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Data Quality in Jamaica
Paul Andrew Bourne
i
Data Quality in Jamaica
Paul Andrew Bourne
ii
First published in Jamaica, 2011 by Paul A. Bourne
© Paul A Bourne
ISBN
All rights of this book are reserved. No part of this publication may be reproduced (electronically or otherwise), stored in retrieval system, or transmitted in any other form (photocopying, recording or otherwise) with the prior permission of the publisher.
iii
TABLE OF CONTENTS
page
List of Tables v
List of Figures ix
Preface x
Acknowledgement xii
Dedication xiv
PART I: HEALTH STATUS: USAGE OF HEALTH DATA 1
Introduction
1 A theoretical framework of good health status of Jamaicans: using econometric analysis to model good health status 5
2 An Epidemiological Transition of Health Conditions, and Health Status of the Old-Old-To Oldest-Old in Jamaica: A comparative analysis using two cross-sectional surveys 26
3 Self-evaluated health and health conditions of rural residents in a middle-income nation 56
4 Disparities in self-rated health, health care utilization, illness, chronic illness and other socio-economic characteristics of the Insured and Uninsured 83
5 Variations in social determinants of health using an adolescence population: By different measurements, dichotomization and non-dichotomization of health 113
6 Self-rated health status of young adolescent females in a middle-income developing country 140
7 Health of females in Jamaica: using two cross-sectional surveys 159
8 Health of children less than 5 years old in an Upper Middle Income Country: Parents’ views 179
9 Health of males in Jamaica 204
iv
PART II: ERRORS IN DATA
Introduction 230
10 Dichotomising poor self-reported health status: Using secondary cross-sectional survey data for Jamaica 232
11 Paradoxes in self-evaluated health data in a developing country 253
12 The validity of using self-reported illness to measure objective health 278
13 The image of health status and quality of life in a Caribbean society 298
Paul A. Bourne, Donovan A. McGrowder, Christopher A.D. Charles, Cynthia G. Francis
14 The quality of sample surveys in a developing nation 317
Paul A. Bourne, Christopher A.D. Charles, Neva South-Bourne, Chloe Morris, Denise Eldemire-Shearer, Maureen D. Kerr-Campbell
Part III: DATA QUALITY 15 Practices, Perspectives and Traditions 349
v
List of Tables page
Table 1.1.1: Good Health Status of Jamaicans by Some Explanatory Variables 22 Table 1.1.2: Good Health Status of Elderly Jamaicans by Some Explanatory Variables 23 Table 1.1.3: Good Health Status of Middle Age Jamaicans by Some Explanatory Variables 24 Table 1.1.4: Good Health Status of Young Adults Jamaicans by Some Explanatory Variables 25 Table 2.2.1. Socio-demographic characteristics of sample 43 Table 2.2.2. Self-reported illness by sex of respondents, 2002 and 2007 44 Table 2.2.3. Self-reported illness by marital status, 2002 45 Table 2.2.4. Self-reported illness by marital status, 2007 46 Table 2.2.5. Self-reported illness by Age cohort, 2002 and 2007 47 Table 2.2.6. Mean age of oldest-old with particular health conditions 48 Table 2.2.7. Diagnosed Health Conditions by Aged cohort
49 Table 2.2.8. Self-reported illness (in %) by health status. 50 Table 2.2.9. Health care-seeking behaviour and health status, 2007 51 Table 2.2.10. Health care-seeking behaviour by health status controlled for aged cohort 52 Table 2.2.11. Logistic regression on Good Health status by variables 53 Table 3.3.1. Demographic characteristics, 2002 and 2007 65 Table 3.3.2: Self-reported health conditions by particular social variables 67 Table 3.3.3. Health care-seeking behaviour by sex, self-reported illness, health coverage, social hierarchy, education, age and length of illness, 2002 and 2007 69 Table 3.3.4. Stepwise Logistic regression: Social and psychological determinants of self-evaluated health, 2002 and 2007 71
vi
Table 3.3.5. Stepwise Logistic regression: R-squared for Social and psychological determinants of self-evaluated health, 2002 and 2007 72 Table 4.4.1. Crowding, expenditure, income, age, and other characteristics by health insurance status 102 Table 4.4.2. Health, health care seeking behaviour, illness and particular demographic characteristics by health insurance status 103 Table 4.4.3. Age cohort by diagnosed illness 104 Table 4.4.4. Illness by age of respondents controlled for health insurance status 105 Table 4.4.5. Age cohort by diagnosed health condition, and health insurance status 106 Table 4.4.6. Logistic regression: Explanatory variables of self-rated moderate-to-very good health 107 Table 4.4.7. Logistic regression: Explanatory variables of self-reported illness 108 Table 4.4.8. Logistic regression: Explanatory variables of health care seeking behaviour 109 Table 4.4.9. Logistic regression: Explanatory variables of self-reported diagnosed chronic illness 110 Table 4.4.10. Multiple regression: Explanatory variables of income 111 Table 4.4.11. Logistic regression: Explanatory variables of health insurance status 112 Table 5.5.1: Demographic characteristic of studied population 134 Table 5.5.2: Particular demographic variables by area of residence 136 Table 5.5.3: Logistic regression: Variables of antithesis of illness among adolescence population 137 Table 5.5.4: Logistic and Ordinal Logistic regression: Factors explaining self-reported health status of adolescents 138 Table 5.5.5: Self-rated health status and antithesis of illness 139 Table 6.6.1: Descriptive analysis of variables of target cohort 157
Table 6.6.2: Socio-demographic and psychological variables of self-related
health status of the sample 158
vii
Table 7.7.1. Sociodemographic characteristics of sample by area of residence, 2002 and 2007 174
Table 7.7.2. Self-rated health status by self-reported illness, 2007 175
Table 7.7.3. Self-rated health status by income quintile, 2007 177
Table 7.7.4. Self-reported diagnosed health condition by per capita income 178
Table 8.8.1. Socio-demographic characteristic of sample, 2002 and 2007 196
Table 8.8.2. Health status by self-reported illness 197
Table 8.8.3. Health status by self-reported diagnosed illness 198
Table 9.9.1. Sociodemographic characteristics of sample, 2002 and 2007 222 Table 9.9.2. Health status and self-rated illness 223 Table 9.9.3. Predictors of poor self-reported illness by some explanatory variables, 2002 224 Table 9.9.4. Predictors of not self-reporting an illness by some explanatory variables, 2007 225 Table 9.9.5. Model summary for 2002 logistic regression analysis 226 Table 9.9.6. Model summary for 2007 logistic regression analysis 227 Table 10.10.1. Socio-demographic characteristic of sample 249 Table 10.10.2. Very poor or poor and moderated-to-very poor self-reported health status of sexes (in %) 250 Table 10.10.3. Odds ratios for very poor or poor and moderate-to-very poor self-reported health of sexes by particular variables 251 Table 10.10.4. Odds ratios of poor health status by age cohorts 252
Table 11.11.1. Socio-demographic characteristic of sample by sex of respondents 273 Table 11.11.2. Socio-demographic characteristic of sample by educational level 274 Table 11.11.3. Socio-demographic characteristic of sample by self-reported illness 275 Table 11.11.4. Stepwise Logistic Regression: Good self-rated health status by socio-demographic, economic and biological variables 276
viii
Table 11.11.5. Stepwise Logistic Regression: Self-reported illness by socio-demographic and biological variables 277 Table 12.12.1. Life expectancy at birth for the sexes, self-reported illness, and mortality, 1989-2007 292 Table 12.12.2. Life expectancy at birth of population and sex of children by self-reported illness 293 Table 13.13.1 Demographic characteristics of sample for CLG and JSLC, 2007 312 Table 13.13.2 Quality of life and health status by gender of respondents, CLG and JSLC 313
Table 13.13.3 Quality of Life and health status by area of residence, CLG and JSLC 314
Table 13.13.4 Quality of life, health status and standardized health status 315
Table 13.13.5 QoL by economic situation of individual and family, CLG 316
Table 14.14.1. Health and curative care visits: 2000-2007 344
Table 14.14.2: Proportion of Survey (Sample) vs. Proportion of Population 345 Table 14.14.3. Descriptive characteristic of samples: Sub-national and National surveys 346 Table 14.14.4. Characteristic of samples: Sub-national and National surveys 347
ix
List of Figures
page
Figure 2.2.1. Diagnosed health conditions, 2002 and 2007 54 Figure 2.2.2. Self-reported illness (in %) by Income Quintile, 2002 and 2007 55
Figure 7.7.1. Mean scores for self-reported diagnosed health conditions, 2002 and 2007 176
Figure 8.8.1. Mean age of health conditions of children less than 5 years old 199 Figure 8.8.2. Health status by Parent-reported illness (in %) examined by gender 200 Figure 8.8.3. Health status by parent-reported illness (in %) examined by area of residence 201 Figure 8.8.4. Health status by parent-reported illness (in %) examined by social classes 202 Figure 8.8.5. Health status by health care-seeking behaviour 203 Figure 9.9.1. Mean age for males with particular self-reported diagnosed illness 228 Figure 12.12.1. Life expectancy at birth for the population by self-reported illness (in %) 294 Figure 12.12.2. Life expectancy at birth for female by self-reported illness of female (in %) 295 Figure 12.12.3. Life expectancy at birth for male by self-reported illness of male (in %) 296 Figure 12.12.4. Mortality (in No of people) and self-reported illness/injury (in %) 297
x
PREFACE
For centuries, academics, researchers, government agents and policy specialists have relied on
cross-sectional data, results and statistics from International Agencies (World Bank; World
Health Organization, WHO; United Nations, UN; International Labour Organization, ILO; et
cetera), Statistical Institute of Jamaica (STATIN) and Planning Institute of Jamaica (PIOJ) as
well as reputable Universities (Oxford, Cambridge, Harvard, Yale, Stanford, University of the
West Indies, et cetera). The fundamental assumption is that the quality of the data is high,
reliable and accurate for usage. Since 1989, STATIN and PIOJ have been collecting self-
reported data from Jamaicans to guide and formulate policies. The data are published in the
Jamaica Survey of Living Conditions (JSLC). Although the JSLC is a collection of results from a
modified questionnaire of World Bank’s Living Standard Household Survey, academics,
researchers and governmental agencies have been using the data, there is a fundamental
assumption that the data quality is reliable, valid and accurate for usage. Relying on an
assumption of data quality is unscientific, non-verification, cannot detect and correct errors.
One of the basic tasks of demography is the production of reliable demographic
estimates. Despite the available demographic tools available to demographers, epidemiologists,
and statisticians, they have been using Survey Data published by the STATIN and PIOJ, without
data quality verification (ie. Content Error Testing).
Data quality in Jamaica may be good (ie Census and JCLC), but this is based on low
coverage errors. There are two main types of errors in data, coverage and content, but much
attention has been placed on coverage errors examinations. Coverage errors refer to the
completeness of inclusion of people or events in a sample. This error can be rectified through
better sampling selection, sampling frame, which has been done for the selection of samples for
the JSLC. The gradual development and consistent updating of sampling frames, from which the
people are drawn for the JSLC, reduced the coverage errors from identification, modification and
rectification. Thus the statistical methods relating to coverage errors have been utilized as
recently as on the 2007-2009 JSLC, making the errors lesser and increasing the completeness of
the sample.
xi
Demographers and Epidemiologists are concerned about pursuing reliable data in order to
increase the quality of their estimates. As such, they evaluate the ‘Content’ of the collected data,
to identify and correct any ‘Content Errors’. This is performed using matching census records
with records from surveys, as apart of the data quality verification and reliability process. In an
effort to correct errors in age data, demographers (such as Preston, Elo, Rosenwaike and Hill;
Caldwell; Ewanks) have used matching studies to assess content errors, testing the consistency of
the data. The assumption here is that data are not of a high quality because they have been
collected from the source(s). The same holds true in Jamaica. It is within the aforementioned
context that we must examine the quality of surveys, censuses, and other data collection methods
in Jamaica and not hide behind tradition, credentials, status and past reputation. By accepting
that data are of a high quality denotes that we are failing to continuously utilize science in the
pursuit of truth as truth is not constant over time (or indefinitely continuous).
This volume is designed primarily to clarify the quality of sample survey data in Jamaica,
particularly the Jamaica Survey of Living Conditions (JSLC). Science is about inquiry, which
means that it can be used to question the cosmology and foundations of current epistemology.
The JSLC publishes collected data on different issues reported by Jamaicans, suggesting that the
estimates from this could be incorrect, unreliable or of low quality without content verification.
Quality is data is critical to the quality estimates, indicating that low quality data can result in
erroneous findings (or estimates). The gradual development of health science cannot rest on the
pillows of unsubstantiated data. It is this unscientific and crucial assumption that can create
fundamental flaws in policy formulation and intervention programmes. This book recognizes the
likeliness of such a situation and seeks to evaluate the content of health data, because the
importance of the health is critical to national development and so cannot be felt to unverified
data.
Readers who seek supplementary coverage of areas which are in this volume can review
odds ratio, confidence interval, multivariate analysis (logistic and multiple regressions),
theoretical and conceptual framework, as these will provide more information on technical issues
used in this book.
The majority of the chapters were taken from publications in different journals. All the
chapters were carefully selecting in keeping with the general theme and focus of the volume,
xii
“Data Quality in Jamaica’. Initially the materials appearing in these pages were rehearsed in a
graduate class in Public Health at the University of the West Indies, Mona and with other
scholars in health sciences. Chapters 12 and 13 were co-authored with other Caribbean and
International scholars, aiding in the coverage of the material and the scope of the volume. All the
other chapters were solely written by the author.
Paul Andrew Bourne
xiii
ACKNOWLEDGEMENT
The pursuit of science cannot rest on unsubstantiated (or unverified) data. Science is about the
pursuit of truth, which denotes that nothing is with verification. Facts cannot be established on
unverifiable information (such as myth, tradition, customs, religious cosmology), but it about is
reaching out to establish truths that are based on logic, gradual development, reliability,
generality, and validity. Thus, the use of health data cannot rely on tradition, authority, and
credibility as the health affects development, which makes it reliability. Effective policies cannot
be fashioned around inaccurate and lowly reliable data as this will void the cost of data
collection.
While science is a gradually developed with trial and errors, verification of data paramount
the final results. Thus, quality data is crux upon which science holds its value. As the quality of
the data collected holds more of the depth of the scientific estimates than the logic and other
scientific approaches. For decades (from 1989), in Jamaica, we have been using survey data,
relying accuracy of the data collector and institutions. This denotes that while we advance
estimates and fact from the data, there exists a scientific unanswered question “How is the data
quality of survey, particularly the JSLC?”
Within the value of science, unanswered questions are good as they for the basis upon which
future studies are conducted, as this will advance knowledge on health matters in Jamaica. The
question of ‘How is data quality in Jamaica?” in respect to the content errors are still
unanswered. This book, therefore, owes itself to the pursuit of truth more that the establishment
of tradition and/or the sanctioning of authority. Thus, the author acknowledges the search for
truth as this the birth of knowledge that can guide effective policy and intervention programmes.
xiv
DEDICATION
This book is dedicated to the
‘Pursuit of Truth’
1
Part I
HEALTH STATUS: USAGE OF HEALTH DATA
2
INTRODUCTION
Many researchers, scholars and academics utilize secondary cross-sectional survey data, because
of the high cost and time allocation in conducting primary research. Secondary cross-sectional
surveys are in response to affordability and time, which create a barrier to primary data collection.
The question that is frequently asked, therefore, by user of those data is “How reliable is the
content and coverage of the already collected data?” Some researchers rely on the credibility of
the data collectors (such as WHO, UN, ILO, World Bank, Statistical Agencies, NASA,
established Universities) in answering the aforementioned question. While those Organizations
are of a high standard, science is not about the non-verification of objects, events and data
estimates, particularly data collected from other sources.
The reliance on the reliability and validity of data source go to the crux of trustworthiness
and not science. This assumption violates the premise of science, verification of issues. Although
science rest on gradual development of issues before conclusions are finalized, many of the
aforementioned Organizations have been in existence for some time and have access to more
resources than single scholar (or researcher), particularly in developing nations, but this does not
denote an arbitrary and unquestionable reliance on them, their data, estimates and findings. The
meaning of unquestionable facts destroys all the pillows upon which science are based, retard
logic and further scientific discoveries. Science is about the pursuit of truths, indicating that
questioning is a normal component in validation, consistency and reliability. Outside of the
verification of truths, there can be no science as everything is mere proposition. It is the logic,
gradual development, continuous inquiry, verification, validity, consistency and reliability that
distinguish sciences from mythology, customs, traditions and opinions.
In Jamaica, researchers, scholars, academics and ordinary citizens rely on the estimates
and results of STATIN, PIOJ, the University of the West Indies and other established
International Organizations. There is an undeniable reality that those Agencies have long
contributed to scientific estimates, results and cosmologies, but this is not sufficient to end
scientific inquiry on their conceptualizations and results. Many discoveries emerged out of the
questioning of the establishments, epistemology, cosmology, customs, traditions, authority, and
3
not accepting things because they were stated. Knowledge is not consistent over all time intervals,
making its changeable on new information at a specified interval.
Science is about the continuation of truth searching, making it a persistent quest of all
things including the establishments, customs, tradition, knowledge, authority, and ‘natural
philosophy’. Facts and knowledge are changeable with logic, gradual development of new facts,
justification of knowledge, refutation of the old knowledge, testing of old and the establishment
of new principles, laws, and methods. Science cannot co-exist indefinitely with unanswered, non-
justifiable and opinionated issues as “What is in an interval (i.e. in time)?” can change with
systematic, logical and conceptual inquiry. Simply put, cosmologies (or world views) are based
on a set of propositions that are flexible. With more knowledge about something, the truth
changes and different paradigms are established to explain events, object, situations and
knowledge. Hence, knowledge is only hidden in time, changeable with time and empiricism. If
knowledge is not stationary throughout time, then the reliability of result can be questioned,
irrespective of the credibility of the data source.
Since 1989, Jamaicans and other scholars have been using the data of STATIN and/or
PIOJ, with some never questioning the content of the results. However, statisticians have
questioned the coverage of the data source that has led to modifications of the sampling frames
and the decreasing of coverage errors. This has increased the generalizability of sample frame,
size and data estimates. Clearly we should question issue to advance science, knowledge of what
is. With the lowering of coverage errors in JSLC, this does not frame any purity about the content.
Because the instrument of the JSLC is a modification of the World Bank’s Living Standard
Household Survey, this does not mean unquestionable estimates, results and content. While the
instrument provide some reliability about questionnaire, reliability does not end with
questionnaire and sample design. Caribbean demographers (such as Paul Bourne, Sharon
Priestley, Julian Devonish), who are cognizant of content errors in surveys as well as censuses,
have neglected to provide a framework for understanding data quality in Jamaica. They as well as
other non-demographers have relied on traditions, authority, agencies and the industrialized
nations to stipulate data quality, without questioning estimates, results and data sources.
The author, who is a trained demographer, has published plethora of articles from health
using the JSLC. Because science is about the pursuit of truths, the author is therefore concerned
4
about data quality, particularly in the JSLC, as the correctness of the estimates relies on data
source. There are two main types of data errors (such as coverage and content errors). On many
occasions statisticians have evaluated coverage errors that have increased the quality of the
sample estimates. Their efforts and works have increased the generality of the sample survey, but
do not ruled out other errors. This means that the quality of the JSLC data is currently higher in
Jamaica, increasing reliability and provision for better generalizability of the population. Like
statisticians, the author is questioning the quality of the JSLC data. This in no way speaks of the
questioning of the credibility of workers – including data gathers, statisticians and workers.
Instead of the author’s questioning of the content of the JSLC data on health, is just an inquiry
that validate and/or improve the estimates and results.
Prior to beginning a comprehensive inquiry of the data quality, the author presents works
that use the data on health. This volume is separated into two Parts. Part I is the presentation of
different topics on health using the JSLC dataset. It is worth adding here chapters on health for
readers to understanding the estimates and results and how this volume will enhance those
estimates and findings.
5
CHAPTER
1 A Theoretical Framework of Good Health Status of Jamaicans: Using Econometric Analysis to Model Good Health Status
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
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
6
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
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):
7
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.
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;
8
number of children living outside of household; and living alone. Unlike Grossman’s study, he
found that current disease conditions accounted for 67.2% of the explained variation in health
status of elderly Barbadians, with life style risks factors accounting for 14.2%, and social factors
18.6%. One of the additions to Grossman’s work based on Hambleton et al.’s study was actual
proportion of each factor on health status and life style risk factors.
A study published in 2004, using life satisfaction and psychological wellbeing to proxy
wellbeing of 2,580 Jamaicans, Hutchinson et al. [21] employed the principles in econometric
analysis to examine social and health factors of Jamaicans. Other studies conducted by Bourne on
different groups and sub-groups of the Jamaican population have equally used the principles of
econometric analysis to determine factors that explain health, quality of life or wellbeing [5, 8, 22,
23]. Despite the contribution of Hutchinson et al’s and Bourne’s works to the understanding of
wellbeing, there is a gap in the literature on a theoretical framework explains good health status of
the life course of Jamaicans. The current study will model predictors of good health status of
Jamaicans as well as good health status of young adults, middle age adults and elderly in order to
provide a better understanding of the factors that influence each cohort.
METHODS
Participants and questionnaire
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
9
Survey of Living Conditions (JSLC). The JSLC was commissioned by the Planning Institute of
Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN). These two organizations are
responsible for planning, data collection and policy guideline for Jamaica.
The JSLC is a self-administered questionnaire where respondents are asked to recall detailed
information on particular activities. The questionnaire covers demographic variables, health,
immunization of children 0 to 59 months, education, daily expenses, non-food consumption
expenditure, housing conditions, inventory of durable goods, and social assistance. Interviewers
are trained to collect the data from household members. The survey is conducted between April
and July annually.
Model
The multivariate model used in this study is a modification of those of Grossman and Smith &
Kington which captures the multi-dimensional concept of health, and health status. The present
study further refine the two aforementioned works and in the process adds some new factors such
as psychological conditions, crowding, house tenure, number of people per household and a
deconstruction of the numbers by particular characteristics i.e. males, females and children (ages
≤ 14 years). Another fundamental difference of the current research and those of Grossman, and
Smith and Kington is that it is area specific as it is focused on Jamaican residents.
The proposed model that this research seeks to evaluate is displayed below [Model (3)]:
Ht = f(Ht-1,Pmc, ED i, Rt, At, Qt, HHt, C i, Eni , MSi, HIi , HTi , SSi, LLi,Xi , CRi , Di, Oi , Σ(NP i,PPi), M i,N i, FSi, Ai , Wi, ε i )….. 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
10
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; HI i is health insurance coverage of person i, 1 if have a health insurance policy, 0 if
otherwise; HTi is house tenure of person i, 1 if rent, 0 if squatted; Xi is gender of person i, 1 if
female, 0 if male; CRi is crowding in the household of person i; Σ(NPi,PPi) NPi is the summation
of all negative affective psychological conditions and PPi is the summation of all positive
affective psychological conditions; Mi is number of male in household of person i and Fi is
number of female in household of person i; Ai is the age of the person i and Ni is number of
children in household of person i; LLi is living arrangement where 1= living with family
members or relative, and 0=otherwise and social standing (or social class), Wi.
Statistical analysis
Statistical analyses were performed using Statistical Packages for the Social Sciences (SPSS) for
Windows, Version 16.0 (SPSS Inc; Chicago, IL, USA). A single hypothesis was tested, which
was ‘health status of rural resident is a function of demographic, social, psychological and
economic variables.’ The enter method in logistic regression was used to test the hypothesis in
order to determine those factors that influence health status of rural residents if the dependent
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.
11
The predictive power of the model was tested using Omnibus Test of Model and Hosmer
and Lemeshow [24] was used to examine goodness of fit of the model. The correlation matrix was
examined in order to ascertain whether autocorrelation (or multi-collinearity) existed between
variables. Cohen and Holliday [25] stated that correlation can be low/weak (0 to 0.39); moderate
(0.4-0.69), or strong (0.7-1.0). This was used in this study to exclude (or allow) a variable in the
model. Where collinearity existed (r > 0.7), variables were entered independently into the model
to determine those that should be retained during the final construction of the model. To derive
accurate tests of statistical significance, we used SUDDAN statistical software (Research Triangle
Institute, Research Triangle Park, NC), and this was adjusted for the survey’s complex sampling
design. Finally, Wald statistics was used to determine the magnitude (or contribution) of each
statistically significant variables in comparison with the others, and the odds ratio (OR) for the
interpreting each significant variables.
Results: Modelling Current Good Health Status of Jamaicans, Elderly, Middle Age and
Young adults
Predictors of current Good Health Status of Jamaicans. Using logistic regression analyses, eleven
variables emerged as statistically significant predictors of current good health status of Jamaicans
(p<0.05, 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 1.1.1).
Ht = f(Ht-1, Rt, Pmc, EDi, MSi, HIi, SSi,ARi, Xi, Σ(NP i,PPi), Mi,N i, ε i)...……………………………..... Model (4)
12
The model [ie Model (4)] had statistically significant predictive power (χ2 (27) =1860.639,
p < 0.001; Hosmer and Lemeshow goodness of fit χ2=4.703, p = 0.789) and overall correctly
classified 85.7% of the sample (correct classified 98.3% of cases of good health status and
correctly classified 33.9% of cases of dysfunctions).
There was a moderately strong statistical correlation between age, marital status,
education, retirement income, per capita income quintiles, property ownership, and so these were
omitted from the initial model (ie model 3). Based on that fact, three age groups were classified
(young adults – ages 15 to 29 years; middle age adults – ages 30 to 59 years; and elderly – ages
60+ years) and the initial model was once again tested. There were some modifications of the
initial model in keeping with the age group. For young adults the initial model was amended by
excluding retirement income, property ownership, divorced, separated or widowed, number of
children in household, and house tenure. The exclusion was based on the fact that more than 15%
of cases missing in some categories and a high correlation between variables.
Predictors of current Good Health Status of elderly Jamaicans. From the logistic regression
analyses that were used on the data, eight variables were found to be statistically significant in
predicting good health Status of elderly Jamaicans (P < 0.5) (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 1.1.2).
Ht = f(Ht-1, EDi, MSi, HIi, ,ARi, Xi, Σ(PP i), Mi,N i, ε i)...…………………………………………………..... 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
13
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, and
number of children in household and previous health status (see Table 1.1.3)
Ht = f(Ht-1, Pmc, Eni, HIi, Xi, Σ(NP i),N i, εi)...........................................……………………………..... Model (6)
Based on table 3, the model had statistically significant predictive power (model χ2 (27)
=547.543, p < 0.001; Hosmer and Lemeshow goodness of fit χ2=4.318, p = 0.827) and overall
correctly classified 87.2% of the sample (correctly classified 98.3% of cases of good or beyond
health status and correct classified 28.2% of cases of dysfunctions).
Predictors of current Good Health Status of young adult in Jamaica. Using logistic regression, two
variables emerged as statistically significant predictors of current good health status of young
adults in Jamaica (p<0.05) (Model 7). These are health insurance coverage, psychological
condition, social class and previous health status (Table 1.1.4).
Ht = f(Ht-1, Wi, HI i, Σ(NP i), εi )...............................................…………………………….....Model (7)
From table 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).
14
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(PP i), 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(NP i,PPi), Mi,Ni , FSi, Ai, Wi,εi)……………………………………………………………………….. Model (3)
The current work is a major departure from Grossman’s theoretical model as he assumed
that factors affecting good health Status over the life course are the same, this study disagreed
with this fundamental assumption. This study revealed that predictors of good health status are
not necessarily the same across the life course, and differently from that of the general populace.
Despite those critical findings, healthy time gained can increase good health status directly and
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.
15
People are different even across the same ethnicity, nationality, next of kin and
socialization. This was not accounted for in the Grossman’s or the current work, as this is one of
the assumptions. Neither Grossman’s study nor the current research recognized the importance of
differences in individuals owing to culture, socialization and genetic composition. Each
individual’s is different even if that person’s valuation for good health Status is the same as
someone else who share similar characteristics. Hence, a variable P representing the individual
should be introduced to this model in a parameter α (p). Secondly, the individual’s good (or bad)
health is different throughout the course of the year and so time is an important factor. Thus, the
researcher is proposing the inclusion of a time dependent parameter in the model. Therefore, the
general proposition for further studies is that the function should incorporate α (p, t) a parameter
depending on the individual and time.
An unresolved assumption of this work which continues from Grossman’s model is that
people choose health stock so that desired health is equal to actual health. The current data cannot
test this difference in the aforementioned health status and so the researcher recommends that
future study to account for this disparity so we can identify factors of actual health and difference
between the two models.
Discussions
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
16
correctly classified, suggesting that using logistic regression to examine good health status of the
Jamaican population with the eleven factors that emerged is both a good predictive model and a
good evaluate or current good health status of the Jamaican population. This is not the first study
to examine current good health status or quality of life in the Caribbean or even Jamaica [6, 21-
23, 26], but that none of those works have established a general and sub-models of good health
over the life course.
In Hambleton et al’s work, the scholars identified the factors (ie historical, current, life
style, diseases) and how much of health they explain (R2=38.2%). However, they did not examine
the goodness of fit of the model or the correctness of fit of the data. Bourne’s works [12,13] were
similar to that of Hambleton et al’s study, as his study identified more factors (psychological
conditions; physical environment, number of children or males or females in household and social
support) and had a greater explanatory power (adjusted r square = 0.459) but again the goodness
of fit and correctness of fit of the data were omitted. Again this was the case in Hutchinson et al.’s
research.
Like previous studies in the Caribbean that have examined health status [6, 21-23, 26],
those conducted by the WHO and other scholars [27-32] did not explore whether social
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
17
wealthy), the current study shows that there are also differences in social and psychological
determinants of health across the life course.
The current study has concluded that the factors identified to determine good health status
for elderly, had the lowest goodness of fit (approximately 68%) while having the greatest
explanatory power (R2= 35%). The findings also revealed low explanatory powers for young
adults (R2=22.6%) and middle age adults (R2=23%), with latter having a greater goodness of fit
for the data as this is owing to having more variables to determine good health. Such a finding
highlights that we know more about the social determinants for the elderly than across other age
cohorts (middle-aged and young adults). And that using survey data for a population to ascertain
the social determinants of health is more about those for the elderly than across the life course of
a population.
Another important finding is of the eleven factors that emerge to explain good health
status of Jamaicans, when age cohorts were examine it was found that young adults had the least
number of predictors (ie health insurance, social class and negative affective psychological
conditions). This suggests that young adult’s social background and health insurance are
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]
18
and purported by Abel-Smith [11] as well as the WHO [27] and affiliated researchers [28-32] are
more for the elderly population than the population across the life course.
Conclusions
There are disparities in the social determinants of health across the life course, which emerged
from the current findings. The findings are far reaching and can be used to aid policy formulation
and how we examine social determinants of health. Another issue which must be researched is
whether there are disparities in social determinants of health based on the conceptualization and
measurement of health status (using self-reported health, and health conditions).
Disclosures
The author reports no conflict of interest with this work.
Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions (JSLC), none of the errors in this paper should be ascribed to the Planning Institute of Jamaica (PIOJ) and/or the Statistical Institute of Jamaica (STATIN), but to the researcher.
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.
19
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Table 1.1.1: Good Health Status of Jamaicans by Some Explanatory Variables
Variable Coefficient Std Error.
Wald
statistic P
Odds Ratio
CI (95%)
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
23
Table 1.1.2: Good Health Status of Elderly Jamaicans by Some Explanatory Variables
Coefficient Std
Error Wald
statistic P Odds 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 Widowed
-0.49
0.15
11.00
0.001
0.61
0.46
0.82
Married -0.33 0.15 4.82 0.028 0.72 0.54 0.97 Never married*
Health Insurance
-3.35
0.22
241.88
0.000
0.04
0.02
0.05
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
24
Table 1.1.3: Good Health Status of Middle Age Jamaicans by Some Explanatory Variables
Coefficient Std
Error Wald
statistic P Odds 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
25
Table 1.1.4: Good Health Status of Young Adults Jamaicans by Some Explanatory Variables
Coefficient Std Error Wald
statistic P Odds Ratio
CI (95%)
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
26
CHAPTER
2 An Epidemiological Transition of Health Conditions, and Health Status of the Old-Old-To-Oldest-Old in Jamaica: A comparative analysis using two cross-sectional surveys
There is a paucity of information on the old-old-to-oldest-old in Jamaica. In spite of studies on this cohort, there has never been an examination of the epidemiological transition in health condition affect this age cohort. The aims of the current study are 1) provide an epidemiological profile of health conditions affecting Jamaicans 75+ years, 2) examine whether there is an epidemiological transition in health conditions affecting old-old-to-oldest-old Jamaicans, 3) evaluate particular demographic characteristics and health conditions of this cohort, 4) assess whether current self-reported illness is strongly correlated with current health status, 5) mean age of those with particular health conditions, 6) model health status and 7) provide valuable information upon which health practitioners and public health specialists can make more informed decisions. In 2007, 44% of old-to-oldest-old Jamaicans were diagnosed with hypertension, which represents a 5% decline over 2002. The number of cases of diabetes mellitus increased over 570% in the studied period. The poor indicated having more health conditions than the poorest 20% of the sample. The implications of the shift in health conditions will create a health disparity between 75+ year adults and the rest of the population.
Introduction The elderly population (ages 60+ years) constituted 10.9% of Jamaica’s population, which means
that this age cohort is vital in public health planning [1]. Eldemire [2] opined that “The majority
of Jamaican older persons are physically and mentally well and living in family units”. This view
was substantiated in an early study; when Eldemire [3] found that approximately 81 percent of
the seniors reported that they were physically competent to care for themselves, without any
form of external intervention. Eldemire’s work revealed that 88.5 percent being physiologically
independent.
27
Many elderly persons are more than physically independent as Eldemire [3] found 65.5
percent of them supported themselves, with males reporting a higher self-support (82.6%)
compared to females, 47.7%. A study conducted by Franzini and colleague [4] found that social
support was directly related to self-reported health, which is collaborated by Okabayashi et al’s
study [5]. The aforementioned situation can explain why many elderly are offered socio-
economic support. Eldemire [3] found that approximately 71 percent of children were willing to
accept responsibility for their parents, with seniors who were older than 75 years being likely to
need support. Seniors ages 75-84 years are referred to as old-old and those 85+ are referred as
oldest-old.
The 2001 Population Census of Jamaica found approximately 66 percent of the elderly
live in private households [6], which imply that the aged are physically and mentally competent.
This is in keeping with Eldemire’s studies [2, 3]. The functional independence of the elderly is
not atypical to Jamaica as DaVanzo and Chan [7], using data from the Second Malaysian Family
Life Survey which includes 1,357 respondents of age 50 years and older living in private
households, noted that some benefits of co-residence range from emotional support,
companionship, physical and financial assistance [8]. Embedded in DaVanzo and colleague’s
work is the issue of ‘Is it functional independence or stubbornness?’ that accounts for the elderly
persons’ report that they are physically and mentally well in order that family and onlookers will
not request that they live in home care facilities. This brings into focus the issues of health status
and health conditions of elderly Jamaicans.
Physical disability and health problems increase with age [9]. Bogue [9] opined that
demand for medical care increases with ageing and that this is owing to health deteriorations. He
[9] also noted that as an individual age, the demands on their children increases and likewise
28
their demand on the public services also increases. Statistics revealed that 15.5% of Jamaicans
reported suffering from an illness/injury in 2007; this was 2.8 times more for individuals ages
65+ and 2.4 times for those people ages 60+ years [10]. This further goes to concurs with
Bogue’s perspective that ageing is associated with increased illness. Concurrently, in 2007,
51.9% of Jamaicans who reported an illness, in the 4-week period of the survey, indicated that
this was recurring compared to 75.1% of the elderly. The elderly also sought more medical care
(72%) compared to the general population (66%), purchased more medication (78.3% compared
to the general population, 73.3%) and had more health insurance coverage (27.8%) compared to
the general population (21.1%) [10]. The aforementioned findings only concur with the work of
Bogue, and still does not provide us with changing in health conditions of the elderly in
particular the old-old-to-oldest old.
Using a sub-sample of 3,009 elderly Jamaicans, Bourne [11] found that the general
wellbeing was low; but, within the context of Bogue’s work, raised the question of the old-old or
the oldest-old’s health status. Bourne [12], using a sub-sample of 1,069 respondents ages 75+
years, found that 51.3% of those 75-84 years had poor health status compared to 52.6% of the
oldest-old. There was no significant statistical difference between the poor health status of old-
old and oldest-old Jamaicans. While poor health status comprised of health conditions, Bourne’s
works do not provide us with an understanding of the health conditions over time and whether
these are changing or not. A study on elderly Barbadians by Hambleton and colleagues [13]
found that current health conditions (diseases) were the most influential predictor of current
health status and adds value to discourse that health conditions provide some understanding of
health status. However, this finding does not clarify the epidemiological transition of health
conditions affecting the old-old-to-oldest-old Caribbean nationals, in particular Jamaicans.
29
An extensive review of health and ageing literature in the Caribbean revealed no study
that has examined an epidemiological transition of health conditions of people 75+ years. In
Jamaica, 4% of the population in 2007 were older than 75+ years, indicating that over 100,000
Jamaicans have reached 75 years or older. This is a critical group that must be studied for public
health planning as more elderly have chronic dysfunctions than any other age cohort in the
population. The aims of the current study are 1) provide an epidemiological profile of health
conditions affecting Jamaicans 75+ years, 2) examine whether there is an epidemiological
transition in health conditions affecting old-old-to-oldest-old Jamaicans, 3) evaluate particular
demographic characteristic and health conditions of this cohort, 4) assess whether current self-
reported illness is strongly correlated with current health status, 5) mean age of those with
particular health conditions, 6) model health status and 7) provide valuable information upon
which health practitioners and public health specialists can make more informed decisions.
Materials and Methods The current study utilized a sub-sample of approximately 4% from each nationally cross-
sectional survey that was conducted in 2002 and 2007. The sub-sample was 282 people ages 75+
years from the 2007 cross-sectional survey (6,783 respondents) and 1,069 people ages 75+ years
from the 2002 survey (25,018 respondents). The survey is known as the Jamaica Survey of
Living Conditions which began in 1989.
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
30
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 [14, 15]. The sample was
weighted to reflect the population of the nation.
The JSLC 2007 [14] was conducted May and August of that year; while the JSLC 2002
was administered between July and October of that year. The researchers chose this survey based
on the fact that it is the latest survey on the national population and that that it has data on self-
reported health status of Jamaicans. A self-administered questionnaire was used to collect the
data, which were stored and analyzed using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL,
USA). The questionnaire was modelled from the World Bank’s Living Standards Measurement
Study (LSMS) household survey. There are some modifications to the LSMS, as JSLC is more
focused on policy impacts. The questionnaire covered areas such as socio-demographic variables
– such as education; daily expenses (for past 7-day; food and other consumption expenditure;
inventory of durable goods; health variables; crime and victimization; social safety net and
anthropometry. The non-response rate for the survey for 2007 was 26.2% and 27.7%. The non-
response includes refusals and rejected cases in data cleaning.
Measures
Age: The length of time that one has existed; a time in life that is based on the number of years
lived; duration of life. Or it is the total number of years which have elapsed since birth [16].
Elderly (or aged, or seniors): The United Nations defined this as people ages 60 years and older
[17].
31
Old-Old. An individual who is 75 to 84 years old [9]
Oldest-old. A person who is 85+ years old [9].
Health conditions (i.e. self-reported illness or self-reported dysfunction): The question was
asked: “Is this a diagnosed recurring illness?” The answering options are: Yes, Cold; Yes,
Diarrhoea; Yes, Asthma; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No.
Self-rated health status: “How is your health in general?” And the options were very good; good;
fair; poor and very poor.
Good health status is a dummy variable, where 1=good to very good health status, 0 = otherwise
Income Quintile can be used to operationalize social class. Social class: The upper classes were
those in the wealthy quintiles (quintiles 4 and 5); middle class was quintile 3 and poor those in
lower quintiles (quintiles 1 and 2).
Health care-seeking behaviour. This is a dichotomous variable which came from the question
“Has a doctor, nurse, pharmacist, midwife, healer or any other health practitioner been visited?”
with the option (yes or no).
Statistical Analysis
Descriptive statistics, such as mean, standard deviation (± SD), frequency and percentage were
used to analyze the socio-demographic characteristics of the sample. Chi-square was used to
examine the association between non-metric variables, and Analysis of Variance (ANOVA) was
used to test the relationships between metric and non-dichotomous categorical variables whereas
independent sample t-test was used to examine a statistical correlation between a metric variable
32
and a dichotomous categorical variable. The level of significance used in this research was 5%
(i.e. 95% confidence interval).
Result
Sociodemographic characteristics of sample
Of the sample for 2002, 57.6% was female compared to 57.4% females in 2007. The mean age in
2002 was 81.3 years (SD = 5.6 years), and this was 81.4 years (SD = 5.4 years) in 2007. More
than two-thirds of the 2002 sample dwelled in rural areas, 20.8%. In 2007, the percent of sample
who resided in urban areas increased by 169.7%, and a reduction by 25.9% of those who dwelled
in rural zones compared to a marginal reduction of 4.3% in semi-urban areas (Table 2.2.1).
Concurrently, in 2007, 51.6% of sample reported suffering from an illness which was a 22%
increase over 2002. Five percent more people sought medical care in 2007 over 2002 (ie 69.2%).
Illness (or health conditions)
A number of shifts in diagnosed health conditions were observed in this study. The number of
cases of hypertension and arthritis were observed between the two periods. The most significant
increase in health conditions was in diabetes mellitus cases (i.e. 576%) (Figure 2.2.1).
A cross tabulation between self-reported illness and sex revealed that there was no
significant statistical correlation between the two variables (Table 2.2.3). Although no statistical
associated existed between the self-reported illness and sex, the percent of men who reported an
illness in 2007 over 2002 increased by 30.5% compared to 16.4% for females.
No significant statistical relationship existed between self-reported illness and marital
status (Tables 2.2.4, 2.2.5). In spite of the aforementioned situation, the divorced sample
33
reported the greatest percentage of increased in self-reported illness (16.7%) followed to married
people (15.7%); separated individuals (11.6%), widowed (5.8%) and those who were never
married reported the least increase in self-reported illness (5.2%).
No significant statistical correlation existed between self-reported illness and age cohort
of respondents – P >0.05 – (Table 2.2.5). Although the aforementioned is true, the percent of
old-old who reported illness in 2007 over 2002 increased by 23.6% compared to a 16.6%
increased in the oldest-old cohort over the same period.
A cross tabulation between diagnosed self-reported health conditions and age of
respondents revealed a significant association between the two variables (Table 2.2.6). On
examination, in 2002, the lowest mean age was recorded by people who indicated that they had
arthritis. However, for 2007, the mean age was the lowest for old-old-to-oldest-old who had
reported the common cold. A shift which is evident from the finding is the mean age of those
with diabetes mellitus in 2002 (79.5 yrs. ± 2.5 yrs), which was the second lowest age of person
with illness in 2002 to the greatest mean age for people with the same dysfunction in 2007 (90.20
yrs ± 3.54 yrs) (Table 2.2.6).
Based on Table 2.2.7, no significant statistical association was found between diagnosed
health conditions and age cohort of the sample – P >0.05. In spite of this reality, some interesting
findings are embedded in the data across the two years. The findings revealed an exponential
increase in diabetes mellitus and the common cold. However, the most significant increase
occurred in diabetic cases in the oldest-old. Reductions were recorded in hypertension, arthritis
and unspecified categorization.
A cross-tabulation between self-reported illness (in %) and Income Quintile revealed a
significant statistical correlation between both variables for 2002 (χ2 (df = 4) = 11.472, P =0.022)
34
and 2007 (χ2 (df = 4) = 10.28, P < 0.05). Based on Figure 2.2.2, the poor had highest self-
reported cases of illness compared to the other social groups. Although this was the case for
2002 and 2007, the wealthy reported more illnesses than the wealthiest 20% of sample.
Concurrently, the poorest 20% reported the greatest increase in self-reported illness for 2007
over 2002 (19.4%) with the wealthy segment of the sample reported the least increase (2.7%).
The first time that the Jamaica Survey of Living Conditions (JSLC) collected information
on self-reported illness and general health status (health status) of Jamaicans was in 2007. Based
on that fact, this study will not be able to compare the health status of the sample for the two
studied years; however, this will be the basis upon which future studies can compare. The cross-
tabulation between the two aforementioned variables was a significantly correlated one (χ2 (df =
2) = 39.888, P < 0.001) (Table 2.2.8).
Health care-seeking behaviour
A cross tabulation of health care seeking behaviour and aged cohort revealed no statistical
relationship between the two variables for 2002 (χ2(df=1) = 0.004, P = 0.947) and for 2007
(χ2(df=1) = 1.308, P = 0.253).
Table 2.2.9 revealed that there is a significant statistical relationship between health care-
seeking behaviour and health status of the sample (χ2 (df = 2) = 10.539, P = 0.005, cc=0.265).
Further examination showed that 57.1% of old-old-to-oldest-old sought medical care, and as
health status decreases the percent of sample seeking medical care increases. Of those who
reported poor health, 86.7% of them have sought medical care in the 4-week period of the
survey. When the aforementioned association was further investigated by aged cohort, the
difference was explained by old-old (χ2 (df = 2) = 11.296, P = 0.004, cc=0.305) and not oldest-
old (χ2 (df = 2) = 0.390, P = 0.823) (Table 2.2.10).
35
Controlling health care-seeking behaviour and health status by aged cohort revealed that
the old-old are more likely to seek more medical care with reduction in their good health status;
but this is not the case for the oldest-old. With one-half of the cells in oldest-old category being
less than 5 items, the non-statistical association possibly is a Type II Error. Type II Error
indicates that there is no statistical significant relationship between variables when there is a
probability that an association does exists.
Multivariate analysis: Predictors of good health status
Good health status of old-old-to-oldest-old Jamaicans can be predicted by self-reported illness
(Table 11). Based on Table 2.2.11, self-reported illness is a negative predictor of good health
status (OR = 0.176, 95% CI = 0.095 - 0.328). Twenty-four percent of the variability in good
health status can be explained by self-reported illness. Concurrently, no other variable except
self-reported illness was significantly correlated with good health status. Furthermore, 75.9% of
the data were correctly classified: 90.5% of good health status and 42.0% of those who has stated
otherwise (poor to fair health status). In addition, an old-old-to-oldest-old Jamaican is 0.824
times less likely to reported good health status.
Discussion
Ageing is directly correlated with increased functional disability [18]. This can be concurred
with the disproportionate number of elderly who continue to outnumber other age cohorts in
visits medical care facilities and number of cases in chronic dysfunctions. Statistics from the
Planning Institute of Jamaica and Statistical Institute of Jamaica revealed that elderly Jamaicans
disproportionately outnumber other ages in diabetes mellitus, hypertension, arthritis and
mortality [10, 16, 17]. The Jamaican Ministry of Health data showed that the prevalence of
36
chronic diseases is greatest for those 65+ years. Is the aforementioned information sufficient
enough for public health policy makers, health care practitioners and academics as a reference to
a comprehensive understanding of the old-old-to-oldest-old in Jamaica? The answer is a
resounding no as this study will show.
Bogue [9] showed that functional capacity, demand for medical care and health problems
increase with ageing which concurs with Erber’s work [18] and other research [19]. The current
study found that 10.3% more old-old-to-oldest-old Jamaicans reported at least one health
condition in 2007 over 2002 and this was associated with at 1.7% increase health care-seekers. In
2007, 73 out of every 100 old-old-to-oldest-old Jamaicans sought medical care which is the
national figure (66 out of every 100 Jamaicans). The research found that significant statistical
association existed between medical care and health status of sample. Interestingly in this study
though, is the fact that as the old-old’s health status fall to poor 89 out of every 100 of them
sought care compared to 53 out of every 100 old-old who had good health. A critical finding of
this study is the fact that after an individual reaches 85 years and beyond, there is no difference
in seeking health care. Intertwined in this finding is the psychological reluctance of prolonged
life at the onset of illness compared to those in the old-old categorization as many of oldest-old
believe that they have lived a long time and so they are able to transcend this life.
People’s cognitive responses to ordinary and extraordinary situational events in life are
associated with different typologies of wellbeing [20]. Positive mood is not limited to active
responses by individual, but a study showed that “counting one’s blessings,” “committing acts of
kindness”, recognizing and using signature strengths, “remembering oneself at one’s best”, and
“working on personal goals” are all positive influences on wellbeing [21,22]. Happiness is not a
mood that does not change with time or situation; hence, happy people can experience negative
37
moods [23]. Within the context of the aforementioned, an individual who has lived or is living
for 85+ years consider this as a blessing and so they are comfortable with that blessing, which
accounts for the psychological reluctance to prolong life if this is accompanied by severity of
illness.
The World Health Organization opined that the among the challenges of the 21st century
will how to prevent and postpone dysfunctions and disability in order to maintain the health,
independence and mobility for aged population. The current research found that 42 out of every
100 old-old-to-oldest old Jamaican reported an illness in 2002 and this increased to 52 out of
every 100. The substantiate matter is not merely the increase in dysfunctions; but it is the
epidemiological transition in typology of diseases. Health conditions were not only reported,
they were substantially diagnosed by a medical practitioner. An alarming finding was the
exponential increase in number of diabetic (576%) and cold cases (330.77%) in 2007 over 2002,
indicating the challenge of revamping lifestyle at older ages. It should be noted here that the
average age for an old-old-to-oldest-old having diabetes mellitus increased from 79.5 years to
90.0 years, and therefore this reinforces the point that psychological reluctance to live with
critical changes that diabetes mellitus may cause.
The challenge for the old-old-to-oldest in Jamaica is not merely the lifestyle changes that
follow diabetes mellitus; but the complication from having more than one illnesses and the issues
surrounding the diseases. These issues include blindness, renal failure and micro-vascular
complications. Forty-four out of every 100 persons in the sample had hypertension in 2007, and
the fact that diabetes mellitus and hypertension are strongly related, the old-old-to-oldest-old will
be experiencing many health problems. A study by Callender [27] found that 50% of individuals
38
with diabetes had a history of hypertension and given that Morrison [28] opined that these are
twin problems for the Caribbean, it is more problematic for the people 75+ years.
Studies have shown that ageing is directly correlated with increased health conditions,
this research found that such a reality dissipates after 75+ years. While this study is not able to
provide an explanation for this finding, factors such as sex, marital status, poverty and area of
residence are no longer contributions to health disparity which contradicts other studies [29-34].
Poverty, which is critical to health determinant [35,36] and the fact that it explains incapacity to
afford food, health care and other necessities, may seem improbable as not being a predictor of
good health of old-old-to-oldest old Jamaicans. However, it is associated with health conditions
for this sample. This means that health status is wider than dysfunction, and how this cohort feels
about life is even broader than the challenge of physical incapacity. In spite of this claim, health
conditions are a strong predictor of health status for the old-old-to-oldest-old in Jamaica. This
concurs with Hambleton and colleagues’ work [13] which found that 33.6% of the total
explanatory power (38.2%) of health status of elderly Barbadians was accounted for by current
health conditions. Embedded in Hambleton et al. [13] and the current study is the critical role
that current health conditions play in determining health status.
Conclusion
This study provides information upon which public health and health practitioners can make
more informed decisions about this age group. A fundamental way for this impetus to proceed is
the immediate diabetes education in the elderly population in particular those 75+ years. On a
panel titled ‘Diabetes Education for the Elderly’ at the 11th Annual international Conference on
‘Diabetes and Ageing’ conference in 2005 at the Jamaica Conference Centre, Merrins [37] called
39
for diabetes care treatment for elderly which indicates that the issue of diabetes education is not
new but that it is even more important today within the context of the current findings.
With over 570% more diabetic cases found in the old-old-to-oldest elderly in Jamaica,
this means that on average 96% more cases are diagnosed each year. This is a massive increase
in such cases, and cannot go unabated. The increase in diabetes mellitus could be accounted for
by the new persons who become 75 years each year or a higher percentage cases that were
formerly undetected become diagnosed. Which ever is the case, a public health promotion thrust
is required to test all Jamaicans 75+ within the context of a disease prevention agenda and
healthy life expectancy. Hence, the implications of the shift in health conditions will create a
health disparity between 75+ year adults and the rest of the population. This requires better
management of older persons [38], which will also require that people 75+ with good health be
tested for diabetes mellitus.
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17. World Health Organization, (WHO). Definition of an older or elderly person. Washington DC: 2009.
18. Erber J. Aging and older adulthood. New York: Waldsworth; 2005.
19. Planning Institute of Jamaica, (PIOJ), Statistical Institute of Jamaica, (STATIN). Jamaica Survey of Living Conditions, 1989-2006. Kingston: PIOJ, STATIN;1989-2007. 20. Lyubomirsky S. Why are some people happier than others? The role of cognitive and motivational process in wellbeing. Am Psychologist. 2001;56:239-249.
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21. Sheldon K, Lyubomirsky S. How to increase and sustain positive emotion: The effects of expressing gratitude and visualizing best possible selves. J of Positive Psychology. 2006;1:73-82.
22. Abbe A, Tkach C, Lyubomirsky S. 2003. The art of living by dispositionally happy people. J of Happiness Studies. 2003;4:385-404.
23. Diener E, Seligman MEP. 2002, Very happy people. Psychological Sci. 2002;13: 81–84.
24. WHO. Health promotion glossary. Geneva: World Health Organization; 1998.
25. WHO. Primary prevention of mental, neurological and psychosocial disorder. Geneva: WHO; 1998.
26. WHO. The world health report, 1998: Life in the 21st century a vision of all. Geneva: WHO;1998.
27. Callender J. Lifestyle management in the hypertensive diabetic. Cajanus. 2000;33:67-70.
28. Morrison E. Diabetes and hypertension: Twin trouble. Cajanus. 2000;33:61-63.
29.WHO. The Social Determinants of Health. Washington DC: WHO; 2008.
30. Victorino CC, Guathier AH. The social determinants of child health: variations across health outcomes – a population-based cross-sectional analysis. BMC Pediatrics. 2009, 9:53
31. Kelly M, Morgan A, Bonnefog J, Beth J, Bergmer V. The Social Determinants of Health: developing Evidence Base for Political Action, WHO Final Report to the Commission; 2007.
32. Wilkinson R, Marmot M, eds. Social Determinants of Health. The Solid Facts. 2nd ed. Copenhagen Ø: World Health Organization; 2003.
33. Solar O, Irwin A. A Conceptual Framework for Analysis and Action on the Social Determinants of Health. Discussion paper for the Commission on Social Determinants of Health. Geneva: WHO; 2007.
34. Graham H. Social Determinants and their Unequal Distribution Clarifying Policy Understanding The MelBank Quarterly. 2004; 82:101-124.
35. Marmot M. The influence of Income on Health: Views of an Epidemiologist. Does money really matter? Or is it a marker for something else? Health Affairs. 2002; 21: 31-46.
36. Alleyne GAO. Equity and health: Views from the Pan American Sanitary Bureau. In: Pan American Health Organization, (PAHO). Equity and health. Washington DC: PAHO; 2001. p. 3-11.
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37. Herd P, Goesling B, House JS. Socioeconomic Position and Health: The Differential Effects of Education versus Income on the Onset versus Progression of Health Problems. J of Health & Soci Behavior. 2007; 48:223-238
38. Merrins C. Special considerations in providing medical nutrition therapy to the elderly with diabetes. West Indian Med J. 2005; 54:39.
43
Table 2.2.1. Socio-demographic characteristics of sample Variable
2002
2007
Frequency
%
Frequency
%
Sex Male 453 42.4 120 42.6 Female 616 57.6 162 57.4 Marital status Married 304 29.2 88 32.4 Never married 255 24.5 66 24.3 Divorced 18 1.7 6 2.2 Separated 22 2.1 7 2.6 Widowed 442 42.5 105 38.6 Income Quintile Poorest 20% 239 22.4 56 19.9 Poor 216 20.2 51 18.1 Middle 195 18.2 74 26.2 Wealthy 194 18.1 58 20.6 Wealthiest 20% 225 21.0 43 15.2 Self-reported illness Yes 441 42.3 141 51.6 No 601 57.7 132 48.4 Health care-seeking behaviour Yes 306 69.2 102 72.9 No 136 30.8 38 27.1 Area of residence Rural 731 68.4 83 50.7 Semi-urban 222 20.8 56 19.9 Urban 116 10.9 143 29.4 Educational level Primary or below 662 66.5 Secondary 309 31.1 Tertiary 24 2.4 Health insurance coverage Yes 48 4.6 26.7 No 998 998 73.3 Age Mean (SD) 81.29 yrs (±5.6yrs) 81.37 yrs (±5.38yrs) Public health care expenditure Mean (SD)
Ja $341.54 (±Ja.$1165.60) Ja $368.89.54 (±Ja.$1518.66)
Private health care expenditure Mean (SD)
Ja. $1436.23 (±Ja.$2060.42) Ja. $1856.04 (±Ja.$4347.78)
44
Table 2.2.2. Self-reported illness by sex of respondents, 2002 and 2007
Self-reported
illness
20021
20072
Male Female Male Female
N (%) N (%) N (%) N (%)
Yes 174 (39.3) 267 (44.6) 60 (51.3) 81 (51.9)
No 269 (60.7) 332 (55.4) 57 (48.7) 75 (48.1)
Total 443 599 117 156
1 χ2 (df = 1) = 2.927, P =0.087
2 χ2 (df = 1) = 0.011, P =0.916
45
Table 2.2.3. Self-reported illness by marital status, 2002
Self-reported illness
Marital status*
Married Never married Divorced Separated Widowed
N (%) N (%) N (%) N (%) N (%)
Yes 140 (46.8) 88 (34.8) 9 (50.0) 10 (45.5) 190 (43.2)
No 159 (53.2) 165 (65.2) 9 (50.0) 12 (54.5) 250 (56.8)
Total 299 253 18 22 440
* χ2 (df = 4) = 9.027, P =0.060
46
Table 2.2.4. Self-reported illness by marital status, 2007
Self-reported illness
Marital status*
Married Never married Divorced Separated Widowed
N (%) N (%) N (%) N (%) N (%)
Yes 55 (62.5) 26 (40.0) 4 (66.7) 4 (57.1) 51 (49.0)
No 33 (37.5) 39 (60.0) 2 (33.3) 3 (42.9) 53 (51.0)
Total 88 65 6 7 104
* χ2 (df = 4) = 8.589, P =0.072
47
Table 2.2.5. Self-reported illness by Age cohort, 2002 and 2007
Self-reported
illness
20021
20072
Old-Old Oldest-Old Old-Old Oldest-Old
N (%) N (%) N (%) N (%)
Yes 333 (42.8) 108 (40.9) 110 (52.9) 31 (47.7)
No 445 (57.2) 156 (59.1) 98 (47.1) 34 (52.3)
Total 778 264 208 65
1 χ2 (df = 1) = .289, P =0.591
2 χ2 (df = 1) = .535, P =0.465
48
Table 2.2.6. Mean age of oldest-old with particular health conditions
Health
conditions
20021
20072
Mean Age (±SD) Mean Age (±SD)
Cold 80.00 - 77.63 (±1.77)
Diarrhoea 86.00 - 85.00 (±9.66)
Asthma 0.00 - 81.00 (±5.20)
Diabetes mellitus 79.50 (±2.50) 90.92 (±4.84)
Hypertension 80.13 (±0.84) 81.21 (±4.95)
Arthritis 79.32 (±0.69) 79.13 (±3.54)
Other 81.64 (±1.75) 83.90 (±6.82)
Total 80.14 (±4.73) 82.75 (±4.50)
F statistic [7,134] = 2.085, P = 0.049
49
Table 2.2.7. Diagnosed Health Conditions by Aged cohort
Diagnosed
Health
conditions
20021
20072
Aged cohort Aged cohort
Old-Old Oldest-Old Old-Old Oldest-Old
% % % %
Cold 1.5 0.0 7.2 0.0
Diarrhoea 0.0 8.3 2.7 3.2
Asthma 0.0 0.0 1.8 3.2
Diabetes mellitus 3.0 0.0 11.1 16.1
Hypertension 47.8 58.3 44.1 45.2
Arthritis 35.8 8.3 12.6 6.5
Other 11.9 25.0 11.7 22.6
No 0.0 0.0 2.7 3.2
1 χ2 (df = 1) = 10.028, P =0.074
2 χ2 (df = 1) = 5.382 P =0.613
50
Table 2.2.8. Self-reported illness (in %) by health status.
Self-reported illness
Health Status
Good Fair Poor
n (%) n (%) n (%)
Yes 21 (25.3) 60 (55.0) 60 (74.1)
No 62 (74.7) 49 (45.0) 21 (25.9)
Total 83 109 81
χ2 (df = 2) = 39.888, P < 0.001, cc=0.357
51
Table 2.2.9. Health care-seeking behaviour and health status, 2007
Health care-seeking behaviour
Health Status
Good Fair Poor
n (%) n (%) n (%)
No 9 (42.9) 21(35.6) 8 (13.3)
Yes 12 (57.1) 38 (64.4) 52 (86.7)
Total 21 59 60
χ2 (df = 2) = 10.539, P = 0.005, cc=0.265
52
Table 2.2.10. Health care-seeking behaviour by health status controlled for aged cohort
Aged cohort
Health status
Total
Good
Fair
Bad
Old-old1 Health Care-
Seeking Behaviour No 7 (46.7) 18 (36.7) 5 (10.9) 30 (27.3)
Yes 8 (53.3) 31 (63.3) 41 (89.1) 80 (72.7) Total 15 49 46 110 Oldest-old2 Health Care-
Seeking Behaviour No 2 (33.3) 3 (30.0) 3 (21.4) 8 (26.7)
Yes 4 (66.7) 7 (70.0) 11 (78.6) 22 (73.3) Total 6 10 14 30
1 χ2 (df = 2) = 11.296, P =0.004, cc=0.305
2 χ2 (df = 2) = 0.390, P =0.823
53
Table 2.2.11. Logistic regression on Good Health status by variables
Variable Coefficient Std. Error
Wald statistic Odds ratio 95.0% C.I.
Self-reported illness -1.735 0.317 29.950 0.176 0.095 - 0.328*** Age
-0.041
0.030
1.910
0.960
0.905 - 1.017
Middle Class
-0.083
0.414
0.040
0.921
0.409 - 2.072
Upper class
0.391
0.759
0.264
1.478
0.334 - 6.546
†Poor Married
0.297
0.393
0.574
1.346
0.624 - 2.907 Divorced, separated or widowed
-0.110
0.376
0.086
0.896
0.428 - 1.872
†Never married Urban area
0.347
0.350
0.981
1.414
0.712 - 2.808 Other town
-0.398
0.414
0.922
0.672
0.298 - 1.513
†Rural area Constant
2.979
2.456
1.471
19.667
-
χ2 =40.083, p < 0.001 -2 Log likelihood = 283.783 Nagelkerke R2 =0.222 Overall correct classification = 75.9% Correct classification of cases of good self-rated health = 90.5% Correct classification of cases of not good self-reported health = 42.0% †Reference group *P < 0.05, **P < 0.01, ***P < 0.001
54
Figure 2.2.1. Diagnosed health conditions, 2002 and 2007
Figure 1 expresses the percentage of people who reported being diagnosed with particular health
conditions in 2002 and 2007. Each number denotes a different health condition: cold, 1;
diarrhoea, 2; asthma,3; diabetes mellitus, 4; hypertension, 5; arthritis, 6; other (unspecified), 7;
and non-diagnosed illness, 8.
55
Figure 2.2.2. Self-reported illness (in %) by Income Quintile, 2002 and 2007
Figure 2 expresses the percentage of people who reported an illness by income quintiles for 2002 and 2007. Q1 denotes the poorest 20% to the wealthiest 20% (ie Q5).
56
CHAPTER
3 Self-evaluated health and health conditions of rural residents in a middle-income nation In Jamaica, in 1989, the national poverty rate was 30.5% and this exponentially fell by 208.1% in 2007, but in the latter year, rural poverty was 4 times more than peri-urban and 3 times more than urban poverty rate. Yet there is no study on health status and health conditions in order to examine changes among rural residents. The present study aims to (1) examine epidemiological shifts in typology of health conditions in rural Jamaicans, (2) determine correlates and estimates of self-evaluated health status of rural residents, (3) determine correlates and estimates of self-evaluated health conditions of rural residents and (4) assist policy makers in understanding how intervention programmes can be structure to address some of the identified inequalities among rural residents in Jamaica. In 2002, 14% of respondents indicated having an illness in the 4-week period of the survey compared in 17% in 2007. For 2002, there are 12 determinants of health: 11 social and 1 psychological determinants. In 2007, there were 7 determinants of health: 6 social and 1 biological variables. The determinants accounted for 22.6% of the explanatory power of the health model for 2002 and 44.7% for 2007. Sixty-eight percentage points of the health status model can be accounted for by self-reported illness (i.e. R squared = 30.4%). With the exponential increase in diabetes mellitus and health inequalities that exists today in rural Jamaica, public health and other policy makers need to use multidimensional intervention strategy to address those inequalities. Introduction The health of a population is critical to all forms of development. This is a justifiable rationale
for governments’ investment in health care and the health system. Despite governments in Latin
America and the Caribbean increased investment in health since the 1980 [1], there are still many
inequities in health among and within their nations [2]. This is evident in the health disparities
indicators as well as the social determinants of health [3-6]. The advancement in technology and
medical sciences have not abated the disparities in infant mortality, poverty, health service
57
utilization, and health differentials among Latin America and Caribbean nations as well as
among the social hierarchies. Casas et al. [4] cited that the improvements in health in the region
are not in keeping with the region’s economic development rates and the same can be said
between the wealthy and the poor. In Jamaica, which is an English-speaking country in the
region, in 1989 the national poverty rate was 30.5% and this exponentially fell by 208.1% in
2007, but in the latter year, rural poverty was 4 times more than peri-urban and 3 times more
than urban poverty rate [9].
Statistics from the WHO for 2007 showed that both life expectancy and healthy life
expectancy at birth was at least 4 years more for females than males [7]. Many empirical studies
have found that rural residents had lower health status and/or more health conditions, greater
levels of poverty and lower levels of education compared to their urban counterparts [8-18], and
these are also the case in Jamaica [19]. Those disparities speak to socio-economic and health
inequalities in many states. Although there is empirical evidence which revealed that health
inequalities and inequities do exist between rural and urban residences as well as among social
hierarchies and between the sexes in Latin America and the Caribbean in particular Jamaica,
only few studies were found that have examined the health status of rural people in the region
[14, 19-28]. The different researches in the region on rural health have not investigated
epidemiological transition of health conditions in the rural areas, and in order to tackle the
identified health disparities and inequalities, intervention techniques must be based on analytic
research on the cohort and not a general understanding of the nation.
Inequity and/or inequalities in health can only be addressed in the region if they are
understood through research within each nation, and that policy makers cannot rely on finding of
studies outside of the region or their countries in order to effectively remedy the challenges that
58
they face. The relationship between poverty and ill health is empirically established, but the
focus of the region since the 1980s has been poverty reduction and while this has been
materializing, the health disparities are still evident today [3]. Embedded in the literature
therefore are income maldistribution, working conditions and health outcome inequalities, health
determinants inequalities, lower material wellbeing and poverty direct influence on health.
Poverty also indirectly influences health service utilization, quality of received care and healthy
life expectancy. With poverty been substantially a rural phenomenon, investment in health in
rural areas require an understanding of the health and changes occurring in health conditions
among the residents. It follows therefore that a research for a nation with area of residence
between an explanatory variable does not provide a comprehensive insight into many of the
issues that are embodied in a particular municipality (or area of residence). For decades (since
the 1980s), Jamaican statistical agencies have been collected data on health status of the people
and these are used to guide policies, but with disproportionately more people in rural areas in
poverty and poverty influences inequalities and/or inequities in a group, then this is rationale for
the research of rural Jamaicans.
The WHO [8] opined that 80% of chronic illnesses were in low and middle income
countries, suggesting that illness interfaces with poverty and other socio-economic challenges.
Poverty does not only impact on illness, it causes pre-mature deaths, lower quality of life, lower
life and unhealthy life expectancy, low development and other social ills such as crime, high
pregnancy rates, and social degradation of the community. According to Bourne & Beckford
[15], there is a positive correlation between poverty and unemployment; poverty and illness; and
crime and unemployment. Embedded in those findings are the challenges of living in poverty,
and the perpetual nature of poverty and illness, illness and poverty, poverty and unemployment,
59
economic deprivation and psychological frustration of poor families. Sen [18] encapsulated this
well when he forwarded that low levels of unemployment in the economy is associated with
higher levels of capabilities. This highlights the economic challenge of unemployment and
equally explains the labour incapacitation on account of high levels of unemployment, which
goes back to the WHO’s perspective that chronic illnesses are more experienced by low-to-
middle income peoples. According to WHO [8], 60% of global mortality is caused by chronic
illness, and this should be understood within the context that four-fifths of chronic dysfunctions
are in low-to-middle income countries.
Within the aforementioned findings, area of residence in particular rural area is too much
of an important variable to be treated as an explanatory concept. The health outcome inequalities
will be decline merely by investing in the health sector of the general population. Montgomery
[17] opined that urban causes of mortality and disability provide understanding into urban-rural
health differentials. The paper provides answers some of urban health disparities in developing
countries and compares those situations with those faced by rural residents. Montgomery’s
findings [17] were generally on developing countries and while it does give some insights to the
urban-rural health inequalities, it cannot be used to formulate policies or intervention strategies
specifically for Jamaica. The rationale embedded in this argument is the fact that not all
developing countries are at the same socio-economic stage of development, and therefore
requires research for any chosen intervention techniques that they decide to utilize to effect
health changes. Concurrent investment in health is critical to economic development [29]; once
again this has not result in removal of health inequalities in Latin America and the Caribbean in
particular Jamaica [3-5]. Therefore more research is needed to understand the health outcome in
rural zones in order to the health disparity gaps in the region and within political states. The
60
present study aims to (1) examine epidemiological shifts in typology of health conditions in rural
Jamaicans, (2) determine correlates and estimates of self-evaluated health status of rural
residents, (3) determine correlates and estimates of self-evaluated health conditions of rural
residents and (4) assist policy makers in understanding how intervention programmes can be
structure to address some of the identified inequalities among rural residents in Jamaica.
Materials and Method
The current study extracted samples of 15,260 and 3,322 rural residents from two surveys
collected jointly by the Planning Institute of Jamaica and the Statistical Institute of Jamaica for
2002 and 2007 respectively [30,31]. The method of selection of the sample from each survey
was solely based on rural residence. The survey (Jamaica Survey of Living Conditions) was
begun in 1989 to collect data from Jamaicans in order to assess policies of the government. Each
year since 1989, the JSLC has added a new module in order to examine that phenomenon which
is critical within the nation. In 2002, the foci were on 1) social safety net and 2) crime and
victimization; and for 2007, there was no focus. The sample for the earlier survey was 25,018
respondents and for the latter, it was 6,783 respondents.
The survey was drawn using stratified random sampling. This design was a two-stage
stratified random sampling design where there was a Primary Sampling Unit (PSU) and a
selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which
constitutes a minimum of 100 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
61
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 labor force. One third
of the Labor Force Survey (i.e., LFS) was selected for the JSLC [30, 31]. The sample was
weighted to reflect the population of the nation.
The JSLC 2007 [30] was conducted in May and August of that year, while the JSLC
2002 was administered between July and October of that year. The researchers chose this survey
based on the fact that it is the latest survey on the national population and that it has data on self-
reported health status of Jamaicans. An administered questionnaire was used to collect the data,
which were stored and analyzed using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA).
The questionnaire was modeled from the World Bank’s Living Standards Measurement Study
(LSMS) household survey. There are some modifications to the LSMS, as JSLC is more focused
on policy impacts. The questionnaire covered areas such as socio-demographic variables such as
education; daily expenses (for past 7-days), food and other consumption expenditures, inventory
of durable goods, health variables, crime and victimization, social safety net, and anthropometry.
The questionnaire contains standardized items such as socio-demographic variables, excluding
crime and victimization that were added in 2002 and later removed from the instrument, with the
except of a few new modules each year. The non-response rate for the survey for 2007 was
26.2% and 27.7%. The non-response includes refusals and rejected cases in data cleaning.
Measurement
Dependent variable
Self-reported illness (or self-reported dysfunction): The question was asked: “Is this a diagnosed
recurring illness?” The answering options are: Yes, Influenza; Yes, Diarrhoea; Yes, Respiratory
diseases; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. A binary
62
variable was later created from this construct (1=no 0=otherwise) in order to be applied in the
logistic regression. This was used to indicate health status (i.e. dependent variable) for 2002.
Self-rated health status: is measured using people’s self-rate of their overall health status [32],
which ranges from excellent to poor health status. The question that was asked in survey was
“How is your health in general?” And the options were very good; good; fair; poor and very
poor. For the purpose of the model in this study, self-rated health was coded as a binary variable
(1= good and fair, 0 = Otherwise) [33-38]. The binary good health status was used as the
dependent variable for 2007.
Covariates
Age is a continuous variable which is the number of years alive since birth (using last birthday)
Social hierarchy: This variable was measured based on income quintile: The upper classes were
those in the wealthy quintiles (i.e. quintiles 4 and 5); middle class was quintile 3 and poor class
was those in lower quintiles (i.e. quintiles 1 and 2).
Medical care-seeking behaviour was taken from the question ‘Has a health care practitioner, or
pharmacist being visited in the last 4 weeks?’ with there being two options Yes or No. Medical
care-seeking behaviour therefore was coded as a binary measure where 1= Yes and 0 =
otherwise.
Crowding is the total number of individuals in the household divided by the number of rooms
(excluding kitchen, verandah and bathroom). Age is a continuous variable in years.
Sex. This is a binary variable where 1= male and 0 = otherwise.
63
Social supports (or networks) denote different social networks with which the individual is
involved (1 = membership of and/or visits to civic organizations or having friends who visit ones
home or with whom one is able to network, 0 = otherwise).
Psychological conditions are the psychological state of an individual, and this is subdivided into
positive and negative affective psychological conditions [39, 40]. 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.
Statistical Analysis
Descriptive statistics such as mean, standard deviation (SD), frequency and percentage were used
to analyze the socio-demographic characteristics of the sample. Chi-square was used to examine
the association between non-metric variables, t-test and an Analysis of Variance (ANOVA) were
used to test the relationships between metric and/or dichotomous and non-dichotomous
categorical variables. The level of significance used in this research was 5% (i.e. 95% confidence
interval).
Results
Demographic
Table 3.3.1 examines the demographic characteristics of the samples for 2002 and 2007. The
samples were 15,260 and 3,322 rural respondents for 2002 and 2007 respectively. The findings
revealed that 96.3% of the sample for 2002 respondents to the question ‘Have you had any
illness in the past 4-weeks and the rate was 97% for 2007. In 2002, 14% of those who responded
64
to the question of illness claimed yes compared to 17% in 2007. When the respondents were
asked to state the experienced health conditions, in 2002, 1.3% answered compared to 14.8% in
2007. Self-reported health conditions showed that exponential increases in influenza and
respiratory conditions in 2007 over 2002. Hypertensive and arthritic cases fell by 44.1% and
75.7% respectively, while diabetes mellitus increased by 150% over the studied period.
Eight-one percentage points of sample claimed to have at least a good health status and
6% at least poor health. Of those who indicated at least good health, 37% stated very good (or
excellent) health compared to 1.1% who claimed very poor health of those who indicated at least
poor health status.
When respondents were asked ‘Why did you not seek medical care for your illness?’ in
2002, 23.2% stated could not afford it; 41.3% was not ill enough and 22.2% used home remedy.
For 2007, 17.4% claimed that they were unable to afford it, 43.3% was not ill enough and 16.8%
stated used home remedy.
65
Table 3.3.1. Demographic characteristics, 2002 and 2007 Variable
2002 2007 n % n %
Sex Male 7,727 50.6 1,654 49.8 Female 7,524 49.3 1,668 50.2 Marital status Married 2,460 25.6 513 24.1 Never married 6,436 66.6 1,462 68.7 Divorced 56 0.6 22 1.0 Separated 104 1.1 20 0.9 Widowed 610 6.3 112 5.3 Social hierarchy Lower 7,298 47.8 1,828 55.0 Middle 3,169 20.8 650 19.6 Wealthy 4,791 31.4 844 25.4 Self-reported illness Yes 1,987 13.5 536 16.6 No 12,713 86.5 2,688 83.4 Self-reported health conditions Acute Influenza 1 0.5 80 16.3 Diarrhoea 4 2.1 19 3.9 Respiratory diseases 6 3.1 51 10.4 Chronic Diabetes mellitus 10 5.2 64 13.0 Hypertension 82 42.9 118 24.0 Arthritis 48 25.1 30 6.1 Other 40 20.9 130 26.4 Medical care-seeking behaviour Yes 1,302 63.8 349 63.3 No 740 36.4 202 36.7 Medical care utilization Public hospitals 499 39.1 127 37.2 Private hospitals 80 6.3 8 2.3 Public health care centres 285 22.3 76 22.3 Private health care centres 528 41.3 158 46.3 Health insurance coverage Yes 1,036 7.0 464 14.5 No 13,714 93.0 2,715 85.5 Age Median, in years, range) 23 (0 to 99) 25 (0 to 99) Length of illness, in days, Median (range) 7 (0 to 90) 7 (0 to 99)
66
Bivariate analyses
Table 3.3.2 presents self-reported health conditions by sex, age, health care-seeking behaviour,
and length of illness of sample. Females were more likely to indicated suffering from the
different health conditions than males except for respiratory diseases. Of those who stated a
particular health conditions, those with chronic illness such as hypertension and arthritis were
more likely to send more time suffering from the diseases.
67
Table 3.3.2: Self-reported health conditions by particular social variables Variable
Health conditions
P Acute conditions Chronic
Influenza Diarrhoea Respiratory Diabetes mellitus
Hypertension
Arthritis Other
2002 Sex (%) 0.045 Male 0.0 25.0 83.3 20.0 30.5 20.8 35.0 Female 100.0 75.0 16.7 80.0 69.5 79.2 65.0 Total 1 4 6 10 82 48 40 Age - in years- Mean (SD) 80.0 (0.0) 1.8 (1.7) 14.0 (24.6) 63.7 (13.2) 68.7
(13.7) 68.4
(12.60 56.0
(23.4) < 0.0001
Health care-seeking behaviour Yes (%) 0.0 75.0 100.0 88.9 79.3 83.3 65.0 0.05 Total 10 14 6 9 82 48 40 Length of illness –in days – Mean (SD)
3 (0) 4 (2) 11 (5) 12 (11) 16 (11) 18 (11) 19 (12) 0.045
2007 Sex (%) <0.0001 Male 42.5 36.8 56.9 20.3 27.1 46.7 43.1 Female 57.5 63.2 43.1 79.7 72.9 53.3 56.9 Total 80 19 51 64 118 30 130 Age - in years- Mean (SD) 19.5
(24.8) 20.1 (28.5) 24.3 (23.8) 56.5 (17.4) 64.0
(17.1) 68.3
(12.0) 36.0
(25.0) <0.0001
Health care-seeking behaviour < 0.0001 Yes (%) 41.3 52.6 62.7 75.0 64.4 46.7 70.5 Total n 80 19 51 64 118 30 129 Length of illness –in days – Mean (SD)
8 (6) 5 (2) 42 (172) 76 (135) 104 (239) 112 (217) 57 (188) 0.004
68
Table 3.3.3 examines health care-seeking behaviour by sex, self-reported illness, health
coverage, social hierarchy, educational levels, age and length of illness for 2002 and 2007. Based
on Table 3, the mean age of someone who sought medical care is greater than someone who does
not. There is no significant statistical association between medical care-seeking behaviour and
self-reported illness, but there is a relationship between length of illness and medical care-
seeking behaviour.
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Table 3.3.3. Health care-seeking behaviour by sex, self-reported illness, health coverage, social hierarchy, education, age and length of illness, 2002 and 2007 Variable
2002 2007 Health care-seeking behaviour Health care-seeking behaviour
Yes No P Yes No P N (%) N (%) N (%) N (%)
Sex 0.011 0.112 Male 511 (39.2) 333 (45.0) 134 (38.4) 89 (44.1) Female 791 (60.8) 407 (55.0) 215 (61.6) 113 (55.9) Self-reported illness 0.360 0.130 Yes 1261 (97.0)) 713 (96.6) 335 (96.3) 199 (98.5) No 39 (3.0) 25 (3.4) 13 (3.7) 3 (1.5) Health insurance coverage 0.197 0.013 Yes 89 (6.9) 40 (5.4) 270 (77.4) 173 (86.1) No 1210 (93.1) 700 (94.6) 79 (22.6) 28 (13.9) Social hierarchy <0.0001 0.104 Lower 545 (41.9) 363 (49.1) 167 (47.9) 115 (56.9) Middle 248 (19.0) 157 (21.2) 79 (22.6) 41 (20.3) Wealthy 509 (39.1) 220 (29.7) 103 (29.5) 46 (22.8) Educational level <0.0001 0.623 Primary or below 402 (40.5) 208 (41.5) 336 (96.3) 191 (94.6) Secondary 569 (57.4) 279 (55.7) 11 (3.2) 9 (4.5) Tertiary 21 (2.1) 14 (2.8) 2 (0.6) 2 (1.0) Age Mean (SD) – in years 46.4 (27.4) 40.4 (28.3) <0.0001 43.5 (27.5) 37.9 (146.8) 0.025 Length of illness Mean (SD) – in days 12 (11) 10 (9) <0.0001 7 (20) 5 (15)
0.01
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Multivariate analyses
Table 3.3.4 represents information on social and psychological determinants of health of rural
residents for 2002 and 2007. Based on Table 4, in 2002, there are 12 determinants of health: 11
social and 1 psychological determinants. On the other hand, in 2007, there were 7 determinants
of health: 6 social and 1 biological variables. The determinants accounted for 22.6% of the
explanatory power of the health model for 2002 and 44.7% for 2007. Sixty-eight percentage
points of the health status model can be accounted for by self-reported illness (i.e. R squared =
30.4%).
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Table 3.3.4. Stepwise Logistic regression: Social and psychological determinants of self-evaluated health, 2002 and 2007 Explanatory variables:
2002 2007 Coefficient Std.
Error Odds ratio
95% CI Coefficient Std. Error
Odds ratio
95% CI
Income 0.000 0.000 1.00 1.00-1.00 0.000 0.000 1.00 1.00-1.00 Age -0.044 0.002 0.96 0.93-0.96 -0.052 0.004 0.95 0.94-0.96 Middle NS NS NS NS 0.321 0.196 1.38 0.94-2.02 Wealthy -0.311 0.090 0.73 0.61-0.88 NS NS NS NS †Lower 1.00 1.00 Total Durable good 0.058 0.013 1.06 1.03-1.09 NS NS NS NS Separated, divorced or widowed -0.367 0.109 0.69 0.56-0.86 NS NS NS NS Married -0.307 0.077 0.74 0.63-0.86 NS NS NS NS †Never married 1.00 NS NS NS NS Tertiary -0.175 0.065 0.84 0.72-0.98 NS NS NS NS †Primary or below 1.00 Social support -0.229 0.070 0.80 0.70-0.90 NID NID NID NID Male 0.803 0.011 2.23 1.95-2.56 0.563 0.134 1.76 1.35-2.28 Negative affective conditions -0.062 0.037 0.94 0.92-0.96 NID NID NID NID Number of females in household 0.123 0.025 1.13 1.05-1.22 NID NID NID NID Number of children in household 0.056 0.006 1.06 1.01-1.11 NID NID NID NID Length of illness -0.039 0.193 0.96 0.95-0.97 NS NS NS NS Crowding NS NS NS NS -0.081 0.029 0.92 0.87-0.98 Medical care-seeking = yes NS NS NS NS -1.01 0.26 0.36 0.21-0.60 Self-reported illness -2.225 0.15 0.11 0.08-0.15 -LL 6,381.3 1,562.6 n 12,666 2,817 Nagelkerke R square 0.226 0.447 χ2 1220.5 670.0 NS – not significant (P > 0.05) NID – not in dataset and/or could not be measured based on the available data
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Table 3.3.5 shows the contribution of each explanatory variable to the model for 2002 and 2007.
Based on Table 5, of the social and psychological determinants of health, age explains more the
variability in health than another other determinant. Income contributed at most 0.2% to health of
respondents. Using the not reporting an illness to measure health of rural respondents, age
accounted for 77% of the health; but when self-reported health status is used to measure health,
age accounted for only 11.5%.
Table 3.3.5. Stepwise Logistic regression: R-squared for Social and psychological determinants of self-evaluated health, 2002 and 2007 Explanatory variables:
2002 2007 R squared R squared
Income 0.1 0.2 Age 17.4 11.5 Middle NS 0.4 Wealthy 0.1 NS Total Durable good 0.2 NS Separated, divorced or widowed 0.1 NS Married 0.2 NS Tertiary 0.1 NS Social support 0.2 NS Male 2.2 1.2 Negative affective conditions 0.4 NID Number of females in household 0.5 NID Number of children in household 0.1 NID Length of illness 1.0 NS Crowding NS 0.2 Medical care-seeking = yes NS 0.8 Self-reported illness 30.4 NS – not significant (P > 0.05) NID – not in dataset
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Discussion
The current health status of rural respondents was good (i.e. 81 out of every 100), but 17 out of
every 100 had an illness. Inspite of reporting an illness, the present study found that 36 out of
every 100 ill respondents had not sought medical care. Of those who did not utilize medical care
although they indicated an illness, at least 41% claimed financial inadequacies and in 2007, 17%
used home remedy. The results revealed that rural respondents have a conceptualization of
illness and the fact that medical care outside of the home should be utilized based on length of
illness and not mere ailments. Concurrently, illness accounts for most of current health status
which emphasizes the dominant of the biomedical perspective in viewing health and health care
in rural Jamaica. While self-reported chronic health conditions fell by over 41% in 2007 over
2002, the percent of those who reported acute conditions increased by over 436%. Of the
increased cases of acute conditions, respiratory diseases accounted for 235% while influenza
accounted for 3160% increase over 2002. Although overall self-reported chronic health
conditions see a decline for 2007 over 2002, diabetes mellitus was the only condition that
showed an increase in the study (i.e. 150%). Interestingly, the current findings showed that
107.1% more rural residents were covered by health insurance in 2007 over 2000, but this was
corresponding to a minimal reduction in those seek medical care. The number of rural residents
who were classified into the lower (i.e. working) class increased by 15.1% and a 19.1% of those
in the wealthy class. With income being positively correlated with good health, an increase in the
number of people the lower class highlights reduction in health for 2007. Males continue to
report better health status than females, but this fell from 2.3 times more in 2002 to 1.8 times in
2007, which suggests that the reduction in income is substantially influence the quality of life of
rural males.
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The current findings concur with the literature that showed that severity of illness (or
length of illness), age, and health coverage are positively related to medical care seeking
behaviour than illness [41-43]. Statistics from national cross-sectional surveys in Jamaica since
1989 [9] revealed that females were approximately more likely to report an illness and utilize
medical care than males. When the absolute figures from the surveys were cross-tabulation, it
was found that the statistical association which existed in 2002 disappeared in 2007. This is not
atypical to Jamaica as a qualitative study in Pakistan on street children found that boys would
attend formal health care are more likely to attend based on severity of illness and if it affects
their economic livelihood [41]. Another study conducted in Anyigba, North-Central, Nigeria
found that [42] 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. In this research it was revealed that 43 out of every 100 rural residents
indicated that they were not ill enough which suggests that they would recover in time.
Health care facilities in Jamaica are primarily operated by females, and with the
perception in the culture that males must be masculine, which include exhibiting strength, power
and avoiding weakness, this is a justification of the rationale for severity of illness account for
medical care-seeking behaviour as against actual illness [41-43]. Dunlop et al’s finding which
found that females utilize health care facilities more than males [44] partially concurs with this
research that found this to be the case in 2002. In 2002, 1.6 times more females sought medical
care than males, but the study found that there was no significant association between sex and
medical care-seeking behaviour for 2007. The explanation of this is embodied in the two things,
(1) income, (2) inflation and (3) the increased number of people who were classified into the
lower class.
75
Income is positively correlated with social hierarchy, health, and employment status [16,
45-50]. Income which is among the social determinants of health, is directly associated with
health through material wellbeing, but it is also associated with occupational and social
hierarchies. The poor receives less of the income than the middle and wealthy classes, which
denotes that an increased in the number of people in the lower class, income will be reduced and
so will health status. It should be noted here that poverty which affect health, is exponential
greater in rural Jamaica and that there are more females in rural household. The health care-
seeking disparity which is diminished can be explained by the inflation over the study. In 2007,
inflation increased by 194% over 2006 [20] and coupled with the lower income, rural
respondents in particular females who are more likely to unemployment, owns less material
resources and increasingly are becoming single parents [9], would justify the narrowing of the
health care-seeking gap that existed in 2002.Williams et al. [42] found that medical care-seeking
behaviour did not differ significant between the sexes, which is in keeping with the situation for
2007 in this study.
The WHO [8] found that poverty is associated with increased health conditions.
Empirical evidence existed that showed the poverty is related to low levels of choices, income,
access to health care services, and opportunities, which is highlighted in this study. Latin
America and the Caribbean governments have increased investment in health care and in the
2006, the Jamaican government introduced the removal of public health care utilization fees for
children (0 to 18 years) and expanded the a drug for the elderly programme to all people who
suffer from particular chronic illnesses. While these undoubtedly increase the health outcomes
which would have been lower if those opportunities were not present, health inequalities still
exist among rural residents.
76
With all the investment in health from decentralization of the health care system, drug for
the elderly programmes, removal of health care user fees to health public care interventions,
there is a rise in acute health conditions in particular influenza and respiratory diseases. The good
news is the reduction in chronic health conditions. This good news is nothing to celebrate as
diabetes mellitus has increased exponentially in the last one half decade. The reduction in
number of hypertensive and arthritic cases correspond to lowered ages in reporting having those
illnesses. The mean age of reporting hypertension has declined by 5 years (to 64 years) and 7.2
years (to 56.5 years). Furthermore, Morrison [51] postulated that hypertension and diabetes are
now twin problems in the Caribbean and although the current study has shown a reduction in
self-reported hypertensive people in rural Jamaica, 24 out of every 100 health conditions were
accounted for by hypertension. Diabetes mellitus accounted for 13 out of every 100 health
conditions, which speaks to a future health rural problem. Another researcher found that 50% of
people with diabetes had a history of hypertension, and this future highlights a health challenge
for policy makers and public health practitioners. The lowered ages of reporting particular
chronic illnesses indicate that rural residents will be living longer with those conditions and this
measure increase burden on the health care system in the future.
A critical issue which emerged from this study is the value that rural residents ascribed to
illness in determining their health status. There is a strong negative statistical correlation between
self-reported illness and good health status. The findings indicated that 68% of the explanatory
power of good health status can be accounted for by illness. This is not atypical as a research by
Hambleton et al. on Barbadian elderly found that illness accounted for 88.0% of health status. It
can be extrapolated from those findings that (1) the older one gets, he/she places more emphasis
on illness in the evaluation of health status, (2) the relationship between illness and health
77
appears to more causal than an associative one, (3) the biomedical approach to measuring health
still predominates people’s perception, and (4) the culture which fashions the conceptualization
of health is influences health care-seeking. Those issues are principally among the reasons that
care is curative and not preventative in Jamaica and this is captured in the finding which showed
that health care-seeking behaviour is negatively correlated with good health. Rural respondents
who seek medical care are 64% less likely to report good health status, indicating embedded
cultural dominance of the biomedical approach in the conceptualization of health. The
dominance of the biomedical approach to the study of health in Jamaica is even high among
medical researchers as a study conducted in 2007/08 examined medical history; health care-
seeking behaviour; health (i.e. diseases, medication consumption), mental health, sexual
practices, dietary habits; lifestyle (i.e. violence and injury; smoking, narcotic and alcohol
behaviour), community and home milieu, suggesting the greater weight on health from the
perspective of illness, its treatment and measureable outcome as against people’s assessment of
their health status [53]. Another limitation of the ‘Jamaica Health and Lifestyle Survey II’ was
the omission of area of residence disaggregation of the collected though limited health data. The
current study bridges this gap, and goes further by using self-assessed heath status in addition to
self-rated health, health care-seeking behaviour and provide other pertinent health matters on
rural Jamaicans.
Conclusion
Health inequalities in rural Jamaica still exist today. The current study found that in the future
health care institutions will be called to invest more in the health system in order to address the
health challenges of increased diabetes mellitus as well as respiratory diseases. On the other
hand, despite investments in health by governments, progress in technology, public health
78
services, increased levels of education and income since the last century, decision makers, public
health practitioners and other health care providers need to recognize that increased life
expectancy and lowered infant mortality rates have not addressed the challenges of in the health
of rural population in Jamaica. General financial investment in health to control communicable
diseases that are particularly detrimental for children such as diarrhoea and respiratory diseases
are on the increase in rural areas, which means that the level of economic development since the
20th Century does not provide answers to the differences in health outcomes within a country.
The identified health disparities in rural Jamaica denote that investment in health and health
intervention strategies are not effectively addressing the health inequalities which are underlying
in the health statistics. This means that the health inequalities in those areas in Jamaica will fuel
future public health challenges for the societies, as well as increase the economic burden of
health care system. The analyses provided in the current study clearly highlight the need for
thinking that will incorporate the health realities of rural population in the agenda of policy
makers.
The way forward
The present work highlights the lingering dominance of the biomedical perspective that
influences health and health care in rural Jamaica. Hence the way forward for government and
policy makers including health care practitioners as well as public health educators in order to
reduce health inequalities is a multi-dimensional approach to health and health care as the current
mechanism is working. The researcher is proposing (1) mobile clinics, (2) community and house
visits from medical practitioners, (3) restructuring health care facilities to reflect a new
preventative thrust, (4) introduced preventative care approach as a subject in all schools, (5) that
the focus should not only be on the extreme of income poverty and health care access, but on
79
opportunities, empowerment, security of poor and rural residents, (6) there is a need for a social
security network that nutritious foods to rural residents, and (7) there is a need for the
modification to the way public health programmes are fashioned and operated as well as a
widening and new definition of the boundaries of public health intervention. These new
mechanisms will be costly, but a reorganization of expenditure means that some of the money
spent for curative care will be reduce as preventative care is the focal point and not curative
health treatment. Another important thing which is needed is research on the value system of
rural residents and this should be done using a longitudinal study in order to provide information
for health care intervention strategies.
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CHAPTER
4
Disparities in self-rated health, health care utilization, illness, chronic illness and other socio-economic characteristics of the Insured and Uninsured
Previous studies which have examined health status as regards the insured and uninsured have used a piecemeal approach. This study elucidates information on the self-rated health status, health care utilization, income distribution and health insurance status of Jamaicans. It also models self-rated health status, health care utilization and income distribution, and how these differ between the insured and uninsured. The majority of health insurance was owned by those in the upper class, (65%) compared to 19% for those in the lower socio-economic strata. No significant statistical difference was found between the average medical expenditure of those who had insurance coverage and the non-insured. 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) more likely to seek 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 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 half a percent of the variance in health care utilization. Health care utilization is a strong predictor of self-reported illness, but it was weaker than illness in explaining health care utilization (61.1% of 66.5%). Public health insurance was mostly acquired by those with chronic illnesses: (76%) compared to 44% private health coverage and 38% without coverage. The findings highlighted that any reduction in the health care budget in developing nations means that vulnerable groups (such as the elderly, children and the poor) will seek less care, and this will further increase mortality among those cohorts. Introduction This study examines the 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
84
health insurance coverage, suggesting that a large percentage of the population are obliged to
make out-of-pocket payments or use government assistance to pay their medical bills.
The health of individuals within a society goes beyond the individual to the socio-
economic development, standard of living, production and productivity of the nation.
Individuals’ health is therefore the crux of human development and survivability, and explains
the rationale as to why people seek medical care at the onset of ill-health. In seeking to preserve
life, people demand and utilize health care services. Western societies are structured so that
people meet health care utilization with a mixture of approaches. These approaches can be any
combination of out-of-pocket payments, health insurance coverage, government assistance and
assistance from the family.
In Latin America and the Caribbean, health care is substantially an out-of-pocket
expenditure aided by health insurance policies and government health care regimes. Within the
context of the realities in those nations, the health of the populace is primarily based on the
choices, decisions, responsibilities and burdens of the individual. Survival in developing nations
is distinct from Developed Western Nations, as Latin American and Caribbean peoples’
willingness, frequency, and demand for health care, as well as their health choices, are based on
affordability. Affordability of health care is assisted by health insurance coverage, as the
provision of care offered by governmental policies means that the public health care system will
be required to meet the needs of many people. Those people will be mostly children, the elderly
and those who belong to other vulnerable groups.
The public health care system in many societies often involves long queues, extended
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
85
of reducing future health care costs as well as an avoidance of the utilization of public health
care. Not having insurance in any society means a dependency on the public health care system,
premature mortality, vulnerability of disadvantaged groups, and often public humiliation. The
insured, on the other hand, are able to circumvent many of the experiences of the poor, the
elderly, children and other vulnerable cohorts who rely on the public health care system.
Insurance in developing nations, and in particular Jamaica, is a private arrangement between the
individual and a private insurance company. Such a reality excludes the retired, the elderly, the
unemployed, the 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 a 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 are in low and middle income countries, and 60% of
global mortality is caused by chronic illnesses [7]. It can be extrapolated from the WHO’s findings that
uninsurance is critical in answering some of the health disparities within and among the different
groups and sexes in the society. The realities of health inequalities between the poor and the
wealthy and the sexes in a society, with those in the lower income strata contracting 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 and low sanitation coupled with inadequate access
to financial resources [11, 13]. Poverty makes it impossible for poor people to respond to illness
unless health care services are free. 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
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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 this emphasizes 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 which 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 provide more
insight into the insured and uninsured. This study elucidates information on the self-rated health
status, health care utilization, income distribution, and health insurance status of Jamaicans. It
also models self-rated health status, health care utilization, income distribution, and how these
differ between the insured and uninsured.
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].
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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 1,994 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 a complex sampling design, weighted to reflect the population of
Jamaica.
Statistical analyses
Statistical analyses were performed using the Statistical Packages for the Social Sciences,
Version 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 in this study as well as chi-square, independent sample t-tests, and analysis of
variance f-tests, multiple logistic and linear regressions.
In analyzing the multiple logistic and linear regressions, correlation matrices were
examined to determine multicollinearity. Where collinearity existed (r > 0.7), variables were
entered independently into the model to determine those that should be retained during the final
model construction. To derive accurate tests of statistical significance, we used SUDDAN
88
statistical software (Research Triangle Institute, Research Triangle Park, NC), and this was
adjusted for the survey’s complex sampling design. A p-value < 0.05 (two-tailed) was used to
establish statistical significance
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 variables and some predisposed independent (explanatory)
variables.
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 used for all categorical variables (using the reference group
listed last). Overall model fit was determined using log likelihood ratio statistics, odds ratios and
r-squared. Stepwise regressions were used to determine the contribution of each significant
variable to the overall model. All confidence intervals (CIs) for odds ratios (ORs) were
calculated at 95%.
Results Demographic characteristics 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 the elderly, 11.9%. The latter
comprised 7.7% young-old, 3.2% old-old and 1.0% oldest-old. The majority of the sample had
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no formal education (61.8%); primary, 25.5%; secondary, 10.8% and tertiary, 2.0%. Two-thirds
of the sample had sought health care 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: colds, 16.7%; diarrhoea,
3.0%; asthma, 10.7%; diabetes mellitus, 13.8%; hypertension, 23.1%; arthritis, 6.3%; and
specified conditions, 26.3%. Marginally more people were in the upper class (40.3%) compared
to the lower socio-economic strata (39.8%). Only 20.2% of respondents had health insurance
coverage (private, 12.4%; NI Gold, public, 5.3%; other public, 2.4%). The majority of health
insurance was owned by those in the upper class (65%) and 19% by those in the lower socio-
economic 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).
Furthermore examination of self-reported health conditions by health insurance status revealed
that diabetics recorded the greatest percentage of health insurance coverage (43.9%) compared to
hypertensives, (28.2%); people with arthritis (25.5%); those with acute conditions (17.0%) and
respondents with other health conditions (18.8%). Sixty-seven percent of respondents who
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reported being diagnosed with chronic conditions had 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 graduates (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 associations 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 socio-
economic 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. Similarly, a significant relationship existed between health care-seeking behaviour and
health insurance status (χ2 = 33.61, P < 0.0001). Fourteen percent of those with health insurance
had 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. Concurring
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with this, 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 taken out 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 mostly acquired 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 the 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 =
15.8) compared to 32.5 years for those with non-chronic conditions. Similarly, 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.
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Table 4.4.1 examines information on crowding index, total annual food expenditure,
annual non-food expenditure, income, age, time in household, length of marriage, length of
illness and number of visits made to medical practitioner by health insurance status.
Self-rated health status, health care seeking behaviour, illness, educational level, social
class, area of residence, health conditions and health care utilization by health insurance status
are presented in Table 4.4.2.
Table 4.4.3 presents information on the 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 4.4.4 examines illness by age of respondents controlled by health insurance status.
There was a significant statistical relationship between illness and age of respondents, but none
between the uninsured and insured, P = 0.410.
Table 4.4.5 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 colds, 19.2 years (SD = 23.9);
diarrhoea, 30.3 years (SD = 31.4); asthma, 22.9 years (SD = 22.1); diabetes mellitus, 60.9 years
(SD = 16.0); hypertension, 62.5 years (SD = 16.8); arthritis, 64.3 years (SD = 14.5), and other
conditions, 38.3 years (SD = 25.3).
Analytic Models Nine variables (see Table 4.4.6), account for 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
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care utilization and illness. Self-reported illnesses accounted for 62.2% of the explained
variability of moderate-to-very good health status.
Table 4.4.7 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 4.4.8).
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 4.4.9).
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 4.4.10).
Table 4.4.11 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 socio-
economic 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
(accounting for 43% of the explained variance, 19.4%).
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Discussion The current study revealed that 15 out of every 100 Jamaicans reported having an illness in the
last 4 weeks, and 57% of those with an illness had chronic conditions. Sixty-one out of every 100
of those with chronic illnesses were 60+ years; 67% of the chronically ill sought medical care as
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 of
the chronically ill who were in the other aged adult cohorts 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 half a 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 in 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, whereas 38% had no coverage at all. The income of those in the
upper income strata was significantly more than those in the middle and lower socio-economic
group, but chronic illnesses were statistically the same among the social classes.
Health disparities in a nation are explained by socio-economic 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,
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17-19]. The present paper revealed that health insurance coverage is mostly acquired by those in
the upper class, with less than 20 in every 100 insured being in the lower socio-economic class.
Although this study found that those in the lower class did not suffer from more chronic illnesses
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.
The uninsured ill are therefore less likely to demand health care, and this economic burden of
health care is going to be the responsibility of either 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, experiencing more acute illness; 38 out of every 100 chronically ill individuals
are in the lower class, and these provide a comprehensive understanding of the insured and
uninsured that will allow for explanations in health disparities between the socio-economic strata
and sexes. With 43 out of every 100 people in the lower socio-economic strata self-reporting
being diagnosed with chronic illness, health insurance coverage, public health systems and other
policy interventions aid in their health, and health care utilization.
Among the material deprivations of the poor is uninsurance. Those in the wealthy socio-
economic group in Jamaica were 3.5 times more likely to be holders of health insurance
coverage than those in the lower socio-economic strata. And Gertler and Sturm [3] identified that
health insurance causes a switching from public health to the private health system, which
indicates that a reduction in public health expenditure and health insurance will significantly
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influence the health of the poor. This research showed that only 19% of those with health
insurance were in the lower class. Therefore, the issue of uninsurance creates future challenges
for the poor in regard to their health and health care utilization. At the onset of illness, those in
the lower income strata without health insurance must first think about their illness and weigh
this against the cost of losing current income, in order to provide for their families; 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 future
product in enhancing a decision to utilize health care. But outside of those issues, their choices
(or lack of choices), the cost of public health care, national insurance schemes and general price
indices in the society all further lower 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 socio-economic strata, as those who do not have health insurance want to
avoid the public health care system, owing to dissatisfaction or lack of means, and will only seek
health care when their symptoms are severe; sometimes the complications from the delay make it
difficult for their complaints to be addressed on their visits. Among the 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 themselves. 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 socio-economic realities of the poor, such as less access to
education, proper nutrition, good physical milieu, poor sanitation and lower health coverage,
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cripple their future health status, and this hinders health care utilization while also accounting for
high premature mortality. It is this lower health care utilization which accounts for their
increased risk of mortality, as the other deprivations such as proper sanitation and nutrition
expose them to disease-causing pathogens, which means that their inability to afford health
insurance increases 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] they
receive worse access to care, and are less satisfied than the insured in the US with the care and
medical services that they receive. This is an indication of further reluctance on the part of the
poor to willingly demand health care, as this intensifies 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. Some of the
reasons why those in the lower socio-economic strata have less health coverage than those in the
wealthy income group are (1) inaffordability, (2) type of employment (mostly part-time,
seasonal, low paid and uninsured positions) which makes it too difficult for them to be holders of
health insurance, and this retards the switch from public-to-private health care utilization.
Recently a study conducted by Bourne and Eldemire-Shearer [21] found that 74% of those in the
poorest income quintile utilized public hospitals compared to 58% of those in the second poor
quintile and 31% of those in the wealthiest 20%. Then, if public health is privatized and becomes
increasingly more expensive for recipients, the socio-economically disadvantaged population
(the poor, the elderly, children and other vulnerable groups) will become increasingly exposed to
more agents that are likely to result in their deaths, with an increased utilization of home
remedies as well as the broadening of the health outcome inequalities among the socio-economic
strata.
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Illness, and particularly chronic conditions, can easily result in poverty before mortality
sets in. With the World Health Organization (WHO) opining that 80% of chronic illnesses were
in low and middle income countries, and that 60% of global mortality is caused by chronic
illness [7], levelling insurance coverage can reduce the burden of care for those in the lower
socio-economic strata. The importance of health insurance to health care utilization, health
status, productivity, production, socio-economic development, life expectancy, poverty reduction
strategies and health intervention must include increased health insurance coverage of the
citizenry within a nation. The economic cost of uninsured people in a society can be measured by
the loss of production, sick leave payment, mortality, lowered life expectancy and cost of care
for children, orphanages and the elderly who become the responsibility of the state. Therefore the
opportunity cost of a reduced public health care budget is the economic cost of the
aforementioned issues, and goes to the explanation of premature mortality in a society.
The chronically ill, in particular, benefit from health insurance coverage, not because of
the reduced cost of health care, but the increased health care utilization that results 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 for their health care utilization and not the condition or illness.
According to Andrulis [22], “Any truly successful, long-term solution to the health problems of
the nation will require attention at many points, especially for low-income populations who have
suffered from chronic underservice, if not outright neglect” Embedded in Andrulis’s work is the
linkage between poverty, poor health care service delivery, differences in health outcomes
among the various socio-economic groups, higher mortality among particular social classes,
deep-seated barriers in health care delivery and the perpetuation of such barriers, and how they
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can increase health differences among the socio-economic strata. The relationship between
poverty and illness is well established in the literature [7, 8, 23] as poverty means being deprived
of elements such as proper nutrition and safe drinking water, and these issues contribute to lower
health, production, productivity, and more illness in the future. Free public health care or lower
public health care costs do not mean equal opportunity to access health care, nor do they
eliminate the barriers to such access, or increase health and wellness for the poor, or remove
lower health disparities among the socio-economic groups. However, lower income, increased
price indices, removal of government subsidies from public health care, increased uninsurance
and lower health care utilization, increase poverty and premature mortality, and lower the life
expectancy of the population.
Increases in diseases (acute and chronic) are largely owing to the 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 of financial resources, and
their voluntary actions will be directly related to survival and not diet, nutrition, exercise or other
healthy lifestyle choices. Lifestyle choices such as diet, proper nutrition, and sanitation and safe
drinking water are costly, and they are choices which, often because of poverty, some people
cannot afford to make. It follows therefore that those in the lower socio-economic strata will
voluntarily make unhealthy choices because they are cheaper. Poverty therefore handicaps
people, and predetermines unhealthy lifestyle choices, which further account for greater
mortality, lower life expectancy, and less health insurance coverage and private health care
utilization.
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Conclusion
Poverty is among the social determinants of health, health care utilization, and health insurance
coverage in a society. While the current study does not support the literature that chronic
illnesses were greater among those in the lower socio-economic strata, they were less likely to
have health insurance coverage compared to the upper class. Poverty denotes socio-economic
deprivation of resources available in a society, and goes to the crux of health disparities among
the socio-economic groups and sexes. Health care utilization is associated with health insurance
coverage as well as government assistance, and this embodies the challenges of those in
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 further increase premature mortality among those in the lower socio-economic
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, along with the construction of clinics and hospitals, and there is definite a
need to include health insurance coverage in their public health measures, as this will increase
access to health care utilization. Any increase in health care utilization will be able to improve
health outcomes, reduce health disparities between the socio-economic groups and the sexes, and
bring about improvements in the quality of life of the poor.
In summary, with the health status of the insured being 1.5 times more than the
uninsured, their health care utilization being 1.9 times more than the uninsured and illness being
a strong predictor of health care-seeking behaviour, any reduction in the health care budget in
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developing nations denotes that vulnerable groups (such as children, the elderly and the poor)
will seek less care, and this will further increase mortality among those cohorts.
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Table 4.4.1. Crowding, expenditure, income, age, and other characteristics by health insurance status Characteristics
Health insurance status P Non-insured mean ± SD
Insured 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 Income1 7703.62±5620.94 12374.89±9713.00 t = - 22.75, < 0.0001 Age (in year) 28.7±21.4 35.0 ±22.7 t = - 9.40, < 0.0001 Time in household (in years) 11.7±1.6 11.8±1.3 t = - 1.62, 0.104 Length of marriage 16.9±14.3 18.3±13.8 t = - 1.55, 0.122 Length of illness 14.7±51.1 14.1±36.2 t = - 0.217, 0.828 No. of visits to medical practitioner 1.4±1.0 1.5±1.2 t = - 0.659, 0.511 1Expenditures and income are quoted in USD (Ja. $80.47 = US $1.00 at the time of the survey)
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Table 4.4.2. Health, health care seeking behaviour, illness and particular demographic characteristics by health insurance status Characteristic
Health insurance status P 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 (87.5) Secondary 80 (9.9) 23 (6.7) 9 (5.7) 577 (11.1) Tertiary 46 (5.7) 4 (1.1) 4 (2.6) 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 (20.8) 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)
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Table 4.4.3. Age cohort by diagnosed illness
Age cohort
Diagnosed illness
Total
Acute condition Chronic condition
Other Cold Diarrhoea Asthma Diabetes mellitus Hypertension Arthritis
n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%)
97 (65.1) 13 (48.2) 51 (53.7) 3 (2.4) 0 (0.0) 0 (0.0) 54 (23.1) 218 (24.5) Children
Young adults 14 (9.4) 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 (14.7) 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 (15.5) 49 (23.8) 14 (25.0) 13 (5.5) 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
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Table 4.4.4. Illness by age of respondents controlled for health insurance status Characteristic
Age of respondents 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
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Table 4.4.5. Age cohort by diagnosed health condition, and health insurance status Characteristic
Diagnosed health condition
Diagnosed health condition
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 (35.5) 110 (26.5) 100 (38.6) 21 (23.3) 37 (29.3) Young-old 49 (9.7) 132 (34.3) 37 (8.9) 82 (31.7) 12 (13.3) 50 (39.7) Old-old 26 (5.2) 82 (21.3) 20 (4.8) 55 (21.2) 6 (6.7) 27 (21.4) Oldest-old 9 (1.8) 21 (5.5) 7 (1.7) 15 (5.8) 2(2.2) 6 (4.8) Total 505 385 415 259 90 126 χ2 = 317.5, P < 0.0001 χ2 = 234.5, P < 0.0001 χ2 = 73.6, P < 0.0001
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Table 4.4.6. 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
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Table 4.4.7. Logistic regression: Explanatory variables of self-reported illness
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
0.001
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
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Table 4.4.8. Logistic regression: Explanatory variables of health care seeking behaviour
Explanatory variable Coefficient Std error
Odds ratio 95.0% C.I.
R2
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
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Table 4.4.9. 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
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Table 4.4.10. Multiple regression: Explanatory variables of income
Explanatory variable
Unstandardized Coefficients
β 95% CI
B Std. Error R2
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
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Table 4.4.11. 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
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CHAPTER
5
Variations in social determinants of health using an adolescence population: By different measurements, dichotomization and non-dichotomization of health
On examining health literature, no study emerged that evaluated whether the social determinants vary across measurement, dichotomization, non-dichotomization and aged cohorts. With the absence of research on the aforementioned areas, it can be extrapolated that social determinants of health are constant across measurement, dichotomization and non-dichotomization, and this assumption is embedded in health planning. This paper seeks to elucidate (1) whether social determinants of health vary across measurement of health status (ie self-rated health status or self-reported antithesis of disease) or the cut-off (dichotomization) and/or the non-cut-off of health status (non-dichotomization), (2) examine the similarities between social determinants found in the literature and that of using an adolescence population, (3) whether particular demographic characteristic as well as illness and health status vary by area of residence of respondents, (4) the health status of the adolescence population, (5) typology of health conditions that they experience, and (6) evaluate the antithesis of illness (disease) and self-rated health. Antithesis of illness is a better measure than self-reported health status in determining social determinants because of its explanatory power (53%) compared to those that used the self-rated health status (at most 38%). There were noticeable variations in social determinants of health among the dichotomized, non-dichotomized health and antithesis of illness. Social determinants of health vary across the measurement and dichotomization and non-dichotomization of health status. The findings provide insights into the social determinants and health, and recommend that we guard against a choiced approach without examining the studied population in question.
Introduction
Adolescents aged 10 to 19 years are among the most studied groups in regard health issues in the
Caribbean, particularly sexuality and reproductive health matters [1-4]. Apart of the rationales
for the high frequency of studies on those in the adolescence years are owing to the prevalence of
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HIV/AIDS, unwanted pregnancy, inconsistent condom usage, mortality arising from the
HIV/AIDS virus, and other risky sexual behaviour. With one half of those who are infected with
the HIV/AIDS virus being under 25 years old [1], this provides a justification for the importance
of researching this aged cohort. Statistics revealed that the HIV virus is the 3rd leading cause of
mortality among Jamaicans aged 10-19 years old (3.4 per 100,000, for 1999 to 2002) [5], and
again this provides a validation for the prevalence of studies on this cohort. Outside of the
Caribbean, sexuality and reproductive health matters among adolescents are well studied [6-11],
suggesting that those issues are national, regional and international.
While sexuality and reproductive health matters are critical to the health status of people
[1], reproductive health problems as well as sexuality form a part of the general health status.
Health is more that the ‘antithesis of diseases’ [12] or reproductive health problems as it extends
to social, psychological or physical wellbeing and not merely the antithesis of diseases [13].
Bourne opined that despite the broadened definition of health as offered by the WHO [14],
illness is still widely studied in the Caribbean, particularly among medical researchers and/or
scholars. A search of the West Indian Medical Journal for the last one half decade (2005-2010), a
Caribbean scholarly journal, revealed that the majority of the studies have been on different
variations of illness, and antithesis of diseases instead of the broadened construct of health.
Outside of the West Indian Medical Journal, few Caribbean studies have sought to
examine the health status of adolescents [15-18] but even fewer published research were found
that examine quality of life of those in the adolescence years [19]. Even though quality of life is
a good measure of general health status, international studies exploring quality of life and self-
rated health status among the adolescence years are many [20-25] compared to those in Jamaica.
A comprehensive review of the literature on health status, particularly among the adolescence
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population, revealed that none has used a national survey data to examine social determinants of
health across different measurement and dichotomization of health (the recoding of the measure
into two groups) to assess whether there is variability in determinants as well as explore the
health of this cohort.
Even among studies which have examined social determinants of health, particularly
among the population [26-34], few have used the elderly population [35-37] and only men in the
poor and the wealthy social strata [37, 38], but none emerged in a literature research that have
used the adolescent population (ages 10-19 years). On examining health literature, no study
emerged that evaluated whether the social determinants of health vary across measurement,
dichotomization and non-dichotomization of health (using the measure in its Likert scale form),
and age cohort. With the absence of research on the aforementioned areas, it can be extrapolated
that social determinants of health are constant across measurement, dichotomization and non-
dichotomization, and this assumption is embedded in health planning. The absence of such
information means that critical validity to the discourse and use of social determinants would
have been lost, as social determinants of health are used in the planning of health policies, future
research and in explaining health disparities.
Statistics revealed that one in every five Jamaican is aged 10-19 years old [39], which
means this is a substantial population and because of its influence of future labour supply it is of
great value. Although Pan American Health Organization (PAHO) [5] stated that adolescents
enjoy good health, and only about 2% of morality in 2003, which was equally the case for
adolescents in the Americas, this information does not indicate distancing examination from their
health status. The current work, therefore, will bridge the gap in the literature by evaluating
social determinants of health among those in the adolescence years across varying measurement
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of health. Using data for 2007 Jamaica Survey of Living Conditions (2007 JSLC), this paper
seeks to elucidate (1) whether social determinants of health vary across measurement of health
status (ie self-rated health status or self-reported antithesis of disease) or the cut-off
(dichotomization) and/or the non-cut-off of health status (non-dichotomization), (2) are there
similarities between social determinants found in the literature and that of using an adolescence
population, (3) whether particular demographic characteristic as well as illness and health status
vary by area of residence of respondents, (4) what is the health status of the adolescence
population, (5) typology of health conditions that they experience, and (6) evaluate the antithesis
of illness (disease) and self-rated health.
Methods and measure
Data
The current study extracted a sample of 1, 394 respondents aged 10 to 19 years old from the
2007 Jamaica Survey of Living Conditions (JSLC). The inclusion/exclusion criterion for this
study is aged 10 to 19 years old. The present subsample represents 20.6% of the 2007 national
cross-sectional sample (n = 6,783). The JSLC is an annual and nationally representative cross-
sectional survey that collects information on consumption, education, health status, health
conditions, health care utilization, health insurance coverage, non-food consumption
expenditure, housing conditions, inventory of durable goods, social assistance, demographic
characteristics and other issues [40]. The information is from the civilian and non-
institutionalized population of Jamaica. It is a modification of the World Bank’s Living
Standards Measurement Study (LSMS) household survey [41]. An administered questionnaire
was used to collect the data.
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The survey was drawn using stratified random sampling. This design was a two-stage
stratified random sampling design where there was a Primary Sampling Unit (PSU) and a
selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which
constitutes a minimum of 100 residences in rural areas and 150 in urban areas. An ED is an
independent geographic unit that shares a common boundary. The country was grouped into
strata of equal size based on dwellings (EDs). Based on the PSUs, a listing of all the dwellings
was made, and this became the sampling frame from which a Master Sample of dwellings was
compiled, which in turn provided the sampling frame for the labour force. One third of the
Labour Force Survey (LFS) was selected for the JSLC.
Overall, the response rate for the 2007 JSLC was 73.8%. Over 1994 households of
individuals nationwide are included in the entire database of all ages [40]. A total of 620
households were interviewed from urban areas, 439 from other towns and 935 from rural areas.
This sample represents 6,783 non-institutionalized civilians living in Jamaica at the time of the
survey. The JSLC used complex sampling design, and it is also weighted to reflect the
population of Jamaica. This study utilized the data set of the 2007 JSLC to conduct our work
[42].
Measure
Age is a continuous variable which is the number of years alive since birth (using last birthday)
Adolescence population is described as the population aged 10 to 19 years old [23]
Self-reported illness (or self-reported dysfunction): The question was asked: “Is this a diagnosed
recurring illness?” The answering options are: Yes, Cold; Yes, Diarrhoea; Yes, Asthma; Yes,
Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. For the antithesis of disease
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(illness) a binary variable was created, where 1= not reported a health condition (no to each
illness) and 0 = otherwise (absence of reporting an illness). The use of two groups for self-
reported illness denotes that this variable was dichotomized into good health (from not reported a
health condition) and poor health (i.e. having reported an illness or health condition). Thus, the
seven health conditions were treated as dichotomous variables, coded as was previous stated.
Self-rated health status: This was taken from the question “How is your health in general?” The
options were very good; good; fair; poor and very poor. For purpose of this study, the variable
was either dichotomized or non-dichotomized. The dichotomization of self-rated health status
denotes the use of two groups. There were four dichotomization of self-rated health status – (1)
very poor-to-poor health status and otherwise; (2) good and otherwise; (3) good-to-very good
health status and otherwise and (4) moderate-to-very good self reported health status and
otherwise. The dichotomized variables were measured as follow:
1= very poor-to-poor health, 0 = otherwise
1= good, 0 = otherwise
1 =good-to-very good, 0 = otherwise
1= moderate-to-very good, 0 = otherwise
The non-dichotomization of self-rated health status means that the measure remained in its Likert
scale form (i.e. very good; good; moderate; poor and very poor health status).
Social class (hierarchy): This variable was measured based on income quintile: The upper classes
were those in the wealthy quintiles (quintiles 4 and 5); middle class was quintile 3 and poor those
in lower quintiles (quintiles 1 and 2).
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Family income is measure using total expenditure of the household as reported by the head.
Statistical analysis
Statistical analyses were performed using the Statistical Packages for the Social Sciences v 16.0
(SPSS Inc; Chicago, IL, USA) for Windows. Descriptive statistics such as mean, standard
deviation (SD), frequency and percentage were used to analyze the socio-demographic
characteristics of the sample. Chi-square was used to examine the association between non-
metric variables, and analysis of variance for metric and non-dichotomous nominal variables.
Logistic regression was used to evaluate a dichotomous dependent variable (self-rated health
status and antithesis of illness) and some metric and/or non-metric independent variables.
However, ordinal logistic regression was used to examine a Likert scale variable (self-rated
health status) and some metric and/or non-metric independent variables. A pvalue of < 5% (two-
tailed) was used to establish statistical significance. Each model begins with variables identified
in the literature (Models 1-5), will be tested using the current data and the significant variables
highlighted using an asterisk (Tables 3 and 4).
Models
The use of multivariate analysis to study health status and subjective wellbeing (i.e. self-reported
health) is well established in the literature [36-38]. Previous works have examined the
dichotomization of health status in order to establish whether a particular measurement of health
status is different from others [43-45]. The current study will employ multivariate analyses to
examine health by different dichotomization and statistical tools to determine if the social
determinants remain the same. The use of this approach is better than bivariate analyses as many
variables can be tested simultaneously for their impact (if any) on a dependent variable.
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Scholars like Grossman [33], Smith & Kingston [34], Hambleton et al. [37], Bourne
[46], Kashdan [47], Yi & Vaupel [48], and the World Health Organization pilot work a 100-
question quality of life survey (WHOQOL) [49] have used subjective measures to evaluate
health. Diener [50,51] has used and argued that self-reported health status can be effectively
applied to evaluate health status instead of objective health status measurement, and Bourne [46]
found that self-reported health may be used instead of objective health. Embedded in the works
of those researchers is the similarity of self-reported health status and self-reported dysfunction
in assessing health. Thus, in this work we will use self-reported health status and the antithesis of
illness to measure health, and dichotomize self-reported health status as follows (1) good health
= 1, 0 = otherwise; (2) good-to-excellent health=1, 0 = otherwise; (3) moderate-to-excellent
health=1, 0 = otherwise; and (4) very poor-to-poor health= 1, 0 = otherwise. Another measure
was that health was evaluated by all the 5-item scale (from very poor to excellent health status),
using ordinal logistic regression.
The current study will examine the social determinants of self-rated health of Jamaican
adolescents and whether the social determinants vary by measurement and dichotomization
and/or non-dichotomization of health. Five hypotheses (models) were tested in order to
determine any variability in social determinants based on the measurement of health status.
Model (1) is the antithesis of disease, non-dichotomization of self-reported health (antithesis of
disease); Model (2) is the non-dichotomization of self-rated health status (ie using the 5-item
Likert scale as a continuous variable), and Models (3-6) are the different dichotomized self-rated
health status (ie. 3= very poor-to-poor; 4=good, 5=moderate-to-very good 6=good-to-very good).
All the models were tested with the same set of social determinants of health, with the only
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variability being the measurement of health status (self-rated health status), cut-off of health
(dichotomization) and/or non-dichotomization of self-rated health status.
HA=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi , ε i) (1)
where HA (i.e. self-rated antithesis of diseases) is a function of age of respondents, Ai;
sex of individual i, Gi; area of residence, ARi; current self-reported illness of individual i,
It; logged duration of time that individual i was unable to carry out normal activities (or
length of illness), lnDi; Education level of individual i, EDi; union status of person i,
USi; social class of person i, Si; health insurance coverage of person i, HIi; logged family
income, lnY; crowding of individual i, CRi; logged medical expenditure of individual i in
time period t, lnMCt; social assistance of individual i, SAi; and an error term (ie. residual
error).
Note that length of illness was removed from the model as it had 93.5% of the cases were
missing as well as union status which had 58.2%.
HND=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi , ε i) (2)
Where HND denotes the non-dichotomization of self-rated health status.
Note that length of illness was removed from the model as it had 93.5% of the cases were
missing as well as union status which had 58.2%.
HD1=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, ε i) (3)
Where HD1 is very poor-to-poor self-rated dichotomized health status.
Note that length of illness was removed from the model as it had 93.5% of the cases were
missing as well as union status which had 58.2%.
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HD2=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, ε i) (4)
Where HD2 is good self-rated dichotomized health status.
Note that length of illness was removed from the model as it had 93.5% of the cases were
missing as well as union status which had 58.2%.
HD1-4=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, ε i) (5)
Where HD3 is very moderate-to-very good self-rated dichotomized health status.
Note that length of illness was removed from the model as it had 93.5% of the cases were
missing as well as union status which had 58.2%.
HD1-4=f (Ai, Gi, ARi, It, lnDi, EDi, USi, Si, HIi, lnY, CRi, lnMCt, SAi, ε i) (6)
Where HD4 is good-to-excellent self-rated dichotomized health status.
Note that length of illness was removed from the model as it had 93.5% of the cases were
missing as well as union status which had 58.2%.
Results
Demographic characteristics of studied population
Table 5.5.1 presents information on demographic characteristic of the sampled
population. Of the population (n = 1,394), 43.9% has primary or below primary level education,
53.1% secondary level and 3.0% had tertiary level education.
Table 5.5.2 presents information on the particular demographic characteristic as well as
health status and self-reported illness of respondents by area of residence.
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Table 5.5.3 depicts information of variables which explain the antithesis of illness among
the adolescence population.
Table 5.5.4 shows the different dichotomizations of self-rated health status and non-
dichotomized self-rated health status, and the various social determinants which explain each.
Table 5.5.5 examines associations between self-rated health status and antithesis of
illness (or disease).
Limitations of study
This study was extracted from a cross-sectional survey dataset (Jamaica Survey of Living
Conditions, 2007). Using a nationally representative cross-sectional survey dataset, this research
extracted 1394 adolescent Jamaicans which denote that the work can be used to generalize about
the adolescent population in Jamaica at the time in question (2007). However, it cannot be used
to make predictions, forecast, and establish trends or causality about the studied population.
Discussion The current work showed that while the majority of Jamaican adolescents have at least
self-rated good health status (92 out of every 100); some indicated at most moderate self-rated
health status. Even though only 1.4% of the sample mentioned that they have very poor-to-poor
health status, 6.5% indicated that they experienced a health condition in the last 30 days. Of
those who reported a health condition, 5.3% were diagnosed with chronic illness (diabetes
mellitus, 3.9%; hypertension, 1.3%). Although 2.4 times more adolescent in rural areas are in the
lower class compared with those in urban areas, rural adolescents have a greater good health
status compared to their urban counterparts, but this was the reverse for rural and periurban
adolescents. Another important finding was that there is no statistical association between health
124
conditions and area of residence, but urban and periurban adolescents were more likely to have
health insurance coverage compared to those in rural areas.
In Jamaica, the adolescence population’s health status is comparable to those in the
United States [23], suggesting that inspite of the socioeconomic disparities between the two
nations and among its peoples, the self-reported health status among adolescent Jamaicans is
good. The high health status of those in the adolescence population in Jamaica speaks good of
the inter dynamics within the countries, but does not imply that they are the same across the two
nations or can it be interpreted that the quality of life of Jamaicans is the same as those in the
United States. Simply put, the adolescence population in Jamaica is experiencing a good health
status although HIV/AIDS, unwanted pregnancies, and inconsistent condom usage are high in
this cohort [1-5].
While the aforementioned results about good health status of Jamaican adolescents
concurs with PAHO’s work in 2003 [5] and others [17], which has continued into 2007, the
current paper provides more information on health matters of adolescents aged 10-19 years than
that offered by PAHO. An adolescent in Jamaica who seeks medical attention is 100% less likely
to report an illness, and those who indicated at least good self-rated health status was 13 times
more likely not to report an illness. Continuing, adolescents in the upper class are 15 times more
likely to report very poor-to-poor health status compared to those in the lower class. And that
those who indicated very poor-to-poor health status are more likely to seek medical care (10
times), live in crowded household and less likely to spend more on consumption and non-
consumption items. On the other hand, those who stated that their health status was at least
moderate were less likely to live in crowded household, spent more on consumption and non-
consumption items. Using a 2007 national probability dataset for the adolescence population in
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Jamaica, we can add value to the existing literature on health status as well as the social
determinants of health.
Grossman introduced the use of econometric analysis in the examination of health in the
1970s to establish determinants of self-rated health [33], which has spiraled a revolution in this
regard since that time. Using data for the world’s population, he identified particular social
determinants of health that was later expanded upon by Smith and Kington [34]. Since the earlier
pioneers’ work on social determinants of health [33, 34], the WHO joined the discourse in 2000s
[27] as well as Marmot [26], Kelly et al. [28]; Marmot and Wilkinson [29]; Solar and Irwin [30];
Graham [31]; Pettigrew et al. [32], Bourne [35], Bourne [36], Hambleton et al. [37] and Bourne
and Shearer [38], but none of them evaluated whether there was variability in the determinants of
health depending on the measurement and/or dichotomization of health.
The variability in social determinants of health was established by Bourne and Shearer
[38] in a study between men in the poor and the wealthy social strata in a Caribbean nation, but
the literature at large has not recognized the variances in social determinants based on the
dichotomization and non-dichotomization self-rated health status, and measurement of heath
(using antithesis of illness and self-rated health status). Such a gap in the literature cannot be
allowed to persist as it assumes that social determinants are consistent over the measurement of
health.
Bourne [43] like Manor et al. [44] and Finnas et al. [45] have dichotomized self-reported
health status and cautioned future scholars about how the dichotomization can be best done.
According to Bourne [43] “The current study found that dichotomi[z]ing poor health status is
acceptable assuming that poor health excludes moderate health status, and that it should remain
as is and ordinal logistic be used instead of binary logistic regression” [43, p.310], and others
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warned against the large dichotomization of self-rated health status [44,45]. Because self-rated
health status is a Likert scale variable, ranging from very poor to very good health status, many
researchers arbitrarily dichotomized it, but the cut-off is not that simple as was noted by Bourne
[43], Manor et al. [44] and Finnas et al. [45].
From data on Jamaicans, Bourne’s work revealed that the cut-off in the dichotomization
of self-rated health status should be best done without moderate health when dichotomizing for
poor health status [43]. All the scholars agreed that narrowed cut-offs are preferable in the
dichotomization of self-rated health status, but only a few variables were used (marital status,
age, social class, area of residence and self-reported illness) [43-45]. Bourne postulated that “By
categorising an ordinal measure (i.e., self-reported health) into a dichotomous one, this means
that some of the original data will be lost in the process.” [43, p.295]. Using many more
variables, the present work highlighted that some social determinants of health are lost as a result
of the dichotomization process. Simply put, the social determinants of health are not consistent
across the dichotomization process which concurs with the literature.
While we concur with other scholars that by dichotomizing self-rated health status some
social determinants are lost in the process [43-45], we will not argue with those who opined that
self-rated health status should remain a Likert scale measure [52, 53]. The evidence is in that
more social determinants in the non-dichotomized self-rated health do not give a greater
explanatory power; instead this model had the least explanation. This indicates that more is not
necessarily better, and such information must be taken into account in a decision to cut-off at a
particular point. The fact that more social determinants of health emerged when health was non-
dichotomized coupled with a lower explanatory power compared with when it is dichotomized as
very poor-to-poor health means that using self-rated health as a Likert scale valve is not
127
preferable to dichotomizing it. A narrower dichotomization of self-rated health status,
particularly very poor-to-poor health, as well as moderate-to-very good health status yielded
greater explanations than non-dichotomizing health status.
This study used both the antithesis of illness and self-rated health status to measure, and
evaluates the social determinants of health, and assess whether antithesis of illness is still a better
measure of health than self-rated health status. A comparison of the social determinants based
on the measurement of health revealed that for the Jamaican adolescence population, antithesis
of illness is a better measure than self-reported health status in determining social determinants
because of its explanatory power (53%) compared to those that used the self-rated health status
(explanatory power at most 38%). On the other hand, the antithesis of illness had fewer social
determinants compared with those in self-rated health status, suggesting that more social
determinants of health should not be preferred to fewer because the latter measure had a greatest
explanation. Like dichotomizing self-rated health status, variation also exists among
dichotomization of health and antithesis of illness. Thus, it appears that the antithesis of illness
may provide a better measure for the social determinants of health than self-rated health status.
Diener [50, 51] had postulated that self-reported health status can be effectively applied
to evaluate health status instead of objective health status measurement (morbidity, life
expectancy, mortality), and Bourne [46] found a strong statistical association between self-
reported illness and particular objective measure of health (life expectancy, r = -0.731); but a
weak relationship between self-reported illness and mortality. Using a nationally representative
sample 6,782 Jamaicans, one researcher warned against using self-reported illness as a measure
of health as he found that men were over-reporting their illness [54], and this means they were
over-rating their antithesis of illness. Those studies highlight the challenges in using subjective
128
measures in evaluating health as they are not consistent like the objective ones such as mortality,
life expectancy, and diagnosed morbidity. Nevertheless, on examining the antithesis of illness
and self-rated health status, it was revealed that 2.9% of those who indicated very good health
status had an illness compared to 20% of those who reported an illness who had very good health
status. From the current work again it emerged that there is disparity between self-reported
illness (or antithesis of illness) and self-rated health status, indicating why caution is required in
using either one or the other.
Other disparities between antithesis of illness and self-rated health status highlighted that
antithesis of illness is a better measure of health than self-rated health status. Clearly despite the
efforts of the WHO in broadening the conceptualization of health away from the antithesis of
illness, the Jamaican adolescence population has not moved to this new frontier. As when they
were asked to report on the antithesis of illness, they gave lower values than indicated for self-
rated health status. Because antithesis of illness captures health more than self-rated health
status, this justifies why the former had a greater explanation when the social determinants of
health were examined than that of self-rated health status. But, where were their differences in
the variables used in one measure compared with the others?
In fact, all the variables used in this study were social determinants that were identified in
the literature [26-38], and many of them were not significant for the adolescence population of
this research. It can be extrapolated from the current work that social determinants of health for a
population are not the same for a sub-population, in particular adolescence population. Thus,
when the WHO [27] and affiliated scholars [26, 28-32] forwarded social determinants of health,
prior to that some scholars like Grossman [33] and Smith and Kington [34] had already social
determinants of health of a population. However, none of them stipulated that there are
129
disparities and variations in these based on the dichotomization, non-dichotomization, sub-
population, and measurement of health (ie self-rated health or antithesis of illness).
Using a cross-sectional survey (2003 US National Survey of Children's Health) of some
102,353 children aged 0 to 17 years, Victorino and Gauthier [55] established that there were
some variations in social determinants of health based on particular health outcomes. The health
outcomes used by Victorino and Gauthier are presence of asthma, headaches/migraine, ear
infections, respiratory allergy, food/digestive allergy, or skin allergy, which are health
conditions. Another research using the 2003 US National Survey of Children's Health (NSCH)
investigated the association of eight social risk factors on child obesity, socioemotional health,
dental health, and global health status [56]. From a research in England, Currie et al. [57] found
disparity in income gradient associated with subjectively assessed general health status, and no
evidence of an income gradient associated with chronic conditions except for asthma, mental
illness, and skin conditions.
This paper concurs with the literature that there are variations in some social
determinants of health status across measurement, dichotomization and non-dichotomization of
health. However, the present work went further than the current literature and found that
particular dichotomization of health had stronger explanatory power, and disparity in
determinants. As such, the variations in social determinants of health vary across the
dichotomization and measurement of health as this paper showed that more social factors do not
translate into greater explanatory power; and that stronger explanation does not denotes more
social determinants. And the social determinants of health had the greatest explanatory power
used antithesis of illness to measure health.
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Conclusion
In summary, the general health status of the adolescence population in Jamaica is good, but 7 in
every 100 have reported an illness of which some had chronic conditions (diabetes mellitus,
3.9% and hypertension, 1.3%), and those who classified as being in the wealthy class were more
likely to report very poor-to-poor health status compared with those in the lower class. Another
important finding was that rural adolescents had a greater health status than urban adolescents,
but periurban adolescents had the greatest health status.
Outside of the aforementioned good health news, the social determinants of self-rated
health status vary across the measurement of and dichotomization and non-dichotomization of
health and the population used. This work provides invaluable insights into how social
determinants should be examined, modify the general social determinants of health offered by
the World Health Organization and some associated scholars. By varying the measurement,
dichotomization and non-dichotomization of health, this work provide some justification as to
whether a particular dichotomization of health is better or non-dichotomization is preferable to
dichotomization.
This researcher will not join the group of scholars who are purporting for the non-
dichotomization of self-rated health status, but we recognized that discourse offers some
information. However, we will chide researchers against arbitrarily using a particular
dichotomization, non-dichotomization and measurement without understanding peoples’
perception of health to which they seek to examine, and evaluate these. Thereby, despite the
international standardized definition of a phenomenon, people may a different view as to this
issue.
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Disclosures
The author reports no conflict of interest with this work.
Disclaimer
The researcher would like to note that while this study used secondary data from the 2007 Jamaica Survey of Living Conditions (JSLC), none of the errors in this paper should be ascribed to the Planning Institute of Jamaica and/or the Statistical Institute of Jamaica, but to the researcher.
Acknowledgement The author thank the Data Bank in Sir Arthur Lewis Institute of Social and Economic Studies, the University of the West Indies, Mona, Jamaica for making the dataset (2007 Jamaica Survey of Living Conditions, JSLC) available for use in this study.
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Table 5.5.1: Demographic characteristic of studied population, n = 1394 Characteristic n Percent Sex Male 672 48.2 Female 722 51.8 Union status Married 1 0.2 Common-law 14 2.4 Visiting 73 12.5 Single 494 84.8 Social assistance Yes 232 17.3 No 1108 82.7 Area of residence Urban 394 28.3 Periurban 287 20.6 Rural 713 51.1 Population Income Quintile Poorest 20% 320 23.0 Second poor 328 23.5 Middle income 287 20.6 Second wealthy 263 18.9 Wealthiest 20% 196 14.1 Self-reported illness Yes 89 6.6 No 1251 93.4 Self-reported diagnosed illness Influenza 22 28.9 Diarrhoea 1 1.3 Respiratory illness (ie asthma) 16 21.1 Diabetes mellitus 3 3.9 Hypertension 1 1.3 Other conditions (unspecified) 33 43.4 Health care-seeking behaviour Yes 50 53.8 No 43 46.2 Self-rated health status Very good 631 47.2 Good 601 45.0 Moderate 84 6.3 Poor 18 1.3 Very poor 2 0.1 Health insurance coverage No 1123 85.3 Yes 194 14.7 Age, mean (Standard deviation, SD) 14.2 years (SD = 2.8 years) Length of illness, median (range) 5 days ( 0 – 36 days)
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Table 5.5.2: Particular demographic variables by area of residence, n = 1,394 Characteristic Area of residence P, χ2
Urban Periurban Rural Self-reported illness n (%) n (%) n (%) 0.628, 0.931 Yes 27 (7.1) 15 (5.4) 47 (6.9) No 352 (92.9) 264 (94.6) 635 (93.1) Self-rated health status 24.82, 0.002 Very good 162 (42.7) 141 (50.4) 328 (48.4) Good 172 (45.4) 132 (47.1) 297 (43.9) Moderate 38 (10.0) 7 (2.5) 39 (5.8) Poor 7 (1.8) 0 (0.0) 11 (1.6) Very poor 0 (0.0) 0 (0.0) 2 (0.3) Social class 172.64, < 0.0001 Lower 101 (25.6) 108 (37.6) 439 (61.6) Middle 88 (22.3) 58 (20.2) 141 (19.8) Upper 205 (52.0) 121 (42.2) 133 (18.7) Educational level 37.79, < 0.0001 Primary or below 138 (36.6) 136 (48.6) 312 (46.1) Secondary 213 (56.5) 136 (48.6) 359 (53.0) Tertiary 26 (6.9) 8 (2.9) 6 (0.9) Sex 1.20, 0.548 Male 213 (54.1) 148 (51.6) 361 (50.6) Female 181 (45.9) 139 (48.4) 352 (49.4) Health insurance coverage 9.36, 0.009 Yes 73 (19.4) 37 (13.6) 84 (12.6) No 303 (80.6) 235 (86.4) 585 (87.4) Length of illness, mean ± SD 6.0 ± 5.7 days 7.8 ± 9.0 days 6.4 ± 6.5 days F = 0.42, 0.857
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Table 5.5.3: Logistic regression: Variables of antithesis of illness among adolescence population, n = 1,280 Characteristic OR CI (95%) Age 1.1 1.0 - 1.3 Health care-seeking (1=yes) 0.0 0.0 - 0.01* Health insurance coverage (1=yes) 1.0 0.4 - 2.5 Primary education (reference group) 1.0 Secondary 1.8 0.9 - 3.7 Tertiary 1.9 0.3 - 15.1 lnMedical 0.8 0.1 - 5.0 Male 1.4 0.7 - 2.6 Social assistance from government 1.6 0.6 - 4.4 Logged family income 0.8 0.3 - 1.8 Rural area (reference group) Urban 1.6 0.7 - 3.8 Periurban 1.2 0.5 - 2.9 Poor-to-Very poor health status (reference group) 1.0 Moderate-to-Very good health status 0.3 0.03 - 2.1 Good-to-Very good health status 12.6 6.0 - 26.3* Lower class (reference group) Middle class 1.6 0.5 - 5.2 Upper 0.8 0.2 - 3.1 Crowding 0.9 0.8 - 1.1 Model χ2, P 287.08, < 0.0001 -2 LL 327.56 R2 0.53 Hosmer and Lemeshow χ2 = 4.40, P = 0.82 OR denotes odds ratio, CI (95%) means 95% confidence interval and *P < 0.05
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Table 5.5.4: Logistic and Ordinal Logistic regression: Factors explaining self-reported health status of adolescents, n = 1,280 Characteristic
Self-rated health status Very poor-to-poor Good Moderate-to-very
good Good-to-very
good All
OR CI (95%) OR CI (95%) OR CI (95%) OR CI (95%) Estimate CI (95%)
Self-reported illness (1=yes) 2.0 0.3 – 15.6 0.1 0.05 – 0.2* 0.5 0.1 – 4.4 0.1 0.05 – 0.2* 1.8 1.1 – 2.4* Age 1.0 0.9 – 1.2 0.9 0.9 – 1.1 1.0 0.8 – 1.2 0.9 0.9 – 1.1 0.02 - 0.03 – 0.1 Health care-seeking (1=yes) 10.0 1.0 – 96.5* 0.7 0.3 – 1.9 0.1 0.01 – 0.5* 0.7 0.3 – 2.1 1.0 0.1 – 2.0* Health insurance coverage (1=yes) 0.3 0.04 – 2.8 1.1 0.6 – 2.2 3.0 0.4 – 25.5 1.2 0.6 – 2.4 0.04 - 0.3 – 0.4 Primary education (reference group) 1.0 1.0 1.0 1.0 1.0 Secondary 0.7 0.3 – 1.9 0.9 0.6 – 1.5 1.4 0.5 – 3.8 1.0 0.6 – 1.6 0.02 - 0.2 – 0.2 Tertiary 0.0 0 – 0.0 0.4 0.1 – 1.0 5E+007 0.0 - 0.4 0.2 – 1.3 0.3 0.4 – 1.0 Logged Medical expenditure 1.6 0.7 – 3.6 0.6 0.4 – 1.2 0.7 0.4 – 1.2 0.5 0.1 – 1.0* Social assistance from government 0.2 0.03 – 1.7 1.2 0.6 – 2.2 4.8 0.6 – 38.5 1.2 0.6 – 2.3 0.1 - 0.2 – 0.4 Lower class (reference group) 1.0 1.0 1.0 1.0 1.0 Middle class 0.6 0.1 – 2.9 2.1 0.9 – 4.5 1.8 0.3 – 9.6 2.2 1.0 – 4.8 - 0.7 - 1.0 - - 0.4* Upper 14.9 1.9 – 118.3 * 0.7 0.3 – 1.4 0.1 0.01 – 0.5* 0.7 0.3 – 1.6 - 0.6 - 1.0 - -0.1 Rural area (reference group) 1.0 1.0 1.0 1.0 1.0 Urban 1.6 0.4 – 3.0 0.6 0.4 – 1.0* 0.9 0.3 – 2.7 0.6 0.4 – 1.0* 0.5 0.2 – 0.8* Periurban 0.0 0.0 - 0.0 3.3 1.3 – 8.2* 2E+0007 3.3 1.53– 8.2* - 0.01 - 0.3 – 0.3 Male 0.9 0.3 – 2.3 1.5 1.0 – 2.4 1.1 0.4– 3.0 1.4 0.9 – 2.2 - 0.1 - 0.3 – 1.2 Logged family income 0.1 0.04 – 0.4* 1.3 0.9 – 2.0* 8.2 2.8 – 23.8* 2.0 1.2 – 3.4* - 0.30 - 0.6 – -0.001* Crowding 1.6 1.3 – 2.0* 0.9 0.8 – 1.0* 0.6 0.5 – 0.8* 0.9 0.8 – 0.98* 0.1 - 0.01 – 0.1* Model χ2, P 59.66, < 0.0001 113.11, < 0.0001 30.37, < 0.0001 113.11, <0.0001 112.94, < 0.0001 -2 LL 146.38 588.76 175.67 588.76 2354.33 R2 0.38 0.20 0.31 0.20 Pseudo R2 = 0.10 Hosmer and Lemeshow χ2 = 4.6, P = 0.82 χ2 = 4.61, P = 0.80 χ2 = 4.36, P = 0.94 χ2 = 4.61, P = 0.80 Goodness of fit,
χ2=5451.14. P < 0.001 OR denotes odds ratio; *P < 0.05
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Table 5.5.5: Self-rated health status and antithesis of illness, n = 1,330 Characteristic
Self-rated health status Very good Good Moderate Poor Very poor
n (%) n (%) n (%) n (%) n (%) Antithesis of illness No 18 (2.9) 38 (6.4) 26 (31.3) 7 (38.9) 0 (0.0) Yes 611 (97.1) 560 (93.6) 57 (68.7) 11 (61.1) 2 (100.0) χ2 = 125.58, P < 0.0001 Characteristic
Good health (Antithesis of illness) No Yes
n (%) n (%) Self-rated health status Very good 18 (20.0) 611 (49.2) Good 38 (42.7) 560 (45.1) Moderate 26 (29.2) 57 (4.6) Poor 7 (7.9) 11 (0.9) Very poor 0 (0.0) 2 (0.2) χ2 = 125.58, P < 0.0001
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CHAPTER
6
Self-rated health status of young adolescent females in a middle-income developing country
The study of young female adolescents in Jamaica is sparse and few, in particular on self-related health status. This research seeks to examine the self-related health status of young female 12-17 years and to model factors that influence good self-related health status of young female adolescents. Four variables emerged as accounting for 20.3% of the variability in reported good self-related health status of young females. Good self-related health status are explained by cost of medical care (OR = 0.996, 95% CI = 0.99 - 1.01), private health care insurance coverage (OR = 0.30, 95% CI = 0.01 - 0.09), number of females in household (OR = 0.73, 95% CI = 0.59 - 0.90), and healthcare seeking behaviour (OR = 1.25, 95% CI = 1.04 - 1.52). The majority of the female adolescents reported good self-related health status. The findings are far reaching and can be used to guide policy. Any policy that seeks to address wellbeing of female adolescents must incorporate the advancement of the household, social and economic factors coupled with the needs of the individual. Introduction Adolescents and young adults represent a large and growing proportion of the populations of
developing countries around the world. In the English-speaking Caribbean countries, adolescents
represent about 20% of the population, or approximately 1.2 million persons according to 2007
population data [1]. Adolescence usually refers to the psychological and physiological processes
of maturation between the ages of about 12 to 18. It is a transitional period of rapid physical
(pubertal), emotional, cognitive and social development [2], and is often characterized by the
clarification of sexual values and experimentation with sexual behaviours [3]. While adolescents
are generally among the healthiest of any age group, they have special biological needs.
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Worldwide, studies on adolescent sexual behaviour show that the years of adolescence and the
transition to adulthood are associated with increases in rates of risky behaviour, including the use
of drugs and alcohol, delinquency, and unsafe sexual practices [4, 5]. Early initiation of sexual
activity among adolescents has been identified as a major risk factor for a number of negative
reproductive health outcomes, including early childbearing and associated implications for
maternal and child health outcomes, as well as increased risk for sexually transmitted infections
(STIs) including human immunodeficiency virus (HIV) [6].
The last two decades have been marked by significant changes in adolescent health in
Caribbean countries. There has been a shift from infectious to social morbidities caused or
contributed by individual risk behaviors and environmental factors [7,8] concurrent with rising
unemployment, increased poverty, and reduced health services. Until in the last ten years we
have known relatively little about the health status of youths residing in the Caribbean. In a study
of a clinical population of young people in Jamaica, Smikle et al. [9] found that the mean age at
onset of sexual intercourse among males was 12.5 years; 4% of sexually active males reported
using condoms consistently. According to the Jamaica Reproductive Health Survey of 2002-03,
sexual initiation occurs on average at 13.5 years for young men and 15.8 years for young women
[10]. The earlier adolescents begin sexual activity, the less likely they are to use contraception,
thus increasing their risk of pregnancy and STIs [11]. Soyibo and Lee [12] reported, among a
general population of Jamaican school-attending adolescents, rates of marijuana, cocaine, and
heroin use of 10.2%, 2.2%, and 1.13%, respectively; the alcohol use rate was 50.2%, and the
tobacco use rate was 16.6%. The country’s adolescent fertility rate has increased in recent years
and, at 112 per 1,000 women aged 15-19, is among the highest in the region. Before they reach
the age of 20, 37% of Jamaican women have been pregnant at least once, and 81% of these
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pregnancies are unplanned [10]. This concur with another study where more than 75 percent of
pregnancies among 15-24-year-olds are unplanned, and about 40 percent of Jamaican women
had at least one child before age 20 [13].
Self-rated health is a subjective and general indicator of overall health status. It evaluates
the health of an individual based on his/her perception of general overall health. This indicator
has been found to capture important information about the individual’s overall health and is a
powerful predictor of mortality and functional ability [14]. While self-rating of health is a good
measure of objective and subjective health [2], it is also a feasible way to measure health in
large-scale surveys [15, 16]. Self-rated health has been extensively studied in older adult
population groups, where a range of factors associated with self-rated health status has been
identified [17, 18]. Much less is known about the self-rated health status of younger populations
such as adolescents in Jamaica, and the available information remains limited in scope. The
published literature suggests that young people preferentially employ psychological or
behavioural factors as a rating frame for their health [19, 20]. In contrast, for older people,
physical well-being plays a more crucial role in assessing their health [21]. Given the
observation that young adults differ from older people in their perception of health, a better
understanding and a separate analysis of the factors associated with self-rated health status is
needed for adolescents. Thus, this research seeks to examine the self-related health status of
young female Jamaicans and to determine the factors that influence the health status of young
females, ages 12 to 17 years.
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Method
Data
The current study is based on data from 2002 Jamaica Survey of Living Conditions (JSLC). The
JSLC is an annual nationally representative survey which collects information on health, health
conditions, health care utilization, and other socio-demographic characteristics of Jamaicans. It is
a modification of the World Bank’s Living Standards Measurement Study (LSMS) household
survey [22].
The survey collects information from the non-institutionalized population between June-
October 2002. The sample size was 25,018 respondents [23]. The current study uses a subsample
of 1,565 young women (ages 12 through 17 years) from the general JSLC survey for 2002. The
mean age of respondents was 14.4 years (±1.7 years). The only inclusion criterion for this study
was female and age (12 through 17 years).
For 2003 to 2006, the Jamaica Surveys of Living Conditions did not collect information
on the health status of Jamaica. Data for 2008 to 2009 are not yet ready, at the time of writing
this paper the researcher was not given access to the 2007 survey data and so the researcher had
to resort to using 2002 survey data to conduct this research
Survey
The survey was drawn using stratified random sampling. The design was a two-stage
stratified random sampling design where there was a Primary Sampling Unit (PSU) and a
selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which
constitutes a minimum of 100 residences in rural areas and 150 in urban areas. An ED is an
independent geographic unit that shares a common boundary. This means that the country is
144
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 (LFS) was selected for the JSLC. The sample was weighted to
reflect the population of the nation. The non-response rate was 26.2%. The non-response
includes refusals and rejected cases in data cleaning.
Over 1994 households of individuals nationwide are included in the entire database of all
ages. 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 weighted to
reflect the population of Jamaica.
Measure
Related health status was operationalized using the number of self-related illness/injury in the
last four weeks. It is a dummy variable, where 0 = bad related health status (proxied by self-
response to having had a least one health condition), 1 = good related health status (proxy by not
reporting a health condition). It is taken from the question, ‘Have you had any illness other than
due to injury? For example a cold, diarrhoea, asthma attack, hypertension, diabetes, or any other
illness? And the options were yes = 1 and no = 2.
Physical environment is the external surroundings and conditions in which the individuals reside.
Natural disaster refers to the number of responses from people who indicated suffering landsides,
property damage due to rains, flooding, and soil erosion.
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Negative affective psychological condition identifies the number of responses from a person on
having loss a breadwinner and/or family member, family having lost its property, household
member being made redundant, family having difficulties meeting its financial obligations.
Crime index = Σ kiTj,
The equation represents the frequency with which an individual witnessed or experienced a
crime, where i denoted 0, 1 and 2, in which 0 indicated not witnessed or experienced a crime, 1
means witnessed 1 to 2, and 2 symbolizes seeing 3 or more crimes.
Ti denotes the degree of the different typologies of crime witnessed or experienced by an
individual (where j = 1 …4, which 1= valuables stolen, 2 = attacked with or without a weapon, 3
= threatened with a gun, and 4 = sexually assaulted or raped. The summation of the frequency of
crime by the degree of the incident ranged from 0 and a maximum of 51.
Education was proxied by the number of self-reported days that an individual goes to schools.
Household crowding (crowding) is the total number of people who are dwelling in the household
divided by the number of rooms that the household occupies excluding kitchen, verandah and
bathroom.
Social hierarchy: Income quintiles were used to measure social class, and these range from
quintile 1 (poorest 20%) to 5 (wealthiest 20%). Lower is measured by those in quintiles 1 and 2;
middle class is represented by those in quintile 3, and upper class indicated those in quintiles 4
and 5.
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Analytic model
Multivariate logistic regression was used to fit the data of the current study. The literature
was used to identify variables for the current paper as well as the dataset. Sixteen variables (Eqn
[1]) were identified based on the literature and the 2002 Jamaica Survey of Living Conditions.
We examined correlation matrices to insure that multicollinearity was not an issue.
Ht = (Pmc, ED,Ai , MR, AR, CR, PA, F, EN, C, M, FM; CH, PHS, HSB,Q)……(Eqn [1])
Eqn [1] expresses current health status Ht as a function of price of medical care Pmc,
education of individual, ED; age of the individual, Ai , marital status, MR; area of residence, AR;
Household crowding (proxy by average occupancy per room), CR; psychological conditions,
PA; existing pregnancy, F; natural disaster, EN; average consumption per person, C; number of
males in household, M; number of females in household, FM; number of children in household,
CH; having private health insurance coverage, PHS; visits to health practitioners, HSB, and per
capita population quintile that the individual’s family below, Q. The model was modified
because of non-response and low variability. Hence, a number of variables were not including in
the final model, which is reflected of the population and the challenges of non-response and low
variability. The following variables were omitted from the analysis because the non response
rates were high (in excess of 40%). These were positive affective psychological conditions
(41.5%, n = 650). Marital status was omitted on two premises; one, non-variability (99.7% of
those who responded were never married (n = 672) given their ages; and two, the non-response
rate (57.1%, n = 893). Only 1.3% of the population were pregnant (n = 14) and this question had
a non-response rate of 29.3% (n = 459).
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The final model was based on those variables that were statistically significant (P <0.05).
Using stepwise logistic regression analyses, all variables that were not significant were removed
from the final model (P > 0.05). Hence, the final model shows that self-related health status is
determined by cost of medical care, Pmc ; number of females in household, FM; having private
health insurance coverage, PHS; visits to healthcare practitioners, HSB (Eqn [2]):
Ht = (Pmc, FM, PHS, HSB)……………………………………………………....(Eqn
[2])
Statistical analysis
Data was stored and retrieved in the SPSS 16.0; descriptive statistics were used to provide
pertinent information on the subsample and logistic regression was used to examine the influence
of socio-demographic and psycho-economic variables on self-related health status (or reported
health status). The dependent variable was self-related health status and the independent
variables were socio-demographic and psycho-economic variables. Means and frequency
distribution were considered significant at P < 0.05 using chi-square, independent sample t-test,
F-test, and multiple logistic regressions. 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 [23]. To derive accurate tests of statistical significance, we used SUDDAN
statistical software (Research Triangle Institute, Research Triangle Park, NC), and this adjusted
for the survey’s complex sampling design.
Results
Table 6.6.1 presents information on the sociodemographic characteristic of the sample. The
sample had 1,565 respondents: mean age, 14.4 years old (S.D. = 1.7 years); 8.3% reported an
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illness and 1.3% were pregnant. The majority (62%) of the female respondents lived in rural
areas, and most (93.8%) had secondary school education.
Table 6.6.2 examines information that is associated with good self-related health status of
respondents. Four variables emerged as accounting for 20.3% of the variability in good self-
related health status of young females. The most influential factors that determine self-related
health status of young females (ages 12 to 17 years) were family ownership of private health
insurance (OR = 0.03, 95%CI: 0.01 - 0.09); the number of females in the household (OR = 0.73,
95%CI: 0.59, 0.90); cost of medical care (OR = 0.996, 95%CI: 1.00, 1.01), and health care
seeking behaviour (visits to health care practitioners), (OR = 1.25, 95%CI: 1.04, 1.52).
Discussion
In this study the majority of adolescents reported that they have good self-related health. The
determinants of good self-related health status in female adolescents in Jamaica were family
owed private health insurance coverage, number of females in household, cost of medical care
and healthcare seeking behaviour (visits to health care practitioners). The findings of this study
concur with those of another study which assessed youth health in the Caribbean countries
including Jamaica where four in five adolescents state that their general health was good [24].
This latter study reported that younger adolescents are more likely to report better health and, by
age 16, one in six youths reported fair to poor health status [24]. In addition, almost 10% of the
young people (more boys than girls) report having a handicap, disability, or chronic illness that
limits their activities. Headaches, physical development and sleep problems are the most
common health concerns of young people in the Caribbean [24]. Poor health in adolescents is
positively associated with risk factors such as abuse and parental problems and negatively
associated with protective factors such as connectedness to family and community [25]. Resnick
149
et al. found that parent/family connectedness and perceived school connectedness were
protective against every health risk behavior measured, except history of pregnancy [26].
In Jamaica, approximately 9% of the population is covered by private health insurance
[27]. Persons in the wealthiest consumption quintile were more than four times more likely to
have health insurance coverage than those in the poorest quintile, 35 per cent and 8.5 per cent
respectively [28]. The family’s health care insurance coverage was the main determinant of good
self-related healthcare status of the female adolescents in Jamaica. Those young females whose
family had them on their private health insurance plan indicated a lower self-related health status
compared to another young female whose family does not have private health insurance. This
suggests that health insurance is purchased in keeping with the high probability of the individual
being likely to become ill (or knowing that the individual suffers from a particular health
condition).
Poverty and lack of health insurance are two powerful socioeconomic influences that
predispose young people to a wide variety of health problems. Poor adolescents typically
experience more health and health-related problems than non-poor adolescents with respect to
acute and chronic conditions that restrict activity; overall self-related fair or poor health; and
higher rates of pregnancy, cigarette smoking and depression [29]. Adolescents from poor
families and those without health insurance are more likely to seek routine medical care from a
public hospital, outpatient clinic, emergency department, or public health center. Uninsured
adolescents are more likely to miss school and fall behind academically, which may affect their
ability to achieve their full potential [30]. In a study done by Newacheck et al. one in every seven
adolescents in the United States, aged 10-18, is uninsured. Uninsured adolescents, as opposed to
insured adolescents, are more likely to be members of poor and minority families [31].
150
The ability of the families of adolescents to afford healthcare is based on their economic
status. An adolescent family economic status can have a strong influence on adolescents’
perceptions of health, their health behaviors and use of health care [32, 33]. The cost of health
care was one of the determinant factors of good health status among the female adolescents in
Jamaica. In a study by Halcon et al. assessing youth health in the Caribbean Community and
Common Market countries including Jamaica, most adolescents (85.9%) reported that they have
a place where they usually receive medical care [34]. However, only 36.2% have had a checkup
in the last two years. Less than half have seen a dentist in the past two years. If they need
contraception, students would go, first to physicians, followed by drug stores, family planning
clinics, and public health clinics. Males are consistently less likely to use healthcare services than
females; and they are more likely to believe that adults will not provide confidentiality [34].
According to Figueroa et al. health-seeking behaviour and/or access to healthcare in
Jamaica appears to have improved between 1993 and 2000 since significantly fewer persons in
2000 than in 1993 reported never having had their blood pressure checked and fewer women
reported they had never had a Pap smear. This may be due to a growing health consciousness in
sectors of the society [35]. In this study, health seeking behavior was one of the determinants of
good health status of female adolescents. The use of healthcare services depends on health status
of respondents. The better the health status of an adolescent the lower the health care services
utilization and vice-versa. The ability of adolescents to obtain healthcare services is an important
indication of whether the healthcare system is meeting their needs. Difficulties experienced by
adolescents in accessing healthcare include: long distance to healthcare centre, lack of transport
services and long waiting time for the healthcare services [36]. Understanding adolescents' health
seeking behaviour is critical for quality service improvement.
151
In a study by Halcon et al. of adolescents in Caribbean countries, crowding was a
significant concern for a number of young people with 29% reporting 2-4 persons slept in a room
and an additional 3.4% indicate more than 5 people slept together [24]. In this study, crowding
did not affect the health status of young females neither did negative affective psychological
conditions; family assets ownership, household income and consumption, and education. It was
also discovered that there was no statistical difference between the health statuses of those who
dwelled in rural, urban or other towns. The number of males in the household and the number of
children in the household did not influence the quality of life of young females. However, the
number of females in the household inversely affects the health status of young female
adolescents.
Although there is no statistical significance between the health status of poor and wealthy
young females, nearly three quarters of young females in the study resided in the rural areas (62
per cent) where incidences of poverty are traditionally higher than those in urban areas. This
further substantiates the fact that household economic status is directly linked to health of
children, and rural children are perhaps more vulnerable than their urban counterparts. There are
several implication associated with phenomenon for young females from rural households.
Among them are vulnerability to diseases brought on by nutritional deficiencies, weak immune
systems and low academic performance. These invariably impact on their life chances,
psychological self actualization and eventually their inability to break the cycle of poverty.
Hence, any policy that seeks to address the wellbeing of female adolescents must incorporate the
advancement of the household, and the social and economic factors coupled with the needs of the
individual.
152
Conclusion
The health status of young females in Jamaica is substantially impacted on by family owed
private health insurance coverage, number of females in household, cost of medical care and
healthcare seeking behaviour (visits to health care practitioner). Embedded in this study is the
importance of family through either the purchase of health insurance, coverage of the cost of
medical care and health visits of young females. This study provided insights into social factors
that determine the good self-related health status of female adolescents, which will enable
healthcare practitioners to devise appropriate programs to address the health concerns of this
group.
Disclosures
The author reports no conflict of interest with this work.
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 and/or the Statistical Institute of Jamaica, 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 available for use in this study, and Dr. Donovan McGrowder for editing and other advice that allowed for the completion the final manuscript.
153
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statechildren’s health insurance program-eligible children in the United States with those of
other groups of insured children: implications for policy. Pediatrics 2000; 106:14-21.
31) Newacheck P, McManus M, Brindis C. Financing health care for adolescents: Problems,
prospects, and proposals, Journal of Adolescent Health Care. 1990; 11:398-403.
32) Garbarino J. Children in Danger: Coping With the Consequences of Community Violence.
San Francisco, Calif: Josey-Bass Publishers; 1992.
33) Gibbs JT, Huang LN. Children of Color: Psychological interventions with minority youth.
San Francisco, California: Josey-Bass Publishers; 1989.
34) Halcón L, Blum RW, Beuhring T, Pate E, Campbell-Forrester S, Venema A. Adolescent
health in the Caribbean: a regional portrait. Am J Public Health. 2003; 93:1851-1857.
35) Figueroa JP, Ward E, Walters C, Ashley DE, Wilks RJ. High risk health behaviours among
adult Jamaicans. West Indian Med J. 2005; 54:70-76.
36) Booth M, Bernard D, Quine S, Kang M, Usherwood T, Alperstein G, Bennett D. Access to
health care among Australian adolescents young people’s perspectives and their
sociodemographic distribution. Journal of Adolescent Health. 2006; 34:97-103.
157
Table 6.6.1: Descriptive analysis of variables of target cohort
Variables Descriptive Analysis
Age 14.4 (±1.7 years)
Residence 62%= Rural
25.4% = Other Town
12.3% = Urban area
Educational level 5.6% = Primary
93.8% = Secondary
0.6% = Tertiary
Average consumption (per year) US$652.30 (± $607.37)
Average income (per year) US$3,699.00 (± $3,167.41)
Crowding 2.3( ±1.5 persons)
Self reported good health 91.7%
Pregnancy (at the time of the survey) 1.3%
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Table 6.6.2: Socio-demographic and psychological variables of self-related health status of the sample
Characteristic β Coefficient
Std Error
Odds ratio
CI (95%)
Middle class 0.46 0.37 1.58 0.77 - 3.25 Upper class -0.36 0.34 0.70 0.36 - 1.37 Referent group (lower class) 1.00 Cost of medical care 0.00 0.00 0.996* 0.99 - 1.01 Crowding -0.02 0.10 0.98 0.80 - 1.19 Environment 0.65 0.37 1.91 0.93 - 3.91 Negative Affective Conditions -0.03 0.04 0.97 0.91 - 1.04 Assets owned by household -0.02 0.06 0.98 0.88 - 1.10 Age 0.004 0.08 1.00 0.86 - 1.18 Health Insurance -3.37 0.46 0.03*** 0.01 - 0.09 Other Towns -0.07 0.29 0.94 0.54 - 1.64 Urban areas -0.05 0.34 0.95 0.49 - 1.85 Referent group (Rural areas) 1.00 Number of male -0.05 0.12 0.95 0.75 - 1.20 Number of females -0.32 0.11 0.73** 0.59 - 0.90 Number of children 0.06 0.09 1.06 0.88 - 1.26 Average Consumption 0.00 0.00 0.997 1.00 - 1.01 Crime Index -0.01 0.01 0.99 0.98 - 1.01 Average Income 0.00 0.00 0.997 1.00 - 1.01 Visits to Health practitioners 0.23 0.10 1.25* 1.04 - 1.52 Education 0.04 0.03 1.04 0.99 - 1.10
Chi-square (19) = 113.87, P < 0.001 -2 Log likelihood = 587.25 Nagelkerke r-squared = 0.203 Overall correct classification = 92.7% Correct classification of cases on good health = 99.2% Correct classification of cases bad health = 18.8% *P < 0.05, **P < 0.01, ***P < 0.001
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CHAPTER
7
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. Current study 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
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
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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
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.
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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.
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.
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The current study fills the gap in the health literature by investigating health of females in
Jamaica. The objectives of the current study 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 study 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
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
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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).
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).
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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
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.
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Table 7.7.1 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 7.7.1. 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 7.7.1).
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 2). Based on Table 7.7.2, 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 ±
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.
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Based on Figure 7.7.1, 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 3). Based on Table 7.7.3, 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 7.7.4).
Discussion
Self-rated health status of female Jamaicans can be measured using self-reported illness. The
current study 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 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.
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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 study 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 study 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
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-
168
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
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
169
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 study 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 study 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
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.
170
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Table 7.7.1. Sociodemographic characteristics of sample by area of residence, 2002 and 2007 Variable
2002
2007
Rural Semi-Urban
Urban Rural Semi-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)
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Table 7.7.2. 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
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Figure 7.7.1. Mean scores for self-reported diagnosed health conditions, 2002 and 2007
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Table 7.7.3. Self-rated health status by income quintile, 2007 Self-rated health status
Income Quintile
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
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Table 7.7.4. Self-reported diagnosed health condition by per capita income Diagnosed health condition
Income Quintile 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
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CHAPTER
8
Health of children less than 5 years old in an Upper Middle Income Country: Parents’ views
Health literature in the Caribbean, and in particular Jamaica, has continued to use objective indices such as mortality and morbidity to examine children’s health. The current study uses subjective indices such as parent-reported health conditions and health status to evaluate the health of children instead of traditional objective indices. The study seeks 1) to examine the health and health care-seeking behaviour of the sample from the parents’ viewpoints; and 2) to compute the mean age of the sample with a particular illness and describe whether there is an epidemiological shift in these conditions. Two nationally representative cross-sectional surveys were used for this study (2002 and 2007). The sample for the current study is 3,062 respondents aged less than 5 years. For 2002, the study extracted a sample of 2,448 under-5 year olds from the national survey of 25,018 respondents, and 614 under-5 year olds were extracted from the 2007 survey of 6,728 respondents. Parent-reported illness status was measured by the question ‘Have you had any illness other than due to injury (for example a cold, diarrhoea, asthma, hypertension, diabetes or any other illness) in the past four weeks? Health condition (i.e. parent-reported illness or parent-reported dysfunction) was measured by the question: “Is this a diagnosed recurring illness?” Self-rated health status was measured by “How is your health in general?” And the options were: Very Good; Good; Fair; Poor and Very Poor, and 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:. The health disparity that existed between rural and urban under-5 year olds showed that this will not be removed simply because of the abolition of health care utilization fees.
Introduction
In many contemporary nations, objective indices such as life expectancy, mortality and
diagnosed morbidity are still being widely used to measure the health of people, a society and/or
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a nation [1-6]. The World Health Organisation (WHO) in the Preamble to its Constitution in the
1940s wrote that health is more important than disease, as it expands to the social, psychological
and physical wellbeing of an individual [7]; and lately that during the 21st century the emphasis
must be on healthy life expectancy [8,9]. In keeping with its opined emphasis, the WHO
formulated a mathematical approach that diminished life expectancy by the length and severity
of time spent in illness as the new thrust in measuring and examining health. Although healthy
life expectancy removes time spent in illness and severity of dysfunctions, it fundamentally rests
on mortality. The WHO therefore, instead of moving forward, has given some scholars, who are
inclined to use objective indices in measuring health, a guilty feeling about continuing this
practice.
The Caribbean, and in particular Jamaica, continues to use mortality and morbidity to
measure the health of children or infants [1-6]. The use of mortality, morbidity and life
expectancy is the practice of Caribbean scholars, and is widely used in Jamaica by the: Ministry
of Health (MOHJ) [10]; Statistical Institute of Jamaica (STATIN) [11]; Planning Institute of
Jamaica (PIOJ) [12]; PIOJ and STATIN [13] as well as the Pan American Health Organization
(PAHO) [14] in measuring health. In spite of the conceptual definition opined by the WHO in
the Preamble to its Constitution in 1946, the health of children who are less than 5 years old in
Jamaica is still measured primarily by using mortality and morbidity statistics. Recently a book
entitled ‘Health Issues in the Caribbean’ [15] had a section on Child Health; however the
articles were on 1) nutrition and child health development [16] and 2) school achievement and
behaviour in Jamaican children [17], indicating the void in health literature regarding health
conditions.
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An extensive review of health literature in the Caribbean region found no study that has
used national survey data to examine the health status of children below 5 years of age. The
current study fills this gap in the literature by examining the health status of children below 5
years of age using cross-sectional survey data which are based on the views of patients. The
objectives of this study are 1) to examine the health and health care-seeking behaviour of the
sample; and 2) to evaluate the mean age of the sample with a particular illness and to describe
whether there is an epidemiological shift in these conditions.
Materials and methods
Sample
The current study used two secondary nationally representative cross-sectional surveys (for 2002
and 2007) to carry out this work. The sub-samples are children below 5 years old, and the only
criterion for selection was being less than 5 years old. The sample in the current study is 3,062
respondents of ages less than 5 years. For 2002, a sub-sample of 2,448 under-5 year olds was
extracted from the national survey of 25,018 respondents in 2002, and information on 614 under-
5 year olds was extracted from the 2007 survey. The survey (Jamaica Survey of Living
Conditions) began in 1989 to collect data from Jamaicans in order to assess government policies.
Since 1989, the JSLC has added a new module each year in order to examine that phenomenon,
which is critical within the nation [18, 19]. In 2002, the focus was on 1) social safety nets, and
2) crime and victimization, while for 2007, there was no focus.
Methods
Stratified random sampling technique was used to draw the sample for the JSLC. This design
was a two-stage stratified random sampling design where there was a Primary Sampling Unit
(PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District
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(ED), which comprises a minimum of 100 residences in rural areas and 150 in urban areas. An
ED is an independent geographical unit that shares a common boundary. This means that the
country was grouped into strata of equal size based on dwellings (EDs). Based on the PSUs, a
listing of all the dwellings was made, and this became the sampling frame from which a Master
Sample of dwellings was compiled, which in turn provided the sampling frame for the labour
force. One third of the Labour Force Survey (i.e. LFS) was selected for the JSLC [18, 19]. The
sample was weighted to reflect the population of the nation [18-20].
The JSLC 2007 was conducted in May and August of that year; while the JSLC 2002 was
administered between July and October of that year. The researchers chose this survey based on
the fact that it is the latest survey on the national population, and that that it has data on the self-
reported health status of Jamaicans. An administered questionnaire was used to collect the data
from parents on children less than 5 years old, and the data were stored, retrieved and analyzed
using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA). The questionnaire was modelled
on the World Bank’s Living Standards Measurement Study (LSMS) household survey. There are
some modifications to the LSMS, as the JSLC is more focused on policy impacts. The
questionnaire covered areas of socio-demographic variables – such as education; daily expenses
(for the past 7 days); food and other consumption expenditures; inventory of durable goods;
health variables; crime and victimization; social safety net and anthropometry. The non-response
rates for the 2002 and 2007 surveys were 26.2% and 27.7% respectively. The non-response
includes refusals and cases rejected in data cleaning.
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Measures
Social class: This variable was measured based on the income quintiles: The upper classes were
those in the wealthy quintiles (quintiles 4 and 5); the middle class was quintile 3 and the poor
were the lower quintiles (quintiles 1 and 2).
Age is a continuous variable in years.
Health conditions (i.e. parent-reported illness or parent-reported dysfunction): The question was
asked: “Is this a diagnosed recurring illness?” The answering options are: Yes, Cold; Yes,
Diarrhoea; Yes, Asthma; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No.
Self-rated health status: “How is your health in general?” And the options were: Very Good;
Good; Fair; Poor and Very Poor.
Medical care-seeking behaviour was taken from the question ‘Has a health care practitioner,
healer or pharmacist been visited in the last 4 weeks?’ with there being two options: Yes or No.
Parent-reported illness status. The question is ‘Have you had any illness other than due to injury
(for example a cold, diarrhoea, asthma, hypertension, diabetes or any other illness) in the past
four weeks? Here the options were Yes or No.
Statistical analysis
Descriptive statistics, such as mean, standard deviation (SD), frequency and percentage were
used to analyze the socio-demographic characteristics of the sample. Chi-square was used to
examine the association between non-metric variables, and Analysis of Variance (ANOVA) was
used to test the relationships between metric and non-dichotomous categorical variables, whereas
an independent sample t-test was used to examine the statistical correlation between a metric
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variable and a dichotomous categorical variable. The level of significance used in this research
was 5% (i.e. 95% confidence interval).
Results
Demographic characteristic of sample
In 2002, the sex ratio was 98.8 males (below 5 years old) to 100 females (below 5 years old),
which shifted to 116.2 under-5 year old males to 100 under-5 year old females. The sample over
the 6 year period (2002 to 2007) revealed internal migrations to urban zones (Table 1): In 2002,
59.6% of respondents resided with their parents and/or guardians in rural areas, which declined
to 5.07%. The percentage of children below 5 years of age whose parents were in the poorest
20% fell to 25.4% in 2007 over 29.6% in 2002. In 2007 over 2002, 1.7 times less children below
5 years of age were taken to public hospitals, compared to 1.2 times less taken to private
hospitals (Table 8.8.1). Approximately 6% more children below 5 years were ill in 2007 over
2002. Based on Table 8.8.1, under-5 year olds with particular chronic illnesses had: diabetes
mellitus (0.6%); hypertension (0.3%) and arthritis (0.3%). However, none was recorded in 2007.
There were some occasions on which the response rates were less than 50%: In 2002,
health care-seeking behaviour was 14.3%; parent-reported diagnosed health conditions, 14.2%;
and visits to health care institutions, 8.9% (Table 1). For 2007, the response rate for health care-
seeking behaviour was 20.2%; parent-reported diagnosed health conditions, 20.2%, and less than
11% for cost of medical care.
Health conditions
Based on Table 8.8.1, the percentage of under-5 year olds with particular acute conditions saw a
decline in colds and asthmatic cases, as well as chronic conditions. Figure 8.8.1 revealed that in
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2007 the mean age of children less than 5 years old with acute health conditions (i.e. diarrhoea,
respiratory diseases and influenza) increased over 2002. On the other hand, the mean age of
those with unspecified illnesses declined from 1.76 years (SD = 1.36 years) to 1.64 years (SD =
1.36 years). Concomitantly, the greatest mean age of the sample was 2.71 years (SD = 1.21
years) for asthmatics in 2007 and 2.59 years (1.24 years) in 2002. It should be noted here that
the mean age of a child below 5 years of age in 2002 with diabetes mellitus was 1.50 years (2.12
years).
Health status
In 2002, the JSLC did not collect data on the general health status of Jamaicans, although this
was done in 2007. Therefore, no figures were available for health status for 2002. In 2007,
43.4% of children less than 5 years old had very good health status; 46.7% good health status;
7.1% fair health status; 2.5% poor and 0.3% very poor health status. The response rate for the
health status question was 96.9%.
Ninety-seven percent of the sample was used to examine the association between health
status and parent-reported illness - χ2 (df = 4) = 57.494, P < 0.001 – with the relationship being a
weak one, correlation coefficient = 0.297. Table 8.8.2 revealed that 24.2% of children below 5
years of age who reported an illness had very good health status, compared to 2 times more of
those who did not report an illness. One percent of parents indicated that their children (of less
than 5 years) who had no illness had poor health status, compared to 5.6 times more of those
with illness who had poor health status.
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Health conditions, health status and medical care-seeking behavior
A cross-tabulation between health status and parent-reported diagnosed illness found that a
significant statistical correlation existed between the two variables - χ2 (df = 16) = 26.621, P <
0.05, cc = 0.422, - with the association being a moderate one, correlation coefficient = 0.422
(Table 3). Based on Table 8.8.3, children below 5 years old with asthma were less likely to
report very good health status (5.9%), compared to those with colds (30.5%); diarrhoea (22.2%);
and unspecified health conditions (22.7%).
When health status by parent-reported illness (in %) was examined by gender, a
significant statistical relationship was found, P < 0.001: males - χ2 (df = 4) = 25.932, P < 0.05, cc =
0.320, and females - χ2 (df = 4) = 39.675, P < 0.05, cc = 0.356. The health statuses of males less than 5
years old in the very good and good categories were greater than those of females (Figure 8.8.2).
However, the females had greater health statuses in fair and poor health status than males, with more
males reporting very poor health status than females.
Based on Figure 8.8.3, even after controlling health status and parent-reported illness (in
%) by area of residence, a significant statistical association was found: urban - χ2 (df = 3) =
10.358, P < 0.05, cc = 0.238; semi-urban - χ2 (df = 3) = 9.887, P = 0.021, cc = 0.273, and rural -
χ2 (df = 3) = 45.978, P < 0.001, cc = 0.365. Concomitantly, children less than 5 years of age were
the least likely to have very good health status (19.4%) compared to rural (25.8%) and semi-
urban children (25.9%). Furthermore, the respondents who resided in urban areas were 2.1 times
more likely to have parent-reported very poor health status, compared to rural respondents.
In examining health status and reported illness (in %) by social classes, significant
statistical relationships were found, P < 0.05: poor-to-poorest classes - χ2 (df = 4) = 52.374, P =
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0.021, cc = 0.393; middle class - χ2 (df = 3) = 8.821, P = 0.032, cc = 0.259, and wealthy class - χ2
(df = 3) = 10.691, P = 0.02, cc = 0.234. Based on Figure 8.8.4, middle class children who are less
than 5 years old had the greatest very good health status (37%) compared to the wealthy class
(26.8%) and the poor-to-poorest classes (16.1%). Fourteen percent of poor-to-poorest class
children who are less than 5 years old had at most poor health status compared to 0% of the
middle class and 4.9% of the wealthy class, while 1.8% of poor-to-poorest classes below 5 years
of age had very poor health status.
When health status and parent-reported illness was examined by age, sex, social class,
and area of residence, the correlation was a weak one – correlation coefficient = 0.295, P <
0.001, n=583.
A cross tabulation between health status and health care-seeking behaviour found a
significant statistical association between the two variables - χ2 (df = 4) = 10.513, P < 0.033 -
with the correlation being a weak one – correlation coefficient = 0.281. A child below 5 years
old was 2.44 times more likely to be taken for medical care if he/she had at most poor health
status. On the other hand, a child who had very good health status was 1.97 times more likely not
to be taken to health care practitioners (Figure 8.8.5).
In 2007, an examination of the health care-seeking behaviour and parent-reported illness
of the sample revealed no statistical correlation - χ2 (df = 1) = 0.430, P = 0.618. It was found that
61.5% of the sample who were ill were taken to health care practitioners, while 38.5% were not.
On the other hand, more were taken for medical care than in 2007 in the 4-week period of the
survey. No statistical correlation was noted for the aforementioned variables in 2002 - χ2 (df = 1)
= 1.188, P = 0.276. Of those who reported ill, 63.7% were taken to health care practitioners.
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Discussion
Infant mortality has been declining since the 1970s, and this has further decreased since 2004
[14]; this, as the literature shows, is not a good measure of health. The current study found that,
using general health status, children below 5 years of age in Jamaica had good health. The
findings revealed that 90 out of every 100 under-5 year olds had at least good health status, with
44 out of every 100 having very good health status. In spite of the good health status of under-5
year olds in Jamaica in 2007, 20.8% of them had an illness in the 4-week period of the survey,
which is a 5.9% increase over 2002. It is interesting to note the shift in this study away from
specific chronic illnesses. In 2002, 30 out of every 1,000 under-5 year olds in Jamaica were
diagnosed with hypertension and arthritis (i.e. parent-reported), with 60 out of 1,000 having been
parent-reported with diabetes mellitus. None such cases were found in 2007, suggesting that in
the case of the children who had those particular chronic illnesses, their parents had either
migrated with them or they had died. Concomitantly, the country is seeing a reduction in
children less than 5 years old with colds; however, marginal increases were seen in diarrhoea,
asthma and unspecified health conditions over the last 6 years. Although there were increased
reported cases of illness over the studied period, in 2007, 62 out of every 100 ill children were
taken to medical practitioners, and this fell from 64 in every 100 in 2002. One of the arguments
put forward by some people is that what retards or abates health care-seeking behaviour is
medical cost. With the abolition of health care user fees for children since 2007, the culture must
be playing a role in parents and/or guardians not taking children who are ill to medical care
facilities for treatment.
189
Medical cost cannot be divorced from the expenditure that must be incurred in taking the
child to the health care facility. In 2007, 25 out of every 100 children below 5 years of age had
parents and/or guardians who were below the poverty line. Although this has declined by 4.2%
since 2002, it nevertheless means that there are children whose parents are incapacitated by other
factors. Marmot [21] opined that the financial inability of the poor is what accounts for their
lowered health status, compared to other social classes. The current study concurs with the
findings of Marmot, as it was revealed that children below 5 years of age from poor households
had the least health status. This means that poverty is not merely eroding the health status of poor
Jamaicans, but that equally it is decreasing the health status of poor children.
Rural poverty in Jamaica is at least twice as great as urban poverty, and approximately 4
times more than semi-urban [13], which provides another explanation for the poor health status
of children below 5 years of age. The current study found that 3.2% of those children dwelling in
urban zones recorded at most poor health status, compared to 13.6% of rural children, suggesting
that the health status of the latter group is 4.3 times worse than the former. This means that
poverty in rural zones is exponential, eroding the quality of life of children who are less than 5
years old. Poverty in semi-urban areas was 4% which is 2.5 times less than that for the nation;
and those below 5 years of age recorded the greatest health status, supporting Marmot’s
perspective that poverty erodes the health status of a people. Hence, the decline in health care-
seeking behaviour for this sample is embedded in the financial constraints of parents and/or
guardians as well as their geographical challenges. The terrain in rural zones in Jamaica is such
that medical care facilities are not easily accessible to residents compared to urban dwellers.
With this terrain constraint comes the additional financial burden of attending medical care
facilities at a location which is not in close proximity to the home of rural residents, and this
190
accounts for the vast health disparity between rural and urban children. As a result of the above,
the removal of health care utilization fees for children under 18 years of age does not correspond
to an increased utilization of medical care services, or lowered numbers of unhealthy children
below 5 years of age. If rural parents are plagued with financial and location challenges, their
children will not have been immunized or properly fed, and their nutritional deficiency would
explain the health disparity that exists between them and urban children who have easier access
to health care facilities.
The removal of health care utilization fees is not synonymous with an increased
utilization of medical care for children less than 5 years old, as 46.5% of the sample attended
public hospitals for treatment in 2002, and after the abolition of user fees in April 2007
utilization fell by 1.7 times compared to 2002. In order to understand why there is a switch from
health care utilization to mere survival, we can examine the inflation rate. In 2007, the inflation
rate was 16.8% which is a 133% increase over 2002 (i.e. 7.2%), which translates into a 24.7%
increase in the prices of food and non-alcoholic beverages, and a 3.4% increase in health care
costs [22]. Here the choice is between basic necessities and health care utilization, which further
erodes health care utilization in spite of the removal of user fees for children.
Health status uses the individual self-rating of a person’s overall health status [23], which
ranges from excellent to poor. Health status therefore captures more of people’s health than
diagnosed illness, life expectancy, or mortality. However, how good a measure is it? Empirical
studies show that self-reported health is an indicator of general health. Schwarz & Strack [24]
cited that a person’s judgments are prone to systematic and non-systematic biases, suggesting
that it may not be a good measure of health. Diener, [25] however, argued that the subjective
index seemed to contain substantial amounts of valid variance, indicating that subjective
191
measures provide some validity in assessing health, a position with which Smith concurred [26].
Smith [26] argued that subjective indices do have good construct validity and that they are a
respectably powerful predictor of mortality risks [27], disability and morbidity [27], though these
properties vary somewhat with national or cultural contexts. Studies have examined self-reported
health and mortality, and have found a significant correlation between a subjective and an
objective measure [27-29]: life expectancy [30]; and disability [28]. Bourne [30] found that the
correlation between life expectancy and self-reported health status was a strong one (correlation
coefficient, R = 0.731); and that self-rated health accounted for 53% of the variance in life
expectancy. Hence, the issue of the validity of subjective and objective indices is good, with
Smith [26] opining that the construct validity between the two is a good one.
The current research found that parent-reported illness and the health status of children
less than 5 years of age are significantly correlated. However, the statistical association was a
weak one (correlation coefficient = 0.297), suggesting that only 8% of the variance in health
status can be explained by parent-reported children’s illnesses. This is a critical finding which
reinforces the position that self-reported illnesses (or health conditions) only constitute a small
proportion of people’s health. Therefore, using illness to measure the health status of children
who are less than 5 years of age is not a good measure of their health, as illness only accounts for
8% of health status. However, based on Bourne‘s work [30], health status is equally as good a
measure of health as life expectancy. One of the positives for the using of health status instead
of life expectancy is its coverage in assessing more of people’s general health status by using
mortality or even morbidity data.
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Conclusion
In summary, the general health status of children who are less than 5 years old is good;
however, social and public health programmes are needed to improve the health status of the
rural population, which will translate into increased health status for their children. The health
disparity that existed between rural and urban children below 5 years of age showed that this will
not be removed simply because of the abolition of health care utilization fees. In keeping with
this reality, public health specialists need to take health care to residents in order to further
improve the health status of children who are less than 5 years old.
Conflict of interest
The author has no conflict of interest to report.
Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, 2007, none of the errors that are within this paper should be ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica as they are not there, but owing to the researcher.
193
References
1. Lindo, J. (2006) Jamaican perinatal mortality survey, 2003. Jamaica Ministry of Health. Kingston, pp. 1-40.
2. McCarthy, J.E., and Evans-Gilbert, T. (2009) Descriptive epidemiology of mortality and morbidity of health-indicator diseases in hospitalized children from western Jamaica. American Journal of Tropical Medicine and Hygiene, 80,596-600.
3. Domenach, H., and Guengant, J. (1984) Infant mortality and fertility in the Caribbean basin. Cah Orstom (Sci Hum), 20,265-72.
4. Rodriquez, F.V., Lopez, N.B., and Choonara, I. (2002) Child health in Cuba. Arch Dis Child, 93,991-3.
5. McCaw-Binns, A., Holder, Y., Spence, K., Gordon-Strachan, G., Nam, V., and Ashley, D. (2002) Multi-source method for determining mortality in Jamaica: 1996 and 1998. Department of Community Health and Psychiatry, University of the West Indies. International Biostatistics Information Services. Division of Health Promotion and Protection, Ministry of Health, Jamaica. Statistical Institute of Jamaica, Kingston
6. McCaw-Binns, A.M., Fox, K., Foster-Williams, K., Ashley, D.E., and Irons, B. (1996) Registration of births, stillbirths and infant deaths in Jamaica. International Journal of Epidemiology, 25,807-813.
7. World Health Organization, (WHO). (1948) Preamble to the Constitution of the World Health Organization as adopted by the International Health Conference, New York, June 19-22, 1946; signed on July 22, 1946 by the representatives of 61 States (Official Records of the World Health Organization, no. 2, p. 100) and entered into force on April 7, 1948. “Constitution of the World Health Organization, 1948.” In Basic Documents, 15th ed. WHO, Geneva.
8. World Health Organization, (WHO). (2004) Healthy life expectancy 2002: 2004 World Health Report. WHO, Geneva.
9. WHO. (2000) WHO Issues New Healthy Life Expectancy Rankings: Japan Number One in New ‘Healthy Life’ System. WHO; 2000, Washington D.C. & Geneva.
10. Jamaica Ministry of Health, (MOHJ). (1992-2007) Annual report 1991-2006. MOHJ,
Kingston.
11. Statistical Institute of Jamaica, (STATIN). (1981-2009) Demographic statistics, 1980-2008. STATIN, Kingston.
12. Planning Institute of Jamaica, (PIOJ). (1981-2009) Economic and Social Survey, 1980-2008. PIOJ, Kingston.
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13. PIOJ, and STATIN. (1989-2009) Jamaica Survey of Living Conditions, 1988-2008. PIOJ and STATIN, Kingston.
14. Pan American Health Organization, (PAHO). (2007) Health in the Americas, 2007, volume II Countries. PAHO, Washington DC.
15. Morgan, W. (ed). (2005) Health issues in the Caribbean. Ian Randle, Kingston.
16. Walker, S. Nutrition and child health development. In Morgan, W. (ed). Health issues in the Caribbean. Ian Randle, Kingston, pp. 15-25.
17. Samms-Vaugh, M., Jackson, M., and Ashley, D. (2005) School achievement and behaviour in Jamaican children. In Morgan, W, (ed). Health issues in the Caribbean. Ian Randle, Kingston, pp. 26-37.
18. Statistical Institute Of Jamaica. (2008) Jamaica Survey of Living Conditions, 2007 [Computer file]. Kingston, Jamaica: Statistical Institute Of Jamaica [producer], 2007. Kingston, Jamaica: Planning Institute of Jamaica and Derek Gordon Databank, University of the West Indies [distributors].
19. Statistical Institute Of Jamaica. (2003) Jamaica Survey of Living Conditions, 2002 [Computer file]. Kingston, Jamaica: Statistical Institute Of Jamaica [producer], 2002. Kingston, Jamaica: Planning Institute of Jamaica and Derek Gordon Databank, University of the West Indies [distributors].
20. World Bank, Development Research Group, (2002). Poverty and human resources.
Jamaica Survey of Living Conditions (LSLC) 1988-2000: Basic Information.
21. Marmot, M (2002) The influence of income on health: Views of an Epidemiologist. Does money really matter? Or is it a marker for something else? Health Affair, 21,31-46.
22. Bourne, P.A (2009) Impact of poverty, not seeking medical care, unemployment,
inflation, self-reported illness, health insurance on mortality in Jamaica. North American Journal of Medical Sciences, 1, 99-109.
23. Kahneman, D., and Riis, J. (2005) Living, and thinking about it, two perspectives. In
Huppert, F.A., Kaverne, B. and N. Baylis, The Science of Well-being, Oxford University Press.
24. Schwarz, N., and Strack, F. (1999) Reports of subjective well-being: judgmental
processes and their methodological implications. In Kahneman, D., Diener, E., Schwarz, N, (eds). Well-being: The Foundations of Hedonic Psychology. Russell Sage Foundation: New York, pp. 61-84.
25. Diener, E. (1984) Subjective well-being. Psychological Bulletin, 95,542–75.
26. Smith, J. (1994) Measuring health and economic status of older adults in developing
countries. Gerontologist, 34, 491-6.
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27. Idler, E.L., and Benjamin, Y. (1997) Self-rated health and mortality: A Review of
Twenty-seven Community Studies. Journal of Health and Social Behavior, 38, 21-37.
28. Idler, E.L., and Kasl, S. (1995) Self-ratings of health: Do they also predict change in functional ability? Journal of Gerontology 50B, S344-S353.
29. Schechter, S., Beatty, P., and Willis, G.B. (1998) Asking survey respondents about health
status: Judgment and response issues. In Schwarz, N., Park, D., Knauper, B., and S. Sudman, S (ed.). Cognition, Aging, and Self-Reports. Ann Arbor. Taylor and Francis, Michigan.
30. Bourne, P.A. (2009) The validity of using self-reported illness to measure objective
health. North American Journal of Medical Sciences, 1,232-238.
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Table 8.8.1. Socio-demographic characteristic of sample, 2002 and 2007
Variable
2002 2007 n % n %
Sex Male 1216 49.7 330 53.7 Female 1231 50.3 284 46.7 Income quintile Poorest 20% 725 29.6 156 25.4 Poor 554 22.6 140 22.8 Middle 474 19.4 126 20.5 Wealthy 402 16.4 117 19.1 Wealthiest 20% 293 12.0 75 12.2 Self-reported illness Yes 345 14.9 125 20.8 No 1969 85.0 475 79.2 Visits to health care facilities (hospitals) Private, yes 17 7.8 5 6.7 Public, yes 100 46.3 20 26.7 Area of residence Rural 1460 59.6 311 50.7 Semi-urban 682 27.9 125 20.4 Urban 306 12.5 178 29.0 Health (or, medical) care-seeking behaviour Yes 221 63.3 76 61.3 No 128 36.7 48 38.7 Health insurance coverage Yes, private 211 9.0 66 11.1 Yes, public * * 33 5.5 No 2123 91.0 496 83.4 Self-reported diagnosed health conditions Acute Cold 185 53.3 60 48.4 Diarrhoea 20 5.8 9 7.3 Asthma 46 13.3 17 13.7 Chronic Diabetes mellitus 2 0.6 0 0 Hypertension 1 0.3 0 0 Arthritis 1 0.3 0 0 Other (unspecified) 54 15.6 22 17.7 Not diagnosed 38 11.0 16 12.9 Number of visits to health care institutions 1.53 (SD = 0.927) 1.43 (SD = 0.989) Duration of illness Mean (SD) 8.51 days (6.952 days) 8.07 days (7.058 days) Cost of medical care Public facilities Median (Range)in USD 2.36 (157.26)1 0.00 (64.62)2 Private facilities Median (Range)in USD 13.76 (117.95)1 10.56 (49.71)2 1USD1.00 = Ja. $50.87 2 USD1.00 = Ja. $80.47 *In 2002, all health insurance coverage was private and this was change in 2005 to include some public option
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Table 8.8.2. Health status by self-reported illness
Health status
Self-reported illness
Yes No
n (%) n (%)
Very good 30 (24.2) 227 (48.3)
Good 61 (49.2) 217 (46.2)
Fair 23 (18.5) 19 (4.0)
Poor 9 (7.3) 6 (1.3)
Very poor 1 (0.1) 1 (0.2)
Total 124 470
χ2 (df = 4) = 57.494, P < 0.001, cc = 0.297, n = 594
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Table 8.8.3. Health status by self-reported diagnosed illness
Health status
Self-reported diagnosed illness Cold Diarrhoea Asthma Unspecified No
Very good 18 (30.5) 2 (22.2) 1 (5.9) 5 (22.7) 5 (31.3)
Good
31 (52.5) 5 (55.6) 4 (23.5) 11 (50.0) 8 (50.0)
Fair
7 (11.9) 2 (22.2) 8 (47.1) 3 (13.6) 3 (18.8)
Poor
2 (3.4) 0 (0.0) 4 (23.5) 3 (13.6) 0 (0.0)
Very good
1 (1.7) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)
Total 59 9 17 22 16 χ2 (df = 16) = 26.621, P < 0.05, cc = 0.422,
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Figure 8.8.1. Mean age of health conditions of children less than 5 years old, 2002 and 2007
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Figure 8.8.2. Health status by Parent-reported illness (in %) examined by gender
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Figure 8.8.3. Health status by parent-reported illness (in %) examined by area of residence
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Figure 8.8.4. Health status by parent-reported illness (in %) examined by social classes
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Figure 8.8.5. Health status by health care-seeking behaviour
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CHAPTER
9
Health of males in Jamaica
Studies in the Caribbean on males have been on marginalization; fatherhood; masculinity and none on the change pattern of diseases, and factors that account for their good health status. The current study fills this gap in the literature by examining males’ health in Jamaica. Study are 1) provide a detailed epidemiological profile of health conditions; 2) indicate the changing pattern of health conditions; 3) calculate the mean age of having reported illness or not; 4) compute the mean age of particular health conditions; 5) state whether the mean age of having particular illness are changing; 6) determine whether there is a significant statistical correlation between health status and self-reported illness; 7) identify factors that correlate with health status; and 8)ascertain the magnitude of each determinant of health status. In 2002, the mean age of a male who reported an illness was 39.32 ± 28.97 years compared to 27.26 ± 20.45 years – t-test = 18.563, P < 0.001. In 2007, the mean age of those with illness marginally increased to 40.64 ± 29.44 years compared to 27.61 ± 19.80 years for those who did not have an illness - t-test = 11.355, P < 0.001. A male who reported good health status with reference to one who indicated poor health status is 17.8 times more likely not to report an illness. Predictors of poor self-reported illness of males in Jamaica for 2002 were age (OR = 1.044; 95% CI = 1.038, 1.049; P < 0.05); urban area (OR = 1.547, 95% CI = 1.172, 2.043; P < 0.05); consumption (OR = 1.183; 95% CI = 1.056, 1.327; P < 0.05). non self-reported illness of males in Jamaica for 2007 can be predicted by good health status (OR = 17.801; 95% CI = 10.761, 29.446; P < 0.05); fair health status (OR = 2.403; 95% CI = 1.461, 3.951; P < 0.05); age (OR = 0.967; 95% CI = 0.957, 0.977; P < 0.05); urban area (OR = 1.579, 95% CI = 1.067, 2.336; P < 0.05); and consumption (OR = 0.551; 95% CI = 0.352, 0.861; P < 0.05). On disaggregating the explanatory power, it was revealed that good health status accounted for 30% (out of 37.6%) of the why males do not report an illness. Interestingly in this work is that the mean age of males who reported being diagnosed with unspecified health conditions has declined by 27 years; but we are not cognizant of what constitutes this category of illness. With average age of contracting this health conditions being 40.7 years, could this group holds some answers to the high mortality of Jamaican males. The way forward must be to research this unspecified health condition grouping as public health cannot plan without research findings.
Introduction
In the Caribbean, studies on males have been masculinity and fatherhood [1-6]; male
marginalization [7-10]; survivability [11], broad health concerns [12-25] and those studies
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exclude the health status of males. The Planning Institute of Jamaica, (PIOJ) & Statistical
Institute of Jamaica, (STATIN) however since 1988 have provided general self-reported illness
and medical care-seeking behaviour of the population and these have been disaggregated by
sexes [26]. The information on health issues of males is insufficient upon which public health
practitioners can sufficient plan for this cohort.
Since 1988, when PIOJ & STATIN began collecting data with a modified World Bank
Living Conditions instrument, males has reported less illness than females; visited health care-
practitioners less than females; yet their life expectancy has been between 2-6 years less than that
of females [27]. These situations suggesting that males’ health cannot be left only up to the
aforementioned for planning their health issues. Concurringly, STATIN’s data revealed that of
the 5 leading cause of mortality in Jamaica, males outnumbered females in 4 categories [28]; and
the morbidity figures published by the Ministry of Health (MOHJ) showed that they
outnumbered females in 7 of the 10 leading cause of illnesses (MOHJ) [29,30]. It is evident from
the aforementioned data that there is health disparity between the sexes in Jamaica; health goes
beyond illness and health care-seeking behaviour.
In the late 1940s, the health discourse was such that World Health Organization (WHO)
in the Preamble to its Constitution joined the debate and offered a conceptual definition of health
[31]. The WHO [31] penned that health is more than the mere absence of diseases to include
social, psychological and physiological wellbeing. This was adopted by Engel [32-36] who even
coined the term ‘biopsychosocial model’ as the new thrust in mental ill patient care. He like the
WHO opined that humans are mind, body and social agents which denote that their care must
incorporate all these facets as against the old biomedical approach, which was only concerned
about diseases and not wellbeing. This approach has revolutionalized the how health care is
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delivered, measured and planned for. Embedded in Engel’s works are how health should be
conceptualized and addressed, and that wellbeing can be attained if it is measured solely using
illness.
In response to a need to expand the measures of health away from diagnosed illness,
mortality and life expectancy (or objective indexes), researchers like Diener [37,38]; Veenhoven
[39]; Frey & Stutzer [40-43]; Diener & Seligman [44]; Diener et al. [45]; Hutchinson et al. [21];
Easterlin [46,47] have used happiness, life satisfaction and some health status [20,48]. Those
measures are subjective indexes, which the scholars opined assess health more than the negative
or narrow objective indexes. In keeping with the limitation of objective indexes, the WHO [49]
devised an approach to discount life expectancy by removing time spent in illness to produce
what is termed healthy life expectancy (or disability adjusted life expectancy). Disability
Adjusted Life Expectancy (DALE) summarizes the expected number of years to be lived in what
might be termed the equivalent of "full health." To calculate DALE, the years of ill health are
weighted according to severity and subtracted from the expected overall life expectancy to give
the equivalent years of healthy life [49]. This approach resulted in Jamaicans losing 9 years of
life owing to disabilities. The healthy life expectancy provides yet another account for health
status of males; but there is a fundamental weakness that has not been address. Healthy life
expectancy is rest on the pillows of mortality patterns and still lacks the coverage that happiness,
life satisfaction and health status gives. Healthy life expectancy therefore lacks extensive
coverage of an individual’s health; but accompanying the subjective indexes are biases and
validity issues.
There are empirical evidences to show that self-reported health is an indicator of general
health. Schwarz & Strack [50] opined that the person’s judgments are prone to systematic and non-
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systematic biases. However, Diener [37] argued that the subjective index seemed to contain
substantial amounts of valid variance, suggesting that subjective measures provide some validity
in assessing health, this was concurred by Smith [51] with good construct validity and is a
respectably powerful predictor of mortality risks [52], disability [53] and morbidity [54], though
these properties vary somewhat with national or cultural contexts [52]. Studies using self-
reported health and mortality found a significant relationship between a subjective and an
objective measure [52, 54]; life expectancy [55]; disability [53]. Bourne [55]) found that the
correlation between life expectancy and self-reported health status was a strong one (correlation
coefficient, R = 0.731); and that self-rated health accounted for 53% of the variance in life
expectancy. Hence, the issue of the validity of subjective and objective indexes is good, with
Smith [51] opined that the construct validity between the two being a good one.
Using subjective indexes to measure health, studies have shown that there are many
predictors (or variables) of these measures. Income, marital status, education, and other
sociodemographic variables [12-18, 20, 21, 40, 46-48, 56] have been found to significant
correlate with health status. Those studies have not singled out males in the examination of
health issues, suggesting that the experiences of males and females are congruent or similar.
WHO [57] forwarded that there is a disparity between contracting many diseases and the gender
constitution of an individual. One health psychologist, Phillip Rice [58], 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’ [58]. With health disparity
between the sexes caused by particular issues with a nation, it is for this reason why health
research must examine the sexes differently in order to understand each subgroup.
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The current study fills this gap in the health literature by examining the health of males in
Jamaica. The objectives of this study are 1) provide a detailed epidemiological profile of health
conditions; 2) indicate the changing pattern of health conditions; 3) calculate the mean age of
having reported illness or not; 4) compute the mean age of particular health conditions; 5) state
whether the mean age of having particular illness are changing; 6) determine whether there is a
significant statistical correlation between health status and self-reported illness; 7) identify
factors that correlate with health status; and 8)ascertain the magnitude of each determinant of
health status.
Materials and methods
The current study used secondary cross-sectional data taken from two nationally representative
surveys. A subsample of 12,332 males out of 25,018 respondents and 3,303 males from 6,783
respondents were extracted from 2002 and 2007 surveys respectively. The only criterion upon
which the subsample was selected was based on being male. The survey (Jamaica Survey of
Living Conditions, JSLC) is a modification of the World Bank Survey on Living Conditions [59-
61] (PIOJ & STATIN, 1988-2008; World Bank, 2002). The JSLC began collecting data since
1988, and each year a new module is included based on particular sociopolitical issues with the
economy leading up to the survey period. 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.
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
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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% [59-61].
Measures
An explanation of some of the variables in the model is provided here. Self-reported illness
status is a dummy variable, where 1 = reporting an ailment or dysfunction or illness in the last
4 weeks, which was the survey period; 0 if there were no self-reported ailments, injuries or
illnesses [17, 18, 62]. 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 [52, 53]. Health status is a binary measure where 1=good to excellent health; 0=
otherwise which is determined from “Generally, how do you feel about your health”? Answers
for this question are in a Likert scale matter ranging from excellent to poor. Age group was
classified as children (ages less than 15 years); young adults (ages 15 through 30 years); other
aged adults (ages 30 through 59 years); young-old (ages 60 through 74 years); old-old (ages 75
through 84 years) and oldest-old (ages 85+ years). Medical care-seeking behaviour was taken
from the question ‘Has a health care practitioner, header, or pharmacist being visited in the last 4
weeks?’ with there being two options Yes or No. Medical care-seeking behaviour therefore was
coded as a binary measure where 1=Yes and 0= otherwise.
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Statistical analysis
Descriptive statistics such as mean, standard deviation (SD), frequency and percentage were used
to analyze the socio-demographic characteristics of the sample. Chi-square analyses were used to
examine the association between non-metric variables; and t-test for metric and dichotomous
variables and F statistic was utilized for metric and non-dichotomous variables. Logistic
regressions analyses the relationship between 1) poor self-reported illness and some socio-
demographic variables (for 2002); as well as 2) not reported an illness and some socio-
demographic, economic variables and health status (for 2007). The statistical packages SPSS
16.0 was used for the analysis. Ninety-five percent confidence interval was used for the analysis,
and the final models (ie equations) were based those variables that P < 0.05. Odds Ratio (OR)
was interpreted for each significant variable. Initially the enter approach was used in logistic
regression followed by stepwise to ascertain the contribution of each significant variable for the
final models.
In order to exclude multicollinearity between particular independent variables, correlation
matrix was examined in order to ascertain if autocorrelation (or multicollinearity) existed
between variables. Based on Bryman & Cramer [63], 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. Moderately to highly correlated variables were excluded from the model. Another
exclusion criterion that was used is 30% of missing cases.
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Results
Demographic characteristic of sample
Table 9.9.1 revealed a shift in percent of divorced (+ 0.8%); widowed (+ 0.7%); separated (-
0.4%); never married (+1.7%) and married males (-1.4%) between 2002 and 2007. There was
also a percentage shift in the sample reported having had an illness in the 4-week period of the
survey. Concomitantly, there was a decline in percent of sample with hypertensive and arthritic
cases in the chronic illness category, with an increase in diabetic cases. In 2007, 62.3% of males
sought medical care compared to 60.7% in 2002. The increase was not limited to medical care-
seeking behaviour as the percentage of males with health insurance coverage increased by 10.5%
to 19.3%. Massive urbanization is occurring in male population as in 2002, 62.7% of males
dwelled in rural zones and this decline to 50.1% in 2007, with 16% more males resided in urban
zones and 3.4% decline in semi-urban males. In the period (2002-2007), consumption and
income increased by 2.24 and 2.17 times respectively.
Health statistics
In 2007, it was the first time in the 2 decade history on collecting data on Jamaicans that health
status was obtained. The findings revealed that 39.0% of sample indicated very good health
status; 46.4% good health; 10.4%, fair health and 4.3% poor-to-poorest health, with 0.8%
indicated very poor health status.
A cross tabulation between health status and self-rated illness revealed a significant
statistical correlations - χ2 (df = 4) = 602.354, P < 0.001, with the association being a weak one,
correlation coefficient = 0.399. Twenty-one percent of the sample indicated having had an illness
that reported poor-to-poorest health status compared to 1.9% of sample that revealed no illness
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recorded poor-to-poorest health status (Table 9.9.2). Continuing, 3.3 times more of the
respondents who indicated not having an illness had very good health status compared to those
who indicated having an illness.
In 2002, the mean age of a male who reported an illness was 39.32 ± 28.97 years
compared to 27.26 ± 20.45 years – t-test = 18.563, P < 0.001. In 2007, the mean age of those
with illness marginally increased to 40.64 ± 29.44 years compared to 27.61 ± 19.80 years for
those who did not have an illness - t-test = 11.355, P < 0.001.
Based on Figure 9.9.1, the mean age of males with particular chronic illness has decline
over the period. Interestingly, the greatest percentage decline was observed in unspecified health
conditions. In 2002, the mean age for males with unspecified health condition was 55.79 ± 28.81
years and this fell to 40.67 ± 27.01 years in 2007. In 2007, the mean age for males with diabetes
mellitus was 61.94 ±12.01 years; 66.76 ± 15.95 years for those with hypertension and 70.29 ±
10.85 years for those with arthritis. Further examination revealed that there is statistical
difference between the mean of those with chronic illness (P > 0.001); but this existed between
the chronic and the acute illnesses as well as the unspecified health conditions: for 2002 – F
statistic = 15.62, P < 0.001 and for 2007 – F statistic = 31.601, P < 0.001.
Multivariate analysis
Predictors of poor self-reported illness by some explanatory variables In 2002, current poor health status of males in Jamaica was found to be significantly correlated
with age; area of residence; consumption, social support and marital status (χ2 = 545.320, P < 0.001
-2 Log likelihood = 4277.79) (Table 9.9.3). Table 9.9.3 revealed that predictors of poor self-reported
illness of males in Jamaica for 2002 were age (OR = 1.044; 95% CI = 1.038, 1.049; P < 0.05);
urban area (OR = 1.547, 95% CI = 1.172, 2.043; P < 0.05); consumption (OR = 1.183; 95% CI =
1.056, 1.327; P < 0.05). Further analysis show that age was the most significant predictor of poor
213
health status accounting for 14.3% of the model (ie 15.1%); with area of residence accounting
for 0.2% (Table 9.9.3).
In 2007, current poor health status of males in Jamaica was found to be significantly
associated with health status; age of respondents; consumption, and area of residence - (χ2 =
463.61, P < 0.001; -2 Log likelihood = 1103.314) (Table 4). Based on Table 9.9.4 revealed that
predictors of poor self-reported illness of males in Jamaica for 2002 were age (OR = 1.044; 95%
CI = 1.038, 1.049; P < 0.05); urban area (OR = 1.547, 95% CI = 1.172, 2.043; P < 0.05);
consumption (OR = 1.183; 95% CI = 1.056, 1.327; P < 0.05). The findings here show that for
each year that a male ages, he is 1.04 times more likely to report an illness; and that urban males
are 1.6 times more likely to report an illness with reference to rural males. Further analysis show
that age was the most significant predictor of poor health status accounting for 14.3% of the
model (ie 15.1%); with area of residence accounting for 0.2% (Table 9.9.5).
Based on Table 9.9.4, non self-reported illness of males in Jamaica for 2007 can be
predicted by good health status (OR = 17.801; 95% CI = 10.761, 29.446; P < 0.05); fair health
status (OR = 2.403; 95% CI = 1.461, 3.951; P < 0.05); age (OR = 0.967; 95% CI = 0.957, 0.977;
P < 0.05); urban area (OR = 1.579, 95% CI = 1.067, 2.336; P < 0.05); and consumption (OR =
0.551; 95% CI = 0.352, 0.861; P < 0.05). On disaggregating the explanatory power, it was
revealed that good health status accounted for 30% (out of 37.6%) of the why males do not
report an illness; age accounted for 5.4%; fair health accounted for 0.8%; consumption, 0.9% and
area of residence, 0.5% (Table 9.9.6). Concomitantly, Table 4 revealed that a male who reported
good health status with reference to one who indicated poor health status is 17.8 times more
likely not to report an illness; and that the more a male spent in consumption expenditure, he is
0.449 times less likely not to report an illness.
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Discussion
The current study revealed that men were willing to state their general health status (using
response rate, 97%); but that they were unwilling to report the typologies of illness that they
were diagnosed with (response rate, 0.7% in 2002 and 12.2% in 2007). Income of males
increased by least 2 times in 2007 over 2002; however, health care-seeking behaviour increased
by only 1.6%. Embedded in the finding is males reluctance to seek medical care, and this again
can be seen in of 8.8% increase in health insurance coverage in 2007 over 2002 7% was due to
public health insurance although this is fee. The number of diabetic cases in 2007 increased by
2.3 times over 2002, and there declines in the mean age at which males reported illness. The
mean age at which a male who had self-reported being diagnosed with diabetes fell to 61.94
years; hypertension, 66.8 years; arthritis, 70.3 years and unspecified health conditions, 40.7
years from 55.8 years. Hence, why the reluctance to seek medical care with the aforementioned
context?
Chevannes [1] provided some explanation for men’s general behaviour using social
learning theory. He forwarded the perspective that a young male imitates the roles of society
members through role modeling as to what constitute acceptable and good roles [1]. Young
males are grown to be strong, masculine, brave and fewer traits must shun the appearance of
weakness and its associated attributes. The male child therefore as a part of his socialization is to
accept that the illness is correlated with weakness, and that he must not be willing participate
into health care seeking behaviour unless it is unavoidable. This definition of unavoidable is
embedded into severity, and being unable to rectify the complaint outside of health care
practitioners. This gender role of sexes is not limited to Jamaica or the Caribbean but a study
carried out by Ali and de Muynck [64] on street children in Pakistan found a similar gender
215
stereotype. A descriptive cross-sectional study carried out during September and October 2000,
of 40 school-aged street children (8-14 years) revealed severity of illnesses and when ill-health
threatens financial opportunities that males sought medical care. Another finding was that
[65]. Chevannes noted males suppressed response a pain, accounting for a low turn out to health
care facilities and justifies a higher mortality rates as on attend medical care facilities it is often
too later and death is probable outcome.
Hence the lowered age with which are diagnosed with particular chronic illness (such as
diabetes mellitus, hypertension and arthritis) does not change this embedded culturalization
which began prior to formal schooling and justifies why higher education does not often time
change this practice. Understanding the psyche of men and how this is fashioned aids in the
comprehension of their reluctance to visit health care facilities. The current findings indicate that
urbanization is taken place with males in Jamaica. The migration to urban zones is primarily to
facilitate economic opportunities which account for the drastic increase in income. Ali & de
Muynck [64] study provides some understanding for the marginal increase in health care seeking
behaviour in Jamaica as this figure is accounted for males who were ill to the point of being
unable to work and that the ill-health threatens their economic livelihood.
Another explanation for males’ withdrawal from visits to health care facilities is due to
the gender composition of those facilities. Males are culturalized to be strong, provide for his
family and chief among these is to show a female his masculinities which are tied to strength,
physique and financial ability. It follows that with the higher percentage of health care workers
being females, this retard the males’ masculinity as he conceptualizes visits to these institutions
as a show of his weakness. In protection of this masculinity, males will go to any extent to
maintain their image, which includes the sacrificing of life. This is embedded in the health
216
reported figures for the sexes. In 2002, 14.6% of females reported an illness compared to 10.2%
for males, and in 2004 the disparity widens as the figures were 13.6% for females and 8.9% for
male [26].
The current work showed the contribution of health status in explaining illness (or non-
illness) of males. Current health status therefore accounted for 80.9% (30% out of 37.1%) of the
variability in current illness (or lack of), which is to Hambleton et al.’s work. Hambleton et al.
found that 87.5% (ie 33.5% out of 38.3%) of current illness account current health status of
elderly Barbadians. This work holds some comparability with Hambleton et al.’s study with
respect to explanatory power and contribution of illness to health status. Hambleton et al.’s
research is not only validating the current study, this work is validating the use of self-rated (or
self-reported) illness or health status in measuring health of an individual.
Many empirical studies have established the strong correlation between marital status and
health status. This work found that there was no significant difference between health status of
married males and males who were never married; but that divorced, separated and widowed
males were 1.4 times more likely to report an illness. A part of this rationale for the higher
probability of increased illness is owing to 1) the lost owing to separation which may be via
death or physical separation, 2) the psychological tenet in investment and its lose from parting;
and, 3) the financial separation cost which are likely to account for depression, suicide and other
forms of illness. A study by Able et al. [66] found that the rate of suicide in male Jamaicans was
9 times higher than that for females, and they opined that a part of this is owing to suppressed
feeling of this sex. Although divorce, separation or widowhood have a psychosocial influence on
males, being married do not provide a benefit of better health.
217
Conclusion
The current study provides a comprehensive examination of males’ health in Jamaica with which
can be used by public health and other policy makers in understanding this cohort. Interestingly
in this work is that the mean age of males who reported being diagnosed with unspecified health
conditions has declined by 27 years; but we are not cognizant of what constitutes this category of
illness. With average age of contracting this health conditions being 40.7 years, could this group
holds some answers to the high mortality of Jamaican males. The way forward must be to
research this unspecified health condition grouping as public health cannot plan without research
findings.
Conflict of interest
The author has no conflict of interest to report.
218
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Table 9.9.1. Sociodemographic characteristics of sample, 2002 and 2007 Variable 2002 2007
n % n % Marital status Married 2007 25.7 522 24.3 Never married 5421 69.4 1528 71.1 Divorced 64 0.8 34 1.6 Separated 85 1.1 16 0.7 Widowed 234 3.0 50 2.3 Self-reported illness Yes 1217 10.2 388 12.1 No 10699 89.8 2820 87.9 Self-reported diagnosed illness Cold - - 69 17.2 Diarrhoea 5 5.7 11 2.7 Asthma 6 6.8 47 11.7 Diabetes mellitus 3 3.4 31 7.7 Hypertension 39 44.3 58 14.4 Arthritis 16 18.2 24 6.0 Other 19 21.6 102 25.4 Not diagnosed - - 60 14.9 Income quintile Poorest 20% 2454 19.9 671 20.3 Poor 2345 19.0 640 19.4 Middle 2440 19.8 636 19.3 Wealthy 2482 20.1 667 20.2 Wealthiest 20% 2611 21.2 689 20.9 Health care-seeking behaviour Yes 769 60.7 253 62.3 No 497 39.3 153 37.7 Health insurance coverage Yes 1251 10.5 612 19.3 No 10699 89.5 2560 80.7 Area of residence Rural 7727 62.7 1654 50.1 Semi-urban 3062 24.8 706 21.4 Urban 1543 12.5 943 28.5 Income Median (Range) Ja $251,795.96
(Ja. $6,423,253.16.72) Ja $545,950.17
(Ja. $5,228,700.28) Age Mean ±SD 28.28 ± 21.7 ears 29.11 ± 21.6 years Consumption Median (Range) Ja $55,508.45
(Ja. $1,992,283.72) Ja $123,697.30
(Ja. $1,621,147.12) Duration of illness Median (Range) 10.5 days (90 days) 7.1 days (15 days) Cost of medical care Public Median (Range) Ja $150.00 (Ja. S12,000) Ja $294.96 (Ja. $20,000) Private Median (Range) Ja $800.00 (Ja $ 29,000) Ja $1130.39 (Ja $ 13,000) In 2002, US $1.00 = Ja. $50.87 In 2007, US $1.00 = Ja. $80.47
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Table 9.9.2. Health status and self-rated illness Health status
Self-rated illness Yes No
Very good 50 (13.0) 1193 (42.6) Good 129 (33.4) 1351 (48.2) Fair 125 (32.4) 205 (7.3) Poor 66 (17.1) 44 (1.6) Very poor 16 (4.1) 8 (0.3) Total 386 2801 χ2 (df = 4) = 602.354, P < 0.001; cc = 0.399
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Table 9.9.3. Predictors of poor self-reported illness by some explanatory variables, 2002
Variable S.E.
Wald statistic P Odds ratio 95.0% C.I.
Age 0.003 222.661 0.000 1.044 1.038 1.049 Urban areas 0.142 9.470 0.002 1.547 1.172 2.043 Other towns
†Rural areas Log Consumption
0.156 0.058
1.312 8.344
0.252 0.004
1.195 1.183
0.881 1.056
1.622 1.327
Separated_Div_Wid
0.148
4.766
0.029
1.382
1.034
1.848
Married 0.097 1.388 0.239 1.121 0.927 1.355 †Never married
Physical environment
0.086
0.885
0.347
1.084
0.916
1.283
Secondary 0.100 0.018 0.893 1.013 0.833 1.232 Tertiary 0.212 0.087 0.768 1.064 0.703 1.612 †Primary or below
Rented – house tenure
0.170
0.017
0.895
0.978
0.700
1.366
Owned 0.123 0.025 0.876 1.020 0.801 1.298 †Squatted
Social support
0.082
6.231
0.013
1.226
1.045
1.440
Constant 0.664 92.874 0.000 0.002 χ2 = 545.320, P < 0.001 -2 Log likelihood = 4277.79 Hosmer and Lemeshow goodness of fit χ2=4.324, P = 0.827 Nagelkerke R2 =0.151 Overall correct classification = 88.9% Correct classification of cases of poor self-rated health = 99.8% Correct classification of cases of good self-rated health =1.8% †Reference group
225
Table 9.9.4. Predictors of not self-reporting an illness by some explanatory variables, 2007 Variable
S.E. Wald
statistic P Odds ratio 95.0% C.I. Good health status 0.257 125.717 0.000 17.801 10.761 29.446 Fair health status 0.254 11.927 0.001 2.403 1.461 3.951 †Poor health status
Age
0.005
39.848
0.000
0.967
0.957
0.977
Middle Class
0.257
0.011
0.918
1.027
0.620
1.701 Upper class 0.364 0.344 0.558 1.238 0.606 2.528 †Lower class
Married
0.194
0.710
0.399
0.849
0.581
1.241 Divorced, separated or
wid 0.313 0.003 0.954 1.018 0.551 1.881
†Never married Health insurance
0.195
0.016
0.899
0.975
0.665
1.430
Urban area
0.200
5.221
0.022
1.579
1.067
2.336 Other towns 0.216 2.858 0.091 1.440 0.944 2.199 †Rural areas
Log Consumption
0.228
6.844
0.009
0.551
0.352
0.861 Constant 2.596 10.301 0.001 4158.196
χ2 = 463.61, P < 0.001 -2 Log likelihood = 1103.314 Hosmer and Lemeshow goodness of fit χ2=4.272, P = 0.832 Nagelkerke R2 =0.376 Overall correct classification = 88.9% Correct classification of cases of poor self-rated health = 99.8% Correct classification of cases of good self-rated health =1.8% †Reference group
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Table 9.9.5. Model summary for 2002 logistic regression analysis
Model Nagelkerke R Square
Age 0.143 Age+urban area 0.145 Age+urban area+consumption 0.148 Age+urban area+consumption+social support 0.149 Age+urban area+consumption+social support+ marital status 0.151
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Table 9.9.6. Model summary for 2007 logistic regression analysis Model Nagelkerke R
Square Good health 0.300 Good health+age 0.354 Good health+Age+fair health 0.362 Good health+Age+fair health+consumption 0.371 Good health+Age+fair health+consumption+urban area 0.376
228
Figure 9.9.1. Mean age for males with particular self-reported diagnosed illness
229
Part II
ERRORS IN DATA
230
INTRODUCTION Content errors refer to the accuracy of characteristics of data system, assessing the reliability of
data sources. This is executed and performed by testing the consistency of data sources,
particularly the content. The exorbitant cost and time consuming nature of primary data
collection makes it increasingly determinable to avoidance of primary data collection. In
response to the challenges of primary data collection, some researchers (academics and scholars)
have resorted secondary data sources. Some people use the credibility of the data collector and
publisher as the yardstick for measuring the usability of secondary data. The tradition, scope,
coverage, authority and traditional contribution of some institutions and agencies make it easier
for people to assume the reliability validity of the data estimates, results and data system.
Institutions and/or agencies like American Diabetes Association; Centers for Disease Control
and Prevention; WHO; Pan American Health Organization, PAHO; United Nations; NASA;
ILO; World Bank; Universities – Cambridge; Harvard; Oxford; Princeton; Yale to name a few).
Repeatedly science has tested and refuted traditions, cosmologies, and authorities. It is as
a result on the unbiasness of science to investigate phenomena, which have led to the
modification and refutation of old knowledge. New paradigms emerged when scientists question
epistemologies. Thus scientists cannot take the biased position that authority is important in
fashioned knowledge. Science seeks to ascertain the truth, which is embedded in the primary
assumption that nothing is truth with testing and verification. With this underlying reality, we
must question data quality irrespective of the data sources’ former credibility, scholastic
accomplishments and authority on knowledge.
In keeping with the pillows upon which science operates, inquiry cannot be done only on
some data estimates and results that of from specific individuals and/or institutions as this violate
the ‘pursuit of truth’. If we assume that we currently know the truth, then more examination of
issues surround that matter and the phenomenon in question cannot be tested in the future, which
assumes that knowledge is constant. Empirical evidence exists that showed the modification of
past knowledge, refutation of some, and paradigm shift of positions. Knowledge is fluid. Fluidity
implies that we must be continuously examining knowledge, because the set of propositions that
held in the past can change and thereby offer a new knowledge of what we thought was. This is
231
one of the tenets that accounts for demographers continuously examining the content of data,
particularly on age in surveys and censuses.
Surveys represent the summation of peoples’ views and quality of recall. It is sometimes
overlooked by people that surveys are critical based on the quality of the recollection of the
respondents and their honesty. Knowing this fact, demographers have formulated and developed
techniques to examine the quality of data. While it is established that coverage errors are low,
because statisticians have continued to improve the quality of the sample frame, sample and
representation of the population, demographers in Jamaica have not examined data quality (ie
content) outside age box (paradigm). This volume seeks to evaluate content errors in health data,
particularly among the JSLC, because of the interconnectivity between health and development
of a society.
Part II of this volume explores content errors in health data that are likely in the JSLC.
The JSLC is not absolute truth as there is no such phenomenon, making an inquiry into the
likeliness of content errors apart of the verification of the data and ascertaining the degree of
truth that is therein. These inquiries are to strengthen the quality of the data as measures and
adjustments can be made in keeping with findings.
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CHAPTER
10
Dichotomising poor self-reported health status: Using secondary cross-sectional survey data for Jamaica
Caribbean scholars continue to dichotomise self-reported health status without empirical justification for inclusion or exclusion of moderate health status in the dichotomisation of poor health. This study will 1) evaluate which cut-off point should be used for self-reported health status; 2) assess whether dichotomisation of self-reported data should be practiced; 3) ascertain any disparity in dichotomisation by some covariates (i.e., marital status, age cohort, social class); and 4) examine the odds of reporting poor or moderate-to-very poor self-reported health status if one has an illness. When moderate self-reported health status was used in poor health status, the cut-off revealed moderate effect on specified covariates across the age cohorts for women. However, for men, exponential effects were used on social class, but not on area of residence or marital status across the different age cohorts. The cut-off point in the dichotomisation of self-reported health status does not make a difference for women and must be taken into consideration in the use of self-reported health data for Jamaica. Introduction
Logistic regression has been widely used by Caribbean and/or Latin American scholars to
examine parameters and weights of determinants of self-reported health status [1-7] or life
satisfaction [8]. This is a global practice [9-14]. Embedded in the use of logistic regression in the
study of self-reported (rated) health is the dichotomisation of health status. Self-rated health
status is a Likert scale variable ranging from very poor to very good health status. This denotes
that the dichotomisation of self-reported health must address where moderate health status
should be placed.
233
The dichotomisation of self-reported health status brings into focus the issue of a cut-off
and the validity of one’s choice. By categorising an ordinal measure (i.e., self-reported health)
into a dichotomous one, this means that some of the original data will be lost in the process.
Another important issue which is unresolved in the choice of a cut-off is the subjective with
which Caribbean scholars have continued to make their decision. Their decision as to what
constitutes bad or good (including excellent) health is not purely subjective, as this practice is
global one. The decision of a cut-off cannot be subject to international norm if there is no
rationale for this approach. Caribbean scholars cannot merely follow tradition in their choice of
conceptualisation and operationalisation of a measure, as this is not a scientific enough rationale
for the use of a particular measure.
Some scholars have opined that self-reported health status should remain a Likert scale
measure or in its continuous form as against the dichotomisation of the measure [15-17]. The
work of Finnas et al. showed that the five-point Likert scale variable of self-reported health
status can be dichotomised. However, there are some methodological issues that must be
considered [18]. Finnas and colleagues’ study revealed that the cut-off point of bad versus good
self-reported health and the decision as to where moderate self-reported health status be placed
does not depend on age. However, when the categorisation of poor self-reported health excludes
moderate self-reported health, the covariate of marital status and educational level were found to
be highly age-dependent. Within the context of the aforementioned findings, Caribbean scholars
need to examine these issues within the available health data in order to be able to empirically
make a choice of 1) dichotomisation or 2) non-dichotomisation of self-reported health status.
The discourse on whether or not to dichotomise self-reported health status is unresolved.,
Therefore, dichotomising the measure simply because it has been done so by non-Caribbean
234
scholars in developed nations is not a sufficient rationale for following suit in Latin America and
the Caribbean. Latin America and the Caribbean are developing nations whose socio-economic
situations are different from those in First World Countries, emphasising the justification of why
Latin America and Caribbean scholars should examine self-reported health data in order to
concretise their choice of dichotomisation or not.
Jamaica, which is a part of Latin America and the Caribbean, has been collecting self-
reported health data since 1988 [19], and these data have been used repeatedly by scholars to aid
public health programmes. An extensive review of the literature did not find a single study that
has examined the validity of dichotomisation of self-reported health status. The same was also
found for the wider Caribbean, suggesting that scholars have been keeping with the tradition and
the practice of using the scholarly information from the developed nations when it comes to
dichotomised self-reported health status. The current study fills this gap in the literature, and will
be used to guide public health practitioners and other users of self-reported health data on
Jamaicans. The objectives of the study are: 1) evaluate which cut-off point should be used for
self-reported health status; 2) assess whether dichotomisation of self-reported data should be
practiced; 3) ascertain any disparity in dichotomisation by some covariates (i.e., marital status,
age cohort, social class); and 4) examine the odds of reporting poor or moderate-to-very poor
self-reported health status if one has an illness.
Materials and Methods
Sample
This study used secondary cross-sectional survey data, which was collected between May and
August, 2007 [20]. The Jamaica Survey of Living Conditions (JSLC), which is used for this
235
study, is a joint research conducted by the Planning Institute of Jamaica (PIOJ) and the Statistical
Institute of Jamaica (STATIN) [19]. The JSLC is an annual survey that began in 1988. It is a
standard exercise; the JSLC’s sample is a proportion of the Labour Force Survey (LFS). In 2007,
it was one-third of the LFS.
For 2007, the JSLC’s sample was 6,783 respondents. The current study extracted 1,583
respondents from the larger sample as the focus was on participants aged 46+ years. 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 dwellings was compiled, which
in turn provided the sampling frame for the labour force. A total of 620 households were
interviewed from urban areas, 439 from semi-urban areas and 935 from rural areas, which
constituted 6,783 respondents. The sample was weighted to reflect the population of the nation.
The non-response rate for the survey for 2007 was 27.7%.
Data collection
The JSLC is a modification of the World Bank’s Living Standards Measurement Study
household survey [21]. Face-to-face interviews over the aforementioned period were used to
collect the data. A structured questionnaire was used and already trained interviewers were then
trained again specifically for this task. The questions covered demographic characteristics,
236
household consumption, health status, health care-seeking behaviour, illnesses, education,
housing, social welfare and related programmes, and inventory of durable goods.
Statistical analyses
Data were stored, retrieved and analyzed using SPSS-PC for Windows version 16.0. Descriptive
statistics were used to provide background information on the sample. Cross tabulations were
done to examine non-metric dependent and independent variables, which provided the
percentages. Percentages were computed for dichotomous health statuses (i.e., very poor or poor
health status, and the other very poor to moderate health status); these were employed for
calculating the odds ratio in each dichotomisation of self-reported health status.
Among men aged 46-54 years, 37.7% of those who reported an illness rated their health status as
very poor or poor, as compared to 7.3% of those who did not indicate an illness. Hence, the odds
ratio of very poor-to-poor health status was 7.7 [(37.7/62.3)/(7.3/92.7)] indicating that men who
reported an illness also have 8 times as high odds of reporting very poor or poor health status
than those who did not report a dysfunction.
In age cohort 46-54 years, the percentage of men who reported very poor, poor or moderate
health status was 81.4% compared to 39.9% of those who did not report an illness. Hence, the
odds ratio of very poor, poor or moderate health status versus non-very poor to moderate health
status was 9.6 [(81.4/18.6)/ (31.2/68.8)].
The current study expanded on the work of Finnas et al. [18], which examined some of the
methodological challenges in self-reported data in Finland. This paper is an expansion of Finnas
et al.’s study in a number of respects, such as: 1) their work used age cohort 35-64 years while
this study used 45-85+ years; 2) self-reported illness was included among the covariates in the
237
examination of self-reported (rated) health status; and 3) social class and access (or lack of
access) to material resources play a critical role in directly and indirectly influencing health, and
so this was added to this paper. Although higher education plays a vital role in health status, 2%
of the sample had tertiary level education and of this, 0.2% was older than 45 years. Most of the
sample had at most primary level education (87.3%), which means that the role of tertiary
education would contribute marginally to this sample. Hence, the researcher excluded it from the
covariate analysis of self-reported health status.
Measurement of variables
Self-reported illness status is a dummy variable, where 1 = reporting an ailment or dysfunction or
illness in the last 4 weeks, which was the survey period, 0 = no self-reported ailments, injuries or
illnesses [11, 12, 25]. While self-reported ill-health is not an ideal indicator of actual health
conditions, because people may underreport, it is still an accurate approximation of ill-health and
mortality [26, 27]. Self-reported health status (or health status) was measured by the question:
Generally, how would you describe your health currently? The options were: very good, good,
moderate (or fair), poor, and very poor. Age group was classified as children (aged less than 15
years), youth (aged 15 through 25 years), and other age cohorts ranging in 5 year intervals from
26-30 years, et cetera. 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 two options were
yes or no. Medical care-seeking behaviour, therefore, was coded as a binary measure where
1=yes and 0= otherwise. Social class is measured using income quintile where it ranges from
poorest 20% to wealthiest 20%.
238
The distribution of the different age cohorts for each sex based on self-reported health status is
given in Figures 1a and 1b. Figures 1a and 1b will be used to argue the case for a cut-off point
for the dichotomisation of self-reported health status in Jamaica.
It is well established in biomedical literature that there is a strong negative correlation between
health and age; the current study using self-reported health status by different age cohort
controlled for sexes revealed that good health decreases as the individual ages and that more
women beyond 80 years old reported very good health status compared to men in the same age
cohorts. Health status, therefore, can be simply explained by age cohorts, and the aforementioned
findings show that sex must be taken into consideration among the covariates in order to
comprehend the effects of particular demographic variables on the statistical interpretations of
health data. The other covariates must include education level, marital status, area of residence,
and social class.
The issue of dichotomising self-reported health status continues to be debated in Jamaica as
researchers continue to grapple with whether to use very poor-to-poor health status versus
moderate-to-very poor health status. The issue of using moderate health in poor or good health
status is critical as this will aid researchers in understanding whether there should be a cut-off
point and where it should be, as this is the crux of the interpretation of the logistic regression
model. Based on Figure 1, the very poor-to-poor health status is marginal at ages below 46 years,
and so for the purpose of dichotomisation, ages 46 years and older will be used.
239
Results
Demographic characteristics
Of the sample (6783), 48.7% was male; 51.3% female; 69.2% never married; 14.9% reported
having an illness in the survey period (4-week); 49.0% dwelled in rural areas; 82.2% reported at
least good health and 4.8% reported at least poor health status (Table 10.10.1). Concomitantly,
61.8% indicated no formal education; 2.0% reported tertiary level education; 20.4% was
classified as in the wealthiest 20% and 19.7% was in the poorest 20%. Continuing, the mean age
of the sample was 29.9 years (SD = 21.8 years) with 25 percent of the sample being 12 years old;
50 percent being 26 years old and 75 percent being 44 years old; 2.1% of the sample was at least
81 years old. Furthermore, 31% of the sample was less than 15 years old and 18.9% youth.
Multivariate analyses
Interpretation of the odds ratios
Comparatively, for ages 46-54 years, the odds ratio for reporting an illness when an individual is
a male who self-reported that he had very poor-to-poor health status was 7.7 times compared to a
male who did not report an illness. For women of the same age cohort, those who reported an
illness who had reported a health status of very poor-to-poor was 3.3 times more likely to report
an illness compared to a female of the same age cohort who did not report a dysfunction.
The findings revealed that the odds ratio of an 85+-year-old male reporting an illness when he
had indicated very poor-to-poor health status was 7.9 times more than for one who had not
indicated a dysfunction. However, the odds ratio of reporting an illness declined for Jamaican
males (Table 10.10.2). On the other hand, the odds of a female of the same age who reported an
240
illness indicating that she had very poor-to-poor health status was greater at 85+ years than a 46-
54-year-old female.
Generally, using the odds ratio, males benefited more by being married (Table 3) than females
(Table 10.10.3). Concomitantly, the variance from adding moderate-to-poor or very poor health
status marginally change the odds ratios over very poor-to-poor health status to very moderate-
to-very poor self-reported health status. This was the same across area of residence for the sexes.
A substantial disparity in the odds ratios occurred in social standing for males, while it was
relatively the same for females. Table 10.10.3 revealed that by adding moderate self-reported
health status to very poor or poor self-reported health status for males, the odds ratios at older
ages (i.e., 75+ years) increased exponentially over very poor-to-poor self-reported health status.
Using odds ratios, the cut-off point for poor health status (excluding moderate health) increased
over the age cohorts. However, when the cut-off point included moderate health status, the odds
ratios from ages 46 years to 84 years showed that as respondent’s age within this age cohort,
their likeliness of reporting poor health increased; this declined for ages beyond 85+ years.
Concurrently, the odds ratios are exponentially higher for the latter dichotomisation than the
former (Table 10.10.4).
Discussion
The findings of the current study show that the choice of cut-off for the dichotomisation of self-
reported health status marginally matters for age, marital status, and area of residence. These
findings concur with Finnas et al.’s work [18]. However, social class matters for males. The odds
ratios for males at the different social classes, when moderate heath status is added to poor health
status, changed substantially. This suggests that the dichotomisation of self-reporting for males
241
will not shift and will produce a different result from if only poor or very poor were the cut-offs
for self-reported health status. The findings of the study showed that the poor or poorest 20% of
males benefitted exponentially when moderate self-reported health status is added to the cut-off
point in dichotomising poor health status (including very poor). Another important finding of this
study, which was not examined by Finnas et al., is the validity of using self-reported illness to
measure the health status of people. Even though the likelihood of a person with an illness
reporting very poor-to-poor health status is greater than one, it should be noted that that
likelihood falls at older ages for males and increases at older ages for females.
For men, when the cut-off point includes moderate health status, the impact of assessing
self-reported illness with poor or very poor health status is higher than if the cut-off was only
poor or very poor health status. Embedded in this finding is the vast difference that is created by
merely changing the cut-off point from poor health status to moderate-to-very poor health status
for males. While this disparity does not emerge for females, health researchers who use sex as a
covariate must be aware of this reality when dichotomising self-reported health status. The cut-
off point for dichotomising self-reported health does not matter if one is examining the health
status of only females, as the marginal difference in odds ratio is insignificant and would not
create a classification disparity in interpreting the final results. However, the same cannot be said
about males, particularly those of older ages. Therefore, with regards to using self-reported
health status, combining people from broad age groups should not be done, as this will not
capture the challenges identified in health data on males in Jamaica.
Studies have shown that health deteriorates with age [22-30]; indicating the critical role
that age plays in the understanding health of people. Therefore, in an examination of poor health
status, cautioned must be used by the researcher(s), as people are less likely to report very poor-
242
to-poor health at ages 15-30 years. On examination of self-reported health status for Jamaicans,
the researcher became aware of this fact and so the study of dichotomisation of poor health did
not use that age cohort. It is this rationale, and why the researcher concurred with Finnas et al.,
that it was decided that these should be used as covariates. Within the context of the current
study, which revealed that small percentages of particular age cohorts are likely to report very
poor-to-poor health status, the researcher chose age cohorts that are more likely to report very
poor-to-poor health status as this was critical to study. Unlike Finnas et al.’s work, which cuts off
at age 64 years, this study extended as far as to study respondents up to 85+ years. In 2007, 3.8%
of Jamaicans were 75+ years (i.e., 101,272); 1% were older than 84 years (26,821), and given
that people at these ages are more likely to report poor or very poor health, the researcher
believes that stopping the study at age 64 would have excluded a critical proportion of those who
are likely to be reporting poor health status.
Among the social determinants of health are social class and area of residence [1-6, 31-
33]. People are not only defined by their ages, but by where they live and the social class in
which they belong. The current study revealed that rural Jamaican women indicated the greatest
percentage of very poor-to-poor health status, while this was not the case for men. However, the
inclusion of moderate health status to poor or very poor health status across the age cohorts by
area of residence revealed marginal differences as was the case without the inclusion of moderate
health status. Among men of 85+ years, the odds ratio of reporting very poor-to-poor health
approximately doubled over the previous age cohort (75-84 years) and this was marginally the
same when moderate health was included in the dichotomisation of very poor-to-poor health. For
women, this was not the case as the odds ratios were mostly the same for the two
dichotomisations.
243
Health literature has shown that the poor had the lowest health status [34]. Among men,
the effect of social class on health showed no consistent pattern and this was the same for
women. However, when moderate health status is included in the cut-off for very poor-to-poor
health status, significant changes were observed over the age cohorts. For men, exponential
increases occurred with the inclusion of moderate health status to the cut-off point, while this
was not the case for women. The current study revealed that the dichotomisation of self-reported
health status fundamentally increased the odds ratio, suggesting that the moderate-to-very poor
exponentially takes in more men based on how self-reported health status is dichotomised in
Jamaica at older ages (75+ years). Embedded in the finding is the disparity between the
percentages of sexes who reported moderate health at older ages for men more than women.
This study included self-reported illnesses, unlike Finnas et al.’s work, and the findings
indicated that cut-off point for dichotomisation of health status was somewhat changed for
women, but exponentially changed for men. The findings revealed that women ages 85+ years—
when self-reported health status was dichotomised using very poor-to-poor health—had the
highest odds of reporting poor health status. When poor health status was expanded to include
moderate health status, the younger ages recorded greater odds of indicating moderate-to-very
poor health status. This indicates that at longer ages using the latter dichotomisation approach the
odds were age-dependent. Men of 85+ years recorded the least odds ratio of very poor-to-poor
and moderate-to-very poor health status. There was no clear pattern of age-dependence of self-
reported illness for men. Embedded in the findings is the greater likelihood of men to report
moderate health than poor health at higher ages (85+ years). This suggests that they are under-
reporting their true very poor-to-poor health status at higher ages. It follows that the narrower
categorisation of age was able to capture this which was lost in a wider categorisation.
244
Marital status as a covariate indicated that marriage benefits Jamaicans men more than it
does women. Among men, the odds of reporting very poor-to-poor status are less than for those
who were unmarried, across the age cohorts. Interestingly, beyond 84 years, the odds ratio of
very poor-to-poor health status of men declines, suggesting that the benefits of marriage at this
age increases compared to earlier ages. When the cut-off point included moderate health status
for men, the odds were relatively the same except for men above age 75. The odds ratios of
reporting poor health (i.e., including moderate health status) for those of 75+ years fell
substantially, which means that health status for men over 75+ years increased with marriage.
Among women, the odds ratio for those less than 55 years who were married was the same as for
their unmarried counterparts. It was found that marriage becomes beneficial for women when
they are older than 75+ years, compared to unmarried women of the same age. When the
dichotomisation of poor health included moderate health, marginal disparities in odds ratios were
found among women in different areas of residence compared to when poor health status
excluded moderate health. Embedded in this finding is the fact that poor health is weakly age-
dependent, as there were not clear patterns for the sexes. However, owing to narrowing age
groups, this is a new finding which has emerged in health research literature for Jamaica—that
marriage substantially benefits women at older ages (75+ years) than their younger counterparts.
One of the critical findings of this study is that a narrower definition of poor health status
(excluding moderate health status) had odds ratios that were closer across the age groups,
suggesting that it would be better to exclude moderate health status from very poor-to-poor
health status on dichotomising health status. However, if researchers decide to include moderate
as a part of the dichotomisation of poor health status, they should be aware of some of the
245
methodological implications of their choice, and how this will impact on the interpretation, in
particular for men, within the different social classes.
Conclusion
In summary, the odds ratios vary substantially for men in different social classes as well as for
self-reported illness based on the dichotomisation cut-off point for poor health. Among women,
there was no clear age dependency based on the cut-off point of poor health; the vast disparity
that was present for men was not found for women in the different social classes. Like the study
conducted by Finnas et al., this paper agrees that the choice of cut-off point in dichotomising
poor health status cannot be made primarily on variables such as age, because sex and social
class must also play a factor in this choice, as well as the nature of the study. Concurrently, this
study differs from Finnas et al.’s work in that with a narrower classification of poor health, the
effect of marital status and area of residence were not found to be highly age-dependent. The
current study found that dichotomising poor health status is acceptable assuming that poor health
excludes moderate health status, and that it should remain as is and ordinal logistic be used
instead of binary logistic regression.
Conflict of interest
There is 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.
246
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Table 10.10.1. Socio-demographic characteristic of sample, n = 6,783 n %
Sexes 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 Self-reported illness Yes 980 14.9 No 5609 85.1 Self-reported health status Very good 2430 37.0 Good 2967 45.2 Moderate 848 12.9 Poor 270 4.1 Very poor 50 0.8 Area of residence Urban 2002 29.5 Semi-urban 1458 21.5 Rural 3322 49.0 Income quintile Poorest 20% 1343 19.8 Poor 1354 20.0 Middle 1351 19.9 Wealthy 1352 19.9 Wealthiest 20% 1382 20.4 Education attainment (level) No formal 4071 61.8 Basic 783 11.9 Primary or preparatory 898 13.6 Secondary 709 10.8 Tertiary 131 2.0
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Table 10.10.2. Very poor or poor and moderated-to-very poor self-reported health status of sexes (in %) Very poor-to-poor Moderate-to-very poor 46-
54yrs 55-64yrs
65-74yrs
75-84yrs
85+yrs 46-54yrs
55-64yrs
65-74yrs
75-84yrs
85+yrs
Men Self-reported illness Yes 37.7 40.0 50.7 46.7 41.7 81.4 87.5 92.5 93.3 91.7 No 7.3 10.4 13.6 21.4 27.3 31.2 39.9 42.4 64.3 72.7 Area of residence Urban 12.1 14.5 21.9 22.0 25.0 49.2 60.9 50.0 55.6 62.5 Semi-urban 18.3 27.0 38.2 50.0 60.0 46.2 65.1 79.4 96.0 90.0 Rural 20.2 24.7 35.3 35.7 30.0 48.3 56.8 70.6 92.9 70.0 Marital status Married 16.8 19.5 31.3 30.0 25.0 48.8 56.4 64.2 60.0 62.5 Not 18.3 25.9 33.8 33.3 35.7 57.2 62.9 72.3 88.9 92.9 Social class Poorest20% 19.6 22.4 28.1 33.3 25 54.6 59.7 65.6 100 100
Poor 20.7 29.4 42.9 50.0 33.3 46.7 58.8 81.0 100.0 100.0 Middle 18.0 24.2 30.3 30.0 20.0 47.0 61.3 66.7 71.4 83.3 Wealthy 18.6 22.0 33.3 50.0 57.1 52.0 62.7 73.3 87.5 85.7 Wealthiest20% 12.0 16.4 20.1 25.0 18.4 40.7 54.5 50.0 25.0 33.3 Total n 266 207 156 97 23 266 207 156 97 23
Women Self-reported illness Yes 29.1 35.1 37.1 41.7 47.4 77.2 81.8 79.8 79.2 73.7 No 11.1 13.6 15.3 18.5 17.4 44.3 51.8 60.0 59.3 52.2 Area of residence Urban 9.7 11.9 16.1 25.0 25.0 53.0 60.6 59.7 56.3 41.7 Semi-urban 14.2 14.5 17.2 28.6 28.6 52.2 62.3 72.4 71.4 71.4 Rural 26.8 33.9 36.9 32.1 34.8 64.5 69.6 77.4 75.0 69.6 Marital status Married 18.6 22.7 32.3 0 0 58.8 69.3 80.6 0.0 0.0 Not 19.0 23.1 25.2 30.0 31.7 58.2 64.3 68.5 70.0 63.3 Social class Poorest20% 28.7 33.8 43.8 33.3 28.6 65.7 70.4 75.0 77.8 71.4 Poor 19.0 23.7 22.9 28.6 27.3 64.0 74.6 77.1 71.4 63.6 Middle 19.0 21.7 26.1 31.3 38.5 57.1 62.7 69.6 62.5 56.8 Wealthy 18.6 22.8 25.8 50.0 50.0 61.9 68.4 71.0 80.0 80.0 Wealthiest20% 9.8 14.5 12.9 20.0 22.2 46.2 53.9 58.1 60.0 55.6 Total n 284 216 172 119 43 284 216 172 119 43
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Table 10.10.3. Odds ratios for very poor or poor and moderate-to-very poor self-reported health of sexes by particular variables Very poor-to-poor Moderate-to-very poor 46-
54yrs 55-64yrs
65-74yrs
75-84yrs
85+yrs 46-54yrs
55-64yrs
65-74yrs
75-84yrs
85+yrs
Men Self-reported illness Yes 7.7 5.7 6.5 3.2 1.9 9.6 10.5 16.8 7.7 4.1 No 1 1 1 1 1 1 1 1 1 1 Area of residence Urban 0.5 0.5 0.5 0.5 0.8 1.0 1.2 0.4 0.1 0.7 Semi-urban 0.9 1.1 1.1 1.8 3.5 0.9 1.4 1.6 1.8 3.9 Rural 1 1 1 1 1 1 1 1 1 1 Marital status Married 0.9 0.7 0.9 0.9 0.6 0.7 0.8 0.7 0.2 0.1 Not 1 1 1 1 1 1 1 1 1 1 Social class Poorest20% 1.8 1.5 1.6 1.5 1.5 1.8 1.2 1.9 large large Poor 1.9 2.1 3.0 3.0 2.2 1.3 1.2 4.3 large large Middle 1.6 1.6 1.7 1.3 1.1 1.3 1.3 2.0 7.5 10.0 Wealthy 1.7 1.4 2.0 3.0 5.9 1.6 1.4 2.7 21.0 12.0 Wealthiest20% 1 1 1 1 1 1 1 1 1 1 Total n 266 207 156 97 23 266 207 156 97 23
Women Self-reported illness Yes 3.3 3.4 3.3 3.2 4.3 4.3 4.2 2.6 2.6 2.6 No 1 1 1 1 1 1 1 1 1 1 Area of residence Urban 0.3 0.3 0.3 0.7 0.6 0.6 0.7 0.4 0.4 0.3 Semi-urban 0.5 0.3 0.4 0.8 0.8 0.6 0.7 0.8 0.8 1.0 Rural 1 1 1 1 1 1 1 1 1 1 Marital status Married 1.0 1.0 1.4 0.0 0.0 1.0 1.3 1.9 0.0 0.0 Not 1 1 1 1 1 1 1 1 1 1 Social class Poorest20% 3.7 3.0 5.3 2.0 1.4 2.2 2.0 2.2 2.3 2.0 Poor 2.2 1.8 2.0 1.6 1.3 2.1 2.5 2.4 1.1 1.4 Middle 2.2 1.6 2.4 1.8 2.2 1.5 1.4 1.7 1.1 1.0 Wealthy 2.1 1.7 2.3 4.0 3.5 1.9 1.9 1.8 2.7 3.2 Wealthiest20% 1 1 1 1 1 1 1 1 1 1 Total n 284 216 172 119 43 284 216 172 119 43
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Table 10.10.4. Odds ratios of poor health status by age cohorts
Poor
Health status
Age cohorts
46-54yrs 55-64yrs 65-74yrs 75-84yrs 85+yrs
Very poor-to-poor health
Yes 0.004 0.020 0.046 0.167 0.228
No 1 1 1 1 1
Moderate-to-very poor
health
Yes 0.091 0.529 1.861 5.444 5.048
No 1 1 1 1 1
Total n 550 423 328 216 66
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CHAPTER
11
Paradoxes in self-evaluated health data in a developing country
Statistics showed that males reported fewer illnesses and greater mortality rates than females, but are outlived by approximately 6 years by their female counterparts, yet their self-rated health status is the same as that of females. This study examines the following questions: (1) Are there paradoxes in health disparity between the sexes in Jamaica? and (2) Is there an explanation for the disparity outside of education, marital status, and area of residence? Good health status was correlated with self-reported illness (OR =0.23, 95% CI = 0.09-0.59), medical care-seeking behaviour (OR = 0.51, 95% CI = 0.36-0.72), age (OR = 0.96, 95% CI = 0.96-0.97), and income (OR = 1.00, 95% CI = 1.00-1.00). Self-reported illness is statistically correlated with sex (OR = 0.25, 95% CI = 0.10-0.62), head of household (OR = 0.33, 95% CI = 0.12-0.96), age (OR = 1.04, 95% CI = 1.01-1.07) and current good self-rated health status (OR = 0.32, 95% CI = 0.12-0.84). This paper highlights that caution must be used by researchers in interpreting self-reported health data of males. Introduction
Jamaica began collecting data on the living standard of its people in 1988, and to date, statistics
have shown that females continue to report more illnesses than males, seek medical care more
frequently than males [1], and outlive males on average by 6 years [2]. A study by Hutchinson et
al. [3] on the wellbeing and life satisfaction of Jamaicans showed that women had lower
psychological wellbeing and less life satisfaction than men, which highlights some of the
paradoxes in the health data. In his study, Bourne [4] found that there was no significant
statistical difference between the current good health status of males and females. However, he
found that there was no statistical correlation between medical care-seeking behaviour and sex of
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respondents, suggesting that reporting more illnesses does not mean that females are any more
willing to address their identified health conditions than males.
A research on rural Jamaican women in the reproductive ages of 15 to 49 [5] showed that
79% were never married, 20% were married, 90% had a secondary level education, 45% were
poor (i.e., 22% below the poverty line), and 15.3% reported an illness while only 5% had health
insurance coverage. In Jamaica, poverty is a rural phenomenon (i.e., in 2007, 15.3% of rural
individuals were living below the poverty line compared to 4% of semi-urban Jamaicans and
6.2% of urban peoples). Males’ per capita consumption was 1.2 times more than that of females;
female-headed households had a higher prevalence of poverty compared to male-headed
households [1], and it follows that socio-demographic and economic challenges faced by females
do not discount from them living longer than men. A study by Bourne [6] showed that elderly
men in Jamaica are healthier than their female counterparts, suggesting that longer life does not
imply healthy life expectancy. Statistics showed that females are more likely to be unemployed
[7], poorer, have longer lives, report more illnesses, visit health care practitioners more
frequently than men, and are less healthy than men in later life. They are also on average more
educated, yet still their health status is generally equal to that of males [8]. Examining mortality
data of the sexes for aged Jamaicans, Bourne et al. [9] found that mortality at older ages was
between 115 and 120 for males to every 100 females. A study by Abel et al. [10] found that the
suicide rate for males was 9 times greater than for females which indicates that mortality for
males is not only greater at older ages but that suicide is occurring voluntarily throughout their
life span.
Using secondary data of 8,373 Jamaican children (aged under 15 years) for 2002 and
2104 for 2007, Bourne [11] found that there was no significant difference between the sexes’
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health conditions. However, female children are taken to health care practitioners more
frequently than male children. In a study of 5229 and 1394 adolescents aged 10 to 19 years in
Jamaica, Bourne [12] found that mortality for males was greater than for females. A significant
statistical correlation existed between health conditions, but none between health conditions and
age cohort of the sample. Furthermore, he found that in 2007, 96% of adolescents did not report
an illness in the past 4 weeks, 54% sought medical care, and 15% had health insurance coverage.
One of the drawbacks of Bourne’s work [12] was the fact that health condition was not
disaggregated by sexes. but invaluable information was provided that showed the low
willingness of adolescents to seek medical care. Another study on children showed that while
there is no significant difference between the health statuses of the sexes, females are taught by
society to seek more medical care than male children [11] and that this continues over their life
course [1].
The literature highlights the fact that the health status disparity does not commence in
childhood, which denotes that females’ longer life and males’ greater health status in later life is
a paradox that must be unravelled by researchers. Interestingly, while the literature explains
Hutchinson et al’s work as to why women have lower psychological wellbeing and life
satisfaction, it does not provide an understanding for the plethora of other studies which showed
no significant statistical difference between the general self-rated health of the sexes [4, 8] and
childhood [11]. Additionally, the health status of elderly males is better than that of females
despite the fact that females report more illness and live longer than males. Another area which
is unexplained by their study is the fact that statistics showed that mortality at all ages for males
is higher than for females [2]. There is a lack of information on the paradox of health disparity
between the sexes in Jamaica and this research seeks to fill this gap in the literature. The current
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research attempts to answer the following questions: (1) Are there paradoxes in the health
disparity between the sexes in Jamaica? and (2) Is there an explanation for the disparity outside
of education, marital status, and area of residence?
Methods and materials
Data
The current study utilised a data set collected jointly by the Planning Institute of Jamaica and the
Statistical Institute of Jamaica [13]. The survey was conducted between May and August of
2007. The Jamaica Survey of Living Conditions (JSLC), which began in 1988, is a modification
of the World Bank’s Living Standards Measurement [1, 14]. The sample size was 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. The sample was weighted to reflect the
population of the nation.
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Instrument
An administered instrument in the form of a questionnaire was used to collect the data from
respondents. The questionnaire covers socio-demographic variables such as education, age,
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 p-value < 0.05
(two-tailed) was selected to indicate statistical significance.
There was no selection criterion used for the current study. On the other hand, for the
model, the selection criteria were based on 1) the literature; 2) low correlations, and 3) non-
response rate. The correlation matrix was examined in order to ascertain if autocorrelation and/or
multicollinearity existed between variables. Based on Cohen & Holliday [15] and Cohen &
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Cohen [16], low (weak) correlation ranges from 0.0 to 0.39, moderate – 0.4-0.69, and strong –
0.7-1.0. Any correlation that had at least a moderate value was excluded from the model in order
to reduce multicollinearity and/or autocorrelation between or among the independent variables
[17-21].
Models
Health is a multifactorial construct. This indicates that it is best explained with many variables
against a single factor. Health is empirically established and is determined by many factors [22-
37], and therefore the use of multivariate regression technique is best suited to explain this
phenomenon than bivariate analyses [22-37]. The current study seeks to establish the socio-
demographic, economic and biological correlates of self-rated health, and self-reported illness so
as to examine the paradoxes in health disparity between the sexes. The aforementioned construct
will be tested in two econometric models. Model [1] is good self-rated health statuses and is
associated with socio-demographic, economic and biological variables; and Model [2] is self-
reported illness and is related to socio-demographic, economic and self-rated health status.
Ht=f(Ai, Gi,HHi, ARi, It, Ji, lnC, lnDi, EDi, MRi, Si , HIi , lnY, CRi, MCt, SAi, Ti , ε i) (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
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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).
It,=f(Ai, Gi ,HHi, ARi, Ji, lnC, lnDi, EDi, MRi, Si, HIi, lnY, CRi, MCt, SAi, Ti , Ht, ε i) (2)
where It (i.e., self-reported illness in last 4-weeks) is a function of age of respondents, Ai
; sex of individual i, Gi; household head of individual i, HHi; area of residence, ARi;
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; self-rated current good health status, Ht; and an error term
(i.e., residual error).
Models [1] and [2] were modified to [3] and [4] owing to collinearity of consumption and
income (r ≥ 0.7) and non-response of injury (over 70%).
Ht=f(Ai, Gi,HHi, ARi, It, lnDi, EDi, MRi, Si, HIi, lnY, CRi, MCt, SAi, Ti, ε i) (3)
It,=f(Ai, Gi ,HHi, ARi, lnDi, EDi, MRi, Si, HIi, lnY, CRi, MCt, SAi, Ti, Ht, ε i) (4)
Measurement of variables
Health in the current study is measured using (1) self-rated health status (self-rated health), and
(2) self-reported illness. Self-rated health status was derived from the question, “Generally, how
260
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].
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 a binary value, where 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 and no. Medical care-seeking behaviour therefore was coded as a binary measure where 1 =
yes and 0 = otherwise.
Total annual expenditure was used to measure income.
Income quintile was used to measure social standing. The income quintiles ranged from
poorest 20% to wealthiest 20%.
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Results
Demographic characteristic of sample
The sample was 6,782 respondents: 48.7% males and 51.3 females. The mean age of the sample
was 30.0 years (SD = 21.8 years). Almost 15% reported having had an illness in the last 4 weeks
and 89.1% reported that the illness was diagnosed by a medical practitioner: cold (14.9%),
diarrhoea (2.7%), asthma (9.5%), diabetes mellitus (12.3%), hypertension (20.6%), arthritis
(5.6%), and unspecified (23.4%).
Bivariate analyses
The findings showed that females were more likely to (1) be widowed (7.3% females to 2.3%
males); (2) be older (mean age: 30.6 years females to 29.1 years males) – t = -2.8, P = 0.05; (3)
report illness (17.5% females to 12.1% males); and (4) spend on medical expenditure (Table
11.11.1). However, there was no significant statistical difference between the sexes (1) seeking
medical care, (2) their social standing, and (3) their educational levels.
Tertiary level graduates were substantially more likely to be in the wealthiest class
(54%), and dwelled in urban areas (63.4%). Concomitantly, they reported more illness than
secondary level respondents (9.2% tertiary to 5.4% secondary), but less than those with primary
education level or below (16.2%) (Table 11.11.2).
Table 11.11.3 showed significant statistical associations between (1) marital status and
self-reported illness (P < 0.05), (2) area of residence and self-reported illness (P < 0.05), and (3)
medical care expenditure and self-reported illness (P < 0.05).
There was a significant statistical association between health care-seeking behaviour (in
%) and social standing of respondents – χ2 =17.12, P = 0.002. The findings revealed that as
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social standing increases from poorest 20% to wealthiest 20%, health care-seeking behaviour (in
%) increases: poorest 20% = 54.7% health care-seeking behaviour; poor = 63.2%; middle class =
66.4%; wealthy = 68.4%, and wealthiest 20% = 73.5%.
Multivariate analyses
Good health status of Jamaicans was correlated with self-reported illness (OR = 0.23, 95% CI =
0.09-0.59), medical care-seeking behaviour (OR = 0.51, 95% CI = 0.36-0.72), age of respondents
(OR = 0.96, 95% CI = 0.96-0.97), and income (OR = 1.00, 95% CI = 1.00-1.00) (Table 4). The
model is a good fit for the data – χ2 = 114.7, P < 0.001, Hosmer and Lemeshow Test P= 0.776.
Furthermore, the aforementioned variables accounted for 20% of the variability in the good
health status of Jamaicans (R-squared = 0.20) (Table 11.11.4).
The self-reported illness of respondents is statistically correlated with sex (OR = 0.25,
95% CI = 0.10-0.62), head of household (OR = 0.33, 95% CI = 0.12-0.96), age of respondents
(OR = 1.04, 95% CI = 1.01-1.07), and current good self-rated health status (OR = 0.32, 95% CI
= 0.12-0.84) (Table 5). The model is a very good fit for the data – χ2 = 33.7, P < 0.001, Hosmer
and Lemeshow Test P = 0.766 (Table 11.11.5).
Discussion
There are enough empirical studies that agree that there was a positive statistical correlation
between income, education, married people, social class and health status of people. The current
study concurs with the literature that there is a positive association between income and health
status. However, this paper did not find a significant statistical correlation between education,
marital status, social class and self-rated health of Jamaicans. The current work highlights a
number of disparities between the literature and this paper. Many studies have shown that
income is strongly and positively correlated with health status [22, 24]. However, this study
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disagreed with those findings, as it found that income’s contribution was 1% of the explanatory
power of 20%. Furthermore, income contributed the least to current good self-rated health status
of Jamaicans. Hambleton et al. [23], studying elderly Barbadians, found that self-reported illness
accounted for the most variability in health status, which concurs with the current study and
therefore emphasises the secondary role that income plays in influencing health status. In
Jamaica, medical care-seeking behaviour is not an indicator of preventative care, as those who
sought health care were 49% less likely to report good health, and those who did not have an
illness spent more on health care compared to those who indicated an ailment. Embedded in this
finding is the concept of health that Jamaicans hold regarding how medical care is still
synonymous with illnesses, but the fact that those who are not sick spent more on health care and
are healthier indicates that preventative care is being practiced by Jamaicans.
Apart from these findings that emerged in the data, a number of health disparities were
identified and some could be considered paradoxical events. The study found that men were 75%
less likely to report an illness than women. However, there was no significant statistical
difference between the health statuses of the sexes. Males reported greater income than females,
yet there was no significance between their health care expenditure and health care-seeking
behaviour. Is it a paradox that males reported fewer dysfunctions, attend health care institutions
as equally frequently as females, and have a health status that is no better than that of females?
The paradox does not cease there, as males are outlived by females, experience greater mortality
at all ages than females, and again indicate fewer ailments than females. Is this a paradox?
Comparatively, using statistics from the Ministry of Health in Jamaica (actual visits to
public hospitals), and statistics from the Planning Institute of Jamaica and Statistical Institute of
Jamaica (i.e., self-reported visits) to measure the validity of self-reported health data in 1997, it
264
was shown that 33.1% of Jamaicans attended public hospitals [38] compared to 32.1% who
actually reported having attended public hospitals. Furthermore, in 2004, 52.9% of Jamaicans
visited public hospitals [38] compared to 46.8% who reported having visited public hospitals.
When the data was disaggregated by sex, in 2004, actual visits for females were 69.8% compared
to 65.7% self-reported; while for males, actual visits were 30.2% compared to self-reported visits
of 64.2%. Using curative visits from the Ministry of Health data, 33% of males visited health
care facilities to address particular illness, yet only 9% of males reported that they had an illness.
Embedded in the data are the extent to which males under-report their illnesses, which further
emphasises the paradoxes in the health data. Self-rated health data for females is therefore highly
accurate, but this is not the case for males. It was a paradox in the health data to find that males
reported fewer illnesses, experienced greater mortality at all ages, and had greater income, yet
their health status was the same as that of females.
There are clearly paradoxes in the health data between the sexes in Jamaica. If males are
under-reporting their illnesses by approximately 50%, statistics on health data are rendered
inaccurate, and so caution must be taken in using self-reported health data for males. The reasons
for this paradox can be unravelled when one takes a closer look at Jamaican culture and society.
Caribbean males and Jamaicans in particular, are persuaded by society to be strong and brave.
Masculinity is tied to these attributes and so justifies the emphasis of physique and strength in
the Jamaican culture. The converse explains why they neglect weakness or the appearance of
weakness, which includes illnesses. Ill health is conceptualised as weakness and within the
context of socialisation and adapting to societal norms, males will not openly speak of illness,
they avoid medical care-seeking behaviour and only visit health care institutions when an illness
becomes severe.
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Statistics from the Ministry of Health showed that since 2000–2004, females outnumber
males by 2 to 1 in terms of visits to health care institutions [38]. However, using reported data
for the same period, the figures were: in 2000 – 57.4% males and 63.2% females; in 2001 –
56.3% males and 68.2% females; in 2001 – 62.1% males and 65.3% females and 2004 – 64.2%
males and 65.7% females. Clearly, the self-reported data is not in keeping with the actual data,
and this denotes that males are over-stating their health care visits. On the other hand, using
2004’s data on actual visits, 69.8% of Jamaican females utilised health care facilities compared
to 66% of females who actually reported health care visits. Within the context of over-statement
of health care-seeking behaviour and understatement of illness by males in Jamaica, this goes to
the crux of the socialisation issue and society’s influence on health care.
A Caribbean anthropologist, Chevannes [39], opined that Caribbean males suppressed
responses to pain, which justifies a low turnout to health care facilities and higher mortality rates.
This is not atypical of Caribbean males. Ali & de Muynck [40], in examining street children in
Pakistan, found a similar gender stereotype. A descriptive cross-sectional study carried out
during September and October 2000 of 40 school-aged street children (8-14 years) showed that
only severe illness that threatens financial opportunities will cause males to seek medical care.
Ali & de Muynck’s study therefore provides some understanding for the reluctance of males
seeking medical care despite having greater income. With 49% of Jamaicans being males, within
the context of socialisation and societal pressures and norms, this explains the fact that income
has a weak correlation with health status. This negative emotional irresponsiveness to medical
care-seeking in Jamaica is not limited to males, as females are a part of the current study which
found no significant statistical difference between them and males seeking health care.
266
Another paradox embedded in the health data is the fact that people who spent more on
medical care reported fewer illnesses – males reported fewer ailments, yet they are not healthier
than females. Once again the explanation for this is embodied in the socialisation and societal
norms, including the negative view that Jamaicans have of health care, health reporting and male
unwillingness to separate caring about health from weakness, weakness from femininity, and
hence how men respond to the interviewers. There is evidence that males are under-reporting
their illnesses in the JSLC’s cross-sectional survey, which means that the self-reported health
data of males cannot be trusted. The researcher is proposing that a part of the rationale of the
under-statement of illnesses by males in Jamaica owes to the sex of the interviewers. Most
interviewers employed by the Statistical Institute of Jamaica to collect data from Jamaicans are
females, and within the context of not wanting to exhibit weakness, males are understating their
illness in order to create the perception that they are strong and healthy. The issue appears to be
extensive because statistics from the Ministry of Health for 2004 showed that for curative visits,
females outnumber males by 2 to 1 [38]. Although the researcher was unable to obtain the
Ministry of Health Annual Report for 2007, the 2006 report showed the same ratios as for 2000–
2004, which implies that gender of the interviewers is a contributing factor when collecting data
on men’s health in Jamaica.
Is it a paradox that the educated are wealthier, have greater income and still are not
healthier than the poor with less financial resources? This study would suggest not, as the weak
relationship between health status and educational level disappears on the inclusion of income.
The current work does show that a bivariate relationship exists between education and healthier
people, but that when income and education are placed in a single model, education no longer
becomes significantly associated with good health status. The current findings concur with the
267
literature which found that when subjective wellbeing, which is a measure of subjective health,
was controlled for income and other variables, the statistical correlation between education and
health disappears [41-43].
Smith & Kington [4] wrote, “Good health is an outcome that people desire, and higher
income enables them to purchase more of it.” This implies that (1) health can be bought and (2)
those with lower incomes will have a lower health status. Although the literature has concurred
with this study (that income is positively associated with health), income’s contribution to health
in Jamaica is weak, indicating that while more income is correlated with better health status,
Smith & Kington’s perspective must be refined, as there was no significant statistical correlation
between socio-economic class and health status. In Jamaica, there is no statistical difference
between the health statuses of the socio-economic classes and this is equally the case when
health is measured using health conditions. On the other hand, there is a clear paradox in the
health data of the current study, as income is correlated with better health status, yet the wealthy
classes do not have greater health status or fewer reported illness than the lower socio-economic
classes.
The rationale that accounts for the paradoxes that emerged from the current study is due
to lifestyle practices of the wealthy and the acceptance of the state of the poor. Marmot [44]
opined that poverty is associated with greater infant mortality, more ill-health, material and
social deprivation, poor conditions, and greater inequality in occupation, employment and
income inequality. Within the inequalities that favour the wealthy, income means that they can
afford, purchase and buy goods. Wilkinson [45] found a weak relationship between average
income and life expectancy in wealthy nations and Sen [46] found that increased life expectancy
in Britain between 1901 and 1960 occurred during slow growth of per capita GDP (Gross
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Domestic Product). Sen went on to say that the improvement in life expectancy was owing to
support policies such as sharing of health care and limited food supply. Another found a non-
linear increase in the probability of dying with increased income [47], suggesting that income
fulfils two roles: (1) provides access to better socio-material resources, and (2) retards the
positives of access to become a negative.
The paradox in income can be seen in the fact that while wealthy Jamaicans have more
income and access to more socio-material and political resources, their health status is not
greater than the under-privileged, poor and poorest 20%. Additionally, the contribution of
income to health status is minimal, which is not the case in the literature. It was expected that
Jamaicans who sought more health care must have been experiencing more ill-health, but this
was not the case. Having established that health data collected from males indicates a low
validity, with 49% of the sample being males, it follows that paradoxes identified in the current
study highlight the difficulties in interpreting health data in Jamaica.
Conclusion
There are some paradoxes in self-reported health data in Jamaica. Although some of these
paradoxes are highlighted in this paper, caution now must be used by researchers in interpreting
self-reported health data collected from males, as they are clearly under-reporting illnesses and
over-stating their health care-seeking behaviour. In spite of the paradoxes in the data, self-
reported health collected on females in Jamaica is of high quality. This denotes that the
paradoxes within the health data have provided critical answers to males’ reluctance in visiting
health care facilities, their unwillingness to openly speak about illnesses and the fact that they
have concealed information on their health. Therefore, a new approach is needed in soliciting
information from males about their health status.
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Conflict of interest
There is 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, but rather to the researcher.
270
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Table 11.11.1. Socio-demographic characteristic of sample by sex of respondents Characteristic Sex
Male Female Total P % % % Educational level > 0.05 Primary or 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 Social standing > 0.05 Poorest 20% 20.3 19.3 19.8 Poor 19.4 20.5 20.0 Middle 19.3 20.6 19.9 Wealthy 20.2 19.7 19.9 Wealthiest 20% 20.9 19.9 20.4 Total 3303 3479 6782 Marital status < 0.05 Married 24.3 22.4 23.3 Never married 71.1 67.4 69.2 Divorced 1.6 1.8 1.7 Separated 0.7 1.0 0.9 Widowed 2.3 7.3 4.9 Total 2150 2384 4534 Area of residence Urban 28.5 30.4 29.5 > 0.05 Semi-urban 21.4 21.6 21.4 Rural 50.1 47.9 49.0 Total 3303 3479 6782 Medical care-seeking behaviour > 0.05 Yes 62.3 67.6 65.6 No 37.7 32.4 34.5 Total 406 599 1005 Self-reported illness < 0.05 Yes 12.1 17.5 14.9 No 87.9 82.5 85.1 Total 3208 3381 6589 Age Mean (SD) in years 29.1 (21.5) 30.6 (21.9) 29.9 (21.8) < 0.05 Medical Expenditure1 Mean (SD) in US$
9.31 (15.48) 11.19 (36.51)
10.46 (30.23)
> 0.05
1Rate in 2007:1US$= Ja$80.47
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Table 11.11.2. Socio-demographic characteristic of sample by educational level Characteristic Educational level
Primary Secondary Tertiary Total P
% % % Social standing < 0.05 Poorest 20% 20.3 19.7 3.8 19.9 Poor 20.0 21.7 7.6 20.0 Middle 19.4 24.5 16.0 19.9 Wealthy 19.9 20.3 19.1 19.9 Wealthiest 20% 20.3 13.7 53.4 20.2 Total 5752 709 131 6592 Marital status < 0.05 Married 25.5 0.0 16.9 23.4 Never married 66.1 99.7 81.5 69.1 Divorced 1.9 0.0 1.5 1.7 Separated 1.0 0.3 0.0 0.9 Widowed 5.5 0.0 0.0 5.0 Total 4048 344 130 4522 Area of residence < 0.05 Urban 28.8 30.0 63.4 29.6 Semi-urban 22.0 19.2 16.4 21.6 Rural 49.2 50.8 20.6 48.8 Total 5752 709 131 6592 Medical care-seeking behaviour >0.05 Yes 65.7 60.0 66.7 65.5 No 34.3 40.0 33.3 34.5 Total 953 40 12 1005 Self-reported illness < 0.05 Yes 16.2 5.4 9.2 14.9 No 83.8 94.6 90.8 85.1 Total 5736 705 130 6571 Health insurance coverage < 0.05 None 79.8 83.7 57.8 79.8 Private coverage 12.0 11.7 35.9 12.5 Public coverage 8.2 4.6 6.3 7.7 Total 5682 689 128 6499 Age Mean (SD) in years 32.0
(22.6) 14.6 (1.7)
26.4 (10.6)
30.0 (21.8
< 0.05
Medical Expenditure1 Mean (SD) in US$
10.44 (30.78)
12.31 (18.73)
5.79 (5.51)
10.46 (30.23)
>0.05
1Rate in 2007:1US$= Ja$80.47
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Table 11.11.3. Socio-demographic characteristic of sample by self-reported illness Self-reported illness P
Yes No Total % % % Social standing 0.05 Poorest 20% 19.7 20.0 19.9 Poor 18.1 20.4 20.0 Middle 20.9 19.8 19.9 Wealthy 20.4 19.7 19.8 Wealthiest 20% 20.9 20.2 20.3 Total 980 5609 6589 Marital status < 0.05 Married 35.9 20.9 23.3 Never married 46.9 73.4 69.2 Divorced 3.1 1.4 1.7 Separated 1.7 0.8 0.9 Widowed 12.5 3.5 4.9 Total 721 3801 4522 Area of residence < 0.05 Urban 26.6 30.1 29.6 Semi-urban 18.7 21.9 21.5 Rural 54.7 47.9 48.9 Total 980 5609 6589 Medical care-seeking behaviour >0.05 Yes 65.1 77.4 65.4 No 34.9 22.6 34.6 Total 970 31 1001 Health insurance coverage < 0.05 None 75.3 80.6 79.8 Private coverage 11.5 12.7 12.5 Public coverage 13.3 6.8 7.7 Total 978 5525 6503 Age Mean (SD) in years 42.0
(27.7) 28.0
(20.0) < 0.05
Medical Expenditure1 Mean (SD) in US$ 9.30 (18.27)
38.80 (126.09)
< 0.05
1Rate in 2007:US$1.00 = Ja$80.47
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Table 11.11.4. Stepwise Logistic Regression: Good self-rated health status by socio-demographic, economic and biological variables Variable SE P Odds ratio
95.0% C.I.
R-squared
Self-reported illness
0.48
0.002
0.23
0.09-0.59
0.02
Medical care-seeking
0.18
0.000
0.51
0.36-0.72
0.02
Age
0.01
0.000
0.97
0.96-0.97
0.15
Income
0.00
0.007
1.00
1.00-1.00
0.01
Constant
0.54
0.000
16.03
-2 LL = 857.3 Hosmer and Lemeshow Test P = 0.776 Χ2 = 114.7, P < 0.001 R-squared = 0.20 N=6049 (89.2%)
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Table 11.11.5. Stepwise Logistic Regression: Self-reported illness by socio-demographic and biological variables
Variable SE P Odds ratio 95.0% C.I.
R-square
Male 0.47 0.003 0.25 0.10-0.63 0.059 Head Household
0.54
0.043
0.33
0.12-0.96
0.024
Age
0.01
0.010
1.04
1.01-1.07
0.021
Good Health
0.49
0.020
0.32
0.12-0.84
0.075
-2 LL = 177.7 Hosmer and Lemeshow Test P = 0.766 χ2 = 33.7, P < 0.001 R-squared = 0.19 N=6049 (89.2%)
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CHAPTER
12
The validity of using self-reported illness to measure objective health
There is a longstanding discourse on whether self-reported health is a good measure of objective health. This has never been empirical examined in Jamaica. Study seeks to 1) examine the relationship between particular subjective and objective indexes; 2) investigate the validity of a 4-week subjective index in measuring objective indexes; 3) evaluate the differences that exist between the measurement of subjective and objective indexes by the sexes; and 4) provide policy makers, other researchers, public health practitioners as well as social workers with research information with which can be used to inform their directions. A strong significant association was found between life expectancy at birth for the Jamaican population and self-reported illness (r = -0.731); and this was weaker females (r = - 0.683) than males (r = - 0.796). However, the relationship between mortality and self-reported illness was a weak non-linear one. Self-reported illness in a 4-week reference period is a good measure of objective health that self-reported illness for males was a better measure for objective health than for females.
Introduction There is a longstanding discourse on whether self-reported health is a good measure of objective
health. Objective health indexes include mortality, life expectancy and diagnosed morbidity,
which provide a great degree of precision in the measurement of health. Those measures have
been used for centuries by mathematicians, demographers and epidemiologists to provide
insights into the health of an individual, community or population. While the objective health
indexes do have a high probability of mathematical empiricism, which make for validity and
reliability in comparisons across different population characteristics, they are narrow in
evaluating a range of issues affecting the health of people. Life expectancy germinates from
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mortality data, which speaks to lived years and not quality of the lived time. Like life expectancy
and mortality, morbidity is caused by some disease causing pathogens that further justify the
causal relation between morbidity and health. Historically, policy makers including doctors
relied on research findings on the causes of particular dysfunctions in order to formulate
measures to address their reduction or eradication. Health therefore was viewed as the absence of
diseases; hence, the alleviation of morbidity meant a healthy person or population. But the
absence of diseases still does not imply that an individual or population is healthy, as this is the
further extreme of the health continuum. It was this gap in the discourse and the accepted
limitation of objective indexes of health that led the World Health Organization (WHO), in the
late 1940s, to forward a conceptual definition of health [1].
The WHO’s definition of health stipulated that it goes beyond the mere absence of
diseases to social, psychological and physical wellbeing. Health was no longer the absence of
diseases but different tenets of ‘wellbeing’. Although WHO’s perspective outlined the way
forward, and sought to provide a platform for which an expansion in objective health could
begin, some scholars opined that it was too vague and elusive a conceptualization [2,3]. In spite
of those critiques, some researchers began using subjective indexes to measure health instead of
the traditional objective indexes. The subjective measures are 1) happiness; 2) life satisfaction, 3)
self-reported health status, and self-reported illness [4-15].
Diener [5, 6] postulated that happiness can be used to measure subjective wellbeing (ie
health). He opined that happiness expends beyond and implicitly takes into account more
aspects of an individual’s life than the objective indexes. Happiness like life satisfaction, self-
reported health has a common denominator, people’s perception of their general quality of life.
Although this is in keeping with that comprehensive broad conceptual definition of health
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forwarded by the WHO – more than the narrow biomedical approach diagnosed morbidity, life
expectancy or mortality – the debate about the validity of those subjective indexes continue.
Scientific literature on health has revealed that self-rated health status is highly reliable a
measure to proxy health and that this ‘successfully crosses cultural lines’ [16]. O’Donnell and
Tait [17] concluded that self-reported health status can be used to indicate wellbeing as they
found that all respondents who had chronic diseases reported very poor health. Another group of
scholars concurred with the aforementioned findings when their findings revealed that the
statistical association between happiness and subjective wellbeing (ie self-reported health) was a
strong one - correlation coefficient r = 0.85 in the 18 OECD countries [18]. In that same study,
the research found a weak relation between objective measures of health and self-reported health.
This highlights the disparity in measures, the need for more empirical studies and implicitly has
not address the biasness in the subjectivity of the subjective indexes.
The subjective indexes introduced the issue of biasness in recall and perception as
subjectivity denotes people’s perceptions. Perception is highly biased as people can provide an
inflated or deflated account of their state in an interview or on a self-administered questionnaire.
It is for this reason why empirical researchers avoid and decry its utilization in the measurement
of health. Although subjective indexes are in keeping with the WHO’s widened definition of
health, their biasness must be understood as challenges for researchers.
The discourse on subjective wellbeing, using survey data, cannot be denied that it is based
on person’s judgement, and therefore must be prone to systematic and non-systematic biases
[19]. In an earlier work, Diener [5] argued that the subjective measure seemed to contain
substantial amounts of valid variance; suggesting that this indicated the validity of subjective
indexes. Kahneman [20] devised a procedure of integrating and reducing the subjective biases
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when he found that instantaneous subjective evaluations are more reliable than assessments of
recall of experiences. This highlights the biasness therefore that remain in cross-sectional survey
that asked people to remember over a long time. Embedded in the aforementioned findings are
whether particular subjective indexes that comprised of recall over 2-4 weeks is a good measure
for objective indexes of health. Embodied in the literature is the need to carry out empirical
research on subjective and objective indexes with emphasis on subjective indexes that are not on
instantaneous assessment.
Using data for Jamaica, the aims of this study are to 1) examine the relationship between
particular subjective and objective indexes; 2) investigate the validity of 2-4 week subjective
index (self-reported illness over a 4-week period) in measuring objective indexes (ie life
expectancy and mortality); 3) evaluate the differences that exist between the measurement of
subjective and objective indexes by the sexes; and 4) provide policy makers, other researchers,
public health practitioners as well as social workers with research information with which can be
used to inform their directions.
Materials and method
The current study utilized secondary published data from the Statistical Institute of Jamaica [21],
and the Planning Institute of Jamaica and the Statistical Institute of Jamaica [22]. Life
expectancy and mortality were from the Statistical Institute of Jamaica, and self-reported illness
from the Planning and Statistical Institutes of Jamaica. Generally, data were for two decades
(1989-2007); however, life expectancy data were only available for some of those years. Life
expectancy for some years was taken from the Human Development Reports [23].
Data were stored, retrieved and analyzed using SPSS for Windows 16.0 (SPSS Inc;
Chicago, IL, USA). Descriptive statistics were used to provide background information on data.
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Scatter diagrams were employed to establish 1) statistical associations, and 2) linearity and non-
linearity between variables under examination. Multiple regression, using the enter method, was
employed to a predictive model of linear associations. Models were built for 1) general life
expectancy and self-reported illness of Jamaicans; 2) life expectancy and self-reported illness of
the sexes. A 95% confidence interval would be used to examine whether a variable is statistical
significant or not.
LEp = ƒ (SPIp, ε) [1]
LEm = ƒ (SPIm, ε) [2]
LEf = ƒ (SPIf, ε) [3]
Where LEp (life expectancy at birth for the population at a given period) is a function of self-
reported illness (SPIp) of population at a given period and some residual error (ε).
LEm is life expectancy at birth for males at a given period
SPIm is self-reported illness for males at a given period
LEf is life expectancy at birth for females at a given period
SPIm is self-reported illness for females at a given period
Measure
Self-reported illness. The percent of people who reported having had an illness/injury in the 4-
week period of the survey for a given year.
Mortality. The number of death of people in Jamaica for a given year.
Life expectancy at birth.
The average number of years of new-born would live if subject to the mortality patterns of the
cross-sectional population at the time of his/her birth.
Subjective health is self-evaluated (or assessed) illness of an individual.
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Objective health. This variable constitutes life expectancy and mortality of a given population at
a particular time.
Results
In 1989, life expectancy at birth for the Jamaican population was 72.5 years and this has
increased to 73.12 year in 2007 (Table 12.12.1). Disaggregating population life expectancy at
birth revealed that in 1989, a female child was likely to outlive a male-child by 3 years. One and
one-half decades later this difference increased to 6 years. Over the 2 decades, the self-assessed
difference in ill status of females increased from 3.5% (in 1989) to 4.7% in 2007. Concurrently,
general self-reported illness over a 4-week period declined from 16.8% to 15.5%, with a mean
self-reported illness of 12.5% (SD = 2.6%). Mortality declined by 9.2%; with a mean mortality
over the 2 decades being 15,829 people (SD = 1,616 people).
Life expectancy of population by self-reported illness (for a 4-week period) Assessing illness from a 4-week period, Figure 12.12.1 found a strong significant association
between life expectancy at birth for the Jamaican population and self-reported illness (correlation
coefficient, r = -0.731). Fifty-four percent of life expectancy can be accounted for by self-
reported illness (R2 = 0.535).
Based on Table 12.12.2, if all other things remain constant (ie not change) which denotes
that self-reported illness would be naught, a Jamaican child at birth on average would be
expected to live for 75.6 years (95% confidence interval: 73.9, 77.3 years). With every 1%
increase in self-reported illness, life expectancy is expected to decline by 0.17 years (ie 2
months).
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Life expectancy of female child at birth by self-reported illness of females (for a 4-week period) Life expectancy at birth of female Jamaica and self-reported illness of female (assessed based on
a 4-week period) are moderately negatively correlated with each other (correlation coefficient, r
= - 0.683). Forty-seven percent of the variance in life expectancy at birth of a female child in
Jamaica can be explained by 1% change in self-reported illness of females (Figure 12.12.2).
Table 12.12.2 revealed that if self-reported illness were equals to zero, life expectancy of
a female child at birth on average would be 83.3 years (9% % Confidence interval = 75.4, 91.3
years). With every 1% increase in self-reported illness, life expectancy will decline by 0.53 years
(or 6 months) (95% confidence interval = -1.031, -0.024 years).
Life expectancy of male child at birth by self-reported illness of males (for a 4-week period)
Life expectancy at birth for a male is strongly associated with self-reported illness of
males (in %) – correlation coefficient, r = - 0.796. Sixty-three percent of the variance in life
expectancy at birth of a male can be explained by self-reported illness (in %) (Figure 12.12.3).
If self-reported illness were zero, average life expectancy of a male child in Jamaica
would be 72.7 years (95% Confidence interval = 71.3, 74.1 years) (Table 2). With each
additional increase in self-reported illness (ie 1%), life expectancy of a male will decline by 0.17
year (2 months) – (95% confidence interval = 0.289, 0.055).
Mortality and self-reported illness of population (in %)
Based on Figure 1 the data for mortality (in number of people) and self-reported illness (in %) is
best fitted by a non-linear curve. Concomitantly, when self-reported illness of the population (in
%) is less than 11%, the significant statistical correlation between self-reported illness and
mortality is a negative one. When self-reported illness lies between 11% and 16%, mortality
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begins to increase indicating the direct statistical association between both variables. When self-
reported illness exceeds 16%, the association between the two variables changed to a negative
one.
Limitation
The use of a single variable to explain the objective indexes may create the impression that only
one explanatory variable is important. This is a limitation of the study as the researcher wants to
examine one independent variable (ie self-reported illness in a 4-week reference period) in order
to establish whether it is a good measure of objective indexes and whether differences exist
between the sexes.
Discussion
Empirical analyses have examined the subjective and objective wellbeing phenomenon, and have
provided some platform for a partial resolution of the matter. Using cross-sectional data,
researchers established that there was a significant statistical relation between subjective
wellbeing (self-reported wellbeing) and objective wellbeing [5, 6, 19]. Diener [5] found a strong
correlation between the two variables, which disagreed with Kahneman and Riis [18], who found
correlation coefficient between subjective happiness and self-reported health to be strong; but the
statistical association between self-reported health and objective health. The current research
concurs with both Diener and not Kahneman and Riis in one instance as the correlation between
self-reported illness (ie subjective index) and objective health (ie life expectancy) for the
population was a strong one, correlation of coefficient, r = 0.731. The evidence here is both that
the association is a strong one and that it is negative, suggesting that life expectancy deteriorates
with more self-reported illness. This justifies the increase in life expectancy at birth for
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Jamaicans in 2007 over 1989 as the percentage of self-reported illness declined by 1.3%.
However on the other hand, when the objective index is mortality, the statistical association
between objective health and self-reported illness (ie subjective index) was very weak.
The studies of Diener and Kahneman and Riis assume that the sexes operate in the same
manner which means that what applies to the general populace is the same across the sexes. This
study did not make that assumption; instead the researcher examined whether there was a
disparity between the sexes and if there were any, what these were. This work revealed that
strong significant correlation between objective health (ie life expectancy at birth for Jamaicans)
and self-reported illness of both sexes differs by male and female. The findings showed that self-
reported illness was more an explanation of life expectancy of males than of females.
Interestingly to note that self-reported illness accounted for less than one-half of life expectancy
of females but close to two-thirds for males.
Kahneman [20] suggested that instantaneous self-assessment of health is a good measure
of subjective health unlike self-evaluations that occur over a longer period of time. This study
found that self-reported illness over a 4-week period of time is not immediate and is still a good
measure of life expectancy; but not mortality. Embedded in this finding is the fact that subjective
index can be instantaneous unlike Kahneman’s finding. The current study did not examine
beyond a 4-week period and while it was not immediate does not say that we can totally
disregard time in recall. The matter may not show any difference for the general population; but
this would be different for particular age cohorts – elderly. Evolutionary biology has shown that
cells degenerate with ageing, suggesting that functional capacity in particular mental faculties
will not on average be as good as in earlier years [24-29]. It is within the context of ageing that
287
Kahneman’s perspective may be even more potent as a 4-week period will not seek challenges in
recall for the young or middle age people but this could be so for the aged.
Gaspart [30] opined on the difficulty of using objective quality of life in measuring
wellbeing and put forward a perspective that self-reported wellbeing should replace this
measurement. 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” [30]
which speaks to the necessity of using a measure that captures more of the multidimensional
construct of health than the traditional income per capita. Wellbeing depends on both the quality
and the quantity of life lived by people, which argues more for subjective indexes than objective
ones [14]. The current study revealed that self-reported health is a good measure of life
expectancy but a poor measure of mortality in Jamaica. Therefore those studies that have used
self-rated illness (or health conditions) [31-34] to evaluate health of Jamaicans or particular sub-
groupings with the population were good in capturing health; but that researchers must be
cognizant of the differences that do exist between the validity of particular objective indexes
used and self-reported illness as well as the sex disparity in validity of subjective index in
measuring health. Self-reported illness therefore is a good measure of health as self-rated health
status or life expectancy. But the former is a better measure for health of males than females.
Hence, this must be taken into consideration in the interpretation of health. Simply put, using
self-reported illness to evaluate health of females is less reliable than of assessing males’ health;
and that subjective health (self-reported illness) is a good measure of objective health (life
expectancy) in Jamaica.
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Conclusion
Life expectancy at birth is widely used to measure quality of life in a country or of a people in
particular geographic region. It is among the objective indexes used by some demographers and
economists to evaluate health status of people and a population. This study found that self-
reported illness in a 4-week reference period is a good measure of objective health (life
expectancy at birth for the population of Jamaica). However, self-reported illness is a poor
measure of mortality. On disaggregating life expectancy and self-reported illness data by sexes,
it was revealed that self-reported illness for males was a better measure for objective health than
for females. The literature revealed that subjective indexes of health is a good measure if people
are asked to report on their health current and not over any long period of time. The current study
disagrees with the literature that for subjective index (ie self-reported illness) to be a good
measure of health it must be instantaneous as this work found that subjective index over a 4-
week was a good measure of life expectancy. This does not denote that the period extends
beyond 4 weeks; but that 1) self-reported illness is a good measure of objective index (life
expectancy); 2) subjective index is a better measure of objective index (life expectancy) for
males than females; 3) subjective index is not a good measure for mortality, and 4) self-reported
illness can be used to measure health as self-rated health status, happiness, or life satisfaction.
Conflict of interest The author has no conflict of interest to report at this time.
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Table 12.12.1. Life expectancy at birth for the sexes, self-reported illness, and mortality, 1989-2007 Year Life expectancy at birth (e0) Ill-health (in %) Mortality
Male Female Total Male Female Total 1989 69.97 72.64 72.5 15.0 18.5 16.8 16400 1990 69.97 72.64 72.5 16.3 20.3 18.3 14900 1991 69.97 72.64 72.5 12.1 15.0 13.7 13300 1992 73.6a 9.9 11.3 10.6 13200 1993 73.7a 10.4 13.5 12.0 13900 1994 11.6 14.3 12.9 13500 1995 74.1a 8.3 11.3 9.8 15400 1996 9.7 11.8 10.7 15800 1997 8.5 10.9 9.7 15100 1998 75.0a 7.4 10.1 8.8 17000 1999 70.94 75.58 73.25 8.1 12.2 10.1 18200 2000 70.94 75.58 73.25 12.4 16.8 14.2 17400 2001 70.94 75.58 73.25 10.8 15.9 13.4 17800 2002 71.26 77.07 74.13 10.4 14.6 12.6 17000 2003 71.26 77.07 74.13 NI NI NI 16900 2004 71.26 77.07 74.13 8.9 13.6 11.4 16300 2005 73.33 NI NI NI 17000 2006 73.24 10.3 14.1 12.2 16400 2007 73.12 13.1 17.8 15.5 14900 a These were taken from the United Nations Development Programme
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Table 12.12.2. Life expectancy at birth of population and sex of children by self-reported illness Explanatory variable
Coefficient Std. Error
Beta t-statistic P 95% CI
Population Constant 75.604 0.738 102.425 < 0.001 73.934, 77.274 Self-reported illness -0.173 0.054 -0.731 -3.217 0.011 -0.295, -0.051 F statistic [1, 9] = 10.350, P = 0.011 R = - 0.731 R2 = 0.535 Female children Constant 83.363 3.375 24.700 < 0.001 75.382, 91.344 Self-reported illness -0.528 0.213 -0.684 -2.478 0.042 -1.031, -0.024 F statistic [1, 7] = 6.138, P = 0.042 R = - 0.684 R2 = 0.467 Male children Constant 72.718 0.587 123.840 < 0.001 71.330, 74.107 Self-reported illness -0.172 0.050 -0.796 -3.478 < 0.010 -0.289, -0.055 F statistic [1, 7] = 12.096, P = 0.010 R = - 0.796 R2 = 0.633
294
Illness/Injury (in %)20.0018.0016.0014.0012.0010.008.00
Life
exp
ecta
ncy
at b
irth:
bot
h se
x (in
yea
rs)
74.50
74.00
73.50
73.00
72.50
R Sq Linear = 0.535
Figure 12.12.1. Life expectancy at birth for the population by self-reported illness (in %).
Life expectancy at birth of Jamaicans and self-reported illness (assessed based on a 4-week period) are strongly negatively correlated with each other (correlation coefficient, r = - 0.731). Fifty-four percent of the variance in life expectancy at birth for the population of Jamaica can be explained by 1% change in self-reported illness.
295
Self-reported Health of female (in %)
22.0020.0018.0016.0014.0012.00
Life e
xpec
tancy
: fem
ale (a
t birt
h in y
ears
)78.00
77.00
76.00
75.00
74.00
73.00
72.00
R Sq Linear = 0.467
Figure 12.12.2. Life expectancy at birth for female by self-reported illness of female (in %).
There is a negative moderate correlation between life expectancy at birth of a female and self-reported illness of female (in %) – correlation coefficient = 0.683. Forty-seven percent of the variance in life expectancy at birth of a female can be accounted for by 1% change in self-reported illness females (in %).
296
Self-reported Health of male (in %)18.0016.0014.0012.0010.008.00
Life
exp
ecta
ncy:
mal
e (a
t birt
h in
yea
rs) 71.25
71.00
70.75
70.50
70.25
70.00R Sq Linear = 0.633
Figure 12.12.3. Life expectancy at birth for male by self-reported illness of male (in %).
There is a strong negative significant statistical correlation between life expectancy at birth of a male and self-reported illness of male (in %) - correlation coefficient, r = - 0.796. Sixty-three percent of the variance in life expectancy at birth of a male can be explained by self-reported illness (in %).
297
Illness/Injury (in %)20.0018.0016.0014.0012.0010.008.00
Morta
lity (in
No. o
f peo
ple)
19000.00
18000.00
17000.00
16000.00
15000.00
14000.00
13000.00
R Sq Cubic =0.106
Figure 12.12.4. Mortality (in No of people) and self-reported illness/injury (in %)
Based on Figure 1 the data for mortality (in number of people) and self-reported illness (in %) is best fitted by a non-linear curve. Concomitantly, when self-reported illness of the population (in %) is less than 11%, the significant statistical correlation between self-reported illness and mortality is a negative one. When self-reported illness lies between 11% and 16%, mortality begins to increase indicating the direct statistical association between both variables. When self-reported illness exceeds 16%, the association between the two variables changes to a negative one.
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CHAPTER
13
The image of health status and quality of life in a Caribbean society
Paul A. Bourne, Donovan A. McGrowder, Christopher A.D. Charles, Cynthia G. Francis
Health is defined as the presence or absence of illness. This conceptualization of health status is dominant in health treatment and in fashioning the health care system. However, very little research has been done on how Jamaicans view health status and quality of life (QoL). This article seeks to understand how Jamaicans conceptualize health status and QoL because definitional content has implications for their health. The majority of the respondents in the CLG (54%) and the JSLC (82.2%) surveys reported good health status. There was a strong statistical relationship between area of residence and health status (P < 0.0001) unlike the relationship between area of residence and quality of life (P < 0.137). The respondents dichotomized health status and QoL and a significant relationship was found between both variables (P < 0.0001). The respondents’ dichotomization of health status and QoL is explained by the significant relationship between health status and self reported illness (P < 0.0001) where respondents view health status as the absence or presence of illness, excluding QoL. Health status means the presence or absence of illness and excludes QoL which is not in keeping with previous findings. This distinction is culturally determined.
Introduction
The satisfaction of basic needs constitutes quality of life (QoL) which is related to health.
Maslow’s theory of human motivation posits that there are five basic interrelated needs. These
are: physiological needs, safety needs, need of love and affection, need to belong , need for
esteem and need for self actualization. All of these operate in a hierarchy of prepotency [1-3].
Each of these needs in the hierarchy has to be satisfied before the higher need can be met [1].
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Understanding these needs is important because the greater the acquisition of knowledge of
people’s natural way of being, the less difficult it becomes to guide people about how to fulfill
their greatest potential, how to respect the self, how to love and be productive, how to be good
and happy [2]. Maslow also posits that healthy people with healthy psyches transcend their
environment. This transcendence occurs because these people are guided by internal values and
rules that foster a self-governing character, detachment and independence [3].
Maslow’s theory can be used to motivate people to become healthy [4]. Scores on belief
in an internal locus of control and neuroticism were predicted by Maslow’s need for satisfaction
[5]. Biopsychosocial health can be explained by the hierarchy of needs. Maslow argues that
people have the potential for growth and innate goodness, and are able to strive when faced with
adversity. Therefore, positive psychology influences health [6]. The hierarchy of needs also
explains gender differences in the meaning of health. Women associated a comfortable life,
pleasure, values and happiness with health, unlike men who associated health with national
security and family. The values of women satisfied their fundamental needs, while those of men
satisfied their higher order needs. This difference suggests that men can be motivated to engage
in healthy behaviour after they have fulfilled their more fundamental needs, compared to women
who may strive for health before they are motivated by other needs [7]. However, there is no
gender difference in self-actualization scores, but women score lower on perceived self-
presentation, confidence, physical self-efficacy and perceived physical ability [8].
Biological and psychological health is related to the hierarchy of needs. For example,
geriatric patients have a hierarchy of needs. Therefore, caring for these patients requires that
their self-actualization and self-esteem needs are met, and not just their physiological health [9].
In addition, the unmet physiological and safety needs of patients who suffer from chronic
300
vestibular dysfunction means that these patients cannot progress to higher order needs. This lack
of progress leads to psychosocial problems that have to be addressed [10]. Maslow’s hierarchy
of needs is also important for health education [11] because the status of people’s basic needs
influences their health-promoting self care behaviour. Some 64% of the variance in health-
promoting self-care behaviour was influenced by the physical: love, belonging, need, satisfaction
and self-actualization [12]. Unhealthy behaviour and health disparities based on race and class
can be reduced through health promotion programmes that respond to the basic needs of people,
which will allow them to achieve self-actualization [13]. This self-actualization influences the
quality of life. The hierarchy of needs was applied to the development of the quality of life in 88
countries between 1964 and 1994. There is a significant association between the predictions of
Maslow’s theory and the quality of life, including part of the S-shaped course and the sequence
of needs achievement [14] which influences health.
Published evidence on the health status and quality of life of Jamaicans is lacking, and
not much research has been done in this area in the English-speaking Caribbean. This study
examined how Jamaicans conceptualize health and quality of life, and investigated any possible
relationship between the two variables.
Materials and Methods
The current study utilized two different cross-sectional probability surveys which were
conducted in 2007 to examine the health status and quality of life of Jamaicans. These two
national surveys were conducted throughout the 14 parishes of Jamaica. The studies were
conducted by (1) the Centre for Leadership and Governance (CLG), Department of Government,
the University of the West Indies (UWI), Mona, and (2) the Planning Institute of Jamaica (PIOJ)
and the Statistical Institute of Jamaica (STATIN) – Jamaica Survey of Living Conditions (JSLC).
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The sample for the current study was 8,120 participants: 1,338 from the CLG and 6,782 from the
JSLC. Each survey was independently collected by the organization, and both the CLG and the
JSLC collected data at the same time.
During the months of July and August 2007, CLG conducted a stratified probability
sample of 1,338 respondents. The sampling design used for the study was that used by STATIN.
Face-to-face interviews were used to collect the data on an instrument which took about 90
minutes. The instrument consisted of questions about Abraham Maslow’s hierarchy of needs
(physiological needs, safety needs, social needs, self-esteem and self-actualization) which were
used to determine the participant’s quality of life [3]. The instrument was administered as part of
a larger CLG study. It was vetted by senior scholars, researchers, and interviewers from STATIN
and the Social Development Commission (SDC). After the vetting phase, the questionnaire was
pre-tested in a number of communities across the 14 parishes of Jamaica, as well as among UWI
faculty members and the student population. Modifications were made at a training symposium,
based on the comments of the different interviewers and the remarks of trained researchers. All
the interviewers employed by the CLG’s team were data collectors from either STATIN or SDC.
The interviewers who are trained data collectors underwent further training with the CLG
team for a 3-day period. The project manager of CLG travelled across the country to verify the
data collection process. A data template was created before the data was entered and data entry
clerks were trained to work with the instrument. Three different groups independently entered
the data, which was cross-referenced and reviewed for accuracy by two members of the research
team, who also validated the data entry process and cleaned the data.
The JSLC was commissioned by PIOJ and STATIN in 1988, and these organizations
have been collecting data since 1989 [15]. The JSLC is done through the administering of
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questionnaires modelled on the World Bank’s Living Standards Measurement Study (LSMS)
household survey [16]. The JSLC questionnaire consists of variables dealing with
demographics, health, the immunization of children aged 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 annually between April and July.
Measure
Quality of life was defined as the overall self-reported life satisfaction of an individual. It was
measured as the mean summation of the five-item needs from Abraham Maslow’s hierarchy.
These items were physiological needs, safety needs, social needs, self-esteem and self-
actualization [1]. Each item was on a 10-point Likert scale. Using Cronbach alpha for the five-
item scale, reliability was 0.841 (or α = 84%).
QoLi = 1/5*∑Ni where i is each need (i.e. I = 1, 2, 3, 4, 5)
where the QoL index is: 0≤QoLi ≤10.
Cohen and Holliday stated that correlation can be very low/weak (0.0-0.19); weak (0.2-0.39);
moderate (0.4-0.69), strong (0.7-0.89) and very strong (0.9-1.0) [17]. Cohen and Holliday’s
interpretation will be applied to categorizing Qoli into five groups: very poor (values range from
0 to 1.9); poor (values from 0.2 to 3.9); moderate (values from 4.0 to 6.9), good (values ranging
from 7.0 to 8.9) and very good (values ranging from 9 to 10). Health status was measured by the
question “Generally, how do you feel about your health?” Answers to this question were on a
Likert scale ranging from excellent to poor.
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Results
In examining the demographic characteristics of the sample as well as QoL and health status
forty three percent of the CLG’s respondents (n = 1338) were males compared to 49% for the
JSLC (n = 6,782; Table 13.13.1). Fifty-four percent of CLG’s respondents indicated at least
good QoL (of which 10.3% claimed very good) compared to 82.2% of those in the JSLC who
indicated at least good health status (of which 37% mentioned very good).
A statistical relationship was found between QoL and gender [QoL – χ2 (df = 4) = 11.9, P
< 0.018], and health status and gender [JSLC – χ2 (df = 4) = 46.5, P < 0.0001; Table 13.13.2]. A
cross-tabulation between QoL and area of residence revealed no significant statistical
relationship [QoL – χ2 (df = 4) = 6.98, P < 0.137; Table 13.13.3]. However there was a
significant relationship between health status and area of residence [JSLC – χ2 (df = 4) = 27.51,
P < 0.0001].
Using the standardized health status and QoL a significant statistical association was
found between the two variables [χ2 (df = 4) = 388.9, P < 0.0001; Table 13.13.4]. In addition, a
statistical relationship was found between the two variables [χ2 (df = 16) = 85.477, P < 0.0001;
Table 13.13.5].
Using data from JSLC’s survey, a statistical relationship was found between the health
status and self-reported illness of respondents’ variables [χ2 (df = 4) = 1323.470, P < 0.0001].
The statistical association was moderate, as given by the contingency coefficient with a value of
0.450. Of those who indicated that they had an illness (n = 976), 3.0% claimed very poor; 17.4%
said poor; 36.8% indicated moderate; 31.3% mentioned good and 11.6% reported very good
health status. In the same way, of those who indicated that they had not experienced an illness in
the last 4-week period (n = 5569), 0.4% reported very poor health status; 1.8% said poor; 8.7%
moderate health status; 47.7% claimed good and 41.4% reported very good health status.
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Discussion
This study examined how Jamaicans view health status and QoL. The majority of the
respondents in the CLG and JSLC surveys stated that they had good health status. The JSLC
survey had the greater majority with 28.2% more of the respondents stating that they had good
health status than their counterparts in the CLG survey. For both surveys there was no
significant gender difference in terms of QoL as there was a weak statistical relationship between
gender and QoL. This latter finding suggests that men and women view their quality of life or
basic needs similarly, despite the patriarchal nature of Jamaican society and the attendant gender
inequality. There was also a weak statistical relationship between the economic situation of the
respondents and their families, and QoL. This finding suggests that the respondents in their self-
reports did not view their economic status as influencing their QoL. Therefore, in the
respondents’ understanding of their basic needs there are other explanatory factors that will have
to be explored in future research.
There was a significant difference in the health status of the respondents in rural and
urban areas because there was a strong statistical relationship between area of residence and
health status, unlike the relationship between area of residence and QoL. These findings suggest
that the respondents, in their self-reporting, view their health status and their QoL
dichotomously, which is different from the results obtained in previous studies [18, 19].
Moreover, in the current study a significant relationship was found between QoL and health
status. This finding suggests that although QoL and health status are related, they are viewed by
the respondents as dichotomous domains in their lives.
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The dichotomous conceptualization of QoL and health status may be explained by the
finding that a significant relationship was found between health status and self-reported illness.
The respondents in this study viewed their health status based on the absence or presence of an
illness, and did not include QoL. The respondents’ exclusion of basic needs in their health status
suggests that their conceptualization of health as the presence or absence of illness is culturally
determined, because this is different from the findings of previous studies [18, 19]. Therefore,
within the Jamaican culture QoL is multi-dimensional and health status is one-dimensional, so
these conceptualizations are antonymous.
The preponderance of illness accounting for most of health is not atypical to Jamaica, as a
study conducted by Hambleton et al. [20] involving elderly Barbadians (60 years) revealed
similar results. Hambleton et al.’s work found that 88% of the variability in health status was
accounted for by current illness. While this study cannot allude to the generalizability of this to
the Caribbean, clearly in both of the aforementioned nations, health still carries a narrow
definition. This narrow definition of health was the justification of the World Health
Organization’s (WHO) concept of health in 1948 [21]. The WHO postulated a definition which
states that health is more than the absence of disease, as it includes social, psychological and
physical wellbeing [21]. Health is therefore more than the absence of illness. This is a negative
approach to the image and study of health, and does not encompass wellbeing or the positive side
to health [22, 23]. Both the WHO in the preamble to its Constitution in 1948, [21] and Engel
[24-26], have sought to conceptualize and provide a rationale for the image and study of health
that extends beyond illness or the antithesis of disease. Despite the contributions of social
scholars as well as the WHO and Engel to the discourse of health, in contemporary Jamaica the
image of health is still the antithesis of illnesses.
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Bok [27] opined that the WHO’s conceptualization of health is too broad, and therefore
poses a problem to operationalize in research. Embedded in Bok’s claim is the difficulty in
quantifying social and psychological conditions in health, and explaining the use of diagnosed
illnesses, mortality and life expectancy instead of wellbeing. Like other scholars [28-30], Bok
sees health as an objective phenomenon which explains the use of life expectancy, diagnosed
illness and mortality. Life expectancy relies on mortality data, and while it is an objective
measure of the health of people or a society, it is similar to the use of the antithesis of illness and
not wellbeing. It is this narrow approach to the use of life expectancy that justifies the World
Health Organization’s (WHO) introduction of healthy life expectancy [31]. Recognizing the
limitations of life expectancy, the WHO discounted life expectancy for disability. Disability
Adjusted Life Expectancy (DALE) summarizes the expected number of years of life of an
individual, which might be termed the equivalent of "full health." To calculate DALE, the years
of ill health are weighted according to severity, and subtracted from the expected overall life
expectancy, to give the equivalent years of healthy life [31].
This study has contributed to our understanding of health status by highlighting the
culture-bound conceptualization of health status in Jamaica, which is different from how it is
conceptualized in the literature which includes QoL. Another contribution is the generalization
of the findings, with the combination of the findings from two large-scale random national
surveys. However, there are a couple of limitations. We did not measure the factors influencing
how Jamaicans conceptualize illness which would inform interventions. Also, the CLG and
JSLC surveys relied on self-reports so there was the possibility of social desirability bias, where
the respondents might have told the interviewers what they wanted to hear to get their approval.
QoL is concerned with how people assess their lives which includes a wide range of
issues from health, life satisfaction, momentary moods, economic wellbeing, happiness to needs
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satisfaction and a global assessment of all aspects of life [32-34]. QoL, therefore, is subjective
wellbeing, and its coverage extends beyond illness [35]. Health status, however, is synonymous
with physical health (illness) which means that collecting data on illness and self-rated health
status is one of the same and therefore adds nothing new to understanding general health as
defined by the WHO [21]. In keeping with the broad definition of health forwarded by the WHO,
QoL should be used in addition to illness or self-rated health status, as self-reported illness and
self-rated health status are the same events.
Conclusion
This study examined how Jamaicans conceptualize health status and QoL. Jamaicans view their
health status and their QoL as distinct domains in their lives. This surprising distinction is
culturally determined because the difference has not been empirically observed elsewhere except
Barbados. The absence or presence of illness influences how Jamaicans conceptualize their
health status. The exclusion of QoL or basic needs from their conceptualization of health status
should be noted by medical practitioners and researchers when they assess the health of
Jamaicans.
The aforementioned findings highlight that collecting data on health status and illness in
Jamaica is one and the same, and therefore other subjective indices such as QoL, life satisfaction
and happiness would yield more information than health status and/or illness. If health is multi-
faceted, then health status would not be a good measure of this broad conceptualization. Further
research is needed to uncover the reasons for the one-dimensional view Jamaicans have of their
health status, and how this conceptualization affects their health.
308
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Table 13.13.1 Demographic characteristics of sample for CLG and JSLC, 2007 Variable CLG JSLC
n % n % Gender Male 574 42.9 3303 48.7 Female 723 54.0 3479 51.3 Social class Working 766 59.0 2697 39.8 Middle 476 36.6 1351 19.9 Upper 57 4.4 2734 40.3 Educational level Primary or below 60 4.6 5752 87.3 Secondary 892 69.1 709 10.8 Tertiary 339 26.3 131 2.0 QoL Very poor 13 1.0
NA
Poor 59 4.5 Moderate 536 40.6 Good 575 43.6 Very good 136 10.3 Health status Very poor
NA 50 0.8
Poor 270 4.1 Moderate 848 12.9 Good 2967 45.2 Very good 2430 37.0 Current economic situation compared to 1 year ago
Very good 58 4.4 NA Good 361 27.1
Moderate 660 49.5 Poor 164 12.3 Very poor 90 6.8 Area of residence Urban 291 21.7 2002 29.8 Rural 1041 77.8 4780 70.5 Age Mean (SD) 35.0 years (13.6) 29.9 years (21.7) NA – Data not available
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Table 13.13.2 Quality of life and health status by gender of respondents, CLG and JSLC
Variable
CLG JSLC
Gender Gender
Male Female Male Female
n (%) n (%) n (%) n (%)
QoL and health status
Very poor 6 (1.1) 7 (1.0) 24 (0.8) 26 (0.8)
Poor 18 (3.2) 40 (5.6) 111 (3.5) 159 (4.7)
Moderate 222(39.3) 292 (41.0) 331 (10.4) 517 (15.3)
Good 245 (43.4) 316 (44.3) 1482 (46.4) 1485 (44.1)
Very good 74 (13.1) 58 (8.1) 1247 (39.0) 1183 (35.1)
Total 565 713 3195 3370
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Table 13.13.3 Quality of Life and health status by area of residence, CLG and JSLC
Variable
CLG JSLC
Area of residence Area of residence
Non-urban Urban Non-urban Urban
n (%) n (%) n (%) n (%)
QoL and Health status
Very poor 9 (0.9) 4 (1.4) 42 (0.9) 8 (0.4)
Poor 47 (4.6) 12 (4.2) 215 (4.7) 55 (2.8)
Moderate 435(42.4) 98 (34.3) 554 (12.0) 294 (15.1)
Good 432 (42.1) 140 (49.0) 2072 (44.9) 895 (46.0)
Very good 104 (10.1) 32 (11.2) 1735 (37.6) 695 (35.7)
Total 1027 286 4618 1947
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Table 13.13.4 Quality of life, health status and standardized health status
Classification QoL JSLC Standardized JSLC
n n n
Very poor 13 50 10
Poor 59 270 54
Moderate 536 848 171
Good 575 2967 596
Very good 136 2430 488
Total 1319 6565 1319
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Table 13.13.5 QoL by economic situation of individual and family, CLG
QoL
Economic situation of family
Much worse
A little worse
Same A little better Much better
n (%) n (%) n (%) n (%) n (%)
Very poor 3 (5.1) 3 (1.2) 2 (0.4) 2 (0.5) 3 (2.7)
Poor 2 (3.4) 24 (9.4) 18 (3.8) 6 (1.5) 7 (6.3)
Moderate 34 (57.6) 124 (48.8) 192 (41.0) 155 (37.5) 29 (26.1)
Good 17 (28.8) 88 (34.6) 213 (45.6) 200 (48.4) 49 (44.1)
Very good 3 (5.1) 15 (5.9) 43 (9.2) 50 (12.1) 23 (20.7)
Total 59 254 468 413 111
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CHAPTER
14
The quality of sample surveys in a developing nation
Paul A. Bourne, Christopher A.D. Charles, Neva South-Bourne, Chloe Morris, Denise Eldemire-Shearer, Maureen D. Kerr-Campbell In Jamaica, population census began in 1844 and many inter-censal ratios performed on the census data show that there is a generally high degree of accuracy of the data. However, statistics from the Jamaican Ministry of Health show that there are inaccuracies in health data collected from males using sample surveys. The objectives of the present research are to (1) investigate the accuracy of a national sample survey, (2) explore the feasibility and quality of using a sub-national sample survey to represent a national survey, (3) aid other scholars in understanding the probability of using national sample surveys and sub-national sample surveys, (4) assess older men’s evaluation of their health status, and (5) determine whether dichotomization changes self-evaluated health status. In Study 1, 50.2% of respondents indicated at least good self-evaluated health status compared to 74.0% in Study 2. Statistical associations were found between health status and survey sample [χ2 (df = 5) = 380.34, P < 0.001]; self-reported illness and study sample [χ2 (df = 1) = 65.84, P < 0.01, phi = 0.16]; health care-seeking behaviour and study samples [χ2 (df = 1) = 21.83, P < 0.05, phi = 0.10]. Substantially more respondents reported an illness in Study 1 (34.3%) than in Study 2 (i.e. 17.5%). Clearly, inconsistencies exist in the health data which indicates that care should be taken in using sample surveys.
Introduction
This paper examines the accuracy of a national survey, assesses the usefulness of using a
sub-national sample survey to understand the national survey, and attempts to act as a guide to
fellow researchers. The article used self-evaluated data from older men on their health status and
seeks to elucidate whether self-evaluated health status changes with the dichotomization process.
Since 1844 census taking has been an irregular decentenial event in Jamaica (with none done in
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the 1930s). Statisticians and researchers have performed many inter-censual ratios on the data
which showed a high degree of accuracy.1 This occurrence is also the case in North America,
Britain, Japan, India, Africa and Europe.1 Vital registration statistics (data on births, deaths,
marriages) have been used for years in the computation of the life expectancy, health and
prosperity of nations. Census-taking and civil registrations are highly expensive data collection
processes, which accounts for the use of sample surveys. The first national sample survey was in
1953 to aid inter-censal planning. The use of survey data by nations in planning denotes that
planners, in particular researchers, rely on the completeness and accuracy of the data.
Sample surveys are widely utilised to examine social conditions. They are also used for
much more than the understanding of social conditions, including life expectancy, mortality
patterns, fertility, termination of marriage, population projections, other demographic
computations and health statistics.1-6 Unlike a census, a survey collects standardized data from a
specific population with the purpose of generalizing this to a wider population.7 In the sampling
and data collection processes, errors are highly likely to enter into the data.
Quality of sample survey data is important for more than the accuracy of using sampling
design in a particular task. The guidance that sample survey methods provide to researchers is
embedded within people. It follows that sample surveys must rely on the accuracy of recall and
the truth of information provided by research participants. This information not only influences
people socially but it impacts on the quality and quantum of their lives. It is within this context
that the accuracy of sample surveys is crucial to researchers, policy makers, and non-academics
as they seek to enhance the quality and quantum of human experience. This mindfulness requires
that researchers take into account the broadest possible range of reasons within the parameters of
the research that brings validity and reliability into disrepute.
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Jamaica is among many countries that collect sample survey data to guide, formulate,
assess and understand their populations. In 1988, Jamaica began collecting sample survey data
on the living standard of its people. The survey is referred to as the Jamaica Survey of Living
Conditions (JSLC). The JSLC is a modification of the World Bank’s Living Standards
Measurement Study (LSMS) household survey, and provides policy makers, including the
government, with vital information on policy implementations and their effect on the living
standard of people. Health, which is more than disease,8 means that the JSLC coverage is
comprehensive enough to allow for the assessment of the health of Jamaicans.
Using Jamaican Ministry of Health Annual Reports on the actual visits made to health
care facilities as well as visits for curative care, Table 1 shows that on average 30% of males
visited health care facilities and 34% received curative care. However, survey statistics for the
same period showed that on average health care visits for males were 62% and self-reported
illness was 10%.9 This highlights inconsistencies in the data sources. Within the context of
disparities which exist in the data sources, it brings into question the reliability and validity of
health survey data, which are collected from males in Jamaica.
A critical assessment of the literature has revealed that there is a paucity of research
investigating the validity of sample survey data in the Caribbean in general and Jamaica in
particular. The Caribbean, like many other regions, has come to rely on sample survey data in
government planning as well as health planning. People’s lives therefore cannot be based on
inaccuracies from sample survey data, and so an examination of the accuracy of surveys will
allay many of the fears of critical stakeholders and non-researchers. Validity is vital for
understanding and interpreting studies already published, as well as guiding new studies and
survey approaches. The objective measurements are infrequently used to validate costly
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questionnaires such as the JSLC. Agreement with reality trumps reliability and validity, where
without question the data is taken as accurate over time, and it is accepted that the instrument is
measuring what it purports to measure.
The current study examines the accuracy of the 2007 JSLC by using another independent
sample survey in the same period. The objectives of the present research are to (1) investigate the
accuracy of a national sample survey, (2) explore the feasibility and quality of using a sub-
national sample survey to represent a national survey, (3) aid other scholars in understanding the
probability of using national sample surveys and sub-national sample surveys, (4) assess older
men’s evaluation of their health status, and (5) determine whether dichotomization changes self-
evaluated health status.
Methods and Materials
Data
For the current study, the data used in the analysis were originally collected in 2007 from
two different studies: (1) the Jamaica Survey of Living Conditions (JSLC) and (2) the Survey of
Older men (SOM). In order to cross-validate self-evaluated data from men in Jamaica, because
complete data were available from JSLC and only data on older men (ages 55+ years) from
SOM, participants 55+ years were selected from each sample, as this was comparable in both
samples. Two thousand, four hundred and eight-three were used for the current study: in Study 1
(i.e. JSLC) 483 participants and 2,000 participants in Study 2 (i.e. SOM). The mean age in Study
1 was 67.7 years (SD = 9.3 years) and in Study 2 it was 67.0 years (SD = 8.2 years). Urban
dwellers comprised 47.0% (n=227) in Study 1 and 49.1% in Study 2 (n = 981) compared to
53.0% and 50.9% in rural areas respectively.
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Sample
Study 1
Data from the Jamaica Survey of Living Conditions (JSLC) for 2007, commissioned by
the Planning Institute of Jamaica and the Statistical Institute of Jamaica, were used to provide the
analyses for this study.9 These two organizations are responsible for planning, data collection
and developing policy guidelines for Jamaica, and they have been conducting the JSLC annually
since 1989. The cross-sectional survey was conducted between May and August 2007 from the
14 parishes across Jamaica and included 6,782 people of all ages.10 The sample for this study
was 1,343 respondents who are classified as being the poorest 20 percent in Jamaica (or the
poorest).
The JSLC used a stratified random probability sampling technique that was drawn to the
original sample of respondents, with a non-response rate of 26.2%. The JSLC survey was based
on a complex design with multiple stratifications to ensure that it represented the population,
marital status, area of residence and social class. The sample was weighted to reflect the
population.
The instrument used by the JSLC was an administered questionnaire where respondents
were asked to recall detailed information on particular activities. The questionnaire was
modelled from the World Bank’s Living Standards Measurement Study (LSMS) household
survey. There are some modifications to the LSMS, as the 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 were trained to collect data from
household members.
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Study 2
The study used primary cross-sectional survey data on men 55 years and older from the
parish of St. Catherine in 2007 (for May and June); it is also generalizable to the island.11-13 The
survey was submitted and approved by the University of the West Indies Medical Faculty’s
Ethics Committee. A stratified multistage probability sampling technique was used to draw the
sample (2,000 respondents), and a 132-item questionnaire was used to collect the data. The
instrument was sub-divided into general demographic profiles of the sample, past and current
health status, health-seeking behaviour, retirement status, social and functional status.
The Statistical Institute of Jamaica (STATIN) maintains a list of enumeration districts
(ED) or census tracts. The parish of St. Catherine is divided into a number of constituencies
made up of a number of enumeration districts (ED). The one hundred and sixty-two enumeration
districts in the parish of St. Catherine provided the sampling frame. The enumeration districts
were listed and numbered sequentially and selection of clusters was arrived at by the use of a
sampling interval. Forty enumeration districts (clusters) were subsequently selected with the
probability of selection being proportional to population size (Table 14.14.2).
The sample population does not only speak to the parish of St. Catherine; it is
generalizable to the island of Jamaica. The sampling frame was men fifty-five years and older in
the parish of St Catherine, and this parish was chosen as previous data and surveys11-13 suggested
that it had the mix of demographic characteristics (urban, rural and age-composition) which
typify Jamaica.
Enumeration districts (ED’s) consisted of not more than 400 households, and they were
used as primary sampling units (PSUs). Interviewers were trained by University of the West
Indies staffers (i.e. Department of Community Health and Psychiatry) and large groups were
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sub-divided into smaller groupings with a supervisor who monitored the interviewers in an effort
to maintain accuracy. All interviewers were given a map of their respective EDs and they were
taken across the geographic boundary of that ED. Stratified random sampling was used to
predetermine those who should be interviewed from particular households. Enumerators
commenced at a fixed point as was stipulated by the Statistical Institute of Jamaica (STATIN)
and the interviewers proceeded based on their map of the predetermined persons in a clockwise
direction. This approach was used in order to exhaust the EDs.
In the event a chosen participant from a household did not wish to participate in the
interview, the interviewer would go to the next identified household on his/her map. For males
55+ years who were not at home when the interview was being conducted, a maximum of 3 call-
backs were used in order to establish a link for a possible interview. In cases where the
interviewer had exhausted all the call-backs and the participants were still unavailable, a
replacement was used from the adjacent household assuming that the person satisfied the
criterion of the study (i.e. male 55+ years). A strict definition of the household was used as a
measure of standardizing those who should be interviewed for the study. A household was where
any individual slept in the dwelling for at least 3 nights and ate at least one meal per week from
the same pot as other individuals. Hence a resident for selection had to be male 55+ years that
slept in the same dwelling as other individuals for at least 3 nights and ate at least once a week
from the same pot as other individuals in that dwelling.
Validity
The current study validated the self-evaluated health data used in the JSLC by using a sub-
national sample survey which was collected during the same time as the former survey. The
JSLC questionnaire could not be assessed as it would be expensive to do so, and the objective of
the latter study sought to examine the health status, health literacy, health decisions and typology
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of dysfunctions that older men have. Both studies used a 5-point self-rated health status question
(Generally, how would you rate your current health status?). The responses ranged from
excellent (i.e. very good) to very poor and this allowed for the validation of health status.
Reliability
One of the purposes of using matching studies is to examine content errors which assess
the reliability of the data sources.1-4 Testing the consistency of information derived from the
National Survey (i.e. JSLC) will be done using a survey of older men (ages 55+ years). The older
men study was conducted in St. Catherine and this will be used to assess the consistency of the
information in the National Survey. Consistency (or inconsistency) was evaluated by using chi-
square analysis. If there was no association between the variable and the different sample, then
the information in one survey was consistent with the information of the other survey.
Conversely, statistical association denoted inconsistency of results.
Ethics
No ethical clearance was sought for the Jamaica Survey of Living Conditions. However,
one was sought and obtained for the sub-national sample survey. The University of the West
Indies Ethical Board approved the sub-national sample survey, and each participant was given a
written informed consent prior to his/her participation.
Statistical Analysis
Data were stored, retrieved and processed using SPSS for windows 16.0 and a 5 percent
level of significance was used to test significance (i.e. 95% confidence interval). Descriptive
statistics were used to provide background information on the samples. Validity was assessed by
comparing levels of self-evaluated data on (1) area of residence; (2) age group; (3) health status;
325
(4) marital status; (5) household heads; (6) medical care-seeking behaviour and (7) self-
diagnosed illnesses. The researcher also used chi-square to measure associations between the two
sample survey results. A statistical association from a chi-square result should be interpreted as
differences between Study 1 (i.e. national sample survey) and Study 2 (i.e. St. Catherine sample
survey). On the other hand, no relationship should be interpreted as demonstrating any difference
between the aforementioned study samples. Contingency coefficient and chi-square were used to
examine the statistical association between variables.
Measurement of variables
Self-rated health status is measured using people’s evaluation of their overall health
status, 14 which ranges from excellent to poor health status. The question that was asked in the
survey was “How is your health in general?” And the options were very good, good, fair, poor
and very poor. For the purposes of the model in this study, self-rated health was coded as a
binary variable (1 = good and fair 0 = Otherwise) (also see studies that have treated self-rated
health status as a binary variable).15-20 Age is a continuous variable which is the number of years
alive since birth (using last birthday). For the present study ages range from 55 years and older.
Data errors for this work are classified into two groups: coverage errors and content
errors. Coverage errors arise due to incompleteness of inclusion of people in a data system.2, 4
This includes misplacement of events in a time or when events are incorrectly classified in one
defined boundary, when there should have been an estimate in another defined unit. Content
errors denote inaccuracy in the characterization of recorded units in a data system.2, 4 Sampling
errors denote negative errors of failure to include elements that should properly belong to a
sample against a population. These arise owing to coverage errors. Non-sampling errors are all
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errors which are theoretically outside of those caused by sample against a population. These
include (1) non-response, (2) content errors, and (3) interviewers’ biases.
Results
Demographic characteristic of sample
The sample was 2,483 respondents [483 for the national survey (i.e. Study 1) and 2,000
for the sub-national survey (i.e. Study 2)]. In Study 1, the mean age was 67.7 years (SD = 9.3
years); while the mean age in Study 2 was 67.0 years (SD = 8.2 years). In Study 1, 50.2% of
respondents indicated at least good self-evaluated health status compared to 74.0% in Study 2
(Table 14.14.3).
In Study 1, 34.3% of the sample indicated an illness compared to 17.5% in Study 2. The
percentage of respondents who indicated having sought more medical care was also more in
Study 1 (i.e. 65.5%) than in Study 2 (i.e. 45.7%,(Table 4). The bivariate analysis follows.
Bivariate Analyses
There was no association between area of residence and study used [χ2 (df = 1) = 0.66, P
> 0.05]. This was also the case for age and study sample [χ2 (df = 5) = 8.66, P > 0.25]. However,
relationships were found between (1) marital status and study sample [χ2 (df = 4) = 15.38, P <
0.01, contingency coefficient = 0.08] and (2) household head and study sample [χ2 (df = 1) =
33.71, P < 0.01, phi = 0.12]. Furthermore, for Study 1, 78% of respondents indicated that they
were heads of their households compared to 88% of those in Study 2. In regard to marital status
and study sample, 10% of those in Study 1 revealed that they were in common-law unions
327
compared to 7% of those in Study 2. Similarly percentage point disparity was found in separated
unions (i.e. 3% in Study 1 and 6% in Study 2).
A cross-tabulation between self-evaluated health status and study sample revealed a
statistical association [χ2 (df = 5) = 380.34, P < 0.001]. The relationship between the two
variables was weak (contingency coefficient = 0.37 or 37%). Substantially more respondents in
Study 2 indicated at least good health compared to those in Study 1. The converse was equally
true as more people in Study 1 reported at least poor health compared to those in Study 2. When
self-evaluated health status was dichotomized, i.e. good versus poor health (with moderate or fair
health being included in poor health], the relationship between it and the study sample became
weaker (i.e. phi = 0.21 or 21%; χ2 (df = 1) = 105.68, P < 0.05) than when health status was non-
dichotomized. When self-evaluated health status was dichotomized, 49.8% of respondents in
Study 1 indicated moderate-to-very poor health status compared to 26% of those in Study 2.
A statistical relationship was found between medical care-seeking behaviour and study
samples [χ2 (df = 1) = 21.83, P < 0.05, phi = 0.10]. In Study 1, 65% of respondents claimed to
have gone to seek medical care compared to 46% in Study 2. Likewise an association was found
between self-reported illness and the study sample [χ2 (df = 1) = 65.84, P < 0.01, phi = 0.16].
Substantially more respondents reported an illness in Study 1 (34.3%) than in Study 2 (i.e.
17.5%).
Discussion
Accuracy of national surveys and sub-sample surveys
A number of scholars as well as the Statistical Institute of Jamaica have found and
purported that stratified sampling of the parish of St. Catherine is a good measure as a
328
representative sample of the wider Jamaican population.1-13 They found that St. Catherine has a
diverse population with congruent characteristics similar to the wider Jamaican population. The
current study found that while a sample of older men in St. Catherine had similar characteristics
to the national sample (Jamaica Survey of Living Conditions), some disparities still exist
between the two samples. If a sample of St. Catherine is similar to that of Jamaica, then there
must not be any disparity in medical expenditure, health status, marital status, being the
household head and self-reported illness. In fact, this study found that even among some
demographic characteristics like household head and marital status, differences were there
between both surveys.
The non-validation in some of the demographic characteristics for both surveys was also
found in the self-evaluated health data. The current research found that there was a difference
between the self-evaluated health data for the St. Catherine and the national sample. In the St.
Catherine sample, none of the respondents indicated having poor health, compared to 18.4% of
those in the national sample. The disparity was more so in the category of good health. In the
national sample, 37.3% revealed having good health, compared to 55.4% in the St. Catherine
cohort. This denotes that 1.5 times more respondents indicated that they had good health in the
latter sample, suggesting that there is either overstatement or understatement in describing health
status among older men in Jamaica. While the current study does not accept that any one of the
two samples is correct over the other, it is evident from the significant inconsistencies between
the two samples that health data from older men is incorrectly reported by them. Embedded in
the health data from older men therefore are non-sampling errors4, 21-24 from a finite population,
suggesting that public health planning with inaccurate health data will yield low quality health
outcomes.
329
Quality of national surveys and sub-sample surveys, and health status of older men
Among non-sampling errors is attitudinal information.1-5 Demographers like Colin
Newell4 believed that despite the possibility of extensive training for sample data collectors, their
attitude and appearance can affect the quality of the information that they receive (or do not
receive) from the interviewee. Caribbean societies, in particular Jamaica, have not been
examining the quality of data collected owing to attitudinal biases. Many of the data collectors in
Jamaica are females, and within the context that males do not want to appear weak, effeminate or
sickly, males reporting illnesses to females clearly are distorting the quality of the data. One
Caribbean anthropologist argued that Caribbean males are socialized to be tough, strong, and
display no signs of weakness.25 Another Caribbean scholar opined that sickness is interpreted by
Caribbean males as a signal of weakness, 26 which justifies their reluctance to speak openly about
illnesses. Males’ unwillingness to speak about illnesses crosses gender types, as they must
preserve their masculinity both among other males as well as females.
Some non-Caribbean researchers found that only 10.5% of men who suffer from erectile
dysfunctions sought medical care, 27 suggesting that males prefer not to speak about or display
signs of weakness. Illness which is an indicator of weakness for males means that health care-
seeking behaviour is usually a last resort, and is most times used in case of severity of illness.28
Statistics from the Ministry of Health (Jamaica) showed that on average 30 out of every 100
males sought medical care, which denotes that older males were substantially under-reporting
their illness in the St. Catherine study. Although 34 out of every 100 older males sought medical
care as indicated by the national survey, within the context that there is a positive relationship
with ageing and poor health status, it can be extrapolated and projected from the Ministry of
330
Health (Jamaica) data29, 30 that this group should not have indicated only 4% more dysfunctions
compared to the general population. The definition of illness for many Jamaicans does not
include the common cold or an upset stomach, to name a few conditions, as these can be
addressed by using a home remedy. Of Jamaicans who reported an illness, statistics showed that
30.2% utilized a home remedy, compared to 28.4% of males, and at least twice more females
seeking medical treatment, 9 which highlights the role of culture in defining and changing health
care seeking in Jamaica.
If illnesses do not disrupt males’ economic livelihood, many of them are highly unlikely
to seek health care as they do not see the need to attend at traditional medical facilities, thinking
that the matter can be rectified at home as they are not ill enough. This is not exclusive to
Jamaica, as in Pakistan27 young males were more likely to seek medical care only if their illness
interfered with their economic livelihood. This explains why many males in Jamaica on average
spend more time receiving medical care than females, 9 and accounts for the higher mortality,12
as during the time it takes them to visit health care institutions the dysfunction would have
increased to being chronic, untreatable and incurable, thus making it highly unlikely for medical
practitioners to make a difference. The culture therefore retards many Caribbean as well as non-
Caribbean males from truthfully reporting health matters, and the fact that females are collecting
information from them about health matters further accounts for the increased non-sampling
errors (i.e. inaccuracies in data as they under-state dysfunctions to create an impression of
strength which is tied to their perception of health).
The findings from the present research revealed an exponential disparity between self-
evaluated illnesses between the two samples. Approximately 2 times more older men reported
illnesses in the national survey compared to the St. Catherine survey. Therefore, there is a
331
difficulty in validating the self-evaluated health data collected from older men in Jamaica.
Statistics from the Ministry of Health (Jamaica) showed that 34 out of every 100 males received
curative care, and with the same number from the national survey, it follows that in the St.
Catherine study there were substantially more under-reported illnesses.
While we can extrapolate an exact value for the number of older males who received
curative care within the biology of an organism, as the body ages this is associated with
increased illness and reduced function, and therefore the researchers suggest that a greater
percentage of older males should have reported an illness that was higher that the national
average. The reality of the unreliability and invalidity of health data is further highlighted in the
self-reported typology of health conditions between the two sample surveys. In both sample
surveys there was no consensus on the typology of dysfunction. In some cases the disparities
were huge as was evident for arthritic cases. One percentage point of respondents indicated that
they had arthritis in the St. Catherine sample compared to 28% in the national sample.
It should be noted that statistics from the Ministry of Health (Jamaica) between 2002 and
200629, 30 showed that females received 2 times more curative care than males. However, self-
reported data from the Planning Institute of Jamaica and the Statistical Institute of Jamaica
showed that 1.5 times was the greatest disparity, with females reporting having more illnesses
and this was 1.4 times more in 2007 (17.8% of females to 13.1% of males). On the other hand,
statistics from the Ministry of Health (Jamaica) on curative visits showed that since 2000 an
average of 34% of males received care.29,30 This further re-enforces the inaccuracies in self-
evaluated health data provided by males in which the case is using elderly samples for two
different sources over the same period. Emerging from the study is that the inaccuracies are not
limited to older men but that this is generalizable to the populace of males across the nation.
332
Another area in which disparity was found is in medical care-seeking behaviour. In the
national survey 66% of older men reported that they visited health care practitioners compared to
46% of the sample in the St. Catherine study. For 2000 and 2006, statistics from the Ministry of
Health (Jamaica) showed that approximately 30% of males had been visiting health care
agencies. Accompanying ageing come increased visits to medical care facilities; but the figure of
66% of older men seeking medical care as revealed by the national survey seems rather high
within the context of the socialization already discussed. The discrepancy may be due to the
participants’ belief that that they should give government agencies the data they want rather than
data that is correct. This further brings into question the quality of self-evaluated health data
collected from males in Jamaica, and how future studies must be interpreted, ergo they must
incorporate the findings of the current study in their analyses.
Dichotomization of self-evaluated health status
In the validation process of the health data what emerged is the loss of some of the
original information from dichotomized self-evaluated health status. Using non-dichotomized
self-evaluated health status, the relationship between this and the study samples was 37%, and
when self-evaluated health status was dichotomized the association fell to 21%. This concurs
with studies that found that it is better to use the continuous nature of self-evaluated health status
than the dichotomized variable,31-33 as in the dichotomization process some of the original
information will be lost. The current research showed that 16% of the original information is lost
owing to the dichotomization process. This highlights a rationale for the non-dichotomization of
self-evaluated health status in Jamaica, as data losses denote the lowering of the quality of health
data, fostering challenges in policy implementation from a dichotomized health status.
333
Some studies have found that dichotomizing self-rated health and using logistic
regression is acceptable34-36 and many studies in Jamaica have followed this procedure,39-44 but
clearly using this operational definition in examining the health of males will not produce the
same interpretation, as some of the original information would have been lost. Recently a study
by Bourne44 found that dichotomizing self-rated health is acceptable for females as there was
minimal variance; however a great deal of variance was found in dichotomizing health for males.
Another study found that when poor self-rated health status was narrowly defined (excluding
moderate health), there was minimal impact on the estimated effects of the covariates45 and this
was re-enforced by Bourne’s work.44 However, Bourne’s finding somewhat disagreed with
Finnas’47 conclusions, as he found substantial disparity for males when health was classified
from very poor-to-moderate compared to very poor-to-poor .
Validity and reliability of using national surveys and sub-sample surveys
Inaccuracies are found in the present study as already outlined but these exclude errors
associated with coverage and content. The national survey (i.e. JSLC) undoubtedly used complex
statistical techniques to design its sample and has reduced coverage errors. The JSLC updated its
sample frame in 2007,9 which adds to the quality of coverage and further reduces coverage errors
as more people would be included in the sample, in order to be better able to select a sample
which is more representative of the population. By widening the sampling frame, negative errors
of failure to include elements are reduced, as more elements that belong to the population will be
included, and therefore can be selected for the national survey sample.46 But the quality of the
sample coverage does not mitigate against content errors which appear to be present in the health
data. Content error also plays a role in influencing sample outcomes and thereby the quality of
data collection.1-4; 21-24 Content errors are a part of response errors and so cannot be neglected in
334
the sampling process as they act jointly with coverage errors to lower the quality of data
collection.46 It can be extrapolated that the inaccuracies found in the health data of older men
cannot be neglected as this will influence health outcomes, the interpretations of those outcomes
and intervention initiatives. This is also a public health challenge, as not having quality data
denotes that policies will address inaccuracies and will further retard all forms of development in
the nation.
Surveys on health are among the epidemiological studies executed and they provide
critical information on various health issues such as dysfunctions, duration of illness,
hospitalization and self-rated health, among other variables. Validity of data assists with
understanding the quality of health data and this is agreed with by many demographers1-4 and
non-demographers in the Caribbean.44 Wilks et al.’s work47 examines the validity of non-
response and concretizes the position that quality health data is based on precision in sample size
and non-response, and the current study goes further to show that content errors will affect the
outcome of the collected data. Interestingly, Wilks et al.’s study is among many researches that
have embarked on sampling errors while avoiding the importance of content quality. Examining
non-response errors assumes that content errors are non-existent or minimal, and even in Wilks
et al.’s work within the context of the current findings there are content errors, as the study
collects data from males who are less likely to report quality information on their health status.
Empirical studies have established that the quality of data in developing countries is
relatively low.1-4, 48 Jamaica is a middle-income developing nation in the Caribbean with a
history of high quality data from statistical data sources. Using intercensal surveys and
demographic ratios, it has been shown that the data collected in the censuses and the Jamaica
Survey of Living Conditions are of high quality. The longstanding nature of data collection and
335
the continuous updating of the sample frame have aided in the reduction of sampling errors and
in the process have reduced coverage errors.9, 11 In spite of the efforts of the statistical agency to
reduce sampling errors, content errors have still been found to be present in the data; this is more
so a gender phenomenon. Inconsistencies in health data collected from males showed that data
collected from them are not accurate and cannot be relied on. This raises the question of the
incentive for males to truthfully report on their health.
Yates22 purported that people can have motives that retard them from providing or
revealing the truth. Studies on the reliability and validity of data sources in developing nations
continue to emphasize the reduction of coverage errors (i.e. sampling errors). While this is
important in data quality, content errors have been substantially left unexplored as a means of
providing explanations for the low quality of data in those developing nations. In the Caribbean,
like many developing countries, males are socialized to be strong, brave, macho, not to show
emotion and not to display weakness, which explains their unwillingness to visit health care
institutions for mere checkups and speak openly about illnesses affecting them. The issue of a
motive that would account for their unwillingness to speak the truth about health matters is
therefore embedded in the (1) culture and (2) definition of illness and its interpretation regarding
their status. Males are sufficiently socialized to suppress weaknesses and within the context of
those societies, they must exhibit to females that they are strong, brave, and healthy which
explains their incorrect response related to health matters when asked by females. Yates, (cit.)
while not stating that those matters are exclusive to males, provided us with justification for low
data quality in the event that those issues are present in a sample.
There are several reasons that may explain the problems with the reliability and validity
of the health data in the present study. There is the case of social desirability bias where the
336
participants say whatever is required to get the approval of the interviewers. Some participants
do not even care about getting social approval - they just want to help the researcher so they tell
the interviewers what they think will help them. The possibility of collecting inaccurate data
increases when government agencies are involved because of the declining trust that citizens
have in government and public institutions in Jamaica. The data given may reflect a rejection of
governmental authority and status. The converse may also be true where a researcher who is
unaffiliated to a government agency receives accurate data. There is also the issue of the time
when the interviewer seeks to interview males because this can adversely affect the data
provided if the interviewers are competing with the important social and recreational times and
activities of the men.
Males are culturally competitive which makes for strength, dominance, physique and
endurance critical to composition25 that will be used to indicate to other males that they are
healthier, superior and stronger than the next competitive male. The challenge therefore is how
do researchers develop an approach to collect data from males in which they have no motive to
conceal the truth, and give accurate answers; and concurrently ensure that interviewers’ biases
can be eliminated, or minimized so that data quality is not reduced in the data collection process.
The challenge of mailing questionnaires to males in developing countries, in particular Jamaica,
is that the response rate would be very low and possibly so minimal that data analysis would
become problematic in providing pertinent information. The low reliability and validity of health
data collected from males poses much public health context as they experience the greatest
mortality, and not understanding their health is to further challenge public health practitioners
and policy makers to institute measures that will mitigate against their wellbeing.
337
To our knowledge a mail survey has never been conducted in Jamaica. We have
reservations about the likely success of this kind of survey as mentioned before, especially with
men who are already under-reporting their health status. Despite our reservations, the best way to
know if a mail survey will work is to do one. However, there should be in-built incentives to
increase the response rate. The pin number for a specified dollar amount of cell phone credit
should be sent to the cell phone of participants whose completed questionnaires are received in
the mail. The foregoing possibility highlights the fact that telephone surveys are also an
underutilized method of data collection in a country where cell phone usage is widespread. The
use of the cell phone has the advantage of allowing the participants to talk to the interviewers at
any place and time that is convenient to them, which should improve the response rate. The
validity and reliability would also be enhanced if the telephone interviewer is male.
Survey researchers more often than not do not use a mixed method research design.
Sometimes the discrepancies within a survey and between surveys are reduced by qualitative
methods such as individual interviews, focus groups and participant observation among other
methods. These methods will repopulate and enrich the text by writing back the individuals and
their characteristics into published research while maintaining the use of regular statistical
procedures.49 No one research method has a monopoly on reality so researchers should be
eclectic in their methodological approach while being mindful that a bundle of techniques is not
synonymous with intellectual sophistication and clarity.50
Reliability and validity can be also be enhanced and discrepancies explained by the
recognition that keeping things simple is best and doing less is more; there is greater clarity in
using fewer variables for more highly targeted research problems. Health researchers should also
be willing to question what was taught about the existing research methodologies and statistics.51
338
In addition, future health researchers should take account of the role of mediator and moderator
variables in influencing the relationship between the independent and dependent variables,
because the measured influence of mediator and moderator variables can sometimes explain the
discrepancies between surveys.52
Conclusion
The current study finds that there are many inconsistencies in health status data collected
from older men in Jamaica. Generally, while this work did not examine males in Jamaica, using
statistics from the Ministry of Health (Jamaica), it appears that the findings can be extrapolated
to males. The wider implications for these findings are the challenges of using self-evaluated
health data from males in planning their health, and that currently we do not understand men’s
health. In researching men’s health the question is not simply to validate the instrument, but
there are challenges in data collection that are unresolved, and which increase non-sampling
errors. Public health practitioners use self-reported health data from the national surveys and
other sub-national surveys and they should understand the challenges faced in interpreting health
data on males. Quality health data from males are not produced by them reporting on their health
status in national or sub-national surveys, as a part of this problem is the data collectors. Studies
have not examined the influence of sex composition on inaccuracies in health data, and this is
clearly causing some noise in health statistics. The quality of health data in Jamaica, and by
extension all nations, is influenced by the attitudes of respondents towards data collectors, the
circumstances surrounding the interviewer, the culture and the belief system of the respondents.
These continue to interface with health data quality and are still under-studied in the Caribbean
as a part of the explanation in understanding men’s health. This should be a public health
concern like epidemics, pandemics and sanitation, as poor quality data will affect policies,
339
programmes and the implementation of strategies in alleviating particular health concerns faced
by people, in particular males. Improvement in quality of life through better health care must
also integrate better quality data collection, as quality care requires accurate health data.
Jamaica is a middle-income developing nation in the Caribbean that has been collecting
data for centuries, and it boasts 20 years of collecting data on health status. Accompanying the
collection of data for a long time is the efficiency and accuracy in using statistical techniques for
gathering data. The Planning Institute and the Statistical Institute of Jamaica have continued to
modify their sampling frame in an effort to reduce sampling errors. The widening and updating
of the sampling frame have reduced coverage errors, as more people will be captured in national
sample surveys. The current study has found that there are still errors in the quality of the health
data collected from males, despite updating the sampling frame in 2007 in an effort to attain
completeness of data coverage. Despite the afore-mentioned errors, the quality of the national
survey, within the context of this study, is moderately good, and care should be taken in
interpreting health data for males owing to the inconsistencies which emerged from this study.
It is clear from the inconsistencies in the health data collected by the relevant agencies
that the reliability of self-reported health data from males will pose a problem in public health
planning. Sample surveys are used for teaching health care professionals; examining health care
staff requirements; community health care; planning health care; planning and determining the
future care of patients; evaluation of public health policies; health care interventions; the
construction of community centres, hospitals and public clinics; and clinical and health service
provisions. Then there are two other issues that emerged from the present findings, firstly, as
dichotomizing self-evaluated health for males loses some of the original information, and
secondly, that a sample of St. Catherine is not the same as sampling the nation, and so a sample
340
from the parish of St. Catherine does not reliably reflect the detailed characteristics of the wider
Jamaican population. Thus, care should be taken in the usage of sub-national samples to
generalize about a population and more so when it comes to data collected from males in regards
to their health. Clearly, there are inconsistencies in the health data collected from men in surveys,
and this needs to be factored into their health intervention, and planning for their health status.
These findings can inform further surveys, and should stimulate an approach of how to collect
data from males on their health status. If public health is to rely on research in order to
effectively implement and attain its objectives, the data collected should be reliable and valid,
and the current findings must be taken into consideration in aiding the process.
Disclosures
The authors report not conflict of interest with this work.
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 (Jamaica Survey of Living Conditions, 2007) available for use in this study.
341
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Table 14.14.1. Health and curative care visits: 2000-2007
Year Health Care Visits Curative Care Visits
Male Female Female: male Male Female Female: male
2000 30.5 69.5 2.3:1 35.0 65.0 1.9:1
2001 30.6 69.4 2.3:1 34.6 65.4 1.9:1
2002 30.3 69.7 2.3:1 34.2 65.8 1.9:1
2003 30.3 69.7 2.3:1 33.5 66.5 2.0:1
2004 30.2 69.8 2.3:1 32.9 67.1 2.0:1
2005 30.4 69.6 2.3:1 33.3 66.7 2.0:1
2006 30.4 69.6 2.3:1 33.5 66.5 2.0:1
2007* 30.5 69.5 2.3:1 33.6 66.4 2.0:1
Figures were computed by Paul A Bourne from Jamaica, Ministry of Health (Jamaica) Annual
Report 2004 and 2006
*Preliminary data from the Jamaica Ministry of Health were used to compute those percentages
and ratios.
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Table 14.14.2: Proportion of Survey (Sample) vs. Proportion of Population Age Group Survey 2001 Census (St.
Catherine) 2001 Census (Jamaica)
(yrs). n % n % N %
55-59 469 23.45 6577 26.7 38645 23.9
60-64 413 20.6 5179 21.1 31828 19.7
65-69 374 18.7 4391 17.8 28901 17.9
70-74 345 17.2 3594 14.6 24856 15.4
75-79 189 9.45 2402 9.78 17711 11.0
80+ 210 10.5 2399 9.77 19552 12.1
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Table 14.14.3. Descriptive characteristic of samples: Sub-national and National surveys Characteristic Sub-national survey
(i.e. Study 2) National survey
(i.e. Study 1) n = 2,000 % n = 483 %
Area of residence Urban 981 49.1 227 47.0 Rural 1019 51.0 256 53.0 Age group (in years) 55-59 469 23.5 120 24.8 60-64 413 20.7 87 18.0 65-69 374 18.7 88 18.2 70-74 345 17.3 68 14.1 75-79 189 9.5 61 12.6 80+ 210 10.5 59 12.2 Marital status Single 686 34.3 150 31.8 Married 894 44.7 217 46.1 Separated 112 5.6 14 3.0 Common-law 136 6.8 49 10.4 Widowed 172 8.6 42 8.7 Household head Yes 1763 88.2 376 77.8 No 237 11.8 107 22.2 Self-rated health status Excellent (or very good) 357 19.0 61 12.9 Good 1038 55.4 177 37.3 Fair (or moderate) 480 25.6 149 31.4 Poor 0 0.0 75 15.8 Very poor 0 0.0 12 2.5 Self-evaluated diagnosed illness Cold - - 14 8.5 Asthma 5 0.3 8 4.8 Diabetes mellitus 129 6.5 24 4.8 Hypertension 193 9.2 24 14.5 Arthritis 20 1.0 46 27.9 Diarrhoea - - 2 1.2 Other: Unspecified - - 22 13.3 Other: Cancer 336 16.8 Heart disease 106 5.3 Kidney/bladder 118 5.9 Prostate problem 143 7.2
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Table 14.14.4. Characteristic of samples: Sub-national and National surveys Characteristic Sub-national survey
(i.e. Study 2) National survey
(i.e. Study 1) n = 2,000 % n = 483 %
Medical care-seeking behaviour Yes 914 45.7 106 65.5 No 1086 54.3 58 34.4
Sought medical care In less than 12 months 289 31.6 NS NS In 12 to 35 months 356 38.9 NS NS In 36 and beyond months 269 29.4 NS NS
Provision of care Home remedy 155 44.3 66 13.7 Public clinic 124 35.4 73 15.2 Hospitals 40 11.4 149 30.8 Private doctor 31 8.9 195 40.3
Self-evaluated illness Yes 350 17.5 162 34.3 No 1650 82.5 310 65.7
NS – Not stated (i.e. was not collected in this Study)
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Part III
Data Quality: Practices, Perspectives and Traditions
349
CHAPTER
15 Practices, Perspectives and Traditions Data quality is a philosophy, theory, principle, legacy which underpins science and the pillows
upon which intellectual inquiry are based. The science is science, because of the processes,
systematic rigors, logic and verification of data. It is the final estimates, results and/or findings
that are lauded as science, but the underlying issues are hidden in the systematic processes that
validate the truth. The methods of discovering the truths are hinged on the scientific methods
applied to the observations, events and subjects (i.e. data sources). Each day we are
systematically processing data, before decisions are taken. The better the information, the less
likely it is to make errors. There is a fundamental assumption in the aforementioned issue;
quality information is all we need to make more accurate decisions. This is simplistic and naïve.
The world is continuously revolving around data, data and more data, these are quality
data. Every time we engage in thinking, we are process data. The knowledge that emerged from
the data sources is limited to the quality of the data. Data collection, therefore, holds the key to
understanding different cosmology, providing clarifications for all epistemologies and is the
primary source for scientific discoveries. While it is simply undeniable that NASA, the defense
force, meteorology, judiciary, politics, and banking and insurance industry rely on data sources,
the quality of the data source is equally important as the estimates, outcomes and purpose of the
data gathered. Embedded in data usage is the assumed accuracy of the material that is sometimes
overlooked by people, because of the past contribution of the data sources. Apart of the rationale
for the justification of the blind usage of data from ‘credible sources’ is low statistical skills of
some researchers. Some people who use data do not the prerequisite statistical techniques to
identify errors, and correct them. As such, they are slaves to alleged credibility of the data
sources instead of using the scientific method of data gathering and data verifications.
Many people have not done an introductory course in statistics and/or demography,
making them unexposed to the techniques (or tools) for indentifying and correcting errors in
data. Within the limitations of their statistical skills, it is easier for them to rely on the
establishment for data quality than validating the data as well as the estimates. In many
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instances, the traditional accuracy of data collection are determined by tradition, authority,
credibility and reputation of the data gather instead of using the tools that are available to
demographers and statisticians for aiding the quality of data. This does not imply for one minute
that unreliable data have been used by non-demographers and/or statisticians, but that
trustworthiness is used as an avenue in the pursuit of truths, because some people give into the
establishments (i.e. authority, credibility, traditions, and titles) as against the scientific methods
in process of knowledge building. It follows that the questioning of cosmologies, epistemologies,
traditions and authority are healthy in validation of things or the creation of new paradigms.
Truth cannot hide for testing, validation and question and still claim truth, fact or soundness. It is
the rigorous testing of issues that establish truths, not the failure to systematic question ‘What
is?’
It has been repeatedly proven facts change because of new data that justify a different
knowledge. With the likelihood of modifying a paradigm or the emergence of an alternative
paradigm, truth as continuously altered and recreated to meet the new data. Data validation
cannot be taken with scant regards as the science of everything is hidden in the data, making
quality data a priority and not an afterthought. The quality of the data speaks to the quality of the
estimates, the contribution to knowledge and the unbiased fear will all for the opening up of the
data, data processes, method and estimates to scrutiny. The same quality of time that is invested
in the estimates must be employed into the data collection, validation, and testing. The coverage
and content of the data must be open to scrutiny, for others to confirm, refute or question the
accuracy of the estimates.
The methods of evaluating data quality are more taught to economists, demographers,
epidemiologists and to a lesser extent undergraduate student, which limits the likeliness of
people investing in data quality as they spend in data collection, for policy planning. As a result
of the mathematical complexities in errors identification and correction, the average person is
oftentimes ignorant of the techniques but may have some intuition that data quality must be
examined with the same ferociousness as the estimates. The mere assumption of credibility,
authority, establishment, cosmology and credentials cannot be used to determine the quality of
data, thereby making people vulnerable to processes of science and not relying on the sideshow.
Owing to the aforementioned realities, in addition to the blind beliefs, there is a high reliance
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‘common sense’, tradition, establishment (including universities, WHO, PAHO, NASA, World
Bank) as the ultimate source in reliable estimates that some people fail to think that those
agencies’ estimates can be questioned for accuracy.
Science is established on data, not on credence, character, status, past traditions, authority
or fame. It is also a gradual development hold onto the tenets of verification, questioning and
logic. The potency of the estimates (i.e. results or outcome) is fashioned by the quality of the
data, data gathers, and rigors adhere to during the scientific process of inquiry. The practice of
researchers is to questioning the estimates, data sources, data system, apparatus, method, and
biases that are likely to result in a particular outcome. The practices, therefore, is understand the
set of propositions that led to the outcome, question the scientificness of the process and any
errors that can create erroneous findings. The perspective must be that the data cannot be flawed
but the estimates are accurate or vice versa.
Whether data collection is via way of census, sample, retrospective, longitudinal, cross-
sectional or other approaches, if you collect data from human subject based on their perspective
and/or recall, because the human element is present there is a probability of error in the data,
estimates and predictions. It is this perspective that justifies data quality inquiry. There is a long
tradition that errors can be present in national registration systems (i.e. births, deaths, marriages,
migrations) as they relate to coverage (completeness), then content errors are likely, inaccuracy
in data because of the quality of human recall or any other external barriers. Empirical evidence
supports content errors in data collection. For centuries, demographers have been examining age
values in sample and/or census to determine the probability of content errors. This is also the
case in Jamaica. Demographers noted that there are age misreporting in census and national
survey data in Jamaica, but limited content errors to age data.
The majority of data collected to aid social scientists are from recall of people,
memorization, perspectives, and responses of the subjects. The primary assumption here is that
people recall is good. Researchers have shown that the recall beyond a particular time interval
may be different. The use of any method to collect data on recall is susceptible to errors (i.e.
content errors). Then when we ask people to recall their health, health conditions, and other
sensitize matters, how sure are we that they accurate report the data and that the data are not
erroneously stated, from which policies are frames.
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Jamaica is among Caribbean nations with a long history of high quality census and
national survey data, but this begs the question ‘Can we further reduce the errors?’ This
proposition does not eliminate the probability of errors, but that errors are present in current
estimates. Such recognition accepts the biasness, subjectivity and likely errors that are associated
with human recall, making errors identification and correction a scientific pursuit as the search
for truths. The trust is bordered by time and quality data. Quality data here refers to good recall,
good interviewers, good sampling frame, and an inbuilt mechanism to valid the entire process.
This volume has empirically showed that data quality have variations, clarifications,
amendments that influence estimates. The evidence is in that the quality of health data for female
is higher than that for males, suggesting the degree of caution in interpreting the estimate of
health data from males. Many recommendations were forwarded to address the challenges in
health data for males; these can be incorporated into the research process to enhance the quality
of the data and the resulting estimates. In data gathering, the human element is such that it can
erode the positives of low coverage errors (completeness in sample selection).
The benefits of using secondary data hold many negatives, which create challenges in
accuracy of data. This raises the question of validity and reliability of data accuracy, knowledge,
facts, truths, and cosmologies. Even in the validity of general data source as it relates to health
matters, there are emerging issues about dichotomization, cut-offs and conceptualization of
health from particular perspectives of Jamaicans – being male or female. Clearly Jamaican males
and females do not have the same world view on health, affecting the data given and outcomes.
These differentials must be, therefore, incorporated into interpreting health estimates and policy
formulations.
Any thinking that supports the neglect of understanding the views of the studied
population without being cognizant of the worldview is not gradually pursuing truth
investigation, but it is on the path of indoctrination as was the case in time of religious
cosmologies. The information of this volume can be used as a panacea to increase the estimates
of policy formulation that rely on health recall data.
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Suggested Readings
Sampling, Sampling Errors, and Other Errors in Survey Data
Biemer, P.A., R.M. Groves, L.E., Lyberg, N.A. Mathiowetz, and S. Sudman (Eds). 1991.
Measurement errors in surveys. New York: Wiley-Interscience.
Cox, P.R. 1976. Demography, 5th ed. Cambridge: Cambridge University Press.
Fink, A (Ed). 1995. The survey kit. Volumes 1-9. Thousand Oaks, CA: Sage.
Groves, R.M. 1989. Survey errors and survey costs. New York: Wiley-Interscience.
Kish, L. 1965/1995. Survey sampling. New York: John Wiley and Sons.
Preston, S.H., P. Heuveline, and M. Guillot. 2001. Demography: Measuring and modeling
population processes. Oxford: Blackwell Publishers.
Siegel, J.S. 2002. Applied demography: Applications to Business, Government, Law, and Public
Policy. Chapter 4. San Diego: Academic Press.
Siegel, J.S., D.A. Swanson (Eds). 2004. The methods and materials of demography, 2nd ed. San
Diego: Elsevier Academic Press.
ABOUT THE AUTHOR
Paul Andrew Bourne is the Director of Socio-Medical Research Institute, Jamaica. He has co-written
monographs on Corruption, Political Culture in Jamaica, Other subjects, and authored books on Growing
Old in Jamaica, Analyzing Quantitative Data, Understanding Health and Health Measurement, and
Sexual Expressions in Jamaica. Dr. Bourne has authored and co-authored plethora of journal articles on
health status, health measurement, sexual and reproductive health, and ageing matters. His works have
been published in top journals, and recently his thrust has been on data quality in national surveys,
particularly in Jamaica.