Overall correct classification = 75.5% (N=1.492)
Correct classification of cases of good or beyond health status =94.6% (N=1,131)
Correct classification of cases of dysfunctions =44.7% (N=361);
*Reference group


                                                                                                       191 

 
Table 7.3: Good Health Status of Middle Age Jamaicans by Some Explanatory Variables
                                                   Std        Wald                 Odds
                                  Coefficient      Error     statistic     P       Ratio       CI (95%)
                                                                                            Lower    Upper
    Middle Quintile                      0.03         0.15       0.04     0.834      1.03     0.76     1.40
    Two Wealthiest Quintiles            -0.29         0.15       3.67     0.055      0.75     0.56     1.01
    Poorest-to-poor Quintiles*

    Retirement Income                   -0.57         0.36       2.44     0.119      0.57     0.28        1.16
    Household Head                       0.50         0.45       1.24     0.265      1.66     0.68        4.01
    Logged Medical Expenditure          -0.09         0.04       6.44     0.011      0.91     0.85        0.98
    Average Income                       0.00         0.00       0.53     0.465      1.00     1.00        1.00
    Environment                          0.31         0.12       7.41     0.006      1.37     1.09        1.71

    Separated or Divorced or
    Widowed                             -0.20         0.23       0.77     0.380      0.82     0.53        1.28
    Married                             -0.18         0.11       2.68     0.102      0.84     0.68        1.04
    Never married*

    Health Insurance                    -3.04         0.17    320.76      0.000      0.05     0.03        0.07

    Other Towns                          0.11         0.12       0.75     0.387      1.11     0.87        1.42
    Urban                               -0.01         0.19       0.00     0.963      0.99     0.68        1.44
    Rural areas*

    House tenure - rented               17.94    20029.78        0.00     0.999               0.00
    House tenure - owned                -1.33        1.12        1.43     0.232      0.26     0.03        2.35
    House tenure – squatted*

    Secondary education                  0.19         0.13       2.11     0.146      1.20     0.94        1.55
    Tertiary education                   0.34         0.23       2.23     0.135      1.41     0.90        2.21
    Primary or below*

    Social support                      -0.08         0.10      0.57      0.450      0.93     0.76        1.13
    Living Arrangement                  -0.19         0.21      0.87      0.351      0.83     0.55        1.24
    Crowding                            -0.05         0.06      0.65      0.419      0.95     0.85        1.07
    Landownership                       -0.13         0.11      1.47      0.226      0.88     0.71        1.08
    Gender                               0.51         0.11     21.41      0.000      1.66     1.34        2.06
    Negative Affective                  -0.08         0.02     24.66      0.000      0.92     0.90        0.95
    Positive Affective                   0.05         0.02       4.51     0.034      1.05     1.00        1.10
    Number of males in house             0.03         0.06       0.23     0.630      1.03     0.92        1.14
    Number of female in house            0.08         0.06       2.09     0.149      1.08     0.97        1.21
    Number of children in house          0.10         0.04       5.47     0.019      1.11     1.02        1.21
    Constant                             3.29         1.25       6.89     0.009     26.77
χ2 (27) =547.543, p < 0.001; n = 3,799
-2 Log likelihood = 2,776.972
Hosmer and Lemeshow goodness of fit χ2=4.318, p = 0.827.
Nagelkerke R2 =0.230
Overall correct classification = 87.2% (N=3,313)
Correct classification of cases of good or beyond health status =98.3% (N=3,143)
Correct classification of cases of dysfunctions =28.2% (N=170);
*Reference group




                                                                                                             192 

 
Table 7.4: Good Health Status of Young Adults Jamaicans by Some Explanatory Variables
                                                                                                       CI (95%)
                                                               Wald                       Odds
                                   Coefficient   Std Error    statistic       P           Ratio     Lower   Upper

    Middle Quintile                     -0.06         0.19        0.10            0.747      0.94    0.65     1.37
    Two Wealthiest Quintiles            -0.59         0.18       11.10            0.001      0.55    0.39     0.78
    Poorest-to-poor quintiles*

    Household Head                      -0.25         0.39        0.41            0.520      0.78    0.36     1.68

    Logged Medical Expenditure           0.01         0.04        0.09            0.760      1.01    0.93     1.10
    Average Income                       0.00         0.00        3.29            0.070      1.00    1.00     1.00
    Environment                         -0.03         0.13        0.04            0.840      0.97    0.75     1.26
    Health Insurance                    -3.73         0.21     321.51             0.000      0.02    0.02     0.04

    Other Towns                          0.23         0.15        2.42            0.120      1.26    0.94     1.69
    Urban                               -0.05         0.18        0.07            0.788      0.95    0.68     1.34
    Rural area*

    Secondary education                 -0.06         0.41        0.02            0.886      0.94    0.43     2.09
    Tertiary education                  -0.39         0.47        0.70            0.405      0.68    0.27     1.69
    Primary and below*

    Social support                      -0.14         0.13        1.22            0.269      0.87    0.68     1.12
    Crowding                             0.04         0.06        0.65            0.420      1.05    0.94     1.16
    Gender                               0.19         0.15        1.60            0.206      1.20    0.90     1.60
    Negative Affective                  -0.04         0.02        4.22            0.040      0.96    0.93     1.00
    Positive Affective                   0.07         0.03        6.81            0.009      1.07    1.02     1.13

    Number of males in house             0.13         0.07        3.67            0.055      1.13    1.00     1.29

    Number of females in house           0.06         0.06        0.87            0.351      1.06    0.94     1.20


    Married                              0.08         0.22        0.13            0.717      1.09    0.70     1.68
    Never married*

    Constant                             2.75         0.67       16.62            0.000     15.57
χ2 (19) =453.733, p < 0.001; n = 4,174
-2 Log likelihood = 2,091.88
Hosmer and Lemeshow goodness of fit χ2=5.185, p = 0.738.
Nagelkerke R2 =0.226
Overall correct classification = 92.6% (N=3,864)
Correct classification of cases of good or beyond health status =99.0% (N=3,757)
Correct classification of cases of dysfunctions =28.2% (N=107);
*Reference group




                                                                                                            193 

 
                                     Chapter 8
                      Sociomedical Public Health in Jamaica


                                     Paul Andrew Bourne
An extensive review of health and health care-seeking behaviour studies revealed that studies
that have examined health care-seeking behaviour and/or health status have used a piecemeal
approach by either investigating health or health care-seeking behaviour. The current research
seeks to examine (1) demographic characteristics of health care-seekers; (2) sociomedical
characteristics of health status; (3) factors that account for health status; (4) factors that explain
health care-seeking behaviour and (5) characteristics of those who reported having been
diagnosed with particular health conditions. The current study used a sample of 6,783
respondents. The survey was drawn using stratified random sampling. An administered
questionnaire was used to collect the data, which were stored and analyzed using SPSS for
Windows 16.0 (SPSS Inc; Chicago, IL, USA). Logistic regressions were used to established (1)
health status and (2) health care-seeking behaviour model. Two-thirds of the variability in health
status was accounted for medical factors such as self-reported illness and length of illness
compared to one-third by social conditions. Four variables emerged as statistically significant
correlates of self-reported health care-seeking behaviour: self-reported illness, (OR = 358.31,
95% CI = 233.31, 550.30); health status, (OR = 0.46, 95% CI = 0.31, 0.67); health insurance
coverage, (OR = 1.74, 95% CI = 1.26, 2.40); age, (OR = 1.01, 95% CI = 1.00, 1.01); and per
capita consumption, (OR = 1.00, 95% CI = 1.00, 1.00). The problems which must be addressed
by public health policy makers are how to address the high percent of Jamaicans who are current
diagnosed with chronic illness (i.e. 43 %) as well the fact that even children are now diagnosed
with diabetes mellitus, and use the social conditions to improve health and quality of life of
Jamaica.




Introduction


The discipline of public health unlike medicine relies on individuals’ perceptions, beliefs,

customs, idiosyncrasies, culture and practices in order to improve health and quality of life and

                                                                                                  194 

 
not merely an understanding the aetiology of diseases. Public health is, therefore, left with the

arduous task of comprehending the human experiences and practices, and using them to enhance,
                                                                                   [1]
modify and change peoples’ unhealthy behavioural lifestyle. Although Albert              opined that

public health can improve health and quality of life of older people, this also extends to all

peoples. In 2005, the Pan-American Journal of Public Health had an exclusive issue which
                                                                                                      [2]
examined health, well-being, ageing, and proposed a framework for public health action.

Public health can only enhance health and quality of life if it understands the people it serves,

and this denotes that its programmes will only be effective if they are supported by sociomedical

research (including epidemiologic inquiry) on national and sub-national populations.


       While peoples’ behaviours share some general similarities across geopolitical boundaries,

a case can also be made equally about the dissimilarities, inequalities and socio-economic

differences in and among people within the same nation. Those similarities and differences are

responsible for the thrust to study and document information on particular phenomena in order to

effectively implement public health programmes that will address the weaknesses, inequalities,

deficiencies and challenges of people. It is for this reason why much information have been
                                                                                              [3-5]
collected and documented on chronic diseases, mortality, disability and health care cost              as

these pose a challenge to the healthy life expectancy of humans.


       The present body of knowledge on mortality, morbidities and disability in the world [3-10],

and in particular the Caribbean, owes much too continuous biomedical research. But by simply

understanding the aetiology of diseases does not mean that technology and medicine can

eradicate the presence of diseases in humans, without an understanding of the social aspects of

the targeted group. Peoples’ beliefs, customs, perception and biases pose a challenge to public

                                                                                                  195 

 
health from attaining its mandate because beliefs guide practices.[11] Within the context that

humans’ perspective is important in science and public health, without an understanding of their

image on things, it will be impossible for medicine and the natural sciences to effectively address

medical conditions that are deemed public health problems.


       Population health and population health in transition is each a function of social,

environment, psychological and biomedical conditions, and not only disease composition and

history. It is for this very rationale why public health must rely on sociomedical research and
                      [12-14]
good quality data.              Hence, this is a justification for researchers’ continuous mode of

investigation of phenomena in order to understand issues experienced by humans. The Caribbean

is no different from the rest of the world in this regards, and this provide some explanation why

Caribbean scholars, the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of

Jamaica (STATIN) continue to embark on social research which include health, lifestyle
                                  [13-20]
practices and data quality                  in order to aid public health practitioners to effectively

understand phenomena and address changes in peoples’ behaviour.


       Pappaioanou et al. [12] forwarded a perspective that the capacity of evidence-based public

health must be strengthened in developing countries in order to identified priority health

problems, respond to public health crises, implement effect strategies and evaluate cost effective

interventions. This therefore justifies PIOJ and STATIN, Wilks et al and Bourne’s continuous

examination of self-reported health, lifestyle of people of Caribbean people in order to set the

platform of public health programmes. Books have been dedicated to ‘Health issues in the

Caribbean,’ ‘Equity and Health’, and ‘Investment in Health’ in Latin American and the
            [21-24]
Caribbean         , but none of those text or other studies in the region, and in particular Jamaica,

                                                                                                  196 

 
have examined in a single research factors that explain health status and health care seeking

behaviour as well as health conditions and the disparities by socioeconomic conditions. An

extensive review of health and health care-seeking behaviour revealed that studies that have

examined health care-seeking behaviour or health status have used a piecemeal approach by

either investigating health, health care-seeking behaviour [25-36] or health conditions. The current

research bridge the gap by examining (1) demographic characteristics of health care-seekers; (2)

sociomedical characteristics of health status; (3) factors that account for health status; (4) health

conditions; (5) factors that explain health care-seeking behaviour and (6) characteristics of those

who reported having been diagnosed with particular health conditions.


Materials and methods
Method

The current study used a sample of 6,783 respondents. The sample was drawn from a large
                                                                        [37]
nationally representative cross-sectional survey of 6,783 Jamaicans.           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 a strata of equal size

based on dwellings (EDs). Pursuant to the PSUs, a listing of all the dwellings was made, and

this became the sampling frame from which a Master Sample of dwellings was compiled, which

in turn provided the sampling frame for the labour force. One third of the 2007 Labour Force

Survey (i.e. LFS) was selected for the survey.


                                                                                                 197 

 
       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. An 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) and independent sample t-test were used to examine 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 [38] were used to examine the goodness of fit

of the model. The correlation matrix was examined in order to ascertain whether autocorrelation

                                                                                            198 

 
(or multicollinearity) existed between variables. Based on Cohen & Holliday [39] 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. The correlation matrix was examined in order to

ascertain if autocorrelation or collinearity existed between variables. Where collinearity existed

(r > 0.7), variables were entered independently into the model to help determine which one must

retained during the final model construction (the decision was based on the variable’s

contribution to the predictive power of the model and the goodness of fit) [40].

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 [35,41] 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
                      [33,34,36]
outside of Jamaica.                This approach allowed for the analysis of a number of variables

simultaneously.    Secondly, the dependent variable is a binary dichotomous one and this

statistical 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 that independently

predict the outcome. A p-value of 0.05 was used for all tests of significance.


Measure

Age is a continuous variable which is the number of years alive since birth (using last birthday)

                                                                                               199 

 
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: “Have you had an

illness such as influenza, asthma et cetera in the past 4-week?”


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) 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); middle class was quintile 3 and poor those in

lower quintiles (quintiles 1 and 2).


Results

                                                                                              200 

 
Table 8.1 presents information on the demographic characteristic of the sample by area of

residence. The sample was 6 782 respondents: 48.7% males and 51.3% females. Based on Table

8.1, 34.2% of urban residents were in the wealthiest 20% compared to 24.1% of those in the peri-

urban and 10.4% in rural areas. On the other hand, poverty was substantially a rural phenomenon

(29.8%) compared to peri-urban (11.5%) and urban areas (9.3%). There is a significant statistical

association between medication purchased by respondents and area of residence. Twenty percent

of respondents who attended public health-care facilities purchased medication, while 81.4% of

those who visited private health-care facilities purchased medication. Twenty-five percent

(25.2%) of those in rural area who attended public health-care facilities purchased medication

compared to 13.6% of those in peri-urban and 14.2% of those in urban areas. Almost ninety-one

percent (90.5%) of those in urban area who visited private health-care facilities purchased

medication compared to 86.8% of peri-urban and 74.7% of rural residents. Rural residents

reported the most illness (16.6%) compared to urban (13.4%) and peri-urban respondents

(12.9%).

       Eighty percent (82.2%) of the respondents indicated at least good health status (with

37.0% said excellent health status) compared to 0.8% who claimed very poor health status. One

percent (1.1%) of the sample was injured in the 4-week period of the survey, while 14.9% was

reported an illness and 43.2% indicated a chronic illness (i.e. Diabetes mellitus, 13.8%;

hypertension, 23.1%; and arthritis, 6.2%) compared to 30.4% reported acute illness (influenza,

16.7%; diarrhoea, 3.0%; and asthma, 10.7%). Almost 66% (i.e. 65.5%) of the sample visited a

health care practitioner (i.e. doctor, nurse, healer, pharmacist) in the 4-week period of the survey;

29.6% was heads of households; married, 23.3%; never married, 69.2%; divorced, 1.7%;

separated, 0.9%; widowed, 4.9%; and the median number of person per room was 4 (range = 1,

                                                                                                 201 

 
17). The median annual income was USD 7 050.66 (range = USD 261.56, USD 6 523.66) and

median per capita consumption was USD 1 523.88 (range = USD 179.57, USD 20 325.55).

          Table 8.2 highlights information on sociomedical characteristics of sample by sex of

respondents. Males were more likely to be married (24.3%) than females (22.4%), and the latter

was more likely to be widowed (7.3%) than the former (2.3%). Females reported more illnesses

(17.5%) than males (12.1%), and they were more likely to have hypertension and diabetes

mellitus than males. However, there was no significant statistical relationship between health

care utilization and sex of respondents: males, 62.3% and females, 67.6% (χ2 = 3.004, P <

0.083).

          There was no significant statistical relationship between those who purchased medication

and their sex. Twenty percent (20.2%) of females who visited public health care facilities

purchased medication compared to 19.7% of males (χ2 = 0.023, P = 0.879). Eight-one percent of

females who attended private health-care facilities purchased medication compared to 81.9% of

males (χ2 = 0.100, P = 0.752). Males were more likely to be household heads (32.7%) than

females (26.7%) - χ2 = 29.207, P < 0.0001.

          When the significant statistical association between marital status and social standing was

disaggregated by sex, this was explained by females (χ2 = 54.48, P = 0.0001) and not males (χ2 =

24.77, P = 0.074). Almost 49% (48.8%) of divorced females were in the wealthiest 20%

compared to 27.0% of those who are married, 21.8% of widowed; 20.0% of separated as well as

those who were never married respondents.

          Table 8.3 shows sociomedical characteristics of sample by marital status. Divorced

respondents were most likely to be in the wealthiest 20% (44.2%) compared to separated

respondents (31.7%); married, 28.3%; never married, 21.8% and widowed respondents, 21.4%

                                                                                                 202 

 
(χ2 = 67.45, P < 0.0001). Forty percent of the widowed respondents indicated an illness

compared to those who are separated, 29.3%; divorced, 28.6%; married, 24.6% and never

married, 10.8%.

       A significant statistical association was found between area of residents and those who

attend public hospitals (χ2 = 7.94, P < 0.019), private hospitals (χ2 = 30.30, P < 0.0001), and

private health care centres (χ2 = 10.19, P < 0.006), while no between area of residents and public

health care centres (χ2 = 4.23, P < 0.13). Rural residents were most likely to visit public hospitals

(37.2%) compared to urban (27.7%) and peri-urban residents (25.6%). With respect to private

hospital utilization, rural residents recorded the least visits (2.3%) than peri-urban (6.8%) and

urban residents (15.0%). Similarly, rural dwellers recorded the least utilization of private health

care centres (46.2%) than urban (52.7%) and peri-urban residents (63.2%).

       Rural dwellers recorded the longest time spent in illness (56.6 days ± 169.3) compared to

urban dwellers (9.6 days ± 17.9) and peri-urban residents (53.3 days ± 154.4) – F-statistic = 9.58,

P < 0.0001.

       There is a significant statistical association between area of residence and educational

levels (χ2=78.02, P < 0.0001). Sixty-eight percent of those with tertiary level education dwelled

in urban areas compared to 16% of those in peri-urban and 20.6% in rural areas.

       No significant statistical association was found between social class and self-reported

illness (χ2=3.28, P < 0.512) as well as between self-reported diagnosed health conditions and

social class (χ2=28.6, P < 0.236).

       Figure 8.1 highlights information on self-reported diagnosed illness by marital status of

respondents disaggregated by sex of respondents. A significant statistical association was found

between self-reported diagnosed illness by marital status even when the data was disaggregated

                                                                                                 203 

 
by sex (male – χ2 = 52.43, P < 0.001; females - χ2 = 56.2, P < 0.0001), but the relationship was

strong for males (contingency coefficient = 0.425) than females (contingency coefficient =

0.339).

          A significant statistical relationship existed between self-reported diagnosed health

conditions and age group (χ2 = 436.8, P < 0.0001). Younger people were more likely to have

acute conditions and older people are likely to have chronic conditions (Figure 8.2). Despite this

fact 1.4% of Jamaica children have diabetes mellitus.


          Table 8.5 examines factors that are correlated with self-evaluated health status of

Jamaicans. Of the 13 variables that were tested in the model, 9 emerged as statistically correlated

with health status and that the model was a good fit for the data (Hosmer and Lemeshow

goodness of fit χ2=18.49 (8), P = 0.78; -2LL = 3321.07). The model (i.e. 9 significant correlates

of self-evaluated health status) accounted for 40.3% of the variability in self-reported health

status: 84.8% of the data were correctly classified, 95.3% of those in good or excellent self-

evaluated health status and 47.5% of those in fair to poor health status. Two-thirds of the

variability in health status was accounted for medical factors such as self-reported illness and

length of illness compared to one-third by social factors (i.e. age, sex, per capita consumption,

health care-seeking behaviour, area of residence, marital status and social class). Of the social

factors, consumption accounted for less than 1% of the variance in self-evaluated health status

(i.e. 0.5%) and social class accounted for 0.1%.

          Table 8.6 presents information on the self-reported health care-seeking behaviour of

respondents by explanatory variables. Four variables accounted for 71.1% of the variability in

self-reported health care-seeking behaviour. Using logistic regression analyses, 4 variables

emerged as statistically significant correlates of self-reported health care-seeking behaviour: self-
                                                                                                 204 

 
reported illness, (OR = 358.31, 95% CI = 233.31, 550.30); health status, (OR = 0.46, 95% CI =

0.31, 0.67); health insurance coverage, (OR = 1.74, 95% CI = 1.26, 2.40); age, (OR = 1.01, 95%

CI = 1.00, 1.01); and per capita consumption, (OR = 1.00, 95% CI = 1.00, 1.00). From the

correlation matrix, there is a moderate statistical correlation between self-reported health illness

and self-evaluated health status (r = 0.64).

Discussion

The current study revealed that 29.8% of rural residents were in the poorest 20% (i.e. poorest

income quintile) in Jamaica compared to 11.5% of peri-urban and 9.3% of urban residents. Rural

poverty lies between 2.5 to 3.3 times more than peri-urban and urban poverty, and 1.3 times

more people report illness in those areas than in peri-urban or urban areas. Rural residents are not

only rural and report more illness than other residents; they are 1.9 times less likely to have

health insurance coverage than urban residents and 1.5 times less likely than peri-urban dwellers.

They are also more likely to utilize public hospitals and spent more time nursing in illness than

other residents, and also had the least consumption per person. However, their self-evaluated

health status was the same as urban dwellers but less than that for peri-urban settlers, and there

was no significant statistical correlation among health care-seekers based on their area of

residences. Concurrently, males were more likely to record greater moderate-to-excellent health

status than females; more likely to be married; less likely to be widowed; less likely to report an

illness; less likely to have diabetes mellitus and hypertension; more likely to have asthma,

arthritis; unspecified conditions and influenza than females. Irrespective of the more female than

males reporting having been diagnosed with chronic conditions, there was no significant

correlation between health care seeking behaviour and sex of respondents. The findings

continued as those in the wealthiest 20% were more likely to be divorced people; but those who
                                                                                                205 

 
were classified as divorced, separated and widowed were less likely to be healthier than those

who were never married. Those who were never married reported the lowest percent of having

had an illness in the 4-week period of the survey. Two-thirds of the variability was accounted

for medical factors such as self-reported illness and length of illness compared to one-third by

social factors (i.e. age, sex, per capita consumption, health care-seeking behaviour, area of

residence, marital status and social class). Four variables accounted for 71.1% of the variability

in self-reported health care-seeking behaviour, and self-reported illness accounted for 70% of the

explanatory power. People who reported moderate-to-excellent health status were 55% less

likely to seek health care and those who reported an illness were 358.3 times more likely to seek

health care. Less than one-half percent of the variance in health care-seeking behaviour can be

explained by health insurance coverage, and that an individual who indicated that he/she is ill is

81% less likely to stated moderate-to-excellent health status.

       Public health is influenced by both the continuous revelations in research as well as

science of people’s behaviour in order to effectively plan behaviour modifications. The

behaviour change required for developing countries must be tailored within the context of the

research findings [42], and cannot be left to the dictates of studies on developed nations. Apart of

the justification for studies on a particular geo-political boundary are based on inequalities,

economic and health disparities among and between people within a nation, and this is

particularly in reference to Latin America and the Caribbean.[43-45] With public health taking

must of its cue from both medical and social sciences, there is obviously a rationale for the social

determinants in the study of public health. The some time ago embarked a thrust of examining

social determinants in understanding health, health conditions and health treatment. In recent

years the World Health Organization (WHO) has increasingly drawn attention to the importance

                                                                                                206 

 
of the relationship between health and social conditions in determining the health of individuals

and populations [46]. The social determinants (non-biological factors), produce inequalities in

health and need to be considered in health development. Addressing social determinants and

health policy now includes the basis for political action both nationally and internationally.[47-51]

       The findings of the present work highlights and concur with the literature about the

dominant of the biomedical conditions in health. The findings revealed that two-thirds of

variability in health status can be accounted for by self-reported illness and length of illness.

Although this fact speaks to the dominance of biomedical conditions, it does also recognize the
                                                                                      [36]
importance of social determinants in health. A study by Hambleton et al.                     on elderly

Barbadians found that as much as 88% of the variability in self-reported health status could be

explained by current diseases. While the current work has a lower percent of explanation model

which is due to the sample that include young people, it highlights a rationale for the ease of use

of the biomedical conditions and in the process sideline the need for the social determinants in

health, health utilisation and health treatment. Clearly illnesses are fundamental in the health

discourse, and it is also critical in the understanding health care-seeking behaviour of people.

       In this research, a respondent who is ill is 358.3 times more likely to seek care. This

highlights not only the dominance of illness to health care-seeking, but the image of health that is

held by Jamaica and how this influence outcome. This is supported by the finding that revealed

that people who self-reported their health status to be moderate-to-good were 54% less likely to

seek health care. Embedded in such a finding is structure of the health care delivery in Jamaica,

which dates back to 130ce to 200ce in Ancient Rome, when health and health care was in

keeping with traditional biomedical model that views the exposure to specific pathogen as the

cause of diseases in organisms. Within this image of health was people’s perception of what

                                                                                                   207 

 
constituted a need to demand health care services which were illness and this fashioned the

health care industry at the time. Clearly the image of health and health care delivery in Jamaica is

framed around the aetiology of diseases and the not the multidimensional approach to the image

of health which is in keeping with the broad definition offered by the WHO in 1948. [52] The

overemphasis on illness, disability and severity of illness in framing people’s willing to seek

medical care is not atypical to Jamaica as this was found in other societies.[26-31, 53]
                                                                                           [54]
        Money is well established as being positively correlated with health status.              Money

does matter in access to resources, opportunities, choices and quality of care. The current

findings found that people whose consumption expenditure are higher have a greater health

status, which concurs with the literature that money does matter for health. Money does not only

matter for health, it also is important for health seeking behaviour. Despite the positive of

money, those who are most likely to be in the wealthiest 20% had lower health status. This paper

found that divorced, separated and widowed Jamaicans were more likely to have a lower health

status than those who were never married and this was also the case for the upper class with

reference to the lower class. Although the finding does indicate that divorced and separated

respondents were wealthier than other marital statuses, this is a negative for their health status.

Also embedded in this finding is the fact that significant statistical association between social

standing and marital status was among females. This denotes that wealthiest females were most

likely to be divorced which offers an explanation that money can buy health, psychological

comfort, happiness and these would have been the case for these females in the study. Divorced

therefore provides females with more economic resources, but this does not compensate for the

lost of the spouse, and further removes the benefits of the economic gains from health. In

addition divorced females recorded the highest percent of diabetes mellitus among all

                                                                                                    208 

 
respondents followed by separated women. Hypertension was substantially more among separate

males and widowed females, suggesting that separation from spouse becomes a disbenefit for

Jamaica and therefore account for the unhealthy life style practices which were not identified in

never married and/or married respondents.

       There are obvious benefits from having money as this was evident in rural residents

having the least money, the most self-reported illness, and the highest public hospital utilisation.

Despite the income inequalities and economic disparities between rural and other residents in

Jamaica, the former residents are able to experience a self-reported health status which is the

same of those in the affluent urban areas. This means that there are some basic standard of living

enjoyed by rural Jamaica which cushioned the wide income inequalities that exist between them

and urban dwellers. Apart of what creates the cushion for rural residents is the quality of primary

health care facilities offered to them by public hospitals in the country. With most rural residents

utilizing public hospitals, public health offerings have played a critical role in removing some of

the health inequalities that could have been owing to income inequalities. Another factor which

mitigates the negatives of income inequalities among the different area of residents is the

communal settings in rural areas, and how this aids in providing socio-economic support among

residents.

       Poverty in rural areas is therefore shared by the wider community as people seek to assist

others in need, vulnerable, less fortunate and economic challenged in life. It is this communal

culture that sees sharing of food, finances and social institutions that helps to retard the negative

of poverty from rural residents. The poor are classified as in the lower socioeconomic status. It is

empirically well established in research that they are less likely to be healthy than those in the
                               [55, 56]
higher socioeconomic groups           , which is not the case in Jamaica. They have a greater self-

                                                                                                 209 

 
evaluated health status than those in the higher socioeconomic groups. Concurrent this research

does not concur with the literature that poverty is more common among the chronically ill [57] or

that the poor reported having more illness than the higher socioeconomic class. This was also

highlighted in the fact that rural residents were substantially more likely to poor, but shared the

same health status as those in urban areas. However what emerged from the current findings is

that peri-urban residents had a greater health status than other residents, and this could be due to

the fact that more of them were in the never married group who had the lowest rate of illness as

well as chronic illnesses. Residents in peri-urban area has greater income than those who dwelled

in rural areas but less than those in urban areas which indicates that some money is important in

health, but that is not responsible for greater health. It can be extrapolated from this finding that

peri-urban residents are more involved in healthier lifestyle choices than residents in other

geographic areas, which is accounting for their health more than money and higher formal

education.

       This study uncovers a paradox between subjective health and objective health. The

present work found that males reported less illness, had greater self-evaluated health status, but
                                                                                                   [58, 59]
using statistics on life expectancy in Jamaica females outlive males between 4 to 7 years.

In 1880-1882, Jamaica females outlive males by 2.9 years and in 2002-2004, this was increased

to 5.8 years.[20] For 2007, statistics published by the WHO revealed that this difference was 5

year. [59] This questions the validity of subjective health data in the evaluation of health, and begs
                                                                                          [17]
the question “How valid is subjective health data in Jamaica?” A study by Bourne                 found a

strong statistical correlation between life expectancy at birth for the Jamaicans and self-reported

illness (r = - 0.731); and this association was weaker females (r = - 0.683) than males (r = -

0.796). Hence, there is validity in the use of subjective index to measure health. This suggests

                                                                                                     210 

 
that the afore-mentioned disparity in subjective and objective indexes to measure health is not a

paradox, but an issue which needs further examination.

              The inverse relationship between health and age is long established in research literature
[1, 34, 35]
              as well as the shift from acute to chronic conditions in old ages. [60, 61] Morrison [60] in an

article entitled ‘Diabetes and hypertension: Twin Trouble’ forwarded that diabetes mellitus and

hypertension have now become two problems for Jamaicans and in the wider Caribbean.
                 [61]                              [60]
Callender               concurred with Morrison           that there is a positive association between diabetic

and hypertensive patients (i.e. 50% of individuals with diabetes had a history of hypertension),

and that this is a public health problem in the Caribbean. This study narrows the chronic

conditions to older people, but also noted that 1.4% of children in Jamaica had diabetes mellitus,

3.5% of young adults and 16.4% of other adults. If Callender’s are true then in a short while one-

half of those afore-mentioned individuals will have dual chronic conditions. A recently
                                         [13]
conducted study by Wilks et al.                 provide some historical background to chronic illnesses in

Jamaica as they found that 31% of Jamaicans indicated that their parent and/or grand parents had

diabetes mellitus; 47% said that hypertension, 17.1% strokes and 15.7% said their parents and/or

grandparents had cancer. Diabetes mellitus and hypertension therefore continue to be silent

killers in Jamaica, and their history dates back to former generations. Public health practitioners

need to urgent begin a campaign of lifestyle practices geared towards children as there is

evidence to support healthy lifestyle practices among all groups, and in particular children, who

are frequently, omitted from healthy lifestyle programmes.

              The current study highlighted that there are many inequalities (i.e. systematic, avoidable

and important difference) in health status among Jamaicans and that these need to be rectified in

order to attain the resolution of the World Health Assembly (WHA48.8).[62] Jamaica now has a

                                                                                                           211 

 
primary health care system which is free to all, but this has still not met equity (i.e. unnecessary

and avoidable differences which are considered to be unfair and unjust) in health care throughout

the society. Free health care for all in Jamaica have not addressed issues such as exposure to

unhealthy, stressful living and working conditions; natural selection or related social mobility;

transient health advantage; gender discrimination; socioeconomic discrimination; inequitable

deployment of resources around the nation; and the organization of some health services around

the country. Inequalities and inequities in Latin America and the Caribbean have been

empirically researched by Pan American Health Organization (PAHO), and further readings can
                                              [23, 24]
be had by examining two of its publications              as well as Whitehead. [63] It is clear from the

current findings that merely making primary health care free for all will not reduce many of the

public health challenges in a nation and among its people. So while Jamaica has done the former,

there are obvious signs that reaching the poor with health care does not address many other

health inequalities and inequities. Using statistics for 90 countries, the WHO [59] revealed that in

many of these nations there are health disparities and inequities between and among people

which is concurred by Global Forum for Health Research [64] and this study. This reinforces the

need for public health practitioners not to rely on national averages and information which

originates from within the health sector but on sociomedical determinants on groups and sub-

groups within the population.

Conclusion

Although biomedical conditions accounted for more of health than social determinants, the

current study highlighted the value of the social determinants in the health discourse. The social

issues in this research brought to the fray the fact that separation from one spouse influenced

health status, healthy behaviour and health conditions. It does not cease there as the image of
                                                                                                    212 

 
health is substantially driven by illness which account for seeking (or not seeking) medical care.

In addition to the afore-mentioned issues, there is a clear public health challenge that exists in

Jamaica which is how to address the unhealthy lifestyle practices of people who have been

separated from their spouses as well as the fact that particular health conditions appear to be

associated with particular social characteristics. Another public challenge is how to change the

image of health in Jamaica from illness to wellness or wellbeing. This public health challenge

must commence with the restructuring of the health care system and its delivery which is

primary driven by the biomedical factors instead of holistic health. Increasing attention must be

placed on this reorganization as if the health care is fashioned more around curative care, then

people will use this image of health care to frame their concept of health and health demands.

Some of the disparities that emerged from the current work from the literature highlights the fact

that public health in Jamaica cannot rely on the research findings in other geo-political

boundaries to craft policies and intervention programmes as the will be ineffective in addressing

its mandate owing to the sociodemographic differences of Jamaicans. Public health therefore

must rely on research findings within it geo-political area while understanding what obtains in

other areas in order to embark on intervention programmes that will improve health and quality

of life of people. Within this context, one of the problems which must be addressed by public

health policy makers is how to address the high percent of Jamaicans who are current diagnosed

with chronic illness (i.e. 43 %) as well the fact that even children are now diagnosed with

diabetes mellitus suggesting that public health must embark on programmes that address living

longer and healthier with (and without) chronic illnesses.

       In sum, the inequalities and/or inequities which emerged in this study are social issues

which explain medical conditions and it is this merger of medicine and sociology that is needed

                                                                                              213 

 
to effectively improve the health and quality of life of people. Concurrently, policy makers need

to change the concept of health of Jamaicans and this can be enhanced by (1) leisure and exercise

facilities in communities as well as in health care facilities; (2) reduce the inequalities in working

and living conditions of the vulnerable and disadvantaged groups; (3) address the health-

damaging behaviour of some social groups; (4) administrative reform of professionals in regards

to the dissemination of information to lay people; (5) examine, monitor and evaluate the

implication of health policies on the socioeconomic groups within the society; (6) pollution

control caps; (7) assist in food hygiene, nutrition, sanitation and health education moreso in times

of economic hardships; and (8) commence a databank that collects data on the cultural and

behavioural practices of people in order to effectively formulate health policies. In addition to

the afore-mentioned issues, while the current study is not a representation of the Caribbean,

based on the Pan American Health Organization research on Latin America and the Caribbean

investment in health and health care modernization have not reduced the inequalities and
                                                                               [23, 24]
inequities in nations among different social groups within those nation               , which is what

emerged from the current work. Clearly, health inequalities and inequities in Latin America and

the Caribbean are very much the same, and any public health intervention programmes that do

not address this reality will be ineffective in aiding health and quality of life of its people. Health

protection therefore must be embedded in science of human behaviour (i.e. social determinants)

as well as an understanding of the pathogenesis of diseases (i.e. sociomedical public health).

Conflict of interest

The author has no conflict of interest to report


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                                                                                                   214 

 
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Figure 8.1. Self-reported diagnosed illness by marital status for sex




                                                                        219 

 
Figure 8.2. Self-reported diagnosed illness by age group




                                                           220 

 
Table 8.1. Demographic characteristic by area of residence
                                Area of residence                                              P
Characteristic                  Urban                   Semi-urban      Rural
Social standing                 n (%)                   n (%)           n (%)             χ2 = 881.51
                                                                                          P < 0.0001
 Poorest 20%                     186 (9.3)            167 (11.5)        990 (29.8)
 Poor                            243 (12.1)           273 (18.7)        838 (25.2)
 Middle                          389 (19.4)           312 (21.4)        650 (19.6)
 Wealthy                         499 (24.9)           354 (24.3)        499 (15.0)
 Wealthiest 20%                  685 (34.2)           352 (24.1)        345 (10.4)
Self-reported Injury                                                                      χ2 = 2.25
                                                                                          P = 0.325
  Yes                            16 (0.8)             16 (1.1)          41 (1.3)
   No                            1933 (99.2)          1408 (98.9)       3186 (98.7)
Sex                                                                                       χ2 = 3.67
                                                                                          P = 0.16
 Male                            943 (47.1)           706 (48.4)        1954 (49.8)
 Female                          1059 (52.9)          752 (51.6)        1668 (50.2)
Marital status                                                                            χ2 = 15.46
                                                                                          P = 0.05
 Married                         326 (23.3)           217 (21.6)        513 (24.1)
 Never married                   972 (69.3)           702 (70.0)        1462 (68.7)
 Divorced                        32 (3.2)             23 (2.3)          22 (1.0)
 Separated                       9 (0.6)              12 (1.2)          20 (0.9)
 Widowed                         63 (4.5)             49 (4.9)          112 (5.3)
Self-evaluated illness                                                                    χ2 = 15.43
                                                                                          P < 0.0001
   Yes                           261 (13.4)           183 (12.9)        536 (16.6)
   No                            1690 (86.6)          1231 (87.1)       2688 (83.4)
Self-reported        diagnosed                                                            χ2 = 29.59
illness                                                                                   P = 0.003
   Influenza                     25 (10.9)            44 (26.0)         80 (16.3)
   Diarrhoea                     4 (1.9)              4 (2.4)           19 (3.9)
   Asthma                        33 (14.4)            11 (6.5)          51 (10.4)
   Diabetes mellitus             32 (14.0)            27 (16.0)         64 (13.0)
   Hypertension                  47 (20.5)            41 (24.3)         118 (24.0)
   Arthritis                     16 (7.0)             10 (5.9)          30 (6.1)
   Other                         72 (31.4)            32 (18.9)         130 (26.4)
Health insurance coverage                                                                 χ2 = 138.80
                                                                                          P < 0.0001
 Yes                             542 (28.0)           310 (22.1)        462 (14.5)
  No                             1397 (72.0)          1091 (77.9)       2715 (85.5)
Health        care-seeking                                                                χ2 = 5.21
behaviour                                                                                 P = 0.07
 Yes                             190 (71.2)           119 (63.6)        349 (63.3)
  No                             77 (28.8)            68 (36.4)         202 (36.7)
Consumption per capita (in       2632.57±2040.89      2223.76±1753.22   1499.18±1095.70   F =344.31,           P <
USD)                                                                                      0.0001




                                                                                                        221 

 
Table 8.2. Sociomedical characteristic by sex of respondents
                                         Sex
                                         Male                Female        P
Characteristic
Consumption per capita (in USD)1         2018.20±1712.76 1962.30±1592.01   t =1.39, P = 0.16
                                      1
Total Expenditure (on food) (in USD) 3488.32±2187.43 3616.80±2201.34       t = -2.41, P = 0.016
No. of days in public health care 6.6 ±6.2                   6.0±4.7       t = 0.35, P = 0.73
facilities
No. of days in private health care 5±0                       1±0
facilities
Medical expenditure - public             3.67±17.51          8.36±67.69    t = -1.02, P = 0.31
(in USD)1
Medical expenditure – private            14.05±21.12         14.15±30.58   t = -0.044, P = 0.97
(in USD)1
Self-reported diagnosed illness                                            χ2 = 30.25, P < 0.0001
  Influenza                              69 (20.2)           80 (14.6)
  Diarrhoea                              11 (3.2)            16 (2.9)
  Asthma                                 47 (13.7)           48 (8.8)
  Diabetes mellitus                      31 (9.1)            92 (16.8)
  Hypertension                           58 (17.0)           148 (27.0)
  Arthritis                              24 (7.0)            32 (5.8)
  Other                                  102 (29.8)          132 (24.1)
Social standing                                                            χ2 = 4.35, P = 0.361
  Poorest 20%                            671 (20.3)          672 (19.3)
  Poor                                   640 (19.4)          714 (20.5)
  Middle                                 636 (19.3)          715 (20.6)
  Wealthy                                667 (20.2)          685 (19.7)
  Wealthiest 20%                         689 (20.9)          693 (19.9)
Self-reported Injury                                                       χ2 = 1.68, P = 0.196
   Yes                                   41 (1.3)            32 (0.9)
    No                                   3169 (98.7)         3358 (99.1)
Marital status                                                             χ2 = 61.94, P < 0.0001
  Married                                522 (24.3)          534 (22.4)
  Never married                          1528 (71.1)         1608 (67.4)
  Divorced                               34 (1.6)            43 (1.8)
  Separated                              16 (0.7)            25 (1.0)
  Widowed                                50 (2.3)            174 (7.3)
Self-evaluated illness                                                     χ2 = 38.12, P < 0.0001
  Yes                                    388 (12.1)          592 (17.5)
  No                                     2820 (87.9)         2789 (82.5)
Health care-seeking behaviour                                              χ2 = 3.004, P < 0.083
  Yes                                    253 (62.3)          405 (67.6)
   No                                    153 (37.7)          194 (32.4)
USD 1.00 = Ja $80.47 at the time of the survey
                                                                                        222 

 
Table 8.3. Sociomedical characteristic by marital status of respondents
                                           Marital status                                                            P
Characteristic                             Married         Never          Divorced     Separated   Widowed
                                                           married
Social standing                            n (%)           n (%)          n (%)                                 χ2 = 67.45, P < 0.0001
 Poorest 20%                               153 (14.5)      564 (18.0)     4 (5.2)      9 (22.0)    43 (19.2)
 Poor                                      181 (17.1)      928 (20.0)     6 (7.8)      3 (7.3)     36 (16.1)
 Middle                                    185 (17.5)      633 (20.2)     18 (23.4)    10 (24.4)   58 (25.9)
 Wealthy                                   238 (22.5)      626 (20.0)     15 (19.5)    6 (14.6)    39 (17.4)
 Wealthiest 20%                            299 (28.3)      685 (21.8)     34 (44.2)    13 (31.7)   48 (21.4)
Self-reported Injury                                                                                            χ2 = 2.16, P = 0.71
  Yes                                      16 (1.5)        37 (1.2)       0 (0.0)      1 (2.4)     3 (1.3)
   No                                      1040 (98.5) 3091 (98.8)        77 (100.0)   40 (97.6)   220 (98.7)
Self-evaluated illness                                                                                          χ2 = 233.86, P < 0.0001
 Yes                                       259 (24.6)      338 (10.8)     22 (28.6)    12 (29.3)   90 (40.4)
 No                                        795 (75.4)      2789 (89.2)    55 (71.4)    29 (70.7)   133 (59.6)
Self-reported diagnosed illness                                                                                 χ2 = 75.36, P < 0.0001
 Influenza                                 18 (7.4)        28 (9.2)       1 (4.8)      1 (8.3)     4 (4.5)
 Diarrhoea                                 2 (0.8)         7 (2.3)        1 (4.8)      1 (8.3)     3 (3.4)
 Asthma                                    10 (4.1)        31 (10.2)      2 (9.5)      0 (0.0)     1 (1.1)
 Diabetes mellitus                         48 (19.7)       39 (12.8)      10 (47.6)    4 (33.3)    19 (21.6)
 Hypertension                              91 (37.3)       69 (22.6)      3 (14.3)     5 (41.7)    37 (42.0)
 Arthritis                                 24 (9.8)        22 (7.2)       1 (4.8)      1 (8.3)     8 (9.1)
 Other                                     51 (20.9)       109 (35.7)     3 (14.3)     0 (0.0)     16 (18.2)
Health insurance coverage                                                                                       χ2 = 127.20, P < 0.0001
 Yes                                       357 (34.1)      552 (17.9)     27 (35.1)    8 (19.5)    60 (26.9)
  No                                       691 (65.9)      2528 (82.1)    50 (64.9)    33 (80.5)   163 (73.1)
Health care-seeking behaviour                                                                                   χ2 = 233.86, P < 0.0001
 Yes                                       173 (65.3)      239 (68.1)     15 (68.2)    8 (61.5)    90 (40.4)
  No                                       92 (34.7)       112 (31.9)     7 (31.8)     5 (38.5)    133 (59.6)
Head of household                                                                                               χ2 = 258.12, P < 0.0001
 Yes                                       564 (53.4)      1163 (37.1)    57 (74.0)    27 (65.9)   181 (80.8)
  No                                       492 (46.6)      1973 (62.9)    20 (26.0)    14 (34.1)   43 (19.2)


                                                                                                                                      223 

 
Table 8.5. Stepwise logistic regression: Self-evaluated health status by explanatory variables
                                                Std.                Odds       95.0% C.I.           R2
 Explanatory variables           Coefficient Error        P         ratio      Lower      Upper     change

    Illness (1= yes)                   -1.648       0.152    0.000   0.192   0.143      0.259       0.266

    Age                                -0.045       0.003    0.000   0.956   0.951      0.961       0.114

    Per capita consumption             0.000        0.000    0.000   1.000   1.000      1.000       0.005

    Health              care-seeking                                                                0.004
                                       -0.720       0.178    0.000   0.487   0.343      0.690
    behaviour

    Sex (1= male)                      0.348        0.091    0.000   1.417   1.184      1.695       0.006

    Upper class                        -0.345       0.164    0.035   0.708   0.513      0.977       0.001
    †Lower class                                                     1.000

    Peri-urban                         0.340        0.114    0.003   1.405   1.125      1.756       0.002
    †Rural                                                           1.000

    Length of illness                  -0.003       0.001    0.004   0.997   0.995      0.999       0.003


    Divorced,      separated      or                                                                0.002
                                       -0.355       0.153    0.021   0.701   0.519      0.947
    widowed

    †Never married                                                   1.000
                                        2
Hosmer and Lemeshow goodness of fit χ =18.49 (8), P = 0.78
Nagelkerke R2 =0.403
-2LL = 3321.07
†Reference group




                                                                                             224 

 
Table 8.6. Stepwise logistic regression: Self-reported health care-seeking behaviour by
explanatory variables

                                                                              95.0% C.I.             R2
    Explanatory variable                         Std.               Odds                             change
                                     Coefficient Error      P       ratio     Lower        Upper
    Health status (1=moderate-to-
    excellent)                    -0.787            0.191   0.000   0.455     0.313        0.662     0.005

    Health insurance                 0.554          0.163   0.001   1.741     1.263        2.398     0.004

    Self-reported illness            5.881          0.219   0.000   358.313   233.307      550.297   0.700

    Age                              0.005          0.003   0.037   1.005     1.000        1.010     0.001

    Per capita consumption           0.000          0.000   0.021   1.000     1.000        1.000     0.001

Hosmer and Lemeshow goodness of fit χ2=7.12 (8), P = 0.52
Nagelkerke R2 =0.711
-2LL = 1525.53
†Reference group




                                                                                              225 

 
                                    Chapter 9
    Modelling social determinants of self-evaluated health of poor
        older people in a middle-income developing nation




                                        Paul A Bourne


Over the last 2 decades (1988-2007), poverty in Jamaica has fallen by 67.5%, and this is within
the context of a 194.7% increase in inflation for 2007 over 2006. It does not abate there, as
Jamaicans are reporting more health conditions in a 4-week period (15.5% in 2007) and at the
same time this corresponds to a decline in the percentage of people seeking medical care. Older
people’s health status is of increasing concern, given the high rates of prostate cancer,
genitourinary disorders, hypertension, diabetes mellitus and the presence of risk factors such as
smoking. Yet, there is a dearth of studies on the health status of older people in the two poor
quintiles. This study examined (1) the health status of those elderly Jamaicans who were in the
two poor quintiles, and (2) factors that are associated with their health status. A sample of 1,149
elderly respondents, with an average age of 72.6 years (SD=8.7 years) were extracted from a
total survey of 25,018 Jamaicans. The initial survey sample was selected from a stratified
probability sampling frame of Jamaicans. An administered questionnaire was used to collect the
data. Descriptive statistics were used to examine background information on the sample, and
stepwise logistic regression was used to ascertain the factors which are associated with health
status. The health status of older poor people was influenced by 6 factors, and those factors
accounted for 26.6% of the variability in health status: Health insurance coverage (OR=13.90;
95% CI: 7.98-24.19), age of respondents (OR=7.98; 95% CI: 1.02-1.06), and secondary level
education (OR=1.82; 95% CI: 1.35-2.45). Males are less likely to report good health status than
females (OR=0.56; 95% CI: 0.42-0.75). Older people in Jamaica do not purchase health
insurance coverage as a preventative measure but as a curative measure. Health insurance
coverage in this study does not indicate good health but is a proxy of poor health status. The
demand of the health services in Jamaica in the future must be geared towards a particular age
cohort and certain health conditions, and not only to the general population, as the social
determinants which give rise to inequities are not the same, even among the same age cohort.




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1. INTRODUCTION


Factors determining the poor health status of the elderly in Jamaica can be viewed from the

perspective of a socio-medical dichotomy. Such factors include poverty (resulting in one’s

inability to access loans, quality education and health care), lifestyle (e.g. smoking, sedentary

habits, sexual and dietary practices and physical inactivity), resulting in prostate cancer,

genitourinary disorders, hypertension, diabetes mellitus and premature death. In 2005, the World

Health Organization began a thrust in examining the social determinants of health, and despite

that reality there is a lack of literature in this regard on the elderly poor people in Jamaica. These

parameters were explored in the current research by using a sample of 1,149 elderly poor

Jamaicans.


       The findings of this paper reveal that the cost of medical care is positively correlated with

health conditions, and that economic constraints account for the decline in the elderly seeking

medical care. Older people in Jamaica do not purchase health insurance coverage as a

preventative measure but as a curative measure. Health insurance coverage in this study does not

indicate good health, but on the contrary, it is a proxy of poor health status. It is also noted that

income is positively correlated with a higher standard of living and life expectancy. In support of

this claim, studies have shown that life expectancy in many developing countries [1], in

particular the Caribbean (Barbados, Guadeloupe, Jamaica, Martinique, Trinidad and Tobago) has

exceeded 70 years, and they are now experiencing between 8-10% of their population living to

60+ years old. Life expectancy, which is a good indicator of the health status of a populace, is

higher in countries with high GDP per capita. This means that income is able to purchase better

quality products [2], and indirectly affects the length of years lived by people. GDP per capita is
                                                                                                  227 

 
used as an objective valuation of standard of living [3-12]. While a country’s GDP per capita

may be low, life expectancy is high because health care is free for the population. Despite this

fact, material living standards undoubtedly affect the health status and wellbeing of people, as

well as the level of females’ educational attainment [6] and the nutrition intake of the poor. On

the other hand, when there is economic growth, the society has more to spend on nutrition, health

care, better physical milieu, better quality food, safer sanitation and education.


        Good health is, therefore, linked to economic growth, something which is established in a

plethora of studies by economists. Developing countries (a term synonymous with poverty) do

not only constitute low levels of democracy, civil unrest, corruption [13], high mortality and

crude birth rates, but one must also include nutritional deficiency [14]. The WHO in 1998 put

forward the position that 20% of the population in developing countries do not have access to

enough food to meet their basic needs and provide vital nutrients for survival.


        In the Caribbean, and in particular Jamaica, poverty is typical, and many of the ills that

affect other developing nations outside of this region are the same. The poor in this society are

facing insurmountable challenges in buying the necessary health care. In 2007, between 51 and

53% of those in the poor quintiles in Jamaica sought medical care, compared to 61-68 % of those

in the middle-to-wealthiest quintiles. When those who had reported that they were ill were asked

why they had not sought medical care, 51% of those in the poorest quintile indicated that they

‘could not afford it’, with 36.7% of those in the poor quintile giving the same response, and the

percentage declines as the wealth of the person increases to the wealthiest quintile (7.7% of those

in the wealthiest quintile).




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        Over the last 2 decades (1988-2007), poverty in Jamaica has fallen by 67.5% and this is

in the context of a 194.7% increase in inflation for 2007 over 2006. Jamaicans are reporting more

health status in a 4-week period (15.5% in 2007) and at the same time this is associated with a

decline in the percentage of people seeking medical care. Older people’s health status is of

increasing concern, given the high rates of prostate cancer, genitourinary disorders, hypertension,

diabetes mellitus and the presence of risk factors such as smoking in earlier life. Yet, there is a

dearth of studies on the health status of older people in the two poor quintiles.


        Works which have examined the social determinants of health have used data for the

population [2,3], but none emerged from a literature research using data for poor old people. This

study examined (1) the health status of those elderly Jamaicans who were in the two poor

quintiles, and (2) factors that are associated with their health status.


2. MATERIALS AND METHODS

2.1 Sample


A sample of 1,149 elderly respondents was extracted from a larger survey of 25,018 Jamaicans.

The sample was based on being 60+ years old, and being classified in the two poorest income

categorizations. The initial survey sample (n = 25, 018) was across the 14 parishes, and was

conducted between June and October 2002. The sample (n=25,018 or 6,976 households out of a

planned 9,656 households) was drawn using a stratified random sampling technique. 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 zones.

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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 dwellings was compiled, and which provided the frame for the labour force. The

survey adopted was the same design as that of the labour force, and it was weighted to represent

the population of the country.


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

Statistical Institute of Jamaica. The data were collected by a comprehensive administered

questionnaire, which was primarily completed by heads of households for all household

members.     The questionnaire was adapted 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, to reflect policy impacts as well. The instrument assessed: (i) the

general health of all household members; (ii) social welfare; (iii) housing quality; (iv) 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 questions.


Data were stored and retrieved in SPSS for Windows, version 16.0 (SPSS Inc; Chicago, IL,

USA). The current study is explanatory in nature. Descriptive statistics were presented 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 the statistical association between some variables, t-test statistics and analysis of variance

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(i.e. ANOVA) were also used 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). The logistic regression was used

because in order to test the association between a single dichotomous dependent variable and a

number of explanatory factors simultaneously, it was the best available technique. A p-value <

0.05 (two-tailed) was selected to indicate statistical significance in this study. 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. To derive accurate tests of statistical

significance, SUDDAN statistical software was used (Research Triangle Institute, Research

Triangle Park, NC), and this was adjusted for the survey’s complex sampling design.


2.2 Measure


Social determinants. These denote the conditions under which people are born, grow, live, work
and age, including the health system.


Crowding. This is the total number of persons living in a room with a particular household.
                   , where is each person in the household and r is the number of rooms
excluding kitchen, bathroom and verandah.
Age: This is a continuous variable in years, ranging from 15 to 99 years.


Old/Aged/Elderly. An individual who has celebrated his/her 60th birthday or beyond.


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

a breadwinner and/or family member, loss of property, having been made redundant, failure to

meet household and other obligations.

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

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

insurance coverage, and 0 for not reporting ownership of private health insurance coverage.


Gender: Gender is a social construct which speaks to the roles that males and females perform

in a society. This variable is a dummy variable, 1 if male and 0 if otherwise.



Health conditions: The report of having had an ailment, injury or illness in the last four weeks,

which was the survey period. This variable is a binary measure, where 1=self-reported health

status or illnesses, and 0=otherwise (not reporting an illness, injured or dysfunctions).


Poverty: In this study, the definition of poverty is the same as that used to estimate poverty in

Jamaica. It is established from the basis of a poverty line. In order to compute the per capita

poverty line in each geographical area (Kingston Metropolitan Area, Other Towns and Rural

Areas), the cost of living for a basket of goods is divided by an average family of five. The

basket of goods is established by the Ministry of Health based on the normal nutrients of the

average family. Based on a per capita approach, there are five per capita income quintiles, with

the poorest being below the poverty line (quintile 1) and the wealthiest being in quintile 5.


Elderly, Aged or Old persons. Using the same definition offered by the United Nations in the

Report of the World Assembly on Ageing, July 26-August 6, 1982 in Vienna, that the elderly are

persons who are 60+ years old.


Older-poor (elderly-poor, aged-poor). All aged persons below and just above the poverty line

(quintiles 1 & 2) in Jamaica.


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3. RESULTS

3.1 Demographic characteristics of sample


Consistent with the demographic characteristics of the ageing population, the sample was 1,149

of which there were 45% males (N=517) compared to 55% females (N=632). The mean age of

the sample was 72.6 years (SD=8.7 years). Most of the sample were married (40%, N=452),

50.5% (N=580) of the sample were in the poorest 20% of per capita income quintile, 95%

(N=1,087) were not receiving retirement income; those who were heads of households (98.3%,

N=1,129), those who had at most primary education (65.2%, N=700) and those who did not have

health insurance coverage (86.0%, N=973) (Table 9.1 ).


        Thirty-seven percent (37.2%) of the sample indicated having had an illness in the last 4-

week period. Approximately 64% of the respondents indicated that they sought health care for

their health conditions. When the respondents were asked if they had visited a health practitioner

for any other reason during the last 12 months, 57.1% reported yes and 30.3% reported going for

‘regular checkups’. Of those who indicated yes, 37.2% visited public health care institutions, and

18.7% went to private clinics, compared to 5.7% who claimed that they attended both health care

facilities. The typologies of illness included colds (1.4%), diabetes mellitus (5.7%), hypertension

(42.9%) and arthritis (31.4%), while 18.6% did not specify their health condition(s). Only 2% of

the respondents had health insurance coverage; 61% purchased the prescribed medication; and

81.8% of those who indicated having not bought their medication reported that they could not

afford it.




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        The median number of days for how long an illness lasted was 7 days, with a median

medical expenditure of US $7.85 (US $1.00 = Ja. $50.97).


3.2 Bivariate Correlation of Health Status and Age Cohort


Of the 1,149 sample respondents for this study, 98.8% (N=1,135) were used for the statistical

correlation between health status and gender. Of the 1,135 respondents, there were 688 young-

old, 327 old-old and 120 oldest-old poor Jamaicans. There was a correlation between the two

above-mentioned variables – χ2 (df=2) = 22.863, p-value < 0.001. On an average, 46% of the

aged-poor (N=523) reported that they had at least one illness/injury in the survey period. The

most health status was reported by the oldest-old poor (59.2%, N=71), 52.9% (N=173) and the

least by the young-old (40.6%, N=279). Embedded in these findings is that for every 1 young-

old poor who indicated that he/she had an illness/injury, there are 1.5 oldest-old and 1.3 old-old

poor.




3.3 Multivariate Analysis


The results of the multiple logistic regression model (in Table 9.2), were statistically significant

[Model χ2 (df=18) = 229.47; -2Log likelihood = 1130.37; p-value < 0.001]. Table 9.2 showed

that 26.6% of the variances in the health status of older people in Jamaica were accounted for by

the independent variables used in the multiple logistic regressions. The mold revealed that there

were 6 statistically significant factors that determined health conditions. These predictors are age

(OR=1.04, 95% CI=1.02-1.06), health insurance coverage (OR=13.90, 95% CI=7.98-24.19),

physical environment (OR=1.42, 95% CI=1.06-1.89), cost of medical care (OR=1.00, 95%

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CI=1.00-1.00), secondary level education (OR=1.82, 95% CI=1.35-2.45) with reference to

primary and below education, and gender of respondents (OR=0.56, 95% CI=0.42-0.75).

Controlling for the effect of other variables, the average likelihood of reporting illness/injury in a

4-week reference period declined by 17 times for those who had dysfunctions.

       The model had statistically significant predictor power (Model χ2 (df=18) = 229.47; -

Homer and Lemeshow goodness of fit χ2= 3.739, P=0.880), and correctly classified 70% of the

sample (correctly classified 55.4% of those with dysfunctions and 82.3% of those without

dysfunctions) (Table 9.2). The logistic regression model can be written as: Log (probability of

dysfunctions/probability of not reporting dysfunctions) = -4.185 + 0.039 (Age) + 2.632 (Health

Insurance coverage, 1= yes, 0=no) + 0.348 (Physical Environment, 1=yes, 0=no) + 0.000 (Cost

of Medical Care) + 0.598 (Secondary level education=1, 0=primary and below) – 0.581 (Sex).




4. DISCUSSION



People are living longer [15], which means that on average the elderly are living 15-20 years

after retirement. Demographic ageing at the micro and macro levels implies a demand for certain

services such as geriatric care. In addition to preventative care, there will be a need for particular

equipment and products (i.e. wheelchairs, walkers etc.). Then there are future preparations for

pension and labour force changes, along with the social and economic costs associated with

ageing, as well as the policy based research to better plan for the reality of these age groups. The

World Health Organization (WHO), in explaining the ‘problems’ that are likely to occur because

of population ageing, argues that the 21st Century will not be easy for policy makers as it is

pivotal in the preparation process to postpone ailments and disabilities, and the challenge of
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providing a particular standard of health for the populace [16]. What constitutes population

ageing? Some demographers have put forward the benchmark of 8-10% as an indicator of

population ageing [17]. Within the construct of Gavrilov and Heuveline’s perspective, the

Jamaican population began experiencing this significant population ageing as of 1975 (using 60+

years for ageing) or 2001 (if ageing is 65+ years). The issue of population ageing will double

come 2050, irrespective of the chronological definition of ageing, but what about the elderly

poor health conditions?

        Let us examine the disparity between long life and quality of lived years. Ali, Christian &

Chung [18] who are medical doctors, cite the case of a 74 year-old man who had epilepsy, and

presented the findings in the West Indian Medical Journal. They write that “Elderly patients are

frequently afflicted with paroxysmal impairments of consciousness, because they frequently

have chronic medical disorders such as diabetes mellitus and hypertension, and can also be on

many medications….Many elderly patients may have more than one cause for this symptom”

[18].


        The case presented by the medical doctors emphasizes the point we have been arguing

that long life does not imply quality of lived years. Although the case study cited here does not

constitute a general perspective on all the elderly, other quantitative studies have concurred with

Ali, Christian and Chung’s general findings. Scientists agree that biological ageing means

degeneration of the human body, and such a reality means that longer life will not mean quality

years. Population ageing is going to be a socioeconomic, psychological and political challenge

today, tomorrow and in the future of developing countries and nations like Jamaica. This

reinforces the position postulated by the WHO that healthy life expectancy [19] is where we


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ought to be going, as the new thrust is not living longer but how many of those years are lived

without dysfunctions. Within the context of healthy life expectancy, studies that will be used to

guide policy are those that incorporate many determinants, and not only biological conditions

[20-25]. But none of those studies examined poor old people. Hambleton [20] and Bourne [23-

25] are Caribbean scholars who have researched social determinants using the population of the

poor, and this gap to date in the literature needs to be addressed, as the elderly constitute a

vulnerable group, and the poor elderly group is even more vulnerable. Any policy which seeks to

reduce poverty must take into account the poor elderly.


        ‘Ageing in poverty’ implies that persons remain in their local environments with the

ability to live in their own home - wherever that might be - for as long as confidently and

comfortably possible. It inherently includes not having to move from one's current residence in

order to secure the necessary support services in response to changing needs. The ageing of

Caribbean populations has been accompanied by a shift to chronic non-communicable diseases

as major causes of morbidity. While overall national trends have been reported, examination of

local patterns of morbidity are increasingly important, as they have implications for the services

to be provided, the mix of human resources, and the maintenance of health and functional status

that facilitate ageing in place.


        Research has shown that crowding is strongly correlated with the wellbeing of the elderly

(ages 60+ years) [23]; however this phenomenon, which is synonymous with poverty, does not

influence the health status of poor elderly Jamaicans. Embedded in this finding is the fact that

older people, in particular those in poor quintiles, interpret people around not as a negative force

but as good social networking and interaction. What, then, influences their health conditions?

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       Poverty speaks to a particular environment; Pacione [26] showed that one’s physical

environment affects one’s quality of life, and other scholars have agreed with this finding. The

current study concurs with Pacione and others, in that the physical milieu is positively correlated

with health conditions. Although Michael Pacione’s work was on the general population,

Bourne’s works [23, 24] examined the elderly population (ages 60+ years) and found a negative

association between physical environment and wellbeing, and this study concurred with that of

the aforementioned researcher on the correlation between physical environment and health

conditions. In this study, an important finding is to refine the correlation.


       Health insurance coverage is among the many indicators of the health-seeking behaviour

of a populace. For the poor elderly, it is the most significant predictor of health conditions. The

correlation is a strong positive one, indicating that health insurance coverage is a good proxy for

more ill-health than good health. The current research found that those elderly poor who owned

health insurance were 14 times more likely to report dysfunctions (or injuries) than those who

did not. Health insurance is, therefore, a cost reducer for those who are aware that they are ill,

and it is not in demand as a preventative measure. Arising from this fact is the role played by the

costs of medical and curative care. Health is influenced by more than disease-causing pathogens.

[27]


       The cost of medical care is positively correlated with health conditions, suggesting that

the more dysfunctions (or injuries) that the elderly poor report, the more they are likely to spend

on medical care. The elderly poor are prevented from seeking preventative care as against

curative care. The latest data published by the Planning Institute of Jamaica and the Statistical

Institute of Jamaica[28] showed that 37.3% of elderly people are at least poor, with 20.6% falling

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in the poorest quintile. This further explains the rationale for the reduction in the demand for

medical care within the context of a precipitous increase in inflation in 2007 over 2006 (194%).

With the steady rise in the cost of health care, as well as the increase in general food and non-

alcoholic beverage prices in Jamaica, coupled with the fact that illness in older age requires care,

the elderly poor are facing increasingly difficult times. The severity of the economic situation

has seen a dramatic increase in the number of Jamaicans not seeking medical care for

illness/injury. Although there is a decline in the general population seeking medical care (66%),

more of the elderly do seek health care (72.3%) and this is owing to recurrent chronic illness

which was shown to affect 74.2% of them28. Illnesses/injuries are precipitously affecting the

elderly, and the data showed that self-reported illness for the elderly was 2.3 times more (36.6%)

than in the general population (15.5%) [28]. In 2007, the elderly poor who constitute 38% of the

poor-to-poorest in the population are mostly household heads (67.3%) and often unemployed,

and within this context they must provide for their own health needs and those of their family,

despite the harsh economic challenges and increased cost of health care.


       In 2002, 12.9% of Jamaicans were unable to afford medical care, and approximately 4

years later, the figure had risen by 162.8% to 33.9% in 2007. This is within the context of a

26.3% decline in poverty for the same period. Generally poverty has been falling over the last 2

decades in Jamaica, and inflation has fluctuated, justifying the increased amount spent on food

and beverages [28], and the corresponding reduction in health care expenditure. In Jamaica

remittances, which subsidize income for many households, have fallen by 7.7% and the

reduction is 33% for those in the poor-to-the-poorest income quintiles. If the cost of medical care

is positively correlated with the health status of the elderly poor, then can it be said that the poor

elderly have more ill-health within the context of biological ageing and lowered access to
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employment income? Marmot [2] opined that there is a direct association between income and

poor health, and this further helps us to understand the embedded health challenge of the elderly

poor, as they must meet the increasing costs of medical care, cost of living, lower income,

illnesses and severity of health conditions. On examining the health statistics for 2007 [28], the

indication was that 50.8% of those in the poorest income quintile were unable to afford to seek

medical care, and the figure was 36.7% of those in the poor quintile. In order to understand the

severity of the situation regarding the aged-poor people in Jamaica, let us analyze the

aforementioned within the context of the aged-poor. The official statistical publication for

Jamaica for 2007 [28] showed that 20.6% percent of the elderly people are in the poorest quintile

and 17.7% in the poor quintile which means that a little over half of the aged-poorest in Jamaica

(10.4%) were unable to afford medical care, and 6.5% of the aged-poor had financial difficulty

affording medical care expenditure. One of the choices that must be made by the aged-poor in

Jamaica is a switch from the formal medical care service to utilizing home remedies and over-

the-counter medications, instead of visiting their personal physicians or health care facilities.


       Since 1988 when the Jamaican authorities began collecting data on self-reported health

conditions, men have been reporting less health status than women [28]. The reporting of less

illness does not mean that men are healthier than women, as the same statistical report [28]

shows that women seek more medical care than men. Morbidity data for the sexes in Jamaica is

typical, as in Mexico City, Havana and Santiago-Chile at least 60% of females compared to 50%

of males aged 60+ years old reported fair-to-poor health [29]. Continuing, Buenos Aires,

Montevideo and Bridgetown-Barbados had twice the figures of the aforementioned geo-political

zones [29]. This is in keeping with women’s protective role of self, and their willingness to have

a regard for their future health status accounts for a higher health status and not a lower one,
                                                                                                    240 

 
although they report more dysfunctions than men. If life expectancy were to be used to proxy

good health status, females are healthier than men given that they outlive them by 6 years in

Jamaica and 8 years in the world. Furthermore, in 2000-2005, life expectancy for men was 69.5

years and 74.7 years for women, and come 2045-2050 they both would have gained an additional

2 and one-quarter years more to their life span. The equal and constant rate of change in the life

expectancy of both sexes in Jamaica highlights the fact that men do not enjoy better overall

health status than their female counterparts. More years of life for both sexes means that the life

course opens itself to coronary heart disease, stroke and diabetes mellitus, and so morbidity must

be examined in this discourse.


       Studies done by the Ministry of Health reveal that of the five leading causes of mortality

in Jamaica, which are malignant neoplasm, heart disease, diabetes mellitus, homicide and

cerebrovascular diseases [30], more men die from more of the aforementioned conditions than

women. Malignant neoplasms are 39% greater for men than women; cerebrovascular diseases

are 14% higher for females than males; heart disease was 71.2 per 100, 000 for men and 66.1 per

100,000 for women; and diabetes mellitus was 64% more for females than males [30]. The

greater vulnerability of men to particular mortality than women is typical across Latin America

and the Caribbean [29], pointing to gender bias (that is feminization) in visits to health care

facilities, which are embedded in the life expectancy rates and visits to health care institutions.

The matter of reporting less health status, once again, does not imply a healthier person, as health

is not on a continuum, with ill-health on one extreme and good health on the other. Health is

more in keeping with cyclical flow, and changes over the life course with time, experiences and

socio-physical environmental conditions. Hence, asking about ill-health is not a good proxy for

health status, as in 2007 a group of Caribbean scholars conducted a national representative
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prevalence survey of some 1,338 Jamaicans, and found that those who indicated themselves to be

of the lower class had the least self-reported health status [13].


       The discipline of gerontology – scientific inquiry into the biological, psychological, and

social aspects of ageing - has shown that ageing is not necessarily without increased health

conditions; it is natural for aged people to complain and die more of dysfunctions than other age

cohorts [31, 32] and that is directly related to their basal metabolic rate [33] and the nature of the

life course of the aged [34]. Here functional ageing is an explanation for the image of ageing,

and it can be measured by normal physical changes, diminished short-term memory, reduced

skin elasticity and a decline in aerobic capacity. It is well established in the research literature

that age is directly correlated with health status for the elderly, and in this study the finding

concurs with the literature. The current research shows that age is the second most significant

predictor of health status for the elderly poor, and explains why the disparity in poor health in

Latin and America and the Caribbean is higher for older persons than younger people [29].

Population ageing is synonymous with more disability and more non-communicable diseases

such as malignant neoplasms, hypertension, diabetes, and heart diseases than younger ages.

Donald Bogue [35] noted that health problems increase with ageing, and that one’s health issues

intensify with ageing. Therefore, an unhealthy lifestyle – tobacco consumption, physical

inactivity, unprotected sex, and unhealthy diet - over the life course will affect the elderly in

latter life, and the declining health of the elderly poor is the same within the sub-categories of the

elderly – young-old, old-old and oldest old.


       Issues of the elderly cannot be discussed without an examination of area of residence.

This study found no correlation between the aged-poor’s health status and area of residence.

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Using data since 1989 (from various issues of the Jamaica Survey of Living Conditions),

population ageing is biased by gender as well as by specific area of residence. Over the last

decade (1997-2007), the number of elderly Jamaicans living in rural areas has declined from

54.3% to 46.6% (a rate of 14.1%). For the same period, the rate of increase of the aged populace

in the Kingston Metropolitan Area (100% cities) was 19.5%, down from 27.2% (in 1997) while

the increase in the aged population over the same period in Other Towns was 12.9% over 18.5%

in 1997. Regarding the prevalence of poverty for the region (2007), rural poverty was 3.8 times

more than that in Other Towns, and 2.5 times more than that in the Kingston Metropolitan Area.

Despite the compounding economic challenges of poverty coupled with ageing, the poor-elderly

in Jamaica do not experience a difference in their health status owing to area of residence. Here

the health issues of the aged poor are independent of their area of residence, suggesting that in

the population the poor are age-residence insensitive. This contradicts research literature on the

health status of the elderly which has shown a correlation between the aged and their areas of

residence [23,24,48], indicating that the physical characteristics of the aged poor are the same in

different areas of residence, and therefore do not account for any poor health, disability,

functional inability or psychological conditions.


       Like the WHO [36], the researcher believes that although ageing is a biological

phenomenon, it cannot be due only to biological conditions, as ageing relates to bio-psycho-

social [20, 25, 37-49] and environmental conditions [23-26], since people – biological organisms

– must operate in a socio-physical milieu throughout their life span, and this demands an

expansion of biological conditions in the ageing discourse. The very nature of gerontology must

coalesce biopsychosocial and environmental conditions in assessing ageing and the health of the

aged, which are in keeping with the WHO’s Constitution of 1948, and this has also been
                                                                                               243 

 
established in many Caribbean scholarships [20,23-25,42-49]. Within the context of the above-

mentioned challenges for elderly people, when this is coupled with poverty which affects 10.2%

of elderly Jamaicans (N=29,794) in 2007, it intensifies the challenges experienced by elderly

people. With the increased cost of food and non-alcoholic beverages, fuel and household

supplies, housing and household operational expenses, the health status of the older-poor will

continue to deteriorate, as they will not be able to afford health care services. The decline in

medical care-seeking behaviour of Jamaicans speaks to the challenges of older people and the

rise in instances of switching to alternative medicine. This is further intensified by poverty; and

rural poverty, which is more severe than that found in urban areas [50], will further compound

the challenges of the health status of the aged populace. Older people who are poor must operate

within the same biopsychosocial and physical environment during their lifetimes as other

persons.


       Even among the WHO commissioned studies [51-53], as well as other studies on the

social determinants of health [2,3, 20-25], the population of the poor elderly were not examined.

Likewise in the Caribbean, scholars have examined the social determinants of the population or

the elderly population, with poverty being an independent variable [20, 23-25]. Any policy that

seeks to address the health status of the elderly poor must take into consideration, or concentrate

and/or rely on, not only the population in general, but the cohort of the elderly in particular. The

experiences and demands of the elderly are not the same as the general population, and the

current study shows that social determinants of health are somewhat different for the general

elderly population and the poor elderly cohort. The WHO [51] opined that the social

determinants of health for the most part account for the health inequities between and within

nations, which substantiates the differences that emerged between the elderly in other studies
                                                                                                244 

 
[20, 23-24] and the current study of the poor elderly. These findings are far-reaching, and can be

used to guide policy and research. The elderly-poor in Jamaica are experiencing ‘health poverty’

which cannot be alleviated by unresearched policies or research policies on the general

population, but by the elderly cohorts in particular.


5. Conclusion

          In summary, the number of elderly persons who reported health conditions in Jamaica is

3 times more than that for the nation (i.e. 12.6%), suggesting that health care expenditure for

Jamaicans is substantially used to address health care needs for the aged population. With the

number of elderly come 2025 estimated to be 14.5% over 10.9% for 2007, health care

expenditure will be primarily absorbed in caring for this age cohort. Public health practitioners

must begin programmes to deal with this pending reality. Ageing is a process which denotes that

the high number of health conditions affecting the elderly would have started earlier, based on

some of the decisions that they undertook (or did not) leading up to their current age. Hence,

there is a need to have a public health campaign geared towards the promotion of healthy

lifestyle practices for ages close to sixty years, in conjunction with one for children and for the

working-age population. The programme should target check-ups, preventative care, signs of the

onset of particular health conditions, and the distinction between ill health and good health care

practices. The demand of the health services in Jamaica in the future must be geared towards a

particular age cohort and certain health conditions, and not only to the general population, as the

social determinants which give rise to inequities are not the same even among the same age

cohort.



                                                                                               245 

 
6. Disclosure

The author reports no conflict of interest for this study.


7. Disclaimer
The researcher 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 researcher.




8. Acknowledgement

The dataset for this study was made available from the databank of SALISES (Sir Arthur Lewis
Economic Institute), Faculty of Social Sciences, the University of the West Indies, Mona,
Jamaica and for this the researcher is indebted and greater appreciate this gesture.




                                                                                            246 

 
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                                                                                                 249 

 
Table 9.1: Socio-demographic characteristics of sample

Description                                        N               Percent

Gender
   Male                                           517               45.0
   Female                                         632               55.0

Marital status
  Married                                         452               40.0
  Never married                                   357               31.6
  Divorced                                         10                0.9
  Separated                                        22                1.9
   Widowed                                        290               25.6

Per capita Income quintile
   Poorest                                        580               50.5
   Poor                                           569               49.5

Retirement Income
  No                                              1087              95.0
  Yes                                              57               5.0

Household head
  No                                               20                1.7
  Yes                                             1129              98.3

Health Insurance coverage
 No                                               973               86.0
 Yes                                              158               14.0

Educational Level
Primary and below                                 700               65.2
Secondary                                         363               33.8
Tertiary                                           10                0.9

Age                                   72.63 years (SD=8.7 years)
Total Medical Care Expenditure         $1,067.64 (SD=$2,000.00)
Per capita consumption                $30,998.07 (SD=$9,833.00)
US $1.00 = JA$50.97




                                                                             250 

 
Table 9.2: Logistic Regression: Socio-demographic correlates of health status of poor older
people in Jamaica, N=1,033
                                                              OR             95.0% C.I.
 Variable
   Age                                                          1.04            1.02 - 1.06***
   Retirement income                                            0.75               0.38 - 1.49
   Per capita consumption                                       1.00               1.00 - 1.02

    Separated, divorced or widowed                              1.07               0.74 - 1.55
    Married                                                     1.11               0.77 - 1.58
    Never married (reference group)                             1.00

    Health insurance                                           13.90          7.98 - 24.19***
    Environment                                                 1.42             1.06 - 1.89*
    Household head                                              3.34             0.37 - 30.01
    Cost of medical care                                        1.00            1.00 - 1.05**

    Secondary                                                   1.82           1.35 - 2.45***
    Tertiary                                                    0.43              0.07 - 2.63
    Primary and below (reference group)                         1.00

    Semi-urban                                                  0.78               0.51 - 1.19
    Urban areas                                                 0.86               0.50 - 1.49
    Rural areas (reference group)                               1.00

    Sex                                                         0.56            0.42 - .75***
    Living arrangement                                          1.20              0.77 - 1.88
    Crowding                                                    0.89              0.78 - 1.02
    Crime index                                                 1.00              0.98 - 1.03
    Positive affective                                          0.96              0.90 - 1.01
Model Chi-square (df =18) = 229.47, p-value < 0.0001
-2Log likelihood = 1130.37;
Nagelkerke R-square = 0.266
Hosmer and Lemeshow test P = 0.880
*P < 0.05, **P < 0.01, ***P < 0.001




                                                                                             251 

 
                                    Chapter 10


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



                                     Paul Andrew Bourne


Education provides choices, opportunities, access to resources and it is associated with an
increased likelihood of higher income. Does this holds true in developing nations like Jamaica,
and does the educated class experience greater self-rated health status than the uneducated
classes? The current study will identify the socio-demographic correlates of self-rated health
status of Jamaicans, examine the effects of these variables, explore self-rated health status and
self-reported diagnosed recurring illness among the educated and uneducated classes, compute
mean income among the different educational types, and determine whether a significant
statistical correlation exists between the different educational cohorts. The current study utilised
the data set of Jamaica Survey Living Conditions which is a cross-sectional survey. It is a
national probability survey, and data were collected across the 14 parishes of the island.
Stratified random sampling techniques were used to draw the sample. Self-rated health statuses
of respondents are correlated with age, income, crowding, sex, marital status, area of residence,
and self-reported illness (es) – χ2= 1,568.4, P < 0.001. Respondents with tertiary level educations
were most likely to be classified in the wealthiest 20% (53.4%) and there was no significant
statistical difference between their health status and the lower educated classes. There is a need
for a public health care campaign that is specifically geared towards the educated classes as their
educational achievement is not translating itself into better health care-seeking behaviour and
health status than the uneducated classes.



Introduction
Health is imperative for socio-economic and political development of people, a society and a

nation. It is within this context that a study of health is critical as it relates to the wider society.

Traditionally, the concept of health is measured using life expectancy, mortality, and diagnosed
                                                                                                    252 

 
illness. In the social sciences, researchers have used self-rated health status [1-9], and self-

reported illness [10-17] to measure health. Apart from those terminologies, other synonyms such

as self-assessed health, self-reported health, perceived health, self assessment of health, global

health status, and health status have all been used to speak about health. It follows from the

aforementioned perspective that all those terms imply the same measurement of health or health

status. Self-rated health status is among the subjective indexes used to measure health, and some

scholars argue that they are not a good assessment of health when it comes to life expectancy,

per capita income, or mortality [18-20].


       The subjective/objective indexes of measuring health emerged as scholars sought to

ensure that the measurement of health was a reliable and valid one. Some scholars opined that

the self-assessment of one’s health status was more comprehensive than objective assessment [3,

5, 21] as it included one’s health and general life satisfaction. Studies have shown that subjective

indexes are a good measurement for mortality [2, 22-24] and life expectancy [25]. Concurringly,

a recently conducted study by Bourne [25] found that self-assessed illness was not a good

measure of mortality; however, it was was very useful when it came to the subject of life

expectancy in Jamaica.


       The subjective indexes in measuring health open themselves up to systematic and

unsystematic biases [26]. People’s perception can be biased as they may inflate or deflate their

status in an interview or on a self-administered instrument (i.e., questionnaire). Another aspect of

bias in subjective evaluation of health is the matter of recall. It is well established in research

literature that as people age, their mental faculties decline [27-32], suggesting that some people

will have difficulties recalling experiences which happened in the past. Within the context of the

                                                                                                253 

 
time recollection, bias can occur in subjective indexes. Kahneman [33] devised a procedure of

integrating and reducing the subjective biases when he found that instantaneous subjective

evaluations are more reliable than assessments of recollection of experiences. Contrary to

Kahmeman’s work, Bourne’s [25] results show that self-assessed health for a 4-week period is a

good measure of life expectancy (objective index). In spite of the fact that subjective indexes are

a good measure of objective health, the former still contains biases, which Diener [34] opines

still have valid variance.


          It is well established in health research that there is a correlation between or among

different socio-demographic, psychological and economic variables [4, 6-17, 20] and self-rated

health status. The correlates include education, marital status, area of residence, education,

income, psychological conditions (i.e., positive and negative psychological affective conditions),

and other variables. Freedman & Martin [35], using data from 1984 and 1993’s panel survey of

Income and Program Participation, noted that there was an association between educational level

and physical functioning of people over 65 years. Another study by Koo, Rie & Park [36], using

multivariate regression, concluded that education was a predictor of increased subjective

wellbeing (t [2523] = 7.83, P<0.001], which means that education was more than associated

with health. Concomitantly, another research found that the number of years of school (i.e., the

Quantity Theory) was a crucial predictor of health status of an individual [37] which indicates

that tertiary level graduates are more likely to be healthier than non-tertiary level educated

people.


          While education provides choices, opportunities, access to resources and is associated

with increased likelihood of achieving a higher income, does it hold true in developing nations

                                                                                               254 

 
like Jamaica that the educated class has greater self-rated health status than the uneducated

classes? A paucity of information (research literature) exists in Jamaica on the educated and

uneducated classes and their self-rated health status, self-reported illness(es), the areas in which

the educated and uneducated classes reside, health care-seeking behaviour among the different

educational classes and the self-rated health status of Jamaicans and its correlates.


       The current study is important, as it uses a statistical technique which accommodates all

items in self-rated health status categories as opposed to dichotomising self-rated health.

Dichotomising self-rated health status in good and poor health means that some of the original

information will be lost; and this explains why some researchers argue for the maintenance of the

Likert nature of the measuring tool over dichotomisation [38-40]. Secondly, the study is

significant as it included more variables: (1) educational levels and area of residence, (2)

educational levels and health care-seeking behaviour, (3) health insurance coverage and

educational levels, (4) self-reported illness(es) and educational levels, (5) social standing and

educational levels. The objectives of the current study therefore are to (1) identify the socio-

demographic and economic correlates of self-rated health status of Jamaicans, (2) examine the

effects of these variables, (3) explore self-rated health status and self-reported diagnosed

recurring illness among the educated and uneducated classes, (4) calculate the mean age of

respondents in the different educational categories, (5) compute mean income among the

different educational types, and (6) determine whether a significant statistical correlation exists

between the different educational cohorts.


Materials and methods

Data
                                                                                                255 

 
A joint survey on the living conditions of Jamaicans was conducted between May and August of

2007 by the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica

(STATIN) [41]. The survey is called the Jamaica Survey of Living Conditions (JSLC) which

began in 1988 and is now conducted annually. The JSLC is a modification of the World Bank’s

Living Standards Measurement Study (LSMS) which is a household survey [42]. The current

study used the JSLC’s data set for 2007 in order to carry out the analyses of the data [43]. It had

a sample size of 6,783 respondents, with a non-response rate of 26.2%.


          The JSLC is a cross-sectional survey which used stratified random sampling techniques

to draw the sample. It is a national probability survey, and data was collected across the 14

parishes of the island. The design for the JSLC 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

residences 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 dwellings was compiled. This, in

turn, provided the sampling frame for the labour force. One third of the Labour Force Survey

(i.e. LFS) was selected for the JSLC. The sample was weighted to reflect the population of the

nation.


Instrument


A self-administered instrument (i.e., questionnaire) was used to collect the data from

respondents. The questionnaire covers socio-demographic variables such as education, age, and
                                                                                               256 

 
consumption, as well as other variables like social security, self-rated health status, self-reported

health conditions, medical care, inventory of durable goods, living arrangements, immunisation

of children 0–59 months, and other issues. Many survey teams were sent to each parish

according to the sample size. The teams consisted of trained supervisors and field workers from

the Statistical Institute of Jamaica.


Statistical Analyses


The Statistical Packages for the Social Sciences – SPSS-PC for Windows version 16.0 (SPSS

Inc; Chicago, IL, USA) – was used to store, retrieve and analyze the data. Descriptive statistics

such as median, mean, percentages, and standard deviation were used to provide background

information on the sample. Cross tabulations were used to examine non-metric dependent and

independent variables. Analysis of variance was used to evaluate a metric and a non-

dichotomous variable. Ordinal logistic regression was used to determine socio-demographic,

economic and biological correlates of health status of Jamaicans, and identify whether the

educated have a greater self-rated health status than uneducated respondents. A 95% confidence

interval was used to examine whether a variable is statistically significant or not.


        There was no selection criterion used for the current study. On the other hand, for the

model, the selection criteria were based on 1) the literature; 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 and Holliday [44] and Cohen and

Cohen [45], low (weak) correlation ranges from 0.0 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. Any correlation that had at

least a moderate value was excluded from the model in order to reduce multicollinearity and/or
                                                                                                 257 

 
autocorrelation between or among the independent variables [46-51]. Another approach in

addressing and/or reducing autocorrelation was to include in the model all variables that were

identified from the literature review with the exception of those where the percentage of missing

cases were in excess of 30%.


          The current study used the ordinal nature of the dependent variable (self-rated health

status or self-rated health) which denotes that none of the original data will be lost as is the case

in dichotomising self-rated health. Ordered regression model is written as:


                                           , s = 1, …k,                                 (1)

          Where x is the vector of covariates with coefficient to be estimated, k is the number of

cut-points for the dependent variable, and αs, αl stand for the intercepts in the regression models.

Anderson [52] opined that ø1=1 and øk, and that other constraints are possible. In the current

study, the researcher set ø1=1 and 0= ø1< ø2 < …< øk =1 to correspond to the levels from very

good to very poor, and other levels of health are relative to “very good”. Based on Anderson’s

arguments, the monotone increase of ‘ø’s are dealt with by varying the sign for β. Within this

context, a positive estimation of coefficient denotes that those with this characteristic would be

negatively associated with good health status and those without would positively associated with

good health status (or self-rated health status). Simply put, positive estimation of coefficients

means poor health and negative estimation of coefficients denotes better self-reported health

status.


Measurement of variables


Dependent variable

                                                                                                 258 

 
Self-rated health status (i.e., self-rated health) was derived from the question, “Generally, how is

your health?” with the options being very good, good, fair (or moderate), poor, or very poor. The

ordinal nature of this variable was used as was the case in the literature [38-40].


Independent variables


Information on self-reported illness was derived from the question, “Have you had any illnesses

other than injury?” The examples given include cold, diarrhoea, asthma attack, hypertension,

arthritis, diabetes mellitus or other illness. A further question about illness asked, “(Have you

been ill) In the past four weeks?” The options were yes and no. This variable was re-coded as

binary value, 1 = yes and 0 = otherwise.


Information about self-reported diagnosed recurring illness was derived from the question, “Is

this a diagnosed recurring illness?” The options were: (1) yes, cold; (2) yes, diarrhoea; (3) yes,

asthma; (4) yes, diabetes mellitus; (5) yes, hypertension; (6) yes, arthritis; (7) yes, other; (8) no.


Information on medical care-seeking behaviour was taken from the question, “Has a health care

practitioner, healer, or pharmacist been visited in the last 4 weeks?” The options were yes or no.

Medical care-seeking behaviour therefore was coded as a binary measure where 1 = yes and 0 =

otherwise.


The term crowding refers to the average number of person(s) per room excluding the kitchen,

bathroom, and veranda (i.e., total number of people in household divided by the total number of

rooms excluding kitchen, bathroom and veranda).


Total annual expenditure was used to measure income.



                                                                                                    259 

 
Income quintile was used to measure social standing. The income quintiles ranged from poorest

20% to wealthiest 20%.


Results

Demographic characteristic of sample and bivariate analyses

The sample was 6,783 respondents: 48.7% males and 51.3% females. Eighty-two percent of

respondents rated their health status as at least good compared to 4.9% who rated it as poor.

Fifteen percent of respondents reported some form of illness within the last 4 weeks. Of those

who recorded an ailment, 89% reported that the dysfunction was a diagnosed recurring one. The

most frequently recurring illness was unspecified conditions (23.4%) followed by hypertension

(20.6%), cold (14.9%), diabetes mellitus (12.3%), and others (Table 10.1).

       The median age of the sample was 29.9 years (range = 99 years). The median annual

income was US $7,050.66 (rate in 2007: 1US$ = Ja$80.47; range = US $4,406.20), and median

crowding was 4.0 persons per room (range = 16 persons).

        A cross-tabulation between educational level and area of residence revealed a significant

statistical correlation – χ2(df = 40 = 78.02, P < 0.001 (Table 10.2). Based on Table 10.2, 0.8% of

rural respondents had tertiary level education and 5.4 times more urban residents had tertiary

level education compared to rural respondents.

       No significant statistical correlation existed between educational level and sex of

respondents – χ2 (df = 2) = 5.61, P > 0.05 (Table 10.3). Similarly, no significant statistical

association was found between purchased prescribed medication and educational levels of

respondents - χ2 (df = 10) = 11.9, P > 0.05.



                                                                                                 260 

 
         A significant statistical difference was found between mean age of respondents who are

at different educational levels – F statistic [2, 6589] = 214.64, P < 0.001. The mean age of

respondents with primary level of education and below was 32.0 years (SD = 22.6, 95% CI =

31.4-32.6) compared to 14.6 years (SD = 1.7, 95% CI = 14.5-14.8) for those with secondary

education level and 26.4 years (SD = 10.6, 95% CI = 24.6-28.2) for those with tertiary education

level.

         A cross-tabulation between self-reported illness and educational level revealed a

significant statistical association - χ2 (df = 2) = 61.33, P < 0.001. Respondents with primary

education level and below recorded the greatest percent of people with illness(es) (16.2%)

followed in descending order by tertiary level (9.2%) and secondary level respondents (5.4%).

The statistical correlation was a weak one – correlation coefficient = 0.10.

         A significant statistical correlation existed between self-reported diagnosed recurring

illness and educational level – χ2 (df = 14) = 42.56, P < 0.001 (Table 10.4). Respondents with

secondary level education (37.5%) had the highest percent of unspecified health conditions

followed in descending order by tertiary (33.3%) and primary level respondents (22.7%).

Hypertension was substantially a phenomenon occurring among those with primary education

level and below: 21.6%, compared to 8.3% of tertiary level individuals. Similarly, diabetes

mellitus (12.8%) was more prevalent among primary level respondents compared to 5.0% of

secondary level respondents. On the other hand, asthma was the greatest among tertiary level

respondents (33.3%) compared to secondary level (22.5%) and primary level respondents

(8.7%).

         Respondents with tertiary level education were most likely to be classified in the

wealthiest 20% (53.4%) compared to those with secondary education who were more likely to be

                                                                                               261 

 
in the middle class and those with primary level education were either in the poorest 20%

(20.3%) or in the wealthiest 20% (20.3) (Table 10.4) – χ2 (df = 8) = 124.53, P < 0.001.

       Of the 20.2% of respondents who had health insurance coverage, tertiary level people

were more likely to have private coverage (35.9%) followed by primary or below (12.0%) and

secondary level individuals (11.6%) – χ2 (df = 4) = 76.95, P < 0.001 (Table 10.4).

       Concurringly, a significant statistical difference existed between the mean age among the

different educational levels in which respondents were categorised (Table 10.4) – F statistic [2,

6589] = 214.6, P < 0.001: mean age for those with at most primary level education was 32.0

years (SD = 22.6) compared to a mean age of 26.4 years (SD = 10.6) for those with tertiary level

education. When educational level of respondents was disaggregated into no formal, basic, and

primary to tertiary, the mean age of respondents with no formal education was 42.7 years (SD =

18.0), 2.7 years (SD = 1.9) for basic school level respondents, and 9.0 years (SD = 2.2) for those

who have primary level education – F statistic [4,6587] = 2207.9, P < 0.001

Multivariate analysis

Self-rated health statuses of respondents are correlated with (1) age, (2) income, (3) crowding,

(4) sex, (5) marital status, (6) area of residence, and (7) self-reported illness(es) – χ2= 1,568.4, P

< 0.001; and that the data is a good fit for the model – LL = 9,218.0. The 7 socio-demographic

and economic correlates accounted for 33% of the variability in self-rated health status (Table

10.5). Based on the Table 10.5, the older the respondents get, the more likely they are to rate

their health status as poor and this was the same for crowding and for those who report an illness

(health condition). Urban residents are more likely to report poor self-rated health status than

rural residents. However, there was no statistical difference between self-rated health status for

rural and semi-urban residents. Married people are more likely to report better self-rated health

                                                                                                  262 

 
status than widowed people, people with more income are more likely to report better health

status, and males are more likely than females to report better health status. However, no

significant statistical difference was found between self-rated health status among the educated

and uneducated cohorts.

Discussion
The current study concurs with the literature in that self-reported illness has the most influence

on self-rated health status of people [8]. In a study of elderly Barbadians (ages 60+ years),

Hambleton et al. [8] found that current illness accounted for 87.7% of the variance in self-rated

health status. In another study on married people in Jamaica, Bourne and Francis [53] found that

73% of self-reported illnesses explains the variability in self-reported health status. Embedded in

the current finding is whether self-rated health is examined on elderly or married people.

Current self-reported illnesses accounted for a critical proportion of self-rated health and can be

used to measure health. Within this context, self-reported illness is a good measure of self-rated

health, and this has been established by other studies [10-17, 25]. A recently conducted research

found that self-reported illness accounted for 54% (r-square) of the variance in life expectancy of

Jamaicans [25], and this increased to 63% for males. Subjective indexes such as self-rated health

and self-reported illness can be used to measure health, but the latter is a better measure and this

must be taken into consideration in the interpretation of findings using this measurement.


       The challenges noted by some researchers in using self-rated health are: (1) bias and (2)

the dichotomisation of the measure. While bias is synonymous with subjective assessment or

evaluation of any construct, the validity of using the measure is high. Diener [34] noted in 1984

that there are still some valid variances, which was validated in a recent study by Bourne [25].

Health literature has long established that subjective indexes such as self-rated health, happiness,
                                                                                                263 

 
and life satisfaction are good measures of health as they are more comprehensive (including

social activities and relationships, psychological conditions, emotions, spirituality, life

satisfaction) while still incorporating the objective component [3, 21, 34]. This is justified by

studies that found strong statistical correlations between subjective health and objective indexes

such as life expectancy [25] and mortality [2, 22-24]. It should be noted here that subjective

indexes (e.g., self-reported illness) and mortality are lowly correlated in Jamaica [25], which

suggests that health literature among regions has revealed different findings. This denotes that

the wholesale use of what is obtained in one nation cannot be applied to another without

understanding socio-demographic characteristics. However, Jamaica, like other nations, can use

subjective indexes to assess health status of its people and by extension its entire population.


       The issue of the dichotomisation of self-rated health, because some of the original values

will be lost, is now resolved by this study as self-rated health was dichotomised and findings

were similar to those who had dichotomised the dependent variable (i.e., self-rated health status).

What are the similarities and dissimilarities between the two statistical approaches in

operationalising subjective health?


       Studies in the Caribbean found that age, marital status, crowding, sex of respondents,

area of residence, income and illnesses were statistically correlated with subjective health [8, 10-

17, 53], which is validated by the current study. Even some non-Caribbean studies have found

the aforementioned variables to be statistically associated with subjective health [7, 9], indicating

that dichotomising self-rated health status does not fundamentally change most of the socio-

demographic, economic, and biological variables.




                                                                                                   264 

 
       Examining data on married people by way of dichotomising self-rated health status,

Bourne [25] found that men had a greater self-reported health status than women, and in the

current study (non-dichotomisation of self-rated health status), males had a higher health status

than females. On the other hand, in Bourne’s work [25], he found in descending order self-

reported illnesses, age, income and sex to be the only factors of self-reported good health while

in the non-dichotomised study more variables accounted for health status. Nevertheless, ranking

of the correlates were similar in both studies as in the current. The factors in descending order

were self-reported illness, age, crowding, income, sex and the others, indicating the closeness of

the statistical approaches. Married people are a component of the general populace and they have

socio-demographic and economic experiences which differ from some unmarried people.


       The literature showed that income is strongly correlated with self-rated health. However,

in Jamaica this is clearly not the case. In Jamaica, income plays a secondary role to illness and

age and when self-rated health is non-dichotomised, it becomes an even weaker variable.

Although income affords one particular choices (or lack thereof), the educated class in Jamaica

received more income than uneducated classes, yet the former class is not healthier than the

latter. This finding is contrary to the literature that showed the association between higher

education and health [7-9]. Education influences social standing and income, but it does not

directly influence good health status in Jamaica. Concurringly, the current work found that

education is positively correlated with more health insurance coverage. However, health

insurance coverage is not significantly associated with better health status. Embedded here is the

fact that health insurance coverage in Jamaica is not an indicator of health care-seeking

behaviour but a product that is purchased for the eventuality of the onset of illness, as it will

lower out-of-pocket medical care expenditure.
                                                                                              265 

 
       Education provides its recipients with knowledge, access to knowledge, access to income

and other empowerment, but it does not mean that the educated classes are more concerned about

their health, and this can be measured using health care-seeking behaviour and knowledge about

the illnesses that are affecting the individual. The current paper found that 25 out of every 100

educated Jamaicans are aware of their health condition(s), and this is greater than that for

uneducated classes. Jamaicans with the least level of education were most cognizant of their

ailments and sought medical care just as much as did educated Jamaicans. Education, therefore,

does not denote empowerment to seek medical care, which is embedded in the culture, in

particular for men. Education is still unable to break the bondages of the perceptions of society

which purport that health is weakness, and that to display weakness as a man removes his

masculinity. This continues to shackle Jamaicans, particularly men, who still subscribe to the

traditional notion that illness is correlated to weakness and that men should not display

weakness. It is this cultural perspective that bars many men from visiting health care facilities,

except in cases of severe illness or if they are married [25]. Hence, mortality being greater for

men is not surprising [54] as many men will die prematurely because of the fact that they are

reluctant to visit health care institutions. This reluctance to seek medical care is not limited to

males. In 1988, when Jamaica began collecting data on the living conditions of its people,

females sought more medical care than males, but the disparity ranged between -2 to 6%. In

2007, 68% of females sought medical care compared to 63% of males, which means that higher

education, which is substantially a female phenomenon in Jamaica, is not fundamentally

improving the health status of females or even males.


       Educated Jamaicans are more likely to live in urban areas and those with primary

education levels or below are more likely to live in semi-urban zones. The current findings found
                                                                                               266 

 
that semi-urban respondents were more likely to have better health status, although they are more

likely to have at most primary level education. In 2007, statistics revealed that 15.3% of

Jamaicans in rural areas were below the poverty line compared to 4% of semi-urban and 6.2% of

urban Jamaicans [41], indicating that poverty is more synonymous with rural areas, yet there is

no significant statistical difference between the self-rated health status of rural and urban

Jamaicans. Income makes a difference in health, as those with more means can access more and

greater resources including health care, but clearly income beyond a certain amount is retarding

the health status of Jamaicans. This study cannot stipulate a baseline income that people should

receive in order to prevent a decline in health status. However, there is clearly a state of

contentment among the poor and very poor who were equally as healthy as the wealthy. The

health disparity between them and the educated showed no significant statistical difference and

this emphasises that wealth does not automatically transfer itself into health. Another issue which

is evident in the data is the variability in the measurement of health among the social classes, as

the poorest 20% reported less illness than the wealthiest 20% [41], yet the former group still

dwells in slums, inner-city neighbourhoods, and violent communities, and they have lower levels

of education. Despite Diener’s findings [34] that the variance is minimal, Bourne’s work showed

a strong association between subjective health (i.e., self-reported illness) and life expectancy – a

correlation coefficient between 50 and 60% for a single variable is strong. However, this

highlights that there are still some challenges embedded in the use of self-rated health status.


Conclusion

While the dichotomisation of self-rated health status loses some of the original data, when self-

rated health is non-dichotomised, socio-demographic and biological variables accounted for 33%

                                                                                                   267 

 
of the explanation of the variance and this was 44% using dichotomisation for married Jamaica,

suggesting dichotomisation of health status still holds some validity. Another critical finding that

emerged from the current work is that education is not improving the health status of Jamaicans.

However, it is correlated with better social standing and higher income. Income is significantly

associated with better health status and it played a secondary role to self-reported illness and age

of respondents. Education is associated with more health insurance coverage, but that health

insurance coverage cannot be used to measure health care-seeking behaviour or measure better

health status of Jamaicans. In summary, there is a need for a public health care campaign that is

specifically geared towards the educated classes as their educational achievement is not

translating itself into better health care-seeking behaviour and health status than the uneducated

which suggests that societal pressures are barring Jamaicans from better health status choices.


Conflict of interest

The author has no conflict of interest to report.


Acknowledgement

Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica
Survey of Living Conditions, 2007, none of the errors that are within this paper should be
ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica, but rather to
the researcher.




                                                                                                  268 

 
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Table 10.1. Demographic characteristic of sample, n=6,783
Characteristic                                                 n     %
Sex
Male                                                        3303   48.7
Female                                                      3479   51.3
Marital status
Married                                                     1056   23.3
Never married                                               3136   69.2
Divorced                                                      77    1.7
Separated                                                     41    0.9
Widowed                                                      224    4.9
Social standing
Poorest 20%                                                 1343   19.8
Poor                                                        1354   20.0
Middle                                                      1351   19.9
Wealthy                                                     1352   19.9
Wealthiest 20%                                              1382   20.4
Area of residence
Urban                                                       2002   29.5
Semi-urban                                                  1458   21.5
Rural                                                       3322   49.0
Self-reported illness
Yes                                                          980   14.9
No                                                          5609   85.1
Self-reported diagnosed recurring illness
Cold                                                        149    14.9
Diarrhoea                                                    27     2.7
Asthma                                                       95     9.5
Diabetes mellitus                                           123    12.3
Hypertension                                                206    20.6
Arthritis                                                    56     5.6
Unspecified                                                 234    23.4
Not reported as diagnosed                                   109    10.9
Health care-seeking behaviour
Yes                                                          658   65.5
No                                                           347   34.5
Self-rated health status
Very good                                                   2430   37.0
Good                                                        2967   45.2
Moderate                                                     848   12.9
Poor                                                         270    4.1
Very poor                                                     50    0.8


                                                                   272 

 
Table 10.2. Educational level by area of residence, n = 6,592
Characteristic                                              Area of residence       Total
Educational level                                Urban       Semi-urban Rural
                                                         %               %       %          %
Primary and below                                     84.8            89.0    88.0        87.3
Secondary                                              10.9             9.6    11.2       10.8
Tertiary                                                4.3             1.5     0.8        2.0
Total                                                 1952            1421    3219       6592
Chi-square (df = 4) = 78.02, P < 0.001, cc = 0.11




                                                                                          273 

 
Table 10.3. Education level by sex of respondents, n = 6,592

Characteristic                                                         Sex               Total
                                                          Male               Female
                                                           %                   %          %
Educational level
Primary and below                                              87.9              86.6        87.3
Secondary                                                       10.5              11.0       10.8
Tertiary                                                         1.6               2.4        2.0
Total                                                          3207              3385       6592
Chi-square (df = 2) = 5.61, P > 0.05




                                                                                              274 

 
Table 10.4. Self-reported diagnosed recurring illness and social standing by educational level
                                                           Educational Level             Total
Characteristic                                 Primary or Secondary Tertiary
                                               below
                                                          %            %              %           %
Self-reported diagnosed recurring
illness1
Cold                                                   15.0         17.5             0.0        14.9
Diarrhoea                                                2.9          0.0           0.0          2.7
Asthma                                                   8.7        22.5           33.3          9.5
Diabetes mellitus                                      12.8          5.0            0.0        12.3
Hypertension                                           21.6          0.0            8.3        20.6
Arthritis                                                5.9         0.0            0.0          5.6
Unspecified condition                                  22.7         37.5           33.3        23.4
Not diagnosed                                          10.5         17.5           25.0         10.9
Total                                                   947           40             12         999
                                      2
Social standing (income quintile)
Poorest 20%                                            20.3         19.7             3.8        19.9
Poor                                                   20.0         21.7             7.6        20.0
Middle                                                 19.4         24.5           16.0         19.9
Wealthy                                                19.9         20.3           19.1         19.9
Wealthiest 20%                                         20.3         13.7           53.4         20.2
Total                                                 5752           709            131        6592
Health Insurance coverage3
No                                                     79.8         83.7           57.8         79.8
Private                                                12.0         11.6           35.9         12.5
Public                                                   8.1          4.6            6.3         7.7
Total                                                 5682           689            128        6499
Age4 Mean (SD) in years                         32.0 (22.6) 14.6 (1.7) 26.4 (10.6) 30.0 (21.8)
Health care-seeking behaviour5
Yes                                                    65.7         60.0           66.7         65.5
No                                                     34.3         40.0           33.3         34.5
Total                                                   953           40             12        1005
Income6 Mean (SD) in US$7                         8,381.88      9,580.20     14,071.67     8,623.84
                                                (6,641.28) (7,712.81)         (9,31.10) (6,874.54)
1
  Chi-square (df = 14) = 42.56, P < 0.001, cc=0.20
2
  Chi-square (df = 8) = 124.53, P < 0.001, cc=0.14
3
  Chi-square (df = 4) = 76.95, P < 0.001, cc=0.11
4
  F statistic [2,6589] = 214.6, P < 0.001
5
  Chi-square (df = 2) = 0.6, P > 0.05
6
  F statistic [2,6589] = 52.4, P < 0.001
7
  Rate in 2007:1US$= Ja$80.47




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Table 10.5. Ordinal logistic regression: Socio-demographic and biological differentials of self-
rated health status of Jamaicans
                                                      Std.                             95% CI
  Characteristic                            Estimate Error Wald        P        Upper          Lower
            Excellent self-rated health            0.0      0.0
            Good self-rated health (ø1)         0.540    0.345     2.456   0.117        -0.135        1.216
            Fair self-rated health (ø2)         3.504    0.625    31.465   0.000         2.279        4.728
            Poor self-rated (ø3)                5.935    0.985    36.327   0.000         4.005        7.865
            Very poor (ø4)                      8.659    1.425    36.909   0.000         5.865       11.452
            Age                                 0.045    0.008    34.055   0.000         0.030        0.060
            Income                        -3.79E-007     0.000    10.636   0.001   -6.06E-007    -1.51E-007
            Crowding                            0.083    0.025    11.130   0.001         0.034        0.132
            Primary or below                   -0.187    0.252     0.553   0.457        -0.681        0.307
            Secondary                           0.042    0.267     0.025   0.874        -0.481        0.566
            Tertiary (=0)
            Sex (female=0)                     -0.221    0.077     8.290   0.004       -0.372        -0.071
            Married                            -0.554    0.200     7.704   0.006       -0.945        -0.163
            Never married                      -0.352    0.192     3.342   0.068       -0.729         0.025
            Divorced                           -0.469    0.319     2.171   0.141       -1.094         0.155
            Separated                          -0.109    0.369     0.087   0.768       -0.832         0.615
            Widowed (=0)
            Poorest 20%                         0.203    0.163     1.554   0.213       -0.116           0.523
            Poor                                0.013    0.140     0.009   0.925       -0.262           0.288
            Middle                              0.028    0.126     0.048   0.826       -0.219           0.274
            Wealthy                            -0.238    0.122     3.782   0.052       -0.477           0.002
            Wealthiest 20% (=0)
            Urban                               0.217    0.090     5.789   0.016        0.040           0.395
            Semi-urban                          0.008    0.085     0.008   0.927       -0.159           0.174
            Rural (=0)

            Private insurance                  -0.175    0.110     2.542   0.111       -0.389           0.040

            Public insurance                    0.026    0.149     0.032   0.859       -0.265           0.318

            Public insurance – other            0.387    0.209     3.433   0.064       -0.022           0.796
            No insurance coverage (=0)

            Illness                             2.377    0.401    35.152   0.000        1.591           3.163
Nagelkerke r-square = 0.33
Chi-square = 1,568.4, P < 0.001
LL = 9,218.0
n=4,433




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                                   Chapter 11
       Retesting and refining theories on the association between
    illness, chronic illness and poverty: Are there other disparities?



                                    Paul Andrew Bourne

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.
The current study used a secondary cross-sectional dataset from the Jamaica Survey of Living
Conditions (JSLC). 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 cross-
sectional survey was conducted between May and August 2002 in the 14 parishes across Jamaica
and included 25,018 people of all ages. 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. 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


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study by Van Agt et al. [8] found that poverty was greater among chronically ill people than the

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
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expectancy [13]. The relationship between poverty and illness is longstanding, and the Director

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

                                                                                                280 

 
care, and not seeking medical care is related to illness, it appears to be a non-issue to re-test the

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


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Standards Measurement Study (LSMS) household survey. The questionnaire covered

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




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(or contribution) of each statistically significant variable in comparison with the others, and the

Odds Ratio (OR) for the interpreting of each significant variable.


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


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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 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.



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       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

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).



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       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 –

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

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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.



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 –

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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

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

                                                                                                  288 

 
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

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

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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%.


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.


                                                                                                290 

 
       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

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
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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

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 B6 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




                                                                                                  292 

 
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-

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
                                                                                                  293 

 
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

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.

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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)



                                                                                                                298 

 
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 15.22±28.91 21.67±37.99 22.54±42.87 33.11±70.35 45.53±79.52        < 0.0001
USD)†
                                                                                                                 299 

 
†USD 1.00 = Jamaican $50.97




                              300 

 
                              


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




                                                                                                       301 
                                    


    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




                                                                                                  302 
                                    


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




                                                                                                     303 
                              



                                   Chapter 12


             Variations in social determinants of health using an
           adolescence population: By different measurements,
           dichotomization and non-dichotomization of health


                                        Paul A. Bourne


On examining health literature, no study emerged that evaluated whether the social determinants
vary across measurement, dichotomization, non-dichotomization and aged cohorts. With the
absence of research on the aforementioned areas, it can be extrapolated that social determinants
of health are constant across measurement, dichotomization and non-dichotomization, and this
assumption is embedded in health planning. This paper seeks to elucidate (1) whether social
determinants of health vary across measurement of health status (ie self-rated health status or
self-reported antithesis of disease) or the cut-off (dichotomization) and/or the non-cut-off of
health status (non-dichotomization), (2) examine the similarities between social determinants
found in the literature and that of using an adolescence population, (3) whether particular
demographic characteristic as well as illness and health status vary by area of residence of
respondents, (4) the health status of the adolescence population, (5) typology of health
conditions that they experience, and (6) evaluate the antithesis of illness (disease) and self-rated
health. The current study extracted a sample of 1, 394 respondents aged 10 to 19 years old from
the 2007 Jamaica Survey of Living Conditions (JSLC). The present subsample represents 20.6%
of the 2007 national cross-sectional sample (n = 6,783). Multivariate logistic and ordinal logistic
regression analyses were used to examine the association between many independent variables
and a single dependent variable. In this study, health was measured using (1) self-rated health
status or (2) the antithesis of illness (not reporting a health condition). The dichotomization of
each denotes the use of two groups, and non-dichotomization means that self-rated health status
was used in its Likert scale form (i.e. very good; good; moderate; poor and very poor). Antithesis
of illness is a better measure than self-reported health status in determining social determinants
because of its explanatory power (53%) compared to those that used the self-rated health status
(at most 38%). There were noticeable variations in social determinants of health among the
dichotomized, non-dichotomized health and antithesis of illness. Social determinants of health
vary across the measurement and dichotomization and non-dichotomization of health status. The
findings provide insights into the social determinants and health, and recommend that we guard
against a choiced approach without examining the studied population in question.



                                                                                                 304 
                               


Introduction

Adolescents aged 10 to 19 years are among the most studied groups in regard health issues in the

Caribbean, particularly sexuality and reproductive health matters [1-4]. Apart of the rationales

for the high frequency of studies on those in the adolescence years are owing to the prevalence of

HIV/AIDS, unwanted pregnancy, inconsistent condom usage, mortality arising from the

HIV/AIDS virus, and other risky sexual behaviour. With one half of those who are infected with

the HIV/AIDS virus being under 25 years old [1], this provides a justification for the importance

of researching this aged cohort. Statistics revealed that the HIV virus is the 3rd leading cause of

mortality among Jamaicans aged 10-19 years old (3.4 per 100,000, for 1999 to 2002) [5], and

again this provides a validation for the prevalence of studies on this cohort. Outside of the

Caribbean, sexuality and reproductive health matters among adolescents are well studied [6-11],

suggesting that those issues are national, regional and international.


       While sexuality and reproductive health matters are critical to the health status of people

[1], reproductive health problems as well as sexuality form a part of the general health status.

Health is more that the ‘antithesis of diseases’ [12] or reproductive health problems as it extends

to social, psychological or physical wellbeing and not merely the antithesis of diseases [13].

Bourne opined that despite the broadened definition of health as offered by the WHO [14],

illness is still widely studied in the Caribbean, particularly among medical researchers and/or

scholars. A search of the West Indian Medical Journal for the last one half decade (2005-2010), a

Caribbean scholarly journal, revealed that the majority of the studies have been on different

variations of illness, and antithesis of diseases instead of the broadened construct of health.




                                                                                                  305 
                              


       Outside of the West Indian Medical Journal, few Caribbean studies have sought to

examine the health status of adolescents [15-18] but even fewer published research were found

that examine quality of life of those in the adolescence years [19]. Even though quality of life is

a good measure of general health status, international studies exploring quality of life and self-

rated health status among the adolescence years are many [20-25] compared to those in Jamaica.

A comprehensive review of the literature on health status, particularly among the adolescence

population, revealed that none has used a national survey data to examine social determinants of

health across different measurement and dichotomization of health (the recoding of the measure

into two groups) to assess whether there is variability in determinants as well as explore the

health of this cohort.


       Even among studies which have examined social determinants of health, particularly

among the population [26-34], few have used the elderly population [35-37] and only men in the

poor and the wealthy social strata [37, 38], but none emerged in a literature research that have

used the adolescent population (ages 10-19 years). On examining health literature, no study

emerged that evaluated whether the social determinants of health vary across measurement,

dichotomization and non-dichotomization of health (using the measure in its Likert scale form),

and age cohort. With the absence of research on the aforementioned areas, it can be extrapolated

that social determinants of health are constant across measurement, dichotomization and non-

dichotomization, and this assumption is embedded in health planning. The absence of such

information means that critical validity to the discourse and use of social determinants would

have been lost, as social determinants of health are used in the planning of health policies, future

research and in explaining health disparities.




                                                                                                 306 
                               


        Statistics revealed that one in every five Jamaican is aged 10-19 years old [39], which

means this is a substantial population and because of its influence of future labour supply it is of

great value. Although Pan American Health Organization (PAHO) [5] stated that adolescents

enjoy good health, and only about 2% of morality in 2003, which was equally the case for

adolescents in the Americas, this information does not indicate distancing examination from their

health status. The current work, therefore, will bridge the gap in the literature by evaluating

social determinants of health among those in the adolescence years across varying measurement

of health. Using data for 2007 Jamaica Survey of Living Conditions (2007 JSLC), this paper

seeks to elucidate (1) whether social determinants of health vary across measurement of health

status (ie self-rated health status or self-reported antithesis of disease) or the cut-off

(dichotomization) and/or the non-cut-off of health status (non-dichotomization), (2) are there

similarities between social determinants found in the literature and that of using an adolescence

population, (3) whether particular demographic characteristic as well as illness and health status

vary by area of residence of respondents, (4) what is the health status of the adolescence

population, (5) typology of health conditions that they experience, and (6) evaluate the antithesis

of illness (disease) and self-rated health.


Methods and measure

Data

The current study extracted a sample of 1, 394 respondents aged 10 to 19 years old from the

2007 Jamaica Survey of Living Conditions (JSLC). The inclusion/exclusion criterion for this

study is aged 10 to 19 years old. The present subsample represents 20.6% of the 2007 national

cross-sectional sample (n = 6,783). The JSLC is an annual and nationally representative cross-

sectional survey that collects information on consumption, education, health status, health

                                                                                                 307 
                                 


conditions, health care utilization, health insurance coverage, non-food consumption

expenditure, housing conditions, inventory of durable goods, social assistance, demographic

characteristics and other issues [40]. 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 [41]. An administered questionnaire

was used to collect the data.

        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 residences in rural areas and 150 in urban areas. An ED is an

independent geographic unit that shares a common boundary. 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 dwellings was

compiled, which in turn provided the sampling frame for the labour force. One third of the

Labour Force Survey (LFS) was selected for the JSLC.


        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 [40]. A total of 620

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. This study utilized the data set of the 2007 JSLC to conduct our work

[42].


Measure

                                                                                              308 
                             


Age is a continuous variable which is the number of years alive since birth (using last birthday)

Adolescence population is described as the population aged 10 to 19 years old [23]


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. For the antithesis of disease

(illness) a binary variable was created, where 1= not reported a health condition (no to each

illness) and 0 = otherwise (absence of reporting an illness). The use of two groups for self-

reported illness denotes that this variable was dichotomized into good health (from not reported a

health condition) and poor health (i.e. having reported an illness or health condition). Thus, the

seven health conditions were treated as dichotomous variables, coded as was previous stated.


Self-rated health status: This was taken from the question “How is your health in general?” The

options were very good; good; fair; poor and very poor. For purpose of this study, the variable

was either dichotomized or non-dichotomized. The dichotomization of self-rated health status

denotes the use of two groups. There were four dichotomization of self-rated health status – (1)

very poor-to-poor health status and otherwise; (2) good and otherwise; (3) good-to-very good

health status and otherwise and (4) moderate-to-very good self reported health status and

otherwise. The dichotomized variables were measured as follow:


       1= very poor-to-poor health, 0 = otherwise


       1= good, 0 = otherwise


       1 =good-to-very good, 0 = otherwise


       1= moderate-to-very good, 0 = otherwise


                                                                                               309 
                               


The non-dichotomization of self-rated health status means that the measure remained in its Likert

scale form (i.e. very good; good; moderate; poor and very poor health status).


Social class (hierarchy): This variable was measured based on income quintile: 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).


Family income is measure using total expenditure of the household as reported by the head.


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 analysis of variance for metric and non-dichotomous nominal variables.

Logistic regression was used to evaluate a dichotomous dependent variable (self-rated health

status and antithesis of illness) and some metric and/or non-metric independent variables.

However, ordinal logistic regression was used to examine a Likert scale variable (self-rated

health status) and some metric and/or non-metric independent variables. A pvalue of < 5% (two-

tailed) was used to establish statistical significance. Each model begins with variables identified

in the literature (Models 1-5), will be tested using the current data and the significant variables

highlighted using an asterisk (Tables 12.3 and 12.4).


Models




                                                                                                310 
                              


The use of multivariate analysis to study health status and subjective wellbeing (i.e. self-reported

health) is well established in the literature [36-38].       Previous works have examined the

dichotomization of health status in order to establish whether a particular measurement of health

status is different from others [43-45]. The current study will employ multivariate analyses to

examine health by different dichotomization and statistical tools to determine if the social

determinants remain the same. 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 [33], Smith & Kingston [34], Hambleton et al. [37], Bourne

[46], Kashdan [47], Yi & Vaupel [48], and the World Health Organization pilot work a 100-

question quality of life survey (WHOQOL) [49] have used subjective measures to evaluate

health. Diener [50,51] has used and argued that self-reported health status can be effectively

applied to evaluate health status instead of objective health status measurement, and Bourne [46]

found that self-reported health may be used instead of objective health. Embedded in the works

of those researchers is the similarity of self-reported health status and self-reported dysfunction

in assessing health. Thus, in this work we will use self-reported health status and the antithesis of

illness to measure health, and dichotomize self-reported health status as follows (1) good health

= 1, 0 = otherwise; (2) good-to-excellent health=1, 0 = otherwise; (3) moderate-to-excellent

health=1, 0 = otherwise; and (4) very poor-to-poor health= 1, 0 = otherwise. Another measure

was that health was evaluated by all the 5-item scale (from very poor to excellent health status),

using ordinal logistic regression.

       The current study will examine the social determinants of self-rated health of Jamaican

adolescents and whether the social determinants vary by measurement and dichotomization

and/or non-dichotomization of health. Five hypotheses (models) were tested in order to



                                                                                                 311 
                               


determine any variability in social determinants based on the measurement of health status.

Model (1) is the antithesis of disease, non-dichotomization of self-reported health (antithesis of

disease); Model (2) is the non-dichotomization of self-rated health status (ie using the 5-item

Likert scale as a continuous variable), and Models (3-6) are the different dichotomized self-rated

health status (ie. 3= very poor-to-poor; 4=good, 5=moderate-to-very good 6=good-to-very good).

All the models were tested with the same set of social determinants of health, with the only

variability being the measurement of health status (self-rated health status), cut-off of health

(dichotomization) and/or non-dichotomization of self-rated health status.



HA=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi)    (1)

       where HA (i.e. self-rated antithesis of diseases) is a function of age of respondents, Ai; sex

       of individual i, Gi; area of residence, ARi; current self-reported illness of individual i, It;

       logged duration of time that individual i was unable to carry out normal activities (or

       length of illness), lnDi; Education level of individual i, EDi; union status of person i, USi;

       social class of person i, Si; health insurance coverage of person i, HIi; logged family

       income, lnY; crowding of individual i, CRi; logged medical expenditure of individual i in

       time period t, lnMCt; social assistance of individual i, SAi; and an error term (ie. residual

       error).

       Note that length of illness was removed from the model as it had 93.5% of the cases were

       missing as well as union status which had 58.2%.



HND=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi)    (2)

      Where HND denotes the non-dichotomization of self-rated health status.



                                                                                                  312 
                               


        Note that length of illness was removed from the model as it had 93.5% of the cases were

        missing as well as union status which had 58.2%.



HD1=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi)     (3)

        Where HD1 is very poor-to-poor self-rated dichotomized health status.

        Note that length of illness was removed from the model as it had 93.5% of the cases were

        missing as well as union status which had 58.2%.

HD2=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi)     (4)

        Where HD2 is good self-rated dichotomized health status.

        Note that length of illness was removed from the model as it had 93.5% of the cases were

        missing as well as union status which had 58.2%.



HD1-4=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi)    (5)

        Where HD3 is very moderate-to-very good self-rated dichotomized health status.

        Note that length of illness was removed from the model as it had 93.5% of the cases were

        missing as well as union status which had 58.2%.



HD1-4=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, εi)    (6)

        Where HD4 is good-to-excellent self-rated dichotomized health status.

        Note that length of illness was removed from the model as it had 93.5% of the cases were

        missing as well as union status which had 58.2%.



Results


                                                                                             313 
                               


Demographic characteristics of studied population


       Table 12.1 presents information on demographic characteristic of the sampled population.

Of the population (n = 1,394), 43.9% has primary or below primary level education, 53.1%

secondary level and 3.0% had tertiary level education.


       Table 12.2 presents information on the particular demographic characteristic as well as

health status and self-reported illness of respondents by area of residence.


       Table 12.3 depicts information of variables which explain the antithesis of illness among

the adolescence population.


       Table 12.4 shows the different dichotomizations of self-rated health status and non-

dichotomized self-rated health status, and the various social determinants which explain each.


       Table 12.5 examines associations between self-rated health status and antithesis of illness

(or disease).

Limitations of study

This study was extracted from a cross-sectional survey dataset (Jamaica Survey of Living

Conditions, 2007). Using a nationally representative cross-sectional survey dataset, this research

extracted 1394 adolescent Jamaicans which denote that the work can be used to generalize about

the adolescent population in Jamaica at the time in question (2007). However, it cannot be used

to make predictions, forecast, and establish trends or causality about the studied population.


Discussion




                                                                                                 314 
                             


        The current work showed that while the majority of Jamaican adolescents have at least

self-rated good health status (92 out of every 100); some indicated at most moderate self-rated

health status. Even though only 1.4% of the sample mentioned that they have very poor-to-poor

health status, 6.5% indicated that they experienced a health condition in the last 30 days. Of

those who reported a health condition, 5.3% were diagnosed with chronic illness (diabetes

mellitus, 3.9%; hypertension, 1.3%). Although 2.4 times more adolescent in rural areas are in the

lower class compared with those in urban areas, rural adolescents have a greater good health

status compared to their urban counterparts, but this was the reverse for rural and periurban

adolescents. Another important finding was that there is no statistical association between health

conditions and area of residence, but urban and periurban adolescents were more likely to have

health insurance coverage compared to those in rural areas.

        In Jamaica, the adolescence population’s health status is comparable to those in the

United States [23], suggesting that inspite of the socioeconomic disparities between the two

nations and among its peoples, the self-reported health status among adolescent Jamaicans is

good. The high health status of those in the adolescence population in Jamaica speaks good of

the inter dynamics within the countries, but does not imply that they are the same across the two

nations or can it be interpreted that the quality of life of Jamaicans is the same as those in the

United States. Simply put, the adolescence population in Jamaica is experiencing a good health

status although HIV/AIDS, unwanted pregnancies, and inconsistent condom usage are high in

this cohort [1-5].

        While the aforementioned results about good health status of Jamaican adolescents

concurs with PAHO’s work in 2003 [5] and others [17], which has continued into 2007, the

current paper provides more information on health matters of adolescents aged 10-19 years than



                                                                                               315 
                             


that offered by PAHO. An adolescent in Jamaica who seeks medical attention is 100% less likely

to report an illness, and those who indicated at least good self-rated health status was 13 times

more likely not to report an illness. Continuing, adolescents in the upper class are 15 times more

likely to report very poor-to-poor health status compared to those in the lower class. And that

those who indicated very poor-to-poor health status are more likely to seek medical care (10

times), live in crowded household and less likely to spend more on consumption and non-

consumption items. On the other hand, those who stated that their health status was at least

moderate were less likely to live in crowded household, spent more on consumption and non-

consumption items. Using a 2007 national probability dataset for the adolescence population in

Jamaica, we can add value to the existing literature on health status as well as the social

determinants of health.

       Grossman introduced the use of econometric analysis in the examination of health in the

1970s to establish determinants of self-rated health [33], which has spiraled a revolution in this

regard since that time. Using data for the world’s population, he identified particular social

determinants of health that was later expanded upon by Smith and Kington [34]. Since the earlier

pioneers’ work on social determinants of health [33, 34], the WHO joined the discourse in 2000s

[27] as well as Marmot [26], Kelly et al. [28]; Marmot and Wilkinson [29]; Solar and Irwin [30];

Graham [31]; Pettigrew et al. [32], Bourne [35], Bourne [36], Hambleton et al. [37] and Bourne

and Shearer [38], but none of them evaluated whether there was variability in the determinants of

health depending on the measurement and/or dichotomization of health.

       The variability in social determinants of health was established by Bourne and Shearer

[38] in a study between men in the poor and the wealthy social strata in a Caribbean nation, but

the literature at large has not recognized the variances in social determinants based on the



                                                                                               316 
                                


dichotomization and non-dichotomization self-rated health status, and measurement of heath

(using antithesis of illness and self-rated health status). Such a gap in the literature cannot be

allowed to persist as it assumes that social determinants are consistent over the measurement of

health.

          Bourne [43] like Manor et al. [44] and Finnas et al. [45] have dichotomized self-reported

health status and cautioned future scholars about how the dichotomization can be best done.

According to Bourne [43] “The current study found that dichotomi[z]ing poor health status is

acceptable assuming that poor health excludes moderate health status, and that it should remain

as is and ordinal logistic be used instead of binary logistic regression” [43, p.310], and others

warned against the large dichotomization of self-rated health status [44,45]. Because self-rated

health status is a Likert scale variable, ranging from very poor to very good health status, many

researchers arbitrarily dichotomized it, but the cut-off is not that simple as was noted by Bourne

[43], Manor et al. [44] and Finnas et al. [45].


          From data on Jamaicans, Bourne’s work revealed that the cut-off in the dichotomization

of self-rated health status should be best done without moderate health when dichotomizing for

poor health status [43]. All the scholars agreed that narrowed cut-offs are preferable in the

dichotomization of self-rated health status, but only a few variables were used (marital status,

age, social class, area of residence and self-reported illness) [43-45]. Bourne postulated that “By

categorising an ordinal measure (i.e., self-reported health) into a dichotomous one, this means

that some of the original data will be lost in the process.” [43, p.295]. Using many more

variables, the present work highlighted that some social determinants of health are lost as a result

of the dichotomization process. Simply put, the social determinants of health are not consistent

across the dichotomization process which concurs with the literature.


                                                                                                 317 
                               


       While we concur with other scholars that by dichotomizing self-rated health status some

social determinants are lost in the process [43-45], we will not argue with those who opined that

self-rated health status should remain a Likert scale measure [52, 53]. The evidence is in that

more social determinants in the non-dichotomized self-rated health do not give a greater

explanatory power; instead this model had the least explanation. This indicates that more is not

necessarily better, and such information must be taken into account in a decision to cut-off at a

particular point. The fact that more social determinants of health emerged when health was non-

dichotomized coupled with a lower explanatory power compared with when it is dichotomized as

very poor-to-poor health means that using self-rated health as a Likert scale valve is not

preferable to dichotomizing it. A narrower dichotomization of self-rated health status,

particularly very poor-to-poor health, as well as moderate-to-very good health status yielded

greater explanations than non-dichotomizing health status.


       This study used both the antithesis of illness and self-rated health status to measure, and

evaluates the social determinants of health, and assess whether antithesis of illness is still a better

measure of health than self-rated health status. A comparison of the social determinants based

on the measurement of health revealed that for the Jamaican adolescence population, antithesis

of illness is a better measure than self-reported health status in determining social determinants

because of its explanatory power (53%) compared to those that used the self-rated health status

(explanatory power at most 38%). On the other hand, the antithesis of illness had fewer social

determinants compared with those in self-rated health status, suggesting that more social

determinants of health should not be preferred to fewer because the latter measure had a greatest

explanation. Like dichotomizing self-rated health status, variation also exists among




                                                                                                   318 
                                  


dichotomization of health and antithesis of illness. Thus, it appears that the antithesis of illness

may provide a better measure for the social determinants of health than self-rated health status.


       Diener [50, 51] had postulated that self-reported health status can be effectively applied

to evaluate health status instead of objective health status measurement (morbidity, life

expectancy, mortality), and Bourne [46] found a strong statistical association between self-

reported illness and particular objective measure of health (life expectancy, r = -0.731); but a

weak relationship between self-reported illness and mortality. Using a nationally representative

sample 6,782 Jamaicans, one researcher warned against using self-reported illness as a measure

of health as he found that men were over-reporting their illness [54], and this means they were

over-rating their antithesis of illness. Those studies highlight the challenges in using subjective

measures in evaluating health as they are not consistent like the objective ones such as mortality,

life expectancy, and diagnosed morbidity. Nevertheless, on examining the antithesis of illness

and self-rated health status, it was revealed that 2.9% of those who indicated very good health

status had an illness compared to 20% of those who reported an illness who had very good health

status. From the current work again it emerged that there is disparity between self-reported

illness (or antithesis of illness) and self-rated health status, indicating why caution is required in

using either one or the other.


       Other disparities between antithesis of illness and self-rated health status highlighted that

antithesis of illness is a better measure of health than self-rated health status. Clearly despite the

efforts of the WHO in broadening the conceptualization of health away from the antithesis of

illness, the Jamaican adolescence population has not moved to this new frontier. As when they

were asked to report on the antithesis of illness, they gave lower values than indicated for self-

rated health status. Because antithesis of illness captures health more than self-rated health

                                                                                                   319 
                                 


status, this justifies why the former had a greater explanation when the social determinants of

health were examined than that of self-rated health status. But, where were their differences in

the variables used in one measure compared with the others?


       In fact, all the variables used in this study were social determinants that were identified in

the literature [26-38], and many of them were not significant for the adolescence population of

this research. It can be extrapolated from the current work that social determinants of health for a

population are not the same for a sub-population, in particular adolescence population. Thus,

when the WHO [27] and affiliated scholars [26, 28-32] forwarded social determinants of health,

prior to that some scholars like Grossman [33] and Smith and Kington [34] had already social

determinants of health of a population. However, none of them stipulated that there are

disparities and variations in these based on the dichotomization, non-dichotomization, sub-

population, and measurement of health (ie self-rated health or antithesis of illness).


       Using a cross-sectional survey (2003 US National Survey of Children's Health) of some

102,353 children aged 0 to 17 years, Victorino and Gauthier [55] established that there were

some variations in social determinants of health based on particular health outcomes. The health

outcomes used by Victorino and Gauthier are presence of asthma, headaches/migraine, ear

infections, respiratory allergy, food/digestive allergy, or skin allergy, which are health

conditions. Another research using the 2003 US National Survey of Children's Health (NSCH)

investigated the association of eight social risk factors on child obesity, socioemotional health,

dental health, and global health status [56]. From a research in England, Currie et al. [57] found

disparity in income gradient associated with subjectively assessed general health status, and no

evidence of an income gradient associated with chronic conditions except for asthma, mental

illness, and skin conditions.

                                                                                                  320 
                              


       This paper concurs with the literature that there are variations in some social

determinants of health status across measurement, dichotomization and non-dichotomization of

health. However, the present work went further than the current literature and found that

particular dichotomization of health had stronger explanatory power, and disparity in

determinants. As such, the variations in social determinants of health vary across the

dichotomization and measurement of health as this paper showed that more social factors do not

translate into greater explanatory power; and that stronger explanation does not denotes more

social determinants. And the social determinants of health had the greatest explanatory power

used antithesis of illness to measure health.


Conclusion

In summary, the general health status of the adolescence population in Jamaica is good, but 7 in

every 100 have reported an illness of which some had chronic conditions (diabetes mellitus,

3.9% and hypertension, 1.3%), and those who classified as being in the wealthy class were more

likely to report very poor-to-poor health status compared with those in the lower class. Another

important finding was that rural adolescents had a greater health status than urban adolescents,

but periurban adolescents had the greatest health status.


       Outside of the aforementioned good health news, the social determinants of self-rated

health status vary across the measurement of and dichotomization and non-dichotomization of

health and the population used. This work provides invaluable insights into how social

determinants should be examined, modify the general social determinants of health offered by

the World Health Organization and some associated scholars. By varying the measurement,

dichotomization and non-dichotomization of health, this work provide some justification as to


                                                                                             321 
                              


whether a particular dichotomization of health is better or non-dichotomization is preferable to

dichotomization.


         This researcher will not join the group of scholars who are purporting for the non-

dichotomization of self-rated health status, but we recognized that discourse offers some

information. However, we will chide researchers against arbitrarily using a particular

dichotomization, non-dichotomization and measurement without understanding peoples’

perception of health to which they seek to examine, and evaluate these. Thereby, despite the

international standardized definition of a phenomenon, people may a different view as to this

issue.


Disclosures


The author reports no conflict of interest with this work.


Disclaimer
The researcher would like to note that while this study used secondary data from the 2007
Jamaica Survey of Living Conditions (JSLC), none of the errors in this paper should be ascribed
to the Planning Institute of Jamaica and/or the Statistical Institute of Jamaica, but to the
researcher.

Acknowledgement

The author 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 (2007 Jamaica Survey
of Living Conditions, JSLC) available for use in this study.




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                                                                                                325 
                                  


Table 12.1: Demographic characteristic of studied population, n = 1394
Characteristic                                                       n                   Percent
Sex
 Male                                                                    672                          48.2
 Female                                                                  722                          51.8
Union status
 Married                                                                   1                           0.2
 Common-law                                                               14                           2.4
 Visiting                                                                 73                          12.5
 Single                                                                  494                          84.8
Social assistance
 Yes                                                                      232                         17.3
 No                                                                      1108                         82.7
Area of residence
 Urban                                                                   394                          28.3
 Periurban                                                               287                          20.6
 Rural                                                                   713                          51.1
Population Income Quintile
 Poorest 20%                                                             320                          23.0
 Second poor                                                             328                          23.5
 Middle income                                                           287                          20.6
 Second wealthy                                                          263                          18.9
 Wealthiest 20%                                                          196                          14.1
Self-reported illness
 Yes                                                                       89                          6.6
  No                                                                     1251                         93.4
Self-reported diagnosed illness
 Influenza                                                                22                          28.9
 Diarrhoea                                                                 1                           1.3
 Respiratory illness (ie asthma)                                          16                          21.1
 Diabetes mellitus                                                         3                           3.9
 Hypertension                                                              1                           1.3
 Other conditions (unspecified)                                           33                          43.4
Health care-seeking behaviour
 Yes                                                                      50                          53.8
 No                                                                       43                          46.2
Self-rated health status
 Very good                                                               631                          47.2
 Good                                                                    601                          45.0
 Moderate                                                                 84                           6.3
 Poor                                                                     18                           1.3
 Very poor                                                                 2                           0.1
Health insurance coverage
 No                                                                      1123                          85.3
 Yes                                                                      194                          14.7
Age, mean (Standard deviation, SD)                                              14.2 years (SD = 2.8 years)
Length of illness, median (range)                                                     5 days ( 0 – 36 days)




                                                                                                          326 
                                   


Table 12.2: Particular demographic variables by area of residence, n = 1,394
Characteristic                                              Area of residence                              P, χ2
                                              Urban              Periurban            Rural
Self-reported illness                         n (%)                n (%)              n (%)         0.628, 0.931
  Yes                                             27 (7.1)              15 (5.4)         47 (6.9)
  No                                           352 (92.9)            264 (94.6)       635 (93.1)
Self-rated health status                                                                                   24.82, 0.002
 Very good                                     162 (42.7)            141 (50.4)       328 (48.4)
 Good                                          172 (45.4)            132 (47.1)       297 (43.9)
 Moderate                                        38 (10.0)               7 (2.5)        39 (5.8)
 Poor                                              7 (1.8)               0 (0.0)        11 (1.6)
 Very poor                                         0 (0.0)               0 (0.0)         2 (0.3)
Social class                                                                                          172.64, < 0.0001
 Lower                                         101 (25.6)            108 (37.6)       439 (61.6)
 Middle                                          88 (22.3)            58 (20.2)       141 (19.8)
 Upper                                         205 (52.0)            121 (42.2)       133 (18.7)
Educational level                                                                                      37.79, < 0.0001
 Primary or below                              138 (36.6)            136 (48.6)       312 (46.1)
 Secondary                                     213 (56.5)            136 (48.6)       359 (53.0)
 Tertiary                                         26 (6.9)               8 (2.9)         6 (0.9)
Sex                                                                                                         1.20, 0.548
 Male                                          213 (54.1)            148 (51.6)       361 (50.6)
 Female                                        181 (45.9)            139 (48.4)       352 (49.4)
Health insurance coverage                                                                                   9.36, 0.009
 Yes                                             73 (19.4)            37 (13.6)         84 (12.6)
 No                                            303 (80.6)            235 (86.4)        585 (87.4)
Length of illness, mean ± SD              6.0 ± 5.7 days         7.8 ± 9.0 days    6.4 ± 6.5 days       F = 0.42, 0.857




                                                                                                               327 
                                    


Table 12.3: Logistic regression: Variables of antithesis of illness among adolescence population, n = 1,280
Characteristic                                                                            OR                CI (95%)
Age                                                                                       1.1                 1.0 - 1.3
Health care-seeking (1=yes)                                                               0.0              0.0 - 0.01*
Health insurance coverage (1=yes)                                                         1.0                 0.4 - 2.5
Primary education (reference group)                                                       1.0
Secondary                                                                                 1.8                 0.9 - 3.7
Tertiary                                                                                  1.9               0.3 - 15.1
lnMedical                                                                                 0.8                 0.1 - 5.0
Male                                                                                      1.4                 0.7 - 2.6
Social assistance from government                                                         1.6                 0.6 - 4.4
Logged family income                                                                      0.8                 0.3 - 1.8
Rural area (reference group)
Urban                                                                                     1.6                 0.7 - 3.8
Periurban                                                                                 1.2                 0.5 - 2.9
Poor-to-Very poor health status (reference group)                                         1.0
Moderate-to-Very good health status                                                       0.3               0.03 - 2.1
Good-to-Very good health status                                                          12.6              6.0 - 26.3*
Lower class (reference group)
Middle class                                                                              1.6                 0.5 - 5.2
Upper                                                                                     0.8                 0.2 - 3.1
Crowding                                                                                  0.9                 0.8 - 1.1
Model χ2, P                                                                                          287.08, < 0.0001
-2 LL                                                                                                          327.56
R2                                                                                                                0.53
Hosmer and Lemeshow                                                                                χ2 = 4.40, P = 0.82
OR denotes odds ratio, CI (95%) means 95% confidence interval and *P < 0.05




                                                                                                                   328 
                                    


Table 12.4: Logistic and Ordinal Logistic regression: Factors explaining self-reported health status of
adolescents, n = 1,280
                                                                                    Self-rated health status
                                      Very poor-to-poor              Good              Moderate-to-very      Good-to-ver
Characteristic                                                                               good               good
                                        OR        CI (95%) OR           CI (95%)         OR       CI (95%)   OR CI (95

Self-reported illness (1=yes)           2.0      0.3 – 15.6     0.1     0.05 – 0.2*        0.5       0.1 – 4.4     0.1 0.05 –
Age                                     1.0        0.9 – 1.2    0.9        0.9 – 1.1       1.0       0.8 – 1.2     0.9     0.9 –
Health care-seeking (1=yes)            10.0     1.0 – 96.5*     0.7       0.3 – 1.9        0.1     0.01 – 0.5*     0.7     0.3 –
Health insurance coverage (1=yes)       0.3      0.04 – 2.8     1.1        0.6 – 2.2        3.0     0.4 – 25.5     1.2     0.6 –
Primary education (reference group)     1.0                     1.0                         1.0                    1.0
Secondary                               0.7       0.3 – 1.9     0.9       0.6 – 1.5         1.4       0.5 – 3.8    1.0     0.6 –
Tertiary                                0.0         0 – 0.0     0.4       0.1 – 1.0     5E+007            0.0 -    0.4     0.2 –
Logged Medical expenditure              1.6       0.7 – 3.6     0.6       0.4 – 1.2                                0.7     0.4 –
Social assistance from government       0.2      0.03 – 1.7     1.2       0.6 – 2.2         4.8     0.6 – 38.5     1.2     0.6 –
Lower class (reference group)           1.0                     1.0                         1.0                    1.0
Middle class                            0.6        0.1 – 2.9    2.1       0.9 – 4.5         1.8       0.3 – 9.6    2.2     1.0 –
Upper                                  14.9   1.9 – 118.3 *     0.7       0.3 – 1.4         0.1    0.01 – 0.5*     0.7     0.3 –
Rural area (reference group)            1.0                     1.0                         1.0                    1.0
Urban                                   1.6        0.4 – 3.0    0.6      0.4 – 1.0*         0.9       0.3 – 2.7    0.6    0.4 –
Periurban                               0.0         0.0 - 0.0   3.3       1.3 – 8.2*   2E+0007                     3.3 1.53–
Male                                    0.9        0.3 – 2.3    1.5        1.0 – 2.4        1.1        0.4– 3.0    1.4     0.9 –
Logged family income                    0.1     0.04 – 0.4*     1.3       0.9 – 2.0*        8.2    2.8 – 23.8*     2.0    1.2 –
Crowding                                1.6       1.3 – 2.0*    0.9       0.8 – 1.0*        0.6     0.5 – 0.8*     0.9 0.8 – 0
Model χ2, P                                59.66, < 0.0001        113.11, < 0.0001             30.37, < 0.0001      113.11, <0.0
-2 LL                                                146.38                  588.76                     175.67               58
R2                                                      0.38                    0.20                       0.31
Hosmer and Lemeshow                       χ2 = 4.6, P = 0.82     χ2 = 4.61, P = 0.80       χ2 = 4.36, P = 0.94    χ2 = 4.61, P =

OR denotes odds ratio; *P < 0.05




                                                                                                           329 

 
                                    


Table 12.5: Self-rated health status and antithesis of illness, n = 1,330
                                                                     Self-rated health status
Characteristic                             Very good              Good          Moderate           Poor   Very poor
                                                n (%)             n (%)             n (%)         n (%)       n (%)
Antithesis of illness
   No                                         18 (2.9)          38 (6.4)        26 (31.3)      7 (38.9)       0 (0.0)
  Yes                                      611 (97.1)        560 (93.6)         57 (68.7)     11 (61.1)    2 (100.0)
χ2 = 125.58, P < 0.0001
                                                               Good health (Antithesis of illness)
Characteristic                                                                No                                Yes
                                                                           n (%)                              n (%)
Self-rated health status
   Very good                                                            18 (20.0)                         611 (49.2)
   Good                                                                 38 (42.7)                         560 (45.1)
   Moderate                                                             26 (29.2)                           57 (4.6)
   Poor                                                                   7 (7.9)                           11 (0.9)
   Very poor                                                              0 (0.0)                            2 (0.2)
χ2 = 125.58, P < 0.0001




                                                                                                                330 

 
                               



                                    Chapter 13


         Childhood Health in Jamaica: changing patterns in health
                  conditions of children 0-14 years



                                     Paul Andrew Bourne

The new thrust by WHO is healthy life expectancy. Therefore, health must be more than
morbidity. It is within this framework that a study on childhood health in Jamaica is of vital
importance. This study 1) expands the health literature in Jamaica and by extension the
Caribbean, 2) will aid public health practitioners with research findings upon which they are able
to further improve the quality of life of children, 3) investigates the age at with children in
Jamaica become influenced by particular chronic diseases and 4) assesses the subjective
wellbeing of children. The current study extracted a sample of 8,373 and 2,104 children 0-14
years from two surveys collected jointly by the Planning Institute of Jamaica and the Statistics
Institute of Jamaica for 2002 and 2007 respectively. A self-administered questionnaire was used
to collect the data. Ninety-one percent of children in Jamaica, for 2007, reported good health.
The number of children who had diarrhoea fell by 84.2% in 2007 over 2002, and a similar
reduction was observed for those with asthma (42.1% in 2002 and 19.7% in 2007). Another
critical finding was that 1.2% of children, in 2007, had diabetes mellitus compared to none in
2002. Public health now has an epidemiological profile of health conditions of children and the
demographic shifts which are occurring and this can be used for effective management and
planning of the new health reality of the Jamaican child.



INTRODUCTION

One of the measures of child health and the health status of the general populace is infant

mortality or mortality, which is well studied in Jamaica and the wider Caribbean [1-11]. The

simple rationale for the use of mortality in evaluating health status is owing to its ease in which it

can be used to precisely measure its outcome unlike other indicators such as quality of life,

subjective wellbeing, happiness or life satisfaction [12-22]. Another reason for the use of infant

                                                                                                  331 

 
                                  


mortality in the measurement of health is because of the strong inverse significant correlation

between it and/or general mortality and life expectancy [23,24]. There is no denial therefore that

infant mortality and/or mortality in general play a critical role in determining health outcomes.

Although life expectancy emerged from mortality, the former only speak to length of life and not

the quality of those lived years. An individual can live for 40 years or even 100 years, of which

all those years were lived in severe morbidity. It is owing to aforementioned rationale why the

World Health Organization (WHO) developed a mathematical technique which discount the life

expectancy by the years spent in disability or morbidity [25]. The WHO therefore emphasized

healthy life expectancy and not life expectancy. Health therefore must be more than morbidity as

it expands to quality of life.


        Within the broadest definition of health conceptualized by the WHO in the 1940s [26], is

social, psychological and physical wellbeing and not the mere absence of diseases suggesting

that health is more than living to the quality of those lived years. Health has been expanded to

mean much more than the absence of diseases to include measures of healthy life expectancy,

happiness, utility, personal preference, and self-reported quality of life [12-22]. Simply put,

wellbeing is subjectively what is ‘good’ for each person [26]. It is sometimes connected with

good health. Crisp [26] offered an explanation for this, when he said that “When discussing the

notion of what makes life good for the individual living that life, it is preferable to use the term

‘wellbeing’ instead of ‘happiness”, which explains the rationale for this project utilizing the term

wellbeing and not good health.


        The issue of wellbeing is embodied in three theories – (1) Hedonism, (2) Desire, and (3)

Objective List. Using ‘evaluative hedonism’, wellbeing constitutes the greatest balance of

                                                                                                332 

 
                                


pleasure over pain [26, 27]. With this theorizing, wellbeing is just personal pleasantness, which

represents that more pleasantries an individual receives, he/she will be better off. The very

construct of this methodology is the primary reason for a criticism of its approach (i.e.

‘experience machine’), which gave rise to other theories. Crisp [26] using the work of Thomas

Carlyle described the hedonistic structure of utilitarianism as the ‘philosophy of swine’, because

this concept assumes that all pleasure is on par. He summarized this adequately by saying that

“… whether they [are] the lowest animal pleasures of sex or the highest of aesthetic

appreciation” [26].


        The desire approach, on the other hand, is on a continuum of experienced desires. This

is popularized by welfare economics. As economists see wellbeing as constituting satisfaction of

preference or desires [26, 27], which makes for the ranking of preferences and its assessment by

way of money. People are made better off, if their current desires are fulfilled. Despite this

theory’s strengths, it has a fundamental shortcoming, the issue of addiction. This forwarded by

the possible addictive nature of consuming ‘hard drugs’ because of the summative pleasure it

gives to the recipient.


        Objective list theory: This approach in measuring wellbeing list items not merely

because of pleasurable experiences nor on ‘desire-satisfaction’ but that every good thing should

be included such as knowledge and-or friendship. It is a concept influenced by Aristotle, and

“developed by Thomas Hurka as perfectionism” [26].              According to this approach, the

constituent of wellbeing is an environment of perfecting human nature. What goes on an

‘objective list’ is based on reflective judgement or intuition of a person. A criticism of this

technique is elitism.     Since an assumption of this approach is that, certain things are good for

                                                                                               333 

 
                              


people. Crisp [26] provided an excellent rationale for this limitation, when he said that “…even

if those people will not enjoy them, and do not even want them”.


       In Arthaud-day et al work [28], applying structural modeling, subjective well was found

to constitute “(1) cognitive evaluations of one's life (i.e., life satisfaction or happiness); (2)

positive affect; and (3) negative affect.” Subjective wellbeing therefore is the individual’s own

viewpoint. If an individual feels his/her life is going well, then we need to accept this as the

person’s reality. One of drawbacks to this measurement is, it is not summative, and it lacks

generalizability.


       In keeping therefore with the broad definition of health forwarded by the WHO, any

study of health must go beyond mortality. A comprehensive search of health literature in the

Caribbean in particular found no research that 1) using national cross-sectional survey(s)

examined health status of children, 2) investigated the changing pattern of morbidity which

affect children ages 0-14 years, 3) investigated whether health status (ie. subjective wellbeing)

and self-reported morbidities (ie health conditions) are correlated, and if they are good measure

for each other, 4) investigated whether from among the health conditions, chronic diseases and

the time they begin to affect children as well as the 5) demographic characteristics of health

conditions affecting children. The current study will examine the aforementioned issues as health

literature in the region on child health must expand beyond infant mortality. The objectives of

the study are to 1) expand the health literature in Jamaica and by extension the Caribbean, 2)

understand the status of child health outside of mortality, 3) aid public health practitioners with

research upon which they are able to further improve the quality of life of children by adding

quality to their lived years, 4) investigate the age at with children in Jamaica become influenced

                                                                                               334 

 
                              


by chronic disease, it typology and 5) evaluate the subjective wellbeing of children as is done for

the general populace and elderly [30-37].


       The current study used two cross-sectional surveys which were conducted jointly by the

Planning Institute of Jamaica and the Statistical Institute of Jamaica (for 2002 and 2007) that

collect data on Jamaicans. A subsample of 8,373 and 2,104 children 0-14 years was extracted

from a sample of 25,018 and 6,783 respondents for 2002 and 2007 respectively. The survey was

a national probability sample of Jamaica, and it was weighted to reflect the populace and sub-

populations. The response rate for each survey was in excess of 72%. 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 Analysis of Variance (ANOVA) was used to test the

relationships between metric and non-dichotomous categorical variables whereas independent

sample t-test was used to examine a statistical correlation between a metric variable and a

dichotomous categorical variable. The level of significance used in this research was 5% (ie 95%

confidence interval).




                                                                                               335 

 
                              


METHODS AND MATERIALS


The current study extracted a sample of 8,373 and 2,104 children 0-14 years from two surveys

collected jointly by the Planning Institute of Jamaica and the Statistics Institute of Jamaica for

2002 and 2007 respectively.[38,39] The method of selecting the sample from each survey was

solely based on an individual being less than or equal to 14 years. The survey (Jamaica Survey of

Living Condition) began in 1989 to collect data from Jamaicans in order to assess policies of the

government. Since 1989, yearly the JSLC adds a new module in order to examine that

phenomenon which is critical within the nation. In 2002, the foci were on 1) social safety net and

2) crime and victimization; and for 2007, there was no focus. The sample for the earlier survey

was 25,018 respondents and for the latter, it was 6,783 respondents.


       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 residence 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 Labour Force Survey (ie LFS) was selected for the JSLC. [40, 41] The sample was

weighted to reflect the population of the nation.


       The JSLC 2007 [40] was conducted May and August of that year; while the JSLC 2002

was administered between July and October of that year. The researchers chose this survey based
                                                                                               336 

 
                                


on the fact that it is the latest survey on the national population and that that it has data on self-

reported 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 variables

– such as education; daily expenses (for past 7-day; food and other consumption expenditure;

inventory of durable goods; health variables; crime and victimization; social safety net and

anthropometry. The non-response rate for the survey for 2007 was 26.2% and 27.7%. The non-

response includes refusals and rejected cases in data cleaning.


Measures


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.


Child. A person who has celebrated less than or equal to 14 years.




                                                                                                  337 

 
                              


Health conditions (ie. 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.


Self-rated health status: “How is your health in general?” And the options were very good; good;

fair; poor and very poor.


Statistical Analysis


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 Analysis of Variance (ANOVA) was

used to test the relationships between metric and non-dichotomous categorical variables whereas

independent sample t-test was used to examine a statistical correlation between a metric variable

and a dichotomous categorical variable. The level of significance used in this research was 5%

(ie 95% confidence interval).


RESULT

For this study there were two samples (8,373 from 2002 data survey and 2,104 from the 2007

survey). In 2002, the sample was 50.7% males and 49.3% females compared to 51.3% males and

48.7% females for 2007. The mean age for the sample in 2002 was 7.2 years (SD = 4.2 years)

and 7.3 years (SD = 4.3 years) for 2007. The proportion of the sample in particular social class

(using population income quintile) was relative the same across the two years. The number of

days recorded as suffering from illness fell by 2 days in 2007 over 2002 (median number of days

experiencing ill-health). In 2002, 9.4% of the sample reported an illness/injury in the 4-week

period of the survey and this increased by 34.0% (to 12.6%). The percent of the sample that
                                                                                               338 

 
                                   


visited health care practitioners marginally increase from 56.7%, in 2002, to 58.6% in 2007.

Concurrently, 9.3% of sample was covered by health insurance (ie total private in 2002) and this

increased by 62.4% and a part of this was accounted for by a 5.1% having public health

insurance coverage. In 2002, 62.6% of the sample dwelled in rural areas, 25.1% in semi-urban

areas and 12.3% in urban areas compared to a shift which was noticed in 2007 as 53.2% resided

in rural areas and 20.2% in semi-urban areas with 26.6% lived in urban zones (Table 13.1).


             The general health status of children in Jamaica, for 2007, was good (91.3%) compared

to 6.7% fair and 2.0% poor.


             Interestingly, in the current study, a shift in health condition was noticed in 2007 over

2002. The number of children who had diarrhoea fell by 84.2% in 2007 over 2002, and a similar

reduction was observed for those with asthma (42.1% in 2002 and 19.7% in 2007). Another

critical finding was that 1.2% of children, in 2007, had diabetes mellitus compared to none in

2002. On the contrary, 37.5% of children, in 2007, had cold which increased from none in 2002

(Table 13.1).


             A cross-tabulation between health conditions and sex of respondents, revealed that no

    significant statistical correlation existed between the two variables and that this was for both

    years: For 2002 - χ2 (df = 2) = 0.232, p> 0.05; and for 2007 - χ2 (df = 5) = 8.915, p> 0.5 (Table

    13.2). In spite of the aforementioned, the new diabetic cases were accounted for by females (for

    2007).

             In 2002, no significant statistical relationship existed between diagnosed health

    conditions and area of residents (χ2 (df = 4) = 1.301, p > 0.05). On the other hand, a statistical

    correlation was observed for 2007 between the aforementioned variables. Furthermore, more
                                                                                                  339 

 
                                 


    children in semi-urban areas had cold than those who dwelled in other areas. On the contrary,

    diabetic cases were found in urban areas and none in other geographical zones. The findings

    revealed also that more rural children had asthma and more urban children had unspecified

    health conditions (Table 13.3).

          Table 13.4 revealed that no significant association was found between diagnosed health

condition and social class (ie population income quintile). However, the diabetic cases were

spread among the lower class (poorest 20%, 1.9%; and poor, 1.8%) and the upper class (wealthy,

2.0%).

          The examination of diagnosed health conditions by mean age of respondents revealed

    that a significant relationship existed between the two aforementioned variables in 2007, F

    statistic = 4.875, p < 0.001; but none in 2002 - F statistic = 3.334, p > 0.05. In 2007, the mean

    age of a child with diabetes mellitus was 12.33 years (SD = 2.1 yrs), 95% CI = 7.16 – 17.5

    (Table 13.5). However the mean age a child with diarrhoea lower than a child and other health

    conditions.

          The first time in the history of the Jamaica Survey of Living Conditions (JSLC) that

    health status and self-reported health condition was collected together was in 2007. Hence, the

    current study will cross-tabulate both in order to determine whether a significant correlation

    exist between them and what is the strength of a relationship if one does exist. Based on Table

    13.6 a weak significant statistical association exist between health status and self-reported

    health condition - χ2 (df = 2) = 174.512, p < 0.0001, cc= 0.282. On further examination of the

    findings, it was observed that no child was classified has having very good health status.

    Ninety-four percent of sample who had no health condition reported good health compared to

    70% of those who had at least one health condition. Of those who had at least one health

                                                                                                 340 

 
                                


    condition, 9.4% of them reported poor health status compared to 1% who had no health

    condition (Table 13.6).

          Using independent sample t-test, in 2002, the current study found that there was a

significant difference between the mean age of those who sought and not seek medical care –

t3.425 , p < 0.001. The mean age of those who do not seek medical care higher, 6.2 years (SD =

4.1), compared to those who seek care, 5.2 years (SD = 4.2 years). However, there was no

difference in 2007: seek care – mean age 5.2 years (SD = 4.1 years) and not seek care – mean

age 5.8 years (SD = 4.2 years).


          On examination as to whether a significant statistical correlation existed between health

care-seeking behaviour and sex of respondents, none was found in each year – p > 0.05 (Table

13.7).

DISCUSSION


It is established in epidemiology that diseases in childhood do influence poor health in adulthood

[42], suggesting the value of child health to health status over the life course. Another

importance to the study of health status is its contribution to all typology of development as

human capital is critical to socio-economic and political systems. In Jamaica, the Statistical

Institute of Jamaica [42] estimated that for 2007, there was 28.3% of the nation’s population was

less than 14 years. Simply put, there are 45 children for every 100 working age (ages 15-64

years) Jamaican; and to omitted the health status of this cohort is to substantially neglect a

critical sector of the population. The current study found that 2 in every 100 children had poor

health status; and that weak significant statistical correlation existed between health status and

self-reported health conditions. This therefore concurs and contradicts another study that found

                                                                                               341 

 
                                


statistical association between health conditions and health status [36]. Hambleton et al. [36],

examining data for elderly Barbadians, found that self-reported health conditions accounted for

most of the variability in health status (ie. current diseases accounted for 33.5% out of R2 =

38.3%).


       This takes the study in the direct of current diseases (ie health conditions) of children in

Jamaica. This study revealed 34% increase in cases of self-reported diseases in Jamaican

children. Only 13 in 100 children in Jamaica, in 2007, had a least one health condition. These

conditions include cold, diarrhoea, asthma, diabetes mellitus and other unspecified diseases. In

2007, 20 in every 100 children had asthma, 5 out of every 100 diarrhoea cases, 38 in every 100

had cold and 21 in every 100 unspecified conditions. Of the different typology of chronic

dysfunctions, 12 in every 1,000 reported diabetes mellitus and no cases were found of

hypertension and arthritis. Given the breadth of the unspecified category, this could include

cancers, HIV/AIDS and other communicable or non-communicable diseases. In spite of this

uncertainty, what emerged from the current research is the change in pattern of health conditions

of children between 2002 and 2007. A study conducted by Walker [43] found that growth

retardation in children influence blood pressure, obesity, and other chronic health conditions, and

that some 5-6% of children in Trinidad and Tobago, and Jamaica are classified in this group.

Walker also found that these children are more likely to experience more episodes of diarrhaea,

fever and other morbidities.


       This research revealed that number of cases of asthma, diarrhoea and unspecified

conditions fell accompanied with a corresponding rise in cold and diabetes mellitus. Interestingly

to note is that the 1.2% of child population that were diagnosed diabetic patients represents 2.3%

                                                                                               342 

 
                               


of the female population. The diabetic cases were not only females, but urban residents. Of those

with diabetes, 1.9% was in the poorest 20%, 1.8% poor and 2.0% of the wealthy social class.

Continuing, the mean age of female diabetic children was 12.3 years; and this indicates the year

age in which diabetes mellitus begin to affect females in Jamaica. The aforementioned finding

explains the disproportionate number of females to males in the general population that have

diabetes -14% females to 7.7% males [40]. Although no cases of hypertension was reported in

this study, it is established that diabetes mellitus is correlated with hypertension.


       Diabetes Mellitus is not the only challenge faced by patients, but McCarthy [44] argues

that between 30 to 60% of diabetics also suffer from depression, which is a psychiatric illness.

Diabetes mellitus does influence the health status of children and follows them across the life

course. It affects lifestyle choice, functional capacity, and like McCarthy said the psychological

state of people. This health condition also affects other disease. Morrison [45] opined that

diabetes mellitus and hypertension have now become two problems for Jamaicans and in the

wider Caribbean. This situation was equally collaborated by Callender [46] who found that there

was a positive association between diabetic and hypertensive patients - 50% of individuals with

diabetes had a history of hypertension [46]. Children with diabetes mellitus therefore are highly

likely to develop hypertension in the future, and so children in Jamaica in the future will have

twin chronic conditions. This envelope further shifts in health conditions of children in Jamaica;

Morrison alluded to a transitory shift from infectious communicable diseases to chronic non-

communicable diseases as a rationale for the longevity of the Anglophone Caribbean populace

and this does not mitigates against lowered healthy life expectancy of the sexes in particular

females who live 6 years more than males [34,42].


                                                                                              343 

 
                               


       Diabetes mellitus and any other typology of chronic diseases do more than affect healthy

life expectancy; they are directly correlated with mortality. Statistics from the Statistical Institute

of Jamaica [42] is the leading cause of deaths in female Jamaicans. The reality of changing

pattern of health conditions from communicable to non-communicable and the fact that this is

accounted with urban poor and wealthy, indicate that public health policies are needed to address

this currently and in the future. Another important fact that embedded in the current study is the

early age in which females are having chronic disease, and this indicates the length of time with

which they will life with this non-curable disease or likeliness of mortality.

       A study on morbidity and mortality patterns in the Caribbean established that the

transition in morbidity is not atypical to Jamaica [47], and that the leading cause of mortality in

region is similar to developed nations. WHO [48] opined that 80% of chronic illnesses were in

low and middle income countries, indicating the preponderance of chronic illness in regions such

as the Caribbean as well as the fact that chronic illnesses are also a part of the landscape of

industrialized nations. With the changing pattern of morbidity of children in Jamaica, this will

support modifications in lifestyle behaviour which must begin from children to the populace.

       Although there is no statistical difference between the 3 area of residents and health

conditions, the fact that the chronic dysfunctions were found in urban areas denote that public

health policies must begin in earnest in those places. There is another situation that must be

explored here and that is response of health services, and the management of care for those who

are affected by chronic illnesses. It should be noted that 57 out of every 100 children were taken

for medical care which speaks to the high proportion of children despite being ill who were not

taken to traditional medical facilities. A part of the rationale for this non-medical care seeking

behaviour of children is adults’ definition of health and the cultural perspective of health.
                                                                                                   344 

 
                               


       Generally, health in Jamaica is defined as the absence of illness which although is

negative and narrow in scope speaks to people’s perspective on the matter. Interestingly in this

discourse is not only the narrowed definition of health, but that severity in health conditions is

substantially what drives medical care-seeking and not on the onset of illness or preventative

care. This goes to the crux of why only 57 out of every 100 children who are ill would be taken

to health care practitioners as their families are less likely to taken then for conditions such as the

cold, but also provide an explanation for the low medical care seeking behaviour for the general

populace.

       Statistics revealed that for the last 2 decades (1988-2007), there were 4 times (years) in

which males sought more medical care than females – 1991 (48.5% males to 47.4% females);

1995(59.0% males to 58.9% females): 1997 (60.0% males to 59.3% females) and 2006 (71.7%

males to 68.8% females) [30, 41, 40], which speaks to some embedded culturalization for this

health care-seeking disparity in nation. While this is not atypical to Jamaica [49-51], that fact

that the current study revealed that there was no significant statistical difference between male

and female children being taken for medical care, the disparity that exist in the general populace

begin in young adulthood. This is the period in which identify formulation begins in adolescents

and when males begin to imitate the practices of adult men. The adolescent male therefore will

seek less medical care because his adult counter believes that this is weak, feminine and reduces

his machoism.

       One anthropologist in seeking to explain the practices of Caribbean men used social

learning theory to examine the lifestyle practices of boys [52]. Chevannes [52] argued that the

young imitate the roles of society members through role modeling of what constitute acceptable

and good roles which is supported by reinforcement. The young male is a subset of the society,
                                                                                                   345 

 
                              


and if men are less likely to seek health care because of a cultural perspective that they form of

ill-health which goes to the crux of their manhood and possibly seeks to threaten it, young males

as soon as they are somewhat responsible for their choices will do more of the same as their

mentors. This gender role of sexes and health disparity which results after childhood is not

limited to Jamaica or the Caribbean but a study carried out by Ali and de Muynck [53] found that

street children in Pakistan had a similar gender stereotype about health, health care and medical

care seeking-behaviour. Using a descriptive cross-sectional study carried out during September

and October 2000 of 40 school-aged street children (8-14 years), they found boys were reluctant

to seek medical care except when there is severity of ill-health, it threatens their economic

livelihood or there is a perceived reduction in functional capacity. The reason being that mild

ailment is not severe enough to barr them from physical functioning and within the context of the

general population that men ought to be tough, this means that they are okay; and so some

morbidity are not for-hospital, which was so the case in Nairobi slums [54]. This again justifies

why some children in Jamaica are not taken to health practitioners as there is a perception that

some illness requires home remedy.

       Statistics revealed that 56.0% of children (ages 0-4) who were not taken for medical

treatment despite having an illness was because home remedies were used, figure was 32.8% for

those 5-9 years and 25.6% for those 10-19 years [40]. Inaffordability accounted for 33%, 32.5%

and 35.9% of those ages 0-4 years, 5-9 years and 10-19 years respectively who were not brought

to health care practitioner even though they were ill.

CONCLUSION

The general health status of children in Jamaica is good; but this mitigate against the relatively


                                                                                              346 

 
                               


low age with which females are reported to have had diabetes mellitus and the changing pattern

of health conditions which have occurred since the 2002. Public health now has an

epidemiological profile of health conditions of children and the demographic shifts which are

occurring and this can be used for effective management and planning of the new health reality

of the Jamaican child. With the removal of health care user fees for children ages 0-18 years

from the health care landscape of Jamaica (since May 28, 2007), the transition to chronic cases in

this cohort means that health care expenditure in the future will rise as we seek to care for those

patients over there life course. It is critical that future research examine the composition of

unspecified health conditions as this constitutes a significant percentage of diseases in 2007

unlike 2002.

Conflict of interest

There is no conflict of interest to report

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Table 13.1. Sociodemographic characteristic of sample
Variable                                                    2002                 2007
                                                           N= 8373              N=2104
Sex
   Male                                                       50.7                51.3
   Female                                                     49.3                48.7
Health care-seeking behaviour
   Yes                                                        56.7                58.6
    No                                                        43.3                41.4
Health insurance coverage
  Yes                                                          9.3                15.1
  No                                                          90.7                84.9
Area of residence
  Rural                                                       62.6                53.2
  Semi-urban                                                  25.1                20.2
  Urban                                                       12.3                26.6
Self-reported illness
  Yes                                                          9.4                12.6
  No                                                          90.6                87.4
Diagnosed Health conditions
 Cold                                                          -                  37.5
 Diarrhoea                                                    31.6                 5.0
 Asthma                                                       42.1                19.7
 Diabetes mellitus (ie diabetes)                               -                   1.2
 Hypertension                                                  -                    -
 Arthritis                                                     -                    -
 Other                                                        26.3                20.8
 Not                                                           -                  17.0
Population Income quintile
 Poorest 20%                                                  26.0                26.0
 Poor                                                         22.9                22.6
 Middle                                                       20.3                19.5
 Wealthy                                                      18.0                18.9
 Wealthiest 20%                                               12.8                13.0
Age Mean (SD)                                           7.2 yrs (4.2 yrs)   7.3 yrs (4.3 yrs)
Length of illness Median                                     7 days             5.0 days
Number of visits to health practitioner(s) median              1.0                 1.0
Crowding mean (SD)                                      2.5 persons (1.5    5.5 persons (2.3
                                                            persons)            persons)




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Table 13.2. Diagnosed health conditions by Sex, 2002 and 2007
Variable                                         20021                   20072




Diagnosed Health conditions              Male            Female   Male           Female

Cold                                       -                      35.7            39.2

Diarrhoea                                 27.3            37.5    3.1             6.9

Asthma                                    45.5            37.5    21.7            17.7

Diabetes                                   -                      0.0             2.3

Hypertension                               -                       -               -

Arthritis                                  -                       -               -

Other                                     27.3            25.0    19.4            22.3

No                                         -               -      20.2            11.5

    1 2
    χ (df = 2) = 0.232, p> 0.05
    2 2
    χ (df = 5) = 8.915, p> 0.5




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Table 13.3. Diagnosed health conditions by area of residence
Variable                                  20021                           20072



                             Rural    Semi-urban   Urban       Rural   Semi-urban   Urban
Diagnosed Health
conditions

Cold                              -        -          -        27.0       56.5      36.0

Diarrhoea                    33.3         40.0       0.0         -        2.2        8.0

Asthma                       41.7         40.0      50.0       25.4       15.2      18.7

Diabetes                          -        -          -          -         -         2.3

Hypertension                      -        -          -          -         -          -

Arthritis                         -        -          -          -         -          -

Other                        25.0         20.0      50.0       20.6       13.0      23.3

No                                -        -          -        27.0       13.0      12.0
    1 2
    χ (df = 4) = 1.301, p > 0.05
    2 2
    χ (df = 10) = 25.079, p = 0.005, cc = 0.297




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Table 13.4. Diagnosed health conditions by Population income quintile

Variable                                   20021                                                 20072




Diagnosed        Poorest    Poor     Middle        Wealthy   Wealthiest   Poorest   Poor   Middle        Wealthy   Wealthiest
Health            20%                                          20%         20%                                       20%
conditions

Cold                 -        -          16.7       14.3       50.0        35.8     37.5    44.3          36.7        30.0

Diarrhoea          75.0       -          66.7       57.1        0.0         3.8     12.5    4.9            2.0        0.0

Asthma              0.0       -           -           -          -         22.6     17.9    18.0          14.3        27.5

Diabetes             -        -           -           -          -          1.9     1.8     0.0            2.0        0.0

Hypertension         -        -           -           -          -           -       -       -              -          -

Arthritis            -        -           -           -          -           -       -       -              -          -

Other              25.0       -          1.0        28.6       50.0        28.3     19.6    16.4          20.4        20.0

No                   -        -                       -          -          7.5     10.7    16.4          24.5        22.5
    1 2
    χ (df = 6) = 8.105, p > 0.05
    2 2
    χ (df = 20) = 25.079, p > 0.05




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Table 13.5. Mean Age of respondent who has a particular health condition

Variable                                               20021                                     20072




Diagnosed Health conditions              Mean age (SD)          95% CI         Mean age (SD)              95% CI

Cold                                             -                  -          4.4 yrs (4.0 yrs)         3.55 – 5.15

Diarrhoea                                1.5 yrs (1.5yrs)      - 0.09 -3.09    3.5 yrs (2.8 yrs)         1.93 – 5.15

Asthma                                   5.0 yrs (3.0 yrs)      2.51-7.49      6.5 yrs (3.5 yrs)         5.51 – 7.47

Diabetes                                         -                  -         12.33 yrs (2.1 yrs)        7.16 – 17.5

Hypertension                                     -                  -                  -                      -

Arthritis                                        -                  -                  -                      -

Other                                    5.4 yrs (3.8 yrs)     0.62 – 10.18    6.0 yrs (4.5 yrs)         4.82 – 7.26

No                                               -                  -            5.8 yrs (4.3)           4.46 – 7.20

    1
        F statistic = 3.334, p > 0.05
    2
        F statistic = 4.875, p < 0.001




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Table 13.6. Health status by self-reported illness
Variable                                              20021                              20072

                                               Self-reported illness             Self-reported illness

                                             None             At least one      None             At least one

Health status                               (in %)              (in %)          (in %)             (in %)

Very good                                      -                   -              -                   -

Good                                           -                   -             94.3               70.2

Fair                                           -                   -             4.7                20.4

Poor                                           -                   -             1.0                 9.4
    1
        In 2002, health status data were not collected. This took place the first time in 2007
    2 2
        χ (df = 2) = 174.512, p < 0.0001, cc= 0.282




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Table 13.7. Health (or medical) care-seeking behaviour by sex

Variable                                      20021                    20072

                                              Sex                      Sex




Health care-seeking behaviour          Male           Female    Male           Female

          Sought care                  42.2            44.5     40.8            42.0

          Did not seek care            57.8            55.5     59.2            58.0
    1 2
    χ (df = 1) = 0.419, p > 0.05
    2 2
    χ (df = 1) = 0.040, p > 0.05




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                                   Chapter 14
                   The uninsured ill in a developing nation



                                    Paul Andrew Bourne


Empirical studies have used a piecemeal approach to the examination of health, health care-
seeking, uninsured people and the health status of those who are chronically ill, but no study
emerged in an extensive literature search, on the developing nations, and in particular Latin
America and the Caribbean, that has investigated health and health care-seeking behaviour
among uninsured ill people in a single research. The current study aims to narrow this divide by
investigating health, self-reported diagnosed health conditions, and health care-seeking
behaviour among uninsured ill Jamaicans, and to model factors which account for their
moderate-to-very good health status as well as health care-seeking behaviour. The current study
utilises cross-sectional survey data on Jamaicans which was collected in 2007. The survey is a
modification of the World Bank’s Living Standard Household Survey. This work extracted a
sample of 736 respondents who indicated that they were ill and uninsured from a sample of
6,783 respondents. Logistic regression analyses examined 1) the relationship between moderate-
to-very 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. Sixty out of every 100 uninsured ill Jamaicans were females;
43 out of every 100 were poor; 59 out of every 100 uninsured ill persons dwelled in rural areas; 1
of every 2 utilised public health care facilities, two-thirds had chronic health conditions, and 22
out of every 100 reported at least poor health. Moderate-to-very good health status was
correlated with age (OR = 0.97, 95% CI = 0.95-0.98); male (OR = 0.60, 95% CI = 0.37-0.97);
middle class (OR = 0.45, 95% CI = 0.21-0.95); logged income (OR = 2.87, 95% CI = 1.50-5.49);
area of residence (Other Town – OR = 2.33, 95^% CI = 1.19-4.54; Urban – OR = 2.01, 95% CI =
1.11-3.62), and health care-seeking behaviour (OR = 0.45, 95% CI = 0.27-0.74). Sixty-one of
every 100 uninsured respondents with ill health sought medical care. Medical care-seeking
behaviour was significantly related to chronic illness (OR = 2.25, 95%CI = 1.31-3.88); age (OR
= 1.03, 95%CI = 1.01-1.04); crowding (OR = 1.12, 1.01-1.24); income (OR = 1.00, 95% CI =
1.00-1.00); and married people (OR = 0.48, 95% CI = 0.28-0.82). Uninsured ill Jamaicans who
resided in rural areas had the lowest moderate-to-very good health status, but there was no
difference in health care-seeking behaviour based on the geographical location of residence.
Despite the fact that there is health insurance coverage available for those who are chronically ill
and elderly in Jamaica, there are still many such people who are without health insurance
coverage. The task of public health specialists and policy makers is to fashion public education
and interventions that will address many of the realities which emerged in this research.


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Introduction
In all cultures, people desire good health and long life. Ill-health, therefore, is a challenge to the

aim of healthy life expectancy, and is the rationale for investments in health options such as

exercise, diet, nutrition, science and technology, medical consultation and/or health care

utilisation. All living organisms will experience ill-health as well as good health over their life

courses; and when ill-health threatens the quality and length of life, it becomes the justification

for humans’ willingness to rectify, address and possibly postpone illnesses. Ill-health (i.e. illness,

sickness or ailment) threatens existence, productivity, development, the individual and the wider

society, and because of that humans demand the best health care options. Demand for health care

must be paid for by (1) a combination of health insurance coverage and out-of-pocket payment,

(2) the state, (3) out-of-pocket payments or (4) relatives, associates and/or family members. Ill-

health can be a burden to the individual, family, community and the nation, and it is a probability

against which people and the society seek to protect themselves. All illnesses require some

typology of treatment, and while this does not necessarily have to be a traditional medical

practitioner, curing illness means that the individual must forego consuming something in order

to restore his/her good health.


       Some illnesses such as the common cold may not require a trained medical practitioner to

cure, but often the individual will be required to spend money on over-the-counter medications,

use a home remedy or utilise non-traditional healers in the quest to restore his/her former healthy

state. There are other illnesses such as diabetes mellitus, heart disease, kidney problems,

hypertension, HIV/AIDS, sexually transmitted infections, and other chronic and non-




                                                                                                  360 

 
                              


communicable diseases, which require the attention of traditional medical experts to address

their cure.


        The traditional medical practitioners require payment in the form of cash and/or health

insurance coverage. Because individuals desire to restore their health, they are expected to

provide payment for health care, which for particular health conditions can be exorbitantly high.

It is this reality which may result in premature mortality if the state does not provide health care

coverage for those who are economically challenged and/or vulnerable. The World Health

Organization (WHO) [1] opined that 80% of chronic illnesses were in low and middle income

countries, suggesting that illness interfaces with poverty. The WHO continued that 60% of

global mortality was 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 [1]. It also

postulated that “In reality, low and middle income countries are at the centre of both old and new

public health challenges” [1]. Embedded in the realities outlined by the WHO are the incapacity

of the poor, the association between poverty and illness, between poverty and premature

mortality, poverty and human suffering, and poverty and future retardation of economic growth,

and the fact that health insurance provides some cushion against this, for the individual and for

society. Other studies have equally found that there is a significant statistical relationship

between poverty and illness [2-4] and poverty and chronic illness, [5] which means that illness

can make the vulnerable less likely to survive and the wealthy become poor.


        The high risk of mortality in developing countries is owing to food insecurity, low water

quality and low sanitation coupled with inadequate access to material resources. Poverty makes

it an insurmountable hurdle for poor people to effectively address illness unless health care

                                                                                                361 

 
                              


services are free. Hence, those in the lower socioeconomic class will be expected to have poorer

health, as they are crippled by their material deprivation and low health options. The WHO

captures this aptly “... People who are already poor are the most likely to suffer financially from

chronic diseases, which often deepen poverty and damage long term economic prospects”. [1]

Among the challenges for people living in poverty is access to health insurance coverage. Such a

possibility means that the burden of health care is an out-of-pocket payment that cannot be

provided by the poor, and this will eliminate life in the process. Cass et al. [6] found that infant

mortality in Peru for those in the poorest quintile (i.e. poorest 20%) was almost 5 times more

than for those in the wealthiest quintile (i.e. wealthiest 20%). This indicates the extent of the

health challenge of the poor, and the role that the lack of health insurance and income play in the

demise of individuals and even their children.


       Another research paper revealed that life expectancy between the poorest 20% and the

wealthiest 20% was 6.3 years, and this figure rose to 14.3 years for disability-free life

expectancy, [7] suggesting that access and lack of access to resources explain health and healthy

life expectancy in and among the social classes in a society. Grossman [8] found a positive

correlation between income and health status, indicating that money makes a difference in

health, health care-seeking behaviour, physical milieu and health care coverage. Smith and

Kington, [9] on the other hand, went further than Grossman when they postulated that money

buys health. This viewpoint is somewhat deceptive, as money provides access to good physical

milieu, the best health care options, nutrition, dietary choices and health information which are

not readily available to the poor, but it does not buy health. Health is not a commodity for sale,

and so it cannot be purchased, but money allows for access to better health choices and by


                                                                                                362 

 
                               


extension can change health outcomes. Those issues could be the intent of Smith and Kington,

when they say that money buys health, and they further exemplify the challenges if an individual

does not have access to it.


        Material deprivation is such that the poor will be far from concerned with health

insurance coverage, proper diet and nutrition, health care choices, but more with survivability.

This denotes that they will be living on the margins of survivability and the decision to purchase

health insurance will be the opportunity cost of food, clothing, shelter, minimal education and

health options. Within the context of material and widespread health deprivation for those in the

lower socioeconomic strata, the state must play a role in aiding improvements in the healthy life

expectancy of those therein. It is through this avenue that public health must act in order to fulfill

the aim of the state in improving the quality of life of all residents in the nation.


        Public health uses information from within and outside the society to improve the health

and quality of people’s lives, and this requires continuous research findings. According to the

WHO, “In Jamaica 59% of people with chronic diseases experience financial difficulties because

of their illness...” Hence, poverty and illness, poverty and chronic illness, and poverty and low

access to material resources are well established in research literature, but a dearth of

information existed in Latin America and the Caribbean, and in particular Jamaica, on the sick

and uninsured. Can we assume that they are all poor people, and use this to plan for them in a

developing nation? An extensive review of the literature in developing nations, and in particular

Latin America and the Caribbean, did not produce a single study that has examined health, and

health care-seeking behaviour among uninsured ill people. The current study aims to narrow this

divide by investigating health, self-reported diagnosed health conditions and health care-seeking

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behaviour, at the same time examining who are the unhealthy and uninsured, and modelling

factors which account for the moderate-to-very good health status of uninsured ill Jamaicans, in

order to provide public health specialists with pertinent information that can be used to address

some of the challenges within the society.


Methods and material


Data


The current study utilised the latest cross-sectional survey data in Jamaica to examine health,

self-reported diagnosed health conditions and health care-seeking behaviour, and to model

factors which account for the moderate-to-very good health status of unhealthy and uninsured

Jamaicans. The Jamaica Survey of Living Conditions (JSLC) began collecting data from

Jamaicans in 1988 and the latest dataset available is for 2007. The JSLC is a modification of the

World Bank’s Living Standard Household Survey [10, 11]. This work extracted a sample of 736

respondents who indicated that they were ill and not insured, from a sample of 6,783 respondents

[12]. The cross-sectional survey was conducted between May and August 2002 in the

14 parishes across Jamaica, and included 6,783 respondents of all ages. The JSLC used a

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.


       The 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 geographical unit that shares a common boundary.
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This means that the country was grouped into strata of equal size based on dwellings (EDs).

Pursuant to the PSUs, a listing of all the dwellings was made, and this became the sampling

frame from which a Master Sample of dwellings was compiled, which in turn provided the

sampling frame for the labour force. One third of the 2007 Labour Force Survey (i.e. LFS) was

selected for the survey.


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

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.




                                                                                             365 

 
                              


       The correlation matrix was examined in order to ascertain if autocorrelation and/or

multicollinearity existed between variables. Based on Cohen and Holliday [13] 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 [14-16]. 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 for retaining or

excluding a variable from the model was based on its contribution to the predictive power of the

model and its goodness of fit [17]. 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.


Measurement

Health status is a binary measure where 1= moderate-to-very good health; 0= otherwise which is

determined from “Generally, how do you feel about your health”? Answers to this question were

analyzed 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). Age

group is a non-binary measure: children (aged 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
                                                                                              366 

 
                                


(ages 75 to 84 years) and oldest elderly (ages 85 years and older). Social hierarchy: This variable

was measured based on income quintile: The upper classes were those in the wealthy quintiles

(quintiles 4 and 5); middle class was quintile 3 and the poor were those in the lower quintiles

(quintiles 1 and 2).


Chronic illnesses: These are ailments or diseases that are prolonged, not likely to be resolved

spontaneously, and are infrequently cured.


Inequity denotes differences that are unnecessary and avoidable, but are also thought to be unfair

and unjust, and these are adjudged based on the context of the customs operating in the society in

general.


Equity in health means (1) equal access to care for equal needs, (2) equal access to utilisation for

equal needs, and (3) equal quality of care for all in the society.


Inequalities in health mean patterns of socioeconomic disparities in health outcome which are

systematic, avoidable and important within a country.


Model


The multivariate model used in this study is in keeping with wanting to capture the multi-

dimensional concept of health and the health care-seeking behaviour of uninsured ill people.

Utilising logistic regression on secondary cross-sectional data, the present study modelled

moderate-to-very good health status and the health care-seeking behaviour of uninsured ill

Jamaicans. Using a p-value of less than 0.05 to indicate statistical significance, each model

reflects only those variables that are statistically significant.

                                                                                                367 

 
                                


Health Model


Hit = f(Ait, Xi, SSit, lnYit, ARit, HSBit, εit) ………………………………. [1]


Health Care-seeking Behaviour Model


Hit = f(Ait, CIit, Hit, lnYit, CRit, MSit, εit) ………………………………. [2]


        Where Hti is current moderate-to-very good health status of uninsured ill person i in time

period t; Ai is age (in years) of person i in time period t; Xi is gender of person i; SSit is social

class of person i in time period t; lnYit is logged income of person i in time period t; ARit is area

of residence in time period time t; HSBit is health care-seeking behaviour in time period t; CRi is

crowding in the household of person i in time period t; CIit is chronic illness of person i in time

period t; MSit is marital status of person i in time period t; εit is residual error of person i - in time

period t.


Results

Table 14.1 presents information on the demographic characteristics of the sample. The sample

was 736 respondents (i.e. 10.85% of the initial survey) who indicated that they were both sick

and uninsured, and of which 40.5% were males. Concurringly, of the sample 95.4% had at most

primary level education and 0.8% had tertiary level education. Children constituted 28.7% of the

sample; young adults, 10.2%; other adults, 31.3%; young-old, 16.4%; old-old, 10.5%; and

oldest-old, 3.0%. The median age was 42.0 years (range = 0 – 99 years). The median total annual

expenditure was USD 5,689.89 (range = USD 261.56 – 32,780.78; US$ 1.00 = J$ 80.47 - at the

time of the survey). The number of visits made to medical practitioner(s) was 1.4 ± 1.0), while
                                                                                                      368 

 
                               


the amount of time spent in private care facilities was 3.0 ± 2.8 compared to 5.2 ± 5.0 for public

care facilities). The mean cost of public medical care was USD 4.44 ± USD 16.14 compared to

USD 13.64 ± USD 28.22 for private medical expenditure.

       Of those who utilised public health care facilities, 22.9% of them purchased the

prescribed medication compared to 78.8% who visited private health care facilities.

       Table 14.2 highlights information on health care-seeking behaviour, health care

utilisation, self-reported illness and area of residence by social hierarchy. Based on Table 14.2,

there were significant statistical associations between (1) health care-seeking behaviour and

social hierarchy; (2) public health care centre utilisation and social hierarchy, and (3) private

health care centre utilisation and social hierarchy.

       Table 14.3 highlights information on monthly food expenditure, per capita consumption,

length of illness, number of visits made to health practitioners, medical expenditure and self-

reported diagnosed illness by area of residence. Based on Table 14.3, there were significant

statistical associations between (1) monthly food expenditure and area of residence and (2) per

capita consumption and area of residence – P < 0.05. However, there were no significant

statistical relationships between the other variables and area of residence – P > 0.05.

       There was a statistical association between health care-seeking behaviour and age group

of respondents – χ2 = 11.1, P = 0.048. As uninsured ill people become older, they are more likely

to seek medical care: Children, 54.8%; old-adults, 54.8%; other-age adults, 64.0; young-old,

63.3%; old-old, 73.3%; and oldest old, 66.7%.

       There was a statistical relationship between having chronic illness and being the

household head – χ2 = 63.3, P < 0.0001. Almost 55% of those with chronic illnesses were



                                                                                              369 

 
                               


household heads, compared to 22.4% who did not have chronic illness but were household

heads.

         A significant statistical association existed between sex and having chronic illness - χ2 =

4.7, P < 0.031. More females had chronic illness (69.8%) than males (61.7%).

         There was a significant statistical association between health status and typology of

illnesses (i.e. acute and chronic conditions) - χ2 = 62.3, P < 0.0001. Thirty-seven percent of

those with chronic illnesses reported at least poor health status compared to 12.2% of those with

acute conditions. On the other hand, 61.1% of those with acute conditions reported at least good

health status compared to 31.3% of those with chronic conditions.

         A statistical difference was found between the mean income of those in the different

social hierarchies – F statistic = 277.50, P < 0.0001. The mean income for those in the poorest

20% was USD 666.07 ± 175.40 followed by the second poor, USD 1,090.68 ± 132.14; middle

class, USD 1,489.69 ± 169.07; second wealthy, USD 2,131.55 ± 254.49 and the wealthiest 20%,

USD 4,201.39 ± 235.26.

Multivariate analysis

         Table 14.5 shows variables which are correlated (or not) with the moderate-to-very good

health status of uninsured ill respondents. Seven variables emerged as significantly associated

with moderate-to-very good health status – Model χ2 = 83.70, P < 0.001, -2 Log likelihood =

482.9 – and they accounted for 23% of the variability in health status. The model is a good fit

for the data - Hosmer and Lemeshow goodness of fit χ2= 3.72, P = 0.88.

         Table 14.6 presents information on variables and self-reported health care seeking

behaviour of uninsured respondents. Six variables emerged as significant statistical correlates of

self-reported health care-seeking behaviour - Model χ2 = 47.9, P < 0.001, -2 Log likelihood =

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486.1. The model is a good fit for the data - Hosmer and Lemeshow goodness of fit χ2= 8.11, P =

0.62.

Discussion

The current research used a sample of respondents who indicated both experiencing ill-health

and having no health insurance coverage. Of the sample of respondents (i.e. n = 736), 60 out of

every 100 were females, 43 out of every 100 were poor, 35 out of every 100 were in the upper

social class, 59 out of every 100 dwelled in rural areas, 3 out of every 100 had been injured

during the last 4 weeks, 61 out of every 100 sought medical care, 50 out of every 100 utilised

public health care, two-thirds reported being diagnosed with a chronic illness, 31 out of every

100 were elderly, and 29 out of every 100 were children. Those in the lower socioeconomic

class were more likely to dwell in rural areas. Those in the poorest 20% were more likely to use

public health centres, and the wealthiest 20% were more likely to utilise private health care

centres. Fifty-four percent of those in the poorest 20% sought medical care in the last 4 weeks

compared to 72% of those in the wealthiest 20%. Concurringly, of the sample, 78.4% indicated

at least fair health status. Moderate-to-very good health status was explained by age, sex, social

class, income, area of residence and health care-seeking behaviour. Rural residents had the least

moderate-to-very good health status among uninsured ill Jamaicans. People who dwelled in

Other Towns were 2.3 times more likely to indicate moderate-to-very good health compared to

those in rural areas, and those in urban areas were 2.0 times more likely to claim moderate-to-

very good health status. Those who indicated having a chronic illness were 37% less likely to

report moderate-to-very good health. In addition, the present sample represents 70% of those

who indicated having an illness in Jamaica for 2007.



                                                                                              371 

 
                              


       Statistics from the Planning Institute of Jamaica and the Statistical Institute of Jamaica

[10] showed that 15.5% of Jamaicans reported ill-health in 2007. Within the context of the

current findings and that of PIOJ and STATIN, it computes that 71% of those who were

experiencing illness were without health insurance coverage. Given that 50% of those who

claimed to be experiencing ill-health utilised the public health care system and the fact that two-

thirds of the illnesses were chronic conditions (3 females for every 2 males were uninsured and

ill, and 6 out of every 10 uninsured ill people were of the dependent age cohort - less than 15

years or 60+ years), the public health care sector in Jamaica needs to recognize the impending

challenges of uninsured unhealthy people.

       Van Agt et al. [5] found that the chronically ill were more likely to be poor, a statement

with which this study concurs. In this paper, 43.2% of the chronically ill were poor (25.2% of

poorest 20%) compared to 35.2% of the upper class (15.3% of the wealthiest 20%). This study

went further than Van Agt et al.’s work, as the chronically ill were more likely to be elderly

(42.5% of the chronically ill were 60+ years), to seek more medical care, were more likely to

utilise public health facilities, more likely to live in rural areas (59.1%), more likely to be

household heads (54.8%) and more likely to be females (63%). Clearly the poor are highly

vulnerable to chronic illness [1, 5] and material deprivation [4], which accounts for more of them

not having health insurance coverage while suffering from ill-health. Hence, those who are

uninsured and ill must interface with chronic health conditions as well as income deprivation.

       Income is well established in the health literature as being associated with health [4, 8, 9],

and this explains the fact that those in the lower socioeconomic class have poorer health than

those in the upper class [18, 19]. This paper found that uninsured ill people with more income

are 2.9 times more likely to report moderate-to-very good health status, and they are also more

                                                                                                 372 

 
                              


likely to seek medical care. The challenge for those in the lower class is more than lower health

status; it is also being deprived of the health care that they need. Statistics revealed that poverty

in Jamaica is substantially a rural phenomenon (prevalence of poverty in rural areas, 15.3%;

semi-urban poverty, 4.0%; urban poverty 6.2%) [10]. This study highlights that those who are ill

and uninsured are likely to dwell in rural zones, explaining how financial deprivation accounts

for lower ownership of health insurance coverage, the worst health being found among those in

rural areas compared to city dwellers. Using per capita consumption to measure income in this

study, it was revealed that urban residents had 1.7 times more income than rural residents, and

that semi-urban residents had 1.3 times more income than rural dwellers, suggesting that the

health disparities between the geographical dwellers is explained by this income inequity. It is

therefore this access to more income that accommodates the greater health status of the urban

and semi-urban respondents, compared to the rural dwellers, and it highlights a real need to

correct income inequality among the socioeconomic groups in the nation. A study by Stronks et

al. [20] found an interrelationship between income, health and employment status, which further

argues for greater health for urban and semi-urban dwellers, as rural residents are more likely to

be seasonally employed, self-employed or have low-income employment.

       While income is related to better health status, which is also the case among uninsured ill

people, concurring with the literature on a population [8, 9, 20], the great health disparity

between the different social classes is more related to income than place of residence. Such a

finding provides clarification for a study done by Vila et al. [21] which stated that great health

disparities in the city of Milwaukee were associated with area of residence by different social

hierarchy. Income has a greater influence on better health than area of residence, and it even

correlates with health care-seeking behaviour among the uninsured ill, unlike area of residence.

                                                                                                 373 

 
                               


Money matters in the health of uninsured Jamaicans as well as the general populace, as it offers a

better explanation for peoples’ choices, accounting for the greater health of those who are able to

choose, than their place of residence. Lack of access to money, therefore, in any geographical

locality, explains health and material deprivation. Hence, it is not the fact of being in a rural area

that accounts for poor health, but material and other deprivations are greater in rural areas, a

factor which provides an understanding for the massive health disparity between them and city

residents.

       Poverty is associated with premature mortality, and the current research provides some

explanation for this established fact. This paper is on uninsured ill Jamaicans, and the findings

highlighted that 54% of those in the poorest 20% visited a health care practitioner, 58% of the

poor compared to 65% of the second wealthy and 72% of the wealthiest 20%. While the affluent

class has access to material and other resources to address health concerns, the poor are not as

privileged as the upper class. This research found that 70.1% of those in the poorest 20% had at

least one chronic health condition, the second poor, 61.2%; the second wealthy, 72.7% and the

wealthiest 20%, 68.7%, which means that non-utilisation of medical care is likely to lead to

complications and possible premature mortality. The WHO had stated that 60% of global

mortality is caused by chronic illness, but clearly poverty, non-treatment of chronic illnesses and

cultural practices are all a part of the rationale for mortality, and not merely the condition.

       Although those who suffer from chronic conditions in Jamaica are able to access public

health insurance which can reduce out-of-pocket payments for treatment and medication, clearly

the culture prevents some people from accessing this facility. This work showed that a large

percentage of uninsured ill people dwelled in rural areas, where poverty was 2.5 times more than

urban poverty and 3.8% more than semi-urban poverty, arguing for the role of the culture in

                                                                                                  374 

 
                              


preventing them from accessing assistance from the state. With this preponderance of

unwillingness on the part of poor and rural residents to access health insurance, accompanied by

their low demand for health services compared to the wealthy, the inference is that many of them

will seek health services based only on severity of illness. Chronic illnesses are such that non-

medical practitioners should not interpret when conditions are serious and warrant health care

assistance. It is this culture underpinning that accounts for the premature mortality and not the

poverty or illness, as those with chronic health conditions in Jamaica are able to access public

health care despite their reluctance to access public health insurance coverage. With not having

health insurance coverage, poverty and illness are likely to become a burden to individuals and

family, and when those social agents are unable to assist with the costing of medical treatment, it

will then become the responsibility of the state.

       This paper did not examine nutrition and health, but a study by Khetarpal and Kochar

[22] found a statistical relationship between nutrition and health in rural women, which offers

some explanation for the great health disparities in geographical areas of residence. Another

study by Foster [23] on low-income rural areas concurs with Khetarpal and Kochar [22] that

nutrition accounts for health or ill-health, as the body requires particular nutrients. It can be

extrapolated from the aforementioned studies, to that of the current one, that great disparities in

health status among the different geographical areas in Jamaica can be explained by the

nutritional intake (or lack of intake) based on where people dwell in this nation. There is a

question which must be addressed in order to provide some explanation for the seemingly low

nutritional intake of rural uninsured residents: Are rural residents less likely to intake the

required nutrients compared to residents in other geographical areas in Jamaica? The answer is

clearly yes as more of the uninsured ill Jamaicans are poor, and this means that they will be less

                                                                                               375 

 
                               


concerned about the required nutrient intake than food consumption and mere survivability.

Poverty is therefore more a factor in insurance, illness, lower health status and health care-

seeking behaviour than the geographical area of residence, but what about the general health

status of the uninsured ill, and is it lower than that of the population of Jamaica?

       Almost 78 out of every 100 uninsured ill Jamaicans claimed to have at least good health

status. A study by Bourne [24] found that 82 in every 100 Jamaicans reported at least good

health status, which is greater than that for the uninsured ill people. Furthermore, 3.3 times more

Jamaicans indicated very good health compared to the uninsured ill Jamaicans. The health

disparities were not only between the good and very good health status of Jamaicans and

uninsured ill Jamaicans, but were also evident for poor health status. Comparatively, 4.4 times

more uninsured ill Jamaicans claimed at least poor health as compared to the general population

(i.e. 4.9%), and 3 times more uninsured chronically ill Jamaicans reported at least poor health

status compared to those with acute health conditions. The current study concurs with (1) Reed

and Tu’s work [25] that uninsured chronically ill people in America reported lower health status

(or worse health) and (2) Bourne and McGrowder [26] which stated that 25.3% of chronically ill

Jamaicans reported at least poor health. Reed and Tu went on to state that the majority of

uninsured people with chronic illnesses delay health care utilisation owing to cost, which

explains an aspect of this study, that although 43.2% of the uninsured ill people were living in

poverty (i.e. poorest 20% and second poor income quintile), 39% did not seek medical care.

       Faced with poverty, no health insurance coverage and chronic illness, uninsured ill

Jamaicans are highly likely to face all kinds of life challenges such as material deprivation,

dietary and nutritional deficiencies, high risk of health complications, high out-of-pocket medical

bills, disruptions in family life, future vulnerabilities and premature mortality. When this burden

                                                                                               376 

 
                              


becomes untenable for the individual, family and wider community, it will then become the

responsibility of the state [27]. This justifies the need to expand public health insurance to

protect the poor, the chronically ill and the vulnerable in a society [28], as chronic illness can

erode the economic livelihood of an individual and therefore delay needed health care [29]. One

study stated that uninsured households are one illness away from financial catastrophe [30],

indicating that if a household was already in poverty this will become the burden of the state or

may lead to premature mortality, as the individual will be unable to access needed health care

owing to his/her inability to afford medical care. This implies that poverty encapsulates

powerlessness, physical weakness, illness, chronic illness, premature mortality, lack of

productive assets, emotional distress, constricted freedom and future impoverishment due to the

aforementioned conditions, if they are not addressed by policy makers.

       While impoverishment in urban areas is highly visible in the form of squalor, dilapidated

edifices, zinc fencing, improper sanitation, squatting and violence, rural poverty is less easily

identifiable and may be overlooked by the naked eye. Clearly, using health disparities between

area of residence and the socioeconomic strata, rural poverty in Jamaica is showing signs of

depleting the human capital more than urban poverty. According to Harpham and Reichenheim

[31], on the disaggregating of rural and urban health indicators, the latter ‘appear’ to have better

health status. This study dispels the notion of ‘appearance’ and goes to the reality of the health

differential using self-reported health among urban, semi-urban and rural uninsured ill

Jamaicans. The discipline of public health cannot only use external findings to carry out its

mandate, or divorce itself from the realities which emerge from the current study; poverty is

destroying the human capabilities and resilience of the Jamaican people and more so in the case

of rural uninsured ill people. Because poverty is strongly associated with illness, and illness can

                                                                                                377 

 
                              


result in poverty [32-34], those who are presently uninsured, ill and poor are highly vulnerable to

ill-health and premature mortality, which argues for an immediate health campaign to address the

challenges among the socioeconomic strata and area of residence, as these were not alleviated

with the introduction of the National Health Fund – NHF [35].

       The NHF is a statutory company which was established by the NHF Act (2003) with a

Chairman and Board of Management appointed by the Minister of Health. It was established in

2003 to provide direct assistance to patients with chronic conditions, to purchase drugs and fund

support to private and public companies for approved projects [35]. The NHF is a social health

insurance which is geared towards alleviating out-of-pocket payments for medication for those

who suffer from chronic illnesses. Fourteen chronic illnesses are covered by the NHF, with

respect to pharmaceutical benefits in direct assistance to ill individuals. The chronic health

conditions that are covered by the NHF are hypertension, diabetes mellitus, breast cancer,

prostate cancer, glaucoma, arthritis, asthma, high cholesterol, rheumatic heart disease, major

depression, epilepsy, psychosis, ischemia and vascular diseases. The NHF became operational in

August 2003, and has undoubtedly aided many chronically ill, non-poor and poor Jamaicans.

With all the investment, the NHF has not failed to have a major coverage of chronically ill

respondents using the Fund. The individuals are mostly rural residents, poor, under 60 years of

age, and female. Such a reality speaks to the administrative and operational failure of the NHF

to improve the lives of its intended population owing to the centralization of its operations in

Kingston, which is an urban area in Jamaica. The verdict is in, that merely instituting an agency

to carry out a particular task (which is to distribute benefits evenly across the socioeconomic

strata, area of residence and sex) will not provide solutions to the inequalities and inequities in

health between the particular groups in Jamaica. This study concurs with one in Finland [36]

                                                                                               378 

 
                              


showing that the poor are more vulnerable to illnesses, and research conducted in the United

Kingdom [37] found that those in the lower socioeconomic stratum were more likely to die

prematurely than those in the upper income groups. Embedded in those findings is the fact that

any equitable distribution of NHF benefits to those in the different socioeconomic strata will

show further unfairness and injustices in the health outcomes which already exist, owing to

income inequalities.

Conclusion

Two-thirds of uninsured ill Jamaicans are chronically ill. The uninsured ill are mostly within the

dependent age cohort (children and elderly), they are female and are rural respondents who are

generally poor people. With one half of the uninsured ill respondents utilising the public health

care system, and only 2 in every 10 of them purchasing medications, there are serious future

challenges for public health in Jamaica. There is an inverse relationship between the health status

of uninsured ill Jamaicans and those in socioeconomic strata. The findings of this study highlight

the likely challenge of the state in assisting uninsured ill Jamaicans. Despite the fact that health

insurance coverage is freely accessible to those who are chronically ill in Jamaica, there are still

many such people who are without health insurance coverage, and some are not even seeking

medical care. Another reality which emerged from this paper is that although health care

utilisation is free in Jamaica for children 18 years and younger, 45 out of every 100 of those

uninsured and ill did not seek medical care, emphasizing people’s interpretation of illnesses that

require medical attention, and how this retards health care demand. The task of public health

specialists and policy makers, therefore, is to fashion public education and intervention

programmes that will address many of the realities which emerged in this research. The great

health disparity between the lower socioeconomic strata and those in the upper strata, as well as
                                                                                                379 

 
                              


those who reside in rural areas, cannot be left to resolve itself, as clearly it has not happened in

the past and the situation cannot be allowed to continue indefinitely in the future.

The Way Forward

The variations in health status and health care-seeking behaviour within and between the

socioeconomic strata who are uninsured ill people, clearly present information that reveals public

health concerns, and highlights many challenges which are still unresolved in Jamaica. The

current study did not examine the emotional distress and mortality patterns of uninsured ill

respondents, and this should be the subject of some future study, as it would provide needed

information about these individuals. Despite the investments in health, the health sector and

poverty alleviation programmes in Latin America and the Caribbean, there is still a need to study

the heterogeneity in health outcome between the socioeconomic strata and area of residence, as

health disparity between and within countries is still great and not in keeping with health

inequality eradication in the region. Another unresolved issue stemming from the present

research is how much of the cognitive dimension explains the health differential between the

socioeconomic strata and the area of residence. In order to understand how to address policy

intervention and health education programmes for people in Jamaica, studies need to examine the

breadth and scope of cognitive dimensions in explaining health inequalities. This will allow

public health technocrats to understand why 70.3% of those who were ill in Jamaica in 2007 did

not have health insurance, and some of the chronically ill people, despite having access to public

health insurance, did not possess such insurance, and did not seek medical care. A critical issue

which needs to be addressed in the future is the structure of the National Health Fund (the NHF

is accessible to, and provides public health insurance coverage for, those experiencing chronic

illnesses). Barrett and Lalta [32] wrote that “The National Health Fund dealt with these issues by

                                                                                                380 

 
                               


treating the non-poor and the poor as part of the same target beneficiary. Survey data and health

officials indicate that the poor suffer as much from chronic diseases as the rich, but are less likely

to seek treatment, or are only able to pay for part of their prescription drugs by reducing out-of-

pocket payment …” This study is 4 years after the operational establishment of the NHF, and

new findings are coming in, which show that the NHF cannot treat different socioeconomic

strata in the same way, neither can it deal equitably with those who reside in different

geographical areas.    The health disparities will not be addressed by merely offering equal

benefits to all within the context of the current findings, as these will only perpetuate health

inequalities and inequities. The NHF therefore needs to be restructured in order to provide

definitions based on socioeconomic class and area of residence, so as to effectively alleviate

some of the challenges which emerged from this research.

Conflict of interest
The author has no conflict of interest to report.

Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica
Survey of Living Conditions, 2007, none of the errors that are within this paper should be
ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica as they are not
theirs, but are instead owing to the researcher.



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Table 14.1. Demographic characteristic of sample, n=736
Characteristic                                            n         %
Sex
  Male                                                        298       40.5
  Female                                                      438       59.5
Marital status
  Married                                                     161       30.8
  Never married                                               276       52.8
  Divorced                                                     14        2.7
  Separated                                                    10        1.9
  Widowed                                                      62       11.9
Social hierarchy
  Poorest 20%                                                 170       23.1
  Second poor                                                 146       19.8
  Middle                                                      165       22.4
  Second wealthy                                              142       19.3
  Wealthiest 20%                                              111       15.4
Area of residence
  Urban                                                       176       23.9
  Semi-urban                                                  128       17.4
  Rural                                                       432       58.7
Injury in last 4-weeks
  Yes                                                          23        3.1
  No                                                          712       96.9
Self-reported diagnosed illness
 Acute conditions
    Influenza                                                 124       18.6
    Diarrhoea                                                  26        3.9
    Asthma                                                     73       10.9
 Chronic conditions
   Diabetes mellitus                                           69       10.3
   Hypertension                                               147       22.0
   Arthritis                                                   40        6.8
   Other                                                      189       28.3
Health care-seeking behaviour
  Yes                                                         446       61.4
   No                                                         280       38.6
Health care utilization
   Public hospital (yes)                                      146       29.9
   Private hospital (yes)                                      27        5.5
   Public health care centres (yes)                            96       19.6
   Private health care centres (yes)                          212       43.4
   Other (yes)                                                  8        1.6
Purchased medication
   Yes                                                        411       58.6



                                                                               385 

 
                               


Table 14.2. Health care-seeking behaviour, health care utilization, self-reported illness and area
of residence by social hierarchy
                                                       Social hierarchy
                                  Poorest     Second         Middle        Second     Wealthiest
Characteristic                      20%         poor                       wealthy         20%
                                                                                                     P
                                   n (%)       n (%)          n (%)         n (%)         n (%)
Health care-seeking                                                                                  0.046
behaviour
   Yes                       90(54.2)       86(59.7)     98(60.1)         91(65.0)    81(71.7)
    No                       76(45.8)       58(40.3)     65(39.9)         49(35.0)    32(28.3)
Health care
utilization
  Public hospitals                                                                                   0.337
   Yes                       32(37.2)       30(35.3)     35(35.7)         30(33.7)    19(23.5)
    No                       54(62.8)       55(64.7)     63(64.3)         59(66.3)    62(76.5)
  Private hospitals                                                                                  0.451
   Yes                         5(5.7)         5(5.9)       3(3.1)           6(6.7)      8(9.9)
    No                       83(94.3)       80(94.1)     95(96.9)         83(93.3)    73(90.1)
  Public health care                                                                                 0.016
centres
   Yes                       29(33.3)       21(24.7)     21(21.4)         15(17.0)    10(12.3)
    No                       59(67.0)       64(75.3)     77(78.6)         73(83.0)    71(87.7)
  Private health care                                                                                0.001
centres
   Yes                       28(31.5)       35(41.2)     49(50.0)         52(58.4)    48(59.3)
    No                       61(68.5)       50(58.8)     49(50.0)         37(41.6)    33(40.7)
  Self-reported diagnosed                                                                            0.200
illness
 Acute conditions
    Influenza                24(15.0)       25(19.1)     34(22.7)         26(20.3)    15(15.2)
    Diarrhoea                  3(1.9)         9(6.9)       7(4.7)           3(2.3)      4(4.0)
    Asthma                   21(13.1)       17(13.0)     17(11.3)           6(4.7)    12(12.1)
 Chronic conditions
   Diabetes mellitus          15(9.4)       15(11.5)       9(6.0)         15(11.7)    15(15.2)
   Hypertension              38(23.8)       23(17.6)     37(24.7)         27(21.1)    22(22.2)
   Arthritis                  15(9.4)         8(6.1)       7(4.7)           6(4.7)      4(4.0)
   Other                     44(27.5)       34(26.0)     39(26.0)         45(35.2)    27(27.3)
Area of residence                                                                                  <0.0001
   Urban                     19(11.2)  21(14.4)  35(21.2)    42(29.6)                 59(52.2)
   Semi-urban                  16(9.4) 25(17.1)  30(18.2)    36(25.4)                 21(18.6)
   Rural                    135(79.4) 100(68.5) 100(60.6)    64(45.1)                 33(29.2)
Length of illness (i.e.     10.6±11.6 12.9±22.7 11.1±15.9 31.5±116.3                 14.9±21.8       0.006
in days) mean± SD



                                                                                                       386 

 
                                  


Table 14.3. Monthly food expenditure, per capita consumption, length of illness, number of visits
made to health practitioner, medical expenditure and self-reported diagnosed illness by area of
residence
                                                    Area of residence
Characteristic                         Urban           Semi-urban            Rural              P
                                       n (%)              n (%)              n (%)
†Monthly food expenditure          280.71±192.00 277.45±162.97           237.07±145.59          0.002
mean ± standard deviation
Per capita consumption            2425.23±1992.1 1923.62±1241.6 1441.30±1179.8 < 0.0001
mean ± standard deviation                         8                  0                  5
Length of illness in day                 9.5±19.1          13.5±23.0           17.7±65.4        0.256
mean ± standard deviation
Number of visits made to                   1.4±0.7            1.4±1.3             1.4±1.0       0.927
health care practitioner in
last 4-weeks
mean ± standard deviation
†Medical expenditure
    Public                              3.47±7.07         4.72±16.51         4.78±18.65         0.787
    mean ± standard deviation
    Private                           13.58±13.21        15.38±15.60        13.14±35.37         0.851
    mean ± standard deviation
  Self-reported diagnosed                                                                       0.162
illness
  Acute conditions
    Influenza                             19(12.3)           34(28.8)            17(17.9)
    Diarrhoea                                3(1.9)             4(3.4)            19(4.8)
    Asthma                                21(13.6)              9(7.6)           43(10.9)
  Chronic conditions
    Diabetes mellitus                     16(10.4)           13(11.0)            40(10.1)
    Hypertension                          37(24.0)           24(20.3)            86(21.7)
    Arthritis                              10(6.5)              6(5.1)            24(6.1)
    Other                                 48(31.2)           28(23.7)          113(28.5)
†Quoted in USD (USD 1.00 = Ja. $ 80.47 at the time of the survey)




                                                                                              387 

 
                             


Table 14.4. Self-reported diagnosed health conditions of uninsured ill respondents by age cohort
                                                                           Age cohort
Characteristic                       Children      Young adults       Other-aged       Young old     Old-old    Oldest-old      P
                                                                         adults
                                      n (%)           n (%)              n (%)           n (%)       n (%)        n (%)
  Self-reported diagnosed                                                                                                    < 0.0001
illness
  Acute conditions
    Influenza                          83(45.6)         10(15.6)             19(9.1)        6(5.1)     6(8.1)       0(0.0)
    Diarrhoea                            13(7.1)          2(3.1)              6(2.9)        2(1.7)     2(2.7)       1(4.5)
    Asthma                             42(23.1)         11(17.2)             13(6.2)        4(3.4)     2(2.7)       1(4.5)
  Chronic conditions
    Diabetes mellitus                     1(0.5)          2(3.1)            32(15.3)      21(17.9)   10(13.5)      3(13.6)
    Hypertension                          0(0.0)          4(6.3)            55(26.3)      41(35.0)   36(48.6)     11(50.0)
    Arthritis                             0(0.0)          0(0.0)             12(5.7)      18(15.4)    9(12.2)       1(4.5)
    Other                              43(23.6)         35(54.7)            72(34.4)      25(21.4)    9(12.2)      5(22.7)




                                                                                                                                 388 

 
                                    


Table 14.5: Logistic regression: Variables of moderate-to-very good health status of uninsured ill
respondents

                                          Coefficien      Std.   Wald                               95.0% C.I.
      Variable                                t           Error statistic         Odds ratio
    Age                                      -0.033       0.008 18.605              0.967***         0.95 - 0.98

    Average Medical Expenditure                 0.000      0.000       1.668               1.00      1.00 - 1.00

    Male                                       -0.511      0.244       4.374             0.60*       0.37 - 0.97

    Middle Class                               -0.807      0.387       4.345             0.45*       0.21 - 0.95
    Upper class                                -1.029      0.553       3.465              0.36       0.12 - 1.06
    †Lower class                                                                          1.00

    Married                                     0.140      0.278       0.253               1.15      0.67 - 1.98

    Divorced, separated or
                                               -0.421      0.349       1.455               0.66      0.33 - 1.30
    widowed
    †Never married                                                                         1.00

    Logged Income                               1.053      0.332     10.063             2.87**       1.50 - 5.49

    Urban area                                  0.696      0.300       5.365             2.01*       1.11 - 3.62
     Other town                                 0.844      0.342       6.092             2.33*       1.19 - 4.54
    †Rural area                                                                           1.00

    Head household                              0.218      0.250       0.761               1.24      0.76 - 2.03

    Dummy health care-seekers                  -0.803      0.255       9.882            0.45**       0.27 - 0.74

    Chronic illness                            -0.456      0.351       1.696             0.63*       0.32 - 0.86

Model χ2 (12) = 83.70, P < 0.001
-2 Log likelihood = 482.96
Nagelkerke R2 = 0.23
Hosmer and Lemeshow goodness of fit χ2= 3.72, P = 0.88
Overall correct classification = 75.1%
Correct classification of cases of self-rated moderate-to-very good health status = 93.4%
Correct classification of cases of not self-rated not moderate-to-very good health status = 26.5%
†Reference group
*** P < 0.0001, **P < 01, *P < 0.05




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Table 14.6: Logistic regression: Variables of self-reported health care-seekers of uninsured ill
respondents
                                                         Std.        Wald
    Variable                            Coefficient      Error      statistic Odds ratio   95% CI
    Chronic illness                          0.812       0.277         8.609     2.25**    1.31 - 3.88

    Age                                        0.024      0.008        9.593      1.03**    1.01 - 1.04

    Moderate-to-very good health              -0.857      0.281        9.274      0.42**    0.24 - 0.74

    Secondary education                        1.117      0.762        2.148        3.06   0.69 - 13.60

    Tertiary education                         1.278      1.222        1.094        3.59   0.33 - 39.42
    †Primary and below
                                                                                    1.00
    education

    Male                                      -0.358      0.244        2.154        0.70    0.43 - 1.13

    Crowding                                   0.114      0.053        4.694       1.12*    1.01 -1.24

    Logged income                              0.000      0.000        4.138       1.00*    1.00 - 1.00

    Length of illness                          0.000      0.002        0.013        1.00    1.00 - 1.00

    Married                                   -0.733      0.274        7.181      0.48**    0.28 - 0.82
    Divorced, separated, or
                                              -0.692      0.384        3.248        0.50    0.24 - 1.06
    widowed
    †Never married                                                                  1.00

    Urban area                                 0.171      0.286        0.359        1.19    0.68 - 2.08
     Other town                               -0.336      0.302        1.238        0.72    0.41 - 1.29
    †Rural area                                                                     1.00
Model χ2 (13) = 47.85, P < 0.001
-2 Log likelihood = 486.1
Nagelkerke R2 = 0.15
Hosmer and Lemeshow goodness of fit χ2= 8.11, P = 0.62
Overall correct classification = 69.0%
Correct classification of cases of self-reported health care-seekers = 89.4%
Correct classification of cases of self-reported health care-nonseekers = 32.2%
†Reference group
*** P < 0.0001, **P < 01, *P < 0.05




                                                                                                     390
                              



                                   Chapter 15
    Determinants of self-rated private health insurance coverage in
                               Jamaica


                        Paul A. Bourne & Maureen D. Kerr-Campbell


The purpose of the current study was to model the health insurance coverage of Jamaicans; and
to identify the determinants, strength and predictive power of the model in order to aid clinicians
and other health practitioners in understanding those who have health insurance coverage. This
study utilized secondary data taken from the dataset of the Jamaica Survey of Living Conditions
which was collected between July and October 2002. It was a nationally representative stratified
random sample survey of 25,018 respondents, with 50.7% females and 49.3% males. The data
was collected by way of a self-administered questionnaire. The non-response rate for the survey
was 29.7% with 20.5% not responding to particular questions, 9.0% not participating in the
survey and another 0.2% being rejected due to data cleaning. The current research extracted
16,118 people 15 years and older from the survey sample of 25,018 respondents in order to
model the determinants of private health insurance coverage in Jamaica. Data were stored,
retrieved and analyzed using SPSS for Windows 15.0. A p-value of less than 0.05 was used to
establish statistical significance. Descriptive analysis was used to provide baseline information
on the sample, and cross-tabulations were used to examine some non-metric variables. Logistic
regression was used to identify, determine and establish those factors that influence private
health insurance coverage in Jamaica. This study found that approximately 12% of Jamaicans
had private health insurance coverage, of which the least health insurance was owned by rural
residents (7.5%). Using logistic regression, the findings revealed that twelve variables emerged
as statistically significant determinants of health insurance coverage in this sample. These
variables are social standing (two wealthiest quintile: OR=1.68, 95% CI=1.23-2.30), income
(OR=1.00, 95%CI=1.00-1.00), durable goods (OR=1.16, 95%CI=1.12-1.19), marital status
(married: OR=1.97, 95%CI=1.61-2.42), area of residence (Peri-urban: OR=1.45, 95%CI=1.199-
1.75; urban: OR=1.83, 95%CI=1.40-2.40), education (secondary: OR=1.57, 95%CI=1.20-2.06;
tertiary: OR=9.03, 95%CI=6.47-12.59), social support (OR=0.64, 95%CI=0.53-0.76), crowding
(OR=1.14, 95%CI=1.02-1.28), psychological conditions (negative affective: OR=0.97,
95%CI=0.94-1.00; positive affective: OR=1.11, 95%CI=1.06-1.16), number of males in
household (OR=0.85, 95%CI=0.77-0.93), living arrangements (OR=0.62, 95%CI=0.41-0.92) and
retirement benefits (OR=1.55, 95%CI=1.03-2.35). This study highlighted the need to address
preventative care for the wealthiest, rural residents and the fact that social support is crucial to
health care, as well as the fact that medical care costs are borne by the extended family and other
social groups in which the individual is (or was) a member, which explains the low demand for
health insurance in Jamaica. Private health care in Jamaica is substantially determined by
affordability and education rather than illness, and it is a poor measure of the health care-seeking
behaviour of Jamaicans.


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1. INTRODUCTION



Literature on private health insurance or health insurance in the Caribbean, and in particular

Jamaica, has been substantially on (i) population density – i.e. coverage, (ii) coverage offerings,

(iii) cost of care – i.e. health economics, and (iv) acceptance (or lack of) by health service

providers of certain insurance coverage. Having extensively perused the literature review on

private health insurance and health care reform in Jamaica, it is obvious that no study has been

conducted identifying the different factors that explain health insurance coverage in this nation.

The individual utilization pattern of health insurance coverage is highly associated over time

with older adults [1, 2] as they prepare for the degeneration of the body; but, what else do we

know about those who have private health insurance in Jamaica? Do insurers attract healthy

patients, and are high risk individuals more likely to become insured as against their low risk (i.e.

less health conditions) counterparts?      Health insurance is a constituent of health seeking

behaviour, suggesting that it is equally important in any study of health, quality of life, and

wellbeing. In this study the researchers will critically examine factors that can be used to predict

private health insurance coverage by using a logistic regression technique to explain the

independent effect; and in the process the researchers will investigate the lives of respondents in

order to understand those who reported having private health insurance coverage.


       Instead of providing an elaborate and extensive description of ‘health insurance’, we will

give a simplified meaning of this construct. Health insurance is protection against medical costs

owing to the possibility of injuries, dysfunctions and other happenings that hinder the body from

performing at some functional standard. In keeping with this definition, a health insurance

policy is the contract that is signed by an insurer (i.e. insurance provider) and an individual or a


                                                                                                 392
                              


group, in which the insurer agrees to pay a specific sum (i.e. a premium). Hence, the

population’s health service is partially dependent on health insurance coverage or the welfare

system of the state. Jamaica does not have a public health insurance system, but one for the

elderly and those who have particular chronic health conditions, such as diabetes mellitus,

hypertension, cancer or a combination. In September 2001, the Cabinet of Jamaica accepted and

approved a proposal for the establishment of a National Health Fund (NHF) that would assists

patients as well as the elderly in Jamaicans. The individual benefits of the NHF (i.e. public health

insurance options) for the elderly and for those with particular chronic health conditions was

officially commenced in 2003 (i.e. August 1, 2003), and so there are only data on private health

insurance coverage from 1988-2002. Despite the fact that Jamaica has instituted a free health-

care service delivery programme for its child population (below 18 years in 2006), the quality of

care which is relatively good is still surrounded by a certain socio-psychological milieu as well

as inequality in health care offerings in the private versus the public sector. This explains the

rationale why some people seek private health care and by extension private health insurance

coverage [3] to meet the impending higher medical cost of care [1, 4-7] and a particular quality

of service – environment, customer service and length of service. The current study will be

examined within the theoretical framework used by Franc, Perronnin, & Pierre. [8]


1.2 Theoretical Framework

A South African Health Inequalities Survey (SANHIS) carried out in 1994 of 3,489 women ages

16 to 64 years was used to model the determinants of health insurance coverage. Kirigia et al.

[8] sought to model health insurance demand among South African women. They used binary

logistic regression analyses to estimate health insurance coverage among the sample and various

determinants of health insurance coverage. Health insurance coverage of the sample was


                                                                                                393
                               


determined by socio-demographic characteristics, health rating, environment rating, bad health

choices (i.e. smoking and alcohol consumption), and contraceptives. These were embodied in

the mathematical formula, (Eq. (1) :


Pij= (α + β1Health rating + β2Environment rating + β3 Residence + β4 Income + β5 Education +
β6Age + β7 Age squared + β8 Race + β9 Household size + β10 Occupation + β11 Employment +
β12Smoking + β13 Alcohol use + β14Contraceptive use = β15Marital status + εi ……………Eq. (1)



       where Pij = 1 if individual I owns insurance (j=1) and equal otherwise (j=0); α is

intercept terms; (β’s) are the estimated coefficients; and εi is the stochastic error term.

       The conceptual framework of Kirigia et al.’s work [8] was on two risks of health care.

They believed that these risks are (1) the risk of becoming ill, with the associated loss in quality

of life, cost of medical care, loss of productive times, more serious cases, mortality, and (2) the

risk of total or incomplete or delayed recovery [8]. This denotes that a person’s decision to buy

health insurance would be based on differentials between the level of expected utility of the

insurance and the expected utility without insurance. It is this binary nature dependent variable

and the desire to determine the effect of particular independent variables that justified the binary

logistic regression technique.

       Eq. (1) allows for the estimation of the individual probability of having or not having

health insurance by some explanatory variables. Kirigia et al. [8] did not stipulate whether

health insurance was public or private coverage, and this was addressed in another research

paper. Using the same principle of econometric analysis as Kirigia et al, a group of researchers

used a single multiple regression equation that identified explanatory variables and the powers of

particular factors that can be used to determine determinants of those who have private health




                                                                                                394
                                


insurance [9]. This captures a standard utility theory model of a demand for private health

insurance coverage, Eq. (2):

        Y = β0+ β1P+β2I+β3Z ………………………………..…………………………..Eq (2)

        where the standard utility theory is expressed in the quantity demanded of health insurance,

Y, can be written as a function of the user price of health insurance, P, income, I, and a vector of

other factors, Z or (with time subscripts suppressed); and β1 and β2 represent, respectively, the price

and income elasticity of the demand for private health insurance.

        Like Kirigia et al., [8] self-rated private health insurance coverage is a binary variable (1=

yes and 0= otherwise), which denotes that a logistic regression model will be used to estimate the

determinants and determine their impact on the dependent variable, as was done by Ahking,

Giaccotto, and Santerre [9] - Eq. (3). Instead of having a vector factor which envelopes

individual characteristics, this research isolates those factors including income, unlike Eqs. (1)

and (2), and added more variables such as psychological conditions, living arrangements and

social support.



HIi = ƒ(Yi, HCi, Eni, MSi, ARi , Ei, SSi, Oi, Pi, Gi, NPi, PPi, Mi, Fi , Di, EWi , Ai, Ri, YPi, Pmci, LLi,
CRi,)……………………………………………………………………………………………..….
.(3)


        where Eq (3) is Private Health Insurance coverage, HIi, is a function of Yi is average

current income per person in household i; HCi is health conditions of person i; Eni is physical

environment of person i; MSi is marital status of person i; ARi is area of residence of person i; Ei

is educational level of person i; SSi is social support of person i; Oi is average occupancy per

person i; Pi is property ownership of person i; Gi is gender per person i; NPi is negative affective

psychological conditions per person i; PPi is positive affective psychological conditions per


                                                                                                      395
                                  


person i; Mi is number of males per household per person i; Fi is number of females per

household per person i; Di is the number of children per household per person i; EWi is durable


goods; Ai is age of person i; Ri is retirement benefits of person i; YPi is social standing of person

i; Pmci is cost of medical care of person i, LLi is living arrangements of person i; and CRi is

crowding.

        The current study found the following determinants of private health insurance of

Jamaica (Eq (4)):

HIi = ƒ (Yi, ARi, MSi, SSi, Ei, ∑(NPi, PPi), Mi , EWi, Ri, YPi,LLi,CRi,)..........................(4)


        where Eq (4) is Private Health Insurance Coverage, HIi, is a function of Yi is average

current income per person in household i; HCi is health conditions of person i; ARi is area of

residence of person i; MSi is marital status of person i; SSi is social support of person i; Gi is

gender per person i; Ei is educational level of person i; NPi is negative affective psychological

conditions per person i; PPi is positive affective psychological conditions per person i; EWi is

durable goods of person i; Di is the number of children per household per person i; Ri is

retirement benefits of person i, YPi is social standing of person i, LLi is living arrangements and

CRi is crowding.


2. MATERIALS AND METHODS

2.1 Method

This study utilized secondary data taken from the dataset of the Jamaica Survey of Living

Conditions which was collected between July and October 2002.                          It was a nationally

representative stratified random sample survey of 25,018 respondents, with 50.7% females

(N=12,675) and 49.3% males (N=12,332). The data was collected by way of an administered

                                                                                                        396
                              


questionnaire. The non-response rate for the survey was 29.7% with 20.5% not responding to

particular questions, 9.0% not participating in the survey and another 0.2% being rejected due to

data cleaning. The current research extracted a sub-sample of 16,118 people 15 years and older

from the survey sample of 25,018 respondents in order to model the determinants of private

health insurance coverage in Jamaica.

       The rationale for the use of the 2002 data set instead of the 2007 is primarily because of

the sample population. In 2002, the institutions that were principally responsible for the data

collection used 10% of the national population to gather pertinent data on the labour force, and

this was for the Survey of Living Conditions. It represents the largest data collected on the

Jamaican population, and data was also collected on crime and victimization and the

environment, these being included for the first time, and omitted in subsequent surveys. Given

the nature of crime, violence and victimization in the nation, we opted to use a survey that had

crime and the environment as among data collected. Another condition for the selection of this

dataset was the fact that it was a large population, as against other years when the population was

less than 3,000. Within the context of a non-response rate that ranges from 10 to 30 per cent, a

larger rather than a smaller sample size coupled with some pertinent variables was preferred to a

smaller sample size without the two critical aforementioned variables. Data were stored,

retrieved and analyzed using SPSS for Windows 15.0. A p-value of less than 0.05 was used to

establish statistical significance. Descriptive analysis will be done on the sampled population in

order to provide background information on the respondents; and the enter method of logistic

regression will be used to establish the determinants of self-reported private health insurance in

Jamaica. Using the principle of parsimony, the final model will consist of only those statistically

significant variables. Where multicollinearity existed (r > 0.7), variables were independently



                                                                                               397
                              


entered into the model to aid in determining which one should be retained during the final model

construction (i.e. the decision therefore was based on the variable’s contribution to the predictive

power of the model and the goodness of fit).

2.2 Measure

Health conditions: The summation of reported ailments, injuries or illnesses in the last four

weeks, which was the survey period; where higher values denote greater health conditions; it

ranges from 0 to 4 conditions. Health status is a dummy variable, where 1 (good health) = not

reporting an ailment or dysfunction or illness in the last four 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 as people may under-report

their health condition, it is still an accurate proxy of ill-health and mortality. Household

crowding: This is the average number of persons living in a room. Physical Environment: This

is the number of responses from people who indicated suffering landsides; property damage due

to rains, flooding or soil erosion. Psychological conditions are the psychological state of an

individual, sub-divided into positive and negative affective psychological conditions.18-19

Positive affective psychological condition signifies the number of responses with regard to being

hopeful and optimistic about the future and life generally. Negative affective psychological

condition means number of responses from a person on having lost a breadwinner and/or family

member, loss of property, being made redundant, or failing to meet household and other

obligations.

Income is proxied by total individual expenditure in USD. The rate was USD1=Ja. $50.97 in

2002 at the survey period. Average income (i.e. per person per household) is total expenditure

divided by the number of persons in the household. Age: The number of years lived, which is



                                                                                                398
                                


also referred to age at last birthday. This is a continuous variable, ranging from 15 to 99 years.

Age group is classified into three categories. These are young adults (ages 15 to 30 years),

middle aged adults (ages 31 to 59 years) and the elderly (ages 60+ years). Retirement benefits

were measured by those who received retirement income. Private Health Insurance Coverage:

This is a dummy variable, where 1 denotes self-reported ownership of private health insurance

coverage and 0 is otherwise.

Durable goods: This variable is the summation of the self-reported durable goods owned by an

individual excluding houses, buildings and property.  


where Di                           ranges from 1 to 28, where higher values denote greater

ownership of durable goods.


Living arrangements are a dummy variable where, 1=living alone, 0= living with family

members or relative.

Social support (or network) denotes different social networks with which the individual has been

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

one’s home or with whom one is able to network, 0=otherwise).


Crime:  




       where Ki represents the frequency with which an individual has 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. Ti denotes the degree of

the different typologies of crime witnessed or experienced by an individual ( where j=1 …4,

where 1=valuables stolen, 2=attacked with or without a weapon, 3= threatened with a gun, and

                                                                                                399
                              


4= sexually assaulted or raped. The summation of the frequency of crime by the degree of the

incident ranges from 0 and a maximum of 51.


Social standing is proxied by per capita population quintile (from poorest-to-wealthiest)


3. RESULTS

3.1 DEMOGRAPHIC CHARACTERISTICS OF SAMPLE

The sample was 16,619 respondents (i.e. 48.6% males and 51.4% females; with 39.2% young

adults, 42.7% middle aged adults and 18.1% elderly). Some 25.8% of the sample resided in peri-

urban areas; 60.2% in rural zones; 14.0% were from urban areas; 16.8% were below the poverty

line (i.e. poorest 20%); while 18.2% were just above the poverty line compared to 21.2% in the

wealthy quintile and 24.1% in the wealthiest 20%. Of the sample, 97.6% responded to the health

status question. Of those who responded to the health status question, 80.6% indicated at least

good health and 19.4% poor health. Ninety-seven percentage points of the sample (n=16,118)

responded to the health insurance coverage question, of that 11.9% revealed having health

insurance coverage.

       Based on Table 15.1, poverty is substantially a rural phenomenon. The findings revealed

that 21.2% of rural residents were below the poverty line (i.e. poorest 20%) compared to 10.7%

of peri-urban dwellers and 9.5% of urban settlers. Health insurance was greatest among urban

residents: Some 20.8% of urban dwellers had health insurance compared to 17.6% for peri-urban

settlers and 7.5% of rural residents. A significant statistical difference was found between area of

residence and crime, and income in this sample.

       Peri-urban residents spent the most statistically on medical care (USD39.16 ± USD85.77,

95%CI: USD31.39-USD46.94) compared to urban (USD30.25± USD61.47, 95%CI: USD22.66-

USD37.83) and rural residents (USD29.33±USD54.15, 95%CI: USD26.58-USD32.06) (Table

                                                                                                400
                              


15.1)

        On examination of the cross tabulation between good health status and social standing, a

statistical correlation was found (P= 0.001) (Table 15.2). Table 15.2 showed that the worst health

was reported by those in the wealthiest quintile (21.8%), the poorest (19.9%), the poor (18.6%)

and so on.

        There is a positive statistical correlation between ageing and self-reported poor health (or

health conditions) of Jamaicans (P =0.001) (Table 15.3). Further examination of Table 15.6

revealed that 10.3% of young adults reported poor health compared to 17.4% of middle aged

adults and 43.6% of the elderly.



3.2 Multivariate Analysis

Table 15.4 presents information on the variables which are correlated (or non-correlated) with

private health insurance coverage in Jamaica of people 15 years and older. Using logistic

regression, twelve variables emerged as statistically significant determinants of health insurance

coverage in this sample (Table 15.7). These variables are social standing (two wealthiest

quintiles: OR=1.68, 95% CI=1.23-2.30), income (OR=1.00, 95%CI=1.00-1.00), durable goods

(OR=1.16, 95%CI=1.12-1.19), marital status (married: OR=1.97, 95%CI=1.61-2.42), area of

residence (Peri-urban: OR=1.45, 95%CI=1.199-1.749; urban: OR=1.831, 95%CI=1.395-2.402),

education (secondary: OR=1.57, 95%CI=1.20-2.06; tertiary: OR=9.03, 95%CI=6.47-12.59),

social support (OR=0.64, 95%CI=0.53-0.76), crowding (OR=1.14, 95%CI=1.02-1.28),

psychological conditions (negative affective: OR=0.97, 95%CI=0.94-1.00; positive affective:

OR=1.11, 95%CI=1.06-1.16), number of males in household (OR=0.85, 95%CI=0.77-0.93),




                                                                                                401
                             


living arrangements (OR=0.62, 95%CI=0.41-0.92) and retirement benefits (OR=1.55,

95%CI=1.03-2.35).

       The model [Eqn (4)] had statistically significant predictive power (model χ2 = 1604.389,

P=0.001; Hosmer and Lemeshow goodness of fit χ2= 5.280, P = 0.727), and correctly classified

91.3% of the sample (Correct classification of cases of reported health insurance coverage

=32.0% and correct classification of cases with no insurance coverage = 98.3%).



4. DISCUSSION


This study found that health insurance coverage is influenced by social standing, durable goods,

income, marital status, area of residence, education, social support, crowding, psychological

conditions, retirement benefits, living arrangements and the number of males in the household,

and that those with good health are more likely to purchase health insurance than those with poor

health. Continuing, rural residents, elderly and poorest, are the least likely to purchase health

insurance coverage in Jamaica.


       In the literature, it is well documented that the majority of uninsured workers in South

Dakota were either employed or self-employed [6]. The poor, elderly and many rural residents

are more likely to be employed on a seasonal basis in the informal sector, and these occupations

and employment types do not have private health insurance, suggesting a further rationale for

why unemployed people within a particular socio-economic status would be less likely to be

holders of health insurance coverage. In this study, it was revealed that more uninsured

Jamaicans were poor, elderly and from rural zones, and these were the ones most likely to be

unemployed in Jamaica. The current study was not able to validate the direct claim of

employability of the uninsured, but the elderly can indirectly validate the literature that more

                                                                                             402
                              


unemployed people do not have health insurance. In addition to the aforementioned fact, another

finding was that poor health is associated with low income, owing to the difficulties it creates

with accessing crucial health care [6].

       This research disagrees with the literature that the poor have lower health statuses,

suggesting that they have more health-related conditions than the wealthy. The rich engage in

highly involved particular lifestyle practices that expose them to health hazards, and this is not

equally comparable to the poor environment of the poor, justifying why they reported the least

health status. Pacione [10] has shown that the quality of the physical environment affects the

quality of life (or health or wellbeing) of people, but that lifestyle behavioural practices play a

significant role in determining one’s health [11] like the physical milieu. [12,13] Moreover, the

high cost of health care is a deterrent for the poor to have health insurance coverage; [6] and we

concur with the literature as we found a positive statistical association between self-rated health

insurance coverage and income. However, in this study we have refined the income variable, as

there is a ceiling to income and its relation with the purchase of health coverage in Jamaica. The

current work has revealed that those in the wealthy-to-wealthiest quintiles were twice as likely to

purchase health insurance coverage as the poor-to-poorest people. Within the context that those

in the wealthiest quintile purchased the most health insurance and indicated the lowest health

status, it can be inferred that the purchase of health insurance is in keeping with their life style

and the perceived role of income in buying good health, as against preventative behaviour.

       Health insurance coverage is an elderly phenomenon, [6] and this work does not concur

with the literature. The argument put forward is that younger people are healthier, and so do not

see the need to invest in health coverage, as the risk of becoming ill is low, hence the willingness

to engage in risky behaviour compared to their older counterparts, [6] suggesting that the



                                                                                                403
                              


futuristic end for health insurance coverage becomes even more critical after 30 years when more

people will have families, as well as the fact that the purchase of health insurance may

materialize owing to futuristic changes in the economic circumstances of the individual.

       There is a statistical relationship between socioeconomic conditions and the health status

of Barbadians, which is not the case in Jamaica. A study by Hambleton et al., [11] of elderly

Barbadians revealed 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,

the percentage fell to 3.2% (a drop of 2%). When the current set of socioeconomic conditions

was 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% which were as a result of current diseases. [11] Despite

this fact, it is obvious from the data that there are other indicators which explain health status;

people do not necessarily pay attention to this fact although they may have more income or

access to more economic resources. This explains the rationale for more health conditions being

reported by the wealthiest as well as the group that purchased the most health insurance, where

the thinking is that money can buy health.


       A study published in the Caribbean Food and Nutrition Institute on the elderly in the

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

services were senior citizens. [14, 15, 16] Among the many issues that the research reported on

are the five major causes of morbidity and mortality, taken from the Caribbean Epidemiology

Centre, which are of paramount importance to this discussion, and their influence on the elderly -

cerebrovascular, cardiovascular, neoplasm, diabetes, hypertension and acute respiratory infection

- and these dysfunctions are highly costly to treat. It should be noted that many of these

dysfunctions are owing to lifestyle behaviour. Hence, the purchase of private health insurance


                                                                                               404
                              


coverage by these people when they become old and approach retirement is in keeping with the

cost of health care and the high likelihood of becoming ill.

       Eldemire, [17] on the other hand, opined that the elderly are not as sick as some people

are making them out to be – “The majority of Jamaican older persons are physically and

mentally well and living in family units” [17]; but the fact is they are preparing for the

eventuality of health conditions owing to the principle of the degeneration of the body with the

onset of old age. Eldemire is somewhat right. The current study found that for every 1 young

adult who reported poor health, there were approximately 2 middle aged adults and 4 elderly

persons. Simply put, there were elderly people with poorer health than other age cohorts; but of

the elderly, more of them indicated good health status (56.4%). The mere fact of living longer

(life expectancy post retirement is at least 15 years), suggests that the aged population will

require more for medical care if they become ill. [18] With ageing the issue is not if they become

ill but when. A group of scholars found that there is a direct association between ageing and

health conditions, [19] a concept with which this study concurs. And this provides the

explanation for the purchase of private health insurance more than other age cohorts, because

they are at a different stage from other age cohorts in a population.

       Health conditions are crucial to the purchase of primary health insurance coverage, and

this is highlighted by ageing. Eldemire’s works [17, 18] have shown that ageing in an individual

does not translate to high physical impairments, but that with ageing come particular changes in

the profile of dysfunctions – Alzheimer’s disease, dementia, cerebrovascular, cardiovascular,

neoplasm, diabetes, hypertension and acute respiratory infection. [21] A study conducted by

Costa [22], using secondary data drawn from the records of the Union Army (UA) pension

programme that covered some 85% of all UA, shows that there is an association between chronic


                                                                                              405
                              


conditions and functional limitation – which includes difficulty walking and bending, blindness

in at least one eye and deafness [22]. Among the significant findings is – (i) the predictability

between congestive heart failure in men and functional limitation (i.e. walking and bending).

Although Costa’s study was on men, this applies equally to women, as biological ageing reduces

physical functioning, and so any chronic ailment will only further add to the difficulties of

movement of the aged, be it man or woman. One study has contradicted the works of Eldemire,

and it showed that a large percentage of the elderly suffer from at least one health condition.

       Women are more involved in health seeking behaviour, compared to their male

counterparts, [20] irrespective of the age factor, and this is owing to the cultural background in

which they live. Unlike women, across the world men have a reluctance to ‘seek health-care’

compared to their female counterparts. It follows in truth that women have bought themselves

additional years in their younger years, and it is a practice that they continue throughout their

lifetime which makes the gap in age differential what it is – approximately a 4-year differential

in Jamaica. In keeping with the preventative care approach to health care, it would be expected

that women would purchase more health insurance coverage than them, but this is not the case in

Jamaica as gender was not a predictor of health status. However, the more men in a household,

the less an individual will purchase health insurance coverage.


       The Planning Institute of Jamaica in collaboration with the Statistical Institute of Jamaica

has shown that while the general health status is commendable, chronic illnesses are undoubtedly

eroding the quality of life enjoyed by people who are 65 years and older [23, 24]. The JSLC

report reveals that the prevalence of recurrent (chronic) diseases is highest among individuals 65

years and over. [23] The findings show that in 2000, the prevalence of self-reported illness/injury

for people aged 65 years and over was 41.7%, for those 60 to 64 years it was 27.6% compared to

                                                                                                  406
                              


19.8% for children less than five years old.          However, the prevalence of self-reported

illness/injury for those 50 to 59 years was 18.8%. Some 36.6% of individuals 65 years and over

reported injuries/illnesses in 2002 which is a 5.6% reduction in self-reported prevalence of

illnesses/injuries over 2000, but the self-reported prevalence of illness/injuries rose by 25.8% to

62.4% in 2004. [25, 26] It should be noted here that this increase in self-reported cases of

injuries/ailments does not represent an increase in the incidence of cases, as according to the

JSLC for 2004,the proportion of recurring/chronic cases fell from 49.2% in 2002 to 38.2% in

2004 [26]. In addition, the PIOJ and STATIN [23] in (JSLC 2000) opined that individuals 60-64

years of age were 1.5 times more likely to report an injury than children less than five years of

age, and the figure was even higher for those 64 years of age and older (2.5 times more). In this

paper, the findings concurred with the literature that health conditions are significantly greater;

but other issues account for them not demanding more health insurance coverage than middle

age adults. This is reinforced in the findings that showed that people who received retirement

benefits were approximately twice as likely to purchase health insurance coverage as those who

did not receive any retirement benefits. Embedded in this finding is the fact that health insurance

is a matter of affordability and education, and not illness, which justifies why rural residents had

the lowest health insurance coverage, yet still the poorest 20% good health status was greater

than that of those in the wealthiest 20%. Statistics revealed that poverty in 2007 for the nation

was 9.9%, and rural poverty was 15.3% compared to 4% in peri-urban and 6.2% in urban areas

[27], accounting for the lowest private health insurance coverage in that group.

5. CONCLUSION


In summary, married Jamaicans are more likely to purchase health insurance coverage compared

to those who were never married, with urban residents being more likely to purchase health

                                                                                                407
                              


insurance than rural dwellers. An individual who has attained tertiary level education was more

likely to purchase health insurance than one with at most primary level education, and those who

lived alone were less likely to purchase health insurance coverage than those who dwelled with

relatives or family members. Moreover the wealthiest were more likely to purchase health

insurance, but were less healthy, and this indicates that income does not buy good health.

Therefore, this study highlighted the need to address preventative care for the wealthiest, and the

fact that social support is crucial to health care, along with the fact that medical care costs are

borne by the extended family and other social groups in which the individual is (or was) a

member, which explains the low demand for health insurance in Jamaica.


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.


Acknowledgement

The author would like to take this opportunity 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 (ie Jamaica Survey of Living Conditions, 2002) available accommodated the
current study.




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Reference
   1. Ettner, S.L. (1997) Adverse selection and the purchase of Medigap insurance by the
       elderly. Journal of Health Economics, 16,543-562.
   2. Liu, T., and Chen, C. (2002) An analysis of private health insurance purchasing decisions
       with national health insurance in Taiwan. Social Science & Medicine, 55,755-774.
   3. Dong, H., Kouyate, B., Cairns, J., Mugisha, F., Sauerborn, R. (2003) Willingness-to-pay
       for community-based insurance in Burkina Faso. Health Economics, 12,849-862.
   4. Carrin, G. (2003) Social health insurance in developing countries: A continuing
       challenge. International Social Security Review, 55, 57-69.
   5. Thomasson, M.A. (2006) Racial differences in health coverage and medical expenditure
       in the United States. Social Science History, 30,529-550.
   6. South Dakota Dept. of Health, the Lewin Group. (2002) Health Insurance Coverage in
       South Dakota: Final Report of the State Planning Grant Program. OA: South Dakota
       Dept. of Health.
   7. Varghese, R,K,, Friedman, C., Ahmed, F., Franks, A.L., Manning, M., and Seeff, L.C.
       (2005) Does health insurance coverage of Office Visits Influence Colorectal Cancer
       Testing. Cancer Epidemiology Biomarkers & Prevention 14,744-747.
   8. Kirigia, J.M., Sambo, L.G., Nganda, B., Mwabu, G.M., Chatora, R., and Mwase, T.
       (2005) Determinants of health insurance ownership among South African women. BMC
       Health Services Research, 5, 1-17.
   9. Ahking, F.W, Giaccotto, C., and Santerre R. (2009) The aggregate demand for private
       health insurance coverage in the U.S. Journal of Risk and Insurance, The American Risk
       and Insurance Association, 76, 133-157
   10. Pacione, M. (2003) Urban environmental quality of human wellbeing–a social
       geographical perspective. Landscape and Urban Planning, 65, 19-30.
   11. Hambleton, I.R., Clarke, K., Broome, H.L., Fraser, H.S., Brathwaite, F., and Hennis, A.J.
       (2005) Historical and current determinants of self-rated health status among elderly
       persons in Barbados. Rev Panam Salud Publica 2005, 17:342-353.
   12. P. Bourne Determinants of well-being of the Jamaican Elderly. M.S. thesis, The
       University of the West Indies, Mona Campus, Jamaica.
   13. Bourne, P. (2007) Using the biopsychosocial model to evaluate the wellbeing of the
       Jamaican elderly. West Indian Medical Journal, 56, (suppl 3), 39-40.
   14. CAJANUS. (1999) Health of the Elderly. Caribbean Food and Nutrition Institute
       Quarterly 32,217-240.
   15. CAJANUS. (1999) Focus on the elderly. Caribbean Food and Nutrition Institute
       Quarterly, 32,179-240.
   16. Anthony, B.J. (1999) Nutritional Assessment of the elderly. Caribbean Food and
       Nutrition Institute Quarterly, 32:201-216.
   17. Eldemire, D. (1995) A situational analysis of the Jamaican elderly, 1992. The Planning
       Institute of Jamaica, Kingston.
   18. Eldemire, D. (1997) The Jamaican elderly: A socioeconomic perspective & policy
       implications. Social and Economic Studies, 46, 175-193.
   19. Zimmer, Z., Martin, L.G., and Lin, H-S. (2003) Determinants of old-age mortality in
       Taiwan. (http://www.popcouncil.org/pdfs/wp/181.pdf)
   20. Rice, P.L. (1998) Health psychology. Brooks/Cole Publishing, CA.
   21. Eldemire, D. (1996) Level of Mental Impairment in the Jamaican Elderly and the Issues


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        of Screening Levels, Caregiving, Support Systems, Carepersons, and Female Burden.
        Molecular and Chemical Neuropathology, 28, E1-E5.
    22. Costa DL. Chronic diseases rates and declines in functional limitation. Demography
        2002, 39:119-138.
    23. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN).
        (2001) Jamaica Survey of Living Conditions 2000. PIOJ and STATIN, Kingston.
    24. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN).
        (1998) Jamaica Survey of Living Conditions 1997. PIOJ and STATIN, Kingston.
    25. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN).
        (2003) Jamaica Survey of Living Conditions 2002. PIOJ and STATIN, Kingston.
    26. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN).
        (2005) Jamaica Survey of Living Conditions 2004. PIOJ and STATIN, Kingston.
    27. Planning Institute of Jamaica, (PIOJ) and Statistical Institute of Jamaica, (STATIN).
        (2008) Jamaica Survey of Living Conditions 2007. PIOJ and STATIN, Kingston.




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Table 15.1: Demographic characteristic of sample by area of residence

                                      Rural          Peri-urban             Urban          P

                                       % (n)           % (n)                 % (n)

Age group                                                                                  0.001
       Young adults                   38.3 (3833)    41.0 (1760)            39.7 (923)
      Middle age adults               41.6 (4160)    44.2 (1895)            44.6 (1039)
       Elderly                        20.1 (2010)    14.8 (634)             15.7 (365)

Health insurance coverage                                                                  0.001
       Yes                            7.5 (722)       17.4 (726)            20.8 (471)
       No                             92.5 (8969)     82.6 (3442)           79.2 (1788)

Gender                                                                                     0.001
          Male                        50.4 (5041)    46.8 (2006)            44.3 (1031)
          Female                      49.6 (4962)    53.2 (2283)            55.7 (1296)

Per capita income quintile                                                                 0.001
       1=Poorest 20%                  21.2 (2118)    10.7 (458)             9.5 (222)
       2                              22.0 (2196)    13.3 (572)             11.2 (261)
       3                              20.8 (2085)    18.7 (800)             16.7 (388)
       4                              19.8 (1978)    22.7 (972)             24.3 (565)
       5=Wealthiest 20%               16.2 (1625)    34.7 (1487)            38.3 (891)

Marital status                                                                             0.001
       Married                        25.5 (2460)    26.9 (1115)            21.0 (475)
       Never married                  66.6 (6433)    66.4 (2755)            71.6 (1619)
       Divorced                       0.6 (56)       1.0 (41)               1.2 (26)
       Separated                      1.1 (104)      1.2 (49)               1.4 (32)
       Widowed                        6.3 (610)      4.5 (187)              4.8 (108)

Crowding mean (SD)                    1.77 ± 1.24    1.75 ± 1.28            1.72 ± 1.18    0.216

Crime index                           1.74 ± 7.37    2.34 ± 8.08            2.83 ± 9.30    0.001

Medical expenditure1 mean (SD)      $29.33±$54.15     $39.16 ± $85.77    $30.25± $61.47    0.012

Income2 mean (SD)                $5496.12 ± $4860.97 $7534.74 ± $5544.26 $8779.26 ±$10568.69 0.001

1
    Medical Expenditure is expressed in USD: 1USD= JA$50.97 for the period 2002
2
    Income is expressed in USD: 1USD= JA$50.97 for the period 2002




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Table 15.2: Good health status by social standing (Per capita population quintile)


                                        Social standing (Per Capita Population Quintile)

    Good health status              1=Poorest     2         3          4       5=Wealthiest   Total


                         Poor         19.9       18.6      17.9       18.4          21.8      19.4


                         Good         80.1       81.4      82.1       81.6          78.2      80.6



    Total                             2738      2975      3208        3413         3883       16217



χ2(4) = 23.273, P= 0.001, contingency coefficient = 0.038




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Table 15.3: Good health status by age group


                                                             Age group

                                                Young     Middle         Elderly
    Good health status                         age(15 to age (31 to       (60+
                                               30 years) 59 years)       years)     Total


                         Poor                       10.3         17.4        43.6      19.4



                         Good                       89.7         82.6        56.4      80.6



    Total                                          6283          6973       2961     16217


χ2(2) = 1458.12, P= 0.001, contingency coefficient = 0.287




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Table 15.4: Logistic regression: Private health insurance coverage by some variables
                                                            Odds
                                               P            Ratio         95.0% C.I.
                                                                        Lower        Upper
    Age                                       0.443          1.00        0.99         1.00
    Middle quintile                           0.174          1.24        0.91         1.71
    Two wealthiest quintiles                  0.001          1.68        1.23         2.30
    †Poorest 20%-to-poor                                     1.00
    Household Head                            0.213          1.80         0.71           4.55
    Logged medical expenditure                0.671          1.01         0.95           1.08
    Average income                            0.009          1.00         1.00           1.00
    Durable goods                             0.000          1.16         1.12           1.19
    Separated or Divorced                     0.608          0.90         0.61           1.33
    Married                                   0.000          1.97         1.61           2.42
    †Never married                                           1.00
    Peri-urban                                0.000          1.45         1.10           1.75
    Urban                                     0.000          1.83         1.40           2.40
    †Rural area                                              1.00
    Environment                               0.116          0.85         0.70           1.04
    House tenure - rented                     0.999          0.00         0.00
    House tenure - owned                      0.950          1.04         0.27           4.03
    House tenure – squatted*                                 1.00
    Secondary                                 0.001          1.57         1.20          2.06
    Tertiary                                  0.000          9.03         6.47         12.59
    †Primary and below                                       1.00
    Social support                            0.000          0.64         0.53           0.76
    Sex                                       0.722          1.03         0.86           1.24
    Crowding                                  0.018          1.14         1.02           1.28
    Crime index                               0.652          1.00         0.99           1.01
    Land ownership                            0.665          0.96         0.79           1.16
    Negative affective                        0.034          0.97         0.94           1.00
    Positive affective                        0.000          1.11         1.06           1.16
    Number of males in house                  0.001          0.85         0.77           0.93
    Number of females in house                0.622          0.98         0.89           1.07
    Number of children in house               0.438          0.97         0.90           1.05
    Living arrangement                        0.017          0.62         0.41           0.92
    Retirement benefits (1=yes)               0.038          1.55         1.03           2.35
    Poor health status                        0.309          0.94         0.83           1.06
-2Log Likelihood= 3982.175
Nagelkerke R Square= 0.359
Model χ2(8)= 1604.389, P-value=0.001
Hosmer and Lemeshow χ2= 5.280, P=0.727
Overall correct classification = 91.3%: Correct classification of cases of reported health insurance coverage =32.0%; Correct
classification of cases with no health insurance coverage =98.3%
†Reference group




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                                   Chapter 16
    Difference in social determinants of health between men in the
       poor and the wealthy social strata in a Caribbean nation


                          Paul A. Bourne & Denise Eldemire-Shearer


Studies that have examined social determinants of health have made their investigations on the
population, but none have reviewed them from the perspective of particular social hierarchies.
The study examined the factors determining the self-reported health of men of different
socioeconomic status, by using models derived through econometric analyses. The study used a
sample of 6,474 respondents: 2,704 from the two poor quintiles and 3,770 from the two wealthy
quintiles. The survey used a random stratified probability sampling technique and involved the
use of self-administered questionnaires. Multiple logistic regression technique was used to
identify variables which are associated with health conditions of men in the two social
hierarchies. The findings revealed that the self-reported health of men in the two wealthiest
quintiles were substantially influenced by private health insurance coverage (Odds Ratio (OR) =
32.9, 95%CI: 20.64, 52.45) and age of respondents (OR = 1.03, 95%CI: 1.02, 1.04) This was
similar for men in the two poorest income quintiles; private health insurance coverage (OR =
16.97, 95%CI: 10.18, 28.27) and age (OR=1.05, 95%CI: 1.03, 1.06). Negative affective
psychological conditions, consumption and medical expenditure affected the self-reported health
of those in the two wealthiest quintiles, while positive affective, secondary levels of education
and living alone influenced those in the two poorest quintiles. This research serves as a
foundation for further work relating to the determinants of self-reported health conditions,
inequity across socio-economic strata for men, and how patient care should be addressed.


INTRODUCTION


In recent years the World Health Organization (WHO) has increasingly drawn attention to the

importance of the relationship between health and social conditions in determining the health of

individuals and populations [1]. Social determinants (conditions, in which people are born, live,

grow, work and age as well as the health system available to them) produce inequalities in

health, and need to be considered in health development. Addressing social determinants and

health policy now forms the basis for political action both nationally and internationally [2].



                                                                                                  415
                              


       Human poverty is defined as more than income poverty; it is the denial of choices and

opportunities for living a tolerable life [3]. Poverty as described above in the Caribbean has been

predominantly a rural phenomenon; however, rising levels of urban poverty have also been seen.

In 1996 the World Bank estimated 38% of the total population (or 25% including Haiti) in the

Caribbean, or more than seven million people, to be poor [4]. One study found that rural poverty

in Argentina, Barbados, Boliva, Brazil, Colombia, Jamaica, Suriname, Trinidad and Tobago, and

Uruguay was at least twice more than urban poverty [5]. According to the Jamaica Survey of

Living Conditions (JSLC), in 2003, the poverty rate stood at 19.1%, and in 2007 it fell to 9.9%

[6]. The JSLC for 2001 [6] indicates 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% [6]. This is a mean per capita annual consumption expenditure of

US$ 3,963.53 compared to US$ 314.48. Gafar found that in some Latin American and Caribbean

countries, between 2 to 8 percentage of income is estimated to be received by those in the

poorest 20% compared to between 42 and 58% that is received by those in the wealthiest 20%

[5] which indicates that income inequalities are vast between the poor and the wealthy within

those societies, and does account for some of the health disparities between the social

hierarchies.


    According to the WHO’s definition, health is not merely the absence of disease but the

highest possible state of physical, social and mental wellbeing. At both a societal and individual

level, the aim is to extend healthy life expectancy, as well as productivity and quality of life at

older ages for as long as possible [7]. Understanding how the social determinants influence

                                                                                               416
                                


health and social wellbeing is an area of considerable research interest. That the unequal

distribution of variables such as income, unemployment and education produce health

inequalities, has been documented [8-10]. Studies have established a statistical relationship

between health status and poverty [11-13], between standard of living and health conditions,

health status owing to a particular natural disaster [14,15], and income and health [16]. It is

recognized that more information is needed at the social level, and that knowledge needs to be

translated into action [17].


    People with lower socioeconomic status have worse health in all adult age groups, including

older ages [18]. Age has been identified as an important social determinant of health. Among

adults, 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 [19].


        Gender is equally as important a social determinant of health. Men are experiencing

poverty. It is important to understand the factors influencing self-reported health. Many studies

that have examined those in the poor and wealthy income groups have used a piecemeal

approach, and in the Caribbean this is also the case. Studies that have examined social

determinants of health [1, 2, 8-17] have made their investigations in the population, but have not

reviewed them from the perspective of particular social hierarchies within a nation, in order to

establish if the factors are the same, and if not, what the disparities are. It is within this

framework that the present study examined factors determining self-reported health among men

in the two poorest and the two richest quintiles in Jamaica, in order to provide public health

specialists and policy makers with research findings on these cohorts.



                                                                                               417
                               


MATERIALS & METHODS

The current study extracted a sample of 6,474 men; (2,704 from the two poorest quintiles and

3,770 from the two wealthiest quintiles) from the dataset of the Jamaica Survey of Living

Conditions (JSLC). The inclusion/exclusion criteria were (1) being males, and (2) being

classified in the poor or wealthy social strata. The survey (JSLC) was a nationally representative

probability sample in which self-administered questionnaires were used to collect data from the

populace [20]. 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.


       The survey was drawn using stratified random sampling. The 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 residences in rural areas and 150 in urban areas. The sample was

weighted to reflect the population of the nation. The non-response rate for the survey was 27.7%.


Measurements

Self-reported health conditions: This is a dummy variable, where 1 = self-reported ailment,

injury or illness in the last four weeks, which was the survey period, 0 = otherwise. Thus, self-

reported health is a binary variable, where 1 = not reporting an illness, and 0 = reporting an

ailment.

Living arrangement: This is a dummy variable, where 1 = living alone, and 0 = otherwise,

                    , where       represents each person in the household, and r is the number of

rooms excluding kitchen, bathroom and verandah.



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


Psychological conditions are the psychological state of an individual, and this is subdivided into

positive and negative affective psychological conditions. Positive affective psychological

condition is the number of responses with regard to being hopeful and optimistic about the future

and life generally. Negative affective psychological condition is the number of responses from a

person on having lost a breadwinner and/or family member, having lost property, having been

made redundant or failing to meet household and other obligations.


Natural disaster: This is the number of responses from people who indicated suffering landslides;

property damage due to rains, flooding and soil erosion.



                             where ki represents the frequency with which an individual

                             witnessed or experienced a crime, where i denote 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

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.


Consumption: The total sum which is spent by an individual on durable and non-durable good

during a 12-month period.



Statistical analysis


                                                                                              419
                               


Statistical analyses were performed using Statistical Packages for the Social Sciences (SPSS)

16.0 software for Windows (SPSS Inc, Chicago IL). Descriptive statistics were used to provide

basic information on the sampled population. Logistic regression analyses were used to establish

the model to ascertain parameters, and determine the strength of each statistically significant

variable (P < 0.05). The predictive power of the model was tested using the Omnibus Test of

Model and Hosmer and Lemeshow [23] 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. Cohen and Holliday [24] stated that correlation can be

low/weak (0 to 0.39); moderate (0.4-0.69), or strong (0.7-1.0). This was used to assist in the

exclusion (or retention) of a variable in the model. In support of this, where collinearity existed (r

> 0.7), variables were entered independently into the model to assist in determining which one

should be retained during the final model construction. The decision to retain (or exclude) was

based on the variables’ contribution to the predictive power of the model and its goodness of fit.

To derive accurate tests of statistical significance, we used SUDDAN statistical software

(Research Triangle Institute, Research Triangle Park, NC), and this adjusted for the survey’s

complex sampling design.


Analytic model


The multivariate model used in this study to examine the sub-sample is a modification of that of

Grossman [21] and Smith & Kington [22] which captures the multi-dimensional concept of

health status and conditions. The present study further refined the two aforementioned works and

in the process added some new factors, such as psychological conditions, crowding, house

tenure, and the number of people in the household. Using econometric analysis the study sought

to model the self-reported health of men in the two wealthiest and poorest quintiles from a

                                                                                                  420
                               


general set of social determinants identified in the literature, as seen in the equation below

(Equation [1]).


Hi = ƒ(Li,Ri,lnC,Eni,ARi,SSi,CRi,(             ),lnEi,HHi,Ai,HIi,Mi,Fi,MRi,EDi,lnMEi) ………[1]



Hi is a function of the 17 variables. Li is living alone of person i, 1 if living alone, 0 if not living

alone; Ri is retirement income of person i, 1 if receiving private and/or government pension, 0 if

otherwise; LnC is the average consumption expenditure of person i, in dollars; En is the natural

disaster, 1 if in the lived milieu there has been flooding, soil erosion, landslide, 0 if not; ARi is

the area of residence, other towns, KMA with the reference group being rural areas; SSi is social

support, 1 if yes, 0 if no; CR is crowding in the household of person i; lnEi is the average total

expenditure of the person i in dollars, which is the proxy for income; HHi is household head of

person, 1 if yes, 0 if no; Ai is age of person i, in years; HIi is health insurance coverage, 1 if

person has a health insurance policy, 0 if otherwise; M is number of males in household of

person i; F is number of females in household of person i; MRi is marital status of person i; EDi

is educational level of person i; lnMEi is medical expenditure of person i;                      NPi is

the summation of all negative affective psychological conditions and PP is the summation of all

positive affective psychological conditions.

        The final model consisted of only those variables which are statistically significant (P <

0.05). Equation [2] represents those factors that explain the health conditions of those in the

poorest 20% and equation [3] denotes variables which are correlated with the health conditions

of those in the wealthiest 20%:

        Hi = ƒ(Li, ,PPi, Ai,HIi,EDi) ………….……………………………………….[2]




                                                                                                    421
                              


       Hi = ƒ(lnCi, NPi, Ai,HIi,lnMEi) ………………………..……………..………[3]



RESULTS

Characteristics of sample

There are diverse dissimilarities between the demographic characteristics of men in the two

poorest quintiles and those in the two wealthiest quintiles. The average consumption per head for

the poor was US$301.79 (SD = US$96.16), which represented 22.1% of the average

consumption expenditure per head of those in the two wealthiest quintiles. Similarly, the

crowding for men in the two wealthiest quintiles was 1 person (SD = 0.798 person) compared to

2.3 persons (SD = 1.4 persons) for those in the two poorest quintiles. Furthermore, 4.6 times

more men in the two wealthiest quintiles resided alone, compared to those in the poorest

quintiles. There was a remarkable difference in the level of tertiary education of the two sampled

groups, as for every 1 man in the two poorest quintiles with tertiary level education there were 88

men in the two wealthiest quintiles. In addition to the aforementioned differences, there are 4

times more men in the two wealthiest quintiles who are receiving retirement income compared to

those men in the two poorest quintiles (Table 16.1). Moreover, those in the two wealthiest

quintiles are more vulnerable to crime (2.5 ± 8.5; Range = 88, 0) compared to those in the

poorest quintiles (1.7 ± 7.3; Range = 88, 0).

       The disparity was narrower for self-reported health conditions, as for every 100 men in

the two poorest quintiles who indicated a health condition there were 109 men in the two

wealthiest quintiles.

Multivariate Analysis

Predicting the health conditions of men in the two poorest quintiles



                                                                                               422
                               


In the investigation of the factors which predict the health conditions of men in the two poorest

quintiles, it was found that the data was a good fit for the model as 89.1% (n = 1,755) of the data

were correctly classified; 98.5% of those who indicated no health condition were correctly

classified, with 28.7% reporting that they had at least one dysfunction (Table 16.2). Moreover,

the 5 factors accounted for 30.6% of the variability in health conditions of this group: -2 log

likelihood =1195.541; Nagelkerke R2 = 0.306; χ2 (21) = 360.02, p < 0.001.



Predicting health conditions of men in the two wealthiest quintiles

In investigating the self-reported health of men in the two wealthiest quintiles, it was found that

the data was a good fit for the model, as 87.6% (n = 2,533) were correctly classified; 99.0% (n =

2,396) of those who indicated no health condition were correctly classified, with 29.0% (n = 76)

of those mentioning that they had at least one dysfunction (Table 16.3). Of the 17 variables that

the researchers tested, only 5 were statistically significant.




DISCUSSION

       This study makes an important contribution to understanding self-reported health in

Jamaican men in two ways. It provides both an econometric model which can be used on sub-

samples of data sets for routine data collection, and it identifies the variables involved in

determining the self-reported health of the poorest and wealthiest Jamaican groups. The study is

timely, given the increasing recognition of the contribution of social determinants to health [1].

The findings of this study suggest that age, average consumption, private health insurance

coverage, level of education, whether or not the person lived alone, medical expenditure and

positive or negative affective psychological conditions were determinants of the self-reported


                                                                                               423
                              


health of the wealthiest and poorest men in Jamaica. Age, health insurance and psychological

conditions are common to both groups, while consumption and medical expenditure are

significant for the wealthiest, and education and living arrangements for the poorest quintiles.

These findings are contrary to those of other studies [21, 22], and therefore contribute to the local

understanding of the relationship between self-rated health status and the socio-economic status

of men in Jamaica.


       Age was the second most significant predictor of self-reported health for both groups.

The Jamaican Healthy Lifestyle Survey Report 2000 [25] noted a prevalence of hypertension of

19.9% among males, which increased with age in both rural and urban populations and in both

sexes. The most common chronic diseases identified among elderly males and females were

hypertension, arthritis, diabetes, cardiovascular arrest, stroke and cancer. Patients in the 60-and-

over age groups accounted for 37.2% and 41.1% respectively, of new hypertensive and diabetic

cases [26]. Diabetes is one of the leading causes of morbidity and mortality among persons aged

65 and older [27].


       Having health insurance was a predictor for both groups of quintiles. Access to services

also depends on the capacity to pay, which can exclude men in the poorest quintile and who

might have lived all their lives in poverty [28]. The health problems of older men often

necessitate prolonged medication and treatment. The high cost of consultations, diagnostic

services and particularly medicines are among the most formidable barriers to appropriate and

timely care. Deprivation earlier in the life cycle, in terms of education and paid employment,

means that older men in the two poorest quintiles are less likely than their counterparts in the two

wealthiest quintiles to be literate, to have participated in the formal labour force, or to receive

retirement pensions or benefits, such as health insurance coverage. Even when they do receive a

                                                                                                 424
                              


retirement pension, this is likely to be lower than that of their wealthier counterparts because of

the lower average wages that they earned when employed. Thus, many lack the means to meet

their needs [28].


       In this study 4.8% of men in the two poorest quintiles possessed medical insurance,

compared with 3.3% of men in the two wealthiest quintiles, and this was lower than the 7.6%

reported in a previous study [29]. This finding suggests that the cost of health care is the

individual’s responsibility and for the poorer quintiles emphasizes the reliance on public

services.


       Being in fair or poor health, or having a chronic health condition, is strongly associated

with being underinsured. Compared to those in better health, individuals who rate their health as

fair or poor are almost three times as likely to be underinsured (19% versus 7%). While this is

true regardless of residence, rural non-adjacent residents in poorer health have the highest

underinsured rate [30]. Studies have also shown that the lack of health insurance coverage is a

significant barrier to treatment, and rural areas have disproportionate populations of uninsured

and underinsured [31, 32]. As a result of a large percentage of rural men being employed in

small businesses or being self-employed, they are more likely to be uninsured. Bennett and

colleagues [33] postulated that rural residents were more likely to be uninsured than urban

residents (17.8% versus 15.3%), and that rural respondents were more likely than urban

counterparts to report having deferred health care because of cost (15.1% versus 13.1%). This

study supports the findings of other studies.


       The current study found that a positive affective psychological condition was a predictor

of self-reported health for those in the two poorest quintiles, while a negative affective condition



                                                                                                425
                             


was a predictive factor for those in the two wealthiest quintiles. This means that the more a

wealthy individual experiences negative affective conditions, he/she is 1.074 times (or 7.4%)

more likely to report health conditions, suggesting that increased negative conditions result in

more hypertension, diabetes mellitus and other types of illnesses. Positive affective

psychological conditions, on the other hand, were inversely correlated with health conditions for

those in the two poorest quintiles. There, those in the two poorest quintiles who experienced

more positive conditions were 8.3% less likely to report health conditions. Embedded in this

finding is the role negative and positive affective conditions play in determining the health

conditions of different sub-groups in the Jamaican population.


       Psychological wellbeing is dependent on a host of factors, including genetic traits, social

support systems, personality types, and the presence of positive and negative psychological

constructs such as happiness, optimism, morale, depression, anxiety, self-esteem, self-efficacy,

and vigour. Psychological wellbeing is particularly important for the prevention or management

of cardiovascular disease, but it also has important implications for the prevention and

management of other chronic diseases such as diabetes, osteoporosis, hypertension, obesity,

cancer and depression [34], which have been identified as significant in the Jamaican population.


       People’s cognitive responses to ordinary and extraordinary situational events in life are

associated with a different typology of wellbeing [35]. It is found that happier people are more

optimistic, and as such they conceptualize life’s experiences in a positive manner. A study by

Diener and colleagues [36] found that self-reported wellbeing (personal happiness) of the

wealthy-affluent (those earning in excess of US 10 million annually) was marginally more than

that of the lower wealthy, suggesting that high incomes do not increase happiness by the same

proportion. The distinction between the importance of the positive and negative affective

                                                                                              426
                               


conditions of the poor and rich respectively, underlines the importance of the state of mind in

perceived health. According to Harris and colleagues [37] and Kashdan [25], negative

psychological conditions affect subjective wellbeing in a negative manner (i.e. guilt, fear, anger,

disgust), and positive factors influence self-reported wellbeing in a direct way. This concurs with

findings in studies conducted by Fromson [38] as well as by other scholars [39, 40].

Furthermore, the poor may become more optimistic, even with a decline in their health status.

Thus the poor remain hopeful irrespective of their health conditions. The rich, on the other hand,

report that a negative affective psychological condition, such as the loss of a family member, is

associated with their decline in health.


       Education was another of the five predictors of self-reported health for those in poor

quintiles. For every eighty-eight men in the two wealthiest quintiles attaining a tertiary level of

education, there was only one man in the two poorest quintiles. Education is closely associated

with an individual’s health status, and high average educational levels are closely associated with

higher average life expectancy [41]. Furthermore, educational attainment is linked to many

aspects of a person’s wellbeing. Research has shown that higher levels of education usually

translate into better health status, higher incomes, and consequently higher standards of living

[42] and better cognitive functioning in older age [43]. Men with less education and who are

poorer are more likely to experience earlier onset of disease, loss of functioning, and physical

impairment [44]. Hayward and colleagues [45] reported onset of diseases and death 5–10 years

earlier for persons with lower socioeconomic status. The average number of biological risk

factors indicating physiological dysregulation is also higher for poorer people and people with

less education [46]. In addition, education significantly affects how effectively people utilize

health care. Education further affects health because well-educated people may be more aware of


                                                                                               427
                              


the benefits and disadvantages of certain types of behaviours associated with personal health

[47].


        Importantly, marital status did not appear to be a proxy for who a person lives with, as it

was not a significant determinant of self-reported health conditions. Smith and Waitzman’s work

[48] noted that men’s gains from marriage were greater than those of women [49]. Smith and

Waitzman [48] offered the explanation that wives dissuaded their husbands from particular risky

behaviours, such as the use of alcohol and drugs, and would ensure that they maintain a strict

medical regimen coupled with proper eating habits [50,51] which accounts for them having

greater wellbeing than their non-married counterparts.          Surprisingly, more men in the two

wealthiest quintiles lived alone. Older men are likely to live alone and be unconnected to any

family unit because of irresponsible patterns of sexual behaviour and parenting or unstable

relations during their younger years [52].


        The wealthiest in the society experience better health, due to their knowledge of health

risks and their access to the resources necessary to avoid such risks, and to treat health conditions

[53, 54]. But with increasing wealth and development there has been an increase in chronic

diseases, as lifestyle changes have had a negative impact [55, 56]. This study found that there

was a large gap between the consumption of the groups, with the poorest only consuming 22% of

the proportional consumption of the wealthiest.


        Among the demographic correlates of health is the cost of medical care [1, 2, 21, 22, 57,

58]. The current study concurs with the literature that the cost of medical care is associated with

health status; but this is only for wealthy Jamaicans. Medical care expenditure was not associated

with self-reported health for the poor to poorest in Jamaica.



                                                                                                 428
                              


Conclusion

The key finding which emerged from this is that social determinants of health are not always the

same across different social hierarchies. The similarities in social determinants across the two

social strata are age of respondents, health insurance coverage, and negative affective

psychological conditions. Educational levels and living arrangements are not associated with

health for men in the upper social strata, and consumption and medical expenditure are not for

those in the lower social strata. This study adds to the literature by showing that social

determinants of health are not the same in a particular cohort, or between different social strata.

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.


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58. Bourne PA. Health Determinants: Using secondary data to model predictors of well-being of
    Jamaicans. West Indian Medical Journal, 2008; 57:476-481.
59. Wilkinson R, Marmot M, (eds). Social determinants of health: the solid facts. 2nd Edition,
    WHO: Copenhagen; 2003 - http://www.euro.who.int/document/e81384.pdf.




                                                                                             432
                             


Table 16.1: Demographic characteristics of sample

                                    Two Poorest quintiles       Two Wealthiest quintile
                                    N=2,704      %              N=3,770      %
Educational attainment
       Primary and below             551            23.4          603         18.2
       Secondary & post-sec.        1787            75.8        2,414         73.0
       Tertiary                       18             0.1          291          8.8
Marital Status
       Married                       593            22.8        1,058         29.0
       Never married                1902            73.1        2,370         65.0
       Divorced                        7             0.3           49          1.3
       Separated                      17             0.7           51          1.4
       Widowed                        83             3.2          116          3.2
Household Head
       No                             94             3.5        1,505         40.0
       Yes                          2610            96.5        2,261         60.0
Age group
       Youth (15 – 25yrs)            973            36.0        1015          26.9
       Older adults (26 -59 yrs)    1214            44.9        2135          56.6
       Elderly (60+ yrs)             517             19.1        620          16.4

Self-reported health conditions
        None                        2229            84.2        3,038         82.7
        At least one                  418           15.8          637         17.3
Receiving retirement income
        No                          2625            97.7        3,426         91.0
        Yes                            63            2.3          339          9.0
Living Arrangement
        With family                 2532            93.6        2,673         70.9
        Alone                         172            6.4        1,095         29.1
Ownership of private health insurance
        No                          2508            95.2        3,462         96.6
        Yes                           127            4.8          118          3.3

†Average annual Consumption US $301.79 (SD=US $96.16) US$1,326.50(SD=US $1,054.97)

Crowding mean (SD)                  2.3 persons (1.4 persons)   1 person (0.798 person)

Crime Index mean(SD)                1.7(7.3); Range=88, 0        2.5(8.5); Range=88,0
†1US$ = Ja. $50.97 (in 2002)




                                                                                          433
                                      


Table 16.2: Logistic regression: Health conditions of men in the two poorest quintiles by some
explanatory variables

    Explanatory Variables                           β                                CI (95%)
                                          Coefficient        Odds Ratio
      Retirement income                        0.166               1.18             0.52 -2.68
      Age                                      0.044               1.05         1.03 - 1.06***
      Household head                          -0.746               0.47            0.15 - 1.50
      Log averaged consumption                -0.033               0.97            0.54 - 1.73

      Separated/Div/Widowed                     -0.123                 0.88         0.48 - 1.64
      Married                                   -0.179                 0.84         0.56 - 1.25
      †Single                                                          1.00

      Other Towns                               -0.237                 0.79         0.50 - 1.26
      Urban areas                               -0.359                 0.70         0.38 - 1.30
      †Rural area                                                      1.00

      Health Insurance                           2.831                 17.0   10.18 - 28.27***
      Natural disaster                           0.032                 1.03         0.75 - 1.41

      Secondary & post secondary                 0.599                 1.82      1.24 - 2.68**
      Tertiary                                  -0.931                 0.39        0.04 - 4.23
      †Primary and below                                               1.00

      Living arrangement                         0.328                 1.39       1.02 - 1.88*
      Crowding                                  -0.072                 0.93        0.80 - 1.08
      Negative affective                         0.007                 1.01        0.96 - 1.06
      Positive affective                        -0.087                 0.92      0.86 - 0.98**
      Logged medical expenditure                 0.038                 1.04        0.93 - 1.16
      Crime index                                0.014                 1.01        1.00 - 1.03
      Number of males per
                                                 0.009                 1.01         0.84 - 1.21
      household
      Number of females per
                                                 0.043                 1.04         0.87 - 1.26
      household
-2 Log likelihood =1195.541
Nagelkerke R Square = 0.306
 Model χ2 (21) = 360.02, P < 0.001
Overall correct classification = 89.1%
Correct classification of cases of no health conditions = 98.5%
Correct classification of cases with al least one dysfunction =28.7%
†Reference group
*P < 0.05, **P < 0.01, ***P < 0.001




                                                                                                  434
                                   


Table 16.3: Logistic regression: Health conditions of men in the two wealthiest quintiles by some
explanatory variables

                                                                                             CI (95%)
    Explanatory Variables                                             Odds
                                                 β Coefficient        Ratio
      Retirement income                                 0.375          1.46              0.73 - 2.91
      Age                                               0.032          1.03           1.02 - 1.04***
      Household head                                    0.396          1.49              0.46 - 4.85
      Log average annual
                                                           0.632       1.88             1.27 - 2.80**
      consumption

      Separated/Div/Widowed                               -0.227       0.80                0.49 - 1.29
      Married                                             -0.178       0.84                0.62 - 1.13
      †Single                                                          1.00

      Other towns                                         -0.124       0.88                0.68 - 1.15
      Urban                                               -0.188       0.83                0.59 - 1.16
      †Rural area                                                      1.00

      Health insurance                                     3.494      32.90        20.64 - 52.45***
      Natural disaster                                    -0.142       0.87              0.67 - 1.13

      Secondary & post-secondary                           0.081       1.08                0.79 - 1.49
      Tertiary                                            -0.243       0.78                0.46 - 1.32
      †Primary and below                                               1.00

      Living arrangement                                  -0.139       0.87                0.69 - 1.10

      Crowding                                            -0.030       0.97              0.81 - 1.17
      Negative affective                                   0.071       1.07           1.04 - 1.11***
      Positive affective                                  -0.019       0.98              0.93 - 1.04
      Logged medical expenditure                           0.086       1.09             1.00 - 1.19*
      Crime index                                          0.007       1.01              1.00 - 1.02
      Number of males in
                                                           0.157       1.17                0.98 - 1.40
      household
      Number of females in
                                                           0.185       1.20                0.99 - 1.47
      household
-2 Log likelihood = 2054.45
Nagelkerke R Square = 0.280
 Model χ2(21) = 522.79, P < 0.001
Overall correct classification = 87.6%, Correct classification of cases of no health conditions = 99.0%
Correct classification of cases with al least one dysfunction =29.0%
†Reference group
*P < 0.05, **P < 0.01, ***P < 0.001




                                                                                                          435
 Health Insurance
         &
      Health
                            By Paul Andrew Bourne


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.



In 2007, statistics revealed that 21 out of every 100 Jamaicans had health insurance coverage and
66 out of every 100 sought medical care, indicating that most of the people who utilized medical
care services did not use health coverage. Within the context of the global economic downturn,
increased job redundancies and prices of commodities, the uninsured will be asked to pay more
for medical care. Apart from the increased odds of not utilizing health care services, little is
known about the uninsured in Latin American and the Caribbean, and in particular Jamaica.



Married Jamaicans are more likely to purchase health insurance coverage compared to those who
were never married, with urban residents being more likely to purchase health insurance than
rural dwellers. An individual who has attained tertiary level education was more likely to
purchase health insurance than one with at most primary level education, and those who lived
alone were less likely to purchase health insurance coverage than those who dwelled with
relatives or family members. Moreover the wealthiest were more likely to purchase health
insurance, but were less healthy, and this indicates that income does not buy good health.

				
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