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FEMALE, HEALTH AND SEX IN JAMAICA

VIEWS: 159 PAGES: 821

This book provides an understanding of the health, reproductive health matters and particular health conditions of females in Jamaica

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									Females, Health
           &
               Sex in Jamaica




           Paul A. Bourne

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      $$                        ££


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Females, Health
          &
              Sex in Jamaica




                  i
Females, Health
              &
                   Sex in Jamaica




         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


Females, Health & Sex in Jamaica


Includes index

ISBN

Bourne, Paul Andrew


All rights reserved. Published , 2011

Cover designed by Paul Andrew Bourne




                                        iii
                                     Contents

Preface                                                                                    vi
Acknowledgement                                                                          viii
                                        Part 1
    Health, Illness and chronic health conditions                                            1
Chapter 1      Health of females in Jamaica: using two cross-sectional surveys               1
Chapter 2      The uninsured ill in a developing nation                                     21
Chapter 3      Self-rated health of the educated and uneducated classes in Jamaica         52
Chapter 4      Health status of patients with self-reported chronic diseases
                    in Jamaica                                                               77
Chapter      5      The changing faces of diabetes, hypertension and arthritis
                    in a Caribbean population                                               103
Chapter      6      Self-assessed health of young adults in an English-speaking
                    Caribbean nation                                                        129
Chapter      7      Disparities in self-rated health, health care utilization, illness,
                    chronic illness and other socioeconomic characteristics of the
                    Insured and Uninsured                                                   160
Chapter      8      Good Health Status of Rural Women in the Reproductive Ages              192
Chapter      9      Determinants of Quality of Life of Jamaican Women                       225
Chapter      10     Examining Health Status of Women in Rural, Peri-urban and
                    Urban Areas in Jamaica                                                  246
Chapter      11     Social determinants of self-reported health across the Life Course     288
Chapter      12     Modeling social determinants of self-rated health status of hypertensive
                    in a middle-income developing nation                                    310


                                        Part 2

    Sex and reproductive practices of females
Chapter 13 Factor Differentials in contraceptive use and demographic profile
                    among females who had their first coital activity at most 16 years
                    versus those at 16+ years old in a developing nation                   335
Chapter      14     Reproductive health matters: Women whose first sexual
                    intercourse occurred at 20+ years old                                  368
Chapter      15     On sexual and non-intimate unions among the general reproductive
                    population of women in Jamaica: A cross-sectional survey               392
Chapter      16     Sociodemographic correlates of age at sexual debut among women

                                             iv
                    of the reproductive years in a middle-income developing nation       430

Chapter      17     Current use of contraceptive method among women in a
                    middle-income developing country                                     458

Chapter      18     Females with multiple sexual partners and their reproductive
                    health matters: A comprehensive analysis of women aged 15-49 years
                    in a developing nation                                               484
Chapter      19     Sexually assaulted females on their sexual debut:
                    Reproductive health matters                                          513
Chapter      20     Females of the reproductive ages who have never used a condom
                    with a non-steady sexual partner                                     538
Chapter      21     Multiple sexual partnerships among young adults in a
                    tropically developing nation: A public health challenge              559

                                        Part 3

    Sex    and reproductive practices of males
Chapter     22 Psychosocial correlates of condom usage in a developing country           592
Chapter     23 Young males whose first coitus began at most 15 years old                 621
Chapter     24 Young males who delay first coitus for the statutory age and
                    beyond in Jamaica                                                    646

                                        Part 4

    Validity and reliability testing of survey data
Chapter 25 The image of health status and quality of life in a Caribbean society 671
Chapter 26 Paradoxes in self-evaluated health data in a developing country          691
Chapter 27 The validity of using self-reported illness to measure objective health 716
Chapter 28 Dichotomising poor self-reported health status:
                    Using secondary cross-sectional survey data for Jamaica              736

Chapter      29     The quality of sample surveys in a developing nation                 758

                                        Part 5
    Additional chapter
Chapter 30 Self-rated health status of young adolescent females in a
                    middle-income developing country                                     791

                                             v
                                           Preface

Many developing countries as well as developed nations continue to experience sexual
explosions, the lowering of the age at first sexual intercourse. It appears that inspite of the
inroads of public health practitioners to effectively tackle sanitary issues, water quality,
vaccination and countless other reproductive health matters; they have failed in their efforts to
adequately address the continuous lowering of the age at first coitus. This is equally the same in
the United States. Jamaica like many societies has tried to revert the lowering of the age at first
sexual relations, but this is to no avail. With the lowering of the age of sexual consent from 18 to
16 years in Jamaica, this is equally responsible for the continued sexual experimentation at an
even younger age among adolescents as well as children (under 16 years). The high diet of
sexual relations is not limited to young children, adolescents and young adults, the prevalence
and incidence of among Jamaicans are exorbitantly high.
          Statistics revealed that almost 23 out of every 25 Jamaicans aged 15-74 years old have
had have sex, 33 out of every 50 Jamaicans aged 15-74 years old had sex at least once per week,
21 out of every 25 Jamaicans aged 15-24 years had sex and about 41 in every 50 Jamaicans of
the early age had sexual intercourse at least once per week (Wilks et al., 2008). The percentage
of reported sexual intercourse increased with age in Jamaica (Wilks et al., 2008), suggesting that
sexual relations must have some cultural underpinnings. Clearly from the aforementioned
findings, Jamaicans are in a highly sexed people. One of the notable irony (or paradox) is that
they (Jamaicans) are not openly expressive about sex, dislike public dialogues on the
phenomenon, the older adults are speechless to advice young children and young adults about
sexual expression, expect to say ‘abstain’, ‘wait for adulthood’ and ‘in time you will know what
to do’.
          The silent sexed culture is equally responsible for rape. Dialectic situations arise when
females are raped (or sexually assaulted) as some people believe that the female is to be blamed
for inviting the peer by her code of dress or ‘common’ behaviour. Then there is sympathy for the
perpetrator (rapist). Some people become empathetic toward the position of the perpetrators,
with social expressions for the behaviour. More so, females are victims and sexually assaulted by



                                                 vi
powerful males because of poverty, and the economic power disparity protect the economically
powerful males.
       In response to the aforementioned issues, this volume collated some search papers on
various issues on health, sex and sexual experiences of females. Even though the issues are
primarily on females, any discussion on sexuality must relate to both sexes. Therefore, I added
an entire section on males’ sexual expression as this will broaden the discourse and provide
clarity to females’ sexual issues.
       This book would have been incomplete if it had not examined the quality of survey data.
I believe that scientists cannot accept cosmology, without questioning, testing and verifying that
knowledge. It is as a result of questioning, further testing of knowledge and truths that truths are
established, modified or changed with more information. Knowledge is not stationary; therefore,
I sought to question the validity and reliability of survey data used in this text. The purpose was
to provide readers with better understanding of findings, their roles in being skeptics, and how
knowledge is created through questioning.
       I believe strongly in readable and engaging writing style, and so many complex concepts
were simplified in keeping with my purpose to engage and connect with the readers. In some
instances technical statistical terms and calculations were unavoidable. In those cases, I tried to
explain the issues surrounding the technical terms for the readers to be adequately informed on
the subject without a thorough knowledge of introductory or advanced statistics. Knowledge of
introductory or advanced statistics will be good but not necessarily for the readers.
       The majority of the chapters are 1) published in peer reviewed journals, and 2) solely
written by yours truly. However, a few chapters are co-authored with Caribbean and
International scholars.
       This book will broaden the discourse as it represents a useful contribution to the literature
that is Jamaican in scope.


                                                                        Paul Andrew Bourne
                                                                                              2011




                                                vii
                                  Acknowledgements


Many people have contributed to the completion of this book. I would like to extend my sincere
gratitude to them. I would like to single out, 1) Ms. Neva South-Bourne for her advice in penning
my ideas, 2) Mrs. Evadney Bourne, my wife, for support, understanding and patience when
things were difficult and surmountable at times, 3) all my co-authors, 4) God, for his wisdom, 5)
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 available for use in this study, and 6) all
my associates (including best friends) whose love, support and encouragement provided the
impetus that I drew from to complete this project.




                                              viii
                                              1
      Health of females in Jamaica: using two
              cross-sectional surveys


The 21st Century cannot have researchers examining self-rated health status of elderly,
population, children and adolescents and not single out females as they continue to be poorer
than males; and are exposed to different socioeconomic situation. The current paper 1) examines
the health conditions; 2) provides an epidemiological profile of changing health conditions in the
last one half decade; 3) evaluates whether self-reported illness is a good measure of self-rated
health status; 4) computes the mean age of females having particular health conditions; 5)
calculates the mean age of being ill compared with those who are not ill; and 6) assesses the
correlation between self-rated health status and income quintile. There is reduction in the mean
age of females reported being diagnosed with chronic illness such as diabetes mellitus (60.54 ±
17.14 years); hypertension (60.85 ± 16.93 years) and arthritis 59.72 ± 15.41 years). In 2007 over
2002, the mean age of females with unspecified health conditions fell by 33%. Although healthy
life expectancy for females at birth in Jamaica was 66 years which is greater than that for males,
improvements in their self-rated health status cannot be neglected as there are shifts in health
conditions towards diabetes mellitus and a decline in the mean age at which females are
diagnosed with particular chronic illnesses.


Introduction


Life expectancy is among the objective indexes for measuring health for a person, society, or

population. In 1880-1882, life expectancy at birth for females in Jamaica was 39.8 years which

was 2.79 years more than that for males. One hundred and twenty-two years later, health

disparity increased to 5.81 years: in 2002-2004, life expectancy at birth for females was 77.07

                                                1
years [1]. For the world, the difference in life expectancy for the sexes was 4.2 years more for

females than males: for 2000-2005, life expectancy at birth for females was 68.1 years [2].

Within the expanded conceptual framework offered by the World Health Organization (WHO) in

the late 1940s, health is more than the absence of morbidity as it includes social, psychological

and physiological wellbeing [3].


       Some scholars [4] opined that using the opposite of ill-health to measure health is a

negative approach as health is more than this biomedical approach. Brannon and Feist [4]

forwarded a positive approach which is in keeping with the ‗Biopsychosocial‘ framework

developed by Engel. Engel coined the term Biopsychosocial when he forwarded the perspective

that patient care must integrate the mind, body and social environment [5-8]. He believed that

mentally patient care is not merely about the illness, as other factors equally influence the health

of the patient. Although this was not new because the WHO had already stated this, it was the

application which was different from the traditional biomedical approach to the study and

treatment of ill patients. Embedded in Engel‘s works were wellbeing, wellness and quality of life

and not merely the removal of the illness, which psychologists like Brannon and Feist called the

positive approach to the study and treatment of health.


       Recognizing the limitation of life expectancy, WHO therefore developed DALE –

Disability Adjusted Life Expectancy – which discounted life expectancy by number of years

spent in illness. The emphasis in the 21st Century therefore was healthy life and not length of life

(ie life expectancy) [9]. DALE is the years in ill health which is weighted according to severity,

which is then subtracted from the expected overall life expectancy to give the equivalent healthy

years of life. Using healthy years, statistics revealed that the health disparity between the sexes in

                                                  2
Jamaica was 5 years in 2007 [10], indicating that self-rated health status of females on average in

Jamaica is better than that for males. This is not atypical to Jamaica as females in many nations

had a greater healthy life expectancy than males.


       The discipline of public health is concerned with more than accepting the health disparity

as indicated by life expectancy or healthy life expectancy, as it seeks to improve the quality of

life of the populace and the various subgroups that are within a particular geographical border. In

order for this mandate to be attained, we cannot exclude the study of females‘ health merely

because they are living longer than males and accept this as a given; and that there is not need

therefore to examine their self-rated health status.


       Many empirical studies that have examined health of Caribbean nationals were on the

population [11-15]; elderly [16-25]; children [26, 27]; adolescents [28-30] and females have

been omitted from the discourse. A comprehensive search of health literature in Caribbean in

particular Jamaica revealed no studies. The values for the healthy life expectancy cannot be

enough to indicate the self-rated health status of females neither can we use self-rated health

status of population, children, elderly and adolescents to measure that of females.


       WHO [31] forwarded a position that there is a disparity between contracting many

diseases and the gender constitution of an individual, suggesting that population health cannot be

used to measure female health. Females have a high propensity than males to contract particular

conditions such as depression, osteoporosis and osteoarthritis [31]. A study conducted by

McDonough and Walters [32] revealed that women had a 23 percent higher distress score than

men and were more likely to report chronic diseases compared to males (30%). It was found that

men believed their health was better (2% higher) than that self-reported by females.
                                                  3
McDonough and Walters used data from a longitudinal study named Canadian National

Population Health Survey (NPHS). Those aforementioned realities justify a study on female

health in Jamaica.


       The current paper fills the gap in the health literature by investigating health of females in

Jamaica. The objectives of the current paper are 1) to examine the health conditions; 2) provide

an epidemiological profile of changing health conditions in the last one half decade (2002-2007);

3) evaluate whether self-reported illness is a good measure of self-rated health status; 4) compute

the mean age of females having particular health conditions; 5) calculate the mean age of being

ill compared with those who are not ill; and 6) assess the correlation between self-rated health

status and income quintile.


Materials and methods

Sample


The current paper extracted subsample of females from two secondary cross-sectional data

collected by the Planning Institute of Jamaica and the Statistical Institute of Jamaica [33, 34]. In

2002, a subsample of 12,675 females was extracted from the sample of 25,018 respondents and

for 2007; a subsample of 3,479 females was extracted from 6,783 respondents. The survey is

called the Jamaica Survey of Living Conditions (JSLC) which began in 1989. The JSLC is

modification of the World Bank‘s Living Standards Measurement Study (LSMS) household

survey. A self-administered questionnaire is used to collect the data from Jamaicans. Trained

data collectors are used to gather the data; and these individuals are trained by the Statistical

Institute of Jamaica

                                                 4
       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. 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 (i.e. LFS) was selected for the JSLC. The sample was weighted

to reflect the population of the nation. The non-response rate for the survey for 2007 was 26.2%

and 27.7%.


Measures


Self-reported illness (or Health conditions): 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 (self-rated health status): ―How is your health in general?‖ And the

options were very good; good; fair; poor and very poor. The first time this was collected for

Jamaicans, using the JSLC, was in 2007.


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).


                                                5
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).


Statistical analysis


The data were collected, stored and retrieved in SPSS for Windows 16.0 (SPSS Inc; Chicago, IL,

USA). Descriptive statistics were used to provide information on the socio-demographic

variables of the sample. Cross Tabulations were employed to examine correlations between non-

metric variables and Analysis of Variance (ANOVA) were utilized to examine statistical

associations between a metric and non-metric variable. The level of significance used in this

research was 5% (ie 95% confidence interval).


       Bryman and Cramer [35] correlation coefficient values were used to determine, the

strength of a relation between (or among) variables: 0.19 and below, very low; 0.20 to 0.39, low;

0.40 to 0.69, moderate; 0.70 to 0.89, high (strong); and 0.90 to 1 is very high (very strong).


Results

Demographic characteristic of sample


In 2002, 14.7% of sample reported an illness and this increased by 19.1% in 2007. Over the same

period, health insurance coverage increased by 81.0% (to 21.0% in 2007); those seeking medical

care increased to 67.6% (from 66.0%); the mean age in 2007 was 30.6±21.9 years which

marginal increased from 29.4 ± 22.3 years; diabetic cases exponentially increased by 227.7% (in

2007, 15.4%); hypertension decline by 45.5% (to 24.8% in 2007) and arthritic cases fell by

66.1% (to 9.4% in 2007). Urbanization was evident between 2007 and 2002 as the number of
                                                 6
females who resided in urban areas increased by 114.7% (to 30.4% in 2007), with a

corresponding decline of 19.4% in females zones.


       Table 1.1 revealed that the increase in self-reported illness was substantially accounted

for by increased cases in the rural sample (from 12.9% in 2002 to 20.0% in 2007). The drastic

increase in health insurance coverage in 2007 was due to public establishment of public health

insurance coverage. The greatest increase was observed in semi-urban areas 17.8%) followed by

urban (9.6%) and rural (7.8%) Table 1.1. The increases in self-reported illness can be accounted

for by diabetes mellitus, asthma and other dysfunctions. Concurrently, most of the increased

cases were diabetic in semi-urban zones (17.1%); other health conditions in semi-urban areas

(12.4%) and asthma in urban zones (12.0%) (Table 1.1).


Bivariate analyses


There was a significant statistical correlation between self-rated health status and self-reported

illness - χ2 (df = 4) = 700.633, P < 0.001; with there being a negative moderate relation between

the variables – correlation coefficient = - 0.412(Table 1.2). Based on Table 1.2, 10.7% of those

who reported an illness had had very good self-rated health status compared to 40.2% of those

who did not indicate an illness. On the other hand, 2.5% of those who did not report a

dysfunction had at least poor self-rated health status compared to 19.8% of those who indicated

having an illness. Even after controlling self-rated health status and self-reported illness by age,

marital status and per capita annual expenditure, a moderate negative correlation was found –

correlation coefficient = - 0.362.

       On further examination of the self-reported illness by age, it was found that in 2002 the

mean age of individual who reported an illness was 43.97 ± 26.81 years compared to 27.05 ±
                                                 7
20.41 years for who without an illness – t-test = 30.818, P < 0.001. In 2007, the mean age of

reporting an illness was 42.83 ± 26.53 years compared to 28.16 ± 19.95 years for those who did

not report an ailment – t-test = 15.263, P < 0.001.

       Based on Figure 1.1, there is an increase in the mean age of females being diagnosed with

diarrhoea (32.00 ± 36.2 years) and asthma (21.73 ± 20.51 years). However, there is reduction in

the mean age of females reported being diagnosed with chronic illness such as diabetes mellitus

(60.54 ± 17.14 years); hypertension (60.85 ± 16.93 years) and arthritis 59.72 ± 15.41 years). The

greatest decline in mean age of chronically ill diagnosed females was in arthritic cases (by 7.41

years). Concurrently, the mean age of females with unspecified health conditions fell by (33%,

from 54.62 ± 21.77 years in 2002 to 36.42 ± 23.69 years in 2007).


       A cross tabulation between self-rated health status and income quintile revealed a

significant statistical correlation - χ2 (df = 16) = 54.044, P < 0.001; with the relationship being a

very weak one – correlation coefficient = 0.126 (Table 1.3). Based on Table 1.3, the wealthy

reported the greatest self-rated health status (ie very good) compared to the wealthiest 20%

(36.7%); with the poorest 20% recorded the least very good self-rated health status.

       No significant statistical correlation was found between diagnosed self-reported illness

and income quintile - χ2 (df = 28) = 36.161, P > 0.001 (Table 1.4).



Discussion

Self-rated health status of female Jamaicans can be measured using self-reported illness. The

current paper found a moderate significant correlation between the two aforementioned

variables, suggesting that self-reported illness is a relatively good measure of female‘s health. In

                                                 8
this study it was revealed that 60 out of every 100 who reported an illness had at most fair self-

rated health status, with 20 out every 100 indicated a least poor health. It is evident from the

findings that self-rated health status is wider than illness, which concurs with the literature [35,

36], which is keeping with the propositions of the WHO that health must be more than the

absence of illness. Self-rated health status is people‘s self-rated perspective on their general self-

rated health status [35], which includes a percentage of poor health (or ill-health). The other

components of this status include life satisfaction, happiness, and psychosocial wellbeing. Using

a sample of elderly Barbadians, Hambleton et al [37] found 33.5% of explanatory power of self-

rated health status is accounted for by illness. There is a disparity between the current paper and

that of Hambleton et al‘s work as more of self-rated health status of the elderly is explained by

current illness with this being less for females in Jamaica. Concomitantly, there is an

epidemiological shift in the typology of illnesses affecting females as the change is towards

diabetes mellitus. In 2007 over 2002, the 15 out of every 100 females reported being diagnosed

with diabetes mellitus compared to 5 in 100 in 2002 indicating the negative effects of life

behaviour of female‘s self-rated health status. Another important finding of the current paper is

that diagnosed illnesses are not significantly different based on income quintile in which a

female is categorized. However, the self-rated health status of females in different social

standing (measured using income quintile) is different. Embedded in this finding is the role of

income plays in improving self-rated health status [38]. Like Marmot [38], this study found that

income is able to buy some improvement in self-rated health status; but this work goes further as

it found that income does not reduce the typology in health conditions affecting females.

       Before this discussion can proceed, the discourse must address the biases in subjective

indexes which are found in studies like this one. Any study on subjective indexes in the

                                                  9
measurement of health (for example, happiness, life satisfaction; self-rated health status, self-

reported illness) needs to address the challenges of biases that are found in self-reported data in

particular self-reported health data. The discourse of subjective wellbeing using survey data

cannot deny that it is based on the person‘s judgement, and must be prone to systematic and non-

systematic biases [40]. Diener [36] argued that the subjective measure seemed to contain

substantial amounts of valid variance, suggesting that there is validity to the use of this approach

in the measurement of health (or wellbeing) like the objective indexes such as life expectancy,

mortality or diagnosed morbidity. A study by Finnas et al [41] opined that there are some

methodological issues surrounding the use of self-reported (or self-rated) health and that these

may result in incorrect inference; but that this measure is useful in understanding health,

morbidity and mortality. Using life expectancy and self-reported illness data for Jamaicans,

Bourne [42] found a strong significant correlation between the two variables (correlation

coefficient, R = - 0.731), and that self-reported illness accounted for 54% of the variance in life

expectancy.

       When Bourne [42] disaggregated the life expectancy and self-reported illness data by

sexes, he found a strong correlation between males‘ health (correlation coefficient, R = 0.796)

than for females (correlation coefficient, R = 0.684). Self-reported data therefore do have some

biases; but that it is good measure for health in Jamaica and more so for males. In spite of this

fact, the current research recognized some of the problems in using self-reported health data

(read Finnas et al. [41] for more information), while providing empirical findings using people‘s

perception on their health.

       Now that the discourse on objective and subjective indexes is out of the way, the next

issue of concern is the reduced aged of reported illness and age of being diagnosed with

                                                10
particular chronic illness. In 2002, the mean age recorded for those who self-reported an illness

was 44 years and this fell by 1 year in 2007, indicating that on average females are becoming

diagnosed with an illness on average 2 months earlier.           When self-reported illness was

disaggregated into acute and chronic health conditions, it was revealed that on average females

were being diagnosed 7.41 years earlier with arthritis in 2007 over 2002; 4.95 years earlier with

hypertension and 1.13 years earlier with diabetes mellitus.



Conclusion

The current paper revealed that rural females recorded the highest percentage of self-reported

illness. Concurrently, in 2007, 20 out of every 100 females in rural Jamaica reported an ailment

which is a 3.7% increase over 2002 compared to a 3.1% increase in urban and 2.2% increase in

semi-urban females. Furthermore, poverty was greatest for rural females. In 2002, poverty

among rural females was 2.2 times more than urban poverty; and this increased to 3.3 times in

2007. In addition to the aforementioned issues, there is a shift in chronic illnesses occurring in

females in Jamaica. Hypertension and arthritis have seen a decline in 2007 over 2002; however,

there were noticeable increases in diabetes mellitus over the same period. The greatest increase

in cases of diabetes mellitus occurred in semi-urban females followed by urban and rural

females.


       In summing, the current paper has revealed that, although healthy life expectancy for

females at birth in Jamaica is 66 years, improvements in their self-rated health status cannot be

neglected as there are shifts in health conditions (to diabetes mellitus) as well as the decline in

ages at which females are being diagnosed with particular chronic illnesses. There is an issue

                                                11
which emerged from the current finding, the increasing cases of unspecified illness among

females and this must be examined as to classification in order that public health practitioners

will be able to address it before it unfolds into a public health challenge in the future.


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                                              13
24. Bourne PA. Good Health Status of Older and Oldest Elderly in Jamaica: Are there
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29. Bourne PA. Demographic shifts in health conditions of adolescents 10-19 years, Jamaica:
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30. Blum RW, Halcon L, Beuhring T, Pate E, Campbell-Forrester S, Venema A. Adolescent
heath in the Caribbean: Risk and protective factors. American Journal of Public Health 2003; 93:
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Science and Medicine 2001; 52:547-559.

33. Statistical Institute Of Jamaica. Jamaica Survey of Living Conditions, 2002 [Computer file].
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[distributors], 2003.

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scientists. London and New York: Routledge; 2005: p. 214-219.


                                               14
35. Kahneman D, Riis J. Living, and thinking about it, two perspectives. In: Huppert FA,
Kaverne B, Baylis N, editors. The science of well-being: Integrating neurobiology, psychology,
and social science. London: Oxford University Press; 2005. p. 285-304.

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current predictors of self-reported health status among elderly persons in Barbados. Revista
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Public Health J 2008;1:32-39.

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American J of Medical Sciences. In print.




                                              15
Table 1.1. Sociodemographic characteristics of sample by area of residence, 2002 and 2007

                                         2002                                          2007
Variable
                        Rural           Semi-           Urban           Rural         Semi-         Urban
                                        Urban                                         Urban
Marital status
 Married                1232 (25.7)     568 (25.7)     243 (19.3)      262 (23.9)    111 (21.0)    161 (21.2)
 Never married          3033 (63.3)    1452 (65.7)     907 (71.9)      723 (65.9)    362 (68.6)    523 (68.9)
 Divorced                  25 (0.5)       16 (0.7)       18 (1.4)        11 (1.0)      16 (3.0)      16 (2.1)
 Separated                 51 (1.1)       27 (1.2)       22 (1.7)        12 (1.1)       5 (0.9)       8 (1.1)
 Widowed                  453 (9.4)      147 (6.7)       71 (5.6)        89 (8.1)      34 (6.4)      51 (6.7)

Income quintile
  Poorest 20%           1864 (24.8)     450 (13.5)     206 (11.4)      498 (29.9)     77 (10.2)      97 (9.2)
  Poor                  1867 (24.8)     511 (15.3)     231 (12.7)      437 (26.2)    146 (19.4)    131 (12.4)
  Middle                1559 (20.7)     652 (19.2)     331 (18.2)      342 (20.5)    161 (21.4)    212 (20.0)
  Wealthy               1340 (17.8)     759 (22.7)     441 (24.3)      237 (14.2)    183 (24.3)    265 (25.0)
  Wealthiest 20%         894 (11.9)     965 (28.9)     605 (33.4)       154 (9.2)    185 (75.2)    354 (33.4)

Health conditions
Diagnosed Acute:
 Cold                        1 (0.7)        0 (0.0)         0 (0.0)      13 (7.8)     21 (20.0)      13 (7.8)
 Diarrhoea                   3 (2.2)        1 (3.0)         0 (0.0)       2 (1.2)       2 (1.9)       2 (1.2)
 Asthma                      1 (0.7)        2 (6.1)         0 (0.0)     20 (12.0)       6 (5.7)     20 (12.0)
Diagnosed Chronic:
 Diabetes mellitus          8 (6.0)        0 (0.0)        1 (4.2)       23 (13.8)     18 (17.1)     23 (13.8)
 Hypertension             57 (42.5)      20 (60.6)      10 (41.7)       33 (19.8)     29 (27.6)     33 (19.8)
 Arthritis                38 (28.4)       8 (24.2)       7 (29.2)         9 (5.4)       7 (6.7)       9 (5.4)
 Other                    26 (19.4)        2 (6.1)       6 (25.0)       45 (26.9)     13 (12.4)     45 (26.9)
 Non-diagnosed              -              -              -             22 (13.2)       9 (8.6)     22 (13.2)

Self-reported illness
  Yes                   1181 (16.3)     384 (12.0)     228 (12.9)      324 (20.0)    104 (14.2)    164 (16.0)
  No                    6051 (83.7)    2811 (88.0)    1540 (87.1)     1298 (80.0)    627 (85.8)    864 (84.0)

Health care-seekers
 Yes                     791 (66.0)     261 (66.8)     145 (64.7)      215 (65.5)     65 (63.1)    125 (74.4)
  No                     407 (34.0)     130 (33.2)      79 (35.3)      113 (34.5)     38 (36.9)     43 (25.6)

Health insurance
  Yes, Private            540 (7.4)      539 (16.7)     341 (19.3)      114 (7.1)     117 (16.3)    191 (18.7)
  Yes, Public                -              -              -            126 (7.8)      56 (17.8)      98 (9.6)
  No                    6723 (92.6)    2690 (83.3)    1430 (80.7)     1361 (85.0)     547 (76.0)    735 (71.8)
Age Mean (SD) in yrs     29.5 (23.0)    28.6 (21.2)    30.0 (21.0)     29.9 (22.3)   30.6 (21.1)   31.6 (22.0)




                                                       16
Table 1.2. Self-rated health status by self-reported illness, 2007

Self-rated health status                                     Self-reported Illness

                                                       Yes                           No

Very good                                                   63 (10.7)                     1114 (40.2)
Good                                                       176 (29.8)                     1305 (47.1)
Fair                                                       234 (39.7)                      281 (10.2)
Poor                                                       104 (17.6)                        55 (2.0)
Very poor                                                     13 (2.2)                       13 (0.5)
Total                                                             590                           2768
χ2 (df = 4) = 700.633, P < 0.001, correlation coefficient = - 0.412




                                                 17
Figure 1.1. Mean scores for self-reported diagnosed health conditions, 2002 and 2007




                                                       18
Table 1.3. Self-rated health status by income quintile, 2007
                                                  Income Quintile
  Self-rated health
 status               Poorest 20%         2.00         3.00       4.00           Wealthiest 20%

Very good
                        196 (30.2)    237 (34.0)     225 (32.4)     282 (42.4)        243 (36.7)



Good
                        287 (44.2)    320 (45.9)     326 (46.9)     268 (40.3)        284 (42.8)



Fair (moderate)
                        105 (16.2)    110 (15.8)     107 (15.4)      87 (13.1)        108 (16.3)



Poor
                           56 (8.6)     23 (3.3)       30 (4.3)       24 (3.6)          26 (3.9)



Very poor
                            6 (0.9)      7 (1.0)        7 (1.0)        4 (0.6)           2 (0.3)

Total                          650          697            695            665               663

χ2 (df = 16) = 54.044, P < 0.001, correlation coefficient = 0.126




                                                19
Table 1.4. Self-reported diagnosed health condition by per capita income
                                                           Income Quintile
 Diagnosed health condition      Poorest 20%        2.00         3.00      4.00         Wealthiest 20%

Yes, Cold                           14 (11.4)         20 (17.5)   21 (15.8) 13 (11.8)         12 (10.3)


Yes, Diarrhoea                         2 (1.6)          5 (4.4)     6 (4.5)   1 (0.9)           2 (1.7)


Yes, Asthma                          12 (9.8)           9 (7.9)    11 (8.3)   3 (2.7)         13 (11.1)


 Yes, Diabetes                      17 (13.8)         14 (12.3)    12 (9.0) 26 (23.6)         23 (19.7)


Yes, Hypertension                   35 (28.5)         27 (23.7)   38 (28.6) 24 (21.8)         24 (20.5)


Yes, Arthritis                       11 (8.9)           5 (4.4)     6 (4.5)   5 (4.5)           5 (4.3)


Yes, Unspecified                    25 (20.3)         27 (23.7)   26 (19.5) 29 (26.4)         25 (21.4)


No                                     7 (5.7)          7 (6.1)    13 (9.8)   9 (8.2)         13 (11.1)

 Total                                    123              114         133       110               117
χ2 (df = 28) = 36.161, P < 0.001




                                                 20
                                                2
      The uninsured ill in a developing nation

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 paper 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. 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.


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
                                                 21
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-

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.

                                                 22
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

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


                                                23
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

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.

                                                 24
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 paper aims to narrow this

divide by investigating health, self-reported diagnosed health conditions and health care-seeking

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
                                                  25
Data


The current paper 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.

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
                                               26
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.


       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

                                                  27
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

(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.



                                                28
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.


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]


                                                   29
        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 2.2.1 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

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.



                                                   30
         Table 2.2 highlights information on health care-seeking behaviour, health care utilisation,

self-reported illness and area of residence by social hierarchy. Based on Table 2.2, 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 2.3 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 2.3, 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

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

                                                 31
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 2.5 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 2.6 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 =

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
                                               32
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.

       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

                                                33
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

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

                                                 34
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.

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.

                                                 35
       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

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

                                                  36
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

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

                                                    37
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 paper 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

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

                                                38
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 paper; 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

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

                                                39
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]

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
                                                40
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

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 paper did not examine the emotional distress and mortality patterns of uninsured ill

                                                 41
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

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

                                                 42
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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                                           45
Table 2.2.1. 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



                                                 46
Table 2.2. 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



                                                 47
Table 2.3. 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)




                                                       48
Table 2.4. 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)




                                                                  49
Table 2.5: 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




                                                         50
Table 2.6: 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




                                                         51
                                                 3
          Self-rated health of the educated and
             uneducated classes in Jamaica

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 paper 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. 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

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
                                                  52
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

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

                                                53
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

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

                                                54
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 paper 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 paper 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




                                                 55
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

paper 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
                                                56
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 paper. 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
                                                 57
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 paper 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

paper, 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

                                                 58
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.



                                                  59
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 3.3.1).

       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 3.3.2). Based on Table 3.3.2, 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 3.3). Similarly, no significant statistical

association was found between purchased prescribed medication and educational levels of

respondents - χ2 (df = 10) = 11.9, P > 0.05.



                                                 60
         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 3.4). 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

                                                61
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 3.4) – 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 3.4).

       Concurringly, a significant statistical difference existed between the mean age among the

different educational levels in which respondents were categorised (Table 3.4) – 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

3.5). Based on the Table 3.5, 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

                                                 62
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 paper 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,
                                                63
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 paper. 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.




                                                 64
       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 paper (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.
                                                65
       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
                                                66
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%

                                                 67
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.




                                                    68
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                                               71
Table 3.3.1. 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


                                             72
Table 3.3.2. 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




                                             73
Table 3.3. 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




                                               74
Table 3.4. 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




                                                75
Table 3.5. 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




                                                76
                                              4
   Health status of patients with self-reported
          chronic diseases in Jamaica

                           Paul A. Bourne, Donovan A. McGrowder


Developing countries such as Jamaica suffer increasingly from high levels of public health
problems related to chronic diseases. To examine the physical health status and use a model to
determine the significant predictors of poor health status of Jamaicans who reported being
diagnosed with a chronic non-communicable disease. Approximately one-quarter (25.3%) of the
sample reported that they had poor health status. Thirty-three percent of the sample indicated
unspecified chronic diseases: 7.8% arthritis, 28.9% hypertension, 17.2% diabetes mellitus and
13.3% asthma. Asthma affected 47.2% of children and 23.2% of young adults. Significant
predictors of poor health status of Jamaicans who reported being diagnosed with chronic
diseases were: age of respondents, area of residence and inability to work. Majority of the
respondents in the sample had good health, and adults with poor health status were more likely
to report having hypertension followed by diabetes mellitus and arthritis, while asthma was the
most prevalent among children. Improvement in chronic disease control and health status can be
achieved with improved patient education on the importance of compliance, access to more
effective medication and development of support groups among chronic disease patients.



Introduction

The rapidly increasing burden of chronic diseases is a key determinant of global public health. In

2001, chronic diseases contributed to approximately 60% of the 56.5 million total reported

                                               77
deaths in the world and approximately 46% of the global burden of disease. The proportion of

the burden of non-communicable diseases is expected to increase to 57% by 2020 [1].


In five out of the six regions of the World Health Organization (WHO), deaths caused by chronic

diseases dominate the mortality statistics [2] and there is evidence that 79% of deaths attributable

to chronic diseases are occurring in developing countries, such as those in the Caribbean,

predominantly in middle-aged men [2]. Most Caribbean countries have experienced a health

transition, with decreases in fertility and mortality rates and changing disease patterns. Leading

up to the mid-1990s, the mortality pattern changed from deaths being mainly due to

communicable diseases to them being mainly due to non-communicable diseases [3, 4]. More

recently, these countries have additionally observed the re-emergence of ‗old‘ communicable

diseases and the emergence of new communicable diseases, along with an increasing prominence

of non-communicable diseases. Furthermore, with 15-20% and 20-25% of the adult population in

English and Dutch-speaking Caribbean countries having diabetes and hypertension respectively,

these non-communicable diseases account for the single largest expenditure in national drug

budgets [5].


Jamaica has undergone a significant demographic transition in the last 5 decades [6, 7]. Some

features of this transition include the increase in the median age of the population from 17 years

to 25 years between 1970 and 2000, the doubling of the proportion of persons older than 60 years

old to over 10%, and the increase in life expectancy at birth from less than 50 years in 1950 to

greater than 70 years in 2000 [8]. As a result, the main causes of illness and death in Jamaica and

many other Caribbean islands and regions at a similar state of development are the chronic non-

communicable diseases [9]. There is an increased prevalence of diet-related chronic non-

                                                78
communicable diseases, such as cardio-vascular diseases, diabetes and obesity. Wilks, et al. [10],

reporting on a survey of body mass index in an urban population, found that 30.7% of the men

were overweight (7.2% were obese) and 64.7% of the women were overweight (31.5% obese). In

this same study, it was found that hypertension had a prevalence of 19.1% among the males and

28.2% among the females, while the prevalence of diabetes was 8.9% and 15.3% among the

males and females respectively [10].


Chronic diseases such as heart disease, cancer and diabetes negatively affect the general health

status and quality of life of individuals [11], and there is an absence in the literature of studies

looking at the health status of persons in the Caribbean with chronic non-communicable diseases.

It is against this background that this study was undertaken. This study was designed to explore

any association between chronic non-communicable disease and health status. The aim of the

study was to examine the self-reported health status of Jamaicans in rural, peri-urban and urban

areas of residence. A model is used to predict the social determinants of poor health status of

Jamaicans who reported at least one chronic non-communicable disease.



Method
The current paper extracted a subsample of 714 people who answered the question of having

sought medical care in the last 4-weeks from a larger nationally representative cross-sectional

survey of 6,783 Jamaicans (Jamaica Survey of Living Conditions, 2007) [12]. 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


                                                79
residents in rural areas and 150 in urban areas. An ED is an independent geographic unit that

shares a common boundary.


This study made use of the Jamaica Survey of Living Conditions (JSLC) 2007 [12], 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 that it has data on self-reported health

status of Jamaicans. Self-administered questionnaires were used to collect the data, and these

were then stored and analyzed using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA).

The questionnaire was modelled pn the World Bank‘s Living Standards Measurement Study

(LSMS) household survey. The questionnaire covered areas such as socio-demographic,

economic and health variables. The non-response rate for the survey was 26.2%.


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

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

the association between non-metric variables, and an Analysis of Variance (ANOVA) was used

to test the relationships between metric and non-dichotomous categorical variables. Logistic

regression examined the relationship between the dependent variable and some predisposed

independent (explanatory) variables, because the dependent variable was a binary one (self-

reported health status is 1 if the respondent reported poor health status and 0 if otherwise).


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 [13], which was used to examine goodness of fit of the

model. The correlation matrix was examined in order to ascertain whether autocorrelation (or
                                                 80
multicollinearity) existed between variables. Based on Cohen & Holliday [14], correlation can be

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

(or allow) a variable in the model. Wald statistics were used to determine the magnitude (or

contribution) of each statistically significant variable in comparison with the others, and the

Odds Ratio (OR) was used to interpret each significant variable.


Multivariate regression framework [15] was utilized to assess the relative importance of various

demographic, socio-economic characteristics, physical environment and psychological

characteristics in determining the reported health status of Jamaicans; this has also been

employed outside of Jamaica [16, 17]. 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 of residence)

and sex of respondents using only those predictors that independently predict the outcome. A p-

value of 0.05 was used to for all tests of significance.


Model
The use of multivariate analysis in the study of health and subjective wellbeing (i.e., self-

reported health or happiness) is well established [18] and this is equally the case in Jamaica and

Barbados [19, 20]. 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. The current paper

examined the social determinants of self-reported health status of Jamaicans (Equation 1).

Equation 1 was again tested and decomposed by (i) sex of respondents and (ii) area of residence

in order to ascertain those social predictors of each sub-group.




                                                  81
Ht=f(Ai, Gi,HHi, ARi, It, Ji, lnC, lnDi, EDi, MRi, Si, HIi, lnY, CRi, MCt, SAi, Ti , εi)    [1]

where Ht (i.e., 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; current self-

reported illness of individual i, It; injuries received in the last 4 weeks by individual i, Ji; logged

consumption per person per household member, lnC; logged duration of time that individual i

was unable to carry out normal activities, lnDi; education level of individual i, EDi; marital status

of person i, MRi; social class of person i, Si; health insurance coverage of person i, HIi; logged

income, lnY; crowding of individual i, CRi; medical expenditure of individual i in time period t,

MCt; social assistance of individual i, SAi; length of time living in current household by

individual i, Ti; and an error term (i.e., residual error).


The final model that was derived from the general Equation [1] can be used to predict health

status of Jamaicans (Equation [2]).

Ht = f(Ai, ARi, lnDUt, εi)                                             [2]

Variables that were investigated include age, self-reported illness (diabetes mellitus,

hypertension) and social class. Age group is a non-binary measure: children (under 15 years);

young adults (15 to 30 years); other-aged adults (31 to 59 years); young elderly (60 to 74 years);

old elderly (75 to 84 years) and oldest elderly (85 years and older).


Results
Demographic characteristics of sample

The sample constituted 714 respondents (36.7% men and 63.3% women) with a mean age of

49.15 years. The majority of the sample was never married (44.7%), 13.4% were widowed, 1.7%

separated, 3.1% divorced and 37.1% married. Some 25.3% of the sample reported that they had

                                                    82
poor health status, 31.9% indicated at least good and 42.8% indicated fair. Thirty-three percent

of the sample indicated unspecified chronic illness: 7.8% arthritis, 28.9% hypertension, 17.2%

diabetes mellitus and 13.3% asthma. Marginally more of the sample was in the upper class

(41.6%), 19.7% in the middle class and 38.7% in the lower class (i.e., poor). The majority of the

respondents were elderly (ages 60 years and older – 41.6%) compared to 33.6% other-aged

adults, 9.7% young adults and 15.1% children. Interestingly, the mean number of persons per

room was 4.07 (S.D. 2.63 persons) and in rural areas it was 4.38 (S.D. 2.75 persons) compared to

3.9 persons in other town areas (S.D. 2.41) and 3.6 persons in urban areas (S.D. 2.42) – F

statistic [2, 711] = 6.642, p = 0.001.


Table 4.4.1 revealed that there is a statistical correlation between social class, self-evaluated

health status, annual income and area of residence (p < 0.001). Just over 50% of the rural

residents were in the lower class (i.e., poor) compared to 26.4% of other town residents and

18.0% of urban dwellers. With regards to self-evaluated health and area of residence, most of the

residents reported fair health status: urban residents (46.5%); other town residents (50.8%) and

rural residents (38.4%). On the other hand, 28.6% of rural residents indicated that they had good

self-evaluated health status compared to 31.7% of other town residents and 38.5% of urban

dwellers. The mean annual income of rural residents was US$5,873.08 compared to

US$8,218.05 for other town residents and US$10,312.41 for urban residents. Most of the rural

respondents were in the lower class (52.9%), while 26.4% of the other town residents were in the

lower class and 18% of the urban dwellers were in the lower class.


Table 4.4.2 revealed that there is a statistical correlation between diagnosed chronic diseases and

age group [χ2 (df = 20) = 297.701, p < 0.001, n = 714]. Asthma was primarily an illness for the

                                                83
younger ages and primarily affects children: 47.2% of children and 23.2% of young adults (Table

4.4.2). The findings revealed that as an individual aged, he/she was more likely to report being

diagnosed with hypertension: 0% of children, 8.7% of young adults, 31.7% of other-aged adults,

35.7% of young old, 50.5% of old-elderly and 48.3% of oldest-elderly. Arthritis was more likely

to affect older ages than young ages: 0% of children, 1.4% of young adults, 7.1% of other-aged

adults, 12.9% of young-old, 14.4% of old-elderly and 6.9% of oldest-elderly. On the other hand,

the findings also revealed that as an individual aged, he/she was more likely to be aware of the

typology of chronic illness that he/she has than they were at young ages (i.e., under 31 years).

Interestingly, 2.8% of children had diabetes compared to 4.3% of young adults, 18.3% of other-

aged adults, 28.7% of young old, 19.6% of old-elderly and 17.2% of oldest-elderly.


Based on Table 4.4.3, no statistical correlation was found between diagnosed chronic disease and

social class [χ2 (df = 8) = 13.882, p = 0.085, n = 714]. On the other hand, a statistical relationship

was found between income, consumption, crowding and chronic disease (p < 0.5; Table 4.4).

Furthermore, there is a similarity across the aforementioned variable as asthma was found to be

associated with the most income, consumption and persons per room; and unspecified chronic

disease was the second leading reported dysfunction. Diabetes mellitus was found to be the third

leading reported chronic disease influencing people with more income and consumption. While

hypertension was the third most reported chronic disease associated with crowding, it was the

fourth most reported dysfunction associated with income and consumption expenditure.


Multivariate analyses

Using logistic regression analyses, of the 17 variables that were tested for this study, only 3

emerged as statistically significant predictors of poor health status of Jamaicans who reported

                                                 84
being diagnosed with chronic diseases (Table 4.5): age of respondents (OR = 1.029, 95% CI =

1.018 – 1. 040), area of residence (urban areas – OR = 0.352, 95% CI = 0.191 – 0.652; other

towns – OR = 0.352, 95% CI = 0.173 – 0.744) and log duration unable to work (OR = 1.711,

95% CI = 1.280 – 2.271).


The model (Equation 2) had statistically significant predictive power [χ2 (4) =59.76.149, p <

0.001; Hosmer and Lemeshow goodness of fit χ2 = 9.956, p = 0.268] and correctly classified

74.4% of the sample (correctly classified 92.6% of those who were in poor health and 31.6% of

those who were not in poor health). The logistic regression model can be written as: Log

(probability of poor health status/probability of not reporting poor health status) = -0.704 + 0.028

(age) -1.041(urban residents) -1.041 (other towns) + 0.537 (log duration unable to work).

Furthermore, the predictors accounted for 24% of the variability in poor health status (Table 4.5).


Discussion
There is an association between chronic disease and health status and the former has a significant

negative impact on the physical aspects of health [21]. Self-reported health status has been

widely used in censuses, surveys, and observational studies and there is evidence suggesting that

self-reported health is an indicator of general health with good construct validity [22] and is a

respectably powerful predictor of mortality risks [23], disability [24] and morbidity [25]. The

results of this study showed that the majority of those sampled reported to be experiencing at

least good or fair health, while approximately one-quarter indicated poor health. These results

concur with those by other researchers from Dominica [26] and Trinidad [27].




                                                85
The current paper revealed that hypertension was the most common chronic disease among the

respondents, followed by diabetes mellitus and arthritis. Hypertension was highest among the

elderly, with the old-elderly recording the most among the elderly cohorts. In a study by

Sargeant et al. [28], hypertension is more common among women and the elderly in Jamaica.

Studies from developed countries have reported prevalence of raised blood pressure among the

elderly to vary from 60% to 80% [29]. Hypertension is one of the most important treatable

causes of morbidity and mortality and accounts for a large proportion of cardiovascular diseases

in the elderly in Jamaica [28]. The age- and sex-adjusted prevalence in Jamaica is 24% [30] with

somewhat higher levels in women than in men. The Jamaican Healthy Lifestyle Survey Report

2000 [31] noted a prevalence of hypertension of 19.9% among males and 21.7% among females;

prevalence increased with age in both rural and urban populations and in both sexes. Among

persons known to be hypertensive, 42% were on treatment, and of this group, 37.7% had been

able to lower and maintain their blood pressure at 140/90 or less. In the Caribbean and the USA,

the higher prevalence of hypertension was associated with an increased prevalence of obesity,

especially in women, and with greater intake of dietary sodium [32, 33].



Diabetes mellitus is an important cause of morbidity and mortality in Jamaica and represents a

significant burden on health services. Diabetes was the second leading cause of chronic disease

in this study and was most prevalent among the young-old with just under one-third reporting

that they have diabetes mellitus. The prevalence of diabetes mellitus is high in Jamaica and the

Caribbean and many patients have poor metabolic control [34]. In Jamaica the prevalence of

diabetes among persons 25-74 years old is estimated to be 12% to 16% [35-37], but of which a

third is unrecognized [36, 37]. There is also evidence that the diabetes prevalence has increased

                                               86
[38]. In the Jamaican Healthy Lifestyle Survey Report 2000 [31], diabetes mellitus was found in

6.3% of males and 8.2% of females and there was a sharp increase with age. Awareness of

diabetes mellitus among those classified as diabetic by the survey was 76.3%. Almost one-third

of those classified as diabetic were not being treated, and 60% of those who reported being on

medication did not have their condition under control. The average length of stay in hospitals

was 8.3 days for diabetes mellitus in 2002, compared to 6.3 days for all conditions [31]. Diabetes

mellitus accounts for about 10% of mortality in Jamaica [39] and is ranked fourth as the principal

cause of death among Jamaicans during the period 1990 to 1994 [40]. But the impact of diabetes

mellitus on mortality is under-reported since the disease may contribute to mortality from such

other conditions as cerebrovascular accidents and myocardial infarctions [41]. Furthermore, there

is evidence that the high prevalence of diabetes in Jamaica is due to the low rates of awareness,

treatment and control among patients with diabetes mellitus [34].


In the Caribbean, there has been growing concern at the apparent increase in asthma in children

and young adults. In 2001, hospital morbidity patterns and primary care data indicated that

respiratory illnesses dominated the list of childhood infirmities among children 0-14 years. For

children aged 0-4 years, asthma was the major condition for which patients were seen in health

facilities, a condition mainly attributable to the high incidence of tobacco smoke to which these

children are exposed [42]. In this study, asthma was the predominant chronic disease affecting

approximately one-half of the children and almost one-quarter of young adults. Asthma is an

important public health issue in Jamaica. Exercise-induced asthma has been reported to occur in

20 percent of school age children [43]. In government hospitals in Jamaica, five percent of clinic

visits are asthma related and 25 percent of respiratory admissions to hospital are due to asthma

[44]. Barnes and colleagues [45] studied asthmatic children in Barbados where treatment was
                                               87
associated with use of inhalers, but no distinction between bronchodilators and corticosteroids

was made [46]. Asthma is a significant cause of mortality in Jamaica, resulting in a death rate of

approximately 5 per 100,000 [47].

Studies conducted over the last three decades in Third World countries have confirmed that

rheumatoid arthritis occurs throughout the world. Rheumatoid arthritis is a chronic systemic

inflammatory disorder that may affect many tissues and organs but particularly the joints, often

progressing to destruction of the articular cartilage and ankylosis of the joints [48, 49]. Due to its

physical, social and psychological burden, patients experience many difficulties in various

aspects of their lives can contribute to their self-reported poor health. Rheumatoid arthritis is the

third chronic illness among the respondents in the study. In India, the prevalence of rheumatoid

arthritis (0.75%) is similar to that in the West [50]. The rarity of rheumatoid arthritis in rural

Africa contrasts with the high prevalence of the disease in Jamaica, where over 2% of the adult

population is affected [51]. In a study in Latin America, rheumatoid arthritis was the reason for

seeking medical advice in 22% of rheumatology clinic patients [52]. Quality of life is

significantly low in patients with rheumatoid arthritis, knee osteoarthritis and fibromyalgia

syndrome, whose depression and/or anxiety scores are high [53]. Therefore, these patients should

be managed using a multidisciplinary approach including psychiatric support.


In this study, just over one-third of the respondents indicated an unspecified chronic illness. The

unspecified chronic diseases could be other chronic non-communicable diseases such as a

malignant neoplasm or a chronic communicable disease. In Jamaica, cancers accounted for 15%

of non-communicable diseases and 9% of total disease burden in 1990. Cancers of the breast and

cervix are the most common neoplasms in women, with rates in 1991 of 22.6 and 19.2 per 1,000


                                                 88
population members, respectively. Prostate cancer was the number one form of cancer found in

men [54]. In 2002, there were 3,769 public hospital discharge diagnoses (4% of total discharge

diagnoses) for malignant neoplasms with an equal gender distribution. The types of neoplasms

involved for males, in order of decreasing frequency, were: trachea, bronchus, and lungs;

prostate; leukemia; and non-Hodgkin‘s lymphoma; representing 56% of all cancers. For females,

the order was as follows: breast; cervix uteri; other malignant neoplasms of female genital

organs; trachea, bronchus, and lungs; leukemia; and non-Hodgkin‘s lymphoma; together

representing 56% of all cancers [55]. The unspecified chronic illness may include HIV/AIDS, a

communicable disease, which has become a serious public health concern in Jamaica. The

national incidence of AIDS in 2000 was 352 per 1,000,000 population members [56]. In

addition, the unspecified chronic disease may include depression and there is evidence to suggest

that depressive disorders frequently accompany other chronic medical diseases. The 2000

Lifestyle Survey found approximately 25 % depressive symptoms in the general population [31].

Anderson et al. [57] concluded that the presence of diabetes mellitus increases the risk of

depression and studies have shown that in persons diagnosed with diabetes mellitus the

prevalence of depression ranges from 6.1% - 60.7% [58].


The majority of the respondents resided in rural areas and just over one-half of these were in the

lower class. The study found that there was an income differential between respondents in rural

compared with urban areas of residence, with those in the rural areas having mean annual income

of approximately two-thirds of their urban counterparts. Diabetes mellitus was found to be the

third leading reported chronic disease influencing persons with greater income and consumption.

While hypertension was the third most reported chronic disease associated with crowding, it was


                                               89
the fourth most reported dysfunction associated with income and consumption expenditure.

According to Sobal and Stunkard [59], in developing societies there is a higher likelihood of

obesity among men in higher socio-economic strata. These men are at increased risk of

developing type 2 diabetes mellitus [60] which is increasing in the adult population. Most of the

respondents in this study were female and single women constitute 45% of Jamaican heads of

household [61]. In Jamaica, female-headed households are poorer than those headed by males

and twice as likely to be unemployed. Male-headed households are smaller and have a per capita

expenditure 10 times higher than female-headed households [62, 63]. The 1999 data from

STATIN show that individuals who live in rural areas, who are in the poorest quintile, and who

are males are less likely to seek health care [64].



Conclusion

The general epidemiological shift from infectious to chronic non-communicable diseases in

Jamaica puts the residents at risk. The majority of the respondents in the sample had good health.

Adults with poor health status were more likely to report having hypertension followed by

diabetes mellitus and arthritis, while asthma was the most prevalent among children. Poor health

status was more prevalent among those of lower economic status in rural areas who reported the

least annual income. Predictors of poor health status of Jamaicans who reported being diagnosed

with a chronic disease were: age, area of residence, and inability to work (therefore being

unemployed). Given the high prevalence and poor levels of control, hypertension and diabetes

mellitus remain formidable issues for public health care in Jamaica and the Caribbean. Poverty,

low education and poor access to health care in rural communities intensify the inertia to the

lifestyle modifications that are necessary to bring about greater levels of control. We suggest that
                                                  90
further improvement in chronic disease control can be achieved with improved patient education

on the importance of compliance, access to more effective medication and development of

support groups among patients with chronic disease(s).




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   Statistical Institute of Jamaica, Kingston, Jamaica, 1999: 45-47.




                                               97
Table 4.4.1: Socio-demographic characteristics of sample
                                                          Area of residence
             Variable                    Urban                Other towns          Rural            P
                                         n (%)                   n (%)             n (%)
Sex                                                                                                0.702
   Male                                        70 (35.0)            48 (39.7)       144 (36.6)
   Female                                   130 (65.0)              73 (60.3)       249 (63.4)
Injury                                                                                             0.347
  Yes                                             4 (2.0)              6 (5.0)         13 (3.3)
  No                                         195 (98.0)            115 (95.0)       380 (96.7)
Self-reported chronic illness                                                                      0.214
  Asthma                                       33 (16.5)              11 (9.1)       51 (13.0)
  Diabetes                                     32 (16.0)            27 (22.3)        64 (16.3)
  Hypertension                                 47 (23.5)            41 (33.9)       118 (30.0)
  Arthritis                                      16 (8.0)             10 (8.3)         30 (7.6)
  Unspecified                                  72 (36.0)            32 (26.4)       130 (33.1)
Social class                                                                                      < 0.001
  Poor                                         36 (18.0)            32 (26.4)       208 (52.9)
  Middle                                       30 (15.0)            27 (22.3)        84 (21.4)
  Upper                                      134 (67.0)             62 (51.3)       101 (25.7)
Self-evaluated health status                                                                      < 0.001
  Good                                         77 (38.5)            38 (31.7)       112 (28.6)
  Fair                                         93 (46.5)            61 (50.8)       150 (38.4)
  Poor                                         30 (15.0)            21 (17.5)       129 (33.0)
Household head                                                                                     0.082
  Yes                                        105 (52.5)             72 (59.5)       189 (48.1)
  No                                          95 (47.5)             49 (40.5)       204 (51.8)
Marital status
  Married                                      60 (35.3)            34 (31.8)       130 (39.8)     0.166
  Never married                                78 (45.9)            48 (44.9)       144 (44.0)
  Divorced                                        7 (4.1)              6 (5.6)          6 (1.8)
  Separated                                       4 (2.4)              4 (3.7)          2 (0.6)
  Widowed                                      21 (12.4)            15 (14.0)        45 (13.8)
Educational level                                                                                  0.466
  No formal                                  160 (80.0)            106 (87.6)       319 (81.2)
  Basic                                          14 (7.0)              5 (4.1)         33 (8.4)
  Primary/Preparatory                            12 (6.0)              5 (4.1)         25 (6.4)
  Secondary/High                                  9 (4.5)              4 (3.3)         13 (3.3)
  Tertiary                                        5 (2.5)              1 (0.8)          3 (0.8)
Age Mean (SD)                     47.5 yrs (25.07 yrs) 53.36 yrs. (23.61) 48.7 (25.79 yr)           0.114
†Annual Income Mean (SD)          USD10,312.41             USD8,218.05         USD5,873.08        < 0.001
                                  (USD9,059.70)            (USD7,653.84)       (US 4,473.51)
Number of visits to health care                 1.4 (1.1)            1.5 (1.5)        1.4 (1.1)    0.842
practitioner Mean (SD)
†Annual Income is quoted in USD (US$ 1.00 = Ja. $ 80.47 at the time of the survey)




                                                       98
Table 4.4.2: Diagnosed chronic recurring illness by age group


                                                                      Age group
                                                  Young      Other-aged                                Oldest
                                     Children     adults       adults      Young old    Old elderly    elderly      Total
 Diagnosed chronic illness            n (%)       n (%)        n (%)         n (%)         n (%)        n (%)       n (%)

Asthma                               51 (47.2) 16 (23.2)         18 (7.5)      7(4.1)        2 (2.1)     1 (3.4)    95 (13.3)



Diabetes mellitus                       3 (2.8)    3 (4.3)      44 (18.3)   49 (28.7)     19 (19.6)     5 (17.2)   123 (17.2)

                                                                                                       14 (48.3)
Hypertension                            0 (0.0)    6 (8.7)      76 (31.7)   61 (35.7)     49 (50.5)                206 (28.9)


Arthritis                               0 (0.0)    1 (1.4)       17 (7.1)   22 (12.9)     14 (14.4)      2 (6.9)     56 (7.8)


Other (unspecified)                  54 (50.0) 43 (62.3)        85 (35.4)   32 (18.7)     13 (13.4)     7 (24.1)   234 (32.8)
                                           108         69             240        171             97          29          714
 Total
χ2 (df = 20) = 297.701, P < 0.001




                                                                 99
Table 4.4.3: Diagnosed chronic illness by social class



                                                   Social class
                                    Poor           Middle class     Upper class     Total
 Diagnosed chronic illness          n (%)             n (%)           n (%)         n (%)

Asthma                               42 (15.2)          19 (13.5)      34 (11.4)     95 (13.3)

 Diabetes mellitus                   41 (14.9)          16 (11.3)      66 (22.2)    123 (17.2)

 Hypertension                        82 (29.7)          48 (34.0)      76 (25.6)    206 (28.9)

 Arthritis                            25 (9.1)           12 (8.5)        19 (6.4)     56 (7.8)

Other (unspecified)                  86 (31.2)          46 (32.6)     102 (34.3)    234 (32.8)

Total                                       276              141             297            714

χ2 (df = 8) = 13.882, P = 0.085




                                                  100
Table 4.4: Crowding, income and annual consumption expenditure by diagnosed chronic disease


                                                                                                                                     95% CI
                                                                                                   Std.
                                                                        N           Mean         Deviation      Std. Error     Lower      Upper
 †Crowding                Asthma                                             95        5.14           2.88             0.30       4.55        5.72
                          Diabetes mellitus                                 123        3.55           2.33             0.21       3.14        3.97
                          Hypertension                                      206        3.86           2.59             0.18       3.51        4.22
                          Arthritis                                          56        3.18           1.93             0.26       2.66        3.69
                          Other (unspecified)                               234        4.30           2.69             0.18       3.95        4.65
                          Total                                             714        4.07           2.63             0.10       3.88        4.26

 †† Annual income*        Asthma                                             95   735796.96      636673.50        65321.32    606099.94   865493.98
                          Diabetes mellitus                                 123   568805.41      441764.01        39832.52    489952.96   647657.86
                          Hypertension                                      206   554090.28      536998.15        37414.43    480323.85   627856.71
                          Arthritis                                          56   460341.89      484472.24        64740.33    330599.37   590084.40
                          Other (unspecified)                               234   649294.58      591718.97        38681.88    573083.63   725505.52
                          Total                                             714   604650.49      554806.11        20763.10    563886.37   645414.61

 †††Annual
 consumption              Asthma                                             95   655299.59      461384.09        47337.01    561310.85   749288.33
 expenditure*
                        Diabetes mellitus                      123 509264.21                     363216.62        32750.14    444432.04   574096.38
                        Hypertension                           206 500635.23                     450169.33        31364.78    438796.32   562474.15
                        Arthritis                                56 404152.14                    352416.40        47093.62    309774.41   498529.87
                        Other (unspecified)                    234 586512.66                     432316.66        28261.42    530832.07   642193.25
                        Total                                  714 543277.73                     429059.15        16057.14    511752.81   574802.65
†Crowding – F statistic [4, 709] = 7.778, P < 0.001
††Income – F statistic [4, 709] = 3.250, P = 0.012,
†††Annual consumption Expenditure – F statistic [4, 709] = 4.472, P = 0.001
*Income and Annual Consumption Expenditure were quoted in Jamaican dollars (Ja. $80.47 = USD1.00 at the time of the survey)


                                                                            101
Table 4.5: Logistic regression: Predictor of poor health status of patients who reported chronic
disease


                                     Wald
                          Std.      statistic         Odds
Predictors                error                       ratio     95.0% C.I.
Age                       0.006    26.131***            1.029    1.018 - 1.040

Urban areas                0.313    11.061**           0.353     0.191 - 0.652

Other towns                0.372     7.582**           0.359     0.173 - 0.744

 Log duration unable to 0.145 13.803***          1.711           1.289 - 2.271
 work
χ2 (df = 4) =59.76.149, P < 0.001; n = 714)
-2 Log likelihood = 332.325
Nagelkerke R2 =0.240
Hosmer and Lemeshow goodness of fit χ2 = 9.956, P = 0.268
†Reference group – rural areas
*P < 0.05, **P < 0.01, ***P < 0.001




                                                102
                                               5
 The changing faces of diabetes, hypertension
   and arthritis in a Caribbean population

  Paul A. Bourne, Samuel McDaniel, Maxwell S. Williams, Cynthia Francis, Maureen D.
                       Kerr-Campbell, & Orville W. Beckford




Globally, chronic illnesses are the leading cause of mortality, and this is no different in
developing countries, particularly in the Caribbean. Little information emerged in the literature
on the changing faces of particular self-reported chronic diseases. This study examines the
transitions in the demographic characteristics of those with diabetes, hypertension and arthritis,
as we hypothesized that there are changing faces of those with these illnesses. The prevalence
of particular chronic diseases increased from 8 per 1,000 in 2002 to 56 per 1,000 in 2007. The
average annual increase in particular chronic diseases was 17.2%. Diabetes mellitus showed an
exponential average annual increase of 185% compared to hypertension (+ 12.7%) and arthritis
(- 3.8%). Almost 5 percent of diabetics were less than 30 years of age (2.4% less than 15 years),
and 41% less than 59 years. Three percent of hypertensive respondents were 30 years and under
as well as 2% of arthritics. The demographic transition in particular chronic conditions now
demands that data collection on those illnesses be lowered to < 15 years. This research
highlights the urgent need for a diabetes campaign that extends beyond parents to include
vendors, confectionary manufacturers and government, in order to address the tsunami of
chronic diseases facing the nation.

Introduction
Globally, chronic illnesses are the leading cause of mortality (60%) [1, 2], and this is no different

in developing countries, particularly in the Caribbean [2-6]. Statistics indicate that 79% of all

mortalities are attributable to chronic diseases, and that they are occurring in developing

                                                103
countries such as those in the Caribbean [3]. Using data for 1989 and 1990, Holder & Lewis [7]

showed that hypertension and diabetes mellitus were among the 5 leading causes of mortality in

the English-speaking Caribbean and Suriname. The findings from Holder and Lewis indicated

that mortality resulting from hypertension was highest in Dominica (over 90 per 100,000 of the

population) and diabetes crude death rates per 100,000 of the population were the greatest in

Trinidad and Tobago (over 85 per 100,000).


       The 20th century has brought with it massive changes in the typology of diseases, where

deaths have shifted from infectious diseases such as tuberculosis, pneumonia, yellow fever,

Black Death (i.e. Bubonic Plague), smallpox and ‗diphtheria‘ to diseases such as cancer, heart

complaints and diabetes. Although diseases have moved from infectious to degenerate, chronic

non-communicable illnesses have arisen and are still lingering in spite of all the advances in

science, medicine and technology. Morrison [8] 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 people in the wider Caribbean. This situation was corroborated

by Callender [9] and Steingo at the 6th International Diabetes and Hypertension Conference,

which was held in Jamaica in March 2000. They found that there is a positive association

between diabetic and hypertensive patients - 50% of individuals with diabetes had a history of

hypertension [9, 10]. Prior to those scholars‘ work, Eldemire [11] found that 34.8% of new cases

of diabetes and 39.6% of hypertension were associated with senior citizens (i.e. ages 60 and

over). In an article published by Caribbean Food and Nutrition Institute, the prevalence rate of

diabetes mellitus affecting Jamaicans is noted to be higher than in North American and ―many

European countries‖ [9].


                                              104
       Chronic illnesses have been on the rise in the Caribbean. In a 1996 study conducted by

Morrison and colleagues in Trinidad and Tobago [12], they noted that there is an alarming rise in

the prevalence rate of diabetes mellitus (15-18%). A study in Barbados found that between 1988

and 1992 the prevalence rate of diabetes mellitus for the population was 17.5%; 12.5% in mixed

population (black/white), 6.0% in white/other and 0.3% in the younger population [13]. Another

research, in Europe, found that the prevalence among newly diagnosed diabetics in Europeans

was 20%; African-Caribbeans, 22%; and in Pakistanis, 33% [14]. They also postulated that there

is an association between poverty and diabetes. Van Agt et al. [15] went further when they found

that poverty was greater among the chronically ill, with which a later study by the World Health

Organization [16] concurred. The WHO [16] stated that 80% of chronic illnesses were in low

and middle income countries, emphasizing the association between not only diabetes and

poverty, but chronic conditions and poverty. The relationship between poverty and chronic

conditions extends to premature mortality [17]. Findings from the WHO [4] showed that 60% of

global mortality is caused by chronic illness, which offers an explanation of the face for those

with these particular conditions. Within the context of a strong association between poverty and

chronic illness, the high prevalence of diabetes mellitus, hypertension and other chronic

conditions in developing countries should not be surprising [16, 18].


       Yach et al. [18] further opined that the global figure for diabetes is projected to move

from 171 million (2.8%) in 2000 to 366 million (6.5%) in 2030. Of this figure 298 million of

these persons will be in developing countries, which reinforces the poverty-illness relationship.

Chronic diseases can be likened to a tsunami [19] in developing nations [20-22], and it seems to

be spiralling because of the unhealthy lifestyle of people. The tsunami of chronic illnesses in the

developing countries is equally reflected in the Americas [20, 21], and particularly Jamaica. The
                                               105
face of chronic illness in developing nations is therefore for (1) lower socioeconomic strata, (2)

rural residents, (3) adults, (4) gender differences, (5) lower educational level, and (6) married

people.


          A great deal of research exists on the management of chronic illnesses, and rightfully so,

as these go to the health status and mortality of a population [23, 24]. The profiles of those with

chronic diseases have never been examined in Latin America and the Caribbean, and studies

outside of this region have used a piecemeal approach to the investigation of chronic conditions.

Hence information is available on one or a few of the aforementioned faces of chronic illness,

and some research has examined diabetes mellitus and hypertension but not arthritis. The present

gap in the literature will be lowered by this study examining the faces of chronic illness from

half a decade of data. Using data for 2002 and 2007, the current paper will investigate the

changing faces of chronic diseases in Jamaica. The study will utilize three chronic diseases (i.e.

diabetes mellitus, hypertension, and arthritis), and analyze health status, health insurance status,

health care utilization, chronic illness and other sociodemographic characteristics in order to

ascertain the transition occurring in the population. We hypothesized that there are changing

faces of those with diabetes, hypertension and arthritis over the last half a decade (2000-2007).


Materials and methods


Data

The current paper extracted a sample of 592 respondents from the 2002 and 2007 Jamaica

Survey of Living Conditions (JSLC). Only respondents who indicated that they were diagnosed

with particular chronic conditions were used for this analysis (i.e. diabetes mellitus,

hypertension, and arthritis). The present subsample represents 0.8% of the 2002 national sample
                                                 106
(25,018) and 5.7% of the 2007 sample (6,783).           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 [25]. 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 [26].                       A self-administered

questionnaire was used to collect the data.

       Overall, the response rate for the 2007 JSLC was 73.8% and 72.3% for 2002. Over 1,994

households of individuals nationwide are included in the entire database of all ages [27]. The

residents of 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


Statistical analyses were performed using the Statistical Packages for the Social Sciences for

Windows 16.0 (SPSS Inc; Chicago, IL, USA). Descriptive statistics such as mean, standard

deviation, 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 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.

                                                 107
Measures


Table 5.1 presents the operational definitions of some of the variables used in this study.


Results

Health care utilization, health insurance status, particular chronic illness (i.e. diabetes mellitus,

hypertension and arthritis), and sociodemographic characteristics are presented in Table 5.2. The

findings in Table 5.2 showed that the average annual increase in the particular chronic illness

was 17.2% between 2002 and 2007. Arthritis showed an average annual reduction of 3.8%,

hypertension, + 12.7% and diabetes mellitus, + 185.0%. Furthermore, the average annual

increase in health care utilization (visits to health care institutions) was 11.9% (public hospital, +

8.2%; private hospital, + 10.7%; public health care centre, + 8.4%; private health care centre, +

17.1%). On average the annual increase in health insurance coverage was + 148%; while the

health care utilization (health seekers) increased by 11.7%. The particular chronic illnesses have

shifted mostly from urban (67.6%) to rural residents (55.1%). This shift could be attributed to

cultural factors affecting how and what individuals eat in rural versus urban areas. The sedentary

lifestyles of urban areas also added to the overall dramatic increase in chronic illnesses.


       Table 5.3 presents information on self-reported diagnosed particular chronic illness by

sex of respondents for 2002 and 2007. On average, the annual increase in particular chronic

illness in males was 19.0% compared to 16.5% in females. Diabetes mellitus showed the highest

annual percentage increase (males 186.7% and females 184.4%), while arthritis fell in females

(average annual 7.9%) compared to an increase in males (average annual 10.0%). Hypertension

increased more in females (average annual 14.0%) compared to 9.7% in males. This could be



                                                 108
attributed to the increasing absorption of females into the upper echelons of management in

stressful occupations such as banking and finance, law, and the police force.


          Table 5.4 examines information on health coverage, health status, health care utilization

and some sociodemographic characteristics by self-reported diagnosed particular chronic

illnesses for 2002 and 2007. Based on Table 5.4, although particular chronic illnesses have

decreased in rural respondents, rural dwellers continue to be the face of chronic conditions as

well as married, primary, uninsured, private health centres and those in the lower class. The

average annual increase in particular chronic illnesses increased by 22.9% for those in the lower

strata compared to 11.0% for those in the middle class and 16.0% for those in the wealthy

socioeconomic strata. However, the greatest increase occurred in diabetics belonging to the

upper class (average annual + 200%) compared to those lower class (116.7%). On the other

hand, the highest average annual increase in hypertension occurred in the lower socioeconomic

group (26.9%) as compared to those in the middle class (7.4%) and upper socioeconomic strata

(7.1%). The massive increase in cases of diabetes within the upper class is clearly not due to the

lack of resources for seeking health care. A more detailed analysis of their diet and lifestyle is

needed to ascertain the real causes for the drastic increase relative to other socioeconomic

groups.


          Table 5.5 presents information on the age of respondents and particular self-reported

chronic conditions for 2002 and 2007. Based on this information, there is a change in the face of

particular chronic ailments in Jamaica. The face is changing to reflect the inclusion of those less

than 30 years of age (including children) as distinct from the elderly population.




                                                 109
Discussion

The present study revealed that the prevalence of particular chronic diseases (i.e. diabetes

mellitus, hypertension and arthritis) increased from 8 per 1,000 in 2002 to 56 per 1,000 in 2007.

The average annual increase of particular chronic illnesses was 17.2%.            Diabetes mellitus

showed an exponential average annual increase of 185% compared to hypertension (+ 12.7%)

and arthritis (- 3.8%). While hypertension remained the most prevalent of the particular chronic

diseases in this study, diabetes mellitus showed the greatest annual increase. The transitions of

particular chronic conditions are accounted for by (1) urban-to-rural shift, (2) female-to-male, (3)

aged-to-young people, and (4) lower socioeconomic strata to upper class. The average annual

increase in particular chronic diseases was greatest among those in the lower socioeconomic

groups. However when the particular chronic ailments were disaggregated, the findings indicated

that those in the wealthy socioeconomic group had the largest prevalence increase in diabetes

mellitus, hypertension was greatest among those in the lower class and those in the upper class

had the greatest reduction in arthritic cases. Particularly of note is the switching from public

health care utilization by particular chronically ill respondents to private health care utilization.

Similarly, the prevalence of health insurance coverage on average saw an exponential annual

increase of 148%, while health care seeking behaviour over the same period showed a marginal

increase of 12%.


       There is an emerging body of literature to support the changing face of people with

particular chronic diseases from old ages (30+ years) to younger people including children [28-

32]. Traditionally chronic conditions such as diabetes mellitus were mostly prevalent among the

elderly. This reality supports the large reservoir of literature on elderly diabetic, hypertensive

                                                110
and arthritic patients. With the emergence of epidemiological and population transition, much

attention was placed on diseases in middle and later ages as well as those conditions that

accounted for most of the mortality and morbidity in a population. Because lifestyle practices

were mostly responsible for chronic illness, many researchers limited their investigation to

people 30+ years old [8-11, 23, 33 and 34].


       The present paper supports the literature that particular self-reported chronic diseases

(such as diabetes, hypertension and arthritis) are found mostly among the elderly (60+ years).

The findings revealed that the mean ages of those with the specific self-reported chronic ailments

have fallen marginally in Jamaica over the period (2002-2007). This is somewhat deceptive as

41% of those with diabetes were less than 60 years of age, compared to 40% of those with

hypertension and 31% of arthritic respondents. Two percent of diabetic respondents were less

than 15 years of age, but no children had hypertension or arthritis. Similarly, increases were

observed in diabetes and arthritis for the young adult (diabetics aged 15 – 30 years) for the

period. This is evidence that self-reported particular chronic diseases are changing face as

almost 5% of diabetics were less than 31 years old in 2007 compared to 0% in 2002. Another

emerging face of particular self-reported chronic illness is that of those with arthritis, as almost

2% of cases were among people ages 15-30 years of age.


       The young face of those with diabetes and other chronic diseases can be accounted for by

(1) maternal nutrition during pregnancy [31], (2) diet [35] and the environment [30]. The

sedentary lifestyles of the youth in the population are further entrenched by the modern

electronic games which have removed the young person from the playing field and see him

spending longer periods on the couch in front of the television. This hooked-on-egame syndrome

                                                111
has also resulted in the increased consumption of sweet snacks and other so-called junk food.

The new face of those with particular chronic diseases is changing, and this reality is therefore a

cause for public health concern. This means that policy makers, health care practitioners,

educators and the wider community need to recognize that chronic conditions such as diabetes,

hypertension and arthritis have begun manifesting in young people as well as children. There is

an urgent rationale for an intervention campaign that will sensitize educators, medical

practitioners, parents, and children about the current reality of children and young adults being

diagnosed with particular chronic illnesses. The intervention programme that should be

formulated must include signs of ailments, place of reference, chronic disease management,

nutrition, and medical practitioners understanding that testing for diabetes, hypertension and

arthritis must be a rudimentary part of medical examinations, even of children, and further, even

if their parents are not experiencing those conditions.


       The emerging young face of diabetics, and hypertensive and arthritis patients requires a

new thrust in the study of mortality and morbidity data for health planning. Although diabetes,

hypertension and arthritis may not be among the 10 leading causes of mortality in Jamaica [36]

or the developing society, the emergence of those conditions requires researchers, demographers,

epidemiologists and policy makers to embark on the inclusion of data on those conditions in

publications in order that they can be examined. In a recently conducted study by Wilks et al.

[37], they used teens of 15+ years to present information on those with particular diseases, but

neglected to mention the new reality of children of younger ages with particular chronic

illnesses. The new reality means that researchers, policy makers and the general society need to

be cognizant of these facts. This will be accommodated by researchers, and in particular the

statistical agency, publishing findings on the new reality in order to commence the discourse and
                                                112
intervention campaign. With the absence of information on the matter, this can be construed as a

miniscule problem. However, the new findings are reflecting the early onset of diabetes (< 15

years) and the provision of data beginning at 15 years omits 0.8% of infected children or 2.4% of

diabetics.


       The present paper unearths more information on the new faces of those with particular

chronic conditions at younger ages. Fifty-four out of every 100 persons with particular chronic

diseases (i.e. diabetes, hypertension and arthritis) had hypertension, 32 out of every 100 had

diabetes and 15 out of every 100 had arthritis. Despite the majority of those with particular

chronic illnesses having hypertension, the prevalence rate for those with diabetes increased

exponentially more than the other conditions. Many studies have established a relationship

between poverty and illness [1, 2, 16 and 22], and particularly poverty and chronic illness [15].

Van et al.‘s work [15] revealed that chronic diseases were greater among those in the lower

socioeconomic strata than the other social classes, but this study found that more people in the

wealthy class had diabetes, while more hypertensive and arthritic respondents were in the lower

socioeconomic group. The current findings are providing some clarification for Van et al.‘s

research.


       Although the prevalence rate of particular chronic illnesses was greater among the

wealthy strata for 2002 and 2007, those in the lower socioeconomic group recorded the greatest

average annual percentage change. On disaggregating the particular chronic diseases, the present

paper showed that the prevalence of diabetes was greater among the upper than the lower class,

and the opposite was noted for hypertension and arthritis. This finding does not only clarify Van

et al.‘s research, but provides pertinent information on the unhealthy lifestyle practices among

                                              113
the wealthy, and reinforces the role of material deprivation on health, health conditions and

mortality.


       Two scholars opined that money can buy health [38], implying that health is a

transferable commodity, and that unhealthy lifestyle practices by the wealthy can be reversed

with money. Clearly Smith and Kington‘s claim [38] can be refuted as 42 out of every 100

chronically ill respondents were in the upper class, and more than half of those with diabetes

were part of the wealthy income group. For any postulation to hold true about money purchasing

health, one of the key axioms that needs to be looked at is the health conditions being lower

among the wealthy than those in the lower class. The wealthy will continue to live by their

desires, and at the onset of chronic ailments, may be able to reverse this by medical expenditure.

It is well established that income is positively correlated with health, as money affords a

particular diet, nutrition, medical facilities, safe drinking water, proper sanitation, leisure and

good physical milieu, but the reality is that whenever unhealthy lifestyle practices become the

choice of an individual, his/her money will not be able to eradicate the onset of diabetes,

hypertension, heart disease, or other chronic diseases. Therefore, money enhances the scope of

better health, but it cannot buy good health as this is not transferable from one person to the next.


       The very reason that health is non-transferable is the rationale behind the mortality of the

wealthy elderly, and morbidity among the upper class. Socioeconomic status was found to be

the strongest determinant of variations in health [39, 40], as wealth allows for particular choices,

opportunities, access, resources and privileges that are not available to the poor. While those

matters provide a virtual door leading to better health, money or wealth does not reduce the risk

of ill-health arising from poor choices. A study by Wilks et al. [37] found that most (71%) of

                                                114
those in the upper socioeconomic strata currently use alcohol which is more than those in the

lower class (59%) and the middle class (64%). Twice as many people in the upper class (14%)

had heart attacks compared to those in the middle class (7%) and 6% in the lower class [37]. The

evidence is in that concretizes and refutes the proposition that ‗money can buy health‘, and

although the association between income and health is well established, unhealthy lifestyle

choices cannot be reversed with money.


       The carbonated soft drink industry is experiencing a boom in the USA and the Caribbean

[41, 42]. Recently, research conducted by Ha et al. [41] found that carbonated soft drinks and

milk were the two most popular non-alcoholic beverages in the USA. They accounted for 39.1%

of total beverage consumption. This explosion in carbonated soft drinks means that added sugar

is infesting the dietary intake of young people and children more than in previous decades.

Another study showed that among children aged 6 to 19 years there was a positive significant

statistical association with soft drink consumption and a negative one with milk intake [43]. A

sedentary lifestyle along with the consumption of sugar, salted food and fast food are accounting

for the overweight and obesity in the world. According to Bostrom and Eliasson [44], over 50%

of men and 33.3% of women between the ages of 16 and 74 years in Sweden are overweight and

obese. Wilks et al. [37] found that 73% of Jamaicans aged 15 to 74 years practice a sedentary

lifestyle, and obesity was the third most popular disease (5.6% of the population, 8.5% of

females and 2.7% of males) behind hypertension (20.2%) and diabetes mellitus (7.6%).


       The growing global tsunami of chronic diseases in developing countries, and in particular

Jamaica, requires urgent policy and public health intervention. The carbonated soft drink

industry has infiltrated the consumption intake of young adults and children. Sugar in the form of

                                               115
sweets (lollipops, candies, et cetera) is sold in every shop and supermarket, and at school gates in

Jamaica. Children and young adults are fed a diet of more sugar than vegetables, beans, legumes,

nuts, protein, diary products, fruits and fibre. Embedded in the increase in diabetes in children

and young adults in Jamaica are parents‘ and children‘s nutritional intake (or lack thereof), as the

dietary habits of Jamaicans have changed to include more fast foods and less nutrient dense diets.

This extends beyond Jamaica to Barbados [44] and the USA [41].


       With the exponential increase in diabetes over the last 5 years in Jamaica, and the

increase in unhealthy lifestyle practices of the people, coupled with the sales explosion of the

carbonated soft drink industry and the increase in fast food outlets, Jamaica is experiencing a

diabetes epidemic which cannot be resolved without government and policy interventions. As is

clearfrom the literature, with the increase in carbonated soft drinks, reduction in milk intake and

influx of fast food entities in the Americas, the diabetes epidemic of Jamaica may become a

reality across the Americas. This is not just affecting countries in the Americas, as studies have

shown that Type 2 diabetes has become a global public health problem [46, 47]. The WHO

contextualized the global public health Type 2 Diabetes epidemic when it stated that during

1999-2025 the prevalence of this ailment will be 40% in the developed nations and 170% in the

developing countries. Clearly this paper is showing that diabetes has now reached an epidemic

state in Jamaica, and may no longer be an epidemic but a pandemic disease. Type 2 diabetes is

no longer an ―adult‖ or ―later life‖ disease, as was the case a generation ago, as it is now being

diagnosed in children in Jamaica and other countries [48, 49].


       This study highlights the changing image of those with particular chronic diseases (i.e.

diabetes, hypertension and arthritis) in Jamaica. With 2 out of every 100 diabetics being children

                                                116
(< 15 years) and the new image of hypertensive and arthritic patients being 15 – 30 years, plus

the exponential increase in diabetes in the wealthy class, the present research highlights

significant public health problems. In the last half a decade (2002-2007), the average annual

increase in diabetes mellitus has risen by 185% indicating the unhealthy lifestyle practices of

pregnant women, children and other young adults.


       The image of particular chronic illness in Jamaica continues to be lower class female and

rural residents, but the average annual increase in diabetes mellitus was 200% for those in the

wealthy class, compared to 117% of those in the lower socioeconomic class. Forty-seven out of

every 100 chronically ill people in Jamaica utilize public health care facilities, which denotes

that the matter is a public one and not solely individual. The cost of public health care in the next

5-10 years will increase phenomenally, as greater proportions of the population who rely on the

public health care system will be afflicted with these chronic diseases. This has serious

implications for the sustainable development of developing countries as well as their future

achievements regarding the United Nations Millennium developments goals. To act now will not

only save lives but will also save the various developing countries billions of dollars that can be

spent on other development programmes.


       The demographic transition, in particular chronic conditions, now demands that data

collection on those illnesses be lowered to < 15 years. Apart from the lowering of the ages in the

data collection process, public health specialists need to address the massive changes in new

diabetic cases. This is an obvious problem, which requires public health intervention as well as

lifestyle management of diabetes. This sensitization and lifestyle management campaign must




                                                117
extend to include educators, parents, children, vendors (especially those at schools), and the

government.


       Governments need to regulate the sugar content of products in Jamaica (carbonated soft

drinks, confectionary and fast food) as this is contributing to a public health problem which will

cost the government and people in the medium to long-term. Diabetes can be likened to a

tsunami in Jamaica and one that demands government intervention. Currently, there is a lifestyle

campaign dealing with sexual behaviour, condom usage, and cancer in Jamaica; this research

highlights the urgent need for a diabetes campaign that extends beyond parents to include

vendors, confectionaries, soft drink manufacturers and government, in order to sensitize the

public about this new public health problem. The gravity of the situation is that such a

programme cannot be delayed for some time in the future as the opportunity costs of delay are

(1) higher public expenditure, (2) increasing cost of diabetic care and management, (3) lower

production cost, (4) increased unemployment benefits, (5) the imputed cost of ignorance, and (6)

an increased mortality rate.


Conclusion

       In summary, the theoretical position that underlines testing for diabetes among other

chronic diseases should be abandoned, as the findings show the need to begin rudimentary health

examinations of all ages. The new thrust of governments, public health specialists and

researchers is to commence a mandate that addresses confectionary products‘ ingredients, and

institution guidelines about the sugar and salt components of manufactured commodities. The

wider confectionary and food industry cannot be left unregulated as the chronic diseases tsunami

is upon us, and it will require a concerted effort from everyone to combat this public health

                                               118
problem as the nation addresses the diabetes epidemic. Diabetes has risen to such epidemic

proportions that it now requires a policy initiative aimed at reducing the level of increases in a

managed way.


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.




                                                119
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Table 5.1: Operational definitions of particular variables

Variable            Operational definition             Coding
Self-evaluated      This is taken from the question ―In general, would you say your health is
health status (or   excellent, good, moderate, poor or very poor?‖
health status)
Sex                 Being male or female
Age group           Age group is classified into 4      Children - ages < 15 years old
                    categories.                         Young adults - 15 to 30 years old
                                                        Other age adults – 31- 59 years old
                                                        Young old – 60 – 74 years old
                                                        Old old – 75 – 84 years old
                                                        Oldest old – 85+ years old
Social hierarchy    Income quintiles were used to       Low = poorest 20% to poor; middle =
                    measure social class, and these     middle quintile and upper = wealthy to
                    range from quintile 1 (poorest      wealthiest 20%
                    20%) to 5 (wealthiest 20%)
Health care-        Visits to pharmacies, medical       1 = visits to health care professionals,
seeking             practitioners, nurses in the last   0=otherwise
behaviour           4-weeks
(health seeking
behaviour)
Self-reported       Have you had any illness or injury during the past four weeks? For example,
illness             have you had a cold, diarrhoea, asthma, diabetes, hypertension, arthritis or
                    other?
Chronic illness     These can be broadly defined as conditions which prolonged, do not resolved
                    spontaneously, and are infrequently curable. This is taken from the question
                    ‗What are the illnesses that you have been diagnosed with –
                    Cold, diarrhoea, asthma, diabetes mellitus, hypertension, arthritis, other
                    chronic conditions (unspecified)?
                    The chronic conditions were diabetes mellitus, hypertension and arthritis.




                                                 124
Table 5.2. Demographic characteristic of sample, 2002 and 2007
                                                    2002              2007
Characteristic                                      n           %     n        %
Chronic illness
  Diabetes mellitus                                12          5.8   123     31.9
  Hypertension                                    126        60.9    206     53.5
  Arthritis                                        69        33.3     56     14.5
Sex
  Male                                             58        28.0    113     29.4
  Female                                          149        72.0    272     70.6
Marital status
  Married                                          95        46.1    163     42.8
  Never married                                    50        24.3    130     34.1
  Divorced                                          1          0.5    14      3.7
  Separated                                         3          1.5    10      2.6
  Widowed                                          57        27.7     64     16.8
Income quintile
  Poorest 20%                                      29        14.0     83     21.6
  Poor                                             40        19.3     65     16.9
  Middle                                           49        23.7     76     19.7
  Wealthy                                          39        18.8     79     20.5
  Wealthiest 20%                                   50        24.2     82     21.3
Health care utilization
  Public hospital                                  51        28.8     72     25.5
  Private hospital                                 15          8.5    23      8.2
  Public health centre                             43        24.3     61     21.6
  Private health centre                            68        38.4    126     44.7
Health care utilization
  Sought medical care                             163        79.1    258     67.4
  Did not seek care                                43        20.9    125     32.6
Health insurance status
  Insured                                          15          7.2   126     32.8
  Uninsured                                       192        92.8    258     67.2
Age cohort
  Children                                          0          0.0     3      0.8
  Young adults                                      2          1.0    10      2.6
  Other age adults                                 49        23.7    137     35.6
  Young-old                                        90        43.5    132     34.3
  Old-old                                          58        28.0     82     21.3
  Oldest-old                                        8          3.9    21      5.5
Area of residence
  Urban                                            24        11.6     95     24.7
  Semi-urban                                       43        20.8     78     20.3
  Rural                                           140        67.6    212     55.1
                                             125
Table 5.3. Self-reported diagnosed chronic illness by sex of respondents, 2002 and 2007
                                                           20021                    20072
Characteristic                                      Sex of respondents       Sex of respondents
                                                       Male        Female       Male      Female
                                                       n (%)         n (%)      n (%)      n (%)
Chronic illness
  Diabetes mellitus                                  3 (5.2)        9 (6.0) 31 (24.7) 92 (33.8)
  Hypertension                                     39 (67.2)     87 (58.4) 58 (51.3) 148(54.4)
  Arthritis                                        16 (27.6)     53 (35.6) 24 (21.2) 32 (11.8)
                                                          58           149        113        272
1 2
 χ = 1.39, P = 0.499
2 2
 χ = 6.09, P = 0.048




                                              126
Table 5.4: Particular demographic and health variable by diagnosed chronic illness, 2002 and 2007
                                        2002                                          2007
                                   Chronic illness                               Chronic illness
Characteristic         Diabetes    Hypertension      Arthritis    Diabetes      Hypertension      Arthritis
                       mellitus                                   mellitus
                             n (%)           n (%)         n (%)        n (%)              n (%)        n (%)
Area of residence
 Urban                     1 (8.3)       15 (11.9)      8 (11.6)    32 (26.0)          47 (22.8)    16 (28.6)
 Semi-urban                1 (8.3)       29 (23.0)     13 (18.8)    27 (22.0)          41 (19.9)     10(17.9)
 Rural                   10 (83.3)       82 (65.1)     48 (69.6)    64 (52.0)         118 (57.3)    30 (53.6)
Marital status
 Married                  4 (33.3)       61 (48.4)     30 (44.1)    48 (40.0)          91 (44.4)    24 (42.9)
 Never married            4 (33.3)       30 (23.8)     16 (23.5)    39 (32.5)          69 (33.7)    22 (39.3)
 Divorced                  0 (0.0)          0 (0.0)       1 (1.5)    10 (8.3)             3 (1.5)      1 (1.8)
 Separated                 0 (0.0)          2 (1.6)       1 (1.5)     4 (3.3)             5 (2.4)      1 (1.8)
 Widowed                  4 (33.4)       33 (26.2)     20 (29.4)    19 (15.8)          37 (18.0)      8 (14.3)
Health utilization
 Public hospital          3 (30.0)       31 (29.5)     17 (23.0)    27 (32.9)          35 (25.5)    10 (32.3)
 Private hospital         1 (10.0)          9 (8.6)       5 (6.8)   11 (13.4)             7 (5.2)     5 (16.1)
 Public centre            2 (20.0)       21 (20.0)     20 (27.0)    23 (28.1)          34 (24.8)      4 (12.9)
  Private centre          4 (40.0)       44 (41.9)     32 (43.2)    21 (25.6)          61 (44.5)    12 (38.7)
Health seekers
 Did not                   1 (9.1)       26 (20.6)     16 (23.2) 34 (27.6)†           66 (32.0)†   27 (48.2)†
 Sought                  10 (90.9)      100 (79.4)     53 (76.8) 89 (72.4)†          140 (68.0)†   29 (51.8)†
Education
  Primary                 8 (66.7)       73 (59.8)     43 (63.2) 121 (98.4)           205 (99.5)   56 (100.0)
  Secondary               4 (33.3)       47 (38.5)     24 (35.3)      2 (1.6)             0 (0.0)      0 (0.0)
  Tertiary                 0 (0.0)          2 (1.6)       1 (1.5)     0 (0.0)             1 (0.5)      0 (0.0)
Health coverage
  Uninsured              11 (91.7)      114 (90.5)     67 (97.1) 69 (56.1)†          148 (71.8)†   41 (74.5)†
  Insured                  1 (8.3)        12 (9.5)        2 (2.9) 54 (43.9)†          58 (28.2)†   14 (25.5)†
Social class
  Lower                   6 (50.0)       35 (27.8)     28 (40.6) 41 (33.3)†           82 (39.8)†   25 (44.6)†
  Middle                   0 (0.0)       35 (27.8)     14 (20.3) 16 (13.0)†           48 (23.3)†   12 (21.4)†
  Upper                   6 (50.0)       56 (44.4)     27 (39.1) 66 (53.7)†           76 (36.9)†   19 (33.9)†
Health status
 Very good                      NI              NI             NI     5 (4.1)           10 (4.9)       1 (1.8)
 Good                           NI              NI             NI   21 (17.1)          45 (21.8)    12 (21.4)
 Fair                           NI              NI             NI   67 (54.5)          91 (44.2)    25 (44.6)
 Poor                           NI              NI             NI   26 (21.1)          52 (25.2)    18 (32.1)
 Very poor                      NI              NI             NI     4 (3.3)             8 (3.9)      0 (0.0)
NI – No information
† Significant (P < 0.05)




                                                  127
Table 5.5. Age of respondent by particular chronic illness, 2002 and 2007
                                     2002                                     2007
                                 Chronic illness                          Chronic illness
Characteristic       Diabetes    Hypertension Arthritis       Diabetes Hypertension Arthritis
                     mellitus                                 mellitus
                          n (%)          n (%)         n (%)       n (%)          n (%)      n (%)
Age cohort
  Children               0 (0.0)        0 (0.0)      0 (0.0)      3 (2.4)        0 (0.0)    0 (0.0)
  Young adult            0 (0.0)        2 (1.6)      0 (0.0)      3 (2.4)        6 (2.9)    1 (1.8)
  Other age adult       5 (41.7)     31 (24.6) 13 (18.8) 44 (35.8)             76 (36.9)  17 (30.4)
  Young-old             5 (41.7)     54 (42.9) 31 (44.9) 49 (39.8)             61 (29.6)  22 (39.3)
  Old-old               2 (16.7)     32 (25.4) 24 (34.8) 19 (15.4)             49 (23.8)  14 (25.0)
  Oldest-old             0 (0.0)        7 (5.6)      1 (1.4)      5 (4.1)       14 (6.8)    2 (3.6)
Age Mean (SD)               62.1    67.2 (12.8)         68.4         60.9    62.5 (16.8)       64.3
                          (12.6)                      (11.5)       (16.0)                    (14.5)
† Significant (P < 0.05)




                                             128
                                              6
    Self-assessed health of young adults in an
          English-speaking Caribbean nation
                 Paul Andrew Bourne & Christopher A.D. Charles




Gender differences in self-assessed health in young adults (i.e. ages 15 – 44 years) are under-
studied in the English-speaking Caribbean. The aims of the current research are to (1) provide
demographic characteristics of young adults; (2) examine self-assessed health of young adults;
(3) identify social determinants that explain good health status for young adults; (4) determine
the magnitude of each social determinant, and (5) gender differences in self-assessed health.
One percent of sample claimed injury and 8% illness. Self-reported diagnosed illnesses were
influenza (12.7%); diarrhoea (2.9%); respiratory disease (14.1%); diabetes mellitus (7.8%);
hypertension (7.8%); arthritis (2.9%) and unspecified conditions (41.2%). The mean length of
illness was 26.0 days (SD = 98.9. Nine social determinants and biological condition explained
19.2% of the variability of self-assessed health. The biological condition accounted for 78.1% of
the explanatory model. Injury accounts for a miniscule percentage of illness and so using it to
formulate intervention policies would lack depth to effectively address health of this cohort.



Introduction



Gender differences in self-assessed health in young adults (i.e. ages 15 – 44 years) are under-

studied in the English-speaking Caribbean. Previous studies that have examined young adults

have focused on reproductive health; survivability; teenage pregnancy; substance use and abuse;

HIV/AIDS; injuries and impact of injuries on health [1-7]. While studies on injuries have shown

                                              129
that young males 15 to 44 years are mostly affected by violent-injuries [6, 7], in Jamaica,

statistics [8] revealed that many of the deaths occurred in this age group can be accounted for by

injuries. Injuries are among reasons for ill-health and by extension do not constitute a significant

percentage of illness. Injuries accounted for most morbidities and/or mortalities in the world [7],

but this is not typical to Jamaica, making studies on injuries germane but lacks extensive

coverage on health. Statistics on Jamaica showed that of the 10 leading causes of mortality, in

2002 [8-10], homicides and injuries were the 5th and 10th causes of deaths respectively [10]. In

2004, statistics from the World Health Organization (WHO) showed that injuries were the 4th

leading cause of morality in Jamaica [11] and in 2006, statistics from the Jamaica Ministry of

Health [9] indicated that injuries was not among the 5 leading cases of hospital utilisation in

Jamaica.


       Policies therefore in Jamaica would not have been formulated using general health status

research, but more so from data on injuries, reproductive health, survivability and mortalities.

Policy intervention on those issues are pertinent and cannot be neglected from the general pursuit

of health, using general health status and health conditions would provide invaluable insights

from the individual‘s perspective on those issues; which would add value to addressing health

concerns that waiting for particular outcomes such as pregnancies, mortality, injuries or crime,

violence and victimization by young adults. A study by Hambleton et al. [12] identified that

illness constituted significant percentage of the explanatory power of self-assessed health of

older Barbadians (ages 60+ years) and while this provides some understanding of the role of

illness on general health status of which may be caused by injuries, the research identified other

factors (i.e. social determinants) that played roles in health status determination.


                                                 130
       Injuries therefore do account for a percentage of ill-health, indicating that a study of their

typologies is imperative but this cannot abate or replace a study on general health of young

adults. An extensive reveal of health literature in the English-speaking Caribbean nations found a

lack of studies on the general health status of young adults. Empirical literature showed that any

study of health must coalesce biological and social determinants [13- 25], which is also lacking

for young adults. Recently a study by Bourne [26] provided invaluable insights into the typology

of health conditions and the demographic shifts in these between 2002 and 2007. Tables 1-3

highlight hospital utilisation for gunshot wounds and suicides, and victim prolife of individuals

in Jamaica for 2005. The data highlights the crime and hospital utilisation profile, which

indicates that health care utilisation and victims of crimes are substantially between 14 and 45

years. Age 15 – 45 years does not only represent most of the victims of crime, mortality and

hospital utilization in Jamaica, it also denotes the group which constitutes arrest for major

crimes. Some of the issues are social and do affect mortality, but what about those persons of this

group who are alive and fear being a victim of violence as well as those who reside in those

communities in which such incidences are perpetuated each day. In addition what about their

general health as well as those members of this age group who are not likely victims owing to

other social conditions such as social hierarchy, area of residence or those who do not reside in

inner-cities communities. It is within this context that the current paper chose to examine self-

reported health of this group in order to provide insights into the health of young adults and the

social determinants that explain their health status.


       The aims of the current research are to (1) provide demographic characteristics of young

adults; (2) examine self-assessed health of young adults; (3) identify social determinants that


                                                 131
explain good health status for young adults; (4) determine the magnitude of each social

determinant, and (5) gender differences in self-assessed health.


Materials and Methods

The Jamaica Survey of Living Conditions (JSLC) was commissioned by the Planning Institute of

Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN) in 1988 [27]. These two

organizations are responsible for planning, data collection and policy guidelines for Jamaica, and

have been conducting the JSLC annually since 1989 [28]. The JSLC is an 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 [28]. There are some modifications to the LSMS, as JSLC is more

focused on policy impacts.           The questionnaire covers demographic variables, health,

immunization of children 0–59 months, education, daily expenses, non-food consumption

expenditure, housing conditions, inventory of durable goods and social assistance. Interviewers

are trained to collect the data from household members. The survey is conducted between April

and July annually. The current paper extracted a sub-sample of 3,024 respondents (i.e. ages15 -

44 years) from a larger nationally cross-sectional survey of 6,782 Jamaicans. This study used the

dataset of the JSLC for 2007 [29].


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,

                                               132
injuries or illnesses. While self-reported ill-health is not an ideal indicator of actual health

conditions because people may underreport, it is still an accurate proxy of ill-health and

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

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

who visit one‘s home or with whom one is able to network, 0 = otherwise). Psychological

conditions 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, 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 the responses were 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.


Statistical analysis


Statistical analyses were performed using the Statistical Packages for the Social Sciences v 16.0

(SPSS Inc; Chicago, IL, USA) for Widows. Descriptive statistics such as mean, standard

deviation (SD), frequency and percentage were used to analyze the socio-demographic

                                               133
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 reported poor health status). The final model was based on

those variables that were statistically significant (p <0.05), and all other variables were removed

from the final model (p >0.05). Categorical variables were coded using the ‗dummy coding‘

scheme or a reference category.


        The predictive power of the model was tested using the ‗omnibus test of model‘ and

Hosmer & Lemeshow‘s [30] 3 technique was used to examine the model‘s goodness of fit. The

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

collinearity) existed between variables. Cohen & Holliday [31] stated that correlation can be

low/weak (0–0.39); moderate (0.4–0.69), or strong (0.7–1). This was used in the present study to

exclude (or allow) a variable. Finally, forward stepwise technique in logistic regression was used

to determine the magnitude (or contribution) of each statistically significant variable in

comparison with the others, and the odds ratio (OR) for interpreting each of the significant

variables.


Model


To study the relationship between self-assessed health status and social determinants, biological

conditions and welfare, and logistic regression was used to estimate the following regression


                                               134
model. Equation [1] denotes the 20 social, SDHij, 3 welfare variables, Wij, and biological

condition, Bi, of self-assessed health status (Hi) and some standard error:



                                                     ,                                  [1]




Table 6.6 presents the results from the econometric exercise, which is captured in Equation [2].

Equation [2] therefore presents only those variables which are significantly correlated with self-

assessed health status of young adults:



                                                     ,                        [2]


       where:

       Hi       is the level of self-assessed health status of person i.

       SDHij denotes the 9 statistically significant social determinants of person i.

Results

The sample was 3,024 respondents: 47.6% males and 52.4% males. The mean age of the sample

was 28.5 years (SD = 8.8 years). Thirty percent of the sample was single; 20.4% common-law;

13% married; and 27.1% in visiting unions. Thirty-six and three-tenth percent of the sample was

poor with 17.1% in the poorest 20% compared to 44.1% in the wealthy social hierarchies, of

which 23.2% was in the wealthiest 20%. Forty-five and nine tenth percent of the sample dwelled

in rural area, 22% in peri-urban and 32.1% in urban areas. Of the sample population, with respect

to the questions on injury and illness 97.1% and 97% responded respectively. Of those

respondents, 1% claimed injury and 8% mentioned illness. When respondents were asked

whether the illness was diagnosed and the typologies of conditions, 100% stated that the health
                                                  135
condition was diagnosed by a medical practitioner. The self-reported diagnosed health conditions

were influenza (12.7%); diarrhoea (2.9%); respiratory disease (14.1%); diabetes mellitus (7.8%);

hypertension (7.8%); arthritis (2.9%) and unspecified conditions (41.2%). The mean length of

illness was 26.0 days (SD = 98.9), with 1 visit made to a health care practitioner in the last 4-

weeks. When respondents were asked if they had visited a health care practitioner (including

healer, pharmacist, nurse, and wife) in the last 4-weeks, 64.2% said yes. The health care

institutions were public hospitals (34.8%); private hospitals (7.0%); public health care centres

(14%); and private health care centre (51.6%). Twenty percent of the sample had health

insurance coverage; 89.6% claimed at least good health (including 42.2% very good self-

assessed health) compared to 1.9% who stated at least poor health (including 0.3% very poor

health).

       A cross-tabulation of health care-seeking behaviour and illness shows no significant

statistical association. Ninety-seven percent of those who seek medical care were ill compared to

94% of those who sought medical care in the last 4-weeks.

A cross-tabulation between illness and age group revealed a significant statistical association – χ2

= 39.4, P < 0.0001. Figure 6.1 provides the information on the age group and percentage of

young adults who indicated that they had an illness in the last 4-weeks.



       No significant statistical association was found between health care-seeking and age

group (P = 0.608): age 15 – 19 years, 60%; age 20 – 24 years, 53.1%; age 25 – 29, 60.0%; age 30

– 34 years, 67.7%; age 35 – 39 years, 68.0% and age 40 – 44 years, 69.%.




                                                136
       No significant statistical relationship was found between health care-seeking behaviour

and social hierarchy (P = 0.339): poorest 20%, 51.1%; poor, 69.2%; middle class, 67.4%;

wealthy, 65.5%, and wealthiest 20%, 67.7%.


       There is a statistical difference between age of respondents who reported having

particular health conditions – F-test = 4.5, P < 0.001. The mean ages of particular health

conditions were influenza, 29.3 years (SD = 9.2); diarrhea, 32.2 years (SD = 8.7); respiratory,

30.3 years (SD = 9.6); diabetes mellitus, 37.3 years (SD = 5.9); hypertension, 36.8 years (SD =

7.1) and other, 29.9 years (SD = 9.3).


       Figure 6.2 highlights young adult who reported injury (%) and illness (%) that dwelled in

particular area of residence controlled for sex of respondents. Figure 6.2 showed that over 50%

of those with illness and injury dwelled in rural areas. However, there was no significant

statistical relationship when illness and injury by area of residence was controlled for by sex of

respondents (illness – male χ2 = 2.6, P < 0.271 and female χ2 = 2.3, P < 0.323; injury – male χ2 =

2.5, P < 0.292 and female χ2 = 0.93, P < 0.628).


       Figure 6.3 shows sex composition of those who utilised health care facilities in Jamaica.

Most young adult males utilised private hospitals (36.4%) compare to females who visited public

health care (72.7%). The least percentage of females visited private hospitals (63.6%) compared

to public health care centres for males (27.3%).


Multivariate analysis

Tables 6 represent the results from the econometric exercise: Of the 24 variables that were tested

in an initial model, 9 were social determinants and 1 a biological variable. Biological variable

(i.e. self-reported illness) accounted for 78.1% of the explanatory power of the model (i.e.
                                               137
15.3%), indicating that the social determinants accounted for 21.9% of the self-assessed health

status of young adults.

Limitations of study

Health is a function of social, psychological, economic, biological and ecological factors. Based

on the multi-dimensional nature of health determinants, the present study used secondary survey

data and variables such as psychological, ecological and some social issues; such as childhood

health history, culture, belief and value system were omitted from the model. Those omissions

reduced the explanatory power of the current paper, but provide a platform with which future

studies can be launched.



Discussion

In the present study, the prevalence of injury in Jamaica for young adults was 1% compared to

8% in illness. A cross-tabulation between self-reported injury and self-reported illness showed a

significant statistical relationship. The association was a very weak one, correlation coefficient =

0.12 (or 12%). Forty-one of every 100 young adults who reported having an injury stated that

they had an illness in the last four-weeks, indicating that less than one- half percent of those with

an injury had an illness. Concurrently, 2 times more young adult-females sought medical care

more than males. On the other hand, males were 2.3 times likely to record injury while females

were 2 times more likely to have an illness in the last 4-weeks. Furthermore, the odds ratio of

recording better good self-assessed health status for males was 1.5 times more than that of

females. Outside of the gender differences in self-assessed health status, medical care-seeking

behaviour, and injuries, the odds ratio of recording good health married young adults was 1.6

times more than their single counterparts and this was similar for peri-urban respondents with
                                                138
reference to rural young adults. On the other hand, a young adult who sought medical care was

65% less likely to record good health; young adults with tertiary level education were 47% more

likely to record good health and those who spent more on medical care (i.e. medical care-

expenditure) were 1% less likely to have good self-assessed health status.

       Empirically, research has established that any investigation of health must coalesce

social, psychological, economic and biological variables [12-25, 32-37]. Hambleton et al. [12]

went further when he disaggregated the contribution of biological and non-medical conditions of

self-assessed health status. They found that 87.7% of the explanatory power of good health status

of elderly Barbadians could be accounted for by current illness. The present study found that

current illness accounted for 78.1%, which suggests that illness accounted for less of young

adults‘ health status than for elderly people. One of the challenges in effectively comparing the

aforementioned issues (which is embedded in the data) is that the perception of people across

different nations are not the same, and this as well as the age component could account for some

aspects of the disparity.. The present study has not only highlighted the role that social

determinants play in health status but also that they play a greater role in the health of younger

adults than old people. Statistics seemingly show a large percent of young adults being victims of

injuries but the current findings indicate that these represent a small part of ill-health of young

adults. The small percent of injuries experienced by young adults denote that using injuries as a

guide in health policy intervention would be addressing an even smaller percent of health status

than illnesses. From the aforementioned results which show that illness contributes more to

health status than social determinants, along with injuries. It is clear that despite the cultural and

biological differences rooted in both figures, current illness is a strong determinant of self-

assessed health status in each region and if health must combine social, biological, psychological

                                                 139
and ecological determinants, public health interventions that are using any one determinant in

particular injuries would not be addressing the health concerns of young adults. This empirical

evidence concretizes the rationale for social determinants in the discussion and research on

health status as well as ill-health.

        The finding in the present paper showed that social determinants of young adults

constituted more explanation than for elderly. Therefore the usage of injuries and/or illness to

measure and guide public intervention denotes that1 in 5 of the health status of young adults

would have been unaddressed in this effort and as much as 9 out of 10 of injury statistics are

used in public policy interventions. Current social determinants of health for elderly Barbadians

accounted for 4.1% of health and historical determinants, suggesting the increased role of

biological determinant in the health process with ageing. Historical determinants which included

education, occupation, children, economic situation, childhood nutrition, childhood health and

diseases theoretically is apart of social determinants. Disaggregating social determinants to

ascertain a value for historical determinants to compare with Hambleton et al.‘s finding in this

study found that education was the only factor of those identified in the Barbadian health status,

and that education accounted for only 0.3% of the explanatory model in this study. Therefore

within the limitations of the current paper, meaningful comparison using disaggregated social

determinants would be close to impossible as the components are not necessarily the same.

        Inspite of the limitations of the current work, the study can effective compare self-

assessed health status as both studies collected this from its population. The current paper which

uses data for 2007 and Hambleton et al‘s work used data for December 1999 to June 2000

showed that young adults‘ health was between 1.5 to 1.9 times more than that for elderly

Barbadians. Although there are time differences which cannot be discounted for in this study,

                                               140
there is emerging information in the reduction of health status with ageing. Ageing is a nature

event. Imagine purchasing a new car, taking this car home and locking it away in the car porch

under cover for 20 years; and on removing the covers although the item was not used, it would

have aged. On using the car however increases the deterioration or depreciation on the human

structure, and therefore account for illness, health care utilisation and lowered health status The

issue of the car symbolizes the natural ageing and progressive depleted state of things and this is

similarly the case for humans. The current paper revealed that as young people age, the odds

ratio (OR = 0.97) of indicating good health falls by 3% and using the aforementioned statistics

would mean that odds ratio of good health for elderly people should fall. A study by Bourne,

McGrowder and Crawford [38] showed that illness affecting elderly Jamaicans was more chronic

than acute compared to the converse in this study. With the changes in the typology of illnesses

from acute to chronic conditions, the elderly‘s health status must be lower than that for young

adults. Hence although homicides accounted for more deaths of young adults that elderly people,

the health status of the former is still greater and this is due largely to lower risk of biological

conditions. Again the biology of an individual accounts for greater percentage of self-assessed

health than external factors such as injuries from accidents. Injuries from accident affect 1 in

every 100 young adults, making its effect on health smaller than illnesses which accounts for 8 in

every 100 young adults.With biological conditions accounting for more of self-assessed health of

older people, this supports lower health status than young adults and greater health care

participation for the former as they seek to address the ageing of the organism and the increased

depreciation owing to old age.

       Gompertz‘s law in Gavriolov and Gavrilova [39] shows that there is a fundamental

quantitative theory of ageing and mortality of certain species (the examples here are as follows –
                                                141
humans, human lice, mice, fruit flies, and flour beetles. Gompertz‘s law went further to establish

that human mortality increases 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 explored in evolutionary biology [40-43]. But studies have

shown that using evolutionary theory for ―late-life mortality plateaus‖, fail because of the

arguable unrealistic set of assumptions that the theory uses to establish itself [44-46].

       Ageing therefore denotes gradual deterioration in living organisms as well other non-

living items, which accounts for demand in medical care. Medical seeking-behaviour could

indicate either preventative or curative care. The present study revealed that the odds ratio of

good health of young adults in Jamaica decline by 65% for those who seek medical care.

Medical care for young adults therefore is a good measure of curative than preventative care.

This also speaks of the cultural impact on health through people‘s conceptual perceptions of

health; that health is illness and so care is sought for ill-health as against preventative care. The

current work revealed that 94 out of every 100 young adults who sought medical care were ill;

reinforcing the cultural perception of illness and the reason why young adults seek health-care is

curative than preventative for this group.

       Illness in the current work is substantially a female phenomenon. Young adult females

were 2 times more likely to report an illness, and this justifies their greater probability to utilize

medical care seeking in order to address ill-health. These findings have a high degree of validity

as statistics from the Ministry of Health (Jamaica) showed that females attended health care

institutions twice as much as men for curative care since 2000-2007 [9]. Since 1988, statistics
                                                 142
obtained from Jamaicans in national cross-sectional surveys revealed that females were

approximately more likely to report an illness and utilize medical care than males. This

reinforced the cultural biasness of illness and health care facilities. Health care facilities are

primarily governed by females for females and this adds to cultural handicap of males afford

attending public health care institution on experiencing ill-health. , The feminization of health

care facilities and the large percent of people in particular females who utilise public health care

institution is another rationale for males use of private health care facilities. Males on the other

hand will attend medical care facilities when ill-health interfaces with their economic livelihood

and the severity is such that this is the only avenue. This is not atypical to Jamaica as a

qualitative study in Pakistan on street children found that boys would attend formal health care if

it affects their economic livelihood and health conditions were severe [47]. Another study

conducted in Anyigba, North-Central, Nigeria found that [48] found that 85 out of every 100

respondents waited for less than a week after the onset of illness to seek medical, and that 57 out

of every 100 indicated that they would recover without treatment.

       A Caribbean anthropologist [49] stated that the macho socialisation of the Caribbean

male accounts for his unwillingness to seek medical care. Caribbean males including Jamaicans

are socialised to be strong, do not show weakness, and be involved in particular tasks to exhibit

their masculinity as a result illness is a signal of weakness, therefore accounting for the reasons

why they are skeptical to visit medical institutions and often times wait for severity. On visiting

medical practitioners, it is sometimes so difficult for traditional medical practioner to offer cure.

This then offers an explanation for females living longing than males. Although the current

findings showed that the odds of recording good health is 1.5 times greater for young adult

males, apart of this is owing to the reality that often times males do not see themselves as ill,
                                                143
visit medical practitioner less and justifies the higher mortality among them than females. The

social determinants are therefore offering explanation for the biological issues as well,

challenges to implement health interventions to improve health of young adults in particular

males are great as definition of illness and severity of symptoms reduce the quality of life of

people and this finding concurs with a previous study by Williams et al. [50]. Unlike this study,

Williams et al. [50] found that medical care-seeking behaviour did not differ significant between

the sexes, with this study finding the opposite. Like this paper, Dunlop et al [51] found that

African American men had few physician contacts than minority and non-Hispanic white

women. The irresponsiveness of young adult males in seeking health care comparable to their

female counterparts in Jamaican extends to even older African American men.

       With the advancement in literacy and numeracy in the world since the 19th century,

specifically in Jamaicans since 1960 (i.e. educational levels), empirical findings showed

education is among the social determinants that influence health status [12-26]. Education affects

health directly and indirectly. A study on twins in USA found that more years in schooling (i.e.

education) was associated with healthier patterns of behaviour. [52], which is an example of the

direct impact of education on health. In the Fujiwara & Kawachi [52] work on increased

schooling was associated with reducing smoking habit and other such healthier practices. The

current paper concurs with the literature as the odds ratio of good health status of young adults

with tertiary level education are 1.5 times more than those with primary or below education. The

indirect way that education affects health can be measured using social hierarchy. The present

findings revealed that the middle class who are the educated ones were 1.5 times more likely to

report good health status and that wealth or income was not correlated with good health status or

for that matter the self-assessed health status of wealthy social hierarchies did not differ from
                                               144
those in the poor social hierarchies.

       Empirical evidence existed that among the social determinants of health is marital status.

Some research showed that married people are healthier than non-married people [12-25, 53-58].

Koo, Rie and Park [54] findings revealed that being married was a ‗good‘ cause for an increase

in psychological and subjective wellbeing in old age. Smith and Waitzman [55] 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 [53, 56]. 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

[57]. They pointed to a paradox within this discourse as ―…this is not observed among men‖.

To provide a holistic base to the argument, the researcher will quote a sentence from the findings

of Delbés and Gaymu [57] study that reads ―The widowed have a less positive attitude towards

life than married people, which is not an unexpected result [57]. The present study concurs with

the literature that the health status of married young adults is greater than those who are single,

but that this was only explained by females. Those findings highlight the value of marriage to

females which commences at an early age, and seemingly that the benefits of marriage are not

for males. This is clearly not the case as study by Bourne [58], using data on Jamaicans, found

that the odds ratio of reported good health was 1.6 times more for married males than their

female counterparts.




                                               145
Conclusion and policy recommendations

In sum, statistics for 2007 revealed one in every two Jamaicans was 15-44 years old. This speaks

to the importance of a research on this age group. With the demographic reality of young adults

in the country, using injury to examine health is grossly inadequate, narrow and fails to

understand the matter of health. Health is more that illness as it incorporates social, economic,

psychological, ecological and biological determinants. While the biological determinant of self-

assessed health of young adult predominates health determinants, injury accounts for a miniscule

percentage of illness and so using injury to formulate intervention policies would be lacking in

depth to effectively address health of this cohort. Although the health of young adult Jamaicans

is very good, there are many health disparities between the sexes which are justifying inequities

in health outcomes between males and females.

       The present study highlights some of the health disparities between the sexes and affords

research findings that can be used to refashion health policies and research focus in the future.

Health policies must utilize the wide spectrum of health determinants in order to address the

multi-dimensional nature of health. The use of injuries to measure and guide policies and

programmes because seemingly there are many young adults who are affected is a misnomer and

does not capture the gamut of illness or even health of this group of people.

       The identified health disparities are among reasons for health inequities in health

outcome, and should justify a call for a research and policy direction that include avoidabilities

such as technical, financial and moral as these would provide additional explanations for health

disparities, choices, inequity and/or inequalities in health outcomes among young adults.




                                               146
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                                         150
Figure 6.1: Illness (%) by age group




                                       151
      Figure 6.2: Area of residence of those with Injury (%) and Illness (%) controlled for by
sex




                                            152
Figure 6.3. Sex composition of those who attend health care facilities




                                               153
Table 6.6.1: Treatment for Gunshot wounds at the Accident and Emergency Depts. Of Public
Hospitals by Gender and Age cohort (in %): 1999-2002

Age cohort                                        Year
                1999               2000                  2001                   2002
            Male Female        Male Female               Male Female        Male Female

< 5 years      0.8    1.3          0.2    3.1             0.2    0.0          0.0    0.0

5-9 years      0.3    3.0          0.7    1.9             0.3    1.1          0.3    0.6

10-19 years    17.9   24.5         16.2   18.5            10.2   17.0         13.9   17.0

20-29 years    39.0   32.5         40.5   30.2            35.8   19.4         36.6   35.2

30-44 years    30.6   23.6         31.1   11.1            32.3   26.9         29.3   32.1

45-64 years    6.6    12.2         6.7    28.4            10.7   22.3         8.9    11.3

65+ years      3.5    3.0          2.3    11.1            6.7    12.7         8.8    3.6

Not unknown 1.4       0.0          2.2    1.2             3.8    0.7          2.3    0.6

Total %       100     100          100     100          100    100             100    100
Calculated by Paul A. Bourne from Annual Report, 2002 published by the Policy, Planning and
Development Division, Ministry of Health, Jamaica




                                            154
Table 6.6.2: Visitation to the Accident and Emergency Depts. Of Public Hospitals for attempted
suicide by Gender and Age cohort (in %): 2000-2002

Age cohort                                         Year

                              2000                 2001                  2002
                             Male Female           Male Female          Male Female

< 5 years                    0.0    0.0            1.0    0.0           1.0     0.9

5-9 years                    0.0    3.4            2.0    0.0           2.0     3.5

10-19 years                  19.0   39.3           13.0   49.4          13.0    38.3

20-29 years                  24.1   36.0           20.0   34.8          20.0    36.5

30-44 years                  34.5   13.5           13.0   6.7           13.0    17.4

45-64 years                  12.1   2.2            4.0    3.4           4.0     0.9

65+ years                    6.9    3.4            4.0    2.2           4.0     0.0

Not unknown                  3.4    2.2            0.0    3.4           1.7     2.6

Total %                     100    100            100   100            100     100
Calculated by Paul A. Bourne from Annual Report, 2002 published by the Policy, Planning and
Development Division, Ministry of Health, Jamaica




                                             155
Table 6.6.3: Victims of Major Crimes by Age Cohorts, 2005

                                                                              Age Group


                                                                                                                                             Carnal
                            Murder                      Shooting                  Robbery                    Breaking           Rape         Abuse
      Age Group
                    Male    Female       Total   Male   Female     Total   Male   Female      Total   Male   Female     Total   Female       Female

0-4                     2            4       6      3         1        4      0           0       0      0         0        0            3            3

5-9                     3            5       8      0         5        5      1           0       1      0         0        0          27         15

10-14                  10            8      18      4        11       15     16        11        27      0         6        6          212       223

15-19                 122        18        140    107        13      120     59        49       108      8        17       25          223       103

20-24                 268        23        291    212        30      242    162       115       277     52        75      127          122            0

25-29                 252        33        285    192        22      214    233       130       363     81       106      187          48             0

30-34                 223        22        245    161        16      177    198       112       310    114       115      229          28             0

35-39                 177        17        194    138        15      153    199       102       301    140       104      244          23             0

40-44                 139        12        151    107        15      122    171        77       248    116       107      223          17             0

45-49                  72        16         88     68         5       73    146        44       190     98        75      173          12             0

50-54                  46            9      55     46         8       54     98        32       130     75        47      122            7            0

55 & Over              81        16         97     50         6       56    152        66       218    171       100      271          16             0

Unknown                91            5      96    408         3      411     28           9      37     32        14       46            8            2

Total                1486       188       1674   1496       150    1646    1463       747     2210     887       766    1653           746       346

Total Reported                            1674                     1646                       2210                      1653           746       346
Source: Statistics department, Jamaica Constabulary Force

                                                                    156
Table 6.6.4: Age Group of Persons Arrested for Major Crimes for 2005
                                                                            Age Group
                       Murder                        Shooting                        Robbery                     Breaking                 Rape   C/Abuse

Age Group              Male     Female       Total   Male    Female       Total      Male   Female       Total   Male    Female   Total   Male   Male         Total

12-15                      6             1       7       4            0          4     10            0      10      54        0     54      12          11      98
16-20                    157             6     163     167            1     168       183            0     183     122        3    125      66          43     748

21-25                    235             8     243     239            1     240       214            1     215     129        3    132      68          52     950

26-30                    160             2     162     137            0     137       120            1     121     105        3    108      73          27     628
31-35                     85             1      86      74            0         74     71            1      72      93        2     95      48          26     401

36-40                     54             3      57      40            1         41     36            1      37      69        0     69      23          19     246
41-45                     15             0      15      12            0         12     13            0      13      44        1     45      18          12     115

46-50                      7             1       8       2            0          2      1            0       1      18        0     18      12            5     46

51-55                      5             1       6       0            0          0      2            0       2       2        0       2      3            2     15

56-60                      1             0       1       1            0          1      6            0       6       1        1       2      1            1     12

61& Over                   0             0       0       2            0          2      2            0       2       3        1       4      2            0     10
Unknown                   40             0      40      86            0         86     23            0      23      11        0     11      10            0    170

Total                    765         23        788     764            3     767       681            4     685     651       14    665     336          198   3439
Source: Statistics department, Jamaica Constabulary Force




                                                                          157
Table 6.6.5. Particular variables by sex of respondents
                                                                  Sex                  P
Variable                                             Male (%)       Female (%)
                                                     n = 1,439      n = 1,585
Injury                                                                                  0.037
   Yes                                                          1.4            0.6
    No                                                         98.6           99.6
Illness
   Yes                                                          5.3           10.5    < 0.0001
    No                                                         94.7           89.5
Self-assessed health status                                                           < 0.0001
    Very good                                                  44.8           39.8
    Good                                                       47.5           47.4
    Moderate                                                    6.3           10.4
    Poor                                                        1.1            2.2
    Very poor                                                   0.4            0.2
Health care-seeking behaviour                                                         < 0.0001
   Yes                                                          3.5            6.8
    No                                                         96.5           93.2
Household head                                                                        < 0.0001
    Yes                                                        34.1           73.0
    No                                                         65.9           27.0
Union status                                                                            0.103
    Married                                                    12.1           15.1
    Common-law                                                 19.7           21.0
    Visiting                                                   28.1           26.2
    Single                                                     30.8           28.9
    Not stated                                                  9.3            8.7
Self-reported diagnosed health condition                                                0.289
    Acute: Influenza                                           12.9           12.7
        Diarrhoea                                               6.5            1.4
        Respiratory                                            16.1           13.4
   Chronic: Diabetes                                            6.5            8.5
        Hypertension                                           11.3           21.1
        Arthritis                                               1.6            3.5
   Other (unspecified)                                         45.2           39.4
Area of residence                                                                       0.756
    Urban                                                      32.2           31.9
    Peri-urban                                                 21.4           22.5
    Rural                                                      46.4           45.6
No. of visits to health care facilities Mean (SD)          1.2 (0.5)      1.5 (1.3)     0.144
Age Mean (SD)                                         28.4 yrs (8.8) 28.5 yrs (8.9)     0.746
Medical expenditure Mean (SD) in US $                 16.67 (42.01) 16.42 (26.82)       0.971
†US$ 1.00 = Ja. $ 80.47

                                               158
Table 6.6. Logistic regression: Explanatory variables of good health status, n = 2, 832

 Explanatory variable                 Std.                                       P        R2
                                      Error      Odds ratio     95.0% C.I.
 Social determinants:
    Age                                  0.01            0.97      0.96-0.99   < 0.0001        0.004
    Crowding                             0.03            0.95      0.90-1.00      0.043        0.003
    Tertiary                             0.28            1.47      1.27-1.81      0.007        0.003
    †Primary                                             1.00
    Male                                 0.14            1.45      1.11-1.91     0.007         0.006
    MiddleClass                          0.18            1.45      1.02-2.07     0.041         0.003
    †Poor classes                                        1.00
    Married                              0.21            1.63      1.09-2.43     0.018         0.004
    †Single                                              1.00
    Other town                           0.18            1.61      1.12-2.30     0.009         0.005
    †Rural                                               1.00
    Medical expenditure                  0.00            0.99      0.99-1.00      0.017        0.006
    Health care- seeking                 0.29            0.35      0.20-0.62   < 0.0001        0.009
 Biological condition:
    Self-reported illness                0.24            0.17      0.11-0.28   < 0.0001        0.153
Hosmer and Lemeshow goodness of fit χ2 = 4.4 (8), P = 0.82
-2LL = 1615.7
Nagelkerke R2 =0.196
†Reference group




                                                     159
                                              7
  Disparities in self-rated health, health care
  utilization, illness, chronic illness and other
 socioeconomic characteristics of the Insured
                   and Uninsured
This study examines self-rated health status, health care utilization, income distribution, and
health insurance status of Jamaicans, and the disparity by the insured and uninsured. It also
models self-rated health status, health care utilization, income distribution, and how these differ
between the insured and uninsured. The 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
groups (such as elderly, children and the poor) will seek less care, and this will further increase
the mortality among those cohorts.



                                               160
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

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

                                               161
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

60% of global mortality is caused by chronic illnesses [7]. It can be extrapolated from the

WHO‘s findings that



                                               162
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

the insured and uninsured. While the current body of health literature provide pertinent


                                               163
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.




                                              164
Statistical analysis


Statistical analyses were performed using the Statistical Packages for the Social Sciences v 16.0

(SPSS Inc; Chicago, IL, USA) for Windows. Descriptive statistics such as mean, standard

deviation (SD), frequency and percentage were used to analyze the socio-demographic

characteristics of the sample. Chi-square was used to examine the association between non-

metric variables, and an Analysis of Variance (ANOVA) was used to test the equality of means

among non-dichotomous categorical variables. Means and frequency distribution were

considered significant at P < 0.05 using chi-square, independent sample t-test, and analysis of

variance f test, multiple logistic and linear regressions.


Analytic Models


Cross-sectional analyses of the 2007 JSLC were performed to compare within and between sub-

populations and frequencies. Logistic regression examined the relationship between the

dichotomous binary dependent variable and some predisposed independent (explanatory)

variables. A pvalue < 0.05 was selected to established statistical significance.


       Analytic models, using multiple logistic and linear regressions, were used to ascertain

factors which are associated with (1) self-rated health status, (2) health care utilization, (3) self-

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%.

                                                 165
Results

Demographic characteristic of sample

The sample was 6,783 respondents (48.7% males and 51.3% females). Children constituted

31.3%; other aged adults, 31.3%; young adults, 25.9%; and elderly, 11.9%. The elderly

comprised 7.7% young-old, 3.2% old-old and 1.0% oldest-old. Majority of the sample had no

formal education (61.8%); primary, 25.5%; secondary, 10.8% and tertiary, 2.0%. Two-thirds of

the sample sought health in the last 4-weeks; 69.2% were never married; 23.3% married; 1.7%

divorced; 0.9% separated and 4.9% were widowed respondents. Almost 15% reported an illness

in the last 4-weeks (43.3% had chronic conditions, 30.4% had acute conditions and 26.3% did

not specify the condition). Of those who reported an illness in the last 4- weeks, 87.9% provided

information on the typology of conditions: cold, 16.7%; diarrhea, 3.0%; asthma, 10.7%; diabetes

mellitus, 13.8%; hypertension, 23.1%; arthritis, 6.3%; and specified conditions, 26.3%. Marginal

more people were in the upper class (40.3%) compared to the lower socioeconomic strata

(39.8%). Only 20.2% of respondents had health insurance coverage (private, 12.4%; NI Gold,

public, 5.3%; other public, 2.4%). Majority of health insurance was owned by those in the upper

class (65%) and 19% by those in the lower socioeconomic strata.

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).

                                              166
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

                                                167
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 =

                                               168
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.

       Table1 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 7.2.

       Table 7.3 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 7.4 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 7.5 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).



                                                169
Analytic Models

Nine variables account for (Table 7.6), 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 7.7 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 7.8).

       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 7.9).

       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 7.10).

       Table 7.11 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

                                                170
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 paper 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



                                                171
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

                                                172
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

                                                 173
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

                                                174
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.

                                                175
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

                                               176
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 paper 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.




                                                177
       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.




                                                178
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Table 7.1. 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)




                                                         181
Table 7.2. 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)

                                                                 182
Table 7.3. 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




                                                           183
Table 7.4. 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




                                              184
Table 7.5. 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




                                                185
Table 7.6. 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




                                                        186
Table 7.7. 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




                                                      187
Table 7.8. 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




                                                         188
Table 7.9. 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




                                                     189
Table 7.10. 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




                                                   190
Table 7.11. 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




                                                      191
                                               8
  Good Health Status of Rural Women in the
            Reproductive Ages

                             Paul A. Bourne & Joan Rhule



Women are traditionally overrepresented among the poor and therefore in the long run, have
less access to remuneration and health resources, including health insurance and social security
services. Women are disadvantaged on some fundamental economic indicators such as
unemployment and access to economic resources. In 2007 in Jamaica, for instance among the
124 500 unemployed persons in the labour force, 65.4 % were women (Planning Institute of
Jamaica, 2008). Thus, women's health and the control that they can exercise over resources are
key factors in achieving effectiveness, efficiency, and sustainability in health interventions. This
study examined the good health status of rural women in the reproductive ages of 15 to 49 years.
Having extensively reviewed the literature, this paper is the first study of its kind in Jamaica and
will provide pertinent information on this cohort for the purpose of public health planning.
Using logistic regression analyses, 6 variables emerged as statistically significant predictors of
current good health status of rural women (i.e. ages 15 to 49 years) in Jamaica. These are social
standing (two wealthiest quintile – OR=0.524, 95%CI: 0.350,0.785); marital status (separated,
divorced or widowed – OR=0.382, 95%CI: 0.147, 0.991); health insurance (OR=0.041, 95%CI:
0.024, 0.069); negative affective psychological conditions (OR=0.951, 95%CI:0.704, 1.284);
asset ownership (OR=1.089, 95%CI:1.015, 1.168) and age of respondents (OR+0.965,
95%CI:0.949, 0.982). Poverty is synonymous with rural area and women, and inspite of this
reality majority of rural women in Jamaica ages 15 to 49 years reported current good health
status. Wealth creates more access to financial and other resources, and makes a difference in
nutritional intake, water and food quality as well as an explanation for better environmental
conditions. In this study, wealth did not mean better health but that poor women had greater
health status than their wealthy counterparts. Another interesting finding was that good health is
inversely correlated with the ownership of health insurance coverage.



                                                192
Introduction



Many studies have shown that there is a statistical relationship between health status and poverty

(Murray, 2006; Marmot, 2002; Muller & Krawinkel, 2005; Bloom & Canning, 2003; Smith &

Waitzman, 1994), standard of living (Pacione, 2003; Bourne, 2007a, 2007b), and other socio-

economic determinants (Grossman, 1972; Smith & Kington 1997; Bourne, 2009; Bourne &

McGrowder, 2009 Benzeval et al, 2001) . According to Abel-Smith (1994), the influence of

income on health decreases as the society shifts from lowers to higher levels of income. And this

is in keeping with the findings that show an inverse relationship between income of a country

and levels of mortality, and the reverse is equally true (Abel-Smith, 1994; Matsaganis, 1992).

Other scholars have refined this association when they opined that it is inequalities of income

within a country that explains higher mortality and not mere income (Cochrane et al, 1978). The

use of mortality to assess health is primary because this is easily measurable unlike the use of

morbidity which is a minimalist‘s approach to the study of health (Grossman, 1972); but the

latter still does not capture quality life expectancy and so is the former measure. The emphasis

on income to provide explanation for health status without incooperating sanitation, education

and lifestyle practices (Bourne, 2007a, 2007b; Hambleton et al, 2005), water and (Abel-Smith,

1994), health care do not provide the core rationale for the health status of a population as the

determinants of health covering, social, economic, psychological, environmental, and biological

conditions.


        In many societies across the world, poverty is rural and gender specific. Poverty is more

than just the lack of income (ie. low income) as it includes the lack of access to services,
                                               193
resources and skills, vulnerability, insecurity and powerlessness. There is another result of

poverty which has a multiple effect on the economy, and that is poor health conditions owing to

malnutrition, low water quality, non-access to primary health care and food insecurity.

According to the WHO (2005), 80% of chronic illnesses were in low and middle income

countries, suggesting that illness interfaces with poverty and vice versa. A study by Bourne,

Beckford and McGrowder (2009), using 2-decade of data on unemployment, self-reported and

health-care-seeking behaviour of Jamaicans (from 1988-2007), found that there was a positive

correlation between poverty and unemployment; poverty and illness; and crime and

unemployment.


       In Jamaica, poverty is substantially a rural and gender phenomena. Statistics from the

Planning Institute of Jamaica and the Statistical Institute of Jamaica (PIOJ & STATIN, 2008)

revealed that in 1997, 19.9% of Jamaicans were poor. Of this figure, 73.3% were in rural areas;

13.1% in semi-urban zones and 13.6% in urban areas. One decade later (ie 2007), the prevalence

of poverty fell to 9.9% of which 71.3% was in rural areas, 8.9% in semi-urban and 19.9% in

urban zones. In the same year (ie 2007), 11.1% of persons living in female-headed households

were classified as poor compared to 8.6% of those residing in male-headed household. Poverty

is not only rural as there has been a rising in its levels in urban areas. The survey determined the

poverty line was US$ 1,070.32 per year (US $2.92 per day) for an individual and US$ 4045.29

per year for a family of five (US $2.22 per person per day). The Jamaica Survey of Living

Conditions (2002) indicated that the wealthiest 20% of the population accounted for 45.9% of

national consumption while the poorest 20% accounted for only 6.1% of national consumption.

On average, the wealthiest 10% of the population consumed approximately 12.5 times more than

the poorest 10%. This is a mean per capita annual consumption expenditure of US$ 3963.53
                                                194
compared to US$314.48. The Jamaica is not atypical in having poor people or having to address

the predominance of this rural phenomenon.        The World Bank (1996) estimated that in 1996,

38% of the total population (or 25% including Haiti) in the Caribbean or more than seven million

people to be poor.


            According to Bourne (2008), in 1880 to 1882, life expectancy at birth for men was 37.02

years and 39.80 years for women with the gap between sexes widening to 5.81 years (71.26 for

men and 77.07 for women). Despite the high life expectancy of women in Jamaica which is

comparable to that of many developed nations (United Nations, 2002), people with lower

socioeconomic status have worse health in all adult age groups, including older ages (House et

al, 2005). Reduced capacity to generate income and the growing risk of illness increase the

vulnerability of the elderly to poverty, regardless of their original economic status, in developing

and industrialized countries (Lloyd-Sherlock, 2000). Poverty, therefore, is age, area and gender

specific.


            Women are traditionally overrepresented among the poor and therefore in the long run,

have less access to remuneration and health resources, including health insurance and social

security services. Women are disadvantaged on some fundamental economic indicators such as

unemployment and access to economic resources. In 2007 in Jamaica, for instance among the

124 500 unemployed persons in the labour force, 65.4 % were women (Planning Institute of

Jamaica, 2008). Thus, women's health and the control that they can exercise over resources are

key factors in achieving effectiveness, efficiency, and sustainability in health interventions.


            According to Marmot (2002), poverty accounts for poor nutrition and physical milieu,

deprivation from material resources and further explains the higher levels of health conditions of
                                                 195
those that are therein. The WHO (2005) concurs with Marmot as it opined that poverty explains

chronic illness and premature death. Women are more likely to be poor, unemployed and have

lower material wealth compared to men. Like the WHO (2005), Marmot (2002) and Abel-Smith

(1997) that showed the health challenges of being poor and by extension female, any study of

health status and women must include not only poverty but other socio-demographic variables.


         Poverty is substantially more than income poverty; it is the denial of choices and

opportunities for living a tolerable life (UNDP, 1997). Over the past two to three decades, our

understanding of poverty has broadened from a narrow focus on income and consumption to a

multi-dimensional notion of education, health, social and political participation, personal security

and freedom, and environmental quality. Hence, those socio-economic factors not only explain

poverty they influence health status for the individual, household, society, country and world.


         Health which is more than the absence of diseases (WHO, 1948) suggests that people

are multi-dimensional and any study of their health status must incorporate the environment

(Pacione, 2), income (Grossman, 1972; Smith & Kingston, 1997; Bourne, 2009). The WHO has

endorsed the evaluation of social determinants in any examination of health status (WHO, 2008;

Kelly et al. 2007). It is the social determinants (ie non-biological factors) which produce the

inequality in income, health and regards health development. Hence, addressing those

determinants account for a percentage of health status (Hambleton et al. 2005). In a study of

elderly Barbadians, Hambleton et al. (2005) found that biological conditions accounted for

67.5% of health status of sample.       This indicates that the social determinants are equally

important in the examination of health status (they account for 32.5% of the explanatory power

of health status).

                                                196
        Concomitantly, Hambleton et al.‘s work reveals that there was a statistical causal

relationship between socioeconomic conditions and the health status of Barbadians.               The

findings reveal that 5.2% of the variation in reported health status was explained by the

traditional determinants of health.       Furthermore, when this was controlled for current

experiences, this percent fell to 3.2% (falling by 2%). When the current set of socioeconomic

conditions were used they account for some 4.1% of the variation in health status, while 7.1%

were due to lifestyle practices compared to 33.5% that was as a result of current diseases (see

Hambleton et al. 2005). It holds that importance place by medical practitioners on the current

illnesses – as an indicator of health status – is not unfounded as people place more value on

biomedical conditions as responsible for their current health status.


       Diener (1984, 2000) and others (Idler & Benyamini 1997; Idler & Kasl, 199) have

showed that wellbeing, happiness or health status is equally good to measure health or subjective

wellbeing. Economists like Grossman (1972) and Smith & Kington (1997) have used self-

reported health status in evaluating health of people. Hence, self-reported health status (health

status) is widely accepted in health literature as a measure of health status. In this study, data

were not collected on health status but on health conditions. The sample was asked to state

whether they have an illness or not, and if they do what were the typology of health conditions.

For this paper the researcher used good health status to indicate not reported a health condition

and poor health to indicate at least one reported health condition. Self-reported ill-health is not an

ideal indicator of actual health conditions because people may underreport; however, it is still an

accurate proxy of ill-health and mortality (Idler & Kasl, 1991; Idler & Benyamini, 1997).




                                                 197
         The reason for the importance of health conditions (illness) is simply that a healthy

population holds the key to development. It is within this framework that a study of health is

required to examine the factors that determine health status of women in the reproductive years

of 15 to 49 years. It is clear from the review of the literature that health is influenced by income

and other social factors. A literature search revealed that no study existing in the Caribbean, in

particular Jamaica has sought to examine factors that determine the health status of rural women

in the reproductive ages of 15 to 49 years. This is the first research of its type in the Caribbean

and in particular Jamaica. It provides an insight into the factors that determine self-reported

health status of women in ages 15 to 49 years, and this can now be used to guide public health

policy. Hence, the purposes of this study are to (i) examine the good health status of women in

the reproductive ages, (ii) model socio-economic determinants of good health status of women in

the reproductive ages, and (iii) provide public health policy makers with research information on

this cohort for better policies design in the future.


Methods


Participants and questionnaire


The current research extracted a sample of 3 450 respondents who indicated that they were rural

women ages 15 to 49 years. This sample was taken from a national cross-sectional survey from

the 14 parishes in Jamaica. The survey used a stratified random probability sampling technique

to drawn the original 25 018 respondents. The non-response rate for the survey was 29.7%. The

study used secondary cross-sectional data from the Statistical Institute of Jamaica (2003) (ie

Jamaica Survey of Living Conditions or JSLC). The JSLC was commissioned by the Planning



                                                  198
Institute of Jamaica and the Statistical Institute of Jamaica. These two organizations are

responsible for planning, data collection and policy guidelines for Jamaica.




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

information on particular activities to trained interviewers from the Statistical Institute of

Jamaica. The questionnaire covers demographic variables, health, immunization of children 0–

59 months, education, daily expenses, non-food consumption expenditure, housing conditions,

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

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


Model


The multivariate model used in this study (a modification of Bourne and McGrowder‘s health

status model) captures a multi-dimensional concept of health and health status. It is

fundamentally different from that of Bourne and McGrowder‘s model (2009) as it is gender

(women) and age specific (15 to 49 years), and a number of new variables were included such as

social standing; crime and pregnancy. Hence, the proposed model that this research seeks to

evaluate is displayed (Eqn (2)):


Ht = f(lnPmc, EDi, Rt, HIi, HTi, Xi, CRi,(ΣNPi, PPi), Mi, Fi, Ni, Ai, εi)               [1]




Where the current good health status of a rural resident, Ht, is a function of 12 explanatory

variables, where Ht is current good health status of person i, if good or above (ie no reported

health conditions in the 4 weeks leading up to the survey period to trained interviewers from the
                                                                199
Statistical Institute of Jamaica), 0 if poor (ie at least one health condition reported to trained

interviewers from the Statistical Institute of Jamaica); lnPmc is the logged cost of medical care of

person i; EDi is the educational level of person i, 1 if secondary, 1 if tertiary and the reference

group is primary and below; Rt is the retirement income of person i, 1 if receiving private and/or

government pension, 0 if otherwise; HIi is the health insurance coverage of person i, 1 if they

have a health insurance policy, 0 if otherwise; HTi is the house tenure of person i, 1 if rent, 0 if

squatted; Xi is the gender of person i, 1 if female, 0 if male; CRi is crowding in the household of

person i; (∑2i=1 NPi,PPi) NPi is the sum of all negative affective psychological conditions, and

PPi is the sum of all positive affective psychological conditions; Mi is the number of males in the

household of person i and Fi is the number of females in the household of person i; Ai is the age

of the person i and Ni is the number of children in the household of person i; LLi is the living

arrangements, where 1 = living with family members or relatives, and 0 = otherwise.


         Variables were identified from the literature, using the principle of parsimony. Only those

explanatory variables that are statistically significant (p <0.05) were used in the final model to

predict current health status of Jamaican women in the reproductive ages of 15 to 49 years. Here,

the final model that accounted for self-reported good health of Jamaican women in the

reproductive years of 15 to 49 years is expressed in Eqn. [2].


Ht = f(Wi, MRi, HIi, NPi,, Di, Ai, εi)                                                [2]


The current good health status of Jamaican women in the reproductive ages of 15 to 49 years, Ht,

is a function of social standing of individual i, Wi; marital status of individual i, MRi; health

insurance of person i, HIi; NPi is negative affective psychological conditions of person i; Di is


                                                200
total number of durable good owned by individual i (excluding property and land) and Ai is the

age of the person i.


Measures

An explanation of some of the variables in the model is provided here. Health status is a dummy

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

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

injuries or illnesses. While self-reported ill-health is not an ideal indicator of actual health

conditions because people may underreport, it is still an accurate proxy of ill-health and

mortality (Idler & Kasl, 1991; Idler & Benyamini, 1997). Social supports (or networks) denote

different social networks with which the individual is involved (1 = membership of and/or visits

to civic organizations or having friends who visit ones home or with whom one is able to

network, 0 = otherwise). Psychological conditions determine 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 in general.

Negative affective psychological condition is the 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. Per capita income quintile was used to measure social

standing. Poor (ie lower class) were all individuals classified as in poorest and poor quintiles (ie

quintiles 1 and 2); middle class were those classified as in quintiles 3 and wealth (upper classes)

were those classified in quintiles 4 and 5 ( quintile 5 being the wealthiest income quintile).


Statistical analysis

                                                201
Statistical analyses were performed using the Statistical Packages for the Social Sciences v 16.0

(SPSS Inc; Chicago, IL, USA) for Widows. Descriptive statistics included frequency, mean and

standard deviation were used to provide background information on the sample. A single

hypothesis was tested, which was: the health status of rural residents 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 the

health status of rural residents. The logistic regression used as dependent variable was binary.

The final model was based on those variables that were statistically significant (p <0.05), and all

other variables were removed from the final model (p >0.05). Categorical variables were coded

using the ‗dummy coding‘ scheme.


The predictive power of the model was tested using the ‗omnibus test of model‘ and Hosmer and

Lemeshow‘s (2000) technique to examine the model‘s goodness of fit. The correlation matrix

was examined in order to ascertain whether autocorrelation (or multi-collinearity) existed

between variables. Cohen and Holliday (1982) stated that correlation can be low/weak (0–0.39);

moderate (0.4–0.69), or strong (0.7–1). This was used in the present study to exclude (or allow) a

variable. Finally, 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

interpreting each of the significant variables.


Results: Demographic Characteristics of sample



Of the sampled respondents (n=3,450), 84.7% reported good health; 3.3% were pregnant; 89.6%

had secondary level education; 20.1% were married; 78.6% were never married; 5.5% had

                                                  202
private health insurance coverage; 58.3% were owners of lands;40.1% had some form of social

support; mean age was 29.7 years (SD=9.9 years); 45.7% belonged to the two poorest quintiles

compared to 34.1% who were classified in the two wealthiest quintiles and 49.6% visited a

public hospital or public health care establishment in the 4-week period of the survey (Table

8.1). On an average, there were 2 persons per household (SD=1 person), with average medical

expenditure being US $26.37 (SD= US$40.81).


       Of the 15.3% of the sample that indicated poor current health status, 69.3% reported

being diagnosed with (chronic) recurring illness. Marginally, more of those who reported being

diagnosed with a recurring ailment had hypertension (36.4%); 31.8% did not specify the

condition; 22.7% indicated arthritis and 9.1% claimed diabetes mellitus. When those who

mentioned having a recurring dysfunction were asked about the length of the last attack, the

median number of days was 7 days. They also indicated that 3 days were the median number of

days that prevented them from carrying out their normal activities.




                                               203
Table 8.1: Demographic characteristic of sample
                                             Number                Percent
Current Health Status:
        Poor                                   511                 15.3
        Good                                 2832                  84.7
Pregnant:
        No                                   3143                  96.7
        Yes                                   106                   3.3
Social Support:
        No                                   2065                  59.9
        Yes                                  1385                  40.1
Educational Level:
        Primary or below                       151                  5.3
        Secondary or post-secondary          2574                  89.6
        Tertiary                               149                  5.2
Visits to:
        Public hospital or establishment       122                 49.6
        Private hospital or establishment      124                 50.4
Social Standing (ie per capita Income quintile):
        1=Poorest                            768                   22.3
        2                                    808                   23.4
        3                                    698                   20.2
        4                                    707                   20.5
        5=Wealthiest                         469                   13.6
Marital status:
       Married                                  665                20.1
       Never married                          2605                 78.6
       Divorced/Separated/Widowed                45                 1.3
Health Insurance:
      No                                     3138                  94.5
      Yes                                      183                  5.5
Land Ownership:
      No                                     1025                  41.7
      Yes                                    1432                  58.3
Age (Mean ± SD)                                     29.7 ± 9.9
Crowding (Mean ± SD)                                 2.1 ± 1.3
Average Annual Consumption per household (Mean ± SD):
      †Ja. $30,216.64± Ja.$39,095.35; (Minimum: Ja.$1,546 to maximum: Ja.$1,876,821)
Medical Expenditure (Mean ± SD)              †Ja.$1,344.22 ± Ja.$2,079.87
†Ja $50.97 = 1 US$




                                           204
        Disaggregating current good health status of the sample by pregnancy or no pregnancy

revealed that there is no statistical difference between the two groups (p=0.356). Approximately

85% of the sample reported good current health status compared to 83% of the women who were

pregnant and 85% for those who were not pregnant (Table 8.2).




Table 8.2: Current Health Status by Pregnancy Status



                                            Pregnancy Status




 Health status                       Not pregnant         Pregnant           Total
                                         n (%)              n (%)            n (%)



                 Poor                      480 (15.3)          18 (17.0)     498 (15.3)



                 Good                     2663 (84.7)          88 (83.0)    2751 (84.7)




Total                                            3143               106           3249



χ2 (1) = 0.231, p=0.356




                                              205
       A cross tabulation between reported recurring illness and per capita population quintile

revealed a statistical correlation (p=0.030) (Table 8.3). Self-reported diabetes mellitus was

reported as illness of wealthy rural women in the reproductive ages of 15 to 49 years (24% for

quintile 4 and 25% for quintile 5). Table 8.3 showed that 42% of those in quintile 2 who

reported a recurring illness had hypertension, 50% of those in quintile 3 and 75% of the

wealthiest quintile. Self-reported arthritis was greater in the wealthy quintile (76%) compared to

28.6% for those in quintile 2. Substantially, more rural women in the reproductive ages of 15 to

49 years reported an unspecified illness (100%) compared to 28.6% of those in the poor quintile

and 50% of those in the middle income quintile.


Table 8.3: Recurring Illness by Per capita Population Quintile

                                    Per Capita Population Quintile


                    1=poorest       2            3             4        5=wealthiest    Total
Recurring Illness     n (%)       n (%)        n (%)         n (%)         n (%)        n (%)


Diabetes mellitus       0 (0.0)    0 (0.0)        0 (0.0)   16 (24.0)      17 (25.0)    33 (9.1)

                                                                                            132
Hypertension            0 (0.0) 49 (42.0)     33 (50.0)       0 (0.0)      50 (75.0)
                                                                                          (36.4)

Arthritis               0 (0.0) 33 (28.6)         0 (0.0)   50 (76.0)        0 (0.0)   83 (22.7)

                                                                                            116
Unspecified          50(100.0) 33 (28.6)      33 (50.0)       0 (0.0)        0 (0.0)
                                                                                          (31.8)
 Total                      50        115              66         66             67          22
χ2 (12) =22.755, p=0.030




                                               206
          There is a statistical correlation between visits to the type of health care facilities and

social standing of rural women in the reproductive ages of 15 to 49 years (χ2 (4) =22.993,

p<0.001). Three times more of the poorest respondents visited public health care establishment

than private health care facilities in comparison to 3 times more of the wealthiest who attended

private than public health care establishment for health care visits (Table 8.4). Here Table 8.4

showed that as ones social standing increases from poorest to wealthiest, they switch from the

usage of public to private health care facilities.


Table 8.4: Visits to Private or Public Health Care Establishment by Social Standing

                                     Per Capita Population Quintile


  Visits to health                                                           5.00=
 care                  1=Poorest 2.00           3.00            4.00         Wealthiest    Total
 establishment          n (%)    n (%)          n (%)           n (%)        n (%)         n (%)


 Private                13 (26.0)   28 (45.9)    19 (50.0)      37 (61.7)     27 (73.0)   124 (50.4)


 Public                 37 (74.0)   33 (54.1)    19 (50.0)      23 (38.3)     10 (27.0)   122 (49.6)

 Count                         50          61              38           60           37         246
χ2 (4) = 22.993, p < 0.001

Results: Multivariate Regression


Using logistic regression analyses, 6 variables emerged as statistically significant predictors of

current good health status of rural women (ie. ages 15 to 49 years) in Jamaica (Table 8.5). These

are social standing (two wealthiest quintile – OR=0.524, 95%CI: 0.350,0.785); marital status

(separated, divorced or widowed – OR=0.382, 95%CI: 0.147, 0.991); health insurance

(OR=0.041, 95%CI: 0.024, 0.069); negative affective psychological conditions (OR=0.951,

                                                     207
95%CI:0.704, 1.284); asset ownership (OR=1.089, 95%CI:1.015, 1.168) and age of respondents

(OR+0.965, 95%CI:0.949, 0.982). Controlling for the effect of other variables, the average

likelihood of reporting good health increased by nearly 5 times.


         Further examination of the model (i.e. Equation (2)) revealed that this had a significant

predictive power (model χ2 = 259.945, p <0.001; Hosmer and Lemeshow‘s goodness of fit

χ2 = 9.649, p = 0.71; Nagelkerke R2 =0.230 or 23.0%) and correctly classified 87.1% of the

sample (correctly classified 98.5% of those who reported good health and 26.2% of those who

indicated poor health status). The logistic regression model can be written as: Log (probability

of good health/probability of not good health) = 3.131 – 0.645 (two health quintiles) -0.964

(Separated, Divorced or widowed) – 3.195 (Ownership of Health Insurance Coverage) – 0.057

(Negative Affective psychological conditions score) + 0.085 (Asset ownership score) – 0.035

(Age).




                                                208
Table 8.5: Logistic Regression of Good Health Status of Women in the Reproductive Ages
                                                                                           Odds      95.0% C.I.
  Variable                                                 Coefficient         Std Error   Ratio    Lower, Upper
   Middle Quintile                                              -0.177            0.207     0.838      0.558, 1.258
   Two Wealthiest Quintiles                                     -0.645            0.206     0.524    0.350, 0.785**
   †Poorest quintile
   Log HealthCare Cost                                             0.000          0.000     1.000     1.000, 1.000
   Separated, Divorced or Widowed                                 -0.964          0.487     0.382    0.147, 0.991*
    Married                                                       -0.037          0.177     0.964     0.681, 1.364
    †Single
    Health Insurance                                              -3.195          0.267     0.041   0.024, 0.069***
    Physical environment                                           0.112          0.166     1.118     0.807, 1.549
    Social support                                                -0.046          0.148     0.956     0.715, 1.277
    Secondary schooling                                           -0.062          0.314     0.940     0.508, 1.741
    Tertiary schooling                                             0.184          0.461     1.201     0.487, 2.966
    †Primary and below
    Living arrangement                                             0.069          0.564     1.071     0.355, 3.234
    Crowding                                                      -0.077          0.062     0.926     0.820, 1.046
    Crime Index                                                    0.001          0.008     1.001     0.985, 1.017
    Landownership                                                 -0.051          0.153     0.951     0.704, 1.284
    Negative Affective                                            -0.057          0.024     0.945    0.902, 0.990*
    Positive Affective                                             0.007          0.033     1.007     0.945, 1.074
    Asset ownership (exclude land)                                 0.085          0.036     1.089     1.015, 1.168*
    Age                                                           -0.035          0.009     0.965   0.949, 0.982***
    Dummy pregnant                                                -0.072          0.425     0.931      0.405, 2.141
    Household Head                                                 0.430          0.485     1.537     0.594, 3.976
    Average Income per head                                        0.000          0.000     1.000     1.000, 1.000
    House tenure (rented)                                         -2.095          1.801     0.123     0.004, 4.197
    House tenure (owned)                                          -0.036          1.092     0.965     0.114, 8.198
    †House tenure (squatted)
    Constant                                                       3.131          1.304    22.902         -
χ2 (23) =259.945, p < 0.001;
-2 Log likelihood = 1316.563
Hosmer and Lemeshow goodness of fit χ2=9.649, p = 0.71
Nagelkerke R2 =0.230
Overall correct classification = 87.1%
Correct classification of cases of good or beyond health status =98.5%
Correct classification of cases of no dysfunctions =26.2%
†Reference group
*p < 0.05, **p < 0.01, ***p < 0.001




                                                                         209
Discussion


The current paper found that of the thirteen socio-economic variables that were examined, six of

them are predictors of good health status of women in the reproductive ages. These socio-

economic determinants are social standing (two wealthiest quintiles); marital status (separated,

divorced, widowed); health insurance coverage; psychological condition (negative affective

psychological condition); asset ownership and age of respondents. This concurs with the findings

of the WHO (2005) that social determinants should be taken into consideration in the study of

health status. Another study (Hambleton et al. 2005) found social, economic and biological

determinants of health status of Barbadian elderlyContinuing, social determinants are




      The use of self-reported health status (ie subjective wellbeing) is well established in

research literature as a good measurement for health or wellbeing. Using people‘s assessment of

their life satisfaction and health is old, and has already been resolved. Nevertheless, it will be

succinct issues here for those who are not cognizant of this discourse. Scholars have established

that there is a statistical association between subjective wellbeing (self-reported wellbeing) and

objective wellbeing (Diener, 2000; Lynch, 2003) and Diener went further when he found a

strong correlation between the two variables (Diener, 1984). Gaspart (1998) opined about the

difficulty of objective quality of life (GDP per capita) and the need to use self-reported wellbeing

in the assessment of the wellbeing of people. He wrote, ―So its objectivism is already

contaminated by post-welfarism, opening the door to a mixed approach, in which preferences

matter as well as objective wellbeing‖ (Gaspart, 1998) This speaks to the necessity of using a

measure that captures more to this multidimensional construct that continues with the traditional
                                                210
income per capita approach. Another group of scholars emphasized the importance of measuring

wellbeing outside a welfarism and/or purely objectification, when they said that ―Although GDP

per capita is usually used as a proxy for the quality of life in different countries, material gain is

obviously only one of many aspects of life that enhance economic wellbeing‖ (Becker et al,

2004) and that wellbeing depends on both the quality and the quantity of life lived by the

individual.


        The discourse of subjective wellbeing using survey data cannot deny that it is based on the

person‘s judgement, and must be prone to systematic and non-systematic biases (Frey & Stutzer,

2005). Diener, an early survey wrote that ―[the] measures seem to contain substantial amounts of

valid variance‖ (Diener, 2000). This will not be addressed in this paper as this is not the nature or

its scope. Despite this limitation, a group of economists noted that ―happiness or reported

subjective well-being is a satisfactory empirical approximation to individual utility‖ (Frey &

Stutzer, 2005) and this justifies its usage in wellbeing research.


        The current research used self-reported health status to examine those factors that

determine good health status of rural women in the reproductive ages 15 to 49 years. Unlike a

recent study conducted by Bourne and McGrowder (2009) – using a randomly selected sample of

5,683     rural Jamaicans, They found that good health status was predicted by medical

expenditure; health insurance; education; house tenure; gender; psychological conditions (i.e.

positive and negative affective psychological conditions); typology of household members and

age of respondents and retirement income. This study concurred with age; negative affective

psychological conditions; health insurance, and added some new factors such as social standing;

marital status, and asset ownership. This research has revealed that there was no statistical

                                                 211
difference between the self-rated good health status of rural women who were pregnant or not

pregnant.




      Bourne and McGrowder‘s work showed that 83 out of every 100 rural residents had good

health status compared to this study that revealed that 85 out of every 100 rural women (ages 15

to 49 years) reported good health. This study has not only highlighted the current good health

status inequality between rural Jamaicans and rural women in the reproductive ages 15 to 49

years in Jamaica, but it showed the health disparity between the typology of variables.


      One of the disparities between the current paper and that of Bourne and McGrowder was

social standing. In the latter work this variable was not significant, while it is in the former one.

The finding in this paper revealed that the odds of self-reported good current health status of

those rural women in two wealthiest quintiles were 48% lower than that of the odds of rural

women in the two poorest quintiles. This contradicts works that have established the correlation

between poverty and health status (Murray, 2006; Marmot, 2002; Muller & Krawinkel, 2005;

Bloom & Canning, 2003; Smith & Waitzman, 1994). Marmot (2002) opined that poverty

influences health through malnutrition, low water and environmental quality, and the non-access

to material resources further validate poor health status. This assumes that wealth accounts for

better environmental quality and good health status.


      While wealth opens access to financial and/or other materials resources, it is an

explanation of poor lifestyle choices. Wealth does not mean that people become more health

conscious. Instead, it means access to liquor, cigars, hard drugs, and many excess that are of


                                                212
themselves health hazards. The issue of poor environment is not a disparity for rural areas in

Jamaica as the quality of milieu in those places is about the same. Hence, the health status

difference between rural women in the reproductive years of the two wealthiest and two poorest

quintiles would be owing to lifestyle practices and access to more financial resources.


      In this study, it can be inferred from the data that although poverty is a health hazard, it is

advantageous for rural women in the reproductive years 15 to 49 years. This is supported by the

morbidity data that showed the five leading causes of health conditions in women in Jamaica

(heart disease, hypertension, diabetes mellitus, arthritis, and neoplasm cancer), most of those

diseases are causes of lifestyle practices (Davidson et al, 2002; Jamaica Social Policy Evaluation,

2003). In an article published by CAJANUS, the prevalence rate of diabetes mellitus affecting

Jamaicans was higher than in North American and ―many European countries‖ (Callender, 2000,

p. 67. Diabetes Mellitus was not the only challenge faced by patients; McCarthy (McCarthy,

2000) argued that between 30 to 60% of diabetics also suffered from depression, which is a

psychiatric disorder.


      The issue of the lifestyle practices accounted for the health disparity between rural women

in the reproductive years of 15 to 49 years and those in the two wealthiest quintiles compared to

those in the two poorest quintiles is reinforced in the fact that there is no statistical difference

between the health status of rural women who were in the two poorest quintiles and those in the

middle quintile. In light of the above, the wealth disparity between the two aforementioned

groups is narrowed and can aid in the explanation of the health disparity between wealthy and

poor rural women in Jamaica. This research showed that hypertension and diabetes mellitus

which are lifestyle causes of non-communicable diseases were higher in the wealthiest quintile

                                                213
than the poorest quintile. An interesting finding was unwillingness of those in the poor to poorest

quintile to declare their dysfunction, unlike those in the middle to upper classes. Of the sample, 4

out of every 100 rural women in the reproductive ages 15 to 49 years reported having

hypertension, 2 out of every 100 had arthritis, 1 out of every 100 had diabetes mellitus and 3 out

of every 100 did not specify their recurring illness.


       Social standing is among the variables that explain health status of rural women in the

reproductive years of 15 to 49 years. Another factor is marital status. Studies have shown that a

statistical correlation existed between marital status and health status. Some studies have shown

that married people have a lower mortality risk in the healthy category than the ‗nonmarried‘

(Goldman, 1993), and this justifies why they take less life-threatening risks (Smith & Waitzman,

1994; Umberson, 1987). According to Delbés & Gaymu (2002), ―The widowed have a less

positive attitude towards life than married people, which is not an unexpected result‖ (Delbés &

Gaymu, 2002, pp. 885-914).


       Using a sample of 1049 Austrians from ages 14 years and over, Prause et al. (2004) found

that married individuals had greater subjective health-related quality of life index (8.3 ) than

divorced persons (7.6) or singles (7.7). Smock, Manning and Gupta (1999) concurred with

Prause et al that there is a direct relationship between married women and economic well-being.

Drawing on longitudinal data from the National Survey of Families and Households for 1987-

1988 (NSHH1) and a follow-up survey (NSFH2) of some 13, 008, a sample size of 2665 females

from 60 years and older was used. Each study had a response rate of approximately 74 % for

NSFH1 and 82% for NSFH2. The research revealed that married women had a higher economic

well-being than divorced females. It was found that females who were remarried experienced an

                                                 214
equally high well-being as their married counterparts, which was higher than that experienced by

single females. The current paper refutes the aforementioned finding as there was no statistical

difference between current health status of married rural women in the reproductive ages of 15 to

49 years and non-married ones. However, in this study, non-married rural women in the

reproductive years 15 to 49 years had a greater current health status than those divorced,

separated or widowed. Furthermore, the odds of reporting good health status for divorced,

separated or widowed rural women in this study was 62% less likely than the odds of reporting

good health status of non-married rural women in the current work.


       This leads to the next variable, which is health insurance coverage. For this study, health

insurance coverage was negatively correlated with good health status which concurs with Bourne

and McGrowder‘s work (2009). In the current research, the odds of good health for rural women

in the reproductive ages 15 to 49 years who had health insurance coverage was 96% less than the

odds of good health for rural women who do not have health insurance coverage. This indicates

that health insurance coverage is not an indicator of health seeking behaviour. Instead, it can be

used to evaluate poor health of rural women in the reproductive ages of 15 to 49 years. In the

pursuit of healthy lifestyle, one of the measures of wellness is health seeking behaviour. Health

insurance is a curative measure of illness as people hold health plan policies more if they are

more likely to be ill than less likely, suggesting that people analyze their health risk and if it is

highly likely to become ill, they will hold health insurance and not the vice versa.


       Age is the next variable which is a predictor of current good health status of rural women

in this sample. It is well established in health literature that there is a negative correlation

between age and health status (Abel-Smith, 1994; Grossman, 1972; Hambleton et al, 2005;

                                                215
Bourne, 2008; Bourne & McGrowder, 2009) and this also extends to biological studies. The

negative association between age and good health status is once again concurred with as the

current work revealed that the odd of reporting good health status for each additional year of the

rural women in the reproductive ages of 15 to 49 years is 3.5% less than the odds of a rural

woman who is one year younger.


       Another variable that is inversely correlated with good health status was negative

affective psychological conditions. Acton & Zodda (2005) aptly summarized these negative

affective psychological conditions and they found that ―expressed emotion is detrimental to the

patient's recovery; it has a high correlation with relapse to many psychiatric disorders‖ (Acton &

Zodda, 2005, pp. 373-399). Studies have revealed that up to 80% of people who committed

suicide had several depressive symptoms (Rhodes et al, 2006). From a 10-year longitudinal study

conducted in the United States by Beck et al (Beck et al, 1985) it is further stated          that

hopelessness was a major predictor of suicidal behaviour which was equally concurred by Smyth

& MacLachlan (2005). In this study negative affective psychological conditions were

operationalized using loss of breadwinners, family members; jobs and general hopelessness of an

individual which further explains the negative association between this variable and good health

status. Continuing, the odds of reporting good health status based on increased negative affective

psychological conditions is 9.8% less than the odds of lowered negative affective psychological

conditions for rural women in ages 15 to 49 years.


       Unlike the other predictors of good health status, asset ownership was the only one that

was positively correlated with current good health status for the sampled respondents. The

findings revealed that the odds of reporting good health status for those who owned more assets

                                               216
was 8.9% more than for those who owned less assets. This concurs with other studies that

showed the direct correlation between asset ownership and health status (Grossman, 1972;

Summers & Heston, 1995) and according to Summers & Heston (1995), ―The index most

commonly used until now to compare countries' material well-being is their GDP POP' [production

of goods and services]‖ ―However, GDPPOP is an inadequate measure of countries' immediate

material well-being, even apart from the general practical and conceptual problems of measuring

countries' national outputs‖ (Summers & Heston, 1995). Generally, from that perspective, the

measurement of quality of life is, therefore, highly economic and excludes the psychosocial

factors, and if quality of life extends beyond monetary objectification then it includes biological,

nutrition, social, cultural, economic and psychological factors. The World Bank went further

when it said that women‘s health status is influenced by a complex set of biological, social,

cultural and psychological variables which are all interrelated (World Bank, 1994).


       An interesting finding that is embedded in this research is the quality of the health care

institutions in Jamaica. The research showed that those in the poorest quintile had a greater

health status than those in the wealthiest quintile, and that those in the poorest quintiles enjoyed

the same good health status as those in the middle class (i.e. quintile 3). Given that 46% of the

sample was in the poorest social standing and that 74% of those who were in this social standing

visited public health care establishment for medical care, then a part of the explanation for the

good health status of this group will be owing to the quality of primary health care and public

medical health care institution in the society. Within the context that those in the wealthy and

wealthiest social standings have a greater access to financial resources, they are both able to

visit private health care institutions and spend substantially more on health care than those in the

poor social standing. This spending does not translate into better health status, suggesting that
                                                217
income cannot buy better health.


Conclusion


Poverty is synonymous with rural area and women, and inspite of this reality majority of rural

women in Jamaica ages 15 to 49 years have reported good current health status. Wealth creates

more access to financial and other resources and makes a difference in nutritional intake, water

and food quality as well as an explanation for better environmental conditions. In this study,

wealth did not mean better health but that poor women had greater health status than their

wealthy counterparts. Another interesting finding was that good health is inversely correlated

with the ownership of health insurance coverage, suggesting that Jamaican rural women (ages 15

to 49 years) do not buy health plans because they are healthy but owing to unhealthy risk factors.

Women‘s health is not merely important because of academic literature; but that it is pivotal to

their earning capacity, health of the children and the general household. Hence, understanding

women‘s health is to comprehend its multiple effects on different areas of the family, the

household and the nation. To summarize, good health in this study can be predicted by 6 factors

(social standing, marital status, health insurance, negative affective psychological conditions,

assets ownership and age of respondents) this adds more information than voluminous amount of

literature on maternal mortality and/or fertility of this age cohort. In keeping with some issues

raised in this paper, the researchers recommend that a lifestyle survey be conducted on this age

cohort in order to provide pertinent information and direction for public health policy

programmes.




                                               218
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                                              224
                                               9
 Determinants of Quality of Life of Jamaican
                  Women


The current paper seeks to examine the quality of life (or subjective wellbeing) of Jamaican
women by building a model that will capture socio-demographic and economic determinants of
their quality of life. The study reveals that the model explains 18.5% (Adjusted R-squared) of the
variability of quality of life of women; with 6 variables accounting for this variance. Further
examination of the sociodemographic determinants revealed that subjective social class (Beta =
0.198; 95%CI:0.380, 1.070) is the most influential factor followed by employment status (Beta =
0.167; 95%CI:0.304, 0.1.077), religiosity(Beta = 0.152; 95%CI:0.214, 0.974), income (Beta =
0.155; 95%CI:0.015, 0.101), the administration of the governance of the nation (Beta = -0.139;
95%CI:-0.893, -0.203) and lastly by interpersonal trust (Beta = 0.094; 95%CI:0.020, 0.676). In
summary, the factors of quality of life of a Jamaican Woman are social class, employment,
income and religiosity, with social class being the most influential of all the variables.
Employment does not merely about the income, but it is about the independence, the choices, the
sense of freedom, the positive psychological attributes that this freedom gives as well as the self-
advancement that it is likely to provide why this variable is of that importance in determining the
quality of life of Women. The current work does not provide all the answers, but it is catalysts
upon which we are able to build, modify and refute research as this provide a platform upon
which this is probable in the future.



Introduction


The current paper seeks to examine the quality of life (i.e. subjective or self-reported wellbeing)

of Jamaican women by building a model that will capture socio-demographic and economic

determinants of quality of life of this cohort. The rationale that underpins the current work is

principally driven by the lack of academic literature on the subjective wellbeing or quality of life
                                                225
on the particular gender. Most studies on quality of life have incorporated gender as a predictive

factor or a determinant of subjective wellbeing studies (or quality of life) (Bourne, 2007; Murphy

and Murphy 2006; Hambleton et al. 2005; Hutchinson et al. 2004; Stutzer and Frey 2003, 2001;

Easterlin 2001a, 2001b, 1995; Lyubomirsky 2001; Cummins 2000; Diener 2000, 1985; Smith

and Kington 1997; Grossman 1972). One study examining a particular quality of life of an

elderly man shows how medical practitioners over many years sought to address a particular

issue that was eroding the wellbeing of a patient who had a certain physiological dysfunctions

(Ali and colleagues 2007). Can medical practitioners and social researchers assume that the

quality of life of sexes is the same, given that they are of the same species? Such a situation is

simple, as the physiological composition of the sexes is different, purchase power party differs,

gender culturalization is dissimilar as well as the disparity between gender opportunities. Within

this context, researchers, medical practitioners and policy makers need to understand the factors

that influence quality of life of each gender as they are sex specify in enhancing the specificity

that is needed to planning for the sex differential. A primary rationale for this awareness is

owing to the opportunity differential because of one sex in society.


       In 1991, the unemployment rate was 22.2 per cent for females compared to 9.4 per cent

for males and in 2007, the figure fell to 6.2 per cent for males and 14.5 per cent for females

(Table 9.4). The more drastic reduction in the unemployment rate for women cannot constitute

any form of betterment of females over their male counterparts as in 2007 the unemployment

rate for female was twice more than that of males. The statistics reveal that men enjoy a 17 per

cent higher employed labour force than females; and this indicates the opportunity of greater

economic resources (Table 9.4). Another good measure that can be used to evaluate betterment

of the sexes is economic resources (i.e. wages or salaries). In the Economic and Social Survey of
                                               226
Jamaica (2004, p. 21.9), the publication showed that on an average the earnings of males (mean

wage = $2.4 million) was 2 times more than that of females ($ 1.7 million); and that 76 per cent

of senior positions were held by males although 54 per cent of executive and managerial

positions were held by females. If males are still receiving greater salaries compared females and

they experience high degrees of employment, we cannot concur with Miller nor Chevannes or

Gayle that they are marginalized despite the fact that they are living fewer years than females

(Table 9.2). From a study, using survey data from 1988 to 1999, conducted in Argentina, Brazil

and Costa Rica, the researchers found that there is no general trend of economic marginalization

of males in those societies (Omar Arias, 2001), which is evident from the some of economic

indicators in Jamaica. On the other hand, what about our women?


       The importance of women in fertility as well as the fact that they have a greater life

expectancy compared to their male counterparts (Table 9.1); it is timely that a research be done

on this cohort to unearth ‗what constitute their quality of life?‘ Scholars who have done studies

on different Caribbean nations like Bourne, 2007; Eldermire 1997, 1996, 1995a, 1995b, 1994,

1987a, 1987b; Hambleton et al. 2005; Brathwaite, their works on the quality of life have been

substantially on elderly people ( ages 60 years or older or 65 years and older) with no particular

interest on a certain sex. Other studies on the same region have looked on the total population

(Hutchinson et al. 2005). Caribbean societies have patriarchal roots and so economic resources

are primarily in the hands of males; but of the quality of life of female? How are they living in

Jamaica?


       Is there is disparity between the quality of life of the sexes? However, a survey done by

Rudkin found that women have lower levels of wellbeing (i.e. economic) than men (Rudkin 1993

                                               227
222). This finding is further sanctioned by Haveman et al (2003) whose study reveal that retired

men‘s wellbeing was higher than that of their female counterparts, because men usually received

had more material resources, and more retired benefits compared to women ages 65 years and

older. Thus with men receiving more than women, and having a more durable possession than

women, their material wellbeing is higher is later life.


       Generally, from the United Nations statistical databases, life expectancy for male is lower

than of females. This is particular true for females in the old aged cohorts (United Nations 2004;

Moore et al. 1997). Moore et al. (1997) added, ―Females‘ life expectancies are likely to remain

above that for males [Elo 2001] for the foreseeable future, among both the population as a whole

and the elderly‖ (Moore et al. 1997, 12). Among the justification for the differential between life

expectancy the sexes is linked with the health consciousness of women and their approach to

preventative care.   Unlike women, worldwide men have a reluctance to ‗seek health-care‘

compared to their female counterpart. 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 life

time which makes the gap in age differential what it is – which is approximately a 4-year

difference in Jamaica.


       A study conducted by McDonough and Walters (2001) revealed that women had a 23

percent higher distress score than men and were more likely to report chronic diseases compared

to males (30%). It was found that men believed their health was better (2% higher) than that

self-reported by females. McDonough and Walters used data from a longitudinal study named

Canadian National Population Health Survey (NPHS). The study was initiated in 1994, and data

were collected every second year for a duration of six years. The information was taken form

                                                 228
20,000 household members who were 12 years and older.


       A research carried out by a group of economists (Headey and Wooden) revealed that

―…women are slightly more likely to report higher levels of life satisfaction than men

(mean=78.3, compared with 77.1 for men…‖ (Wooden and Headey 2003, 14). Based on the

nature of the study, ‗…subjective wellbeing and ill being‘, the reported wellbeing (measure by

life satisfaction) of women is higher than that for men but that males have a higher financial

wellbeing than females (Headey and Wooden 2003, 16). Thus, the discourse is inconclusive and

we will not add to the literature in this regard but will examine quality of life of women as this is

the first of its kind in Jamaica and in the wider Caribbean literature.


Theoretical Framework


The overarching theoretical framework that will be adopted in this study is an econometric

model that was developed by Grossman (1972), and further modified by Smith and Kington

1997. The initial model (i.e. Eqn. [1]) by Michael Grossman reads:


       Ht = ƒ (Ht-1, Go, Bt, MCt, ED) ……………………………………………………… [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)- (Smith and Kington 1997, 159-160). Grossman‘s model

further expanded upon by Smith and Kington to include socioeconomic variables (Eqn.2).


       Ht = H* (Ht-1, Pmc, Po, ED, Et, Rt, At, Go) ……………………………..………..Eqn. [2]



                                                 229
       Eq. (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,). Thus, the current paper will test this general hypothesis in

seeking to establish a quality of life model for Jamaican Women (Eqn. [3]):


QoLj = ƒ(Gj, PPIj, Yj, Rj , lnAj , Ci , Oj , Tj , SSj , ARj , Xj , Ej , ESj , RAj , WSj εi)…….…Eqn.[3]


Method

The current paper uses a sample of 723 women, with a mean age of 34.33yrs ±13.4 yrs. This
study is taken from a general study conducted by the Centre of Leadership and Governance,
Department of Government, The University of the West Indies between July and August 2006 of
some 1,338 Jamaicans. The survey uses a questionnaire of some 166 items, which probes issues
relating to the orientation of democracy, leadership and governance in Jamaica. The survey was a
stratified random sample of the fourteen parishes of Jamaica, using the descriptive research
design. Data were collected and stored using the Statistical Packages for the Social Sciences
(SPSS). Descriptive statistics were done to provide background information on the sample; tests
were done for Cronbach alpha to examine the validity of the construct – i.e. wellbeing and
political participation. Then, multiple regressions were used to build a model for quality of life
of Jamaican Women.

Measures:

             5
QoLj = 1/10∑Lij , where Li i=1…5 denotes each Need Item of Abraham Maslow‘s 5-Need
Hierarchy
            i=1
(Each is a 10-point Likert Scale: Health status; Basic Necessities; Social Needs; Self-Esteem;
Self-Actualization). Reliability analysis of the 5-Need Likert Scale Item is 0.748 (or 75%).
Quality of Life Index ranges from: 1≤Quality of Life Index≥10; where from 1 to 3.9 are low, 4 to
6.9 are moderate and with high being from 7 to 10.

Sex. Sex is the biological makeup of males and females. This is a binary measure, where
1=male and 0=female.
                                                  230
Area of residence. This means the geographic location of one‘s place of abode It is a dummy
variable, 1=St. Andrew, Kingston and St. Catherine, 0=Other1
Subjective Social Class. This is people‘s perception of their social and economic position in
life, based on social stratification.
   socialcl1                        1=Middle class
   socialcl2                        1=Upper class
   Referent group is lower class.
Interpersonal Trust. The survey instrument asked the question ‗Generally speaking would you
say that most people are essentially good and can be trusted, or that most people are not
essentially good and cannot be trusted. The variable was then dummied, 1 if most people
essential good and can be trusted, 0 if otherwise. Trust is on a continuum, and so low trust is a
proxy for distrust.

Occupation is a dummy variable, 1 if in high occupation, 0 if otherwise. Those categories
which are classified within this are – teachers, doctors, lawyers, businessmen, managers and/or
supervisors whereas in the low category the following were includes – farmers, tradesmen,
unskilled worker, shopkeeper, haggler, vendor, office workers and so on.

Confidence in sociopolitical institutions. This is the summation of 22 likert scale questions,
with each question on a scale of (4) a lot of confidence, (3) some confidence, (2) a little
confidence, to (1) no confidence. The heading that precedes the question reads: I am going to
read to you a list of major groups and institutions in our society. For each, tell me how much
CONFIDENCE you have in that group or institution. Confidence index = summation of 22
items, with each question being weighted equally; and 0≤confidence index≤88, with a Cronbach
α for the 22-item scale being 0.896. The higher the scores, the more people have confidence in
sociopolitical institutions within the society. Thus, the confidence index is interpreted as from 0
to 34 represents very little confidence; 35 to 61 is low confidence; 62 to 78 is moderate
confidence and 79 to 88 is most confidence.

Age. Age is a continuous variable, which is recorded in years.
Religiosity. The frequency with which people attend religious services, which does not include
attending functions such as (1) graduations, (2) weddings, (3) christenings, (4) funerals. This
variable was recorded as:
        Religiosity1 1=High religiosity (i.e. church attendances more than once per week)
        Religiosity2 1=Moderate religiosity (i.e. church attendance once per week or
        fortnightly)
        Referent group is low religiosity (i.e. none to several times per year)

Income. Income is an ordinary variable with twenty-categories, ranging from (1) under $5,000
to (20) $250,000 and above. Based on the nature of this variable, it will be treated as a
continuous variable.
1
 Others constitute St. Thomas, Portland, St. Mary, St. Ann, Trelawny, St. James, Hanover, St. Elizabeth,
Westmoreland, Manchester, and Clarendon.

                                                        231
Political Participation Index. Based on Trevor Munroe‘s work, ‗political participation‘ ―...the
extent to which citizens use their rights, such as the right to protest, the right of free speech, the
right to vote, to influence or to get involved in political activity‖ (Munroe, 2002:4; Munroe,
1999:33), w use that construct to formulate a PPI = Σbi, bi ≥ 0, and bi represents each response to
a question on political behaviour, such as voting, involvement in protest, with 0≤PPI≤19. The
Cronbach alpha for the 22-item scale, which is used to constitute this Index, is 0.828.

Governance of the country, G, is defined as people‘s perception of administration of the society
by the elected officials. This is a dummy variable, where 1 denotes in favour of a few powerful
interest groups or the affluent, 0 is otherwise

Extent of the Welfare System of governance:

Results: Sociodemographic Characteristics of Sampled Population

The findings of the current research has a sampled population of 723 women ages 16 to 85 years

with a mean age of 34.3 years ± 13.4 years. Most of the respondents report that they are Blacks

(78%) with some indicates Browns – i.e. Mixed - (14%), Caucasians (6%). Approximately 6 out

of 10 women indicate that they are in the lower class. The demographic characteristics of the

sample also reveal that approximately 7 out of every 10 women indicate that they are employed

(i.e. full-time, part-time, temporarily, seasonally and self-employed). On an average the quality

of life of the sample was high (i.e. 6.8 ±1.7: Range 10: 10– 0). Furthermore, the findings (Table

9.2) indicate that political participation for Jamaican Women is low (i.e. 3.6 ±3.5: Range 17: 17–

0). On the contrary, the population has moderate confidence in the various socio-political

institutions in Jamaica (56.3±10.8: Range 79:86 – 7); with a sample report a high ‗welfare

system of governance‘ of the Jamaican state.

                                        Insert Table 9.2 here

Findings: Multivariate Analysis

Using econometric analysis (i.e. multiple regressions) – of the surveyed research data of some

723 Jamaican women – we found that the final model (Eqn. [3]) explains 18.5% (Adjusted R-

                                                 232
squared) of some 6 variables. The model is a good fit (F statistic [15, 410] = 7.413, p value =

0.001]. (Table 9.3).


QoLj = ƒ(Gj, PPIj, Yj, Rj , lnAj , Oj , Tj , SSj , ARj , Xj , Ej , ESj , RAj , WSj ,εj)………….…Eqn.[3]



where QoLj                    the quality of life of person j;
Gj                            self-reported administration of the governance of the nation of
person j;
PPIj                          political participation index of person j
Yj,                           income of person j
Rj ,                          religiosity of person j
lnAj ,                        logged age of person j
Oj ,                          occupation of person j
Tj ,                          interpersonal trust of person j
SSj ,                         subjective social class of person j
ARj ,                         area of residence (i.e. parish of residence) of person j
 Xj ,                         gender of respondent of person j
Ej ,                          educational level of person j
 ESj ,                        employment status of person j
 RAj ,                        ethnicity of person j
WSj ,                         extent of welfare state of a nation as reported by person j


QoLij = ƒ(Yj, Rj ,Tj , Gj, SSj , ESj , εj)…………………………………….……………..…Eqn.[4]

Examination of the sociodemographic determinants in Eqn. [4] revealed that subjective social

class – middle class with referent to lower class - (Beta = 0.198; 95%CI:0.380, 1.070) is the most

influential factor followed by employment status (Beta = 0.167; 95%CI:0.304, 0.1.077), income

(Beta = 0.155; 95%CI:0.015, 0.101), religiosity- high religiosity - (Beta = 0.152; 95%CI:0.214,

0.974), the administration of the governance of the nation (Beta = -0.139; 95%CI:-0.893, -0.203)

and lastly by interpersonal trust (Beta = 0.094; 95%CI:0.020, 0.676) (Table 9.3).


                                       Insert Table 9.3 here




                                                233
       Further examination of the findings will now be forwarded to provide a more in-depth

understanding of determinants in model (i.e. Eqn. [4]). An individual who is in the self-reported

middle class with referent to lower class contributes the most to quality of life of Jamaican

Women.     However, those in the upper class with referent to lower class contribution are

marginally more than interpersonal trust that influence is the least. A woman who trusts other

people has a greater quality of life compared to another who reported that she does not trust other

people. A similar result was observed for employment status as an employed woman has greater

quality of life compared to those who are unemployed, and the greater the income of a person the

higher is the quality of life of that individual. Religiosity is the fourth most significant factor of

quality of life of sampled population. The religiosity with which we speak is high church

attendance (i.e. church attendance at least twice per week) with referent to low religiosity (i.e.

from no church attendance to once per year). Those who reported that the governance of the

nation (i.e. political administration) benefits mostly equally with referent to those who indicated

that it favours the rich have a lower quality of life. In addition to what has been reported so far,

those who cited being in moderate religiosity had a greater quality of life compared to those with

had a low religiosity. Thus, a woman‘s quality increases with greater church attendance.


Limitation of the Model

Although the current model used data from a cross-sectional study by way of stratified
probability sampling technique, it has an adjusted R-square of less than 20%. Some statisticians
argue that a cross-sectional study that is less than 30% and over is not a good predictor of the
phenomenon. The current research is the first of its kind, and is more so a platform for future
studies than a conclusion on the matter of quality of life of women in Jamaica.

Discussion




                                                 234
The physiological composition of the sexes explains the rationale of some typologies of diseases

affecting a particular sex (WHO 2005). One health psychologist, Phillip Rice, in concurring

with WHO, argued that differences in death and illnesses are the result of differential risks

acquired from functions, stress, life styles and ‗preventative health practices‘ (Rice 1998).

Biomedical studies showed that there are gender specific diseases. The examples here are

prostate cancer (affect only men) and cervical cancer (plague only women). Rice believed that

this health difference between the sexes is due to social support.    According to Rice (1998),

Rodin and Ickovics (1990) this can be explained by epidemiological trends. Lifestyle practices

may justify the advantages that women enjoy compared in men concerning health status.

However, a survey done by Rudkin found that women have lower levels of wellbeing (i.e.

economic) than men (Rudkin 1993 222). This finding is further sanctioned by Haveman et al

(2003) whose study reveal that retired men‘s wellbeing was higher than that of their female

counterparts, because men usually received had more material resources, and more retired

benefits compared to women ages 65 years and older. Thus with men receiving more than

women, and having a more durable possession than women, their material wellbeing is higher is

later life.


         The issue extends beyond those two types of chronic illnesses as Courtenay (2003) noted

from research conducted by the Department of Health and Human Services (2000) and Centers

for Disease Control (1997) that from the 15 leading causes of death except Alzheimer‘s disease,

the death rates are higher for men and boys in all age cohorts compared to women and girls.

Embedded within this theorizing are the differences in fatal diseases that are explained by gender

constitution (Seltzer and Hendricks 1989, 7), to which Courtenay (2003) explained are due to

behavioural practices of the sexes and goes to explain the fact that men are dying 6 years earlier
                                               235
than females (U.S. Preventive Services Task Force, 1996). The current research does not expand

on past literature, but provides new information on factors that explain variability in quality of

life of females (or women) in Jamaica.


       Among the fundamental characteristics of research are that adding something new to the

discourse, modifying what exists and so in keeping with these epistemological traditions, we

will maintain these traditions in the current work. Religion is gender bias, and this dates back to

nation‘s slavery past. In contemporary Jamaica, church attendance is substantially a woman

issue; and many theologians continue to argue that there are reality benefits to have from this

practice. It is well accepted that religiosity is positively associated with wellbeing; and that is

goes beyond the theologians‘ views (Krause 2006; Moody 2006: Jurkovic and Walker 2006;

Ardelt 2003; Graham et al. 1978). According to Kart (1990), religious guidelines aid wellbeing

in that through restrictive behavioural habits which are health risk such as smoking, drinking of

alcohol, and even diet. The current paper has concurred with the literature that religiosity is

positively associated with quality of life; and this is the fourth most influential predictor of

quality of life of a Jamaican Woman. We go further to say that the quality of life Jamaican

Woman is the highest when she has the greatest degree of church attendance followed by

moderate religiosity and lastly by the lowest religiosity.


       Traditionally income was used to proxy wellbeing (i.e. economic wellbeing), and that

Richard Easterlin (2001a, 2001b) showed that income is important to happiness, but that income

does not buy unlimited happiness. In a paper titled Poverty and Health, Murray (2006) argued

that there is a clear interrelation between poverty and health. She noted that financial inadequacy

prevents an individual from accessing – food and good nutrition, potable water, proper

                                                236
sanitation, medicinal care, preventative care, adequate housing, knowledge of health practices -

and attendance at particular educational institutions among other things. The issue of resource

insufficiency affects the ability and capacity of the poor from accessing the quality of goods and

services comparable to the rich that are better able to add value to wellbeing. This is succinctly

forwarded by Murray in her monograph that:


       Poverty also leads to increased dangers to health: working environments of poorer people
       often hold more environmental risks for illness and disability; other environmental
       factors, such as lack of access to clean water, disproportionately affect poor families
       (Murray 2006, 923)

       Michael Grossman‘s work had established the direct link between income and health

(Smith and Kington 1997; Murray 2006; Sen 1999); and that income‘s contribution to the quality

of life of a Jamaican Woman is highly important as the current paper reveals that it (income) is

the third most influential factor in determining quality of life of the sampled population. This

contradicts the work of Edward Diener. Diener (1984), states that the correlation between

income and subjective wellbeing was small in most countries. According to Diener (1984, 11),

―…, there is a mixed pattern of evidence regarding the effects of income on SWB [subjective

wellbeing]‖.   The current research was subjective wellbeing (i.e. self-reported quality of life

using Abraham Maslow‘s 5 Need Item scale), and it shows that income is the third most valued

predictor of quality of life of a Jamaican woman. Arendt, using ordered logistic models, found

that ―it cannot be rejected that the income effects are causal‖ and this proceeded the finding that

―[a] robust relations exist between income and some measures of wellbeing of [the] elderly‖

(Ardent 2005, 327). Although the current work counter the findings of Edward Diener‘s work

(1984), what contributes the most to quality of life of a Jamaican woman?


       The answer is social class followed by employment status. The quality of life of a
                                               237
Jamaican Women is primarily determined by her social class; with middle class women having

the greatest quality of life and working class female experiencing the least quality of life. In this

study education was not related to quality of life, which contravenes the finding of many studies

(Diener 1984; Grossman, 1972; Hambleton et al. 2005; Bourne, 2007). Hambleton et al.‘s work,

on the other hand, found that the statistical relation was a weak one. Employment‘s contribution

to the quality of life of woman is highly important because of significant of employment in

socio-economic independent, opportunities, choices and freedom and power of independency in

this regard.


Conclusion


       In summary, the factors of quality of life of a Jamaican Woman are social class,

employment, income and religiosity, with social class being the most influential of all the

variables. Employment does not merely about the income, but it is about the independence, the

choices, the sense of freedom, the positive psychological attributes that this freedom gives as

well as the self-advancement that it is likely to provide why this variable is of that importance in

determining the quality of life of Women. The current work does not provide all the answers,

but it is a catalyst upon which we are able to build, modified and refute as these are pillows upon

which research is based.




                                                238
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Acknowledgement


The authors would like to single out the Centre for Leadership and Governance, Department of
Government, The University of the West Indies, Mona, Jamaica for allowing them to utilize the
dataset which facilitates this study.




                                            241
Table 9.1: Expectation of Life at Birth by Sex, 1880-1991, Jamaicans
Period                              Average Expected Years of Life at Birth
                                    Male                            Female
                                    e0                              e0
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); Statistical Yearbook of Jamaica, 1999 and
*
  Economic and Social Survey, Jamaica 2005 (Quoted in Bourne, 2007, p. 150)
Note e0 is life expectancy at birth




                                                      242
Table 9.2: Demographic Characteristics of Sampled Population, N=723

                                                                      Number         Percent

Subjective Social Class
         Working (lower) class                                                 409             58.7
         Middle class                                                          259             37.2
         Upper class                                                            29              4.2
Ethnicity
         Caucasian                                                              46             6.4
         Blacks                                                                562             77.9
         Browns                                                                104             14.4
         Other                                                                   9               1.2
Educational Level
         No formal Education                                                   8               1.2
         Primary/Preparatory and All Age school                                116             16.8
         Secondary                                                             246             35.5
         Post-secondary                                                        127             18.4
         Tertiary                                                              195             28.1
Employment Status
         Unemployed                                                            222             31
         Employed                                                              494             69

Age                                                       34.33yrs ±13.4 yrs.: Range 69: 85 – 16 yrs.
Quality of Life                                           6.8 ±1.7: Range 10: 10– 0.
Political Participation Index                                      3.6 ±3.5: Range 17: 17– 0.
Extent of Welfare System of governance                    6.8 ± 1.5: Range 8.7:10 – 1.2.
Confidence in sociopolitical institution index                     56.3±10.8: Range 79:86 – 7.




                                                    243
Table 9.3: Quality of Life of Jamaican Women by Some Explanatory Variables
                                Unstandardized
                                 Coefficients
  Variable                                            Beta             P          CI (95%)
                           Coefficient Std. Error
 (Constant)                       4.317       1.060                     0.000    2.233       6.401
 Tertiary Education               0.252       0.225        0.064        0.263   -0.189       0.693
 Religiosity (1=High)             0.594       0.193        0.152        0.002    0.214       0.974
 Religiosity (1=Middle)           0.466       0.194        0.116        0.017    0.085       0.847
 Area of Residence               -0.031       0.204       -0.007        0.879   -0.432       0.370
 Extent of Welfare
 System of Governance             0.034       0.053        0.028        0.522   -0.071       0.139
 Dummy Occupation
 (1=Lower level )                 0.050       0.209        0.013        0.813   -0.362       0.461
 socialcl1                        0.725       0.175        0.198        0.000   0.380        1.070
 socialcl2                        0.820       0.387        0.096        0.035   0.060        1.581
 Trust                            0.348       0.167        0.094        0.038   0.020        0.676
 Dummy
 Governance(1=Benefit
 s most equally,                 -0.548       0.176       -0.139        0.002   -0.893       -0.203
 0=Favours Rich)

 Income                          0.058      0.022          0.155       0.008    0.015        0.101
 Employment status               0.691      0.197          0.167       0.000    0.304        1.077
 Race2 (1=black and
 brown)                          0.235      0.293          0.036       0.424    -0.341       0.810
 Index of Political
 Participation                  -0.042      0.022         -0.088       0.063    -0.086       0.002
 lnAge                           0.222      0.254          0.044       0.383    -0.277       0.721
R = 0.462
R2 = 0.213
Adjusted R2 = 0.185
N=425
F-test [15, 410] = 7.413, P = 0.001< 0.05
Standard error of the estimate 1.598




                                                    244
Table 9.4: Particularized Labour Force Indicators By Sex, 1990-2007
                                                                       Year
                 1990    1991    1992   1993    1994    1995    1996   1997      1998   1999   2000   2001   2002   2003   2004   2005   2006   2007
Male:
Labour Force     564.    571.    570.   571.    574.    617.    614.   613.      614.   611.   615.   618.   618.   611.   663.   661.   695.   699.
(000’s)          6       8       1      3       8       9       6      8         3      7      0      1      4      1      5      9      6      1
Employed         896.    518.    516.   509.    519.    551.    553.   549.      552.   550.   552.   554.   552.   552.   610.   611.   646.   656.
Labour Force     3       1       0      2       9       0       3      0         9      3      4      8      8      3      9      4      8      1
(000’s)
Unemploymen      15.3    9.4     9.5    10.9    9.6     10.8    10.0   10.6      10.0   10.0   10.2   10.3   10.6   9.7    7.9    7.6    7.0    6.2
t Rate (in %)


Female:
Labour Force     494.    500.    504.   511.    515.    532.    528.   520.      514.   507.   490.   486.   506.   487.   531.   529.   557.   562.
(000’s)          0       7       8      7       8       2       2      0         2      4      3      7      1      7      3      1      5      2
Employed         513.    389.    389.   397.    403.    412.    406.   397.      400.   393.          384.   401.   402.   444.   445.   476.   480.
Labour Force     1       6       7      1       2       4       5      9         7      6             7      6      3      3      6      9      8
(000’s)
Unemploymen      9.1     22.2    22.8   22.4    21.8    22.5    23.1   23.5      22.1   22.4   22.3   21.0   20.7   17.6   16.4   15.8   14.5   14.5
t Rate (in %)
Compiled by Paul A. Bourne from Planning Institute of Jamaica (in Economic and Social Survey 1990 – 2007)




                                                                           245
                                            10
Examining Health Status of Women in Rural,
  Peri-urban and Urban Areas in Jamaica

            Paul A. Bourne, Denise Eldemire-Shearer, Donovan McGrowder and

                                     Tazhmoye Crawford 3




A comprehensive review of the literature revealed that less information is available in
literature on health status of women, and health status of women in 3 geographical zones in
Jamaica. This study examined data on the health status of women in Jamaica in order to
provide some scientific explanation of those factors that account for their health status; and
differences based on area of residence. Rural women had the lowest health status (OR =
0.819, 95% CI = 0.679-0.989) among all women (peri-urban OR = 1.054, 95% CI = 0.842-
1.320; urban OR = 1.00) and that they were the least likely to have health insurance
coverage. Health insurance was the critical predictor of good health status of women in
Jamaica, and this was equally the same across the 3 geographic areas; and that married
women were 1.3 times more likely (OR 1.3, 95 CI = 1.036-1.501) to report good health
compared to those who were never married. This study provides an understanding of
women‘s health status in Jamaica as well as the disparity which correlates based on the
different geographical regions.



Background

Latin America and the Caribbean have the second highest urbanization level in the world. For

every 13 persons there are in the region, 10 of them live in cities (78.3% in 2007) [1]; and 20 of

the region‘s largest cities are home to nearly 20% of its population. Jamaica is a predominantly


                                               246
Afro-Caribbean society, 75% black and 13% mixed, with a class structure based on land and

wealth rather than race. Although a developing country, it possesses features of a developed

country. While there is much industrialization and modernization, customs, cultural and social

habits of several centuries are common-place.


  Jamaica is the third largest English speaking Caribbean island (total area of 10,991 km2) with

an estimated population of 2.7 million (2007). The country is classified into three geographical

planes (Cornwall, Middlesex and Surrey) and has 14 parishes. Cornwall covers the Western belt

which includes parishes such as Westmoreland, Hanover, St. James, St. Elizabeth. Middlesex

constitutes the middle proportions of the island with parishes such as Clarendon and St.

Catherine. Surrey comprises the Eastern region with parishes such as Kingston, St. Thomas and

Portland. Another classification is cities (urban areas) which constitute 27.3% of the population,

peri-urban 30.2% and rural areas, 42.5% in 2007.


  In 2007, Jamaica‘s poverty rate was 9.9%, and this was 15.3% in rural areas, 4.0% in peri-

urban areas and 6.2% in urban areas. Furthermore, the mean annual per capita consumption for

country was US $2,059.91 while it was US $2,736.60 for urban dwellers, US $2,231.04 and US

$1,513.17 for rural Jamaicans. Statistics for the same period showed that the sex ratio of the

population was 97 per 100 and 84 per 100 for older ages (60 years and over). This indicates that

there are marginally less men than women in the population, and an even greater feminization at

older ages. It was estimated that 10.9% of the population was 60 years and over which indicates

an ageing population that began in the 1960s [2-4], 28.3% under 15 years, and 53.5% in the

reproductive years of 15 to 49 years. Women comprised 50.7% of the population and elderly

women accounted for 13.0% compared with 11.4% for elderly men. It was also found that 46.6%

                                                247
of household heads were women; life expectancy at birth for women was 77.1 years (2002 to

2004). The unemployment rate in 2007 was 65.4% for women [3-5] with women participation rate

being 55.4% compared to 72.9% for men. Fifty-three percent of women in the poorest quintile

were heads of households compared to 46.9% of men. An important difference between the sexes

was the mean annual per capita consumption. Statistics revealed that the mean annual

consumption for male headed-household was US $2,188.03 compared to US $1,892.92 for female

headed-household.


  It is well established that health status is determined by socio-physiological factors (age,

income, education, culturalization, crime and negative psychology) and that lifestyle practices

also account for good (or poor) health status [6-8]. Women‘s health therefore is intricately a mix

of socio-physiological response or outlay and is expressed through behaviour relating to culture,

religion, and legal norms [6]. Although recent attention has been directed towards exploring the

ramifications of women‘s health in the Western Hemisphere including the Caribbean, an

extensive review of the literature revealed that only a few studies have examined health

determinants of women in the Caribbean, including Jamaica [7, 8].


  Using secondary data from a stratified probability survey on political culture of 1,338

respondents, Bourne [7] extracted a sample of 722 women in investigating the determinants of

quality of life of women in Jamaica. The study showed that the mean quality of life of Jamaican

women was moderately high (6.8 out of 10; SD =1.7). Six variables (social class, employment,

income, religiosity, governance of the nation and interpersonal trust) accounted for 18.5% of the

variability of quality of life. Eldermire [8] investigated the general life situation of elderly

Jamaican women and found that their life situations were on an average good.

                                               248
   Many economic indicators showed that women are disadvantaged in Jamaica and the wider

Caribbean when compared to men [9-11]. In 2007, Statistics for Jamaica showed that the mean

consumption per capita on food was US $2,378.39 for male head-household compared to US

$1,898.56 for women. Studies have showed that women seek more health care then men [12-14],

and that this commences in earlier childhood. Therefore, the similarities and dissimilarities based

on area of residence of women was examined (via econometric models) in order to determine the

composition of women‘s health status.


   Econometric models such as Bourne‘s health determinant model [15] denotes that an

individual‘s health is a function of cost of medical care and other factors such as: educational

level, age, the environment, gender, marital status, area of residence, psychological status which

include positive and negative affective status, occupancy per room, home tenure, property, and

crime and victimization. Bourne‘s work modelled health determinants of Jamaicans, and with the

aforementioned issues surrounding women as were outlined above, a study on Jamaicans is not

necessarily providing an understanding of women‘s health and significant of particular factors

determining their health status or the disparity in health status of women based on the 3

geographic sub-regions in the island. This study sought to examine 1) the consumption

expenditure of women in the different income quintiles (or social classes); 2) health insurance

coverage, and visits to health care facilities by area of residence; 3) health status by age cohorts

(ie young, other adults and elderly women); 4) diagnosed illness by age cohorts; diagnosed illness

by area of residence; 5) the health status of women in Jamaica using a modification of Bourne‘s

health determinant model; 6) the health status of women in and sub-regions namely urban, peri-

urban and rural residence; and 7) the strength of those factors which affect health status of women

in the nation and the sub-regions.
                                                249
Materials and methods

Materials and Methods


The sub-sample for the current paper was all 8,541 women (ages of 15 to 100 years) extracted

from a nationally representative cross-sectional survey of 25,018 Jamaicans, the Jamaica Survey

of Living Status (JSLC, 2002) [16]. This survey was drawn using stratified random sampling. The

design was two-stage stratified random sampling, where there was a Primary Sampling Unit

(PSU) and a selection of dwelling from the primary units. The PSU is an Enumeration District

(ED), which constitutes a minimum of 100 dwellings in rural areas and 150 in urban 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 provided the sampling frame for the labour force. Ten percent was

selected for the JSLC. The survey was weighted to represent the population of Jamaica.


   This study used JSLC 2002 which was conducted by the Statistical Institute of Jamaica

(STATIN) and Planning Institute of Jamaica (PIOJ) between June and October 2002. The

researchers selected this survey because it was the second largest sample size for the survey in its

history (since 1988 to 1998), and in that year, the survey had questions on crime and

victimization, and the physical environment unlike previous years. A self-administered

questionnaire was used to collect the data, which was stored and analyzed using SPSS for

Windows 16.0. The questionnaire was modeled from the World Bank‘s Living Standards

                                                250
Measurement Study (LSMS) household survey. There were some modifications to the LSMS as

JSLC was more focused on policy impacts. The questionnaire covered questions such as: socio-

demographic, economic and wealth, crime and victimization, social welfare, health status and

services, nutrition, housing, immunization of infants and physical environment. The survey was

weighted in order for it to represent the population. The non-response rate for the survey was

27.7%.


   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 association between non-metric variables; an Analysis of Variance (ANOVA) was used

to evaluate 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, with 1 if good health status was reported and 0 if poor health).


   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 Omnibus Test

of Model and Hosmer and Lemeshow [17] was used to examine goodness of fit of the model. The

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

collinearity) existed between variables. Based on Cohen and Holliday [18], correlation can be low

(weak), from 0 to 0.39; moderate, 0.4 to 0.69, and strong, 0.7 to 1.0. This was used to exclude (or

allow) a variable in the model as any variable that had at least moderate correlation was excluded

from the final model. Wald statistics was used to determine the magnitude (or contribution) of




                                                 251
each statistically significant variables in comparison with the others, and the odds ratio (OR) for

interpreting each significant variables.


    Multivariate regression framework [19, 20] was used to assess the relative importance of

various demographic, socio-economic characteristics, physical environment and psychological

characteristics in determining the health status of women in Jamaica. This approach allowed for

the analysis of a number of variables simultaneously. Secondly, the dependent variable is a binary

dichotomous one which has enabled the use of this statistic technique to be utilized in the past to

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

logistic regression techniques, final models were build for women in general as well as for each

geographical sub-regions (rural, peri-urban and urban areas) using only those predictors that

independently predict the outcome. The level of significance for this study is 95% (ie P < 0.05).


    Equation 1 is a modification of Bourne [21, 22] health determinant model which was

previously used to determine the health status of the elderly in Jamaica.


Hi = ƒ(Wi, HHi, Pmci, Ci, MRi, ARi, EDi, SSi, CRi, (∑NAi, PAi), Mi, Fi, CHi, At, Ai, HIi, LLi, Eni, Yi, Vi,εi)   (1)


    The health status of person i, Hi, is a function of Wi, the two wealthiest quintiles of person i

with 1 if yes or 0 for the two poorest quintiles; HHi, household head of person i, with 1 if yes or 0

if otherwise; Pmci, cost of medical care of person i, in United States (US) dollars; Ci, average

consumption per person in household, in US dollars; MRi,is marital status of person i; ARi,, area

of residence of person i; EDi, educational level of person i; SSi, having social support of person i

with 1 if yes or 0 if no; CRi, crowding of person i, in numbers; (∑NAi, PAi), psychological status

which is the summation of negative affective status of person i, NAi where values are in


                                                                 252
continuous number; PAi, positive affective psychological status of person i, where values are in

continuous numbers; Mi, number of men in household of person i; Fi, number of women in

household of person i; CHi, number of children below the age of 14 years of person i; At, asset

owned of person i, in continuous numbers; Ai, age of person i, in continuous numbers; HIi, of

private health insurance (proxy ); LLi, living arrangement where 1 is living with family members

or relative, 0 if otherwise; Eni, physical environment of person i, with 1 if affected by flood,

landslides, soil erosion or 0 if not affected; Yi, average income per person in household (this

variable is proxied by total expenditure); Vi, crime of person i, where values are continuous

numbers, and εi is the residual error.


Measures


   Self-reported health status is self-assessed illness (cold, diarrhoea, asthma attack, hypertension,

diabetes mellitus or any other illnesses) reported by respondents in the last 4-weeks of the survey

period. Good health status is a dummy variable; where 1 is good health (not reporting an ailment,

injury or dysfunction) and 0 is poor health (self-reported illness, injury or ailment).


   Household crowding is the average number of persons living in a room excluding kitchen,

bathroom and verandah. Physical environment is the summation of responses reported by

respondents on suffering landsides; property damage due to rains, flooding; or soil erosion in the

last 4-weeks.


   Psychological conditions are the psychological state of an individual, and this is sub-divided

into positive and negative affective psychological status. Positive affective psychological status

refers to the number of responses that are hopeful and optimistic about the future and life


                                                 253
generally. Negative affective psychological status refers to the number of adverse events occurred

to the respondents over the last 4-week period. Each event was equally weighted.


   Age is the number of years lived, which is also referred to age at last birthday. This is a

continuous variable, ranging from 15 to 100 years. Age group is classified into three sub-groups.

Young are ages 15 to 30 years, other adults 31 to 59 years, and elderly 60 years and over. Age is

used as a continuous variable for the logistic regressions.


   Crime and victimization index (crime index) measures the number of cases and severity of

crimes committed against a person or his/her family members but not against property.


   Social support (or network) denote different social networks with which the individual has or

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

home or with whom one is able to network, 0 = otherwise).


   Living arrangement denotes whether the individual is living alone or with family, friends or

associates; where 1 = living with family members or relatives, and 0 = otherwise.




   Results

Demographic characteristics of sampled population


The sub-sample consisted of 8,541 respondents (ages 15 to 100 years), with a mean age of 40.1

years (SD 19.29 years). Of the sub-sample of respondents, 65.2% were never married, 24.7%

married and 10.1% divorced, separated or widowed. The mean annual consumption per person

per household was US$762.35 (SD US$917.81) (rate in 2002: 1US$ = Ja$50.97) with the

                                                 254
maximum consumption being US$136,822.08. Moreover, 36.6% of the sample was in poverty

with 17.5% being below the poverty line (i.e. poorest poor) compared to 44% who were in the

two wealthiest quintiles, of which 23% were in the wealthiest quintile (Table 10.1).


   On examination of area of residence by age group, it was found that 21% of rural women were

60+ years compared to 15.2% of peri-urban women and15.1% of urban women (Table 10.1) – P <

0.001.


   Of the population, 17.2% reported poor health status (suffering from an illness, ailment, or

injuries) in a 4-week period of the survey, with 82.8% indicated good health status. Of the 17.2%

of women who reported poor health status, 6.5% visited public-private health care facilities for

treatment. Of this 6.5%, 6.3% visited public health care institutions compared to 0.2% who visited

private health care facilities, 66.1% of those who had reported an illness in the 4-week survey

period bought the prescribed medication, with 40.9% of them took the medication in full. Some

5.6% of the sample reported that they resided alone (living arrangement), and 57.8% indicated no

social support.


   Based on Table 10.1, there was a significant statistical correlation between good health status

and area of residences – P < 0.001. Rural women recorded the lowest health status among all

women of the three geographic areas (Table 10.1): Rural women recorded the least good health

status (75.5%) compared to 77.0% of urban women and 81.8% of semi-urban women.


   More crowding was in the rural sample (1.9 ± 1.3 persons per room) compared to 1.8±1.3

persons per room in peri-urban and urban areas – P = 0.020. A statistical difference was found

between area of residences and mean number of visits made to health care facilities – P = 0.023:


                                               255
1.6 ± 1.1 days for rural women; 1.7 ± 1.3 days for peri-urban women and 2.0 ± 2.7 days for urban

women. A statistical correlation was found between social standing and area of residences – P <

0.001: 22.3% of rural women were in the poorest 20% compared to 11.5% of peri-urban women

and 9.5% of urban women. Rural women had the most of primary or below level education

respondents (23.5%) compared to 18.4% of peri-urban and 14.6% urban – P < 0.001.

Concomitantly, mean income of rural women was US$ 2,871.86 ± US$2,646.39 which was

76.1% of the income of peri-urban women and 64.5% of that of urban women – P < 0.001.




       The general positive affective psychological condition of Jamaican women was moderate

(3.5 out of 6 ± 2.4) and negative affective psychological condition of the same sample was low

(4.6 out of 15 ± 3.4). On disaggregating both affective conditions by area of residences revealed a

significant statistical difference: positive – F statistic =36.205; P < 0.001 and negative – F statistic

= 30.774, P < 0.001. Based on Table 10.1, rural women had the highest negative affective

psychological conditions – 4.8 out of 15 ± 3.2 compared to peri-urban (4.2 out of 15 ± 3.5) and

urban women (4.3 out of 15 ± 3.8). However, there was no statistical difference between the

negative affective psychological conditions of peri-urban and urban women (P = 0.655). Rural

women had a lower mean score in positive psychological conditions (3.3 out of 6 ± 2.4) than peri-

urban women (3.9 out of 6 ± 2.3) – P < 0.001; however there was no significant statistical

difference between rural and urban women‘s positive affective psychological conditions ( 3.4 out

of 6 ± 2.4) – P = 0.990.




                                                  256
        Table 10.1. Demographic characteristics of sample.
Variable                            Rural areas        Peri-urban          Urban           P
                                    n (%)              n (%)                n (%)
                                    n = 4,962          n = 2,283           n = 1,296
Marital status                                                                             < 0.001
   Married                         1232 (25.7)         568 (25.7)          243 (19.3)
   Never married                   3032 (63.3)         1451 (65.7)         907 (71.9)
   Divorced                        25 (0.5)            16 (0.7)            18 (1.4)
   Separated                       51 (1.1)            27 (1.2)            22 (1.7)
   Widowed                         453 (9.5)           147 (6.7)           71 (5.6)
Social Standing                                                                            <0.001
  Poorest 20%                      1106 (22.3)         263 (11.5)          123 (9.5)
  Poor                             1162 (23.4)         320 (14.0)          149 (11.5)
  Middle                           1014 (20.4)         433 (19.0)          222 (17.1)
  Wealthy                          973 (19.6)          522 (22.9)          321 (24.8)
  Wealthiest 20%                   707 (14.2)          745 (32.6)          481 (37.1)
Good Health status                                                                         <0.001
  No                               1184 (24.5)         405 (18.2)          292 (23.0)
  Yes                              3641 (75.5)         1820 (81.8)         979 (77.0)
Educational level
  Primary and below                1010 (23.5)         355 (18.4)          159 (14.6)      < 0.001
  Secondary                        3099 (72.0)         1360 (70.4)         807 (74.0)
  University                       194 (4.5)           216 (11.2)          125 (11.5)
Social support                                                                             < 0.001
  No                               2724 (54.9)         1418 (62.1)         741 (57.2)
  Yes                              2238 (45.1)         865 (37.9)          555 (42.8)
Living arrangement                                                                         0.005
  With family or relative          4714 (95.0)         2148 (94.1)         1202 (92.7)
  Without family (alone)           248 (5.0)           135 (5.9)           94 (7.3)
Age group                                                                                  < 0.001
  Young (15 – 30 years)            1865 (37.6)         910 (39.9)          501 (38.7)
  Older adults (31 – 59 years)     2055 (41.4)         1025 (44.9)         599 (46.2)
  Elderly (60+ years)              1042 (21.0)         348 (15.2)          196 (15.1)
Age Mean (SD)                      41.02         yrs   38.65 yrs (18.19)   39.12 yrs       < 0.001
                                   (20.06)                                  (17.91)
Crowding Mean (SD)                 1.9 (1.3 persons)   1.8 (1.3 persons)   1.8        (1.2 0.020
                                                                           persons)
Mean Income per person (SD)          US 2871.86         US$3773.41           US$4451.23    < 0.001
                                   (US $2646.39)       (US $2752.03)       (US 5181.68)
Mean consumption per person (SD)    US $614.04          US$888.24           US$1108.34     < 0.001
                                    (US $871.47)       (US $727.32)        (US 1217.18)
Mean number of visits for health   1.6days (1.1)       1.7days (1.3)       2.0 (2.7)       0.023
care (SD)

Negative affective Mean (SD)       4.8 ± 3.2           4.2 ± 3.5           4.3 ± 3.8       < 0.001
Positive affective Mean (SD)       3.3 ± 2.4           3.9 ± 2.3           3.4 ± 2.4       < 0.001
* Rate in 2002 was US$ 1= Ja.$50.97
The recorded p-value is for each variable by area of residence (ie rural, peri-urban and urban)
                                                 257
   Upon examination of consumption and per capita income quintile (social standing), a

significant statistical difference was found between consumption of women in different social

standing F = US$22.32, P< 0.001 (Table 10.2). Those in the poorest quintile had a mean

consumption per person per household of Jamaican US$225.38 (rate in 2002:1US$ = Ja$50.97)

which was 67% less than those in quintile 2; 133% less than those in quintile 3; 237% less than

quintile 4 and 659% less than those in the wealthiest quintile (quintile 5). Those in the wealthiest

quintile had an average consumption per person per household of 125% more than those

respondents in the wealthy quintile (quintile 4). Owing to the wide disparity in values, the best

measure for average consumption per person per household is the median consumption –

US$554.39 (rate in 2002: 1US$ = JA$50.97).




                                                258
Table 10.2. Average consumption per person per household by per capita income quintile.

                                          Std.                      95% CI
                     N          Mean      Deviation Std. Error      Lower        Upper

                                US$       US$         US$           US$          US$


  Poorest 20%        1492       225.38    64.03       1.66          222.13       228.63


  Poor               1631       376.37    45.66       1.13          374.15       378.59


  Middle class       1669       525.07    70.19       1.72          521.7        528.44


  Wealthy            1816       759.91    123.34      2.89          754.24       765.59


  Wealthiest 20%     1933       1709.65   1550.86     35.27         1640.47      1778.83

  Total              8541        762.35   917.81      9.93          742.88       781.82
   F statistic = US$22.32, P < 0.001
   Rate in 2002 was US$1 = Ja$50.97




                                             259
   There was no statistical difference between visits to either public or private health care

facilities and area of residence of the sample (Table 10.3) – P > 0.05. However, a statistical

difference existed between health insurance coverage and area of residents in Jamaica (χ2 = 24.4,

P< 0.001), with there being a weak correlation (contingency coefficient = 0.167). Of those who

responded (n = 8,268), 12.5% of had private health insurance coverage. The least number of rural

women had health insurance coverage (7%) compared to 16.5% of peri-urban women and 18.7%

of urban women.




Table 10.3. Health insurance, self-reported good health status by area of residence (in %).

                                   Area of Residence

          Details
                                   Rural            Peri-urban              Urban
                                   n = 4796         n = 2216                n = 1263

          Health insurance          7.0             16.5                    18.7
              Yes                  93.0             83.5                    81.3
              No

          Self-reported visits
          to public facilities
          for health care
               Yes                 7.1              4.4                     6.1
               No                  92.9             95.6                    93.9

          Self-reported visits
          to private facilities
          for health care
               Yes                  0.3               0.0                     0.0
               No                  99.7             100.0                   100.0

       Health insurance - P< 0.001
       Self-reported visits to public health care facilities – P < 0.386
       Self-reported visits to private health care facilities – P < 0.617


                                                 260
   Table 10.4 revealed that there was a negative statistical correlation between self-reported good

health status and age group (χ2 = 820.397, P< 0.001), with the association being a moderate one

(cc = 0.301). The findings indicated that 7.5% of young respondents reported a poor health status

compared to 15.6% other adults and 40.9% of elderly respondents indicating the substantial

erosion of good health status of women as they age.




Table 10.4. Self-reported good health status by age group.

                      Age group


                      Young               Other Adults       Elderly
                      (n = 3,114)         (n = 3,579)        (n = 1,544)

Good        health
status

No                    234 (7.5)           558 (15.6)         631 (40.9)


Yes                   2,880 (92.5)        3,021 (84.4)       913 (59.1)


χ2 = 413.247, P< 0.001

   Of the 1,417 respondents who reported an illness, 7.0% indicated that it was diagnosed as

chronic recurring illness. A statistical correlation was found between illness being recurring and

age group of respondents (χ2 = 413.247, P< 0.001), with relationship between the two

aforementioned variables being a moderate one (cc = 0.473). Based on Table 10.5, Diabetes

mellitus, hypertension and arthritis were found to be more an elderly chronic illness than the other

age sub-samples. Simply put, as women age, chronic illness such as diabetes mellitus,



                                                261
hypertension and arthritis increased, with arthritis having the greatest increase in elderly

compared to middle age.


Table 10.5. Diagnosed with recurring illness by age group.

                                Age Group                                   Total
Diagnosed/recurring illness     Young Age       Middle Age     Elderly
                                (n = 236)       (n = 562)      (n = 636)    (n = 1,434)

   Cold
                                64 (27.1)       62 (11.1)      21 (3.3)     149 (10.4)
   Diarrhoea
                                9 (3.8)         10 (1.8)       7 (1.1)      26 (1.8)
   Asthma
                                37 (15.7)       33 (5.9)       12 (1.9)     82 (5.7)
   Diabetes
                                5 (2.1)         82 (14.6)      150 (23.6)   237 (16.5)
   Hypertension
                                17 (7.2)        206 (36.7)     275 (43.2)   498 (34.7)
   Arthritis
                                3 (1.3)         20 (3.6)       91 (14.3)    113 (7.9)
   Other
                                65 (27.5)       99 (17.6)      65 (10.2)    229 (16.0)
   No
                                36 (15.3)       49 (8.7)       15 (2.4)     100 (7.0)

χ2 = 413.247, P < 0.001, cc = 0.473




   When a correlation was performed between the duration of illness (How long did the last

episode of illness last?) and area of residence, a relationship was found between the two variables

(F = 7.513, P < 0.001). On an average, the mean duration for the illness was 11.09 days (SD

10.742 days), 95% CI: 10.51-11.67 days. Rural residents reported suffering from illness for a

mean of 11.74 days (SD 10.691 days), 95% CI: 11.04-12.44 days which was not statistically

different from mean number of days reported by peri-urban residents, 10.50 days (SD 10.573

                                               262
days), 95% CI: 9.21-11.79 days, P < 0.001. The mean number of reported days in which rural and

urban resident were ill [8.20 days (SD 10.879 days), 95% CI: 6.44-9.96 days,    P = 0.233]. There

was no statistical difference between the mean duration of illness for peri-urban and urban

residents, P = 0.091.


   The findings revealed that health status by area of residence had no statistical correlation, P=

0.051 (Table 10.6). Despite the no statistical difference, in excess of 30.0% in each area of

residence suffered from hypertension, 16% from diabetes mellitus, 13% other and 5% arthritis. Of

the 1,434 respondents who indicated poor health status, 93.0% said that these were diagnosed as

recurring and acute.


Table 10.6. Diagnosed/recurring illness by area of residence.

                         Area of Residence             Total
                                       Peri-
                                       urban   Urban
                         Rural Areas (n      = (n    =
                         (n = 961)     292)    181)    (n = 1,434)
    Diagnosed illness
                         98 (10.2)      27(9.2)      23(12.7)   148(10.3)
    Cold
    Diarrhoea            12(1.2)        10(3.4)      4(2.2)     26(1.8)
                         57(5.9)        13(4.5)      12(6.6)    82(5.7)
    Asthma
                         159(16.5)      49(16.8)     29(16.0)   237(16.5)
    Diabetes
                         342(35.6)      95(32.5)     61(33.7)   498(34.7)
    Hypertension
                         82(8.5)        17(5.8)      15(8.3)    114(7.9)
    Arthritis
                         157(16.3)      47(16.1)     25(13.8)   229(16.0)
    Other
                         54(5.6)        34(11.6)     12(6.6)    100(7.0)
    No

    No significant association (P > 0.05)

                                               263
Multivariate Analyses


Of the 20 predisposed variables that were used in the used in Eq. [2], one was excluded because

the correlation coefficient between it (consumption) and income was 0.68. Nine variables were

found to be determinants of good health status of women in Jamaica (Table 10.7). The model had

a statistical significant predictive power (χ2 = 1,249.19 P < 0.001; Hosmer and Lemeshow

goodness of fit χ2 = 5.606, P = 0.691). In addition, it was revealed that overall 84.9% (n = 7,251)

of the data were correctly classified: 97.7% of those who indicated good health status and 37.8%

of those who indicated poor health status.


   On examination of data (Table 10.7), it was revealed that private health insurance was the most

significant factor predicting good health status of women in Jamaica (OR = 27.5, 95% CI = 21.1-

35.8)followed by assets owned (OR = 1.1; 95% CI = 1.0-1.0); age of the respondents (OR = 0.9,

95% CI = 0.9-0.9); positive affective psychological status (OR = 1.1, 95%CI = 1.0-1.1); number

of men in the household (OR = 0.9, 95% CI = 0.8-1.0); income (OR = 1.000, 95% CI = 1.000-

1.000); marital status – married – (OR = 1.2, 95% CI = = 1.1-1.6); crowding (OR = 0.9, 95%CI =

0.8-1.0); area of residence and negative affective psychological status (OR = 1.0, 95% CI = 0.9-

1.0). All the factors explain 36.0% of the variability in health status of women in Jamaica.

Income positively influences good health status of women (OR = 1.0, 95%CI 1.0-1.0) (Table

10.7).


   The current work has found that women who have health insurance were 27.5 times likely to

report good health than those who do not have health insurance coverage. Rural women were less


                                               264
likely to report good health status compared to urban women; and that peri-urban women were

1.1 times more likely to report good health status compared to urban women. Married women

were 1.2 times more likely to report good health status with reference to those who were never

married. Women who were experiencing greater positive affective psychological conditions were

1.1 times more likely to report good health status; and women who experienced greater negative

affective psychological conditions were 0.03 times less likely to report good health status. The

older women get, they are 0.19 times less likely to report good health status (Table 10.7).




                                                265
       Table 10.7. Logistic regression of the general good health status of Jamaican
       women by some explanatory variables, n = 8,541
                                                                      Std.    Odds
                      Explanatory variables                           Error   Ratio    95% CI
                        Two wealthiest quintiles                      0.102   1.136    0.931 - 1.387
                        Household Head                                0.288   0.891    0.507 - 1.566
                        Log medical Expenditure                       0.027   1.003    0.951 - 1.059
                         Separated, Divorced , Widowed                0.131   1.231    0.952 - 1.591
                         Married                                      0.095   1.247    1.036 - 1.501*
                         †Never married                                       1.000

                         Rural                                        0.096   0.819    0.679 - 0.989*
                         Peri-Urban                                   0.115   1.054    0.842 - 1.320*
                         † Urban                                              1.000

                         Secondary level                              0.104   1.084    0.883 - 1.330
                         Tertiary level                               0.183   1.168    0.817 - 1.671
                         †Primary and below level

                         Social support                               0.077   1.123    0.965 - 1.306

                         Crowding                                     0.045   0.907    0.831 - 0.991*

                         Psychological conditions
                            Positive Affective                        0.012   1.055    1.030 - 1.080***
                            Negative Affective                        0.017   0.966    0.935 - 0.998*

                         No. of males in household                    0.044   0.887    0.814 - 0.966**
                         No. of females in household                  0.042   0.965    0.889 - 1.048
                         No. of children in household                 0.033   0.989    0.928 - 1.055
                         Age                                          0.018   0.910    0.878 - 0.943***
                         Asset owned                                  0.003   1.035    1.029 - 1.041***
                         Health Insurance                                              21.111           -
                                                                      0.134   27.478
                                                                                       35.765***
                         Living Arrangement                           0.179   0.879    0.619 - 1.248
                         Physical Environment                         0.112   0.945    0.759 - 1.177
                         Average Income                               0.000   1.000    1.000 - 1.000**
                         Crime Index                                  0.004   1.008    0.999 - 1.017
                         Constant                                     0.417   0.063              -

Nagelkerke R-square = 36.0%
-2 Log likelihood= 4656.637
Hosmer and Lemeshow chi-square=5.606; P=0.691
Model: Omnibus Test - chi-square=1,249.19, P < 0.001
Overall correct classification = 84.9%
Correct classification of cases of poor health status = 37.8%
Correct classification of cases of good health status = 97.1%
†Reference group
*P< 0.05. **P < 0.01, ***P < 0.001




                                                                266
   Using a sub-sample of 4,962 rural residents, 20 initial predisposed explanatory variables were

tested to ascertain factors and degree of significance of each factor (P < 0.05), one was omitted

(consumption, because the correlation coefficient between it and income was 0.68). Of the 19

predisposed variables that were examined in the initial model, nine of them explained 38.6% of

the variability in health status of rural women in Jamaica (Table 10.8).

   The model had a statistical significant predictive power (χ2 = 884.476 P < 0.001; Hosmer and

Lemeshow goodness of fit χ2 = 8.498, P = 0.386). Overall, 84% (n = 2,940) of the data were

correctly classified: 96.8% (n = 2,593) of those who had indicated good health status and 42.3%

(n = 347) of those with poor health status.


   Continuing, Table 10.8 revealed that health insurance was the most influential factor

determining the good health status of rural women in Jamaica (OR = 25.0, 95% CI =18.0-34.9)

followed by assets owned (OR = 1.0, 95% CI = 1.0-1.1); age (OR = 0.9, 95% CI = 0.8-0.9);

number of men in household (OR = 0.8, 95% CI = 0.7-0.9); positive affective psychological status

(OR = 1.1, 95% CI = 1.0-1.1); educational attainment – secondary and post-secondary level

education (OR = 1.4, 95% CI = 1.1-1.8); Social support (OR = 1.3, 95% CI = 1.1-1.6); marital

status – married (OR = 1.4, 95% CI = 0.8-2.3), and lastly income (OR = 1.0, 95% CI = 1.0-1.0).


   The current findings revealed that income plays the least role in determining good health status

of rural women; women with health insurance are 25.0 times more likely to have good health

status than those without health insurance coverage; married rural women are 1.4 times more

likely to report good health status with reference with those who were never married; those rural

women with social support were 1.3 times more likely to report good health status compared to

those who did not have social support, and as rural women become older, they are 0.102 times

                                                267
less likely to report good health status. More males in the household will reduced the good health

status of rural women (OR = 0.83, 95% CI = 0.75-0.93): indicating that more males in a

household will decrease rural women‘s good health status by 0.17 times compared to less males

in the household.


       Table 10.8. Logistic regression of the good health status of rural-Jamaican women
       by some explanatory variables, n = 3,498
 Explanatory variables                                     Odds
                                             Std. Error    Ratio       95% CI
    Two wealthiest quintiles                 0.142         1.043       0.790 - 1.378
    Household Head                           0.385         0.512       0.241 - 1.087
    Log medical Expenditure                  0.035         1.040       0.971 - 1.114

    Separated, divorced                      0.169         1.309       0.940 - 1.822
    Married                                  0.121         1.360       1.074 - 1.724*
    †Never married                                         1.000

    Secondary or post-secondary              0.132         1.413       1.090 - 1.832**
    Tertiary level
                                             0.271         1.352       0.795 - 2.298
    †Primary and below
                                                           1.000
    Social support                           0.100         1.292       1.063 - 1.571*

    Crowding                                 0.058         0.961       0.858 - 1.075

    Psychological conditions
    Positive Affective                       0.017         1.053       1.019 - 1.087**
    Negative Affective                       0.021         0.964       0.924 - 1.005

    Number of males in house                 0.058         0.834       0.745 - 0.933**
    Number of females in house               0.056         0.899       0.805 - 1.003
    Number of children in house              0.043         0.950       0.873 - 1.033
    Age                                                                0.855         -
                                             0.025         0.898
                                                                       0.942***
    Assets owned                                                       1.031         -
                                             0.004         1.038
                                                                       1.046***
    Health Insurance                                                   18.006        -
                                             0.167         24.955
                                                                       34.586***
    Living Arrangement                       0.248         0.770       0.474 - 1.252
    Physical Environment                     0.127         0.922       0.718 - 1.183
    Average Income                           0.000         1.000       1.000 - 1.000**
    Crime Index                              0.007         1.005       0.991 - 1.018
    Constant                                    0.541         0.069        -
Nagelkerke R-squared = 38.6%
2 Log likelihood=2,774.82
Hosmer and Lemeshow chi-square = 8.498; P=0.386; Model: Omnibus Test - chi-square=884.476, P < 0.001
Overall correct classification = 84% ; Correct classification of cases of poor health status =42.3%
Correct classification of cases of good health status = 96.8% ; †Reference group; *P < 0.05. **P < 0.01, ***P < 0.001


                                                              268
   With regard to peri-urban areas in Jamaica, a sub-sample of 2,283 respondents were used to

establish the good health status model. This model had a statistical significant predictive power

(χ2 = 285.807 P < 0.001; Hosmer and Lemeshow goodness of fit χ2 = 7.226, P= 0.512). Upon

reviewing the classification table, overall, 88.2% of the data were correctly classified: 98.8% of

those classified as having had good health status and 35.5% of those who had indicated poor

health status (Table 10.9).


   Of the 19 predisposed variables that were tested in the initial model, six factors accounted for

36.6% of the variability in good health status of women in peri-urban area in Jamaica (Table

10.9). The factors that predict good health status of peri-urban Jamaican women in descending

order were health insurance (OR=57.7; 95%CI: 29.8-111.7); asset ownership (OR=1.0; 95%CI:

1.0-1.0); age of respondents (OR=0.9; 95%CI: 0.9-1.0); number of men in household (OR=0.8;

95%CI: 0.6-1.0); negative affective psychological status (OR=0.9; 95%CI: 0.9-1.0); positive

affective psychological status (OR=1.1; 95%CI: 1.0-1.1) and consumption (OR=1.0; 95%CI: 1.0-

1.0).


   The findings revealed that income contributed the least to good health status of peri-urban

residents. Another interesting finding of the current paper is peri-urban women who had health

insurance coverage is 57.7 times more likely to report good health status compared another who

do not have this coverage. The older peri-urban women get, they are 0.1 times less likely to

record good health; more men contributes 0.2 times less to their good health; the more asset they

own this increased their good health by 1.0 times more another with less assets and that the more

they are positive, this direct increase their good health status and the converse is the case for those

with greater scores in negative affective psychological conditions.

                                                 269
       Table 10.9. Logistic regression of the good health status of peri-urban-Jamaican
       women by some explanatory variables, n=2,283
                                          Std.        Odds
 Explanatory variables                    Error       Ratio      95% CI
    Two wealthiest quintiles              0.220       0.949      0.616 - 1.461
    Household Head                        0.722       2.554      0.620 - 10.523
      Log medical Expenditure             0.058       0.953      0.851 - 1.068
      Average Income                      0.000       1.000      1.000 - 1.000

      Separated, divorced, widow          0.291       0.853      0.482 - 1.510
      Married                             0.203       1.143      0.767 - 1.704
      †Never married                                  1.000

      Secondary or post-secondary         0.235       0.704      0.444 - 1.115
      Tertiary level                      0.367       0.622      0.303 - 1.277
      †Primary and below                              1.000

      Social support                      0.169       0.849      0.609 - 1.182

      Crowding                            0.095       0.874      0.726 - 1.051

      Psychological conditions
      Positive Affective                  0.027       1.062      1.008 - 1.120*
      Negative Affective                  0.037       0.923      0.859 - 0.992*

      Number of males in house            0.105       0.780      0.634 - 0.959*
      Number of females in house          0.103       0.961      0.786 - 1.175
      Number of children in house         0.074       0.958      0.829 - 1.107
      Age                                 0.038       0.935      0.867 - 1.008***
      Assets owned                        0.006       1.031      1.018 - 1.044***
      Health Insurance                    0.337       57.659     29.785 - 111.619***
      Living Arrangement                  0.363       0.919      0.451 - 1.870
      Physical Environment                0.286       0.961      0.549 - 1.683
      Crime Index                         0.009       1.005      0.988 - 1.023
      Constant                            0.999       0.065                       -
Nagelkerke R-square=36.6%
-2 Log likelihood = 1,071.43
Hosmer and Lemeshow chi-square=7.226; P=0.512
Model: Omnibus Test - chi-square=285.807, P < 0.001
Overall correct classification = 88.2%
Correct classification of cases of poor health status =35.5%
Correct classification of cases of good health status = 98.8%
†Reference group
*P < 0.05. **P < 0.01, ***P < 0.001




                                                                270
  A sub-sample of 1,296 women of urban Jamaica was used to build the good health status

model. The model had a statistical significant predictive power (model chi-square = 263.08 P <

0.001; Hosmer and Lemeshow goodness of fit χ2 = 8.481, P = 0.388). Upon observation of

classification, overall, 83.4% of the data were correctly classified: 97.1% of those who had

indicated good health status and 31.8% of those who reported poor health status.


       Of the 19 predisposed variables that were examine in the initial model, six of them

accounted for 30.7% of the variability in good health status of urban women in Jamaica (Table

10.10). Health insurance had the most impact on good health status of urban women (OR = 22.2;

95%CI: 11.3-43.7) followed by in descending order are age of respondents (OR = 0.94; 95% CI=

0.9-1.0); two wealthiest quintiles (OR = 1.8; 95%CI: 1.1-2.9); asset ownership (OR=1.0; 95%CI:

1.0-1.0); positive affective psychological status (OR = 1.1; 95%CI: 1.0-1.1) and number of men in

the household (OR = 1.2; 95%CI: 1.0-1.5).


  Embedded in the current findings are that urban women with health insurance coverage were

22.2 times more likely to record good health status compared to those who do not have health

insurance coverage; the older urban women get, they are 0.1 times less likely to record good

health status and that more men in urban household contributed 1.2 times more likely to good

health status. Concomitantly, urban women in the two wealthiest quintiles were 1.8 times more

likely to report good health status with reference to women in the poor-to-poorest 20%.




                                               271
       Table 10.10. Logistic regression of the good health status of urban-Jamaican
       women by some explanatory variables, n = 1,296

                                          Std.         Odds
 Explanatory variables                    Error        Ratio          95 % CI
   Two wealthiest quintiles               0.247        1.808          1.113 - 2.935*
   Household Head                         0.602        1.044          0.321 - 3.399
   Log Medical Expenditure                0.074        0.960          0.830 - 1.110
   Average Consumption                    0.000        1.000          1.000 - 1.000

    Separated, divorced, widowed          0.327        1.541          0.812 - 2.923
    Married                               0.251        1.154          0.706 - 1.886
    †Never married                                     1.000

    Secondary or post-secondary           0.279        0.714          0.413 - 1.234
    Tertiary level                        0.400        1.147          0.524 - 2.509
    †Primary and below                                 1.000

    Social Support                        0.200        0.911          0.616 - 1.346

    Crowding                              0.116        0.829          0.661 - 1.040

    Psychological conditions
    Positive Affective                    0.027        1.066          1.010 - 1.124*
    Negative Affective                    0.043        1.019          0.937 - 1.108

    Number of males in house              0.106        1.237          1.005 - 1.522*
    Number of females in house            0.096        1.163          0.964 - 1.405
    Number of children in house           0.092        1.127          0.941 - 1.349
    Age                                   0.044        0.936          0.858 - 1.021***
    Assets owned                          0.007        1.028          1.013 - 1.043***
    Health Insurance                      0.345        22.222         11.312 - 43.655***
    Living arrangement                    0.390        1.475          0.687 - 3.167
    Environment                           0.482        1.722          0.669 - 4.431
    Crime Index                           0.010        1.008          0.988 - 1.027
    Constant                              1.029        0.043                -
Nagelkerke R-square=41.5%
-2 Log likelihood = 738.894
Hosmer and Lemeshow chi-square=8.481; P=0.388
Model: Omnibus Test - chi-square=263.08, P <0.001
Overall correct classification = 82.9%
Correct classification of cases of poor health status =37.0%
Correct classification of cases of good health status = 97.3%
†Reference group
*P < 0.05. **P < 0.01, ***P < 0.001



Discussion

The findings of the current paper showed that poverty for rural women was 2.4 times more than

that for urban women and 1.9 times more than that for peri-urban women. An interesting finding
                                                                272
is that on average urban women received income which was 1.6 times more than rural women and

1.2 times that of peri-urban women. Rural women‘s consumption expenditure was 45% less than

that for urban women and 31% less than for peri-urban women. Another fundamental disparity

was in education as 161 rural women for every 100 urban women had at most primary education

and the ratio was 127 to 100 rural women for every peri-urban woman respectively. Those

socioeconomic disparities between sub-regions in Jamaica, accounted for rural women having the

lowest good health status. Overall, Jamaican women report good health status (over 80%). Those

with poor health status were more likely to report having hypertension followed by diabetes

mellitus, and the rates of these two chronic diseases were similar in the three geographical

locations. Hypertension (43.2%) and diabetes mellitus (23.6%) was more prevalent in the elderly

than in the other adult and young respondents. Interestingly, only 7.5% elderly had private health

insurance coverage and the mean consumption expenditure for the poorest was 13% of that for

those in the wealthiest income group, supporting the that poverty was a rural phenomenon and

that this significantly retards consumption pattern of rural women in Jamaica. A critical finding of

this study was that health insurance coverage accounted for the most influence on good health

status of women in the 3 sub-regions; but that it had the most impact on good health for peri-

urban women and the least for urban women. Another important finding was that income played a

secondary role to factors such as health insurance, age of respondents and other psychosocial

factors. Education did not explain good health status for peri-urban or urban women; and that

more males contributed positively to the health status of urban women and negatively for women

in the two other sub-regions.


   When health status of Jamaican women was deconstructed into area of residence, some major

similarities were observed among them. The study revealed that the most significant factor
                                                273
predicting health status of women in Jamaica across the three sub-regions was health insurance

coverage. Embedded in this study is the fact that health insurance aids in the health care-seeking

behaviour of women; but that it is more so for peri-urban women. For peri-urban women, those

with health insurance coverage were approximately 60 times more likely to report good health

status than those without this coverage, suggesting that lifestyle practices of these women account

for their health status. This finding can be supported by the fact that women in peri-urban zones

visited health care practitioners more than that of rural but less than urban women.


   Financial resource availability plays an important role in health care decisions. The resources

regarding health care decision-making could be health insurance or monetary resources. Health

insurance is important for access to health care and being uninsured significantly reduces access

to health services and substantially increases health problems. Uninsured persons with poor health

status are much more likely than their insured counterparts to report that they or a family member

did not receive doctor‘s care or prescription medicines [23]. Shi [24] reported that income was the

most significant predictor of lack of health insurance coverage, which explains why rural women

in this study had the least health insurance coverage, the lowest income and consumption and the

lowest good health status. Low-income adult women tended to have lower health status and

uninsured women tended to have problems accessing health care services [25], which are

concurred by this study. Mead et al [26] noted that low-income women were less likely to have

health insurance, while they were more likely to have health care access problems, chronic illness

and lower overall health status than their richer counterparts.


   In Jamaica, Life of Jamaica and Blue Cross Jamaica Limited are the only total health insurance

companies catering to the widest cross- section of Jamaica‘s population. These companies offer a

                                                 274
wide range of health insurance products to best suit the needs of clients from individuals,

students, executives, associations and companies. This study revealed that an overall 11 out of

every 100 of sample had health insurance coverage. In terms of geographical areas, 7 out of every

100 rural women, 17 out of every 100 peri-urban women and 19 out of every 100 urban women

possessed health insurance coverage, which reinforced the aforementioned findings that income

plays a critical role in health insurance coverage and health status. These results are not in

agreement with findings from a study by Wong and Diaz [27], who found that almost three-

quarters of the urban population (73%) have coverage compared to 38% of those in rural areas;

women showed a slightly higher and similar coverage (56%) than men (52%). Bennett et al. [28]

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%). In studies done in rural areas,

the probability of a worker being covered by an employer-sponsored insurance plan is lower than

for urban workers [29, 30]; and therefore account for the health insurance disparity between the 3

sub-regions. The authors found that small firm size and low wages in rural areas are the main

reasons for this difference. In this study 7.5% of women residents in all three regions reported

having health insurance coverage, which is similar to 7.6% reported in a previous study [31].

Hence this justifies why rural women recorded the least number of visits to health care

practitioners; because health care cost will be substantially an out of pocket expense that they

would be unable to afford.


  Good health is a determinant of the individual and societal economic status. Unemployed

women were reported to have poorer mental and physical health status than employed women

[32, 33]. This causes low-income women to frequently face health care decisions. However, low-
                                               275
income women often experience conflicts between their poor health status and lack of resources.

Wagstaff and Doorslaer [34] reported that an individual‘s absolute income affected his/her

mortality. These authors supported Rodgers‘ [35] argument that the relationship between an

individual‘s health and income is concave. This means that each additional dollar of income raises

an individual‘s health status, but the increase gets smaller as income increases and justifies why

income plays a secondary role to health insurance coverage.            Another fact that this study

highlights is the increased indirect role that income plays, which is weaker than it direct role.


   Poverty is related to poor health, and urban poverty is a dynamic status. An individual or

household‘s position can decline or improve over short periods according to changing

circumstances such as illness, unemployment, eviction or other events. The causes of urban

poverty are interlinked, stemming from such factors as employment insecurity, sub-standard

housing, poor health, low levels of income generation, vulnerability to market shocks, and limited

education [36-39]. According to Hinrichson [40], most urban poverty does not result from a lack

of jobs, but from a lack of well-paying, steady jobs. Unemployment rates are generally below

15% in most developing country cities, but wage rates are depressed in the formal sector, and

many are self-employed in the informal sector. Average incomes in rural areas are often lower

than in urban areas [41, 42]. In rural areas, poverty leads to health-related problems not only for

single mothers but also for mothers with partners, while in urban areas this problem is usually

observed in single mother-headed households. Rural Americans are more likely to be poorer [43]

and less healthy than their urban counterparts, which is also the case in Jamaica. This study goes

farther as it found that urban women in the two wealthy quintiles were 1.8 times more likely to

report good health, and this was not the case for rural or peri-urban women. Although social class

(ie wealthy class) is a predictor of good health status of urban women, once again peri-urban
                                                 276
women had the greatest good health status. This indicates that after certain sum of wealth, income

adds increasing less to good health status. Income therefore will add substantially more to the

good health status of poor women than it is likely to increase good health for middle and wealthy

women‘s health status.


   Non-communicable diseases such as cardiovascular diseases, cancer, chronic respiratory

diseases and diabetes mellitus are rapidly increasing problems for the socially disadvantaged [44].

In this study, findings of diagnosed chronic health conditions show patterns of worse health status

among elderly women living in rural areas. The prevalence of hypertension and diabetes mellitus

among respondents in the three regions were similar. However, reports of cancer, influenza,

asthma and arthritis are low compared with hypertension and diabetes mellitus. Hypertension was

higher in rural than urban and peri-urban areas. The self-report of disability and chronic status is

higher for older than younger residents. Rural women tend to have higher rates of chronic status

of hypertension, arthritis, spinal disorders, bursitis, hearing, and visual impairments than their

urban counterparts. They also make fewer doctor visits than urban women. Furthermore, when

seeking medical services, they are more likely to be ill, hospitalized than women in urban areas

[45]. In this study, the duration of sickness in women residents in rural areas was longer than their

counterparts in urban and peri-urban areas. In addition, health care facilities in rural areas are

unfavorable compared to non-rural areas due to limited medical resources and shortage of

physicians [46]. We can deduce from current paper that with rural women having less economic

resources and lowered visits to health care facilities, they would be using more home remedy or

non-traditional healers to treat their ill-health. Hence this would account for an aspect of pre-

mature mortality of these women.


                                                277
   In this study a higher percentages of the elderly in the rural areas reported poor health status.

Bennett et al. [28] found that residents in rural area were more likely to report fair to poor health

status than were residents of urban counties (19.5% versus 15.6%). Rural adults were more likely

to report having diabetes mellitus than were urban adults (9.6% versus 8.4%). The authors also

found that urban residents are more likely to use preventative care than their rural counterparts,

but there seemed to be no differential use of doctor visits or hospitalizations [28]. According to

Brenzel et al. [47] chronic diseases such as diabetes mellitus and hypertension are either

undetected or medically untreated, or in the case of those who do receive treatment, the clinical

management of the status is poor. In Jamaica, available hospital records show that between 1990

to 98 showed that twice as many women than men were admitted for hypertension and diabetes

mellitus [48]. The predominance of women with chronic disease visiting health care facilities

(82%) is in keeping with the experience of other public health areas for chronic diseases. In

addition, women are more likely to report an illness; with 15% women compared to 12% men

reported suffering from an illness or injury in the previous four weeks in 1991. The gender gap is

widest for hypertension with twice as many women as men (12% vs. 6%) reporting having the

disease [47]. In the current paper, the researchers found that diabetes mellitus for elderly women

was 11.2 times more than that for young women and 1.6 times more than for middle aged adult

women. Continuing, hypertension in elderly women was 6.0 times more than that in young

women and 1.2 times more than in middle aged adults. Arthritis was 10.8 times more in elderly

women compared to young women and 4.0 times more in elderly than in middle aged adult

women. On the other hand, acute dysfunctions such as cold, diarrhoea and asthma decreases as

women become older and the same was recorded for unspecified illness.



                                                278
   Women‘s education also affects attitudes toward health. The more highly educated are likely to

better understand the importance of proper health care. Ross and Miroswky [49] reported that

education significantly improved self-reported health and physical functioning. In addition,

knowledge of and experiences with health care were found to affect an individual‘s health care

behaviour more so than age. The latter was believed to be the most dominant determinant of

health care behavior [50]. Majority of the women residents in this study attained secondary level

education. Education is strongly associated with the level of health service utilization, the type of

provider, the choice of private versus public provider, dietary and child-feeding, and sanitary

practices [51]. However, studies have found that it is not just general education, but also health-

specific knowledge that is important. Barrera [52] and Caldwell [53,54] argued that educated

mothers are more likely than the uneducated ones to take advantage of modern medicine and

comply with recommended treatments because education changes the mother‘s knowledge and

perception of the importance of modern medicine in the care of her children. In contrast,

Rosenzweig and Schultz [55] viewed women schooling and health care services as partial

substitutes for information regarding knowledge of diseases, treatment of illness and child-care

practices, and hypothesized that the effect of education on child health becomes less important as

access to public health care services improves. Presumably, in areas where such services are

readily accessible, they are used by both educated and uneducated women, and thus the advantage

conferred by schooling on health outcomes is narrowed. It is unlikely that the observed effects of

maternal education on child-health outcomes simply reflects health knowledge and habits

acquired in school, although they may play some role [56]. Education could thus influence a

woman‘s beliefs about disease causation and cure and the value she places on modern medicine.

Mansfield et al. [57] compared the health practices of rural women with those of a large

                                                279
metropolitan area. They found that rural women adopted more health practices overall than their

urban counterparts, and younger women in both groups exhibited more awareness of health

promotion. In addition, they found that there is higher utilization of doctors‘ visits and

preventative care among persons with the highest level of education and in the highest income

groups. However, higher education or income seems to have no association with differential use

of hospitalizations [57]. The current paper both concurs and disagrees with the aforementioned

works. Education was not found to be significantly correlated with good health status of Jamaican

women. However, this was not the case for rural women. An irony that lies in this study is the

fact that there is a health disparity between women who have had a most primary education

compared to those with secondary education, but there was none between tertiary and at most

primary education for rural women.


  Human emotions are a mix of not only positive status but also negative factors [58]. Hence,

depression, anxiety, neuroticism and pessimism are seen as measures of the negative

psychological status that affect subjective wellbeing [59, 60]. Negative psychological status (loss

of family members, friends etc) affect subjective wellbeing in a negative manner (guilt, fear,

anger, disgust [60, 61] and that the positive factors influence self-reported wellbeing in a direct

way. This was concurred in a study conducted by Fromson et al [62] and other researchers [59,

63]. In this study, negative affective psychological status was inversely affect good health status

of Jamaican women, and the opposite was true for positive affective psychological conditions. On

disaggregating the good health status by the 3 sub-regions, only positive affective conditions

influence good health status of urban women while positive and negative affective psychological

conditions determined good health status for rural and peri-urban women. Rural residents are

more likely than their urban counterparts to experience negative circumstances such as
                                               280
unemployment, lower rate of health insurance coverage, poor health status, and lowered

consumption and earnings and this retards their health care seeking behaviours and further

becomes challenges for their health. Hambleton et al. [20] found that an individual‘s

psychological state influences his/her health status, which this study concurs with. People in rural

areas are more likely to have characteristics that are strongly associated with depression, poor

health status, chronic diseases and poverty. Probost et al. [64] found the prevalence of depression

were slightly higher in residents in rural than in urban areas. Depression is subsumed in negative

affective psychological condition, and so this work agrees with the literature. The current paper

however found that there is no significant statistical difference between the negative

psychological state of peri-urban and urban women in Jamaica as well as between positive

affective psychological conditions and urban and urban women. Embedded in these findings are

the higher over affective conditions of peri-urban women, and this fact accounts for peri-urban

women having the greatest health status.


   Some limitations must be considered in interpreting these results as this study was completely

based on data reported by interviewed residents, and of course, persons do not always answer

factually in interview surveys. Therefore, survey participants could be subject to recall bias in

their health status. Interviewers and supervisory staff were aware of this problem, and interviewer

instructions included directions for probing participants on these issues. However, the strength of

the study's sample design and data collection procedures compensated for these limitations.


Conclusions


The findings revealed that rural women had the least good health, while peri-urban women

recorded the greatest self-reported good health. Concurrently, rural women were older; poorer;
                                                281
received the lowest income per person; had the greatest percentage of primary level eduction;

recorded the highest negative affective psychological conditions; were the least likely to have

health insurance coverage and they recorded the lowest consumption expenditure. This study

therefore provides a comprehensive understanding of health of women in Jamaica and the 3 sub-

regions as well as the disparity in socio-demographic correlates of health based on the different

geographical regions. Concomitantly, poverty continues to reduce the self-rated health status of

women and while they are living 6 years longer than men, this does not mean that we neglect the

reality that poverty is eroding their health status.




                                                  282
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                                            11
       Social determinants of self-reported
          health across the Life Course

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. 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


                                               288
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
                                                 289
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.
                                                290
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 paper 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.




                                                  291
METHODS


Participants and questionnaire

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
                                                292
≤ 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.


           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

                                                             293
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

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.




                                                294
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, see Model 4). The factors are retirement income, logged medical expenditure, marital

status, 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 11.1).


       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.

                                                 295
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) (see Model 5). These factors were

education, marital status, health insurance, area of residence, gender, psychological conditions,

number of males in household, number of children in household and previous health status (see

Table 11.2).


       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 (see Table 11.3)

       Ht = f(Ht-1, Pmc, Eni, HIi, Xi, Σ(NPi),Ni, εi).................. ………..... Model (6)

       Based on Table 11.3, 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).


                                                   296
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 11.4).

                Ht = f(Ht-1, Wi, HIi, Σ(NPi), εi)........................... ….....Model (7)

       From Table 11.3, 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)




                                                           297
       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

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

                                                298
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

       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

                                                299
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

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 paper shows that there are also differences in social and psychological

determinants of health across the life course.


       The current paper 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.

                                                 300
       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

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.

                                                301
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.

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.




                                              302
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                                             305
Table 11.1: 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

                                                        306
Table 11.2: 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


                                                       307
Table 11.3: 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




                                                       308
Table 11.4: 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




                                                       309
                                            12
   Modeling social determinants of self-rated
   health status of hypertensive in a middle-
           income developing nation

                         Paul A. Bourne & Christopher A.D. Charles




A piecemeal approach has been taken in studies on hypertension, but there is a void in the
literature on (1) the socio-demographic profile of those with the disease in a Latin American and
Caribbean nation, (2) healthcare seeking behaviour, (3) healthcare utilization, and (4) modelling
social determinants of self-rated health status. The aim of this paper is to elucidate information
on hypertension and the socio-demographic profile of those with the disease in a Latin American
and Caribbean nation as well as to model self-rated health status of the hypertensive. Twenty-
seven in every 100 hypertensive persons had at least good self-rated health status. The current
paper found that 2.5 times more females than males were affected by hypertension; and the
hypertensives were more likely to: be married, be elderly, utilise private health care facilities,
record moderate health status, be in the lower socioeconomic strata, and be rural dwellers.
Most had sought medical care during the last 4-week period. Rural hypertensives recorded the
greatest very poor health status, and two variables emerged as statistically significant factors of
the self-rated health status of hypertensives in Jamaica. The findings provide policy makers with
evidence that can be used to enhance policy formulation and intervention programmes.



Introduction
In 2007, statistics revealed that there were 2,682,120 Jamaicans (end of year population) [1], of

whom 22.4% had hypertension [2]. A study conducted in 2007/2008 on Jamaicans between 15
                                               310
and 74 years of age found that 25% of population had hypertension as well as obesity [3]. This

denotes that between 1 in 5 and 1 in 4 Jamaicans are living with at least one chronic illness [2,

3]. In the 1950s, tuberculosis, heart diseases, nephritis, syphilis, pneumonia and influenza were

the leading causes of mortality in the Caribbean, and in the 1980s, a shift occurred which saw

cardiovascular disease, heart disease, malignant neoplasm, hypertension and diabetes being the

leading causes of death. Another shift was observed in the 1990s when malignant neoplasm,

cardiovascular disease, diabetes mellitus, ischaemic heart disease, other heart diseases and

hypertension were among the 10 ten leading causes of death. In 2007, hypertension stood as the

third leading cause of mortality in females and the 6th cause for males. Hypertension is not only a

silent killer; it is an epidemic and needs to be examined as such in the developing world.


       Globally, chronic diseases account for 60% of deaths, and this is as high as 80% in low-

to-middle income nations [4]. Jamaica like the rest of the developing world is experiencing an

epidemic in cardiovascular diseases, as they are the leading cause of mortality [5], but despite

this reality, obesity is the studied epidemic in the Americas, and not the face behind hypertension

[6]. While 11 to 21% of Latinos in the Americas are obese, obesity accounts for between 20 to

33 1/3% of the populations in Chile, Jamaica, Mexico, Peru and Venezuela [3, 5]. Hypertension,

on the other hand, increases exponentially in middle to late ages and accounts for more deaths in

the world as well as in developing countries, than obesity.


       Diabetes, cardiovascular disease, cancers, and hypertension are among the main causes of

death in the world except in South Asia and sub-Saharan Africa. The sedentary lifestyle of urban

dwellers explains much of the chronic illness in the world, and come 2030 with 80% of the

globe‘s population residing in cities compared to over 50% in 2008, more people will be

                                               311
expected to die from chronic diseases. Urban zones continue to attract many people and some of

them, being poor, will not be able to change their lifestyles (cigarette consumption, sugar, diet,

saturated fat and environmental factors) like the wealthy. While urban settings appeal to too

many people, the better financial pull factors that appear to people do not mean that they will

have less chronic illness. In fact, it is well established that there is a direct relationship between

poverty and chronic illness [7- 9], which suggests that those in the lower socioeconomic strata in

the developing world will in the future be vulnerable to more illnesses, and in particular chronic

diseases, despite urban-rural migration.


        In 1998, Forrester et al. [10], using hypertension as an indicator of the emergence of

chronic cardiovascular diseases, found that early blood pressure problems were virtually non-

existent in rural Africans, and were modest in Caribbean people. They noted, however, that in

recent times hypertension in Nigeria, Jamaica and the US has seen remarkably steep gradients. In

Jamaica [2, 3], as in Nigeria, hypertension is an important cardiovascular risk factor which

affects between 20-25% of the population [11]. Clearly, hypertension in Jamaica as well as some

nations in Africa is a silent epidemic [12], and while researchers have recognized this as the case

in the latter state, those in the former are still to admit this reality.


        Studies on hypertension have shown differences between areas of residence [13, 14],

stressors [15], diet [16], Western lifestyle [10], sex [17], measurement and treatment [18], and

educational level [19, 20], income [20] and advanced aging [21-23]. Since blood pressure was

measured for the first time in 1733 by Stephen Hales, many piecemeal studies have been

conducted on the matter. An extensive research of the literature unearthed no study on self-

reported hypertension that evaluates who hypertensives are, as well as modelling their self-rated

                                                    312
health status. In 2001, Swab et al. [24] stated that 3 in every 10 Jamaicans (ages 30+ years) had

hypertension, and in 2007 1 out of every 4 Jamaicans had the disease. The face of hypertension

is no longer middle-to-late ages in Jamaica, as the current paper found that 2.9% are young

adults (15-30 years).


         Chronic diseases are the next tsunami facing developing countries. The swelling

increases in those conditions, and in particular the high prevalence of hypertension which is a

predisposing factor for cardiovascular diseases [25, 26]; highlight the importance of a

comprehensive study of the face of the hypertensive person. This is no longer a silent epidemic,

as mortality figures indicate that a ‗red alert‘ needs to be sounded for hypertension among the

other chronic ailments in developing countries. If the ‗Rule of Halves‘ (half of those detected are

treated or controlled) holds true [27-29], hypertension requires an immediate assessment of the

sociodemographic characteristics and health status of its patients. Thus, the aim of this paper is

to elucidate information on hypertension and the socio-demographic profile of those with the

disease in a Latin American and Caribbean nation as well as to model self-rated health status of

hypertensive.


Methods and materials

Sample

The current paper used the 2007 Jamaica Survey of Living Condition (JSLC) dataset to carry out

the analyses. The 2007 JSLC was conducted in May and August of that year. The current paper

extracted a sub-sample of 206 respondents who indicated being diagnosed with hypertension

from a larger nationally cross-sectional survey of 6,782 Jamaicans. The JSLC was conducted by

the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN).
                                               313
       The PIOJ and STATIN are non-profit organizations focusing on data collection and

policy assessment, and they aid in the evaluation of government‘s social programmes including

census taking, among other issues. Funded by the central government, the organizations deliver

evidence-based information. Since 1989, the organizations have been collecting data on

Jamaicans in order to evaluate social programmes instituted by the government. The data is

collected by way of an administered questionnaire, and published in a document entitled the

Jamaica Survey of Living Conditions (JSLC). The JSLC is a modification of the World Bank‘s

Living Standards Measurement Study (LSMS) household survey [30].


       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 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 [30, 31].

The sample was weighted to reflect the population of the nation.


Measurement


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

Age group is a non-binary measure: children (ages less than 15 years); young adults (ages 15 to

30 years); other-aged adults (ages 31 to 59 years); young elderly (ages 60 to 74 years); old

elderly (ages 75 to 84 years) and oldest elderly (ages 85 years and older).
                                                314
Self-reported illness (or self-reported dysfunction): The question was asked: ―Is this a diagnosed

recurring illness?‖ The answering options were: Yes, Cold; Yes, Diarrhoea; Yes, Asthma; Yes,

Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No.


Self-reported 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) [32-34].


Social class: This variable was measured based on income quintile: The upper classes were those

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

classes quintiles 1 and 2.


Income is measured using total expenditure.


Analytic model


Using econometric analyses (multiple logistic regressions), Bourne and McGrowder [27]

modeled social determinants of health of rural Jamaicans. The chosen method allows for the

testing of many possible variables which account for health status, which was measured as a

binary variable. The literature has shown that health status can be dichotomized into good-to-

very good health status and poor-to-moderate health status [32-34]. Clearly, based on the

findings in the literature, care should be taken in where moderate health status is placed as

Bourne [34] opined that moderate health status is best fitted into good-to-very good health status.

Thus, for this study the dichotomization of health status was moderate-to-very good and very

poor-to-poor. Furthermore, the selected variables which used in this model building were based

                                                315
on the established evidence on social determinants of health. Some modifications were made to

Bourne and McGrowder‘s model as not all the variables which emerged in that model were

applicable to the current work. In this model building, the variables were entered in block from

which the significant ones emerged as factors which account for moderate-to-very good health

status of hypertensive in Jamaica.


Statistical analysis


We used the SPSS computer statistical package, Version 16.0 (SPSS Inc; Chicago, IL, USA),

and STATA. Cross tabulations were performed in order to examine demographics, health, and

particular variables, and where 33.3% of the cells are less than 5 data vales, Fisher exact test was

used instead of Chi-square. Multiple logistic regressions were used to analyze possible

explanatory variables (health care-seeking behaviour in the last 4weeks, health insurance

coverage, medical expenditure, marital status, income, area of residence, sex, household head

and age) of self-rated health status. The results were presented using β coefficients, Wald

statistics, and Odds ratio, with a confidence interval of 95% (CI 95%). The predictive power of

the model was tested using the Omnibus Test of Model, and Hosmer & Lemeshow [36] was used

to examine goodness of fit of the model. In order to develop accurate tests of statistical

significance, the researchers used SUDAAN statistical software (Research Triangle Institute,

Research Park, NC; 1989), adjusted for the survey‘s complex sampling design [37]. A p-value <

0.05 was selected to indicate statistical significance. The final model was based on those

variables that were statistically significant (p < 0.05). Categorical variables were coded using the

‗dummy coding‘ scheme.



                                                316
       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. The final decision

on whether to retain was based on the variables‘ contribution to the predictive power of the

model and its goodness of fit [38].


Results
Table 12.1 presents information on the sociodemographic characteristics of the sample, illness,

health care utilisation, and health care-seeking behaviour. The sample was 206 respondents

(mean age = 62.5 years ± 16.8 years). Twenty-eight percent of respondents had health insurance

coverage (private, 8.3%). The majority of the respondents indicated fair self-rated health status

(44.2%) compared to 26.7% who said at least good (very good, 4.9%) and 29.1% who mentioned

at least poor (very poor, 3.9%). Most of the sample purchased the prescribed medication

(70.2%), and 3.9% had been involved in an accident in the last 4weeks. The preferred health care

utilisation of the sample was private health facilities (including hospitals, 55.2%).

Predominantly, the face of hypertension in Jamaica was elderly (60+ years, 60.2%). The average

number of visits to medical practitioners(s) in the last 4 weeks were 1.3 (SD = 0.7); and the mean

length of illness of the sample was 24.8 days (SD = 85.3 days). The mean cost of private medical

expenditure (USD 15.54± 36.95) was 3.7 times more than that for public medical expenses – (US

$1.00 = Ja. $80.47, in 2007).


       Table 12.2 examines sociodemographic characteristics and health care utilisation by self-

rated health status. A significant statistical association emerged between area of residence and

self-rated health status (χ2 = 24.69, P = 0.002, contingency coefficient = 0.33).



                                                317
       Table 12.3 presents information on sociodemographic characteristics and health care

utilisation by population income quintile of sample.

       No significant statistical association existed between self-reported illness and self-rated

health status (χ2 = 2.98, P = 0.562); health care-seeking behaviour and population income

quintile (χ2 = 5.49, P = 0.241) as well as between health care-seeking behaviour and sex (χ2 =

0.072, P = 0.788).

       Table 12.1 presents information on the health care-seeking behaviour of people in

different marital statuses and sex of respondents. Married people had sought the most medical

care (42.1%) in the last 4weeks, compared to never married people (36.4%) and other social

partnerships. Married men were 2.2 times more likely to have visited a health care practitioner in

the last 4 weeks compared to never-married men.

Multivariate analyses

       Using logistic regression analyses, one variable emerged as a statistically significant

factor of the self-rated health status of hypertensive Jamaicans (Table 12.3): area of residence

(urban: OR = 4.15, 95% CI =1.44 – 11.97; other towns: OR = 3.47, 95% CI = 1.23 – 9.78). The

model had statistically significant associative power (Model χ2 = 32.6, P = 0.003; Hosmer and

Lemeshow goodness of fit χ2 = 9.6 (8), P = 0.8), and it correctly classified 75.1% of the sample

(correctly classified 93.4% of those who self-rated their health as moderate-to-very good and

31.6% of those who self-rated their health as poor-to-very poor).

Discussion

Diabetes mellitus, cardiovascular diseases and neoplasm are among the leading causes of

mortality in the world, and more so in developing countries. While infectious diseases, low


                                               318
nutrient intake, and accidents continue to claim lives, chronic conditions are rising faster and will

account for more deaths in the future. Despite this reality hypertension, which is an important

cardiovascular risk factor, does not have a clear face, or factors which explain the self-rated

health status of this group. The current paper found that 2.5 times more females than males are

affected by hypertension; and the hypertensives are more likely to be married, elderly, to utilise

private health care facilities, to record moderate health status and to be in the lower

socioeconomic strata or rural dwellers. Most had sought medical care in the last 4 weeks, rural

hypertensives recorded the greatest very poor health status, and two variables emerged as

statistically significant factors of the self-rated health status of hypertensives in Jamaica.


       More Jamaicans have hypertension than any other type of chronic condition, yet more

extensive and comprehensive studies have been conducted on diabetes, heart disease, neoplasms

and arthritis. Traditionally, chronic diseases were viewed as middle-to-late life ailments, but

there is a growing decrease in the age of contracting those conditions. In this paper, the findings

concur with the literature that hypertension is a middle-to-later life ailment [20-23], as 97 out of

every 100 hypertensive persons were ages 31+ years and 60 out of every 100, 60+ years old.

What is evident is that 3 out of every 100 hypertensives are 15-30 years old, which supports the

changing image of hypertension, and how we research this fact. Studies have used 30+ years old

to examine chronic illness [24], which means that public health planning, relying on research,

will be under-planning for a critical cohort in the population.


       Public health planners use information from within and outside of their geopolitical

boundaries to enhance decision-making. While outside information affords a pertinent source of

data in understanding a phenomenon, this may not provide the correct knowledge about a

                                                 319
localized group with different socioeconomic, biological and environmental conditions.

Urbanization is well established in the literature as having a key role to play in human health

conditions such as hypertension, diabetes mellitus and other chronic ailments. While

urbanisation affects people‘s lifestyle in relation to the food they eat, where they work, the

surrounding environmental conditions and concern as to what they are exposed to, and their

sedentary lifestyle, with almost 50% of Jamaicans residing in cities, 6 out of every 10

hypertensive person in this nation dwells in urban zones.


       Clearly, low nutritional intake and poverty account for more hypertensive people than the

‗bad‘ elements of urbanization. In Jamaica, statistics reveal that 71% of poverty is in rural areas

[2]. Poverty means the incapacitation of financial resources, material deprivation, nutritional

deficiency and environmental degradation, which are associated with low health and higher

morbidity and mortality. Those realities form the core of the rationale for developing nations

having more deaths owing to chronic illness than the developed world. A study by Van et al. [7]

found that chronically ill people in the Netherlands were more likely to be poor, suggesting that

material deprivation is directly associated with particular health conditions. This research

concurs with Van et al.‘s work, and went further to find that poverty is associated with area of

residence, area of residence is related to illness, and by extension hypertension is higher among

rural respondents.


       Smith and Kington [39] postulated that money is able to buy health, from which it can be

extrapolated that poverty is associated with low health, increased morbidity and mortality. While

their argument is not entirely true, as health is not exchangeable (cannot be bought), money

provides access to better nutrition, lifestyle, choice of health care services, good sanitation and

                                               320
physical milieu, which otherwise is difficult for the poor to obtain without governmental or other

interventions. In this paper 40 out of every 100 hypertensive persons were poor compared to 37

out of every 100 in the wealthy social strata, which somewhat supports Smith and Kington‘s

postulation. So when it is said that chronic illness is becoming the next tsunami in developing

countries, the swelling increases in chronic illness, and in particular hypertension, are more

evident among those in the lower socioeconomic group in those societies.


       The push-pull factors associated with migration in developing countries are accounted for

by poverty, among other psychosocial conditions. Poverty hinders opportunity, life expectancy,

quality of life, economic progress, and brings nutritional deficiencies, and material deprivation,

which are the very reasons that pull rural residents to urban areas. In this research, urban

dwellers were 4.1 times more likely to record moderate-to-very good self-rated health status than

their rural counterparts; and those who live in semi-urban areas were 3.5 times more likely to

have greater moderate-to-excellent self-rated health status. Material deprivation in rural areas in

Jamaica is accounting for more morbidity and low health status, and clearly this will be a push

factor for urban-rural migration, despite the negatives of urban living.


       In this study no significant statistical relationship existed between health care-seeking

behaviour and population income quintile (social standing). This may appear paradoxical, as

financial deprivation should affect people‘s ability to afford health care, and rightfully so, but

since 2005, the Jamaican government has instituted free health care in all public hospitals except

the University Hospital of the West Indies, which means that money will influence the choice of

care and not health care demand. This therefore accounts for the greater percentage of

hypertensives having sought medical care in the last 4 weeks (68%) compared to the population

                                                321
(66%) [2]. Despite the removal of access fees from public health care institutions, there is a

preference for private health care utilisation.


       The preference for private health care utilisation among hypertensives is embedded in

long queues, low privacy, social treatment of patients, and milieu – the environment of public

health care facilities - that push people into private health care demand. The reality still exists

that public health care is the choice of 44 out of every 100 hypertensive Jamaicans, suggesting

that public health will be required to plan for this group. While the onset of hypertension

commences at 15 years in Jamaica, the non-children public health care system needs to cater to

this cohort, as their choices, lifestyle, demands and tolerance for disrespectful behaviour are not

the same as elderly or middle-aged adults.


       A public health concern must be the ratio of males to females with hypertension in

Jamaica. Swaby et al. [24] opined that there is a preponderance of females with chronic illness

and treatment for chronic illness, as compared to males, but this study found that the disparity

was as much as 2.5 females to 1 male (using hypertension). There was no statistical association

between the health care-seeking behaviour of male (67.2%) and female hypertensives (69.2%) in

Jamaica, which refutes Swaby et al.‘s [24] earlier, findings. Furthermore, the preponderance of

females to males with hypertension accounts for why this health condition is the third leading

cause of mortality in the former, and the sixth leading cause for the latter group.


       Hypertension is brought on by various stressors in lifestyle practices, and with the influx

of females into the labour force, top managerial positions, higher education and single parents,

they are now exposing themselves to the risk factors associated with those social roles that were

once dominated by males. Statistics reveal that the unemployment rate for females (14.3%) is 2.6
                                                  322
times more than that for males [40], indicating that unemployment, as well as other types of

social deprivation, are associated with greater hypertension among females. A study by Atallah

et al. [41] found that hypertension was greater among unemployed Caribbean people than those

who were employed, which also emerged in the current research. The unemployed females are

vulnerable to the dictates of males, and during this period there are the social challenges of child

rearing for mothers, the psychological stressors of unemployment, the psychological situation of

a dictatorial male, the material deprivation, dietary deficiency, and these influence the higher

blood pressure count seen in them, compared to males.


       The 21st Century has brought with it urbanization, lifestyle and role changes, and risk

factors related to chronic diseases for many Caribbean peoples, as well as the economic burden

of chronic illnesses such as diabetes mellitus and hypertension. For some time now Caribbean

governments have instituted data collection units to examine epidemiological data [42] on

prevalence, gender-specific population and age-specific mortality, but for the purpose of

effective public health policy planning more information is needed on the face behind

hypertension. The current work opens a comprehensive discussion and analysis of the

hypertensives in Jamaica, and while economic development is associated with economic growth,

increased employment of females in the labour force means lower male dependency, and while

money reduces material deprivation, the side effect is increased hypertension among this group.


       Interestingly, in this study there is a greater prevalence of hypertension among married

than non-married Jamaicans, but no difference in the self-rated health status between the groups.

According to Smith and Waitzman [43] ―many observers have theorized that married individuals

have access to more informal social support than do non-married individuals‖, which explains

                                                323
the social reality of a higher quality of life for married couples than ‗non-married‘ individuals

[44]. Furthermore, studies have shown that married people have a lower mortality risk in the

healthy category than the ‗non-married‘ [45], and this justifies why they take less life-threatening

risks [46]. Clearly, the benefits of marriage as put forward by other scholars do not provide

protection from hypertension among this cohort. In fact they recorded a greater prevalence of

hypertension than other marital states.


       Married people are more likely to seek medical care than non-married people, and this

accounts for the greater prevalence of hypertension among them. Although males do not like to

seek medical care, those who are married seek more care on the request of their wives which

accounted substantially for more of them visiting a medical practitioner in the last 4-week

period, compared to those who were never married. Smith and Waitzman [43] opined that wives

were found to dissuade 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. With more married people utilising health care services, this means that more non-

married Jamaicans would be ill but have not yet been diagnosed. If the ‗Rule of Halves‘ (half of

those detected are treated or controlled) holds true [27-29], the greater prevalence of

hypertension among married people is as a result of the greater half seeking more medical care

than non-married people. This speaks to a public health problem, as the treatment and prevalence

of hypertension is undoubtedly greater than the percentage currently planned for in the nation.


       There is a need to have more people seeking medical care, but this must be done in a

holistic way, as outlined earlier from the findings of this paper. The hypertension epidemic is

clearly highlighted as an important public health problem, but in order to effectively combat this

                                                324
reality,    poverty,   opportunity,   social   exclusion,   unemployment,     malnutrition,   disease

management, early testing and lifestyle practices must be coalesced by health planners. A study

as early as in the 1980s had stated that hypertension was the most prevalent chronic illness in the

West Indies [47] and in 2000 Barcelo [48] called it a silent killer, but researchers have continued

to examine its aetiology, management, programmes and even a study conducted in 2007/08 [3],

like its predecessors, used the standard age-specific, gender and education-specific conditions.


           The social explanations are rarely examined, and when done the traditional variables

(age, gender, and educational level) are examined by scholars, instead of the more demographic

variables such as marital status, area of residence, social class and health care utilisation, as well

as self-rated health status. This study is more comprehensive than other works and provides

research experts with social justification for the face behind hypertension in Jamaica. It should

be used to help public health practitioners, policy makers and governments to understand the

complexity of effectively implementing programmes to address the management of

hypertension, as well as other chronic illnesses. Poverty is the underlying challenge to greater

health in the population, despite the gains of economic development, growth, removal of health

care user fees, and social programmes.


Conclusion

In summary, the current evidence shows that hypertension has changed compared to the

traditional late life disease to middle-to-late years, and that it mostly affect females, rural

residents, married respondents and marginally inflect the poor more than those in the wealthy

social strata. And that the social determinants of self-rated health status are fundamentally

different from those identified in the literature on the population, or other sub-populations.

                                                  325
Conflict of interest
The authors have 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, 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.




                                                326
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    265.




                                           329
Table 12.1. Health seeking behaviour (in %) by marital status and sex




                                              330
Table 12.1. Sociodemographic characteristics of study population, n = 206
Characteristic                                                               n      %
Sex
   Male                                                                      58   28.2
   Female                                                                   148   71.8
Marital status
   Married                                                                   91   44.4
   Never married                                                             69   33.7
   Divorced                                                                   3    1.5
   Separated                                                                  5    2.4
   Widowed                                                                   37   18.0
Partner in household
   Yes                                                                       93   45.1
   No                                                                        12    5.8
   Did not respond                                                          105   51.0
Social assistance (PATH)
   Yes                                                                       41   19.9
    No                                                                      165   80.1
Area of residence
    Urban                                                                    47   22.8
     Semi-urban                                                              41   19.9
    Rural                                                                   118   57.3
Population income quintile
    Poorest 20%                                                              47   22.8
    Poor                                                                     35   17.0
    Middle                                                                   48   23.3
    Second wealthy                                                           38   18.4
    Wealthiest 20%                                                           38   18.4
Age cohort
    Young adults                                                              6    2.9
     Other aged adults                                                       76   36.9
     Young-old                                                               61   29.6
     Old-old                                                                 49   23.8
     Oldest-old                                                              14    6.8
Illness (self-reported)
   Yes                                                                      205   99.5
    No                                                                        1    0.5
Health care seeking behaviour
   Yes                                                                      140   68.6
    No                                                                       64   31.4
Health care utilization
   Public hospital                                                           35   22.7
   Private hospital                                                           7    4.5
   Public health centre                                                      34   22.1
   Private health centre                                                     78   50.7




                                                  331
Table 12.2. Sociodemographic characteristics and health care utilization by self-rated health status
                                                                                       Self-reported health status
                                                       Very good              Good                 Fair              Poor         Very poor
Characteristic                                           n (%)                n (%)               n (%)              n (%)         n (%)
Area of residence*
    Urban                                                    1 (10.0)           13 (28.9)           26 (28.6)          7 (13.5)         0 (0.0)
    Semi-urban                                               5 (50.0)             6 (13.3)          24 (26.4)           5 (9.6)        1 (12.5)
    Rural                                                    4 (40.0)           26 (57.8)           41 (45.1)         40 (76.9)        7 (87.5)
Population income quintile
    Poorest 20%                                              1 (10.0)             9 (20.0)          22 (24.2)         13 (25.0)        2 (25.0)
    Second poor                                              1 (10.0)             8 (17.8)          16 (17.6)          8 (15.4)        2 (25.0)
    Middle                                                   2 (20.0)           11 (24.4)           17 (18.7)         16 (30.8)        2 (25.0)
    Second wealthy                                           2 (20.0)             7 (15.6)          19 (20.9)          9 (17.3)        1 (12.5)
    Wealthiest 20%                                           4 (40.0)           10 (22.2)           17 (18.7)          6 (11.5)        1 (12.5)
Health care seeking behaviour
    No                                                       3 (30.0)           21 (46.7)           22 (24.7)         16 (30.8)        2 (25.0)
    Yes                                                      7 (70.0)           24 (53.3)           67 (75.3)         36 (69.2)        6 (75.0)
Age cohort
   Young adults                                              1 (10.0)              3 (6.7)            2 (2.2)           0 (0.0)         0 (0.0)
    Other aged adults                                        6 (60.0)           20 (44.4)           34 (37.4)         16 (30.8)         0 (0.0)
    Young-old                                                2 (20.0)           14 (31.1)           26 (28.6)         17 (32.7)        2 (25.0)
    Old-old                                                    0 (0.0)            6 (13.3)          22 (24.2)         16 (30.8)        5 (62.5)
    Oldest-old                                               1 (10.0)              2 (4.4)            7 (7.7)           3 (5.8)        1 (12.5)
Sex
    Male                                                     3 (30.0)             8 (17.8)          26 (28.6)         17 (32.7)        4 (50.0)
    Female                                                   7 (70.0)           37 (82.2)           65 (71.4)         35 (67.3)        4 (50.0)
Marital status
   Married                                                   3 (33.3)           16 (35.6)           43 (47.3)         25 (48.1)        4 (50.0)
   Never married                                             6 (66.7)           21 (46.7)           28 (30.8)         14 (26.9)         0 (0.0)
   Divorced                                                    0 (0.0)             1 (2.2)            1 (1.1)           1 (1.9)         0 (0.0)
   Separated                                                   0 (0.0)             1 (2.2)            1 (1.1)           2 (3.8)        1 (12.5)
   Widowed                                                     0 (0.0)            6 (13.3)          18 (19.8)         10 (19.2)        1 (12.5)
*P < 0.05


                                                                        332
Table 12.3. Sociodemographic characteristics and health care utilization by Population Income Quintile
                                                                                     Population Income Quintile
                                                      Poorest 20%         Second poor          Middle      Second wealthy      Wealthiest 20%
Characteristic                                           n (%)                n (%)            n (%)            n (%)              n (%)
Area of residence
    Urban                                                      3 (6.4)           8 (22.8)         5 (10.4)        10 (26.3)          21 (55.3)
    Semi-urban                                               7 (14.9)             3 (8.6)        16 (22.9)        12 (31.6)           8 (21.0)
    Rural                                                   37 (78.7)           24 (68.6)        32 (66.7)        16 (42.1)           9 (23.7)
Health care seeking behaviour
    No                                                      26 (56.5)           27 (77.1)        32 (66.7)        26 (70.3)          29 (76.3)
    Yes                                                     20 (43.5)            8 (22.9)        16 (33.3)        11 (29.7)           9 (23.7)
Age cohort
   Young adults                                                1 (2.2)            0 (0.0)          1 (2.1)           1 (2.7)           3 (7.9)
    Other aged adults                                       14 (29.8)           12 (34.3)        15 (31.3)        17 (44.7)          18 (47.4)
    Young-old                                               16 (34.0)           10 (28.6)        13 (27.1)         9 (23.7)          13 (34.2)
    Old-old                                                 12 (25.5)            8 (22.8)        15 (31.2)        11 (28.9)            3 (7.9)
    Oldest-old                                                 4 (8.5)           5 (14.3)          4 (8.3)           0 (0.0)           1 (2.6)
Sex
    Male                                                    12 (25.5)            8 (22.9)        10 (20.8)        14 (36.8)          14 (36.8)
    Female                                                  35 (74.5)           27 (77.1)        38 (79.2)        24 (63.2)          24 (63.2)
Marital status
   Married                                                  19 (40.3)           17 (50.0)        20 (41.7)        19 (50.0)          16 (42.1)
   Never married                                            18 (38.3)           10 (29.4)        13 (27.1)        12 (31.6)          16 (42.1)
   Divorced                                                    1 (2.1)            0 (0.0)          0 (0.0)           1 (2.6)           1 (2.6)
   Separated                                                   1 (2.1)             0(0.0)          2 (4.1)           2 (5.3)           0 (0.0)
   Widowed                                                   8 (17.0)            7 (20.6)        13 (27.1)         4 (10.5)           5 (13.2)
Self-reported illness
  Yes                                                      47 (100.0)         35 (100.0)         47 (97.9)       38 (100.0)         38 (100.0)
   No                                                          0 (0.0)            0 (0.0)          1 (2.1)           0 (0.0)            0 (0.0)
Health Insurance* – no coverage                             38 (80.9)           23 (65.7)        38 (79.2)        27 (71.0)          22 (57.9)
                      private                                  1 (2.1)            3 (8.6)          0 (0.0)         6 (15.8)           7 (18.4)
                      public                                 8 (17.0)            9 (25.7)        10 (20.8)         5 (13.2)           9 (23.7)
*P < 0.05


                                                                      333
Table 12.4. Logistic regression: Variables of self-rated health status

                                                                       Std.       Wald       Odds
 Variable                                          β Coefficient       error     statistic   ratio     CI (95%)
 Health seeking behaviour                                     -0.57     0.41        1.99       0.57   0.26 - 1.25

 Health insurance (1=Yes)                                     0.04      0.41        0.01       1.04   0.47 - 2.31

 Logged medical expenses                                      -0.36     0.19        3.41       0.70   0.48 - 1.02

 Never married (reference)                                                                     1.00
 Married                                                      -0.48     0.44        1.19       0.62   0.26 - 1.47
 Divorced, separated or widowed                               -0.75     0.55        1.87       0.48   0.16 - 1.38

 Lower class (reference)                                                                       1.00
 Middle class                                                 -0.09     0.49        0.03       0.92   0.35 - 2.39
 Upper class                                                   0.03     0.61        0.00       1.03   0.31 - 3.41

 Logged income                                                0.06      0.54        0.01       1.07   0.37 - 3.07

 Rural area (reference)                                                                         1.00
 Urban area                                                   1.42      0.54        6.92     4.15** 1.44 - 11.97
 Other town                                                   1.24      0.53        5.51      3.47* 1.23 - 9.78

 Sex (1= male)                                                -0.31     0.42        0.53       0.74   0.32 - 1.68

 Household head                                               -0.26     0.41        0.38       0.76   0.35 - 1.74

 Age                                                          -0.02     0.01        1.21       0.99   0.96 - 1.01
Model chi-square = 32.6, P = 0.003
Hosmer and Lemeshow goodness of fit χ2 = 9.6 (8), P = 0.8
-2Log Likelihood = 201.7
Nagelkerke R2 = 0.22
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 self-rated poor-to-very poor health status = 31.6%
*P < 0.05, **P < 0.01, ***P < 0.001




                                                        334
                                           13
Factor Differentials in contraceptive use and
demographic profile among females who had
  their first coital activity at most 16 years
versus those at 16+ years old in a developing
                      nation

Previous studies have examined age at first sexual intercourse and factors which determine
contraceptive use, but none have explored factors which determined method of contraception use
between females whose first coital activity began at 16+ years and those who started < 16 years
old. This research aims to bridge the gap in the literature by elucidating information on the
differentials in factors of contraceptive use between females whose first coital activity was < 16
years and 16+ years old as well as sociodemographic and reproductive health characteristics of
these respondents. More females whose first coitus was < 16 were currently in a sexual union
(83%) compared with 79% of those who began at 16+ years old. Factor differentials on
contraceptive use emerged between the two cohorts. These were social class (upper class: OR =
0.72, 9%% CI = 0.55 – 0.94) for those who begin < 16 years old but not for those 16+ and area
of residence (Rural area: OR = 1.26, 95% CI = 1.07 – 1.47) for the latter but not the former.
The current results are far reaching and can be used to guide new public health intervention
programmes.



Introduction


For decades, the developing countries like the developed nations have been experiencing

lowered age at first coital activity, which commences during the adolescence years. Young

people (ie. adolescents) continue to be engaged in sexual activities outside of marriage and even

                                               335
the statutes. The continuity of early sexual debut means that there are some health and social

matters that will face the society because of early sexual relationships. It is well documented that

early sexual initiation is associated with increased HIV, human papillomavirus (HPV), cervical

cancers, teenage pregnancy, unwanted pregnancies, abortion (safe and unsafe), and lowered

levels of education and financial opportunities [1-6]. While the developing nations have been

plagued by the HIV/AIDS epidemic and lowered age at sexual debut, the developed world is

more so experiencing lowered age at first sexual debut than the prevalence and incidence of

HIV/AIDS epidemic faced by the developing societies. A previous study established that the

lowering of the age of first coital activity has been so for the past 3 decades in developed nations,

and particularly in New Zealand [7]. Furthermore, Dickson et al.‘s work [7]; using a longitudinal

study of a cohort born in Dunedin in 1972-3, found that there were young people who were

engaged in sexual activities before 13 years old. This concurs with a five community

ethnographic study carried out by Chevannes in the Caribbean [8], which found that sex among

adolescents‘ starts as early as 14 years. The aforementioned early sexual debut in the Caribbean

and New Zealand is also obtained in the United States [9], and a group of researchers found that

almost 12 out of every 25 individuals aged 15-19 years in the United States reported having had

sexual intercourse at least once [10].


       In United States, the median age at first sexual debut was 17 years, which is higher than

that in Jamaica (15.0 years) [11, 12]. Like United States, New Zealand and Jamaica, some

African nations (such as Uganda, Kenya, Ghana, Tanzania, Zambia and Zimbabwe) had a

median age which is statistical the same, suggesting that premarital sexual behaviour is similar in

many developing and particular developed societies. A previous study conducted by Wilks et al

[13], using a national probability same survey of 2,848 Jamaicans aged 15-74 years, found that
                                                336
22 out of every 25 people aged 15-24 years have had sexual intercourse - 21 out of every 25

males aged 15-24 years and 19 out of every 25 females of the same age [13]. The sexual

expression and practices of young Jamaicans (aged 15-24 years) is embedded in the fact that 11

out of every 25 have sex at least once per week - 11 out of every 25 males and 10 out of every 25

females [13]. Statistics also showed that 2.6% of Jamaicans aged 15-24 years had a STI in the

last 12 months compared with 2.4% of Jamaicans aged 15-74 years old. Comparatively between

the United States and Jamaica, less Americans aged 14-22 years were sexually active compared

to Jamaicans aged 15-24 years [9, 13]. However, there were similarities between Jamaica and the

United States as the age at sexual debut for males and females was relatively close [9, 13],

suggesting congruency in sexual expressions.


       Using dataset for the 2002 Reproductive Health Survey in Jamaica [12], the mean age at

first coitus was 14.7 years (SD = 3.1, median age at first intercourse = 15.0, range = 13 – 16

years) [14], and the median age of first coitus among females aged 16-49 years was 16.0 years in

2001, this fell from 17.3 years in 1997 [12]. The rationales for using < 16 years and 16+ are (1)

the age of individual sexual consent is 16 years, and (2) the median age of first coitus among

females aged 15-49 years was 16 years.


       Inspite of public health campaigns to address (1) the lowering of age of sexual

intercourse, (2) HIV/AIDS among the population, particularly among adolescents and young

adults, (3) sexual promiscuity, (4) inconsistent condom usage, (5) unwanted pregnancies and (6)

better sexual practices in the world, particularly in Jamaica, the society has seen the continuous

erosion of values because the aforementioned matters continue unabated and there seems to be

no end in sight. Many developed nations such as New Zealand and the United States is

                                               337
experiencing the early age of sexual debut epidemic like Jamaica. Apart of the justification of

this public health challenge is that lifestyle practices, cultural values and expectation as well as

orientations which are changing in the 21st century.


       Although females in world have been living longer than males (life expectancy or healthy

life expectancy), which is the case in Jamaica, statistics revealed that the incidence of STIs

among female for 2007/2008 in Jamaica were greater for them than their male counterparts [13].

This is within context of increased public health education campaigns on sexual responsibility

and the rise of HIV/AIDS in the nation. Embedded in the incidence of STIs are the cultural

values, lifestyle, norms, beliefs and sexual practices of females, which will not easily change

because external agents such as health educators and professionals say that they are to do this.


       The literature on age at first sexual intercourse is extensive but recent and factors that

determine contraceptive use of female [2-7, 15, 16], but no research existed that examined

differentials in factors of contraceptive use between females whose first coital activity was < 16

years and 16+ years old.      Bourne et al. [16] eight factors were statistical associated with

contraceptive use among females aged 15-49 years. The factors were age (OR = 0.95, 95%CI =

0.98 – 0.99); social class (upper class, OR = 0.83, 95%CI = 0.73 – 0.95); area of residence (rural,

OR = 1.16, 95%CI = 1.02 – 1.32); currently pregnant (OR = 0.01, 95%CI = 0.00 – 0.02); had sex

in last 30 days (OR = 2.29, 95%CI = 1.95 – 2.70); number of sexual partners (OR = 1.85, 95%CI

= 1.57 – 2.17); age began using method of contraception (OR = 0.99, 95%CI = 0.98 – 1.00), and

crowding (OR = 1.4, 95%CI = 1.21 – 1.60). If research provides an understanding of issues in

our physical and social milieu, then, a study on the aforementioned is critical and timely as it

would provide insights into their behaviour, thereby allowing health practitioners and educator to

                                                338
better understand how to address the increasing HIV/AIDS virus and other public health

problems such as unwanted pregnancies and unsafe abortions. With previous studies having

demonstrated that early sexual activities are associated with increased HIV/AIDS infections,

cervical cancers and other health problems [1-6, 15], understanding early sexual activity (before

the statutory age 16 years in Jamaica) and post the statutory age will provide invaluable insights

into practices and measure that can be formulated to address the lifestyle of these individuals.


       This current paper, recognizing limitations of previous research on the aforementioned

issue within the context of the increased HIV/AIDS virus, unwanted pregnancy, abortions and

high fertility [17-19] coupled with the continuous lowering of age of sexual debut over the

decades, can add value to public health by studying factor differentials in contraceptive use

between females whose first coital activity was < 16 years and those 16+ years old as well as

their demographic profile. Such a research is timely and will guide policy formulation and

intervention programmes. The rationales for the study are primarily based on (1) females

vulnerability in contracting HIV/AIDS and other STI, (2) females being less economic

independent than their male counterparts, (3) the vetoing power of males over females‘

reproductive health choices in developing nations, (4) income inequalities between the genders,

and (5) the issue of survivability. This research aims to elucidate information on the differentials

in factors of contraceptive use between females whose first coital activity was < 16 years and

16+ years old and to provide a socio-demographic and reproductive health profile of these

individuals.


Methods

Sample (participants) and procedures
                                                339
A descriptive cross-sectional study was carried out by the National Family Planning Board

(Reproductive Health Survey or RHS). There are two sets of inclusion criteria, which are females

and ages. The eligibility criterion for age was 15 to 49 years at last birthday. In 2002, RHS

collected data on Jamaican men ages 15-24 years as well as women 15-49 years old. The current

paper extracted only females aged 15-49 years from 2002 Reproductive Health Survey (RHS)

dataset to carry out this research. The female sample for the 2002 RHS was 7,168 women of the

reproductive ages, with a response rate of 77.6%. Of those who responded (n=5, 565), 32.5% had

first coitus before 16 years old compared with 67.5% who began at 16+ years old. Thus, the

entire female sample for the 2002 RHS that responded to the survey was used for this study.


       Stratified random sampling was used to design the sampling frame from which the

sample was drawn. Using the 2001 Census sector (or sampling frame), a three-stage sampling

design was used. Stage 1 was the use of a selection frame of 659 enumeration areas (or

enumeration districts, EDs). This was calculated based on probability proportion to size. Jamaica

is classified into four health regions, which constitute particular parishes (there are 14 parishes).

Region 1 is composed of Kingston, St. Andrew, St. Thomas and St. Catherine; Region 2

comprises Portland, St. Mary and St. Ann; Region 3 is made up of Trelawny, St. James, Hanover

and Westmoreland, with Region 4 being St. Elizabeth, Manchester and Clarendon. The 2001

Census showed that Region 1 comprised 46.5% of Jamaica compared to Region 2, at 14.1%;

Region 3 at 17.6% and Region 4 at 21.8% [12].


       In stage 2, the households were clustered into primary sampling units (PSUs), and each

PSU constituted an ED, which in turn was comprised of 80 households. The previous sampling

frame was in need of updating, and so this was performed between January and May 2002. The

                                                340
previous sampling frame was in need of updating, and so this was carried out between January

2002 and May 2002. The new sampling frame formed the basis upon which the sampling size

was computed for the interviewers to use. Again, the sample was selected based on probability

proportion to size of the four regions, and interviewers were given particular ED(s) which they

exhausted in a clockwise manner.


        Stage 3 was the final selection of one eligible female from each sampled household and

this was done by the interviewer on visiting the household [12].


        The Statistical Institute of Jamaica (STATIN) provided the interviewers and supervisors,

who were trained by McFarlane Consultancy, to carry out the survey. The instrument

administered was a 35-page questionnaire. The data collection began on Saturday, October 26,

2002 and was completed on May 9, 2003. Prior to the date of the final data collection, pre-testing

of the instrument was conducted between March 16 and 20, 2002. Modifications were made to

the pre-tested instrument (questionnaire), after which the final exercise was carried out. Validity

and reliability of the data were conducted by many statisticians, statistical agency, and university

scholars before the data was used as the data are for national policy planning [12]. After which it

was released to the University of the West Indies, Mona, Data Bank for use by scholars. The data

was weighted in order to represent the population of female aged 15 to 49 years in the nation

[12].


Measures


Age at first sexual debut (or initiation or intercourse) was measured based on a respondent‘s

answer to the question ―At what age did you have your first intercourse? Crowding is the total

number of persons in a dwelling (excluding kitchen, bathroom and verandah). Age is the number
                                                341
of years a person is alive up to his/her last birthday (in years). Contraceptive method comes from

the question ―Are you and your partner currently using a method of contraception? …‖, and if

the answer is yes ―Which method of contraception do you use?‖ Age at which began using

contraception was taken from ―How old were you when you first used contraception? Area of

residence is measured from ―In which area do you reside?‖ The options were rural, semi-urban

and urban (1 = rural, 0 = otherwise; 1 = semi-urban, 0 = otherwise, and urban is the reference

group). Currently having sex is measured from ―Have you had sexual intercourse in the last 30

days?‖ (1=yes, 0 = otherwise). Education is measured from the question ―How many years did

you attend school?‖ Marital status is measured from the following question ―Are you legally

married now?‖, ―Are you living with a common-law partner now? (that is, are you living as man

and wife now with a partner to whom you are not legally married?)‖, ―Do you have a visiting

partner, that is, a more or less steady partner with whom you have sexual relations?‖, and ―Are

you currently single?‖ Age at menarche is measured from ―How old were you when your first

period started (first started menstruation)?‖ Gynaecological examination is taken from ―Have

you ever had a gynaecological examination?‖ (1 = yes, 0 = no). Pregnancy was assessed by ―Are

you pregnant now?‖ (1=yes, 0 = otherwise or no). Religiosity was evaluated from the question

―With what frequency do you attend religious services?‖ The options range from at least once

per week to only on special occasions (such as weddings, funerals, christenings et cetera)

(1=frequent attendance from response of at least once per week, 0 = otherwise). Subjective social

class is measured from ―In which class do you belong?‖ The options are lower, middle or upper

social hierarchy (1 = middle class, 0 = otherwise; 1 = upper class, 0 = otherwise; reference group

is lower class). Forced to have sexual relations was assessed from the question ―Were you forced

to have sex at your first intercourse?‖ and the options were yes, no, don‘t know and refused to

                                               342
answer (1= yes, 0 = otherwise). Age at first sexual debut, age at menarche, age at first

contraceptive use, and years of schooling were used as continuous variables. Early sexual debut

is having sexual intercourse before the statutory legal age to do so (in Jamaica, this is 16 years

old).


Statistical analyses


Data were entered, stored and retrieved using SPSS for Window, Version 16.0 SPSS Inc;

Chicago, IL, USA). Descriptive statistics were performed on particular sociodemographic

characteristics of the sample (frequency, mean, standard deviation (SD), and range). All metric

variables were tested for normality (age at first sexual debut, crowding, age, and years of

schooling). Where skewness was found to be less than 0.5, the variable was used in its current

form and a value more than 0.5 was normalized by natural log. Independent sample t-test was

used to examine differences in age at sexual debut between those who frequently attend churches

and those who infrequently visit churches and F-statistic was employed for age of sexual debut

and subjective social class (Table 13.4). Chi-square analyses were used to examine two non-

metric variables (Table 13.4). Pearson Product Moment correlation was used to evaluate

statistical association between age of first sexual intercourse and number of sexual partners for

the sample. Stepwise logistic regression analyses were used to fit the one outcome measure

(contraceptive use) by different sociodemographic as well as reproductive health variables. Thus,

only explanatory variables (i.e. statistically significant variables) are shown in Table 13.5. 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 [19]. To derive accurate tests of

statistical significance, we used SUDDAN statistical software (Research Triangle Institute,

                                                343
Research Triangle Park, NC), and this adjusted for the survey‘s complex sampling design. A p-

value < 0.05 (two-tailed) was used to establish statistical significance.


Results
Demographic characteristic of sample

Table 13.1 presents information on the demographic characteristic of the studied population by

age at first coital activity (< 16 years or 16+ years old). Of the studied respondents, 7.3% had

their first sexual intercourse at most 13 years old, 16.7% at most 14 years old, 32.5% at most 15

years old, 51.4% by at most 16 years, 92.6% by at most 20 years old and 99% by at most 26

years old. Twenty one percentages of the respondents had no sexual partner, 75.6% had one

sexual partner compared with 3.4% who had 2+ sexual partners.


        Table 13.2 highlights particular reproductive health characteristic of studied population

by age at first coital activity (< 16 years or 16+ years old).


        Table 13.3 displays information on methods of contraception Method of contraception

and when began using by age at first coital activity (i.e. < 16 or 16+ years old).


        Table 13.4 forwards information on particular demographic variables by subjective social

class of respondents controlled for by age at first coital activity (i.e. < 16 or 16+ years old).


        On examination of age at first sexual intercourse and number of sexual partners for the

past month and the former 3 months, a significant statistical correlation was found between (1)

age at first sexual intercourse and number of sexual partners in the last 4 weeks (rxy = - 0.034, P

= 0.011), and (2) age at first sexual intercourse and number of sexual partner in the last 12 weeks

(rxy = - 0.037, P = 0.006).

                                                  344
       A significant statistical difference was found among the subjective social classes and age

at first sexual intercourse (F = 187.4, P<0.0001). Females in the lower socioeconomic stratum

began having sex at 16.0 years (SD = 2.3) compared with 16.5 years (SD = 2.4) for those in the

middle class and 17.8 years (SD = 3.2) for those in the wealthy socioeconomic stratum.

However, no statistical difference emerged among the subjective social classes and number of

sexual partners (F = 2.23, P = 0.107).


       On average, crowding was 1.9 persons (SD = 0.30) among females who were in the lower

socioeconomic stratum compared with 1.8 persons (SD = 0.43) for those in the middle stratum

and 1.3 persons for those in the wealthy socioeconomic stratum – F-statistic = 252.03, P<0.0001.


       Females who frequently attend church begins having sex at 17.4 years (SD = 3.5)

compared with 16.4 years for those infrequent female church attendees (t-test = - 12.56,

P<0.0001).


Multivariate analyses


       Table 13.5 shows explanatory factors which account for contraceptive use among females

in Jamaica aged 15-49 years based on age at first sexual activity that the individual is classified

in (i.e. < 16 or 16+ years old).


Discussion

A previous study had that ―Experiences at sexual debut may be linked to reproductive health

later in life‖ [21, p. 1] and that the age of first sexual debut is associated with future reproductive

health outcomes [1-6]. The current works concurs with the literature, and provide detailed

information on the differences on demographic profile and factor differentials in contraceptive
                                                 345
use between the two cohorts (females aged 15-49 years who began having sexual intercourse <

16 years and those who started at 16+ years). This research found that females whose first sexual

intercourse happened before 16 years old were less likely to use a condom with a steady partner,

do Pap smear and gynaecological examination as well as utilize the pill as a method of

contraception, but they were more likely to be in the lower socioeconomic stratum, live in rural

areas, have a lower educational level, first sexual intercourse was forced, use injection as a

method of contraception, shared sanitary convenience, currently in a sexual relationship, sexual

partnerships in last 3 months and unemployed. Factor differentials on contraceptive use emerged

between the two cohorts. These were social class (upper class: OR = 0.72, 9%% CI = 0.55 –

0.94) for those who begin < 16 years old but not for those 16+ and area of residence (Rural area:

OR = 1.26, 95% CI = 1.07 – 1.47) for the latter but not the former. Embedded in those findings is

the fact that females who are in the upper socioeconomic stratum that commenced sexual

intercourse before 16 years are engaged in riskier sexual practices than those in the lower class.


       In Jamaica, statistics revealed that females are poorer and less employed compared with

males [22, 23]. This reality means that there is high economic dependence of females on males

for financial survivability, making young females within the lower socioeconomic stratum

having different reproductive health outcome than those in the wealthy socioeconomic strata

because of their socio-economic marginalized situation. Many of these females commenced sex

at an early age because of economic vulnerability, and so they are likely to be engaged high-risk

behaviours [21].


       On the other hand, in order to provide for themselves many females who are within the

lower socioeconomic stratum become involved with older men who expose them to the same risk

                                                346
of pregnancy, STIs, and HPV. With females in the lower socioeconomic stratum having more

people in a dwelling area compared with those in the other socioeconomic strata, they will turn

outside the household for financial assistance and oftentimes this is provided in visiting sexual

unions in which the males are older. In such unions, because females are in a socioeconomic

vulnerable position and by extension poorer and marginalized, males are able to dictate many

things including reproductive health choices. Females, therefore, in those income class will bear

children as an economic flows and/or some will have unsafe abortions, but those in the upper

class are able to carry out safe abortions compared with those in the lower class because of

access to financial resources, and where they consider their lives. Thus, the aforementioned

arguments justify female who began sexual intercourse at most 15 years who are more likely to

be in the lower class, dwell in rural areas, unemployed, have multiple sexual partners and less

educated were more likely to be engaged in sexual relationships, and forced into sexual

activities. Their economic vulnerabilities account for the rationale of using fewer condoms as a

method of contraception because this is vetoed by the male.


       Money is important to women, but the risky sexual behaviour of upper class females

whose first sexual activity begins before 16 years old is not for the money as those in the lower

socioeconomic strata. The high risk sexual behaviour among upper class females whose first

sexual intercourse was before 16 years, suggests that many of them would have abortions, STIs

and even HPV because of their lifestyle practices. The work also showed a negative correlation

between number of sexual partners and age at first coitus, indicating that younger females are

more promiscuous and that this changes with age at they move into stable sexual unions. Simply

put the adolescence years are about fun, frolic, sexual freedom, sexual expression, inconsistent


                                              347
condom usage and sexual carelessness, which seems to continue even in the adult years among

wealthy females.


       Even though money is important to particular reproductive health outcomes (such as safe

abortions), early sexual intercourse comes with less likeliness of a method of contraception,

which is because of ignorance. It was revealed from the findings that those females who

commenced sexual intercourse at older ages were more likely to use a particular method of

contraception (pill) than condoms that expose them to STIs, HPV, HIV/AIDS and pregnancies,

which is in keeping with the literature from other nations [2, 21, 24,25]. Embedded in this

finding is the influence of knowledge of contraceptive with age, and not money. While money is

associated with employment and other socioeconomic benefits, it is not responsible for lower

method of contraception among Jamaicans females.


       Rural poverty in Jamaica is about twice urban poverty, with more people residing in rural

areas and a sex ratio that is greater for females than males [22, 26], if money matters, then rural

females who begins having sexual intercourse at 16+ years would not be 1.3 times more likely to

use a method of contraception compared with those in urban areas. Or, those in those whose

families are in the wealthy strata would be more likely to use a method of contraception

compared with those in the lower socioeconomic stratum, but the reverse is true in Jamaica.

Embedded in these findings are inexperience and the euphoria surrounding first sexual activity as

well as the age of the initiating partner that account for lower contraceptive use based on age at

first sexual coital activity than money. According to Gomez et al. [21], ―Sixty-five percent of

women reported sexual initiation with a partner younger or less than 5 years older, 28% with a

partner 5 to 10 years older, and 7% with a partner 10 or more years older‖, and in Jamaica a

                                               348
study revealed that many young women began their sexually initiation with men at least 5 years

older than them [12]. Embodied here is an understanding of the lifestyle of adolescents in

regarding to sex, and how older men can expose them to sexually transmitted infections. The

media continues to glamorize sex and sexuality, which are capturing the attention and practices

of young people. The young females are culturized in sex, and this they see to explore as they

become cognizant of sex during the adolescent years when there is growth and development of

the body.


       Even with age, knowledge, exposure and high accessibility to method of contraception

and low cost of contraceptives, inconsistent condom use and condom use is low among Jamaican

women aged 15-49 years. The current work revealed that 42.5% of those who began having sex

before 16 years old currently use a condom consistently with their steady partner and the figure

was 2.5% more among those who started at 16+ years old. This finding provides evidence of the

difficulty to change lifestyle practices as although the majority of people in Jamaica have been

exposed to public health education and intervention programmes [12], this has not significantly

change their sexual behaviour as the age of sexual initiation continues to fall as well as an

increase prevalence of HIV/AIDS among the populace. Abel-Smith is correct, therefore, when he

claimed that people are prisoners of their lifestyle [27], suggesting that values, customs, norms

and early socialization are difficulty to change, but that it is still possible over time. Apart of the

Caribbean culture is that a woman is not a woman without bearing children, like the man [8, 28].

Such an orientation and culture, implies and dictates a diet of sex, inconsistent contraceptive use

and risky sexual practices.




                                                 349
       School is an agent of socialization, in which people are provided the tools of

socioeconomic survivability, has become a place of indirectly promoting sex through sexual

education and peers of different socioeconomic situations and background. The current findings

revealed that 43% those whose first sexual activity started before 16 years old began using a

method of contraception during school compared with 7% who started at 16+ years. With there

being an inverse association between age and contraceptive use [4-7, 10, 16], it can be deduced

that high contraceptive use is associated with sexual activities. Like Gomez [21], this study

recognizing the importance of age and gender-based power differentials between the sexes

regarding sex note that delaying sexual debut must understand those differences as well as the

educational system.


       Dickson [7] opined that adolescent sexual behaviour is influenced by social factors. It can

be deduced from Dickson‘s work that educational system is able to change sexual practices and

particular reproductive health outcome. From the current research, the educational system has

modified the use of contraception, but not increasing the age at sexual debut. During school,

children are not only exposed to health and reproductive health education and subjects‘ trainings,

they are interfacing with other children of different socialization, lifestyle, values and

orientations. With the glamorization of sex in the media, on cable television, many children are

exposed to a diet of sex, and some will seek to practice this while attending school. This is

reinforcing sex, sexuality and orientation of sex that is even covertly reinforced with

reproductive health education in schools.


       Based on Bourne et al.‘s work [29] that ―Health education and health promotion are

driven based on understanding lifestyle practices of a population‖ [29], the current findings

                                               350
provide some critical information that can be used for a new thrust into public health intervention

programmes in the future that can be used to modify current practices. As formal educational is

not able to change the sexual practices and/or reproductive health behaviour of females because

more than 55% of the sample have tertiary level education (or have attained this level) compared

to only 9.6% who have at most primary level education. The social and cultural values,

orientation, beliefs, and expectations of the society are such that formal education is not

modifying the lifestyle practices that public health specialists and behaviouralists would want to

change.


       Clearly, a public health problem that emerged from the current paper is that 1.5 times

more females who had sex before 16 years were sexually assaulted compared to those who began

at 16 years and older. Outside of the obvious that many early sexual encounters among females

at most 16 years is as a result of rape, the perpetrators are normally friends, family members

and/or acquaintances who carry out these acts against the physical vulnerable adolescents and

children [30, 31]. Such abase leave an indelible psychological scar for the adolescent and Lowe

et al. [32] posited that this leaves immense psychological trauma which are sometimes are

suicidal. Another psychological matter which is a consequence of sexual assault of is aggression

on the path of the victim [33], suggesting that the sexual appetite of Jamaican males is exposing

female adolescent and children to future psychological traumas as well as reproductive health

problems.


       This matter becomes even more complex when the adolescent is found to be pregnant,

family is poor, lowly educated, unemployed and religious. One researcher found positive

statistical correlations between poverty and not seeking medical care (R = 0.576), and poverty

                                               351
and unemployment (R = 0.48) [34], indicating that economic vulnerable adolescents and their

families are likely to see the young female doing unsafe abortion, carrying the pregnancy to term

and going into depression and/or other psychological traumas because of socioeconomic

deprivation. No or little access to money means less choices including abortion for females who

become pregnant as a result of rape and the economic power of the perpetrator is also able to

change the outcome of criminal conviction. Thus public health practitioners need to recognize

money and power as influencing reproductive health, and how these may retard self autonomy of

the females, particularly those young females who are from low socioeconomic background. The

socio-economic consequences of poverty, low educational attainment, self-esteem and social

isolation can, therefore, influence public health intervention programmes [36], making it difficult

for public health practitioners to be effective in meeting their objectives without addressing those

inadequacies and the social structure in the society.


       Religiosity is associated with better sexual practice as it increased the age of first sexual

intercourse, which concurs with the literature [20, 37, 38]. The church which is a part of the

social structure is delaying sexual intercourse among Jamaican females aged 15-49 years by one

year, which speaks to the embedded sex culture and the difficulty in changing this practice

without structural and cultural changes, over time. Again this reinforces the fact that delaying

early sexual behaviour is also a future good as people will continue bad practices if they start

early in life. Research evidence demonstrates that the religiosity network in which the adolescent

involved as well as the friends‘ religious positively lowers age at first coital activity [39]. With

the number of churches in Jamaica, particularly in the lower socioeconomic areas, it is

paradoxical that age at first sexual intercourse continues to fall. Some of those issues can be

explained by the economic deprivation in inner-city communities and the culture values, beliefs
                                                352
and customs within the society as well as the sub-cultures and countercultures on sex and

sexuality.


       Clearly, the culture in inner-city communities coupled with crowding are fostering early

sexual intercourse because those in the lower socioeconomic stratum commenced sexual

intercourse on average at 16 years compared with 16.5 years for those in the middle class and

17.8 years among those in the wealthy stratum. It can be deduced and extrapolated from those

figures that men are using the economic vulnerability of young females against them, and this is

resulting in those females becoming engaged in transactional sex. They are exchanging sex for

good, commodities and other support things for sex from older men. Although the same is not

the case for females in the wealthy socioeconomic stratum, those who starting having sex before

16 years old are currently engaged in risky sexual behaviour. This speaks to the early lifestyle

practices, values which were garnered during that period and its bearing on current practices.

Thus, old habits are difficult to change. This is the difficulty that public health practice need to

tackle, those who began having sexual intercourse at most 15 years old as they are high sex risk

takers even in the adults years. One study demonstrates this aptly as the researchers found that

―…children are significantly more likely to become sexually active before age 14 if their mother

had sex at an early age and if she has worked extensively‖ [40]


       Previous studies have demonstrated that many of the cases of sexual assault and rapes are

perpetrated by acquaintances. With the crowding being an issue in inner-city communities (or

lower socioeconomic areas), a number of the sexual initiations occur as a result of this fact. The

adolescents are sometimes gullibly encourages to become involvement in sexual activities with

family members, household members and friends. With the crowding in inner-city communities

                                                353
means that many of the rapes are perpetrated by non-household members by acquaintances in the

area. The next issue is the associations of the adolescents, and whether those networks are among

religious members or non-religious individuals. Hardy and Raffaelli [38] provide an explanation

for the previously mentioned situation. They opined that religiosity delay the transition of

adolescents venturing into sexual activity, suggesting that religion is a social control. It follows,

therefore, that adolescents who are friends of non-religious individual would not have this level

of control and will initiate sexual intercourse early. The peer group influences the reproductive

health outcome of people, particularly children and/or adolescents as well as adults [41] and

increases early sexual practices which in this case justify future sexual behaviour of adults. It is

this explanation why public health practitioners need to address social institutions in thwarting a

campaign that will foster better sexual practices of adults as early as childhood and during their

adolescence years.


       The traditional approach to health behaviour modification was to give people knowledge

about a particular issue, practice or happenings within their sociophysical milieu and instruct

them into a new path [42]. According to one group of researchers, in 2009, ―Knowledge about

the prevalence of sexual risk behaviour (SRB) in adolescence is needed to prevent unwanted

health consequences‖ [43], and this justifies the continuation of poor sexual practices in the

future. Such an argument implies that lifestyle behaviour is easily changeable, which is the

fartherest from the reality. This is captured in the current work which showed that educational

attainment is not associated with usage of contraceptives. On the contrary, those in the wealthy

income stratum had the greatest prevalence of tertiary level education, yet those who started

having sexual intercourse before 16 years were less likely to use a method of contraception.

Thus, education cannot easily change peoples‘ behaviour and so it is about knowledge on a
                                                354
particular issue. This is capture in the Wilks et al.‘s work [13] which found that in 2002 78.3% of

Jamaicans aged 15-74 years used a condom with their main partner and this fell to 43.1% in 2008

although the percentage of Jamaicans with secondary-to-tertiary level education had increased,

with 11.3% having had tertiary level training. They also found that more people were engaged in

visiting and/or single unions compared with married and common law, and the more people had

2+ sexual partner in 2008 (24.4%) compared to 2000 (23.0%).


       The current work that showed that adult women who began having sex at 16+ years were

more likely to use a method of contraception than those who started before 16 years, this

suggests that risky sexual behaviour which commenced early in life is likely to continue into

adulthood. Again, people are prisoners to their culture, social structure, values, beliefs, and

socialization. Cohen, Scribner and Farley [44] developed a model for behaviour change using

structural modeling which addresses physical structures, social structures, cultural and media

messages. Like Cohen et al. [44], Bourne et al. opined that health promotion for Jamaicans must

include social, economic, and lifestyle choices [29]. In the previous works, the authors

recognizing the complexity of humans have coalesced a multidimensional apparatus to address

behaviour change and not simply imparting knowledge or by formal education. Although a group

of scholars found that the women‘s level of education and that of her spouse and age determine

contraceptive use, this concurs and disagrees with those findings [45, 46].


       For the current work, age is a factor in contraceptive use, which is supported by the

literature [16, 45], but the same cannot be said about education. Education is not changing sexual

practice as it relates to contraceptive use among Jamaica females, despite its provision in

imparting knowledge and behaviour medications. People are not barrels in which they are fed a

                                               355
diet of information from external sources such as health educators to want them to carry out a

particular action or cease one because the social and environmental factors influence behaviour,

particularly contraceptive use [47]. Hogan et al. [47], provided some clarifications to the social

and other factors which are associated with contraceptive use, when they postulated that, ―Social

and environmental variables were found to affect contraceptive preparedness at 1st intercourse

only, and not subsequent initiation of contraceptive practice‖ [47]. Outside of this clarification, it

is evident that the culture, physical milieu, values, and beliefs impact on people behaviour and

this include education, but that this is not the case among female Jamaicans aged 15-49 years old

whether sexual initiation was < 16 years or 16+ years old. There is cultural conflict among

female Jamaicans, the health care system and the health care educators because the symbols of

the culture and ways of life are not supported by the health care educators, particularly related

with sexual practices, sex and reproductive health matters. Embedded in the current findings is

the value of the social environment in which these females live and grow, which fashion their

cultural development, identification and belief system. Those are the reasons why ―Morally

unacceptable policies designed to pressure or compel people to limit their fertility have been

shown to be unnecessary and thus have been abandoned, except in China‖ [48] as well as being

ineffective in behaviour medication, and any such similar public health intervention programmes

that used force, moral suasion or dictatorial stance.


Conclusion

Early sexual initiation is influencing future health and reproductive health outcomes among

Jamaican women aged 15-49 years old. Those outcomes include more coital activity,

involvement in sexual unions, and less contraceptive use. Despite reproductive health education

                                                 356
programmes in Jamaica, the culture is clearly retarding good reproductive health practices and

sexual lifestyle. In Jamaica, although fertility is lower and educational advancement is greater in

urban than in rural areas, rural females whose first coitus began at 16+ years were more likely to

use a method contraception compared with their urban counterparts. Clearly, there is a lifestyle

change occurring among females in rural areas which needs examination, and equally so is the

risky sexual practices of affluent females who started having sex before 16 years old.


       With the global economic downturn, sexual autonomy of female Jamaicans will be

further reduced, particularly those in the lower socioeconomic stratum, unemployed, uneducated,

and young because males will now have greater vetoing powers over sex, sexuality and

reproductive health matters. Public health practitioners have not begun to address those realities

in the communities and human rights of women will be thwarting because money is important in

survivability. Sexual rights of women cannot be supported by merely ascribing it to them or

penning social constructions in this regards in must be supported by economic independency.

While legislation and policies that promote sexual autonomy are good, the reality is money is

power, and with the economic downturn in the Jamaican economy there will be greater

promiscuity as women seek more assistance in sexual relationships, which is embedded in Wilks

et al.‘s work which showed an increase in visiting unions and number of sexual concurrent

partners between 2000 and 2008.


       Because money is associated with better education, physical milieu, social opportunities,

good nutrition and sexual autonomy; to asked the question ―If women are so keen to avoid

pregnancy, why do they not use a method of contraception?” [49] is to deny people of their

social environment and the role of money in it. There will be in social justice in society that does

                                                357
not understand the factors which are associated with sexuality, rights and sexual justice; and the

role of money in influencing health and reproductive health matters. It means that apart of the

sexual lifestyle of females is justified by the economic situation in the communities [50, 51],

nation and the world. Such social and financial environments means that public health must

begin to address the new reality as all the gains that have been accomplished in past decades will

be erodes because of the increased economic vulnerability of peoples and economic

marginalization of the poor, particularly among young, uneducated, and unemployed females.


       In summary, delaying age at first sexual intercourse influences contraceptive use, by

increase methods of contraception. It also fosters good sexual practices in the future. Clearly, the

reproductive health problems in Jamaica are structurally driven which care embedded in the

cultural values that make it difficult for public health practitioners to address without including

those issues in health education, communication and intervention programmes. Because people

are sexual being, sex will always be a part of their social existence and an issue that cannot be

left unaddressed by public health policies makers within the current findings and the global

economic downturn. There is a need for structural changes in developing as well as developed

nations to address many reproductive health matters. The factors of method of contraception are

not the same across the age cohort at which a female began having sexual intercourse, and they

are also some different to those of women in the reproductive ages 15-49 years old. The findings

which emerged from the current results are far reaching and can be used to guide new public

health intervention programmes.


Disclosures


The authors report no conflict of interest with this work.
                                                358
Disclaimer
The researchers would like to note that while this study used secondary data from the
Reproductive Health Survey, none of the errors in this paper should be ascribed to the National
Family Planning Board, but to the researchers.

Acknowledgement

The authors 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 (2002 Reproductive
Health Survey, RHS) available for use in this study, and the National Family Planning Board for
commissioning the survey.




                                             359
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                                          362
Table 13.1: Demographic characteristics of studied population
                                                Age at first coital activity
                                            < 16 years old        16+ years old         χ2, Pvalue
Characteristic                                 n = 1811              n = 3754
                                                 n (%)                 n (%)
Area of residence                                                                   19.48, < 0.0001
   Urban                                            265 (14.6)          668 (17.8)
   Semiurban                                        470 (26.0)         1156 (30.8)
   Rural                                          1076 (59.4)          1930 (51.4)
Educational level                                                                    195.95, < 0.0001
  Primary and below                                 225 (12.4)            306 (8.1)
  Secondary                                         832 (45.9)         1096 (29.2)
  Tertiary                                          739 (40.8)         2352 (62.7)
Shared sanitary convenience                                                           40.36, < 0.0001
   No                                             1380 (76.9)          3101 (83.0)
   Yes                                              414 (23.1)          636 (17.0)
Social class                                                                         182.61, < 0.0001
   Lower                                            603 (33.3)          771 (20.5)
   Middle                                           839 (46.3)         1560 (41.6)
   Upper                                            369 (20.4)         1423 (37.9)
Employed                                                                              71.05, < 0.0001
   No                                             1158 (63.9)          1938 (51.6)
   Yes                                              653 (36.1)         1816 (48.4)
Frequent church attendance                                                            47.40, < 0.0001
   No                                              1289(71.2)          2799 (62.0)
   Yes                                              522 (28.8)         1714 (38.0)
Partner main source of financial                                                          0.001, 0.979
support
   No                                                89 (42.6)            93 (42.3)
   Yes                                              120 (57.4)          127 (57.7)
Age at first coital activity mean (SD)          14.1 yrs (1.1)      29.8 yrs (26.3) t=-40.01, <0.0001
Current age of respondents, mean (SD) 30. 5 years (9.2 yrs)          33.1 yrs (8.4) t=10.27, <0.0001
Crowding, mean (SD)                        1.8 persons (0.42) 1.7 persons (0.5)        t=9.02,<0.0001
SD denotes standard deviation




                                               363
Table 13.2: Particular reproductive health characteristic of studied population
                                                                     Age at first coital activity
                                                                   < 16 years old     16+ years old         χ2, Pvalue
Characteristic                                                      n = 1811             n = 3754
                                                                      n (%)                n (%)
Want to be pregnant                                                                                          0.005, 0.943
   No                                                                  228 (12.6)           486 (12.9)
   Yes                                                                   83 (4.6)             172 (4.6)
   Missing                                                           1500 (82.8)           3096 (82.6)
Had sex (in last 30 days)                                                                                     9.71, 0.002
   No                                                                  573 (31.6)          1318 (35.1)
   Yes                                                               1238 (68.4)           2436 (64.9)
Forced to have sex (ever)                                                                                 64.19, <0.0001
   No                                                                1321 (73.1)           3072 (82.0)
   Yes                                                                 485 (26.9)           675 (18.0)
Forced to have sex (first time had coital activity)                                                       82.18, < 0.0001
   No                                                                1491 (82.9)           3367 (90.5)
   Yes                                                                 309 (17.1)             356 (9.5)
Currently pregnant                                                                                           0.481, 0.488
   No                                                                1725 (95.3)           3592 (95.7)
   Yes                                                                   85 (4.7)             161 (4.3)
In sexual union                                                                                           16.22, < 0.0001
   No                                                                  310 (17.1)           789 (21.0)
   Yes                                                               1501 (82.9)           2965 (79.0)
Currently used method of contraception                                                                        2.98, 0.084
   No                                                                  601 (34.3)          1302 (36.0)
   Yes                                                               1151 (65.7)           2312 (64.0)
Frequency of condom usage
    With steady partner                                                                                       8.58, 0.073
       Always                                                          221 (42.5)           463 (45.0)
       Most times                                                      259 (49.8)           493 (47.9)
       Seldom                                                            29 (5.6)              66 (6.4)
       Never                                                              2 (0.4)               1 (0.1)
       Never had a steady partner                                         9 (1.7)               7 (0.7)
       Missing                                                       1291 (71.3)           2724 (72.6)
    With non-steady partner                                                                               22.23, < 0.0001
       Always                                                           93 (18.1)           111 (10.8)
       Most times                                                        41 (8.0)              60 (5.9)
       Seldom                                                             0 (0.0)               2 (0.2)
       Never                                                             30 (5.8)              59 (5.8)
       Never a non-steady partner                                      351 (68.2)          7923 (77.3)
       Missing                                                       1296 (71.6)           2730 (72.7)
Number of sexual partners in last month – mean (SD)              0.7 person (0.7)     0.7 person (0.8)      t=1.78, 0.076
Number of sexual partners in last 3 months - mean (SD)           1.1 person (1.4)     0.9 person (1.2)      t=3.02, 0.003




                                                     364
Table 13.3: Method of contraception, when began using, gynaecological and Pap Smear
examination by age at first coital activity (ie. < 16 or 16+ years old)

                                                       Age at first coital activity
                                                       < 16 years     16+ years old    χ2, Pvalue
Characteristic                                             old
                                                        n = 1811         n = 3754
                                                          n (%)            n (%)
Contraceptive method used (or using)                                                      25.22, 0.009
Female sterilization (tubal ligation)                       216 (11.9)    388 (10.3)
Implant (Norplant)                                             5 (0.3)       6 (0.2)
Injection                                                   251 (13.9)    380 (10.1)
Pill                                                        274 (15.1)    706 (42.9)
Morning after pill (ECP)                                       1 (0.1)       2 (0.1)
IUD/coil                                                      20 (1.1)      42 (1.1)
Withdrawal                                                    35 (1.9)     100 (2.6)
Rhythm, calendar                                               5 (0.3)      19 (0.5)
Condom                                                      447 (24.7)    969 (25.8)
Foaming tablets/cream/jelly                                    1 (0.1)       0 (0.0)
Other                                                          0 (0.0)           5(

Were you in or out of school, when you began using
method of contraception
 In                                                          33 (41.8)       4 (6.8)
 Out                                                         32 (40.5)     46 (78.0)
 Both                                                         14 (0.8)      9 (15.3)
Gynaecological examination                                                                 4.57, 0.033
  No                                                        420 (61.9)   1272 (57.3)
  Yes                                                       258 (38.1)    947 (42.7)
Pap Smear                                                                              22.73, < 0.0001
  No                                                       1474 (81.4)   3423 (75.8)
  Yes                                                       337 (18.6)   1090 (24.2)




                                                     365
Table 13.4: Particular demographic variables by subjective social class of respondents controlled for by age at first coital activity
                                           Subjective social class                                  Subjective social class
                                       Lower         Middle      Upper      χ2, Pvalue      Lower          Middle             Upper
Characteristic                     n = 603          n = 839     n = 369                                                                      χ2, Pvalue
                                               %            %         %                             %                 %               %
Area of residence                                                                71.72,                                                    234.20, 0.0001
                                                                                 0.0001
 Urban                                        9.3        15.6       21.1                           8.6             19.0            21.4
 Semiurban                                  20.9         25.1       36.0                         18.6              28.7            39.6
 Rural                                      69.8         59.2       42.8                         72.6              52.3            38.9
Educational level                                                                78.72,                                                    248.36, 0.0001
                                                                                 0.0001
 Primary or below                           20.0         11.5        9.5                         15.0               8.0              4.6
 Secondary                                  52.7         45.4       36.0                         39.3              32.9            19.7
 Tertiary                                   29.2         43.1       54.5                         45.7              59.1            75.8
Partner main source of financial                                           0.559, 0.756                                                      0.006, 0.997
support
 No                                         39.5         44.6       45.2                         42.6              42.2            42.0
 Yes                                        60.5         55.4       54.8                         57.4              57.8            58.0
Employed                                                                         20.39,                                                    228.21, 0.0001
                                                                                 0.0001
 No                                         69.8         63.4       55.6                         70.0              55.4            37.5
 Yes                                        30.2         36.6       44.4                         30.0              44.6            62.5
In sexual union                                                             1.81, 0.405                                                      0.647, 0.723
 No                                         18.7         16.6       15.7                         21.4              20.4            21.5
 Yes                                        81.3         83.4       84.3                         78.6              79.6            78.5
Currently pregnant                                                          1.18, 0.555                                                       4.36, 0.113
 No                                         94.7         95.3       96.2                         94.4              96.3            95.8
 Yes                                          5.3         4.7        3.8                           5.6              3.7              4.2
Forced to have sex (in life)                                                1.20, 0.549                                                      11.58, 0.003
 No                                         72.6         72.6       75.4                         77.9              82.5            83.7
 Yes                                        27.4         27.4       24.6                         22.1              17.5            16.3
                                                                                       1
Crowding mean (SD)                            1.9         1.8        1.4      < 0.0001             1.9              1.8              1.4        < 0.00012
1                                    2
  F-statistic = 209.22, P<0.0001;      F-statistic = 537.28, P<0.0001

                                                                          366
Table 13.5: Logistic regression analyses: Explanatory variables of use of contraception by age at first coital activity (ie. < 16 or 16+
years old)


                                             Age at first coital activity ( < 16 years old)1                     Age at first coital activity ( ≥ 16 years old)2


     Dependent variable: Method of                     Std       Wald       Odds                                                     Wald         Odds
    contraception                    β coefficient    error      Lower      ratio       CI (95%)       β coefficient   Std error     Lower        ratio        CI (95%)

    Age of respondents                       -0.02      0.01        7.90      0.98       0.97 -1.00            -0.03       0.01        32.07         0.97      0.96 - 0.98
    Upper class                              -0.33      0.14        6.05      0.72      0.55 – 0.94                -            -            -            -               -
    Lower class (reference group)                                               1.0                                -            -            -            -               -
    In sexual union (1=yes)                   1.63      0.14     135.10       5.09      3.87 – 6.69             2.24       0.10      533.33          9.37     7.75 - 11.38

    Currently pregnant (1=yes)                                                                                 -4.72       0.46      105.74          0.03      0.00 - 0.02
                                             -5.51      1.01       29.82      0.01      0.00 – 0.03
    Rural                                        -         -           -         -                -             0.23       0.08         7.93         1.26      1.07 - 1.47
    Urban (references)                           -         -           -         -                -                                                  1.00
                                              0.08      0.22        0.11      1.08                 -           -0.20       0.18         1.44         0.80                 -
    Constant
1
 Model chi-square = 320.74, P<0.0001
-2 Log likelihood = 1909.11
Nagelkerke r-squared = 0.234
n = 1728
Hosmer and Lemeshow test, χ2 = 8.22, P = 0.412; Overall correct classification = 74.1%
Correct classification of cases in sexual union = 90.7%
Correct classification of cases not in sexual union = 42.4%
2
 Model chi-square = 951.90, P<0.0001
-2 Log likelihood = 3737.40
Nagelkerke r-squared = 0.319
n = 3588
Hosmer and Lemeshow test, χ2 = 7.95, P = 0.439; Overall correct classification = 77.3%
Correct classification of cases in sexual union = 91.4%
Correct classification of cases not in sexual union = 52.2%


                                                                                  367
                                            14
 Reproductive health matters: Women whose
 first sexual intercourse occurred at 20+ years
                       old

The evidence is in that public health interventions have failed to effectively address HIV/AIDS
infections and lowering the age at first sexual intercourse in the developing nations, particularly
in Jamaica despite the amount received and spent on those programmes. The new way of
addressing the issues identified earlier is to examine those issues from the perspectives of those
who wait until 20+ years old to have sexual intercourse. This study seeks to elucidate
information on the reproductive health matters of those who whose first sexual engagement
starts at 20+ years old. The current paper found that 9 in every 100 women aged 15-49 years
commenced having sexual intercourse at least 20 years. Of those whose sexual relations begin at
20+ years old, 2 out of every 5 are married; 13 out of every 25 are frequent church attendees (at
least once per week); 4 out of every 5 have never had a non-steady sexual partner; 14 out of
every 25 were in the upper class; 1 out of every 10 shared sanitary convenience; and they began
using contraceptives on average at 24 years old. Frequent church attendees on average start
having sexual intercourse at 22.7 years, which is 1.2 years later than those who infrequently visit
church. The new paradigm is on education and creating economic dependency and not first on
safe sex, abstinence and/or on consistency condom usage among young women.


Introduction


Health and reproductive health literature is filled with studies that have examined age at first

sexual intercourse (or sexual relations, coitus, sexual debut or sexual initiation) [1-5], and

rightfully so because of its association in explaining HIV/AIDS infection, unwanted pregnancies,

teenage pregnancy, sexual promiscuity, sexual and reproductive health matters, and general

health status [6-8]. In Jamaica, statistics showed that the median age of first coitus among
                                               368
females was 16.0 years in 2001, which fell from 17.3 years in 1997 [9]. Early sexual relation is

an adolescent phenomenon, and it is falling more during the adolescence years in Jamaica. First

sexual intercourse during the adolescence years is not atypical to Jamaica as this is equally the

case in Antigua and Barbuda, Haiti, Guyana, Trinidad and Tobago, Dominica Republic [10],

America [11], China [12] and many other developing countries, particularly in Africa [13,14] as

well as in the United States of America [3,15].

       The prevalence of sexually transmitted infections (STIs) has been on the rise over the

decades in Jamaica [16], China [12, 17] and the wider developing nations [18]. Given that most

of these occur in individuals aged 15-44 years, particularly 50 percent among people less than 25

years old [19], this is undoubtedly a public health concerns in many respects. In 2007, 1 in 4

Jamaicans were under 25 years old [20]; of those females aged 15-24 years old, only 24.6%

reporting never having sex and 67.8% had at least one sexual partner.

       Previous studies have examined reproductive health matters of adolescents and age at

sexual debut [1-5, 10, 19], but there is none which investigated the reproductive health matters of

those who commenced sexual intercourse at least 20 years old. For decades, the developing

nations have been suffering from increased HIV/AIDS infections, teenage pregnancies,

unwanted pregnancies, and lowering of the age at sexual intercourse, yet plethora of studies

which have been conducted have not resulted in a fundamental change in the public health

problems previously identified. The answers to changing those issues are not beyond us, it is just

that a new avenue should be taken in understanding the phenomena.

       Clearly, the evidence is in that public health interventions have failed to effectively

address HIV/AIDS infections and lowering the age at first sexual intercourse in the developing

nations, particularly in Jamaica despite the amount received and spent on intervention

                                                  369
programmes. This study emerged out of the wanting to provide answers to public health

practitioners to change the old approach in viewing a problem that continues to reoccur in the

developing nations. The current work will elucidate information on the reproductive health

matters of women who delay their first sexual encounter until 20+ years old as this will offer

some explanation that can be used to curb the reproductive health practices of those who

commenced sexual intercourse during the adolescence years.

         The present study is therefore a part of larger initiative to change the old approach in

examining reproductive health matters of adolescents in wanting to modify (1) age at first sexual

intercourse, (2) gynaecological examination; (3) currently used a method of contraception; and

(4) the role of church attendance influence sexual behaviour. The new way of addressing the

issues identified earlier is examine those issues from the perspectives of those who wait until 20+

years old.

Methods and material

Sample

This descriptive cross-sectional study used a secondary dataset from the National Family

Planning Board (Reproductive Health Survey, RHS). There are two sets of inclusion criteria,

which are females and ages. The eligibility criterion for age was 15 to 49 years at last birthday.

Since 1997, the National Family Planning Board (NFPB) has been collecting information on

women (ages 15-49 years) in Jamaica regarding contraception usage and/or reproductive health.

In 2002, the Reproductive Health Survey (RHS) collected data on Jamaican men ages 15-24

years as well as women 15-49 years old. The current paper extracted 649 females who began

having sexual intercourse at 20+ years old. The study population from which the current sample

is drawn was 7,168 women of the reproductive ages [9].
                                               370
       Stratified random sampling was used to design the sampling frame from which the

sample was drawn. Using the 2001 Census sector (or sampling frame), a three-stage sampling

design was used. Stage 1 was the use of a selection frame of 659 enumeration areas (or

enumeration districts, EDs). This was calculated based on probability proportion to size. Jamaica

is classified into four health regions. Region 1 is composed of Kingston, St. Andrew, St. Thomas

and St. Catherine; Region 2 comprises Portland, St. Mary and St. Ann; Region 3 is made up of

Trelawny, St. James, Hanover and Westmoreland, with Region 4 being St. Elizabeth, Manchester

and Clarendon. The 2001 Census showed that Region 1 comprised 46.5% of Jamaica compared

to Region 2, at 14.1%; Region 3 at 17.6% and Region 4 at 21.8% [9].


       In stage 2, the households were clustered into primary sampling units (PSUs), and each

PSU constituted an ED, which in turn was comprised of 80 households. The previous sampling

frame was in need of updating, and so this was performed between January and May 2002. The

previous sampling frame was in need of updating, and so this was carried out between January

2002 and May 2002. The new sampling frame formed the basis upon which the sampling size

was computed for the interviewers to use.


       Stage 3 was the final selection of one eligible female from each sampled household and

this was done by the interviewer on visiting the household.


       The Statistical Institute of Jamaica (STATIN) provided the interviewers and supervisors,

who were trained by McFarlane Consultancy, to carry out the survey. The instrument

administered was a 35-page questionnaire. The data collection began on Saturday, October 26,

2002 and was completed on May 9, 2003. Prior to the date of the final data collection, pre-testing

of the instrument was conducted between March 16 and 20, 2002. A total of 175 instruments
                                               371
were pre-tested, of which 40.6% were given to eligible men. Modifications were made to the pre-

tested instrument (questionnaire), after which the final exercise was carried out. Validity and

reliability of the data were conducted by many statisticians, statistical agency, and university

scholars before the data was used as the data are for national policy planning. After which it was

released to the University of the West Indies, Mona, Data Bank for use by scholars. The data was

weighted in order to represent the population of female aged 15 to 49 years in the nation [9].


Statistical analyses


Data were entered, stored and retrieved using SPSS for Window, Version 16.0 SPSS Inc;

Chicago, IL, USA). Descriptive statistics were performed on particular sociodemographic

characteristics of the sample (frequency, mean, standard deviation (SD), and range). All metric

variables were tested for normality (age at first sexual debut, crowding, age, and years of

schooling). Where skewness was found to be less than 0.5, the variable was used in its current

form and a value more than 0.5 was normalized by natural log, or another method. Independent

sample t-test was used to examine differences in age at sexual debut between those who

frequently attend churches and those who infrequently visit churches and F-statistic for age of

respondents by age at sexual debut. Finally, ordinary least square (OLS) regression was used to

fit the data because the dependent variable (age at sexual debut) was a continuous one. Stepwise

multiple linear regression was used to fit the one outcome measure (age at first sexual debut) by

different sociodemographic variables. Thus, only explanatory variables (i.e. statistically

significant variables) are shown in Table 14.3. 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, we used SUDDAN

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statistical software (Research Triangle Institute, Research Triangle Park, NC), and this adjusted

for the survey‘s complex sampling design. A p-value < 0.05 (two-tailed) was used to establish

statistical significance.


Measures


Age at first sexual debut (or initiation or intercourse) was measured based on a respondent‘s

answer to the question ―At what age did you have your first intercourse? Crowding is the total

number of persons in a dwelling (excluding kitchen, bathroom and verandah). Age is the number

of years a person is alive up to his/her last birthday (in years). Contraceptive method comes from

the question ―Are you and your partner currently using a method of contraception? …‖, and if

the answer is yes ―Which method of contraception do you use?‖ Age at which began using

contraception was taken from ―How old were you when you first used contraception? Area of

residence is measured from ―In which area do you reside?‖ The options were rural, semi-urban

and urban (1 = rural, 0 = otherwise; 1 = semiurban, 0 = otherwise, and urban is the reference

group). Currently having sex is measured from ―Have you had sexual intercourse in the last 30

days?‖ (1=yes, 0 = otherwise). Education is measured from the question ―How many years did

you attend school?‖ Marital status is measured from the following question ―Are you legally

married now?‖, ―Are you living with a common-law partner now? (that is, are you living as man

and wife now with a partner to whom you are not legally married?)‖, ―Do you have a visiting

partner, that is, a more or less steady partner with whom you have sexual relations?‖, and ―Are

you currently single?‖ Age at menarche is measured from ―How old were you when your first

period started (first started menstruation)?‖ Gynaecological examination is taken from ―Have

you ever had a gynaecological examination?‖ (1 = yes, 0 = no). Pregnancy was assessed by ―Are

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you pregnant now?‖ (1=yes, 0 = otherwise or no). Religiosity was evaluated from the question

―With what frequency do you attend religious services?‖ The options range from at least once

per week to only on special occasions (such as weddings, funerals, christenings et cetera)

(1=frequent attendance from response of at least once per week, 0 = otherwise). Subjective social

class is measured from ―In which class do you belong?‖ The options are lower, middle or upper

social hierarchy (1 = middle class, 0 = otherwise; 1 = upper class, 0 = otherwise; reference group

is lower class). Forced to have sexual relations was assessed from the question ―Were you forced

to have sex at your first intercourse?‖ and the options were yes, no, don‘t know and refused to

answer (1= yes, 0 = otherwise). Age at first sexual debut, age at menarche, age at first

contraceptive use, and years of schooling were used as continuous variables. In stable union

measured (1) being legally married or (2) in a common-law union (are you living with a

common-law partner now that is, are you living as man and wife now with partner to whom you

are not legally married).


Results
       Demographic characteristics of study population


       Table 14.1 summarizes the demographic characteristics of the studied population.

Furthermore, 59.4% of the population currently use a method of contraception, and the mean age

at sexual debut was 22.1 years (SD = 2.8 years). Almost 89% of the sample had their first sexual

encounter before 26 years old, 57.3% at least 21 years and 2.3% at least 30 years old. None of

the sample had their first sexual experience after 36 years old.

       Marginally more of the sample indicated currently using a method of contraception

(59.4%). The methods were pill (29.9%); condom (29.3%); female sterilization (22.2%);

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injection (9.0%); and other. Of those who used a condom, with a steady partner, 42.7% remarked

always, 51.7% mentioned most times and 5.6% said seldom (Table 14.2).

       Bivariate analyses

       Of the sample, on average women in the lower social class had 2.9 children (SD = 2.3)

compared to 2.1 children for those in the middle class (SD = 1.8) and 1.5 children for those in the

upper class (SD = 1.4) (F statistic = 319.4, P < 0.0001). Those with primary or below education

had 3.3 children (SD = 2.9) compared to 2.4 children (SD = 2.1) among those with secondary

level education and 1.7 children (1.6) among those with tertiary level education (F statistic =

185.9, P < 0.0001).

       There exists a statistical association between educational levels and subjective social

class (χ2 = 507.48, P < 0.0001). Seventy percentages of those in the upper class had tertiary level

education compared to 52.6% of those in the middle class and 37.2% of those in the lower class.

       Table 14.3 presents information on marital status, employment status, raped, currently

using a method of contraception, shared sanitary convenience, subjective social class, area of

residence, age at sexual debut, age of respondent, years of schooling, age of menarche, age began

using method of contraception, crowding and age at marriage by frequency of church attendance.

       Multivariate analyses

       Age at sexual debut can be explained by 6 explanatory variables (F statistic = 38.05, P <

0.0001, R2 = 0.376, Table 14.4). These are age, frequent church attendance, number of live

births, gynaecological examination done in the last 12 months, education and in a stable union.

       Age at marriage can be explained by age of respondent, area of residence, age at sexual

debut and crowding (F statistic = 6.422, P < 0.0001, R2 = 0.075, Table 14.5).



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         Three explanatory variables account for 29.1% of the variance in currently using a

method of contraception: In a stable union; subjective social class and age at sexual debut

(Model chi-square = 98.48, P < 0.021; -2 log likelihood = 447.86). Almost 75% of the data were

correctly classified (Table 14.6).

         Six variables emerged as statistically significant correlates of a women having had a

gynaecological examination in the past 12 months (Model chi-square = 160.28, P < 0.0001, -2

Log likelihood = 611.18). The factors (area of residence; subjective social class; employment

status; age of respondent, education and Pap smears in the last 12 months) account for 32.3% of

the variance in gynaecological examination in the last 12 months. Seventy-three percentage of

the data were correctly classified (Table 14.7).

Discussion

The current paper found that 9 in every 100 women aged 15-49 years commenced having sexual

intercourse at least 20 years. Of those whose sexual relations begin at 20+ years old, 2 out of

every 5 are married; 13 out of every 25 years are frequent church attendee (at least once per

week); 4 out of every 5 have never had a non-steady sexual partner; 14 out of every 25 were in

the upper class; 1 out of every 10 shared sanitary convenience; and they began using

contraceptives on average at 24 years. Among the factors that positively influences age at first

sexual intercourse are frequency in church attendance, age, educational attainment and in a stable

union.

         According to Bourne and Charles [21], church attendance is among the factors which

account for young men‘s (aged 15-24 years) lowered age at first sexual intercourse. This

research concurs with Bourne and Charles‘ study that frequent church attendance is responsible

for increased age at sexual debut among those who began having sexual intercourse at least 20
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years. In this work, it was revealed that 13 out of every 25 women who delay sexual intercourse

for 20+ years attend church on a regular basis. It was also found that among the studied

population, those who are frequent church attendees on average commenced their sexual

encounter age 22.7 years which is 1.2 years later than those who are infrequent attendees. One

study which explored sexual initiation of persons within the age range of 15-44 years, found that

protestants (similar to those of non-religion) were more likely to have their first sexual initiation

within their 16th year, compared to the Catholics (within their 17th year) and those of other

religion (18th year) [22]. It can be concluded that the cultural values and orientation of the

churches that occasional attendance do not change women delaying sexual debut, but that

frequent attendance is one of the media that increase delaying age at sexual relations.

       Another justification which account for delaying age at sexual debut among women is a

stable sexual union. Embedded in this finding is fact that women who starts in stable unions such

as marriage or common-law sexual unions are least likely to search for such a union as in the

case of women who are in visiting relationship. Furthermore, the cultural values of the church is

such that frequent membership is more fostered in marriage and therefore accounts for why 29

out of every 50 women who frequently attend church are married compared to 10 out of every 50

of those who infrequently visit churches. This is also embedded in the current finding which

showed that 4 out of every 5 women who delay having sexual intercourse at 20+ years old

indicated that they have never had a non-steady partner, suggesting that visiting union are more

about sex than stable union. Thus, when Wilks et al. [16] found that 8 out every 25 women aged

15-24 years had never had a sexual partner (or in the last 12 months), it follows that these

females are seeking for stable than transitional sexual union that are likely to result in another

sexual relationship.

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       Another issue which emerged from current work about the studied population is

subjective social class. It was found that 14 out of every 25 women who begin having sexual

relationship at 20+ years old are in the upper class, and that 3out of every 25 are in the lower

class. One scholar postulated that money is positively associated with health [23], but is appears

that economic disparity accounts for the delaying of sexual intercourse, advanced educational

attainment and number of live births. The current research found that women who delay sexual

intercourse for 20+ years old is money, which fosters accessing higher level of education, less

children, and have a greater sexual autonomy of their live than those in the lower class. Like

Marmot, money matters for women health as well as their reproductive health and the age at

which they begin having sexual intercourse. Poverty, economic deprivation and the social

settings among those who are poor is account for the early sexual relation.

       Those in the sample have higher educational attainment are among the upper class. This

work shows that educational level is positively associated with increased age at first sexual

intercourse, suggesting that poverty increases people search for social relationship as a source of

material goods. Those individuals have less sexual autonomy, and sexual intercourse is left up to

the male who wants this earlier than later and not for the purpose of desiring a stable union.

Because those sexual unions are mainly visiting and/or transitional partnerships, knowing the

vulnerability of some females, males will dictate condom usage as a price for economic support.

The results of inconsistent condom usage are STIs, unwanted pregnancies, and females being

caught in the cycle of more such relationship for financial support because the last one offered

less and they desire continuous assistance owing to their economic deprived status. Thus, this

explains the negative association between number of live births and age at sexual debut, meaning

that as more economic independency or family with economic resources have more sexual and

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reproductive rights which justifies them delaying sexual intercourse for a later time (20+ years).

       If Yan et al‘s postulations holds true in developing nations outside of China that ―Safe

sexual behaviors include having a single sex partner and using condoms in every sexual

encounter, and these behaviors also reduce risk of HIV/STDs.‖ [12, p. 2], then women in instable

unions will continue to inconsistently use condoms as they are economically dependent on a

male partner for some level of survivability. In addition to the aforementioned, thus, this

supports the finding that stable unions influence age at sexual intercourse and that money matters

in reproductive health matters as well as sexual autonomy. This is supported by the a World

Health Organization‘s (WHO) postulation that stated, ―In high-income countries, communicable