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HEALTH DEMOGRAPHY

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HEALTH DEMOGRAPHY Powered By Docstoc
					Health Demography




Paul Andrew Bourne
Health Demography




        i
      Health Demography


              Paul Andrew Bourne
Director, Socio-Medical Research Institute (Formerly, Biostatistician, Dept. of
Community Health and Psychiatry, Faculty of Medical Sciences, The University of
the West Indies, Mona, Jamaica)




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©Paul A. Bourne, 2011

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


National Library of Jamaica Cataloguing Data


Health Demography



Includes index

ISBN

Bourne, Paul Andrew


All rights reserved. Published, 2011

Cover designed by Paul Andrew Bourne




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                                      Preface
For many decades, the study of demography excluded the sub-discipline of health, health
demography. Demography which concerned itself with the study of human populations (size,
distribution, composition of human population, fertility, mortality, migration, data source, life
tables, stable and stationary populations, standardization, morbidity, age-sex composition and
population growth) did not see it fitting to include health. Demographic literature and
conferences avoided the study of health, although extensive examinations were made on
mortality and morbidity. With sub-disciplines like mortality and mortality, health should have
been a theme or sub-theme therein, but this was not the case.
        The issues of mortality and morbidity are critical components of health. Those themes
cannot be hidden from each other as the absence of morbidity was traditionally viewed as health,
and mortality was used to compute life expectancy which is an indicator of the health status of a
population or people. Based on the World Health Organization’s (WHO) conceptualization of
health (“Health is state of complete physical, mental and social wellbeing, and not merely being
the absence of disease or infirmity”1), demographers like other health scientists must recognize
the importance of death and illness in health discourses as well as the broaden definition of
health. However, much of the works on health have been widely expounded upon by non-
demographers. Health cannot be divorced from mortality and/or morbidity, making health
demography pivotal in mortality and morbidity studies.
        Despite the emergence of the biopsychosocial model which (George Engel) is an
alternative paradigm to the dominant traditional medical model (etiology of diseases or germ
theory), the latter model is well established in medical literature because of its scientific basis
and its usefulness in addressing particular health conditions. The medical model (or biomedical
model) is a causal one in explaining disease causing pathogens, which focuses on acute rather
than chronic conditions. In Jamaica, life expectancy has more than doubled since the 1900s. This
doubling of the life expectancy means demographic transition and health care transformation.
The major transformation is the shifting from acute (communicable) to chronic conditions (non-
communicable diseases). The shift accounts for demographic transition, the ageing of the
population (shift of the population from younger to older ages). With the demographic transition
and health care transformation arising from epidemiological transition (disease transitions), the
biomedical model is well suited as its emphasis is on diseases. From the limitation of the
biomedical model to address non-physical conditions, illness (morbidity) cannot be the focus of
demographers. Health should not be measured other extreme of the illness pendulum, it should
be conceptualized in a broader scope including the social, environmental, economical and
psychological factors. Health in the study of demography cannot be about illness, health
demography must be all-inclusive of the biological, social, economical, environmental and
psychological conditions.
        Health demography which is the application of demographic methods to the study of
health and health behaviour, therefore, is demography like mortality and morbidity.
Demographers for centuries have been studying mortality, morbidity, and building model to
explain mortality (life tables), yet the discipline excluded health demography. It was during the
1980s that health demography emerged as sub-discipline in the field of demography. Despite this

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reality, in the Caribbean, particularly in Jamaica, the majority of studies on health demography
have been conducted by non-demographers including sociologists, and epidemiologists.
         In the 1950s, Mortimer Spiegelman (demographer) wrote a book entitled ‘Introduction to
Demography’ in which a chapter (Chapter 7) examined ‘Health Statistics’. He recognized the
importance of health in the discourse of demography, yet most the chapter was spent examining
illness. Although in paragraph 4 (section 7.1.1), he introduced the definition of health as was
offered by the WHO in the Preamble to its Constitution (in 1946), the emphasis was on
morbidity statistics (chronic and acute conditions) as well as disability. For some unknown
reason when health is mentioned by many scholars, they tend to revert to illness, negative health.
There is no denial that illness, curative care and morbidity statistics are crucial in health
examination, but that this is a miniscule part in the health discourse. Based on the definition of
health forwarded by WHO, health is synonymous with wellbeing and not morbidity (acute and/or
chronic conditions). Why then did Spiegelman dedicate a chapter on health to morbidity? Even
in a section entitled health status (section 7.7, page 197), he discussed ‘acute conditions’ (pages
197-199), ‘chronic conditions’ (pages 199-200) and ‘disability’ (pages 200-2005). Like many
academics before him, Spiegelman followed the cosmology of natural scientists examined the
clear causal linkage between illness and particular pathogens instead of venturing to the broad
purview of health, wellbeing and not the mere absence of illness.
         Other demographers like Donald T. Rowland (in Demographic methods and concepts,
published in 2003), Peter R. Cox (in Demography, published in 1950), H.S. Shryock, J. S.
Siegel, and Associates (in The methods and materials of demography, published in 1976) and
Newell (In Methods and models in demography, published in 1988) did not pen a single chapter
on health and/or health demography. Recognizing the paucity of information from demographers
on health, Jacob S. Siegel and David A. Swanson (in The Methods and Materials of
Demography, second edition, published in 2004) included a chapter on health demography
(written by V Lamb and Jacob Siegel). In this Chapter (Chapter 14, pages 341-370), Lamb and
Siegel expanded on negative health as forwarded by Spiegelman to 1) problems with health data
sources, 2) measures of health status, functioning, and use of health statistics, 3) reproductive
health, 4) mortality and morbidity, and 5) general analytic devices and health projections models.
         Clearly, the emergence of health demography cannot be denied by demographers. Lamb
and Siegel forwarded that health studies can use multivariate logistic regression (dichotomous
dependent variable), proportional hazards model, and meta-analysis. Embedded in Lamb and
Siegel’s work is the use of complex statistical model to examine health status of the population.
This suggesting that there will be exogenous and endogenous variables. Health which is a
multidimensional variable is best examined using econometric model, to identify the various
exogenous factors, while health is treated as an endogenous variable.
         Michael Grossman (an economist) used econometric model to establish exogenous
variables that influence health status, with health being an endogenous variable. Among the
exogenous variables were many demographic factors (including age, sex, marital status, social
class, education). Demography which is concerned about population matters, having recognized
the importance of modeling health status, must model health of the population, and sub-
populations, demographic models of health. Using econometric model, we can identify a system
of simultaneous linear equation that link health changes with germane factors, exogenous
variables. Yet Caribbean demographers lag behind in the use of demographic tools to health data.


                                                v
         Traditionally, demographers have sought to analyze and provide information on health
from the perspective of mortality and/or morbidity. From the mortality tenet, they have captured
and measure health status, by using life expectancy. Life expectancy may be an adequate
indicator of length of life. But, this is from a biomedical perspective. Such a yardstick of health
status is not in keeping with the conceptual definition furnished by the World Health
Organization. It should be noted that this conceptual definition which is in the Preamble to the
constitution of the WHO which was signed in July 1946 and became functional in 1948,
according to one scholar, from the Centre of Population and Development studies at Harvard
University, it is mouthful of sweeping generalization, that is difficult to attain, and at best it is a
phantom. (Bok 2004)
         While we accept that the definition of health as offered by the WHO is comprehensive, a
mouthful, and an idealistic state, it is not elusive. Based on WHO’s conceptualization of health,
it is an end as well as an ingredient to other product. As an end, health can be model in order to
understand its exogenous factors. This book recognizes the discourse on the conceptualization of
health, understand its importance and so will furnish a discussion on wellbeing and health
(Chapters 1 & 2). Is there a shift taking place in demography, as the journal entitled
‘Demography’ (Demography 1997) published an article by Smith and Kington on the exogenous
factors of health status? The maturing of the field of health demography denotes that is it time
for this to be the case in Caribbean studies.
         With the increased ageing of the world’s populations, which is more so a case in the
developing regions, its implications are multi-fold. This means changes, shifts and new direction
in (i) dependency ratios; (ii) employability and labour force; (iii) pension packages; (iv)
mortality patterns and hospitalization care; (v) longevity – life span and life expectancy; (vii)
health status and family health-care – as the elderly have a wide spectrum of health challenges,
and the health costs faced by this age cohort are normally higher, (viii) social and economic
development (including building and other infrastructure) and (ix) demographic and
epidemiological transition. Thus, there is a need for demographers to utilise demographic
techniques to study the health status of population, particularly among older adulthood (60+
years old).
         This volume consists of 25 chapters. I have introduced happiness in this text in keeping
with the expanded definition of health. Health which is a subjective measure (using WHO’s
definition of health) cannot be discourse with examining other subjective indexes such as
happiness and life satisfaction (chapters 20 & 21). Initially when happiness was put forward by
Ed Diener as a measure of wellbeing, it was rigorously opposed by some scholars as subjective
and so cannot be used to measure health or wellbeing.
         Happiness, life satisfaction, and health status are among some of the subjective indexes
used to evaluate health (or wellbeing) of an individual, community or population. Subjective
indexes cover a wider gamut of an individual’s life compared to diagnosed health conditions,
morbidity, reproductive health and life expectancy. Jamaica has been collecting data since 1989,
and this has been used to measure health of the population, gender of the participants and health
within area of residence. The use of illness to measure health is negative and does not cover
health. Happiness therefore like life satisfaction and health status provide a comprehensive
coverage of people’s quality of life than ill-health. The use of objective indexes such as
diagnosed illness, gross domestic product; life expectancy and mortality are among measured


                                                  vi
that are said to be limited in scope, and justify the usage of the subjective indexes by some
scholars.
         I believe that self-rated health is just as subjective as happiness and life satisfaction. It
follows therefore that we (demographers) must examine happiness and life satisfaction as a part
of health demography. If happiness is synonymous with life satisfaction and self-rated health
(used as health thorough this text), then demographers should not shy away from evaluating
different definitions of health and factors that influence the conceptualization of health. Health is
an endogenous variable like happiness and life satisfaction. As such, I am forwarding that
happiness and life satisfaction should be incorporate into health demography. Chapters 20 and 21
evaluate health, life satisfaction and health of older adult men in Jamaica. Although this cannot
be stated as generalizable to the population, it provides an understanding of the three endogenous
variables that are important in the health discourse, health demography.
         Chapter 23 examines a subjective index of health (self-reported illness) and an objective
index (life expectancy), and 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).
This is validating the use of illness (or morbidity) by academics in measuring health.
         The penultimate chapter (Chapter 24) evaluates the statistical associations between 1)
happiness, 2) life satisfaction and 3) health status. Happiness is the degree to which people judge
their overall quality of life as favourable. According to Konow and Earley, happiness was
correlated with unemployment, positive and negative life-events, social networks and intimate
friendships. In chapter 24, happiness was strongly correlated with life satisfaction as both
subjective indexes are broader than health status and incorporate many aspect of life. Hence, the
finding that very happy older men are highly likely to be very satisfied with life and vice versa
suggests that heart disease, hypertension, digestive disorders and headaches are temporal and as
such in assess ones quality of life, they are lowly value and do not contribute to this overall
measure of wellbeing.
         Empirical evidence exists in this volume which showed that health is still narrowly
conceptualized by Jamaicans (self-reported illness), and that happiness and life satisfaction is
more comprehensive measures that health (or illness). Within the context of the broadened
definition of health, illness should not be used to evaluate health or vice versa. Using a sample of
older adult men, it appears that happiness and life satisfaction are more composite that health
(self-rated). Within the Jamaican context, health demography, therefore, must include
happiness and life satisfaction.
         This book is written primarily for audience in health care, academic and private sector
demographers, students in demography, health care administrators and social workers who seek
to understand health as an endogenous variable and demographic and other factors that account
for changes in health status of peoples in Jamaica.
         The etiology of disease is a component to health and health care; it is not the end because
health must include the non-physical components. This book recognizes the alternative paradigm
(biopsychosocial model), forwards the various chapters on illness and the wider
conceptualization of health. The volume is written partly as a result of the paucity of health
demography in the Caribbean (including Jamaica) and need to commence the discourse on health
demography in Jamaica.
                                                                           Paul Andrew Bourne
                                                                                   February, 2011
                                                 vii
                       Acknowledgement

Like many other authors, I am indebted to many people who contributed in different ways to the
completion of this book. These individuals are 1) Mrs. Evadney Bourne, 2) Kimani Bourne, 3)
Kerron Bourne, 4) Paul Andrew Bourne, Jnr, who stayed up with me on countless nights, and
longer on Saturdays and Sundays. Ms. Neva South-Bourne, whose tireless efforts and endless
patience in proofreading some of the chapters as well as Mrs. Cindi Schofield. I am also indebted
to the Derek Gordon Databank, University of the West Indies, Mona (Jamaica) that made the
dataset available from which many of the chapters emerged. The majority of the chapters are
published works in different journals, and I am grateful for their permission to use the materials
in this book (North American Journal of Medical Sciences, Health, Current Research Journal in
Social Sciences, and Journal of Clinical and Diagnostic Research). Finally, I would like to thank
all my co-authored who wrote different articles with me.




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                                   Contents
Preface                                                                                   iv

Acknowledgement                                                                          viii

   1. A conceptual framework of wellbeing in some Western nations                          1

   2. Health Measurement                                                                  23

   3. Why demographers should study wellbeing of the aged?                                49

   4. Wellbeing Discourse                                                                 56

   5. Variations in social determinants of health using an adolescence population:

          By different measurements, dichotomization and non-dichotomization of health    107

   6. Self-reported health and health care utilization of older people                    134

   7. Socioeconomic correlates of self-evaluated health status of elderly with

          diagnosed chronic medical conditions, Jamaica                                   174

   8. Child Health Disparities in an English-Speaking Caribbean nation:

          Using parents’ views from a national survey                                     201


   9. Health Status of Patients with self-reported Chronic Diseases in Jamaica            230

   10. Retesting and refining theories on the association between illness, chronic

          illness and poverty: Are there other disparities?                               260

   11. Demographic shifts in health conditions of adolescents aged 10-19 years,
       Jamaica: Using cross-sectional data for 2002 and 2007                              285

   12. Determinants of quality of life of youths in an English-speaking Caribbean nation 309

   13. Self-rated health of the educated and uneducated classes in Jamaica                330



                                                   ix
14. An Epidemiological Transition of Health Conditions, and Health Status of the

    Old-Old-To-Oldest-Old in Jamaica: A comparative analysis                              355

15. Health of females in Jamaica: using two cross-sectional surveys                       385

16. Gender differences in self-assessed health of young adults in an

    English-speaking Caribbean nation                                                     405

17. Disparities in self-rated health, health care utilization, illness, chronic illness

    and other socio-economic characteristics of the Insured and Uninsured                 446

18. Self-evaluated health and health conditions of rural residents in a developing
    Country                                                                               478

19. Health of males in Jamaica                                                            506

20. Happiness among Older Men in Jamaica: Is it a health issue?                           535

21. Happiness, life satisfaction and health status in a Caribbean nation:

    Using a cross-sectional survey                                                        567

22. Decomposing Mortality Rates and Examining Health Status of the Elderly in

    Jamaica                                                                               600

23. The validity of using self-reported illness to measure objective health               622

24. A cross-sectional survey of the health status, life satisfaction and happiness

    of older men in Jamaica - associations between questionnaire scores                   642

25. The changing faces of diabetes, hypertension and arthritis in a

    Caribbean population                                                                  666




                                                x
CHAPTER 1



A conceptual framework of wellbeing in some Western nations


The aim of this study is to examine and highlight the narrow definition of wellbeing that still
exists in some contemporary Western societies. This definition is in keeping with the biomedical
model that views the exposure to specific pathogens as the cause of diseases in organisms. Such
an approach began during the 130ce to 200ce in Ancient Rome, and despite the efforts of the
WHO in 1946 to expand the concept, health in Caribbean societies and in particular Jamaica is
still substantially seen as the ‘absence of diseases’ or dysfunctions in the body, which is what is
used to indicate wellbeing. Health and wellbeing are multidimensional constructs and so there
is a need for academics to begin vociferously working to encapsulate an operational definition of
wellbeing that can be used in the images of wellbeing and patient care. This paper presents and
examines a conceptual framework on health (or wellbeing) from a biopsychosocial perspective,
as well as including an environmental perspective as this is in keeping with an expanded
conceptualization of health as forwarded by the WHO in its constitution. Within the discourse,
arguments will be presented on both subjective and objective measurements of wellbeing.



Introduction

The traditional view of Western Societies is that health is conceptualized as the ‘absence of
diseases’. This approach is both narrow and negative in scope as regards health. According to
one school of thought, the aforementioned conceptualization of health emphasizes the absence of
some disease-causing pathogens, and not health (Longest, 2002; Brannon, and Feist, 2007; Rice,
1998). Such a perspective is in keeping with the traditional biomedical model that views the
exposure to specific pathogens as the cause of diseases in organisms. This began during 130ce
to 200ce in Ancient Rome and despite the efforts of the WHO as early as 1946 to expand this
construct (WHO, 1948), health in Caribbean societies, in particular Jamaica, is still substantially
viewed as the ‘absence of diseases’ or dysfunctions, with wellbeing being the opposite of that
state. Humans are multifaceted and so any conceptualization of health that seeks to measure an
aspect of their existence cannot be uni-directional or bi-directional, as health, wellbeing and
wellness are multidimensional, which would be in keeping with the complexities of people.


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Lynch (2003) opines that everything that we do, feel, think and experience interfaces with our
health; hence, wellbeing cannot be operationally defined solely based on functional limitation
because of pathogens, as many events affect the quality of life outside of that space. Thus, this
paper recognizes the need for the discourse, as it will allow for a better measurement of the
concept. In addition to health measurement, this paper seeks to broaden the scope of the
determinants of health, and in the process help policy-makers to understand this concept. In a
nationally representative survey of Jamaicans, using observational data on some 2,320 elderly
people (ages 65+ years), Bourne (2007) finds 12 factors that determine the wellbeing of elderly
Jamaicans. Bourne’s wellbeing model is different to that presented in many other studies, as he
uses a combination of physical dysfunctions, income and material possessions to conceptualize
wellbeing.   Bourne’s overall model explains 40.1% of the variance in wellbeing. Again,
wellbeing is influenced by more than just biological conditions. However, one scholar [Bok,
2004] opined that the WHO’s operationalization of health (or wellbeing) is too broad and by
extension difficult to measure. This begs the question, why have we reverted to the ancient
conceptualization of wellbeing (or health) and its images to guide patient care? Hence, what are
the different discourses on wellbeing? Therefore, the paper presents and examines a conceptual
framework on health (or wellbeing) from a biopsychosocial perspective, in addition to including
the physical environment in the discourse as well as providing other images within the health
discourse, with the aim of aiding health outcome research and patient care.


Result and Discussion

       Wellbeing defined

       The concept of health according to the WHO is multifaceted. “Health is the state of
complete physical, mental and social wellbeing and not merely the absence of disease or
infirmity” (WHO, 1948).      From the WHO’s perspective, health status is an indicator of
wellbeing (Crisp, 2005). Wellbeing for some, therefore, is a state of happiness – positive feeling
status and life satisfaction (Easterlin, 2003; Diener et al., 1985; Diener, 1984) satisfaction of
preferences or desires, health or prosperity of an individual (Diener, and Suh, 1997a, b; Jones,
2001; Crisp, 2005; Whang, 2006), or what psychologists refer to as positive effects. Simply put,
wellbeing is subjectively what is ‘good’ for each person (Crisp, 2005).          It is sometimes


                                                2
connected with good health. Crisp offered an explanation for this, when he said that “When
discussing the notion of what makes life good for the individual living that life, it is preferable to
use the term ‘wellbeing’ instead of ‘happiness” (Crisp, 2005).

       Ergo, the term wellbeing is used interchangeably with words such as ‘happiness’, ‘life
satisfaction’, and ‘welfare’ by a number of researchers and/or people in intelligentsia (Diener,
1984; Easterlin, 2003; Veenhoven, 1993). While some scholars argue that happiness and life
satisfaction are but a fraction of wellbeing, what is embedded in Diener and Easterlin’s usage of
those terminologies instead of wellbeing aptly shows that, within the context of a
multidisciplinary global market place in which people must operate, the quality of life that
people enjoy (or do not enjoy) must be understood before the goals of policy-planning and
decision-making on the desire to improve the welfare, quality of life and/or standard of living of
a people can materialize.

       Happiness, according to Easterlin (2003) is associated with wellbeing, and also with ill-
being (for example depression, anxiety, dissatisfaction). Easterlin (2003) argued that material
resources have the capacity to improve one’s choices, comfort level, state of happiness and
leisure, which militates against static wellbeing within the context that developing countries and
developed countries had at some point accepted the economic theory that economic wellbeing
should be measured by per capita Gross Domestic Product (GDP) – (i.e. total monetary value of
goods and services produced within an economy over a stated period per person). Amartya Sen,
who is an economist, writes that a plethora of literature exists showing that life expectancy is
positively related to Gross National Product (GNP) per capita. (Anand and Ravallion, 1993; Sen,
1989). Such a perspective implies that mortality is lower whenever an economic boom exists
within the society and that this is believed to have the potential to increase development, and by
extension the standard of living. Sen, however, was quick to offer a rebuttal in that data
analyzed have shown that some countries (i.e. Sri Lanka, China and Costa Rica) have had
reduced mortality without a corresponding increase in economic growth (Sen, 1989), and that
this was attained through other non-income factors such as education, nutrition, immunization,
expenditure on public health and poverty removal. The latter factors undoubtedly require income
resources, and so it is clear that income is unavoidably a critical component in welfare and
wellbeing. Some scholars believe that economic growth and/or development is a measure of


                                                  3
welfare (Becker et al., 2004).

       Therefore, those studies on economic wellbeing were able to offer a plethora of answers
to national governments on the health status of the people, or the wellbeing and/or illbeing of
their citizens. No policy formulation on improving the quality of life of the citizens of a
particular space should proceed without firstly unearthing the ‘real’ determinants of wellbeing.
From Crisp’s perspective (2005), wellbeing is related to health and the strength of those
associations, and secondly planning requires information that is made available by research. Is
traditional economists’ operationalization of wellbeing still applicable in contemporary societies,
knowing it to be purely objective?

       If happiness is a state of wellbeing, then if we were to impute depression, anxiety, stress,
and illness and/or physical incapacitation, spirituality and environment within the objective
measurement of wellbeing, a more holistic valuation would be reached. With the inclusion of
subjectivity conditions in the measurement of wellbeing, we come closer to an understanding of
people’s state of wellness, health and quality of life, as better nutrition, efficient disposal of
sewage and garbage, and a healthy lifestyle also contribute to health status (i.e. wellbeing). It
should be noted that the biomedical model that is objective, conceptualizes health as the absence
of diseases. This leads to the question, are any of the following diseases – (i) depression, (ii)
stress, (iii) fatigue, and (iv) obsession? Hence, an issue arises, does the lack of objectivity mean
it should be accepted with scepticism?

       In order to put forward an understanding of what constitutes wellbeing or illbeing, a
system must be instituted that will allow us to coalesce a measure that will unearth peoples’
sense of the overall quality of life from either economic-welfarism (Becker et al. 2004) or
psychological theories (Diener et al., 1997; Kashdan, 2004; Diener, 2000). This must be done
with the general construct of a complex man. Economists like Smith and Kington, and Stutzer
and Frey as well as Engel believe that the state of man’s wellbeing is not only influenced by
his/her biologic state, but that it is always dependent on his/her environmental, economical and
sociological conditions. Some studies and academics have sought to analyze this phenomenon in
a subjective manner by way of general personal happiness, self-rated wellbeing, positive moods
and emotions, agony, hopelessness, depression, and other psychosocial indicators (Arthaud-day
et al., 2005; Diener et al., 1999; Skevington et al., 1997; Diener, 1984).



                                                  4
       An economist (Easterlin) studying happiness and income, of all social scientists, found an
association between the two phenomena (Easterlin, 2001a, b), (Stutzer and Frey, 2003). He
began with a statement that “the relationship between happiness and income is puzzling”
(Easterlin, 2001a: p. 465), and found that people with higher incomes were happier than those
with lower incomes – he referred to it as a correlation between subjective wellbeing and income
(Stutzer, and Frey, 2003). He did not cease at this juncture, but sought to justify this reality,
when he said that “those with higher incomes will be better able to fulfil their aspirations, and
with other things being equal, on an average, feel better off.” (Easterlin, 2001a: p. 472).
Wellbeing, therefore, can be explained outside of the welfare theory and/or purely on
objectification-objective utility (Kimball, and Willis, 2005; Stutzer, and Frey, 2003).

       Whereas Easterlin found a bivariate relationship between subjective wellbeing and
income, Stutzer and Frey revealed that the association is a non-linear one. They concretized the
position by offering an explanation that “In the data set for Germany, for example, the simple
correlation is 0.11 based on 12, 979 observations” (Stutzer, and Frey, 2003). Nevertheless, from
Stutzer and Frey’s findings, a position association does exist between subjective wellbeing and
income despite differences over linearity or non-linearity.

       The issue of wellbeing is embodied in three theories – (1) Hedonism, (2) Desire, and (3)
Objective List. Using ‘evaluative hedonism’, wellbeing constitutes the greatest balance of
pleasure over pain (Crisp 2005; Whang 2006: p. 154). With this theorizing, wellbeing is just
personal pleasantness, which postulates that the more pleasantries an individual receives, the
better off he/she will be. The very construct of this methodology is the primary reason for a
criticism of its approach (i.e. ‘experience machine’), which gave rise to other theories. Crisp
(2005), using the work of Thomas Carlyle, described the hedonistic structure of utilitarianism as
the ‘philosophy of swine’, because this concept assumes that all pleasure is on par.          He
summarized this adequately by saying that “… whether they [are] the lowest animal pleasures of
sex or the highest of aesthetic appreciation” (Crisp, 2005).

       The desire approach, on the other hand, is on a continuum of experienced desires. This
is popularized by welfare economics, as economists see wellbeing as constituting the satisfaction
of preference or desires (Crisp, 2005; Whang 2006: p. 154), which makes for the ranking of
preferences and assessment by way of money. People are made better off if their current desires


                                                 5
are fulfilled. Despite this theory’s strengths, it has a fundamental shortcoming, the issue of
addiction. This is exemplified by the possible addictive nature of consuming ‘hard drugs’
because of the summative pleasure it gives to the recipient.

        Objective list theory: This approach in measuring wellbeing lists items not merely
because of pleasurable experiences, nor on ‘desire-satisfaction’, but states that every good thing
should be included, such as knowledge and/or friendship. It is a concept influenced by Aristotle,
and “developed by Thomas Hurka (1993) as perfectionism” (Crisp, 2005). According to this
approach, the constituent of wellbeing is an environment of perfecting human nature. What goes
on an ‘objective list’ is based on the reflective judgement or intuition of a person. A criticism of
this technique is elitism (Crisp, 2005), since an assumption of this approach is that certain things
are good for people. Crisp (2005) provided an excellent rationale for this limitation, when he
said that “…even if those people will not enjoy them, and do not even want them.”

        In the work of Arthaud-Day et al. (2005), applying structural modelling to subjective
wellbeing was found to constitute “(1) cognitive evaluations of one's life (i.e., life satisfaction or
happiness); (2) positive affect; and (3) negative affect.” Subjective wellbeing, therefore, is the
individual’s own viewpoint. If an individual feels his/her life is going well, then we need to
accept this as the person’s reality. One of the drawbacks to this measurement is, it is not
summative, and it lacks generalizability.

        Studies have shown that subjective wellbeing can be measured on a community level
(Bobbit et al., 2005; Lau, 2005) or on a household level (Lau, 2005; Diener 1984), whereas other
experts have sought to use empiricism (biomedical indicators - absence of disease symptoms, life
expectancy; and an economic component - Gross Domestic Product per capita; welfarism -
utility function).

        Powell (1997) in a paper entitled ‘Measures of quality of life and subjective wellbeing’
argued that psychological wellbeing is a component of quality of life. He believed that this
measurement, in particular for older people, must include Life Satisfaction Index, as this
approach constitutes a number of items based on “cognitively based attitudes toward life in
general and more emotion-based judgment”(Powell, 1997).              Powell addressed this in two
dimensions. Where those means are relatively constant over time, and while seeking to unearth



                                                  6
changes in the short-run, ‘for example an intervention’, procedures that mirror changed states
may be preferable. This can be assessed by way of a twenty-item Positive and Negative Affect
Schedule or a ten-item Philadelphia Geriatric Centre Positive Affect and Negative Affect Scale
(Powell, 1997).

       In a reading entitled ‘Objective measures of wellbeing and the cooperation production
problem’, Gaspart (1998) provided arguments that support the rationale behind the
objectification of wellbeing. His premise for objective quality of life is embedded within the
difficulty as it relates to consistency of measurement when subjectivity is the construct of
operationalization.   This approach takes precedence because an objective measurement of
concept is of exactness as non-objectification; therefore, the former receives priority over any
subjective preferences. He claimed that for wellbeing to be comparable across individuals,
population and communities, there is a need for empiricism.

       Gaspart discussed a number of economic theorizings (Equal Income Walrasian equilibria,
objective egalitarianism, Pareto efficiency; Welfarism), which saw the paper expounding on a
number of mathematical theorems in order to quantify quality of life. Such a stance proposes a
human predictable, rational form, from which we are able to objectify plans. The very axioms
cited by Gaspart emphasized a particular set of assumptions that he used in finalizing a
measurement for wellbeing for man who is a complex social animal. The researcher points to a
sentence that was written by Gaspart that speaks to the difficulty of objective quality of life; he
wrote, “So its objectivism is already contaminated by post-welfarism, opening the door to a
mixed approach, in which preferences matter as well as objective wellbeing” (Gaspart, 1998).
Another group of scholars emphasized the importance of measuring wellbeing outside of
welfarism and/or purely objectification, when they said that “Although GDP per capita is usually
used as a proxy for the quality of life in different countries, material gain is obviously only one
of many aspects of life that enhances economic wellbeing” (Becker et al., 2004), and that
wellbeing depends on both the quality and the quantity of life lived by the individual (Easterlin,
2001). This is affirmed in a study carried out by Lima and Nova (2006), which found that
happiness, general life satisfaction, social acceptance and actualizations are all directly related to
the GDP per capita for a geographic location (Lima and Nova, 2006). Even though in Europe




                                                  7
these were found not to be causal, income provides some predictability of subjective wellbeing,
and more so in poor countries than in wealthy nations. (Lima, and Nova, 2006)

       It should be understood that GDP per capita speaks to the market economic resources,
which are produced domestically within a particular geographic space. So increased production
in goods and/or services may generate excess, which can then be exported, and vital products
(such as vaccination, sanitary products, vitamins, iron and other commodities) can be purchased,
which are able to improve the standard of living and quality of the life of the same people
compared to the previous period. One scholar (Caldwell, 1999) has shown that life expectancies
are usually higher in countries with high GDP per capita, which means that income is able to
purchase better quality products, which indirectly affects the length of years lived by people.
This reality could explain why in economic recession, war and violence, when economic growth
is lower (or even non-existent) there is a lower life expectancy. Some of the reasons for these
justifications are government’s failure to provide for an extensive population in the form of
nutritional care, public health and health-care services. Good health is, therefore, linked to
economic growth, which further justifies why economists use GDP per capita as an objective
valuation of standard of living; and why income should definitely be a component in the analysis
of health status. There is another twist to this discourse as a country’s GDP per capita may be
low, but the life expectancy is high because health care is free for the population. Despite this
fact, material living standards undoubtedly affect the health status and wellbeing of a people, as
well as the level of females’ educational attainment.

       Ringen (1995) in a paper entitled ‘Wellbeing, measurement, and preferences’ argued that
non-welfarist approaches to measuring wellbeing are possible despite its subjectivity. The direct
approach for wellbeing computation through the utility function according to Ringen is not a
better quantification as against the indirect method (i.e. using social indicators). The stance
taken was purely from the vantage point that utility is a function ‘not of goods and preferences’
but of products and ‘taste’. The constitution of wellbeing is based on choices. Choices are a
function of individual assets and options. With this premise, Ringen put forward arguments
showing that people’s choices are sometimes ‘irrational’, which is the make for the departure
from empiricism.




                                                 8
       Wellbeing can be computed from either the direct (i.e. consumption expenditure) or the
indirect (i.e. disposable income) approach (Ringen, 1995).       The former is calculated using
consumption expenditure, whereas the latter uses disposable income. Rigen noted that in order
to use income as a proxy for wellbeing, we must assume that (1) income is the only resource, and
(2) all persons operate in identical market places. On the other hand, the direct approach has two
key assumptions. These are (1) what we can buy is what we can consume and (2) what we can
consume is an expression of wellbeing.         From Rigen’s monograph, the assumptions are
limitations.

       In presenting potent arguments in favour of non-empiricism in the computation of
wellbeing, Ringen highlighted a number of drawbacks to welfarism. According to Ringen:

       Utility is not a particularly good criterion for wellbeing since it is a function not only of
       circumstances and preferences, but also of expectation. In the measurement of wellbeing,
       respect for personal preferences is best sought in non-welfarist approaches that have the
       quality of preference neutrality; …As soon as preferences are brought into the concept of
       wellbeing, it cannot but be subjective. (Ringen, 1995)



       The difficulties of using empiricism to quantify wellbeing have not only been put forward
by Ringen, as O’Donnell and Tait (2003) were equally forthright in arguing that there were
challenges in measuring quality of life quantitatively. O’Donnell and Tait believed that health is
a primary indicator of wellbeing. Hence, self-rated health status is a highly reliable proxy of
health, which “successfully crosses cultural lines” (O’Donnell, and Tait, 2003). They argued
that self-reported health status could be used, as they found that all the respondents of chronic
diseases indicated that their health was very poor.

       To capture the state of the quality of life of humans, we are continuously and increasingly
seeking to ascertain more advanced methods that will allow us to encapsulate a quantification of
wellbeing that is multidimensional and multifaceted (Pacione, 2003). Therefore, an operational
definition of wellbeing that sees the phenomenon in a single dimension such as physical health,
medical perspective (Farquhar, 1995), material (Lipsey, 1999) and would have excluded
indicators such as crime, education, leisure facilities, housing, social exclusion and the
environment (Pacione 2003; Campbell et al., 1976) as well as subjective indicators, cannot be an



                                                 9
acceptable holistic measurement of this construct. This suggests that wellbeing is not simply a
single space; and so, the traditional biomedical conceptual definitions of wellbeing exclude many
individual satisfactions and in the process reduce the tenets of a superior coverage of quality of
life.

         One writer noted that the environment positively influenced quality of life (Pacione,
2003) of people; in order to establish the validity and reliability of wellbeing, empirical data
must include issues relating to the environment. The quality of the environment is a utilized
condition in explaining the elements of people’s quality of life. Air and water quality through
industrial fumes, toxic waste, gases and other pollutants, affect environmental quality. This is
directly related to the maintenance or lack thereof of societal and personal wellbeing (Pacione,
2003).

         Studies have conclusively shown that environmental issues such as industrial fumes and
gases, poor solid waste management, mosquito infestation and poor housing are likely to result in
physiological conditions like respiratory track infections (for example lung infection)and asthma.

         According to Langlois and Anderson (2002), approximately 30 years ago, a seminal
study conducted by Smith (1973) “proposed that wellbeing be used to refer to conditions that
apply to a population generally, while quality of life should be limited to individuals’ subjective
assessments of their lives …” They argue that a distinction between the two variables has been
lost with time.    From Langlois and Anderson’s monograph, during the 1960s and 1970s,
wellbeing was approached from a quantitative assessment by the use of GDP or GNP (Becker et
al., 2004), and unemployment rates; this they refer to as a “rigid approach to the (enquiry of the
subject matter) subject.” According to Langlois and Anderson (2002), the positivism approach
to the methodology of wellbeing was objectification, an assessment that was highly favoured by
Andrews and Withley (1976) and Campbell et al. (1976).

         In measuring quality of life, some writers have thought it fitting to use Gross Domestic
Product per capita (i.e. GDP per capita) to which they referred as standard of living (Lipsey,
1999; Summers, and Heston 1995). According to Summers and Heston (1995), “The index most
commonly used until now to compare countries' material wellbeing is their GDP          POP' .”   The
United Nations Development Programme has expanded on the material wellbeing definition put



                                                10
forward primarily by economists, and has included life expectancy and educational attainment
(UNDP, 2005: p. 341) as well as other social indicators (Diener, 1984; Diener, and Suh, 1997).
This operational definition of wellbeing has become increasingly popular in the last twenty-five
years, but given the expanded definition of health as cited by the WHO, wellbeing must be
measured in a more comprehensive manner than merely using material wellbeing as seen by
economists.

        Despite the fact that quality of life extends beyond the number of years of schooling and
material wellbeing, generally wellbeing is substantially construed as an economic phenomenon.
Embedded within this construct of a measure is the emphasis on economic resources, and we
have already established that man’s wellbeing is multifaceted. Hence, any definition of the
quality of life of people cannot simply analyze spending or the creation of goods and/or services
that are economically exchangeable, the number of years of schooling and life expectancy, but it
must include the psychosocial conditions of the people within their natural environment.

        GDP is the coalesced sum of all the economic resources of people within certain
topography, so this does not capture the psychosocial state of man in attaining the valued GDP.
By this approach, we may arrive at a value that is higher than in previous periods, making it
seem as though people are doing very well. However, with an increase in GDP, this single
component is insufficient to determine wellbeing, as the increase in GDP may be from (1) more
working hours, (2) higher rates of pollution and environmental conditions, (3) psychological
fatigue, (4) social exclusion, (5) human ‘burn out’, (6) reduction in freedom, (7) unhappiness, (8)
chronic and acute diseases and so forth. Summers and Heston (1995) note that “However,
GDP POP is an inadequate measure of countries' immediate material wellbeing, even apart from
the general practical and conceptual problems of measuring countries' national outputs.”
Generally, from that perspective, the measurement of quality of life is therefore highly economic
and excludes the psychosocial factors, and whether quality of life extends beyond monetary
objectification.

        In developing countries, Camfield (2003), in looking at wellbeing from a subjective
vantage point, notes that Diener (1984) argues that subjective wellbeing constitutes the existence
of positive emotions and the absence of negative ones within a space of general satisfaction with
life.   According to Camfield (2003) and Cummins’ (1997a, b), this perspective subsumed


                                                11
‘subjective and objective measures of material wellbeing’ along with the absence of illnesses,
efficiency, social closeness, security, place in community, and emotional wellbeing, which
implies that “life’s satisfaction” comprehensively envelopes subjective wellbeing.

       Diener (2000) in an article entitled ‘Subjective Wellbeing: The Science of Happiness and
a Proposal for a National Index’ theorizes that the objectification of wellbeing is embodied
within satisfaction of life. His points to a construct of wellbeing called happiness.

       He cited that:

       People's moods and emotions reflect on-line reactions to events happening to them. Each
       individual also makes broader judgments about his or her life as a whole, as well as about
       domains such as marriage and work. Thus, there are a number of separable components
       of SWB [subjective wellbeing]: life satisfaction (global judgments of one's life),
       satisfaction with important domains (e.g., work satisfaction), positive affect
       (experiencing many pleasant emotions and moods), and low levels of negative affect
       (experiencing few unpleasant emotions and moods). In the early research on SWB,
       researchers studying the facets of happiness usually relied on only a single self-reported
       item to measure each construct (Diener, 2000).



       Diener’s theorizing on wellbeing encapsulates more than the marginalized stance of other
academics and researchers who enlightened the discourse with economic, psychosocial, or
subjective indicators. He shows that quality of life is multifaceted, and coalescing economic,
social, psychological and subjective indicators is more far-reaching in ultimately measuring
wellbeing. This work shows a construct that can be used to operationalize a more
multidimensional variable, wellbeing, which widens the tenet of previous operational definition
on the subject.     From the theorizing of various writers, it is clear that wellbeing is
multidimensional, multidisciplinary and multispatial. Some writers emphasize the environmental
components of subject matter (Pacione, 1984; Smith, 1973), from the psychosocial aspect
(Clarke et al., 2000) and from a social capital vantage point (Glaeser 2001; Putnam 1995;
Woolcock 2001).

       Smith and Kington (1997), using H t = f (H t-1 , P m G o , Bt , MC t ED, Ā t , to conceptualise a
theoretical framework for “stock of health,” noted that health in period t, Ht, is the result of



                                                  12
health preceding this period (H t-1) , medical care (MC t) , good personal health (G o) , the price of
medical care (P m ), and bad medical care (Bt) , along with a vector of family education (ED), and
all sources of household income (Ā t ). Embedded in this function is the wellbeing that an
individual enjoys (or does not enjoy) (Smith, and Kington, 1997).

       In seeking to operationalize wellbeing, the United Nations Development Programme
(UNDP) in the Human Development Reports (1997, 2000) conceptualized human development
as a “process of widening people’s choice as well as the level of achieved wellbeing”.
Embedded within this definition is the emphasis on materialism in interpreting quality of life.
From the UNDP’s Human Development (1993), the human development index (HDI) “…is a
normative measure of a desirable standard of living or a measure of the level of living”, which
speaks to the subjectivity of this valuation irrespective of the inclusion of welfarism (i.e. gross
domestic product (GDP) per capita). The HDI constitutes adjusted educational achievement (E=
a 1 * literacy + a 2 * years of schooling, where a1, = 2/3 and a2 = 1/3), life expectancy
                                                                   1-e
(demographic modelling) and income (W (9y) = 1/ (1 - e) * y          ). The function W(y) denotes
“utility or wellbeing derived from income”. This income component of the HDI is a national
average (i.e. GDP per capita, which is then adjusted for income distribution (W*(y) = W(y) {1 -
G}), where G = Gini coefficient). In wanting to disaggregate the HDI within a country, the
UNDP (1993) noted that data are not available for many countries, which limits the possibility.

       An economist writing on ‘objective wellbeing’ summarized the matter simply by stating
that “…one can adopt a mixed approach, in which the satisfaction of subjective preferences is
taken as valuable too” (Gaspart, 1998; Cummin, 1997), which is the premise to which this paper
will adhere in keeping with this multidimensional construct, wellbeing. Wellbeing, therefore, in
the context of this paper, will be the overall health status of people, which includes access to and
control over material resources, environmental and psychosocial conditions, and per capita
consumption.

       New Focus: Healthy Life Expectancy

       One of the drawbacks to the use of life expectancy is the absence of capturing ‘healthy’

years of life.   Traditionally, when life expectancy is measured it uses mortality data to

predetermine the number of years of life that are yet to be lived by an individual, assuming that


                                                 13
he/she subscribes to the same mortality patterns of the group. The emphasis on this approach is

on length of life, not on the quality of those lived years. The rationale why healthy life

expectancy is important in ageing comes against the background that age means increased

dysfunction and the unavoidable degeneration of the human body. Hence, we must seek to

examine more than just the number of years that an individual is likely to survive, and we should

be concerned about the quality of those years. Therefore, in attempt to capture ‘quality of lived

years’, the WHO in 1999 introduced an approach that will allow us to evaluate this, ‘disability

adjusted life expectancy’ (DALE). DALE is not only concerned with length of years to indicate

the health and wellbeing status of an individual or a nation, but the number of years without

disabilities and the severity of their influence by reducing the quality of lived years.


       DALE is a modification of the traditional ‘life expectancy’ approach in assessing health.

It uses the number of years lived as an equivalent to ‘full health’. In calculating DALE, the

number of years of ill-health is weighted based on severity. This is then subtracted from the

expected overall life expectancy to give what is referred to as years of healthy life. Embedded in

this approach is reduction in years because of numbers, and severity of dysfunctions and HIV

experienced by the individual or people within a particular socio-political geography.

       Having arrived at ‘healthy life expectancy’, the WHO has found that poorer countries lost

more from their ‘traditional life expectancy’ than developed nations. The reasons put forward by

the WHO are the plethora of dysfunctions and the devastating effects of some tropical diseases

like malaria that tend to strike children and young adults. The institution found that these

account for a 14 percent reduction in life expectancy in poorer countries and 9 percent in more

developed nations (WHO, 2000b). This system is in keeping with a more holistic approach to

the measure of health and wellbeing, which this study seeks to capture.                By using the



                                                  14
biopsychosocial model in the evaluation of the wellbeing of aged Jamaicans, we will begin to

understand the factors that are likely to influence the quality of lived years of the elderly, and not

be satisfied with the increased length of life of the populace. The rationale behind this study is

that it will assist policy-making on health and social services, long term care and pension scheme

planning, and will aid in the understanding of future health needs and the evaluation of future

health programmes.


       Conclusion


       The discourse on health began centuries ago, but today the issues have a changed focus
because of new information, and a modification in epistemology about health. In this discourse
some scholarships have used the ‘absence of diseases’ or dysfunctions as a conceptual definition
of health, and in so doing they work substantially to see health from a mechanistic approach.
Such an approach treats patient care from a biomedical science standpoint, and the emphasis is
on the biology of the organism. The biomedical model as a study of health fails to appreciate that
long before any ailments (or dysfunctions) appear within an organism, the socio-physical,
cultural and psychological milieu would have had an impact on the quality of that organism.
Thus, the use of symptomology as the identification of ill-health, and using the opposite of this to
indicate health, is one-dimensional, and fails in its bid to encapsulate all the possible aspects that
influence the quality of life, wellbeing, and health of people.

       Following the clear limitations with the construct of health from the perspective of the
biomedical sciences [model], in 1946 the WHO conceptualized a definition of wellbeing that was
composite and far reaching, and one scholar (Crisps) refers to this as an elusive dream, which is
difficult to operationalize. Although the debate continued for years, George Engel was the first
scholar and psychiatrist to map out a conceptual framework for the WHO’s new construct for
health as a working definition that guides how he approached patient care. Engel, in the 1950s,
began using what he called the biopsychosocial model in treating psychiatric patients. He
believed that when a patient goes to a doctor, the individual’s ailment is a complex apparatus of
different tenets, and not merely the outward appearance, which is the identified symptomology.



                                                 15
Engel proposed that the medical fraternity should commence approaching patient care from the
vantage point of mind, body, and social conditions. Although some scholars and practitioners
concurred with Engel’s beliefs, and practiced this new model [biopsychosocial], and he (Engel,
1978, 1977a, 1977b, 1960) got Rochester Medical School to institute this approach in the
curriculum of medical training, substantially the biomedical approach was widely practiced.

       Traditionally, people were socialized to use symptomology to identify ill-health and the
reverse of this meant ‘health’, so much so that scientists still continue to research in this
tradition. Some scholarships argue that Engel’s biopsychosocial model is but an ‘abstraction’ (or
a theoretical construct), and so with the objective realities of patient care, the use of morbidity is
still the best indicator of the extent of wellbeing. Gradually, the culturalized tradition of the
supremacy of the biomedical model began to be seriously challenged in the 20th century.

       A group of authors claim that the United States, in the 20th century, expanded their
operational definition of health from the traditional ‘absence of diseases’ to the biopsychosocial
approach argued by Engel (Brannon, and Feist, 2007; Engel, 1960). It was not until the 1970s
that a scholar, using empirical data, finally provided an econometric model that encapsulates
what Engel was arguing some 2 decades before (Grossman). Using data, Grossman (1972)
showed that the health status of people in the world is influenced by both biological, and a
plethora of other social conditions. He laid the foundation that has shaped the present landscape
of social science research on health, wellbeing and quality of life, so much so that a group of
scholars have used the advanced quantitative method to model happiness, which was conceptual
to measuring wellbeing.

        Today Grossman’s model, with some modifications, is being used by some Caribbean
scholars (Bourne, 2007; Hambleton et al., 2005). Using data from Barbados, Hambleton et al.
(2005) showed that health (proxy physical functioning) is a function of biological, cultural and
social conditions. Bourne (2007), using data from Jamaica, expanded on the operational
definition of health (or wellbeing) from physical functionality used by Grossman and Hambleton
et al. (2005) to that of a composite index which captures physical, functional and economic
wellbeing (material possessions). Bourne’s work did not only add to the operational definition of
health, but he showed that environmental and psychological conditions in addition to social
factors do influence health.


                                                  16
       In sum, only a few studies in the Caribbean have sought to expand the narrow definition

of health inspite of the WHO’s efforts as well as others. The narrow definition of health is still

dominant in contemporary Jamaica as well as other Caribbean nations, and this primarily

accounts for the image of health that is held by many peoples. It is this narrow definition of

health that fashions the health care system, patient care, data collected on health and peoples’

image of health, health care and lifestyle practices. This is not only a challenge for public health

specialists, but for the general populace as one image of health influences his/her perception of

health care, lifestyle and views on preventative health.




                                                 17
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                                              22
CHAPTER 2


Health Measurement


Jamaicans are not atypical in how they conceptualize health and/or how they address patient
care as the antithesis of diseases or dysfunctions (i.e. health conditions). In the 1900s and
earlier, Western Societies were using the biomedical model in the measurement and treatment of
health, health attitudes and the utilization of health services. This approach emphasizes sickness,
dysfunction, and the identification of symptomology or medical disorders to evaluate health and
health care. Such an approach places significance on the end (i.e. genetic and physical
conditions), instead of the multiplicity of factors that are likely to result in the existing state, or
issues outside of the space of dysfunctions. Notwithstanding the limitations of the biomedical
approach, it is still practiced by many Caribbean societies, and this is fundamentally the case in
Jamaica. The current paper is an examination of health measurement, and provides at the same
time a rationale for the need to have a more representative model as opposed to the one-
dimensional approach of using pathogens in measuring health. Owing to the importance of
health in development, patient care and its significance for other areas in society, this paper
seeks to broaden more than just the construct, as it goes to the core of modern societies in
helping them to understand the constitution of health and how patient care should be treated.
Thus, it provides a platform for the adoption of the biopsychosocial model, which integrates
biological, social, cultural, psychological and environmental conditions in the assessment of
health and the outcome of research, by using observational survey data.



1. INTRODUCTION

The construct of health is more than a concept. It is a “leading characteristic of the members of a
population...” [1] and, ergo, it plays a direct role in the images of health and health care. Among
the plethora of reasons for the importance of health are not merely the images created by the
construct, but also its contribution to the production of different tenets of human existence –
illness, morbidity, comorbidity, disability, mortality, life expectancy, wellbeing, and so on, as
well as the guide that it affords for health interactions and interventions. In addition to the
aforementioned issues, it is of germane significance in aiding us to understand many of the
things that we see. The definition of this single term ‘health’ is important, as a precise use of the



                                                  23
construct fashions and connects other important applications such as growth and development,
productivity, health care and people’s expectations of health care professionals. One scholar, in
helping us to understand the meaning of a construct, says that “without a well-defined construct,
it is difficult to write good terms and to derive hypotheses for validation purposes” [2].
Embedded in Spector’s argument is the ‘theoretical abstraction’ of the construct, and how we
may use it for outcome research. In this paper, the author will review the existing literature and
identify particular measures of health, examining how these differ from the WHO’s conceptual
definition of health [3]. At the same time, within the limitations of the biomedical model, the
study will evaluate the usefulness of the biopsychosocial model in health and how the image of
health influences the health care of people.

       1.2 Image of Health

       Health, however, is more than a ‘theoretical abstraction’. There is an ‘objective reality’ to
this construct. It explains life, and life is an objective reality. Furthermore, health is a valuable
tool that ‘drives’ health policies and influences the determinants of health care. Then there is the
issue of health care and how this is planned for, as well as the role that health plays in the
development of a society. Health, wellbeing and poverty are well documented in developmental
economics by scholars such as Amartya Sen, Paul Streeten and Martin Ravallion as having
critical roles in understanding human development (or the lack of it). The fascination with health
and wellbeing in developmental studies is primarily because of the direct association between
development and health.

       Jamaica is not atypical in how its people conceptualize health and/or how they address
patient care. In the 1900s and earlier, western societies used the biomedical approach in the
measurement and treatment of health [5].         The biomedical approach emphasizes sickness,
dysfunction, pathogens, and disability and medical disorders in the construction of health. This
approach places importance on the outcome (or the end) instead of the multidimensional
conditions that are likely to result in the existing state. Notwithstanding the limitations of the
biomedical approach, it is still practiced by many Caribbean societies, and this is fundamentally
the case in Jamaica. This is atypical in many Western nations, as contemporary demographers
still use the antithesis of illness and disability to write about health [6-8]. Rowland wrote that
“Measures of population health are of general interest to demographers, sociologists,


                                                 24
geographers and epidemiologists. Interdisciplinary concerns here include comparing national
progress through the epidemiologic transition, and identifying social and spatial variations within
countries in patterns of disease and mortality” [5].

       The United States has left many Caribbean societies behind in how they conceptualize
health and treat health care. As early as the commencement of the 20th century [4], the United
States shifted their focus from negative wellbeing (i.e. antithesis of diseases) to positive
wellbeing. The antithesis of diseases assumes a bipolar opposite between health and diseases.
Embedded in this bipolar thinking is that for one to be healthy, he/she must not be experiencing
any symptomology of dysfunctions. Hence, the health of people is measured by mortality or
morbidity statistics. Health, however, is more than just the antithesis of diseases to positive
psychology, inclusive of socio-cultural conditions and the environment. Positive wellbeing
encapsulates the biomedical model in addition to psychological, socio-cultural and
environmental conditions. The name that Engel gave to this new approach is the biopsychosocial
model. The current paper is a discourse on the limitation of the biomedical model, which will
provide a rationale for the need to have a more representative model as against this one-
dimensional approach to the measurement of health.

       Traditionally, health was conceptualised as the ‘antithesis of diseases’ [4]. Using the
antithesis of diseases, this construct utilizes a minimization approach or a negative perspective,
adopted by western societies, which saw health as the absence of dysfunctions, morbidity
conditions or comorbidity.      “This definition of health has been largely the result of the
domination of the biomedical sciences by a mechanistic conception of man. Man is viewed by
physicians primarily as a physio-chemical system” [9]. With this thinking, health professionals’
evaluation of patient care and diagnostic treatments is based primarily on the identification of
any symptomology of dysfunctions. Hence the standard that is used in the evaluation of health is
the established norm of any deviation from diseases. Rather than conceptualizing health and
stating its determinants, this approach uses the identification of symptomology to measure
health. Therefore, life expectancy is used here as a measure of health. This assumes that once
an individual is alive, it is because there are no dysfunctions to cause death. Embedded in this
association is the influence of dysfunctions on health, but there are no other determinants of
health except the various symptomologies of diseases.



                                                 25
        Outside of diseases, there are other determinants of health. Based on the biopsychosocial
model that George Engel [10, 11] developed, he proposed an approach to the treatment of the
health care of psychiatric patients that included biological, social and psychological conditions.
Such a conceptual framework, unlike the biomedical sciences, introduces and identifies factors
that are responsible for the health, and by extension the wellbeing, of a population. One scholar
cites that “the states of health and disease [are] the expressions of the success or failure
experienced by the organism in its efforts to respond adaptively to environmental changes” [12].
Again, when health is defined as the antithesis of diseases its determinant is solely biological, but
this is clearly one-dimensional, and many scholars have shown that health is, in fact,
multidimensional, and composed of biopsychosocial and environmental conditions.

        Another aspect to health is the positive association between the determinants of health
and health care policies. Health care policy makers use the determinants of health as the
benchmark that directs their planning. Therefore, when health policies are too narrow, the health
determinants which fashion a population’s health care will take a minimal approach, as this is
based on the image of health. One scholar puts it succinctly, “…health policies affect health
through their effects on health determinants” [13], which speaks to the importance of ‘good’
hypotheses in the schema of things. It should be noted that the hypotheses allow us to derive the
possible determinants of health, which would be used to evaluate the effectiveness of the health
policy, and so show how they affect health (see Figure 1).




                               Determinants
                               of Health –
   Health Policy                                                       Health
                               Bi l   i l

Figure 1: The relation between health policy and health, and the roles of health determinants




                                                     26
       The goal of the policy is to decrease the incidence of chronic diseases, high risk
       sexual behaviour/violence and injury through the adaptation of appropriate
       behaviours by the population and particularly young children, adolescents and
       young adults [14].


       The general conceptualization of health in Jamaica is the “antithesis of diseases”. This
explains why many people emphasize health care for morbidity conditions, genetics, or physical
functioning (i.e. their biology). Another indicator of the usage of this perspective can be seen in
how data are collected on health in Jamaica and/or in the wider Caribbean. Such a situation
highlights the minimization or substantially negative approach in the construct of health. Despite
the title of the Ministry of Health’s ‘National Policy for the Promotion of Healthy Lifestyle in
Jamaica’, throughout the paper the MOH [14] emphasizes mortality, diseases, dysfunctions and
reproductive health, which highlights Jamaicans’ perspective on health. This is also evident in
the Planning Institute of Jamaica which is responsible for policy, along with the Statistical
Institute of Jamaica, collecting information on health by way of (1) preventative (i.e. behaviour
modification), curative (surgical procedures, visits to health practitioners), restorative (physical
rehabilitation), and palliative (i.e. pain management) measures, and ownership of health
insurance. Thus, the hypotheses that arise from the collected data are in keeping with the
narrowed definition for which the data was initially gathered by the research design exercise.
The hypothesis of the presence of pathogens such as poor air being the cause of diseases, or
classification of ill-health, is ancient, within the context that health has been expanding from
mere physical functioning for some time. This hypothesis assumes that a person who does not
have an ailment (or disease condition) is healthy, which is categorically false, as health
psychologists have shown that psychological conditions do influence wellbeing [4].             This
perspective dates back to Galen in Ancient Rome (i.e. 130 CE – 200 CE). A point is even more
forcefully made in a study by two economists, which found a strong direct relationship between
happiness and wellbeing [15]. Other researchers found an association between ‘positive and/or
negative’ mood(s) and wellbeing [16]. This paper is in two parts, designed: (1) to provide
detailed evidence that will support the rationale for an expanded concept which looks at health
and wellbeing, and (2) to illustrate the purpose and significance of the expanded model that
Engel termed the biopsychosocial model.               This paper however is not arguing for a


                                                 27
biopsychosocial hybrid model, which would include biological, economic, social, cultural,
psychological and ecological conditions.

2. P HYSICAL F UNCTIONING

       Caring for patients suffering from ill-health has a long history, which dates back to the
Agrarian societies. During those earlier periods, man in his quest to address health conditions
did so primarily from the standpoint of physical functionality. Based on the annals of time, the
literature showed that people would treat biological dysfunctions and sometimes the ‘spirit’ in
their pursuit of making man healthier. This approach dates back as far as ancient Rome (i.e. 130
CE – 200 CE). Despite the WHO offering us a better way in the pursuit of happiness and
wellness, man continues to return to the biomedical model of health. One of the reasons for the
continued acceptance of the use of the biomedical model is the dominance of technology in this
process. As technology is still primarily intended to address physical dysfunctions and the
absence of pathogens, many studies conducted in early societies have not only linked the concept
of health to medical conditions and by extension health care, but have served as another
important indicator in determining lifespan.

       In 1884, an Englishman named Francis Galton who was both a mathematician and
medical doctor researched the ‘physical and mental functioning’ of some 9,000 people between
the ages of 5 and 80 years [17]. Galton wanted to measure the human life span in relation to the
physical and mental functioning of people, so he sponsored a health exhibition that would allow
him to collect data for analysis. Health was traditionally defined as the “antithesis of diseases”,
which explains the predominance of physical functioning in policy making and health care, and
justifies Galton’s wanting data on the physical functioning of people.

       The 20th century has brought with it massive changes in the typologies of dysfunctions,
where deaths have shifted from infectious diseases such as tuberculosis, pneumonia, yellow
fever, Black Death (i.e. Bubonic Plague), smallpox and ‘diphtheria’ to illnesses such as cancer,
heart disease and diabetes [14]. Although diseases have shifted from infectious to degenerate,
chronic non-communicable illnesses and science, medicine and technology have expanded since
then, and the image of health in contemporary Jamaica still lags behind many developed nations.
Morrison [18] titled an article ‘Diabetes and Hypertension: Twin Trouble’ in which he
establishes that diabetes mellitus and hypertension have now become problems in Jamaicans and


                                                28
in the wider Caribbean. This situation was equally corroborated by Callender [19] and Steingo
[20] at the 6th International Diabetes and Hypertension Conference, which was held in Jamaica in
March 2000. They found that there is a positive association between diabetic and hypertensive
patients - 50% of individuals with diabetes had a history of hypertension [19, 20]. Prior to those
scholars’ work, Eldemire [21] found that 34.8% of new cases of diabetes and 39.6% of
hypertension were associated with senior citizens (i.e. ages 60 and over). Accompanying this
period of the ‘age of degenerative and man-made illnesses’ are life expectancies that now exceed
50 years.

       Before the establishment of the American Gerontology Association in the 1930s and their
many scientific studies on the ageing process [17], many studies were done based on the
biomedical model, i.e. physical functioning or illness and/or disease-causing organisms [4].
Many official publications used either reported illnesses or the prevalence of seeking medical
care for measuring sicknesses. Some scholars have still not moved to the post biomedical
predictors of health status. The dominance of this approach is so strong and present within the
twenty-first century, that many doctors are still treating illnesses and sicknesses without an
understanding of the psychosocial and economic conditions of their patients. To illustrate this
more vividly, the researcher will quote a sentiment expressed by a medical doctor in ‘The
Caribbean Food and Nutrition Institute’s Quarterly [22].       A public health nutritionist, Dr.
Kornelia Buzina [23], says, “When used appropriately, drugs may be the single most important
intervention in the care of an older patient … and may even endanger the health of an older
patient …” This proposition highlights the paradox in biomedical sciences as well as showing the
need to expand the image of health beyond this negative approach to it.

       Within the context of the WHO’s definition and growing numbers of studies that have
concluded that health should be a multidimensional construct, in 2007 a group of medical
practitioners used physical functionality and dysfunctions to treat an elderly patient who was
suffering from a particular health condition [24]. The researchers put forward an examination of
a 74-year old man who with “...a long history of ischaemic heart disease, presented with
increasingly prolonged episodes of altered consciousness” [24].           The physicians cite the
argument that “many elderly patients may have more than one cause for this symptom” [24],
which summarizes their perspective and reliance on understanding medical disorders in the
dispensing of patient care. Throughout the study, the scholars and medical practitioners did not


                                               29
seek to evaluate the psychological, social, and environmental conditions and their possible
influence on the current state of dysfunction of the elderly patient.        Despite the seeming
complexity of the result of the detailed inquiry into the neurological conditions of the patient,
and the keen medical examination of the patient, his medical condition continued for years
unabated. This emphasises the dominance of the biomedical model, and it goes beyond this
single study, as a review of publications in the West Indian Medical Journal – a medical journal
in Jamaica – from 1960-2009 revealed a few studies that have gone beyond the use of the
biomedical approach to the examination of patient care.

       In seeking to treat the 74-year old patient, the medical practitioners examined and re-
evaluated various medical problems. Thus, owing to the thinking of this group of researchers,
they used ‘multiple medications’ in the treatment of the patient’s condition. It was clear from the
perspective of the scholars that what guided their intervention were the biomedical sciences (i.e.
physical functionality or dysfunctions). In this case, health is the ‘antithesis of diseases’. It is
the narrow definition of health – negative health (i.e. biomedical approach) – which explains the
image of health and health care for those scholars and researchers. Apart from the reasons for the
use of diagnosed conditions, life expectancy and other physical issues are utilized in examining
health, because of the precision in using them to evaluate health as against other approaches that
are more holistic and broader in scope.

       2.2 Health measurement

       The narrow definition of health is the “antithesis of diseases” which Longest [13] says is
the “…absence of infection or the shrinking of a tumour” which can be called dysfunctions (see
[1, 4]. As we mentioned earlier, the ‘antithesis of diseases’ idea dates back to Galen in Ancient
Rome. It was widespread in the 1900s, and so medical professionals used this operational
definition in patient care. Another fact during this time was that technology was fashioned in this
regard, addressing solely physical dysfunctions. This definitional limitation may be a rationale
for the World Health Organization, nearing the mid-1900s, declaring that health is the “state of
complete physical, mental, and social wellbeing, and not merely the absence of diseases or
infirmity” [3]. It should be noted that this conceptual definition which is in the Preamble to the
constitution of the WHO which was signed in July 1946 and became functional in 1948,
according to one scholar, from the Centre of Population and Development studies at Harvard



                                                 30
University, is a mouthful of sweeping generalizations. According to Bok [25], the definition
offered by the WHO is too broad and difficult to measure, and at best it is a phantom. Other
intelligentsia point to the WHO’s definition as a difficulty for policy formulation, because its
scope is ‘too broad’ [26]. The question is “Is the conceptual definition formulated by WHO so
broad that those policies faced difficulty in formation”, and by extension should we regress to a
pre-1946 conceptualization of health because a construct is difficult to operationalize today?
Undoubtedly, health extends beyond diseases and is tied to cultural and psychological elements,
personal responsibility, lifestyle, environmental and economic influences as well as quality
nutrition [27-41]. Those conditions are termed determinants of health [26].

       The WHO’s perspective must have stimulated Dr. George Engel to pursue a modification
of the narrow approach to the health and health care debate. Dr. Engel was a psychiatrist who
formulated the construct called the biopyschosocial model in the 1950s. He believed that when a
patient comes to a doctor, for example for a mental disorder, the problem is a symptom not only
of actual sickness (biomedical), but also of social and psychological conditions [10, 11]. He
therefore campaigned for years for physicians to use the biopsychosocial model for the treatment
of patients’ complaints, as there is an interrelationship between the mind, the body and the
environment. He believed so deeply in the model, convinced that it would help in understanding
sickness and providing healing, that he introduced it into the curriculum of Rochester medical
school [42, 43]. Medical psychology and psychopathology was the course that Engel introduced
into the curriculum for first year medical students at the University of Rochester. This approach
to the study and practice of medicine was a paradigm shift from the biomedical model that was
popular in the 1980s and 1990s.

       The Planning Institute of Jamaica and the Statistical Institute of Jamaica employ the
biomedical model in capturing the health status and/or wellbeing of the populace. This approach
was obsolete by the late 20th century, as in 1939 E.V. Cowdy, a cytologist in the United States;
expanded on how ageing and health status should be studied in the future. Cowdy broadened the
biomedical model in the measurement of the health status of older adults by including social,
psychological and psychiatric information in his study entitled the “Problem of Ageing” [17].
The Ministry of Health [MOH] [14], however, has published a document in which it shows that
health interfaces with biomedical, social and environmental conditions. One of the reasons put
forward by the MOH to help in understanding why they arrived at the aforementioned position,


                                               31
was the rationale behind the explanation for the changes in the typology of diseases – that is,
from infectious and communicable diseases to chronic conditions. The institution cites that this is
substantially because of the lifestyle practices of Jamaicans. One of the ironies within the
document was in the ‘main components of the policy for the promotion of a healthy lifestyle in
Jamaica’, which cites that the goal of the policy was to reduce the incidence of communicable
and infectious diseases, which speaks to society’s subconscious emphasis on the biomedical
model in conceptualizing health and its treatment. Embedded within the MOH’s 2004
publication are repetition and the focus on seeking to reduce physiological conditions that affect
the individual. The MOH admits, however, that health interfaces with body and environment,
which is an expansion of the biomedical model, but all indications in their document point to the
biomedical science approach in the application of the policy. The institution recognized that
psychological factors (for example, self-esteem, and resilience) play a role in influencing health,
so much so that it included these within its ‘goal of the strategic approach’, but they were not
supported in the ‘broad objectives of the strategic approach’.

       Critical to all of this is the acceptance that the definition of health is fundamental to the
construction of those hypotheses that are used to formulate health policies. According to Longest
[13], the conceptualization of health is indeed critical to all the things that rely on its definition.
Longest writes:

       The way in which health is conceptualized or defined in any society is important because
       it reflects the society’s values regarding health and how far the society might be willing
       to go in aiding and supporting the pursuit of health among its members [13].

       In Jamaica health policies are still driven by physical functioning, which is an obsolete
approach to addressing health and by extension wellbeing. This limited approach to health and
wellbeing means that little consideration is given to other factors such as lifestyle, psychological
state, the environment, crime and violence, among others. This of course implies that Jamaica’s
health policy is limited in its orientation, as it is largely driven by hypotheses that support
physical functioning.




                                                  32
2.3 Biomedical Approach

       Dr. Buzina admits that wellbeing is fundamentally a biomedical process [23]. This
conceptual framework derives from the Newtonian approach of basic science as the only
mechanism that could garner information, and empiricism being the only apparatus to establish
truth or fact. It is still a practice and social construction that numerous scholars and medical
practitioners [24] continue to advocate despite new findings. Simply put, many scholarships still
put forward a perspective that the absence of physical dysfunction is synonymous with wellbeing
(or health, or wellness). Such a viewpoint appears to hold some dominance in contemporary
societies, and this is a widespread image held in Jamaica. Then there are issues such as the death
of an elderly person’s life-long partner; a senior citizen taking care of his/her son/daughter who
has HIV/AIDS; an aged person not being able to afford his/her material needs; someone older
than 64 years who has been a victim of crime and violence and continues to be a victim; seniors
who reside in volatile areas who live with a fear of the worst happening, the inactive aged, and
generally those who have retired with no social support, are equally sharing the same health
status as the elderly who have not been on medication because they are not suffering from
biomedical conditions to the extent that they need to be given drugs.

       Two medical doctors writing in Kaplan and Saddock’s Synopsis of Psychiatry noted that
physicians are frequently caught in theorizing that normality is a state of health [44]. They
argued that doctors’ definition of normality correlates with a traditional model (biomedical) that
emphasizes observable signs and symptoms. Using psychoanalytic theories, Saddock and
Saddock [44] remarked that the absence of symptoms as a single factor is not sufficient for a
comprehensive outlook on normality. They stated, “Accordingly, most psychoanalysts view a
capacity for work and enjoyment as indicating normality…” [44]. Among the challenges
associated with this method (biomedical model), is its emphasis only on curative care. Such an
approach discounts the importance of lifestyle and preventative care. In that, health is measured
based on experiences with illnesses and/or ailments, with limited recognition being placed on
approaches that militate against sickness and/or diseases. The biomedical approach is somewhat
biased against an understanding of multi-dimensional man, which is not in keeping with the
holistic conceptualization of health as offered by the WHO.




                                                33
The new paradigm




       34
                                  2.4 Biopsychosocial Approach

       In the 1950s, George Engel, a physician, teamed with John Romano, a young psychiatrist,
to develop a biopsychosocial model for inclusion in the curriculum of the University Of
Cincinnati College Of Medicine, which measured the health status of people. It is referred to as
Engel’s biopsychosocial model. Engel’s biopsychosocial model [10, 11, 43], recognized that
psychological and social factors coexisted along with biological factors. It was a general theory
of illness and healing, a synergy between medicine, psychiatry and the behavioural sciences [42].
Therefore, from Engel’s model, wellbeing must include factors such as motivation, depression
(or the lack thereof), biological conditions (such as illnesses and diseases), social systems,
cultural, environmental and familial influences on the appearance and occurrence of illness.

       Some scholars may argue that this paper appears to believe that only quantitative studies
may provide answers to the examination of the determinants of health. This is absolutely not so,
and we use a qualitative study to show people’s perception of what contributes to a particular
medical condition. In a qualitative study that uses in-depth interviews with some 17 Malaysian
men aged between 40 and 75 years old, some scholars examined the perception of these men in
relation to erectile dysfunction (ED) – the sample was a convenient one of men who were
suffering from ED and who were willing to speak about their condition. When the interviewers
asked the participants about the possible causes of ED, many of them outlined biomedical
conditions such as diabetes, hypertension, medications, past injuries, ageing and then came
lifestyle practices (i.e. smoking) and psychosocial factors [45]. Embedded in this perception is
the respondents’ emphasis on pathophysiological conditions in health measurement and
intervention. Although the sampled respondents do believe that psychosocial factors play a role
in health status, it should be noted here that they did not itemize those conditions. This speaks to
the conceptualization of health that these respondents have come to accept, and the fact that they
believe that health is not limited to biomedical sciences. Using their definition of health, the
study shows how culture plays a pivotal role in determining how men will seek health care
irrespective of the nature of their condition.

       According to a number of demographers [46, 47], health has been conceptualized as
“functioning ability”. These pundits categorized “functioning ability” as – (i) being able to
provide both personal care and independent living but having some difficulty in performing these


                                                 35
tasks or in getting about outside the home, (ii) having no functioning difficulties, (iii) being
unable to independently provide personal care, and finally (iv) being able to provide personal
care but not able to manage life in the home independently” [46].

3.0 EXPANSION OF THE B IOMEDICAL MODEL

       Studies reveal that positive moods and emotions are associated with wellbeing [48] as the
individual is able to think, feel and act in ways that foster resource building and involvement
with particular goal materialization [49].     This situation is later internalized, causing the
individual to be self-confident, from which follow a series of positive attitudes that guide further
actions [50]. Positive mood is not limited to active responses by individuals, but a study showed
that “counting one’s blessings,” “committing acts of kindness”, recognizing and using signature
strengths, “remembering oneself at one’s best”, and “working on personal goals” all positively
influence wellbeing [50,51].      Happiness is not a mood that does not change with time or
situation; hence, happy people can experience negative moods [52].

       Human emotions are the coalescence of not only positive conditions but also negative
factors [53]. Hence, depression, anxiety, neuroticism and pessimism are seen as a measure of the
negative psychological conditions that affect subjective wellbeing [54-56]. From Evans and
colleague [54], Harris et al. [55] and Kashdon’s monographs [56], negative psychological
conditions affect subjective wellbeing in a negative manner (i.e. guilt, fear, anger, disgust); and
the positive factors influence self-reported wellbeing in a direct way - this was corroborated in a
study conducted by Fromson [57]; and by other scholars [53, 58,59]. Acton and Zodda [60]
aptly summarized the negative affective of subjective wellbeing in the sentence that reads
“expressed emotion is detrimental to the patient's recovery; it has a high correlation with relapse
to many psychiatric disorders.”

       From the theologians’ perspective, spirituality and religiosity are critical components in
the lifespan of people. They believe that man (including woman) cannot be whole without
religion. With this fundamental concept, theologians theorize that man cannot be happy, or feel
comfortable without a balance between spirit and body [62]. In order to achieve a state of
personal happiness, or self-reported subjective wellbeing, some pundits put forward a construct
that people are fashioned in the image of God, which requires some religiosity before man can be
happy or less stressed. Religion is, therefore, association with wellbeing [63-65] as well as low


                                                36
mortality [66]. Religion is seen as the opiate of the people from Karl Marx’ perspective, but
theologians, on the other hand, hypothesize that religion is a coping mechanism against
unhappiness and stress. According to Kart [67], religious guidelines aid wellbeing through
restrictive behavioural habits which are health risks, such as smoking, drinking alcohol, and even
diet.

        The discourse of religiosity and spirituality influencing wellbeing is well-documented
[68, 69]. Researchers have sought to concretize this issue by studying the influence of religiosity
on quality of life, and they have found that a positive association exists between those two
phenomena [70]. They found that the relationship was even stronger for men than for women,
and that this association was influenced by denominational affiliation. Graham et al.’s [71] study
found that blood pressure for highly religious male heads of households in Evans County was
low. The findings of this research did not dissipate when controlled for age, obesity, cigarette
smoking, and socioeconomic status. A study of the Mormons in Utah revealed that cancer rates
were lower (by 80%) for those who adhered to Church doctrine [72, 73] than those with weaker
adherence.

        In a study of 147 volunteer Australian males between 18 and 83 years old, Jurkovic and
Walker [65] found a high stress level in non-religious as compared to religious men. The
researchers in constructing a contextual literature quoted many studies that have made a link
between non-spirituality and “dryness”, which results in suicide. Even though Jurkovic and
Walker’s research was primarily on spiritual wellbeing, it provides a platform that can be used in
understanding the linkages between the psychological status of people and their general
wellbeing. In a study which looked at young adult women, the researchers found that spirituality
affects the physical wellbeing of a populace [69]. Embedded within that study is the positive
influence of spirituality and religion on the health status of women. Edmondson et al.’s work
constituted of 42 female college students of which 78.8 percent were Caucasian, 13.5 percent
African-American, 5.8 percent Asian and 92 percent were non-smokers.

        Health psychologists concurred with theologians and Christians that religion influences
psychological wellbeing [74, 75]. Taylor [74] argued that religious people are more likely to
cope with stressors than non-religious individuals, which explains the former’s better health
status. She put forward the position that this may be done through avoidance or vigilant


                                                37
strategies. This response is an aversive coping mechanism in addressing serious monologue or
confrontational and traumatic events. Coping strategies, therefore, are psychological tools used
by individuals to problem-solve issues, without which they are likely to construct stressors and
threaten their own health status. Taylor [74] said that "some religious beliefs also lead to better
health practices", producing lower mortality rates from all cancers in Orthodox Christians.

4. EVIDENCE OF USE FOR BIOPSYCHOSOCIAL MODEL

         Even though policy makers are cognizant of the importance of healthy lifestyle practices
and their influence on wellbeing [76], we continue to sideline them in understanding health
status, and using this concept in the formulating of hypotheses that will drive a broader policy
focus of health care for the populace. This is evident in our neglect to expand studies for policy
purposes that collect data on health using the biopsychological model, meaning that policy
formulators are emphasizing physical vulnerability or dysfunction to measure health status. Is
there a study that has sought to use a maximization definition of health that will be able to better
evaluate and plan for the wellbeing of Jamaicans?

         A study conducted in Barbados reveals that there is a statistical causal relationship
between socioeconomic conditions and health status. The findings revealed that 5.2% of the
variation in reported health status was explained by the traditional determinants of health
(disease indicators – See Table 5.1.1).      Furthermore, when this was controlled for current
experiences, the percentage fell to 3.2% (falling by 2%). When the current set of socioeconomic
conditions were used they accounted for some 4.1% of the variations in health status, while 7.1%
were due to lifestyle practices, compared to 33.5% that were as a result of current diseases [34].
It holds that the importance placed by medical practitioners on the current illnesses – as an
indicator of health status – is not unfounded as people place more value on biomedical
conditions as being responsible for their current health status. Despite this fact, it is obvious
from the data – using 33.5% - that there are other indicators that explain some 67.5% of the
reason why health status should be as it is. Furthermore, with an odds ratio of 0.55 for number
of illnesses, there is a clear suggestion that the more people reporting illnesses, the lower will be
their health status [34]; and this was equally so for more disease symptoms – odds ratio was
0.71).




                                                 38
       Figure 1 above is a depiction of the use of the biopsychosocial model in the study of
health status. This research was conducted in Barbados between 1999 and 2000, in which health
status was predicted by a composite function of five general typologies of variables. The model
shows that health status is not primarily limited to biomedical conditions – such as diseases and
ailments – as has been the custom of many scholars. While different indicators as used by these
researchers may not be possible in this paper because of the limitation of the secondary dataset –
for example ‘current lifestyle risk factors’, ‘childhood nutrition’, ‘childhood diseases’,
‘environmental factors’, to name a few – despite the data’s shortcomings, the study emphasizes
the use of a multidimensional approach in the study of wellbeing.

       Bourne [27], using secondary data, encapsulates George Engel’s conceptual idea of a
multidimensional model which incorporates biological, social, psychological, environmental and
social conditions in examining wellbeing. Wellbeing is operationally defined as material
resources, illness and total expenditure of households. The sample is drawn from a nationally
representative survey of 25,018 Jamaicans, some 9.3% of the sample being elderly. From a
sample of 2,320 elderly Jamaicans (ages 65+ years), Bourne [27] found that 10 of the 14
predisposing variables explain 36.8% of the variance in wellbeing.         Of the 10 statistically
significant variables, the five most important ones, in descending order, are (1) area of residence
(β=0.227), (2) cost of medical care (β=0.184), (3) psychological conditions – [total positive
affective conditions] - (β=0.138), (4) ownership of property (β=0.135), and (5) crime (β=0.111).
Among the other factors, which are the 5 least important conditions, are negative affective
conditions, marital status, educational level, average occupancy per room, age of residents, and
the environment. Thus, whether or not we use Grossman’s model [77], Hambleton et al.’s model
[34] or Bourne’s models [27-33] it is clear from them that wellbeing extends beyond biological
conditions to include psychological, environmental, and social conditions.

       Another study was conducted by Bourne [30] of some 3,009 elderly Jamaicans (60 years
and older), with an average age of 71 years and 10 months ± 8 years and 6 months, of which
67% (n=2,010) resided in rural areas, 21% (n=634) dwelled in Other Towns and 12% (n=365)
lived in the Kingston Metropolitan Area. The mean General Wellbeing of elderly Jamaicans was
low (3.9 out of 14 ± 2.3). Bourne’s model [30] identified 10 explanatory variables which explain
40.1% (adjusted R-squared) of the variance in general wellbeing. In this study he deconstructed



                                                39
the general model into (1) economic wellbeing and (2) physical wellbeing (proxy by health
conditions). Using the same set of explanatory variables, the latter model explains 3.2% of the
variability in wellbeing (proxy by health conditions) compared to 41.3% for the former model
(i.e. economic wellbeing using material economic resources). General Wellbeing was operational
as material resources and functional limitation (or health conditions). Material economic
resources constitute ownership of durable goods (such as motor vehicles, stereo, washing
machines, et cetera); income (proxy by income quintile); and financial support (e.g. social
security and other pensions). Hence, it follows that the biopsychosocial model is a better proxy
for wellbeing; and that functional limitation is still not a good proxy for wellbeing as used by
Hambleton et al. Grossman and even Smith and Kington [78].

       Globally, regionally and especially domestically, the most popular space in research
concerning wellbeing is the biomedical approach; its popularity is fuelled by the combination of
the traditional operational definition of health (good physical health) and the dominance of the
medical sciences in this field of enquiry.      The number of studies on mortality, structural
alterations and functional declines in body systems, genetic alterations induced by exogenous
and endogenous factors, prevalence and incidence of diseases, and certain diseases as
determinants of health, clearly justifies establishing leniency towards medical science in the
study of health and health care. Engel [10, 11] accredited the biomedical model that governs
health care to the practice of pundits over the last 300 years.        This model assumes that
psychosocial processes are independent of the disease process.        Engel argued for the bio-
psychosocial model that it includes biological, psychological, and social factors, which is a close
match to the multi-dimensional aspect of man. With this as the base, it can be construed from
Engel’s thrust behind the biopsychosocial model that the previous model is a reductionistic
model. Engel’s biopsychosocial model in analyzing health emphasizes both health and illness,
and maintains that health and illnesses are caused by a multiplicity of factors.           Engel’s
theorizing, therefore, is better fitted for the definition of health coined by the World Health
Organization.

       In Jamaica, only a miniscule number of studies have sought to analyze the effect of the
death of a family member or close friend, violence, joblessness, psychological disorders and
sexual abuse, on wellbeing, or social change on health, area of residence on quality of life and



                                                40
the perception of ageing and its influence on health conditions. Morrison [18] alluded to a
transitory shift from infectious communicable diseases to chronic non-communicable diseases as
a rationale for the longevity of the Anglophone Caribbean populace. This was equally endorsed
by Peña [79], the PAHO/WHO representative in Jamaica. They argued that this was not the only
reason for the changing life expectancy. Morrison summarized this adequately, when he said
that:

        Aiding this transition is not only the increased longevity being enjoyed by our islanders
        but also the changing lifestyle associated with improved socioeconomic conditions [18]



        With the post-1994 widened definition of health as put forward by the WHO, people are
becoming increasingly cognizant of the fact that socio-cultural factors such as geographical
location, income, household size and so on, as well as several psychological factors, explain
wellbeing; hence the new definition of health has coalesced biomedical variables and socio-
cultural and psychological variables in the new discourse on wellbeing.

        Stressors may arise from within the individual or outside his/her environment. One such
external stressor that may affect the individual is the death of loved ones. Response to the
mortality of close family members may be more traumatic, depending on expectancy or non-
expectancy. Bereavement influences the incidence of mortality. This may result in exhaustion of
the individual's 'adaptive reserve'. The person’s body wears down and becomes highly vulnerable
to morbidity and even death. Rice put forward a study that contradicted an association between
bereavement and mortality. He wrote that "Fathers who lost sons in war had lower mortality
rates than those who lost son in accidents" [75]. Despite that study, Rice quoted other studies
[80] that showed the impact of stress on human physiology. He argued that it is suppression
during and after bereavement that creates the stressors, which become potent devices for
mortality and morbidity. Lusyne, Page and Lievens’ [81] study finds that there is an association
between bereavement and mortality. However, this is more likely to occur in the short-run (i.e.
during the first 6 months after the death of the spouse). As there are a number of confounding
situations which in the long-run could offset the likelihood of mortality, such as remarriage,
social support from other family members, grandchildren and so on, bereavement may not
necessarily be a constant in one’s life. Nevertheless, Lusyne, Page and Lievens affirm with other


                                               41
studies that the loss of a long-time partner may result in the death of the living spouse. The
explanations given for this eventuality are – (i) role theory as the surviving partner may find the
role played by the other partner too stressful and so (ii) may not be able to adapt to the new role
alone; this is more a male phenomenon [81].

       The Planning Institute of Jamaica and Statistical Institute of Jamaica collect data on ill-
health, and questions are asked based on visits to health practitioners, healers and pharmacies,
injuries, ailments, ownership of health insurance, duration of the disease or illness, cost of
treatment for ailments and injuries, and mental disability. Those questions are clearly derivatives
from the biomedical model, as they seek to address physical functioning without equally
emphasizing culture, lifestyle behaviour, depression, stress, fatigue, trust for others, perception
of one’s position in current society and the likelihood of one’s place in the future, religiosity,
time periods, HIV/AIDS of family members or the individual and how it is likely to influence the
his/her health and wellbeing, social involvement in various institutions, and issues on positive
affective conditions.

5. CONCLUSION

       In sum, any definition of the construct of health must be multidimensional in nature.
Such a definition must include (1) personal and environmental conditions, (2) social factors, (3)
psychological conditions, (4) diagnosed illness, and (5) self-determination of wellbeing. If health
is solely based on illnesses (i.e. biomedical model), we would have failed in our bid to
operationally define a construct that is comprehensive enough to encapsulate all the tenets that
would capture man in his complex milieu. Health is not simply a construct. It plays a critical
role in the formulation of policy for health care, and in the development of the society. Thus, if
we emphasize only the biomedical approach to the study of health, its underpinnings could only
be symptomology. This approach fails to capture issues outside of the mechanistic structure of
man’s conception of biomedical sciences. Concurringly if health care professionals were to use
as their premise dysfunctions to indicate health, which is the deviation from the norm, this image
of health would affect policy formulation and intervention programmes which are geared
towards this narrow conceptualization. But this approach lacks are clear characteristics outside
of illnesses that will encapsulate wellness, wellbeing, and healthy life expectancy in a
multidimensional human. Thus, the biomedical model relies on illness identification to capture


                                                42
health and this fashions the health care system, which also limits health coverage outside of this
negative view of health. This is undoubtedly suboptimal, and does not account for health. The
health services in the Caribbean, and in particular Jamaica, are best described as medical
services, as they are still fundamentally structured around the biomedical model which is
embedded as the image of health, and not psychosocial, economic and ecological wellbeing.
Although the WHO as early as the 1940s provides a definition of health that is comprehensive
and complex, some scholars believe that it is elusive and by extension immeasurable. There are
merits to the argument of those academics, but the emphasis should not be the difficulty of how
operationalizing the construct labels it ‘elusive’.     Instead the goal should have been for
researchers and academics alike to formulate a working definition of the conceptual framework
created by the WHO. Thus, when Grossman in the 1970s moved away from the difficulty posed
by the WHO’s conceptual framework, he developed an econometric framework that laid the
foundation for the measure of this seemingly ‘elusive’ construct. Other scholars have built on
the initial theoretical model introduced by Grossman, and Bourne in particular has added
psychological and environmental conditions to the already established factors of the health
model. The constitution of the World Health Organization (WHO) states that “Health is a state
of complete physical, mental and social well-being and not merely the absence of diseases or
infirmity”, [3]. Hence, any use of morbidity statistics, dysfunctions, sickness, diseases or ill-
health to conceptualize health is limited, and by extension is a negative approach to the treatment
of this construct. Health, health care, and patient care are critical components in development, as
unhealthy people will not be able to offer to the society their maximum, neither will they be able
to comparatively contribute the same to productivity and production as their healthy
counterparts. Therefore, the conceptualization of health is not merely a concept but a working
product that affects all aspects of society.




                                                43
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    Involvement and Psychological Well-Being Among Urban Elderly African Americans.
    Journal of Counseling Psychology, 52, 583-590.
69. Edmondson, K.A., Lawler, K.A., Jobe, R.L., Younger, J.W., Piferi, R.L. and Jones, W.H.
    (2005) Spirituality predicts health and cardiovascular responses to stress in young adult
    women. Journal of Religion and Health, 44, 161-171.
70. Franzini, L., and Fernandez-Esquer, Maria Eugene. (2004) Socioeconomic, cultural, and
    personal influences on health outcomes in low income Mexican-origin individuals in
    Texas. Social Sciences and Medicine, 59, 1629-1646.
71. Graham, T. W., B. H. Kaplan, J. C. Cornoni-Huntley, S. A. James, C. Becker, C. G.
    Hames, and S. Heyden. (1978) Frequency of church attendance and blood pressure
    elevation. Journal of Behavioral Medicine, 1, 37-43.
72. Gardner, J.W., and Lyon, J.L. (1982) Cancer in Utah Mormon men by lay priesthood
    level. American Journal of Epidemiology, 116, 243-257.
73. Gardner, J.W., and Lyon, J.L. (1982) Cancer in Utah Mormon women by church activity
    level. American Journal of Epidemiology, 116, 258-265.
74. Taylor, S. (1999) Health psychology, 4th ed. United States of America: McGraw-Hill.
75. Rice, P. L. (1998) Health psychology. Brooks/Cole Publishing, Los Angeles.
76. Jamaica Social Policy Evaluation [JASPEV]. (2003). Annual Progress Report on
    National Social Policy Goals 2003. Cabinet Office, Kingston.
77. Grossman, M. (1972) The demand for health- a theoretical and empirical investigation.
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78. Smith, J. P., and Kington, R. (1997) Demographic and Economic Correlates of Health in
    Old Age. Demography, 34, 159-170.
79. Peña, M. (2000) Opening Remarks and Greetings from the Pan American Health
    Organization. Cajanus, 33, 64-70.
80. Jemmott, J.B., and Locke, S.E. (1984) Psychosocial factors, immunologic mediation,
    and human susceptibility to infectious diseases: How much do we know? Psychological
    Bulletin, 95:78-108. In Health Psychology, P. L. Rice. 1998. Brooks/Cole, Los Angeles.
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    Belgium 1991-96: The unexpected effect of education. Population studies, 55, 281-28.




                                          48
CHAPTER 3



Why demographers should study wellbeing of the aged?

Traditionally, demographers have sought to analyze and provide information on health from the

perspective of mortality. From the mortality tenet, they have captured and measure health status,

by using life expectancy and diseases. Life expectancy may be an adequate indicator of length of

life and from a biomedical perspective a yardstick for health status, but such a construct is not in

keeping with the conceptual definition furnished by the World Health Organization – that “health

is a state of complete physical, mental and social wellbeing and not merely the absence of

diseases or infirmity”. It should be noted that this conceptual definition which is in the Preamble

to the constitution of the WHO which was signed in July 1946 and became functional in 1948,

according to one scholar, from the Centre of Population and Development studies at Harvard

University, it is mouthful of sweeping generalization, that is difficult to attain, and at best it is a

phantom. (Bok 2004). This paper recognizes this debate, and understands its importance but

unfortunately will not be providing information hereafter thereon as it is not the purpose of this

study. Is there a shift taking place in demography, as the journal ‘Demography’, 1997, has

published an article by Smith and Kington who looked at health status from biopsychological

conditions? Is this an indicator of shift taking place as some universities do have one department

called epidemiologic and population studies, population and development studies, population and

public health studies?


       Now, there is a need not to subjectively qualify the health status of older people, but to

provide a demographic study of this group’s wellbeing. As demographic information is




                                                  49
paramount in the analysis of the health status of the elderly, it is highly probable that such a

study will provide the bedrock upon which invaluable information can be garnered on this group.

In the past, demographers such as Shryock, Siegal and Associates (1976) did not see it fitting to

study and provide information on health in their textbook ‘The Methods and Materials of

Demography’ primarily because they were more concerned about issues such as – mortality, life

expectancy, fertility, projections and estimations, population composition and distribution.

Nevertheless, the issue of health, health status and wellbeing has increasingly become an

important issue to demographers so much so that in the second edition of the text “The Methods

and Material of Demography” edited by Siegal and Swanson; the authors included an entire

chapter on health (Chapter 14, 341-370). Our world is always evolving, and with this comes a

new set of questions that the old paradigm may be unable to address. This is one of the

dynamics of science, addressing new issues that cannot be fitted within the old epistemology of

the paradigm, which is there to explain particular phenomenon. Demographers have been for

years assessing life expectancy and diseases as indicators of health status of people (see for

example Elo 2001) - believing that it is the best measure to evaluation a people’s general

wellbeing, - but with the increases in non-communicable ailments it is obvious that longevity

does not pronounced quality lived years (see for example Jamshidi et al. 1992, 172). In keeping

with the science of the disciplines, demographers like Siegel and Swanson and Professor Emily

Grundy have recognized the importance of the study of ageing, quality of life of the lived person,

and other issues surrounding health of a population in keeping with the shift in population ageing

across the globe. The old paradigm was not able to account for distinction between the lived

years and the quality of lived years, hence, this is one of the primate reason begin the paradigm

shift in health focus by demographers.      Evidence in paradigm shift is so clear today that




                                                50
Professor Emily Grundy is not only a demographer but she is a demographic gerontologist. She

have done extensive work on ageing, quality of lived experience of the aged, demography and

public health and ageing policy studies.

       Among the many challenges of contemporary societies is the reality of demographic

transition. This implies that the human population is living longer but that this is within the

context of reduced mortality and fertility along with increases in diseases from the epidemiologic

transition. Accompanying the changes in the population structure is not only the shifts which are

likely to present populations but the delay demographic shift that will continue if they are not

reversed. Professor Grundy is keeping with the challenges of health woes which are likely to

befall contemporary societies if policy makers do not act today as a precursor for this

tomorrow’s problem. Knowing that demography studies population dynamics, in particular

mortality, morbidity, fertility and migration, it would be simplistic if they do not venture in the

study of ageing from the perspective of characteristics of the age-cohorts 65 and beyond years, as

well as the interrelation between population change and human health. Those issues had to be

resolved, and so who are more competent than demographers and/or demographic gerontologists

to examine population issues – with which health and ageing falls. In attempt to understand the

determinants of the population ageing, health must be brought within the existing model. So by

introducing health within the old model, a number of discourses have begun to emerge which

include (i) differential of longevity and quality lived years; (ii) public health and longevity; (iii)

the feminization of population ageing, (iv) health issues– physical limitation and frailty,

wellbeing, nutritional deficiencies- and health care costing of population ageing, and (v) health

transition – the composite of epidemiologic transition and ‘response of society to health and

disease processes’.



                                                  51
       These shifts are in keeping with the world population ageing. Their implications include

fertility transition (decline), mortality decline, old-age dependency ratios, challenges to public

health, along with the economic development indictors such as labour force participation rates,

and lowered savings.     Thus, population ageing is not simply an indicator of demographic

transition but that it speaks to a whole shift in the socio-economic indicators and health

consequences.      With this said, demographers have twinned health and demography.

Demography is concerned with understanding population dynamics, which include fertility,

mortality, migration and life expectancy.        From all indications, with demography being

concerned about how population changes occur, health is one such consequence.

       In “The Methods and Material of Demography”, Lamb and Siegel commence the chapter

on Health Demography with the following statement: “Health is a leading characteristic of the

members of a population, akin other demographic and socioeconomic characteristics” (Lamb and

Siegel 2004, 341). They did not cease there but continue that “...increased life expectancy, …has

shifted the focus of population health from quantity of life to the quality of life, …” Embedded

in this thesis is the importance that demographers must now place on quality of life as against the

quantity of life (life expectancy). Thus, the length of life expectancy cannot be used as an

indicator of health as the absence of ailments is not necessarily an indicator of a ‘good’ quality of

life experienced by an individual. As shown in the JSLC (1997-2002), that the aged populace

have the highest rate of number of days spent in health care, and they share the highest

proportion of illnesses and ailment with the children (less than 5 years. These are clearly

indicators that longer life is not necessarily spent as healthier days. With the measures related to

functioning being increasingly an issue for the elderly, the wellbeing of this group must be

studied from a demographer’s vantage point.



                                                 52
       Spiegelman (1980) outlines the importance of health in demography, which explains his

rationale for the inclusion of ‘Health Statistics’ in chapter 7 of the text ‘Introduction to

Demography’. In this, the author furnishes definitions, and he emphasizes the significance of

‘attitude toward health maintenance.’ Still little attention was given to ‘quality of health-care’

outside of morbidity. One of the ironies of this text is the author’s recognition that

       ..Health statistics encompasses not only morbidity statistics, but also data relating to its
       socio-economic correlates including health attitudes and to utilization of health services
       (Spiegelman 1980, 171)
Nevertheless, despite his acceptance of the importance of socio-economic variables including

those two to which he refers, the text’s primary focus is on morbidity, which is keeping with a

uni-directional approach to the study of health. Hence, this study is timely. It will be able to

provide a new focus in the study of health demography in Jamaica as well as providing a more

holistic understanding of the state of the elderly.

       One scholar who works with the London School of Hygiene and Tropical Medicine,

University of London, Professor Emily Grundy, have coined both medical demography and

social gerontology in order to study ageing, ageing and wellbeing, demography and health, life

cycle influences on migration, fertility annals and health in later years, and primarily on the

demography of ageing with a public health and policy focus. Her professorial status is in

demographic gerontology, which speaks to the linkage that has created between social

gerontology and medical demography. By the merging the two fields into one, Professor Grundy

has sought to highlight the need to understand population ageing, demographic transition,

mortality and morbidity patterns, health outcomes, and determinants which explain the state of

people who happen to have had escape many of the challenges of mortality’s battles.

       Embedded in Professor Grundy’s works is the recognition that the negative



                                                  53
functionality’s consequences of ageing are vital; and so is ageing from demographic vantage

points, which include a detailed analysis of longevity beyond mortality and fertility while

incorporating all the likely conditions that may influence this demographic transition. Other

focuses of Professor Grundy’s demographic gerontology are – (i) questioning the differential

between longevity and quality life; (ii) understanding the implications of present population

ageing and the role that it will play on future population age structure; (iii) recognizing that

population ageing must be met with a coordinated effort to play for its future socio-economic

challenges such as lowered labour force participation, Medicare expenditure, and (iv) the burden

that the working class will need to absorbed in order to afford the aged population. Despite the

paved way that has been set for Caribbean demographers, limited works exist on the aged from a

demographic perspective. From a demographic gerontology view point, increasingly fewer

works exist in the Caribbean and more so Jamaica from demographers. Therefore, the time is

right for a demographer to explore demographic gerontology as the discipline is a

multidimensional drawn from economics, sociology, geography, epidemiology, and gerontology.

Hence, this work is a move afoot in this direction of integrative demography – the use of

demographic gerontology within the Jamaican context.



Contextualizing wellbeing, from the aged perspective


       Ageing is a significant but neglected dimension of social stratification and the life-course
       is an essential component of the analysis of status (Turner 1998, 299)
       Ageing has emerged as a global phenomenon in the wake of the now virtually universal
       decline in fertility and, to a lesser extent, of increases in life expectancy (Marcoux 2001,
       1)



       In developing countries over the last fifty years, the issue of age and senior citizens’


                                                54
quality of life has taken center stage in development planning on a planetary scale. The cogent

reason for this growing interest is largely due to an understanding by the populace that wellbeing

is not necessarily simply a function of material wealth but is most frequently used as what is

‘non-instrumental’ or eventually good for a person. Embedded in this reality are the

psychosocial, environmental and economic conditions that are likely to result from the socio-

demographic transition of ageing. Associated with population ageing are quality of life issues

that go far beyond geriatric care, genetics, and determinants of life expectancy. Wellbeing of the

elderly extends outside the popular use of physical functioning. What is wellbeing? According

to the Webster’s lexicon, wellbeing is ‘a state of being happy, healthy, or prosperous’. Good

physical health is, therefore, a precondition for wellbeing, but happiness constitutes good life for

an individual, which are based on many elements. Moreover, do not forget, from Marcoux

(2001) account, ageing is a global phenomenon that is here to stay. Furthermore, we should not

turn a blind eye ageing as was pointed out by Turner (1998).




                                                 55
CHAPTER 4

Wellbeing Discourse

The term wellbeing is used interchangeable with words such as ‘happiness’, ‘life satisfaction’,

and ‘welfare’ by a number of researchers and/or people in intelligentsia (for example by Diener

1984; Easterlin 2003; Veenhoven1993). While some scholars argue that happiness and life

satisfaction are but a fraction of wellbeing, what is embedded in Diener and Easterlin’s usage of

those terminologies instead of wellbeing aptly showed that, within the context of

multidisciplinary global market place in which people must operate, the quality of life that

people enjoy (or not enjoy) must be understood before the goals of policy, planning and decision

on wanting to improve welfare, quality of life and/or standard of living of a people can

materialize.


       Diener et al. (1999) forward a perspective that emphasized the importance of hindrance to

wellbeing and limitations to its expansion. They write that:




       The influence of genetics and personality suggests a limit on the degree to which policy
       can increase SWB [subjective wellbeing] . . . Changes in the environment, although
       important for short-term wellbeing, lose salience over time through processes of
       adaptation, and have small effects on long-term SWB (Diener, Suh, Lucas and Smith
       1999, 227)


       It is clear from the writing of Diener et al that policy implementation and execution offers

little to change the subjective wellbeing of people, and this it would appear must be equally the

same for the elderly. Contrary to this stance, economics theorists suggested that wellbeing can

be expanded by income and employment (see for example Oswald 1997, Pigou 1932), which


                                                56
was also supported by Keister (2003). According to Keister [in an article titled Sharing the

Wealth: The effect of sibling on adult’s wealth ownership, forwarded that] there is “…little doubt

that material resources can improve quality of life and well...” (Keister 2003, 522). Wellbeing,

therefore, can be improved with time through material resources, which is counter to the

particular perspective lauded by some psychologists (see for example Diener ey al. 1999).

       Happiness, according to Easterlin (2003) is associated with wellbeing, and so does ill-

being (for example depression, anxiety, dissatisfaction). Easterlin (2003) argued that material

resources have the capacity to improve ones choices, comfort level, state of happiness and

leisure, which militates against static wellbeing. Within the context that developing countries

and developed countries had at some point accepted the economic theory that economic

wellbeing should be measured by per capita Gross Domestic Product (GDP) – (i.e. total money

value of goods and services produced within an economy over a stated period per person).

Amartya Sen, who is an economist, writes that plethora of literature exist that show that life

expectancy is positively related to Gross National Product (GNP) per capita. (Anand and

Ravallion 1993; Sen 1989, 8). Such a perspective implies that mortality is lower whenever

economic boom exists within the society and that this is believed to have the potential to increase

development, and by extension standard of living. Sen, however, was quick to offer a rebuttal

that data analyzed have shown that some countries (i.e. Sri Lanka, China and Costa Rica) have

had reduced mortality without a corresponding increase in economic growth (Sen 1989, 9), and

that this was attained through other non-income factors such as education, nutrition

immunization, expenditure on public health and poverty removal. The latter factors,

undoubtedly, require income resources and so this is clear that income is unavoidable a critical

component in welfare and wellbeing. It is believed by some scholars that economic growth



                                                57
and/or development is a measure of welfare (see Becker, Philipson and Soares 2004).

        Therefore, those studies on economic wellbeing were able to offer a plethora of answers

to national governments on the health status of the people, or wellbeing and/or ill-being of the

citizens. No policy formulation on improving the quality of life of citizens of a particular space

should precede without firstly unearthing ‘real’ determinants of wellbeing. From Crisp’s

perspective (2005), wellbeing is related to health, and the strength of those associations, and

secondly planning requires information that is made available by research. Is traditional

economists’ operationalization of wellbeing still applicable in contemporary societies, knowing

that this is purely objective?

        If happiness is a state of wellbeing, then if we were to impute depression, anxiety, stress,

and illness and/or physical incapacitation, spirituality and environment within the objective

measurement of wellbeing, a more holistic valuation would be reached. With the inclusion of

subjectivity conditions in the measurement wellbeing, we come closer to an understanding of

people’s state of wellness, health and quality of life. As even better nutrition, efficient disposal

of sewage and garbage, and a health lifestyle also contribute to health status (i.e. wellbeing). It

should be noted that the biomedical model that is objective, conceptualizes health as the absence

of diseases. This leads to the question, are any of the following diseases – (i) depression, (ii)

stress, (iii) fatigue, and (iv) obsession? Hence, an issue arises, does the lack of objectivity means

accept with skepticism?

        Two medical doctors writing in Kaplan and Saddock’s Synopsis of Psychiatry noted that

physicians are frequently caught in theorizing that normality is a state of health (Saddock, and

Saddock 2003). They argued that doctors’ definition of normality correlates with a traditional

model (biomedical) that emphasizes observable signs and symptoms. Using psychoanalytic



                                                 58
theories, Saddock and Saddock remarked that the absence of symptoms as a single factor is not

sufficient for a comprehensive outlook on normality.          They stated, “accordingly, most

psychoanalysts view a capacity for work and enjoyment as indicating normality…” (Saddock

and Saddock 2003, 17). Among the challenges with this method (biomedical model), is its

emphasis only on curative care. Such an approach avoids the importance of life style and

preventative care. In that, health is measured based on experiences with illnesses and/or ailment,

with limited recognition being placed on approaches that militates against sickness and /or

diseases. The biomedical approach is somewhat biased against an understanding of multi-

dimensional man, which is not keeping with the holistic conceptualization of health as offered by

the World Health Organization (WHO).

        If we intend to utilize the multi-faceted definition of health as forwarded by the WHO,

the absence of physical illnesses are insufficient as an approach in analyzing wellbeing. It is

generally perceived that the biological system of the aged will degenerate (Saddock and Saddock

2003; Parshad et al. 1987). One expert thoroughly penned the sentiments of innumerable persons

that “the elderly represents a vulnerable group with several medical conditions, degenerate

diseases, cancer …” (Grell 1987, 7). Hence, wrongfully, we conceptualize ill-health primarily

using physical capacities and cognitive abilities.

       In the 1950s, George Engel, a physician, teamed with John Romano, a young psychiatrist,

to develop a biopsychosocial model, into the curriculum of University of Cincinnati College of

Medicine, which measured the health status of people.             It is referred to as Engel’s

biopyschosocial model.      Engel’s biopsychosocial model (in Brown 2000), recognizes that

psychologic and social factors coexisted along with biologic factors. It was a general theory of

illness and healing, a synergy between medicine, psychiatry and the behavioural sciences (in



                                                 59
Dowling 2005). Therefore, from Engel model, wellbeing must include factors such as –

motivation, depression (or the lack thereof), biologic conditions (such as illnesses and diseases),

social systems, cultural, environmental and familial influences on the appearance and

occurrences of illness.

       Nevertheless, according to demographers (Crimmins, Hayward and Saito 1994; Portrait

et al 2001), the terminology, because of its multidimensional tenets, is conceptualized using

indicators of “functioning ability”. These pundits categorized “functioning ability” as – (i) being

able to provide both personal care and independent living but having some difficulty in

performing these tasks or in getting about outside the home, (ii) having no functioning

difficulties, (iii) being unable to independently provide personal care, and finally (iv) being able

to provide personal care but not able to manage life in the home independently” (Crimmins,

Hayward and Saito 1994, 162).


       The concept of health according to the WHO is multifaceted. “Health is state of complete

physical, mental and social well, and not merely being the absence of disease or infirmity”

(Whang 2005, 153). From the WHO’s perspective, health status is an indicator of wellbeing

(See also, Crisp 2005). Wellbeing for some, therefore, is a state of happiness – positive feeling

status and life satisfaction (see for example, Easterlin 2003; Diener, Larson, Levine, and

Emmons 1985; Diener 1984) satisfaction of preferences or desires, health or prosperity of an

individual (Diener and Suh 1997; Jones 2001; Crips 2005; Whang 2005), or what psychologists

refer to as positive effects (Headey and Wooden 2004). Simply put, wellbeing is subjectively

what is ‘good’ for each person (See for example, Crisp 2005). It is sometimes connected with

good health. Crisp offered an explanation for this, when he said that “When discussing the

notion of what makes life good for the individual living that life, it is preferable to use the term


                                                 60
‘wellbeing’ instead of ‘happiness” (Crisp 2005), which explains the rationale for this project

utilizing the term wellbeing and not good health.


       In order to forward an understanding of what constitutes wellbeing or ill being, a system

must be instituted that will allow us to coalesce a measure that will unearth peoples’ sense of

overall quality of life from either economic-welfarism (see Becker et al. 2004) or psychological

theories (Diener, Suh, and Oishi, 1997; Headey and Wooden 2004; Kashdan 2004; Diener 2000).

This must be done with the general construct of a complex man. Economists like Smith and

Kington, and Stutzer and Frey as well as Engel believe that state of man’s wellbeing is not only

influenced by his/her biologic state but that is always dependent on his/her environment,

economic and sociologic conditions. Some studies and academics have sought to analyze this

phenomenon within a subjective manner by way of general personal happiness, self-rated

wellbeing, positive moods and emotions, agony, hopelessness, depression, and other

psychosocial indicators (Arthaud-day et al. 2005; Diener et al. 1999; Skevington et al.1997;

Diener 1984).


       An economist (Easterlin) studying happiness and income, of all social scientist, found an

association between the two phenomena (Easterlin 2001a, 2001b), (see also Stutzer and Frey

2003). He began with a statement that “the relationship between happiness and income is

puzzling” (Easterlin 2001a, 465), and found people with higher incomes were happier than those

with lower incomes – he referred to as a correlation between subjective wellbeing and income

(see also, Stutzer and Frey 2003, 8). He did not cease at this juncture, but sought to justify this

realty, when he said that “those with higher income will be better able to fulfill their aspiration

and, and other things being equal, on an average, feel better off” (Easterlin 2001a, 472).




                                                61
Wellbeing, therefore, can be explained outside of welfare theory and/or purely on objectification-

objective utility (See for example, Kimball and Willis 2005; Stutzer and Frey 2003).


       Whereas Easterlin found a bivariate relationship between subjective wellbeing and

income, Stutzer and Frey revealed that the association is a non-linear one. They concretized the

position by offering an explanation that “In the data set for Germany, for example, the simple

correlation is 0.11 based on 12, 979 observations” (Stutzer and Frey 2003, 9). Nevertheless, from

Stutzer and Frey’s findings, a position association does exist between subjective wellbeing and

income despite difference over linearity or non-linearity.


       The issue of wellbeing is embodied in three theories – (1) Hedonism, (2) Desire, and (3)

Objective List. Using ‘evaluative hedonism’, wellbeing constitutes the greatest balance of

pleasure over pain (See for example, Crisp 2005; Whang 2005, 154). With this theorizing,

wellbeing is just personal pleasantness, which represents that more pleasantries an individual

receives, he/she will be better off. The very construct of this methodology is the primary reason

for a criticism of its approach (i.e. ‘experience machine’), which gave rise to other theories.

Crisp (2005) using the work of Thomas Carlyle described the hedonistic structure of

utilitarianism as the ‘philosophy of swine’, because this concept assumes that all pleasure is on

par. He summarized this adequately by saying that “… whether they [are] the lowest animal

pleasures of sex or the highest of aesthetic appreciation” (Crisp 2005).


       The desire approach, on the other hand, is on a continuum of experienced desires. This

is popularized by welfare economics. As economists see wellbeing as constituting satisfaction of

preference or desires (Crisp 2005, 7; Whang 2005, 154), which makes for the ranking of

preferences and its assessment by way of money. People are made better off, if their current




                                                 62
desires are fulfilled. Despite this theory’s strengths, it has a fundamental shortcoming, the issue

of addiction. This forwarded by the possible addictive nature of consuming ‘hard drugs’ because

of the summative pleasure it gives to the recipient.


        Objective list theory: This approach in measuring wellbeing list items not merely

because of pleasurable experiences nor on ‘desire-satisfaction’ but that every good thing should

be included such as knowledge and-or friendship. It is a concept influenced by Aristotle, and

“developed by Thomas Hurka (1993) as perfectionism” (Crisp 2005).                  According to this

approach, the constituent of wellbeing is an environment of perfecting human nature. What goes

on an ‘objective list’ is based on reflective judgement or intuition of a person. A criticism of this

technique is elitism (Crisp 2005). Since an assumption of this approach is that, certain things

are good for people. Crisp (2005) provided an excellent rationale for this limitation, when he

said that “…even if those people will not enjoy them, and do not even want them”.


        In Arthaud-day et al work, applying structural modeling, subjective well was found to

constitute “(1) cognitive evaluations of one's life (i.e., life satisfaction or happiness); (2) positive

affect; and (3) negative affect.”      Subjective wellbeing, therefore, is the individual’s own

viewpoint. If an individual feels his/her life is going well, then we need to accept this as the

person’s reality. One of drawbacks to this measurement is, it is not summative, and it lacks

generalizability.


        Studies have shown that subjective wellbeing can be measured on a community level

(Bobbit et al.2005; Lau 2005; Boelhouwer and Stoop 1999) or on a household level (Lau 2005;

Diener 1984), whereas other experts have sought to use empiricism (biomedical indicators -

absence of disease symptoms, life expectancy; and an economic component - Gross Domestic




                                                   63
Product per capita; welfarism - utility function).


       Powell (1997) in a paper titled ‘Measures of quality of life and subjective wellbeing’

argued that psychological wellbeing is a component of quality of life. He believed that in this

measurement in particular for the older, must include Life Satisfaction Index, as this approach

constitutes a number of items based on “cognitively based attitudes toward life in general and

more emotion-based judgment”(Powell 1997).            Powell addressed this from two-dimensions.

Where those means are relatively constant over time while in seeking to unearth changes in the

short-run, ‘for example an intervention’, procedures that mirror changed states may be

preferable. This can be assessed by way of a twenty-item Positive and Negative Affect Schedule

or from a ten-item Philadelphia Geriatric Centre Positive Affect and Negative Affect Scale

(Powell 1997).


       In a reading titled ‘Objective measures of wellbeing and the cooperation production

problem’, Gaspart (1998) provided arguments that support the rational behind the objectification

of wellbeing. His premise for objective quality of life is embedded within the difficulty as it

relates to consistency of measurement when subjectivity is the construct of operationalization.

This approach takes precedence because an objective measurement of concept is of exactness as

non-objectification; therefore, the former receives priority over any subjective preferences. He

claimed that for wellbeing to be comparable across individuals, population and communities,

there is a need for empiricism.


       Gaspart discussed a number of economic theorizing (Equal Income Walrasian equilibria,

objective egalitarianism, Pareto efficiency; Wefarism), which saw the paper expounding on a

number of mathematical theorems in order to quantify quality of life. Such a stance proposes




                                                 64
that human predictable, rational from which we are able to objectify their plans. The very

axioms cited by Gaspart emphasized particular set of assumptions that he used finalizing a

measurement for wellbeing for man who is a complex social animal. The researcher points to a

sentence that was written by Gaspart that speaks to the difficulty of objective quality of life; he

wrote, “So its objectivism is already contaminated by post-welfarism, opening the door to a

mixed approach, in which preferences matter as well as objective wellbeing” (Gaspart 1998).

Another group of scholars emphasized the importance of measuring wellbeing outside a

welfarism and/or purely objectification, when they said that “Although GDP per capita is usually

used as a proxy for the quality of life in different countries, material gain is obviously only one

of many aspects of life that enhance economic wellbeing” (Becker et al. 2004, 1), and that

wellbeing depends on both the quality and the quantity of life lived by the individual (see also

Easterlin 2001). This is affirmed in a study carried out by Lima and Nova (2006), that found

happiness, general life satisfaction, social acceptance and actualizations are all directed related to

GDP per capita for a geographic location (see Lima and Nova 2006, 9). Even though in Europe

these were found not to be causal, income provides some predictability of subjective wellbeing

more so in poor countries than in wealthy nations. (see Lima and Nova 2006, 11)


       It should be understood that GDP per capita speaks of the market economic resources

which are produced domestically within a particular geographic space. So increased production

in goods and/or services may generate excess which can then be exported, and vital products

(such as vaccination, sanitary products, vitamins, iron and other commodities) can be purchased

that is able to improve the standard of living and quality of the life of the same people over the

previous period. One scholar (Caldwell 1999) has shown that life expectancies are usually higher

in countries with high GDP per capita, which means that income is able to purchased better



                                                 65
quality products, which indirectly affects the length of years lived by people. This realty could

explain why in economic recession, war and violence, when the economic growth is lower (or

even non-existence) there is a lower life expectancy. Some of the reasons for these justifications

are government’s unavailability to provide for an extensive population in the form of nutritional

care, public health and health-care services. Good health is, therefore, linked to economic

growth, which further justifies why economists use GDP per capita as an objective valuation of

standard of living; and why income should indefinitely be a component in the analysis of health

status. There is another twist to this discourse as a country’s GDP per capita may be low, but the

life expectancy is high because health care is free for the population. Despite this fact, material

living standards undoubtedly affect the health status and wellbeing of a people, as well as the

level of females’ educational attainment.


       Ringen (1995) in a paper titled ‘Wellbeing, measurement, and Preferences’ argued that

non-welfarist approaches to measuring wellbeing are possible despite its subjectivity. The direct

approach for wellbeing computation through the utility function according to Ringen is not a

better quantification as against the indirect method (i.e. using social indicators). The stance

taken was purely from the vantage point that utility is a function ‘not of goods and preferences’

but of products and ‘taste’. The constitution of wellbeing is based on choices. Choices are a

function of individual assets and options. With this premise, Ringen forwarded arguments which

show that people’s choices are sometimes ‘irrational’, which is the make for the departure from

empiricism.


       Wellbeing can be computed from either the direct (i.e. consumption expenditure) or the

indirect (i.e. disposable income) approach (Ringen 1995, 8). The former is calculated using

consumption expenditure, whereas the latter uses disposable income. Rigen noted that in order


                                                66
to use income as a proxy for wellbeing, we must assume that (1) income is the only resource, and

(2) all persons operate in identical market places. On the other hand, the direct approach has two

key assumptions. These are (1) what we can buy is what we can consume and (2) and that what

we can consume, is an expression of wellbeing. From Rigen’s monograph, the assumptions are

limitations.


        In presenting potent arguments in favour of non-empiricism in the computation of

wellbeing, Ringen highlighted a number of drawbacks to welfarism. According to Ringen:


        Utility is not a particular good criterion for wellbeing since it is a function not only of
        circumstances and preferences, but also of expectation. In the measurement of wellbeing,
        respect for personal preferences is best sought in non-welfarist approaches that have the
        quality of preference neutrality; …As soon, as preferences are brought into the concept of
        wellbeing cannot but be subjective. (Ringen 1995, 11)



        The difficulties in using empiricism to quantify wellbeing has not only be


Forwarded by Ringen as O’Donnell and Tait (2003) were equally forthwith in


arguing there were challenges in measuring quality of life quantitatively. O’Donnell and Tait

believed that health is a primary indicator of wellbeing. Hence, self-rated health status is a highly

reliable proxy of health which “successfully crosses cultural lines” (O’Donnell and Tait 2003,

20). They argued self-reported health status can be used as they found that all the respondents of

chronic diseases indicated that their health was very poor.


        To capture the state of the quality of life of humans, we are continuously and increasingly

seeking to ascertain more advance methods that will allow us to encapsulate a quantification of

wellbeing that is multidimensional and multifaceted (Pacione, 2003). Therefore, an operational




                                                  67
definition of wellbeing that sees the phenomenon on single dimension such as physical health

(Steward and King 1994), medical perspective (Farquhar 1995), material (Lipsey 1999) and

would have excluded indicators such as crime, education, leisure facilities, housing, social

exclusion and the environment (Pacione 2003; Campbell et al 1976) as well as subjective

indicators cannot be an acceptable holistic measurement of this construct. This suggests that

wellbeing is simply not a single space; and so, the traditional biomedical conceptual definitions

of wellbeing exclude many individual satisfactions and in the process reduce the tenets of a

superior coverage of quality of life.


       One writer noted that the environment positively influenced the quality of life (Pacione

2003, 20) of peoples; in order to establish validity and reliability of wellbeing, empirical data

must include issues relating to the environment. The quality of the environment is a utilized

condition in explaining elements of quality of life of people. Air and water quality through

industrial fumes, toxic waste, gases and other pollutants affect environmental quality. This

directly related to maintenance or lack thereof of societal and personal wellbeing (Pacione 2003).


       Studies have conclusively shown that environmental issues such as industrial fumes and

gases, poor solid waste management, mosquito infestation and poor housing are likely to result in

physiological conditions like respiratory track infections (for example lung infection), asthma.


       According to Langlois and Anderson (2002), approximately 30 years ago, a seminal

studies conducted by Smith (1973, 2) “proposed that wellbeing be used to refer to conditions that

apply to a population generally, while quality of life should be limited to individuals’ subjective

assessments of their lives …” They argue that a distinction between the two variables have been

lost with time.    From Langlois and Anderson’s monograph, during the 1960s and 1970s,




                                                68
wellbeing was approached from a quantitative assessment by the use of GDP or GNP (also See,

Becker, Philipson and Soares 2004), and unemployment rates; this they refer to as a “rigid

approach to the [enquiry of the subject matter] subject”. According to Langlois and Anderson

(2002), the positivism approach to the methodology of wellbeing was objectification, an

assessment that was highly favoured by Andrews and Withley, 1976 and Campbell et al 1976.


       In measuring quality of life, some writers have thought it fitting to use Gross Domestic

Product per capita (i.e. GDP per capita) to which they referred to as standard of living (Lipsey

1999; Summers and Heston 1995; Hanson 1986). According to Summers and Heston (1995),

“The index most commonly used until now to compare countries' material wellbeing is their

GDP   POP' .”   The United Nations Development Programme has expanded on the material

wellbeing definition forwarded primarily by economists, and has included life expectancy and

educational attainment (Human Development Reports, 2005, p. 341) and other social indicators

(Diener 1984; Diener and Suh 1997). This operational definition of wellbeing has become

increasingly popular in the last twenty-five years, but given the expanded definition of health as

cited by the WHO, wellbeing must be measured in a more comprehensive manner than using

material wellbeing as seen by economists.


       Despite the fact that quality of life extends beyond the number of years of schooling and

material wellbeing, generally wellbeing is substantially construed as economic phenomenon.

Embedded within this construct of a measure is the emphasis on economic resources, and we

have already establish that man’s wellbeing is multifaceted. Hence, any definition of the quality

of life of people cannot just simply analyze spending or the creation of goods and/or services that

are economically exchangeable, number of years of schooling and life expectancy but this must

include the psychosocial conditions of the people within their natural environment.


                                                69
       GDP is the coalesced sum of all economic resources of people in certain topography, so

this does not capture the psychosocial state of the man in attaining the valued GDP. By this

approach, we may arrive at a value that is higher than in previous periods, making it seem as

though people are doing very well. However, with this increase in GDP, this single component is

insufficient to determine wellbeing. As the increase in GDP may be by (1) more working hours,

(2) higher rates of pollutions and environmental conditions, (3) psychological fatigue, (4) social

exclusion, (5) human ‘burn out’, (6) reduction in freedom, (7) unhappiness, (8) chronic and acute

diseases and so forth.     Summers and Heston (1995) note that “However, GDP POP is an

inadequate measure of countries' immediate material wellbeing, even apart from the general

practical and conceptual problems of measuring countries' national outputs.” Generally, from

that perspective, the measurement of quality of life is, therefore, highly economic and excludes

the psychosocial factors, and if quality of life extends beyond monetary objectification.


       In developing countries, Camfield (2003) in looking at wellbeing from a subjective

vantage point notes that Diener (1984) argues that subjective wellbeing constitute the existence

of positive emotions and the absence of negative ones within a space of general satisfaction with

life. According to Camfield, Cummins’ (1997) perspective subsumed ‘subjective and objective

measures of material wellbeing’ along with the absence of illnesses, efficiency, social closeness,

security, place in community, and emotional wellbeing, which implies that “life’s satisfaction”

comprehensively envelopes subjective wellbeing.


       Diener (2000) in an article titled ‘Subjective Wellbeing: The Science of Happiness and a

Proposal for a National Index’ theorizes that the objectification of wellbeing is embodied within

satisfaction of life. His points to a construct of wellbeing called happiness.




                                                 70
       He cited that:


       People's moods and emotions reflect on-line reactions to events happening to them. Each
       individual also makes broader judgments about his or her life as a whole, as well as about
       domains such as marriage and work. Thus, there are a number of separable components
       of SWB [subjective wellbeing]: life satisfaction (global judgments of one's life),
       satisfaction with important domains (e.g., work satisfaction), positive affect
       (experiencing many pleasant emotions and moods), and low levels of negative affect
       (experiencing few unpleasant emotions and moods). In the early research on SWB,
       researchers studying the facets of happiness usually relied on only a single self-report
       item to measure each construct (Diener, 2000, 34).




       Diener’s theorizing on wellbeing encapsulates more than the marginalized stance of other

academics and researchers who enlightened the discourse with economic, psychosocial, or

subjective indicators. He shows that quality of life is multifaceted and coalescing economic,

social, psychological and subjective indicators is far more reaching in ultimately measuring

wellbeing. This work shows a construct that can be used to operationalize a more

multidimensional variable, wellbeing, which widens the tenet of previous operational definition

on the subject.      From the theorizing of various writers, it is clear that wellbeing is

multidimensional, multidisciplinary and multispatial. Some writers emphasize the environmental

components of subject matter (Lui 1976; Pacione 1984; Smith 1973), psychosocial aspect

(Clarke and Ryff 2000) and from a social capital vantage point (Glaeser 2001; Putnam 1995;

Woolcock 2001).


       According to Smith and Kington (1997), using H t = f (H t-1 , P m G o , Bt , MC t ED, Ā t , ) to

conceptualize a theoretical framework for “stock of health” noted that health in period t, Ht, is

the result of health preceding this period (H t-1) , medical care (MC t) , good personal health (G o) ,

the price of medical care (P m ), and bad ones (Bt) , and a vector of family education (ED), and all


                                                  71
sources of household income (Ā t ). Embedded in this function is the wellbeing that individual

enjoys (or not enjoys) (see Smith and Kington 1997, 159-160).


       In seeking to operationalize wellbeing, the United Nations Development Programme

(UNDP) in the Human Development Reports (1997, 2000) conceptualized human development

as a “process of widening people’s choice as well as the level of achieve wellbeing”. Embedded

within this definition is the emphasis of materialism in interpreting quality of life. From the

UNDP’s Human Development (1993), the human development index (HDI) “…is a normative

measure of a desirable standard of living or a measure of the level of living”, which speaks to the

subjectivity of this valuation irrespective of the inclusion of welfarism (i.e. gross domestic

product (GDP) per capita). The HDI constitute adjusted educational achievement (E= a 1 *

literacy + a 2 * years of schooling, where a1, = 2/3 and a2 = 1/3), life expectancy (demographic
                                                      1-e
modeling) and income (W (9y) = 1/ (1 - e) * y           ). The function W(y) denotes “utility or

wellbeing derived from income”. This income component of the HDI is a national average (i.e.

GDP per capita, which is them adjusted for income distribution (W*(y) = W(y) {1 - G}), where

G = Gini coefficient). In wanting to disaggregate the HDI within a country, the UNDP (1993)

noted that data are not available for many countries, which limits the possibility.


       An economist writing on ‘objective wellbeing’ summarized the matter simply by stating

that “…one can adopt a mixed approach, in which the satisfaction of subjective preferences is

taken as valuable too” (Gaspart 1998, 111) (see also Cummin1997a, 2001), which is the premise

upon which this paper will adhere in keeping with this multidimensional construct, wellbeing.

Wellbeing, therefore, for this paper will be the overall health status of people, which include

access to and control over material resources, environmental and psychosocial conditions, and

per capita consumption.


                                                 72
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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. The current study extracted a sample of 1, 394 respondents aged 10 to 19 years old from
the 2007 Jamaica Survey of Living Conditions (JSLC). The present subsample represents 20.6%
of the 2007 national cross-sectional sample (n = 6,783). Multivariate logistic and ordinal
logistic regression analyses were used to examine the association between many independent
variables and a single dependent variable. In this study, health was measured using (1) self-
rated health status or (2) the antithesis of illness (not reporting a health condition). The
dichotomization of each denotes the use of two groups, and non-dichotomization means that self-
rated health status was used in its Likert scale form (i.e. very good; good; moderate; poor and
very poor). Antithesis of illness is a better measure than self-reported health status in
determining social determinants because of its explanatory power (53%) compared to those that
used the self-rated health status (at most 38%). There were noticeable variations in social
determinants of health among the dichotomized, non-dichotomized health and antithesis of
illness. Social determinants of health vary across the measurement and dichotomization and
non-dichotomization of health status. The findings provide insights into the social determinants
and health, and recommend that we guard against a choiced approach without examining the
studied population in question.



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


                                                107
for the high frequency of studies on those in the adolescence years are owing to the prevalence of

HIV/AIDS, unwanted pregnancy, inconsistent condom usage, mortality arising from the

HIV/AIDS virus, and other risky sexual behaviour. With one half of those who are infected with

the HIV/AIDS virus being under 25 years old [1], this provides a justification for the importance

of researching this aged cohort. Statistics revealed that the HIV virus is the 3rd leading cause of

mortality among Jamaicans aged 10-19 years old (3.4 per 100,000, for 1999 to 2002) [5], and

again this provides a validation for the prevalence of studies on this cohort. Outside of the

Caribbean, sexuality and reproductive health matters among adolescents are well studied [6-11],

suggesting that those issues are national, regional and international.


       While sexuality and reproductive health matters are critical to the health status of people

[1], reproductive health problems as well as sexuality form a part of the general health status.

Health is more that the ‘antithesis of diseases’ [12] or reproductive health problems as it extends

to social, psychological or physical wellbeing and not merely the antithesis of diseases [13].

Bourne opined that despite the broadened definition of health as offered by the WHO [14],

illness is still widely studied in the Caribbean, particularly among medical researchers and/or

scholars. A search of the West Indian Medical Journal for the last one half decade (2005-2010), a

Caribbean scholarly journal, revealed that the majority of the studies have been on different

variations of illness, and antithesis of diseases instead of the broadened construct of health.


       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.


                                                 108
A comprehensive review of the literature on health status, particularly among the adolescence

population, revealed that none has used a national survey data to examine social determinants of

health across different measurement and dichotomization of health (the recoding of the measure

into two groups) to assess whether there is variability in determinants as well as explore the

health of this cohort.


       Even among studies which have examined social determinants of health, particularly

among the population [26-34], few have used the elderly population [35-37] and only men in the

poor and the wealthy social strata [37, 38], but none emerged in a literature research that have

used the adolescent population (ages 10-19 years). On examining health literature, no study

emerged that evaluated whether the social determinants of health vary across measurement,

dichotomization and non-dichotomization of health (using the measure in its Likert scale form),

and age cohort. With the absence of research on the aforementioned areas, it can be extrapolated

that social determinants of health are constant across measurement, dichotomization and non-

dichotomization, and this assumption is embedded in health planning. The absence of such

information means that critical validity to the discourse and use of social determinants would

have been lost, as social determinants of health are used in the planning of health policies, future

research and in explaining health disparities.


       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


                                                 109
social determinants of health among those in the adolescence years across varying measurement

of health. Using data for 2007 Jamaica Survey of Living Conditions (2007 JSLC), this paper

seeks to elucidate (1) whether social determinants of health vary across measurement of health

status (ie self-rated health status or self-reported antithesis of disease) or the cut-off

(dichotomization) and/or the non-cut-off of health status (non-dichotomization), (2) are there

similarities between social determinants found in the literature and that of using an adolescence

population, (3) whether particular demographic characteristic as well as illness and health status

vary by area of residence of respondents, (4) what is the health status of the adolescence

population, (5) typology of health conditions that they experience, and (6) evaluate the antithesis

of illness (disease) and self-rated health.


Methods and measure

Data

The current study extracted a sample of 1, 394 respondents aged 10 to 19 years old from the

2007 Jamaica Survey of Living Conditions (JSLC). The inclusion/exclusion criterion for this

study is aged 10 to 19 years old. The present subsample represents 20.6% of the 2007 national

cross-sectional sample (n = 6,783). The JSLC is an annual and nationally representative cross-

sectional survey that collects information on consumption, education, health status, health

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.


                                                110
        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



                                               111
(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).



                                               112
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




                                                  113
determinants remain the same. The use of this approach is better than bivariate analyses as many

variables can be tested simultaneously for their impact (if any) on a dependent variable.

        Scholars like Grossman [33], Smith & Kingston [34], Hambleton et al. [37], Bourne

[46], Kashdan [47], Yi & Vaupel [48], and the World Health Organization pilot work a 100-

question quality of life survey (WHOQOL) [49] have used subjective measures to evaluate

health. Diener [50,51] has used and argued that self-reported health status can be effectively

applied to evaluate health status instead of objective health status measurement, and Bourne [46]

found that self-reported health may be used instead of objective health. Embedded in the works

of those researchers is the similarity of self-reported health status and self-reported dysfunction

in assessing health. Thus, in this work we will use self-reported health status and the antithesis of

illness to measure health, and dichotomize self-reported health status as follows (1) good health

= 1, 0 = otherwise; (2) good-to-excellent health=1, 0 = otherwise; (3) moderate-to-excellent

health=1, 0 = otherwise; and (4) very poor-to-poor health= 1, 0 = otherwise. Another measure

was that health was evaluated by all the 5-item scale (from very poor to excellent health status),

using ordinal logistic regression.

       The current study will examine the social determinants of self-rated health of Jamaican

adolescents and whether the social determinants vary by measurement and dichotomization

and/or non-dichotomization of health. Five hypotheses (models) were tested in order to

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).




                                                114
All the models were tested with the same set of social determinants of health, with the only

variability being the measurement of health status (self-rated health status), cut-off of health

(dichotomization) and/or non-dichotomization of self-rated health status.



H A=f (A i , G i , AR i , It , lnD i , ED i, US i , S i , HIi , lnY, CR i , lnMC t , SA i , ε i )     (1)

          where H A (i.e. self-rated antithesis of diseases) is a function of age of respondents, A i ;

          sex of individual i, G i ; area of residence, AR i ; 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), lnD i ; Education level of individual i, ED i ; union status of person i,

          US i ; social class of person i, S i ; 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, lnMC t ; social assistance of individual i, SA i ; 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%.



H ND=f (A i , G i , AR i , It , lnD i , ED i, US i , S i , HIi , lnY, CR i , lnMC t , SA i , ε i )     (2)

        Where H ND 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%.



H D1 =f (A i , G i , AR i , It , lnD i , ED i, US i , S i , HIi , lnY, CR i , lnMC t , SA i , ε i )   (3)

          Where H D1 is very poor-to-poor self-rated dichotomized health status.




                                                                115
          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%.

H D2 =f (A i , G i , AR i , It , lnD i , ED i, US i , S i , HIi , lnY, CR i , lnMC t , SA i , ε i )     (4)

          Where H D2 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%.



H D1-4 =f (A i , G i , AR i , It , lnD i , ED i, US i , S i , HIi , lnY, CR i , lnMC t , SA i , ε i )    (5)

          Where H D3 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%.



H D1-4 =f (A i , G i , AR i , It , lnD i , ED i, US i , S i , HIi , lnY, CR i , lnMC t , SA i , ε i )    (6)

          Where H D4 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 5.1 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.




                                                                116
        Table 5.2 presents information on the particular demographic characteristic as well as

health status and self-reported illness of respondents by area of residence.


        Table 5.3 depicts information of variables which explain the antithesis of illness among

the adolescence population.


        Table 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 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



                                                117
lower class compared with those in urban areas, rural adolescents have a greater good health

status compared to their urban counterparts, but this was the reverse for rural and periurban

adolescents. Another important finding was that there is no statistical association between health

conditions and area of residence, but urban and periurban adolescents were more likely to have

health insurance coverage compared to those in rural areas.

        In Jamaica, the adolescence population’s health status is comparable to those in the

United States [23], suggesting that inspite of the socioeconomic disparities between the two

nations and among its peoples, the self-reported health status among adolescent Jamaicans is

good. The high health status of those in the adolescence population in Jamaica speaks good of

the inter dynamics within the countries, but does not imply that they are the same across the two

nations or can it be interpreted that the quality of life of Jamaicans is the same as those in the

United States. Simply put, the adolescence population in Jamaica is experiencing a good health

status although HIV/AIDS, unwanted pregnancies, and inconsistent condom usage are high in

this cohort [1-5].

        While the aforementioned results about good health status of Jamaican adolescents

concurs with PAHO’s work in 2003 [5] and others [17], which has continued into 2007, the

current paper provides more information on health matters of adolescents aged 10-19 years than

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-




                                               118
consumption items. On the other hand, those who stated that their health status was at least

moderate were less likely to live in crowded household, spent more on consumption and non-

consumption items. Using a 2007 national probability dataset for the adolescence population in

Jamaica, we can add value to the existing literature on health status as well as the social

determinants of health.

          Grossman introduced the use of econometric analysis in the examination of health in the

1970s to establish determinants of self-rated health [33], which has spiraled a revolution in this

regard since that time. Using data for the world’s population, he identified particular social

determinants of health that was later expanded upon by Smith and Kington [34]. Since the earlier

pioneers’ work on social determinants of health [33, 34], the WHO joined the discourse in 2000s

[27] as well as Marmot [26], Kelly et al. [28]; Marmot and Wilkinson [29]; Solar and Irwin [30];

Graham [31]; Pettigrew et al. [32], Bourne [35], Bourne [36], Hambleton et al. [37] and Bourne

and Shearer [38], but none of them evaluated whether there was variability in the determinants of

health depending on the measurement and/or dichotomization of health.

          The variability in social determinants of health was established by Bourne and Shearer

[38] in a study between men in the poor and the wealthy social strata in a Caribbean nation, but

the literature at large has not recognized the variances in social determinants based on the

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.




                                                 119
According to Bourne [43] “The current study found that dichotomi[z]ing poor health status is

acceptable assuming that poor health excludes moderate health status, and that it should remain

as is and ordinal logistic be used instead of binary logistic regression” [43, p.310], and others

warned against the large dichotomization of self-rated health status [44,45]. Because self-rated

health status is a Likert scale variable, ranging from very poor to very good health status, many

researchers arbitrarily dichotomized it, but the cut-off is not that simple as was noted by Bourne

[43], Manor et al. [44] and Finnas et al. [45].


       From data on Jamaicans, Bourne’s work revealed that the cut-off in the dichotomization

of self-rated health status should be best done without moderate health when dichotomizing for

poor health status [43]. All the scholars agreed that narrowed cut-offs are preferable in the

dichotomization of self-rated health status, but only a few variables were used (marital status,

age, social class, area of residence and self-reported illness) [43-45]. Bourne postulated that “By

categorising an ordinal measure (i.e., self-reported health) into a dichotomous one, this means

that some of the original data will be lost in the process.” [43, p.295]. Using many more

variables, the present work highlighted that some social determinants of health are lost as a result

of the dichotomization process. Simply put, the social determinants of health are not consistent

across the dichotomization process which concurs with the literature.


       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


                                                  120
particular point. The fact that more social determinants of health emerged when health was non-

dichotomized coupled with a lower explanatory power compared with when it is dichotomized as

very poor-to-poor health means that using self-rated health as a Likert scale valve is not

preferable to dichotomizing it. A narrower dichotomization of self-rated health status,

particularly very poor-to-poor health, as well as moderate-to-very good health status yielded

greater explanations than non-dichotomizing health status.


       This study used both the antithesis of illness and self-rated health status to measure, and

evaluates the social determinants of health, and assess whether antithesis of illness is still a better

measure of health than self-rated health status. A comparison of the social determinants based

on the measurement of health revealed that for the Jamaican adolescence population, antithesis

of illness is a better measure than self-reported health status in determining social determinants

because of its explanatory power (53%) compared to those that used the self-rated health status

(explanatory power at most 38%). On the other hand, the antithesis of illness had fewer social

determinants compared with those in self-rated health status, suggesting that more social

determinants of health should not be preferred to fewer because the latter measure had a greatest

explanation. Like dichotomizing self-rated health status, variation also exists among

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


                                                  121
sample 6,782 Jamaicans, one researcher warned against using self-reported illness as a measure

of health as he found that men were over-reporting their illness [54], and this means they were

over-rating their antithesis of illness. Those studies highlight the challenges in using subjective

measures in evaluating health as they are not consistent like the objective ones such as mortality,

life expectancy, and diagnosed morbidity. Nevertheless, on examining the antithesis of illness

and self-rated health status, it was revealed that 2.9% of those who indicated very good health

status had an illness compared to 20% of those who reported an illness who had very good health

status. From the current work again it emerged that there is disparity between self-reported

illness (or antithesis of illness) and self-rated health status, indicating why caution is required in

using either one or the other.


       Other disparities between antithesis of illness and self-rated health status highlighted that

antithesis of illness is a better measure of health than self-rated health status. Clearly despite the

efforts of the WHO in broadening the conceptualization of health away from the antithesis of

illness, the Jamaican adolescence population has not moved to this new frontier. As when they

were asked to report on the antithesis of illness, they gave lower values than indicated for self-

rated health status. Because antithesis of illness captures health more than self-rated health

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,


                                                 122
when the WHO [27] and affiliated scholars [26, 28-32] forwarded social determinants of health,

prior to that some scholars like Grossman [33] and Smith and Kington [34] had already social

determinants of health of a population. However, none of them stipulated that there are

disparities and variations in these based on the dichotomization, non-dichotomization, sub-

population, and measurement of health (ie self-rated health or antithesis of illness).


       Using a cross-sectional survey (2003 US National Survey of Children's Health) of some

102,353 children aged 0 to 17 years, Victorino and Gauthier [55] established that there were

some variations in social determinants of health based on particular health outcomes. The health

outcomes used by Victorino and Gauthier are presence of asthma, headaches/migraine, ear

infections, respiratory allergy, food/digestive allergy, or skin allergy, which are health

conditions. Another research using the 2003 US National Survey of Children's Health (NSCH)

investigated the association of eight social risk factors on child obesity, socioemotional health,

dental health, and global health status [56]. From a research in England, Currie et al. [57] found

disparity in income gradient associated with subjectively assessed general health status, and no

evidence of an income gradient associated with chronic conditions except for asthma, mental

illness, and skin conditions.


       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


                                                 123
social determinants. And the social determinants of health had the greatest explanatory power

used antithesis of illness to measure health.


Conclusion

In summary, the general health status of the adolescence population in Jamaica is good, but 7 in

every 100 have reported an illness of which some had chronic conditions (diabetes mellitus,

3.9% and hypertension, 1.3%), and those who classified as being in the wealthy class were more

likely to report very poor-to-poor health status compared with those in the lower class. Another

important finding was that rural adolescents had a greater health status than urban adolescents,

but periurban adolescents had the greatest health status.


       Outside of the aforementioned good health news, the social determinants of self-rated

health status vary across the measurement of and dichotomization and non-dichotomization of

health and the population used. This work provides invaluable insights into how social

determinants should be examined, modify the general social determinants of health offered by

the World Health Organization and some associated scholars. By varying the measurement,

dichotomization and non-dichotomization of health, this work provide some justification as to

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


                                                124
international standardized definition of a phenomenon, people may a different view as to this

issue.


Disclosures


The author reports no conflict of interest with this work.


Disclaimer
The researcher would like to note that while this study used secondary data from the 2007
Jamaica Survey of Living Conditions (JSLC), none of the errors in this paper should be ascribed
to the Planning Institute of Jamaica and/or the Statistical Institute of Jamaica, but to the
researcher.

Acknowledgement

The author thank the Data Bank in Sir Arthur Lewis Institute of Social and Economic Studies,
the University of the West Indies, Mona, Jamaica for making the dataset (2007 Jamaica Survey
of Living Conditions, JSLC) available for use in this study.




                                                125
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                                               128
Table 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)




                                                       129
Table 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




                                                        130
Table 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




                                                         131
Table 5.4: Logistic and Ordinal Logistic regression: Factors explaining self-reported health status of adolescents, n = 1,280
                                                                                    Self-rated health status
                                      Very poor-to-poor              Good              Moderate-to-very         Good-to-very                          All
Characteristic                                                                                good                   good
                                        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




                                                                         132
Table 5.5: Self-rated health status and antithesis of illness, n = 1,330
                                                                                                Self-rated health status
Characteristic                                                      Very good               Good             Moderate                Poor    Very poor
                                                                        n (%)               n (%)                n (%)              n (%)        n (%)
Antithesis of illness
   No                                                                  18 (2.9)           38 (6.4)               26 (31.3)        7 (38.9)       0 (0.0)
  Yes                                                                611 (97.1)         560 (93.6)               57 (68.7)       11 (61.1)    2 (100.0)
χ2 = 125.58, P < 0.0001
                                                                                           Good health (Antithesis of illness)
Characteristic                                                                                           No                                        Yes
                                                                                                      n (%)                                      n (%)
Self-rated health status
   Very good                                                                                         18 (20.0)                               611 (49.2)
   Good                                                                                              38 (42.7)                               560 (45.1)
   Moderate                                                                                          26 (29.2)                                 57 (4.6)
   Poor                                                                                                7 (7.9)                                 11 (0.9)
   Very poor                                                                                           0 (0.0)                                  2 (0.2)
χ2 = 125.58, P < 0.0001




                                                                                  133
CHAPTER 6
Self-reported health and health care utilization of older people




Paul A. Bourne, Christopher A.D. Charles, Cynthia G. Francis & Stan
Warren




Many studies have examined health status, physical functionality, lifestyle and living
arrangements of the elderly (ages 60+ years); but have not investigated the health, health care-
seeking behaviour and health conditions of people 80+ years in developing nations, in
particular Jamaica. Global statistics reveal that the 80+ age group is growing faster than the
younger old population and that come 2050 1 in 9 elderly will be 80+ years. The aims of this
study are to (1) examine the health status of the 80+ year population in Jamaica, (2) evaluate
whether there are shifts in the typology of dysfunctions over the last 6 years (2002-2007), (3)
examine whether health status and self-reported dysfunctions are correlated for the 80+ age
cohort, (4) evaluate the health care-seeking behaviour of those who are 80+ years, and (5)
compare and contrast the results of the 80+ year cohort with the general 60+ year cohort. The
current study extracted a sample of 722 elderly 80+ years from the dataset of the Jamaica
Survey of Living Conditions (JSLC). The JSLC is a modification of the World Bank’s Household
Living Standard Survey. In 2007 compared to 2002, diabetes mellitus had the greatest increase
of 550% over the studied period compared to 9% increase in unspecified diseases, and -76.6% in
arthritic cases. No significant statistical association was found between health status and area of
residents (χ2 = 11.899, P > 0.05) likewise between health status and sex of respondents (χ2 =
3.867, P > 0.05) and between health status and health care-seekers (χ2 = 3.350, P > 0.05). A
statistical correlation existed between health status and self-reported illness; (χ2 = 13.036, P =
0.011), and between health status and income quintile (χ2 = 26.716, P < 0.045); as well as
between health status and health insurance coverage (χ2 = 21.913 P = 0.039). Health care-
seeking is an inelastic commodity as more health insurance coverage or total annual expenditure
this could see an incremental change in health care-seeking behaviour. Money continues to
explain greater health status for the wealthiest 20% of 80+ age cohort in Jamaica. One of the
ironies in this study is the fact that the poorest 20% recorded the second highest health status.




                                               134
Introduction
In the Caribbean and Latin America, issues on the elderly (ages 60+ years) have been reviewed

by scholars such as Alvarado et al. [16]; Bourne [12-15]; Brathwaite [10, 11]; Eldemire [1-6];

Grell [7]; Hambleton and colleagues [9]; Lawson [8]; and others [17-20]. However, Bourne [15],

Hambleton et al. [9] and Menéndez et al. [20] have examined health status of the elderly in the

Latin America and the Caribbean with only the former investigating the health status of the old-

to-oldest elderly (ages 75+). While Hambleton and colleagues, and Menéndez and collaborators

have examined health status, health conditions and functional capacity of people 60+ years,

Bourne [15] studied health and health conditions of elderly 75+ years which means that none of

those studies have researched the changing pattern of acute and chronic diseases, health care-

seeking behaviour and health status for the population 80+ years.


       Many studies examine the elderly age cohort as a single entity, indicating that they treat

health challenges facing the elderly population (60+ year) as though they are the same across the

elderly age cohort - young-old (ages 60-74 years); old-old (ages 75-84 years) and oldest-old

(ages 85+). In 2000, statistics revealed that there were 1.4% of the Caribbean population 80+

years, 1.8% of Jamaica, 0.9% of Latin America and the Caribbean and 6.9% of the World [21].

Based on Figures 1 and 2, the rate of growth in the population 60+ years is greater than that for

the 65+ and 80+ years in the Caribbean as well as in Jamaica; but this does not mean that the 80+

age cohort given that they constituted less than 2% should be excluded from research inquiry.

The reality is they do exist, and information are needed on them in order to guide public health

policies and programmes as current plans are made for this age cohort using information on the

general elderly population.




                                              135
       Data from the Statistical Institute of Jamaica (STATIN) on mortality goes up to age 75+

years [22], but no mortality data are available for those 80+ years. Mortality which is used to

compute life expectancy is therefore not presented for people 80+ years neither are health status

nor health conditions. United Nations’ publication reported data on survival rates, life

expectancy, growth rate, sex ration, population and percentage of people in older ages for those

up to 80+ age cohort which is not the case for STATIN. Neither of the two institutions has

examined in a single study the health, health conditions and health care-seeking behaviour of

those 80+ years. Statistics from Pan American Health Organization (PAHO) [23] presented

information on health of people 60+ years and again no data is available on those 80+ years and

they constituted 2% of the Jamaica’s population and 1.1% of the world’s population. The

traditional approach is life expectancy, and health status of the elderly population (60+ years) by

disaggregating the old population and this assumes that the conditions affecting this age cohort

remains constant during old age.


       Bogue opined that the health problems, health conditions and health care-seeking

behaviour (ie health demand) increases with ageing [24], suggesting that aggregating elderly in

60+ and examining this age cohort will not provide the health practitioner with a better

understanding of the different ageing segment and issues surrounding that age cohort. Statistics

from the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica revealed

that elderly Jamaicans had the greatest hospital visits and, health conditions than all other age

cohorts [25]. Again the data were not disaggregated by the three elderly age cohorts, which

meant that information is lost in the aggregated data. Concomitantly, 37.9% of all new diabetic

cases and 39.8% of new hypertensive cases between January and June 2007 seen in public health

facilities were accounted by senior Jamaicans (ages 60+ years) [26]. At the same time, 43.8% of


                                               136
elderly (ages 65+ years) in 2007 reported an illness in the 4-week reference period and 75.9%

indicated that the illness was a recurring one compared to 74.2% of those 60-64 years who

reported that they were suffering from a recurring illness and 36.6% revealed that they were ill

[25]. There is a gap in the literature about the health status, health conditions and health care-

seeking behaviour of people 80+ years in the Caribbean in general and Jamaica in particular.


       The health literature that evaluates functional capacity, health status, health conditions

and health care-seeking behaviour of the elderly 60+ years or even 65+ year span the 3

categories of elderly (i.e. young-old; old-old and oldest-old) and does not provide a

comprehensive understanding of the elderly cohorts.         Hypertension, diabetes mellitus and

arthritis are among the five leading cause of morbidity in the elderly (ages 60+ years) population

in Jamaica [26] and this is also the case in Barbados, St. Lucia, Guyana, and Trinidad and

Tobago, and Uruguay [13, 27]. Using data on a PAHO/WHO survey on health, wellbeing and

aging (i.e. SABE), Rossi and Triunfo [28] disaggregated the data for the three elderly age cohorts

but like previous studies, they did not use this to ascertain information on chronic illness, or

health status. While this data provides invaluable information on the population 60+ years, by

not disaggregating the elderly into the 3 aforementioned categories the data assume that they are

affected at the same rate across the life course.


       Outside of South America, and Latin America and the Caribbean, using information on

elderly 75+ years from Israel, Benyamini et al. [29] found that the health status of young-old

(75-84 years) was lower than that for the old-old (85-94 years). A study in Newcastle conducted

by Collerton et al. [30] on elderly 85+ years revealed that 77.6% of them rate their health status

as at least good, indicating that the remainder were experiencing at least poor health. A study by



                                                    137
Bourne et al. [14], using data for Jamaicans 55+ years, they found that as people age increases,

their poor health status increases..


        However, Bourne et al’s work [14] found that 52% of elderly 70+ years reported poor

health compared to 22% of the participants 85+ years in the Newcastle study. Extrapolation

from the two studies suggests that there is a positive relationship between increased health

conditions and age of old people. Inspite of the literature, there is a gap in the health literature on

the health status, health conditions and health care-seeking behaviour of those 80+ years.

Therefore, the aims of this study are to (1) examine health status of the 80+ year population in

Jamaica, (2) evaluate whether there are shifts in the typology of dysfunctions over the last 6

years (2002-2007), (3) examine whether health status and self-reported dysfunctions are

correlated for those 80+ age cohort, (4) evaluate the health care-seeking behaviour of those 80+

years, and (5) compare and contrast the results of the 80+ year cohort with the general 60+ year

cohort. We now turn to the material and methods we used in the current study.




Source: Extracted from Department of Economic and Social Affairs Population Divisions, United Nations, (UN).
World Population Ageing 1950-2050. New York: 2002.

Figure 6.1. Caribbean Elderly population as a percentage of total population

                                                   138
Source: Extracted from Department of Economic and Social Affairs Population Divisions, United Nations, (UN).
World Population Ageing 1950-2050. New York: 2002.

Figure 6.2. Jamaica Elderly population as a percentage of total population



Materials and Methods

Sample
The current study extracted a sample of 722 elderly participants 80+ years from the dataset of the

Jamaica Survey of Living Conditions (JSLC): 566 and 159 respondents from 2002 and 2007.

The JSLC is jointly administered by the Planning Institute of Jamaica (JSLC) and the Statistical

Institute of Jamaica (STATIN). JSLC is a national cross-sectional probability survey which is

conducted normally between April and May of each year. An-administered instrument

(questionnaire) is used to collect data from Jamaicans. 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 socio-demographic variables – such as education; daily expenses (for past 7-day; food


                                                     139
and other consumption expenditure; inventory of durable goods; health variables; crime and

victimization; social safety net and anthropometry.


       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

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% for 2002. The non-response includes refusals and rejected cases in data cleaning.


       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 study; and, chi-square was used to examine association between

non-metric variables and ANOVA was utilized to examine the equality of means among the non-

dichotomous variables. In addition to the aforementioned, elasticity of demand and income

elasticity of demand were computed for health status.




                                               140
Measurement


       Elasticity of demand measures consumers’ demand responsiveness to changes in

particular product attributes such as price. In this paper, the research will examine health

insurance elasticity of health care-seeking behaviour, and income elasticity of health care-

seeking behaviour.


Elasticity is calculated as a percentage of the change in demand (in this case health care-seeking

behaviour) divided by the percentage change in (1) health insurance; and (2) income (ie total

annual expenditure).


Elasticity of health care-seeking behaviour with reference health Insurance = % Δ HSB/ %Δ HI

       Where HSB is health care-seeking behaviour and HI is health insurance coverage

Elasticity of health care-seeking behaviour with reference to income = % Δ HSB/ %Δ Y

       Where Y is income (ie total annual expenditure).


Elasticity of health care-seeking behaviour

with reference to self-reported illness = % Δ HSB/ %Δ SRI

       Where SRI is self-reported illness


The values below will be used to compute the elasticities.




                                               141
Table 6.0. Health insurance, health care-seekers and median total annual expenditure for 2002
and 2007
                                                          20021                  20072
Health insurance coverage                               16 (2.9%)              45 (29%)
Health care-seekers                                    163 (68.2%)             59 (77.6)
Self-reported illness                                  239 (43.5%)            76 (48.7%)
Median Total Annual Expenditure (Range)               Ja $170,019.8          Ja $396,576.9
                                                     (Ja $1,954,053)        (Ja $5,213,338)
1
  Ja $50.47 + US $1.00
2
  Ja $80.97 = US $1.00


Health conditions (ie. self-reported illness or self-reported dysfunction): The question was asked:

“Is this a diagnosed recurring illness?” The answering options are: Yes, Cold; Yes, Diarrhoea;

Yes, Asthma; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No.


Self-rated health status: “How is your health in general?” And the options were very good; good;

fair; poor and very poor.


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).


National Health Fund (NHF). This is a health coverage provided by the Jamaican Government to
reduce the burden on the health care sector. It provides prescription health benefits to all
residents. This was established under the National Health Fund Act which came into being in
2003. The NHF subsidies drugs for residents who are affected by any of the 15 illnesses. These
are (1) arthritis; (2) asthma; (3) benign prostate hyperplasia (BPH); (4) breast cancer; (5) diabetes
mellitus; (6) epilepsy; (7) glaucoma; (8) high cholesterol; (9) hypertension; (10) ischemic heart
disease; (11) major depression; (12) prostate cancer; (13) psychosis; (14) rheumatic heart
disease, and (15) vascular disease.

                                                142
Jamaica Drug for the Elderly Programme (JADEP). The JADEP was established by the Ministry
of Health in July 2004 to provide drugs for elderly Jamaicans (ages 60+ years), and this was later
handed over to the NHF as an individual benefit. It enables elderly people to access drugs
through subsidized payment by the government if the individual is suffering from any
combination of eleven dysfunctions. These are 1) arthritis; 2) asthma; 3) benign prostate
hyperplasia; 4) enlarged prostate; 5) cardiac or heart disease; 6) diabetes mellitus; 7) high
cholesterol; 8) hypertension (or high blood pressure); 9) psychiatric conditions; 10) vascular
conditions (or circulatory problems), and 11) glaucoma.

Results
A sample of 566 elderly 80+ years was extracted for 2002 (39.9% men and 60.1% women) with

a mean age of 85.4 years (4.6 years), while the sample for 2007 was 157 elderly 80+ years

(37.1% men and 62.9% women) with a mean age of 85.0 years (4.5 years) (Table 6.6.1). Forty-

four percent of the 2002 sample reported suffering from an illness and this increased to 48.7% in

2007. In 2002, 42.2% of the sample responded to the question of ‘have you visited a health care

practitioner in the last 4-weeks’ compared to 47.8% in 2007. Of those who responded, 68.2%

reported yes in 2002 and 77.2% claimed yes in 2007. Based on Table 6.1, there is a shift toward

urban areas: 30.2% resided in urban areas in 2007 compared to 10.1% in 2002; 18.7% in semi-

urban areas in 2002 and 20.8% in 2007. Shifts were also taking placing in health conditions over

the two periods studied. In 2007 over 2002, in the acute illness category, there were reductions in

the number of elderly suffering from cold and asthma to increases in those suffering from

asthma., Hypertension was the most prevalently diagnosed disease among chronic conditions in

2007 (46.8%) and this represented a 1% decline over 2002. Diabetes mellitus had the greatest

increase by 550% over the period studied compared to +9% in unspecified diseases, and -76.6%

in arthritic cases (Table 6.1). Similarly, the numbers of 80+ years widowed Jamaicans increased

by 3.8% and the percentage of married sample increased to 25.8% (from 23.3% in 2002). Three

percentage of the elderly 80+ years had health insurance coverage in 2002 and by 2007 this had
                                               143
increased exponentially to 900%. The increased in health insurance coverage can be substantially

accounted for by public coverage (+782.8%).


       Based on Table 6.2, no significant statistical correlation was found between diagnosed

health conditions and area of residents and that this was the case in both years. In spite of this

reality, the reduction in hypertensive cases can be accounted for by the decline in number of

cases in semi-urban residents, while the urban cases increased. The greatest reduction in arthritic

cases were among urban residents followed by rural dwellers and an increase was also observed

among semi-urban residents. Increases in diabetic cases were observed in all three geographic

regions with the greatest increase in the semi-urban areas.


       No significant statistical association was found between health status and area of

residents (χ2 (df = 8) = 11.899, P > 0.05). No comparison is possible as there were no figures for

2002 as the first time that data on health status was recorded was in 2007 (Table 6.3).

       Table 6.4 showed that a statistical correlation existed between health status and self-

reported illness but that the relationship was a weak one (contingency coefficient = 0.278).

Furthermore, 46.1% of those who indicated an illness had health status was poor-to-very-poor,

with only 2.6% of them having a very good health status, 11.8% a good health status and 39.5%

a moderate health status. On the other hand, the 25.1% of those who indicated that they had no

illness reported poor-to-very-poor health status compared to the 10.0% who had very good

health status and the 25.0% who had good health status.

       A cross-tabulation between health status and sex of respondents revealed no significant

statistical correlation (χ2 (df = 4) = 3.867, P > 0.05) (Table 6.5).

       A statistical relationship exists between health status and income quintile (χ2 (df = 16) =

26.716, P < 0.045); but that the association was a weak one, cc=0.381.Further examination

                                                144
revealed that the wealthiest 20% had the greatest very good and good health status compared to

the other social classes (Table 6.6). Table 6.6 indicated that the poorest 20% had a very good

health status which was greater than that of the other classes. Only 17.4% of the wealthiest 20%

reported a poor health status compared to 36.7% of the poorest 20%; 23.3% of the poor; 27.7%

of the middle class and 25.9% of the wealthy. The greatest very poor health status was recorded

by wealthy respondents (14.8%) followed by the poor (13.3%); middle class (8.8%) and the

poorest 20% (3.3%).


       No significant statistical association was found between health status and health care-

seekers (χ2 (df = 4) = 3.350, P > 0.05) (Table 6.7).


       Based on Table 6.8, a statistical correlation existed between health status and health

insurance coverage (χ2 (df = 4) = 21.913 P = 0.039); but that the relationship was a moderate

weak one, contingency coefficient = 0.352. On examination, it was revealed that the those with

private health insurance was most likely to have good health status; those with national health

fund (NHF) was most likely to report moderate health and those with other public coverage also

so likely to have moderate health status along side those without health insurance coverage.


       Although no significant statistical association was found between diagnosed health

conditions and health care-seeking behaviour for both 2002 (χ2 (df = 5) = 5.381, P > 0.05) and

2007 (χ2 (df = 7) = 6.209, P > 0.05), in 2007, the percentage of the sample with hypertension was

seeking less medical care and this also the case for the arthritic patients, and those with cold.

However, increases were recorded for diabetes mellitus, diarrhoea and unspecified dysfunction

cases (Table 6.9).




                                                145
        Significant statistical difference was found between those with particular health status

and the amount of money they were able to spend (F statistic [5,152] = 7.134, P < 0.001) (Table

6.10). Those with the greatest expenditure had the highest health status (ie very good) followed

by those with good. However, those with moderate health status had the least mean annual

expenditure, with those who recorded very poor health status spent more than those with a poor

health status.


        Table 6.6.11 revealed that no significant statistical correlation was found between self-

reported health conditions and total expenditure (F statistic [5, 30] = 0.396, P > 0.05).


       In 2007, a significant statistical correlation was found between self-reported health

conditions and total annual expenditure (F statistic [7, 69] = 2.935, P = 0.009) (Table 6.6.11).

Based on Table 6.6.11, of those who were diagnosed with either acute or chronic health

conditions, diarrhoea patients spent the most (Ja $597,953.39) followed by diabetic patients (Ja

$568,441.75) and the least was spent by asthma patient (Ja $42,703.27).


       No significant statistical difference existed between the expenditure on particular health

care facilities and self-reported health conditions: for 2002 (F statistic [7, 53] = 0.288, P = 0.955)

and for 2007 (F statistic [7, 46] = 0.119, P = 0.997) (Table 6.12).


        A cross-tabulation between area of residents and sex of respondents revealed no

significant statistical correlation for 2002 (χ2 (df = 2) = 0.612, P > 0.05) or 2007 (χ2 (df = 2) =

3.958, P > 0.05). Although there was no statistical association between the two aforementioned

variables, it was observed that in 2002 10.6% of women lived in urban areas compared to 9.3%

of men and in 2007 the figures increased by 230.2% and 136.6% for women and men

respectively. For 2002, 73.0% of women resided in rural areas compared to 73.0% of men and


                                                146
the percentages fell to 50.8 and 48.0 for men and women respectively. However, in 2007, the

percentage of men who lived in semi-urban areas increased by 53.1% while the number of

women declined by 12.4%. These findings indicate urbanization of 80+ year population in

Jamaica.


       Based on Figure 6.3, the greatest percentage increase in the ownership of health

insurance coverage was in the poor cohort (3600%) compared to the poorest 20% (2575%);

middle class (855%); wealthy (640%) and the wealthiest 20% (458%).




Figure 6.3. Percentage of population 80+ years with health insurance coverage, 2002 and 2007



Elasticity of health care-seeking behaviour

Health care-seeking behaviour with respect to health insurance = 0.352, which indicates that

health care-seeking behaviour of Jamaicans is highly unesponsive to changes in health insurance




                                              147
coverage. With all other things being equal, a 1% change in health insurance will cause a less

than 1% change in health care-seeking behaviour of the Jamaicans who are 80+ years of age.


       Health care-seeking behaviour with respect to total annual expenditure (median) = 0.382.

The value denotes that health care-seeking behaviour is less responsive to changes in income.

With all other things being equal, a 1% change in health insurance will cause a less than 1%

change in health care-seeking behaviour of the Jamaicans who are 80+ years of age.


       Elasticity of health care-seeking behaviour with reference to self-reported illness = 0.94.

This finding emphasizes the reluctance of the participants in the sample to seek medical care

even when illnesses are on the rise. Over the period, the percentage change in self-reported

illness was 68.2% which result in a 63.8% change in health care-seeking behaviour with all other

things being held constant. Health care-seeking therefore is an inelastic commodity because

more health insurance coverage or total annual expenditure will see an incremental change in

health care-seeking behaviour.




Discussion
The present research highlighted that at least 1 of every 2 elderly 80+ years reported an illness

and 35 out of every 100 indicated at least poor health status. There was a 5.7% reduction in

number of respondents with diagnosed chronic illness and13.8% more respondents sought

medical care in 2007 than in 2002. The gender differences in health status were not statistically

significant as well as health conditions by area of residence, and health status by area of

residence. Some 41% of those who sought medical care indicated at least poor health status with

hypertension being the most prevalent health condition.



                                              148
       In Rossi and Triunfo’s work [28], 6.5% of elderly 60+ years indicated poor (bad) health

status. Benyamini et al. [29], found that one-third of elderly (75+ years) in Israel reported poor

health status which is similar to the findings in the current study (35.1%). These findings indicate

that as people become older their health status decline and this is supported somewhat by the

findings of Collerton et al. [30]. They found that 32.4% of the elderly 85+ years reported poor

health status. Comparatively, although the age cohort for the present study is not the same as that

Collerton et al’s study, marginally more of the 80+ year Jamaicans had poor health compared to

the elderly in Collerton et al’s study. In the present research unlike the ones mentioned earlier,

there is a significant statistical association between self-rated health status and self-reported

illness. Almost two times more 80+ year olds in this study who reported an illness indicated poor

health status compared to those who did not report an illness. This finding indicates that illness

can be used to offer some explanation for the poor self-rated health status of the elderly.


       The prevalence of self-reported illness for the 80+ year old population in Jamaica was 3.1

times more than that of the population, 1.3 times more than that for the 60-64 years old and 1.1

times more than that for the 65+ year old Jamaicans. Concurringly, the most prevalent diseases

in the current work and that of Collerton et al was hypertension, with 57.5% of elderly 85+

having the condition compared to 46.8% of 80+ years, and this was 43% in Rossi and Triunfo’s

study. In Jamaica, the prevalence of people with hypertension was 2.1 less than the 80+ year

population. There was even a difference in the prevalence of hypertension among those 65+years

and 80+ years with hypertension 1.3 times more prevalent in the latter group compared to the

former. Furthermore, 1.5 times more of the 80+ age group had diabetes mellitus compared to the

population. These findings indicate the health disparity between young old and the 80+ age




                                                149
group as well as the population and 80+ age cohort. The high prevalence of chronic illness in

older people accounts for a higher percentage of them utilising health care practitioners.


       Statistics from the Planning Institute of Jamaica and the Statistical Institute of Jamaica,

for 2 decades (1989-2007), showed that elderly (ages 60+ or 65+ years) sought more health care

services and reported greater number of cases in chronic illnesses than all other age cohorts [25].

This paper found that 78 out of every 100 of those 80+ year old sought medical care in the last 4-

weeks which is greater than that for those 65+ years (ie. 72%) and the population (66%). In

Jamaica, the 80+ age cohort sought more medical care in the last 4-weeks compared to the 85+

year olds in Newcastle (i.e. one third visited outpatient clinic in the last 3 months, averaging 10%

in 4-weeks).


       Unlike the other studies on the elderly, our study presents comparative information across

two periods. It was revealed that 5.2% more of those 80+ years of age reported an illness in 2007

over 2002. Although the percentage of those diagnosed with chronic diseases declined by 5.4%

over the two periods, in disaggregating the data it was found that there was a 550% increase in

the number of 80+ year olds with diabetes mellitus in 2007 than in 2002; and a 76.6% decline in

arthritic cases. While there were declines in chronic diseases in the 80+ age cohort, no significant

statistical association was found between (1) diagnosed health condition and area of residences

over the period; (2) health status and area of residence, (3) health status and gender of

participants, (4) health status and health care-seeking behaviour, and (5) diagnosed health

condition and health care-seeking behaviour.


       The World Health Organization (WHO) opined that “The health implications of healthy

ageing – the physical and mental characteristics of old age and their associated problems – need


                                                150
to be better understood” [31]. This view implies that there should be a better understanding of

the demands, preparations, and social and economic factors of ageing through policy base

research to better plan for the reality of an ageing population in particular the 80+ age cohort.

This study corroborates the literature that the health problems of ageing are extensive; but it goes

further to show the remarkable differences between the 60+ and 80+ age cohorts in terms of

health condition .


       In 2007, 15.5% of the elderly Jamaicans reported an illness and 66% of them sought

medical care [25], suggesting that 34 out of every 100 Jamaican who indicated a health

conditions did not seek health care but may have used home remedy. Statistics revealed that

30.2% of those who indicated an illness used home remedy [25], suggesting that there were ill

Jamaicans who did not seek professional medical care. For the elderly population the statistics

are somewhat different as 43.8% of those 65+ and 36.6% of 60-64 year cohorts reported a health

condition, with 75.1% of the 60+ population sought medical care which is 2.1% less than the

number of 80+ years who visited a health care practitioners in the same 4-week period.


       Thirty-five in every 100 of the 80+ year cohort reported poor-to-very poor health status,

yet there was no significant difference in medical expenditure. Interestingly to note is the fact

that those who reported diarrhoea and diabetes mellitus spent more than those with other

diagnosed health conditions but this greater spending was not used for medical expenditure.

Those 80+ year olds with very poor health status had a greater total annual expenditure than the

poor, with those in very good health spending the most for a year. Further examination of health

status and social class (ie income quintile) showed that the wealthiest 20% had the greatest

health status followed by poorest 20%. Marmot [32] opined that income is positively associated

with better health status which is equally the same among 80+ year olds. Continuing, the

                                               151
wealthiest 20% recorded the greatest health status; and their good health was 2.4 times more than

those in the poorest 20%. Those in the poor 20% evaluated their good health to be greater than

that for those in the wealthy socioeconomic strata. This finding somewhat supports Marmot’s

work, but shows that the upper socioeconomic strata does not always have better health

compared to the poor income groups.


       Studies have revealed the significant statistical association between health status and

gender. Although there are more studies which show that men have a greater health status than

women, some have found that women having a greater health and others revealed little gender

differences [29, 33-40]. Rudkin [33] found that women have lower levels of wellbeing (i.e.

economic) than men, and Benyamini et al [29] found that they had lower self-rated health status

than men. Rudkin’s finding was further sanctioned by Haveman et al [34] whose study revealed

that retired men’s wellbeing was higher than that of their female counterparts, because men

usually received and had more material resources, and more retired benefits compared to women

ages 65 years and older. Therefore, with men receiving more than women, and having a more

durable possession than women, their material wellbeing is higher in later life. Courtenay [35]

noted from research conducted by the Department of Health and Human Services [36] and the

Centers for Disease Control [37] that from the 15 leading causes of death except Alzheimer’s

disease, the death rates are higher for men and boys in all age cohorts compared to women and

girls. Embedded within this theorizing are the differences in fatal diseases that are explained by

gender characterisics [38], to which Courtenay [35] explained are due to behavioural practices of

both genders. The foregoing explains the fact that men are dying 6 years earlier than females

[39]. However this study does not concur with the literature in anyway because no statistical

correlation was found between the health status and gender of the 80+ year cohort. This work


                                               152
also disagrees with Smith and Kington [40] that income can buy health as this research found

that the poorest 20% among the 80+ year olds in Jamaica had greater good health that those in

the wealthy socioeconomic strata. However, in the present work the self-reported good health

status for those in the wealthiest 20% was 2.4 times more, and this seemingly supports Smith and

Kington’s work. Health is not a product which is transferrable from one human to another,

suggesting that it cannot be bought. It can be extrapolated from the present research that those in

the wealthiest 20% lifestyle, income, sociophysical milieu and health choices are such that they

foster greater good health, and this does not indicate a purchase of good health over those in the

poorest 20%.


       Using a sample of 1,006 Jamaicans who indicated that they sought medical care in a 4-

week period in 2007, Bourne [41] found that there was no significant statistical association

between medical care and health insurance coverage. The current study contradicts that of

Bourne’s work as it was found that 12% of the variability in medical care-seeking behaviour can

be explained by health insurance coverage and other studies [42]. In 2002, health insurance

coverage was totally private which saw 3 out of every 100 of the 80+ year elderly having

coverage. In the post-2003 period when health took on a public aspect, coverage increased to 29

out of every 100 in 2007. These findings explain the increase in health care seeking behaviour

which was recorded in 2007 over 2002: 68.2% in 2002 and 77.6% in 2007. In 2007, 21 out of

every 100 poorest 20% had health insurance compared to 1 in every 100 in 2002. This

substantial increase was also recorded for the poor with 33 out of every 100 having health

insurance compared to 1 in every 100 in 2002. Hence, the increase in the number of health care

seekers is 2007 is due to the poor and poorest who were unable to previously afford health care

because of financial constraints were now able to do so.


                                               153
       In 2007, 43 out of every 100 elderly ages 60-64 years indicated that they were unable to

afford medical care compared to 27 out of 100 elderly 65+ years. Concurrently, 22% of the

elderly 60-64 years indicated that they used home remedy compared to 24% of the elderly 65+

years. Through the JADEP and National Health Insurance programme the out of pocket

expenditure on medical care is substantially reduced, yet only 21% of the poorest 20% had

accessed to this or private insurance; 33% of the poor; 28% of the middle class; 22% of the

wealthy and 44% of the wealthiest 20%. This finding suggests more than cost constraint, it is a

self-perception that they are not sufficiently medically ill to require care, the cultural biasesin

favor of folk medicine and their perspectives on living longer. These factors may account for the

irresponsiveness of this age cohort to seek medical care within the context of increased health

insurance coverage and expenditure. This is not atypical as Borghesi and Vercelli [43] showed

that elderly people have a progressively lower elasticity of aspirations to outcome, suggesting

their unwillingness to carry out some functions and attain particular events is low. This view is a

possible explanation for the low responsiveness of the 80+ age cohort in their health care-

seeking behavior despite having more health care-choices.


       The issue which must be raised and addressed in this study is the validity of the self-

reported health as a measure of health. The relation of self-reported health to health has been

known for some time. The scientific literature has shown that self-rated health status is highly

reliable to proxy for health and that this has successfully crossed cultural lines [44]. Another

study conducted by O’Donnell and Tait [45] concluded that self-reported health status can be

used to indicate wellbeing as all respondents who had chronic diseases reported very poor health.

It is for this rationale why some studies have used self-reported health conditions and health

status instead of life expectancy or other objective indices to measure health [46-48] as the latter


                                                154
is narrower than the former and does not encapsulate the extent life as subjective measures. This

work has revealed that there is statistical relationship between health status and self-reported

illnesses of elderly (80+ years) Jamaica, but that the association was a weak one. Benyamini et

al. [29] found that self-reported health status was strongly associated with shorter term mortality

(within the next 4 years) than longer-term mortality (9 years of follow-up) of elderly Israelis.


       Medical practitioners, social workers, health education and promotion specialists and

public health practitioners as well as policy makers are now provided with an extensive review

of the health conditions, shifts in patterns of illness, health care-seeking behaviour and practices

of elderly 80+ years in Jamaica. In excess of 77% of those who reported ill-health sought

medical care in the 4-week reference period of the survey, which indicates that there are some

80+ age individuals who are likely to use home remedy and not seekmedical care because of

financial constraints, and the perception that the illness is not severe enough or they just do not

want to visit a traditional medical practitioner. Close to one-half of those who reported a health

condition suffered from hypertension and despite only 18 out of every 100 ill 80+ age elderly

had diabetes mellitus, the number of cases of people suffering from this illness increased by

550% in 2007 over 2002. This increased number of reported cases in diabetes mellitus is

alarming and must be addressed with urgency by public health specialists.


       With the urbanization of the 80+ age cohort to urban and semi-urban areas in Jamaica,

health practitioners and other specialists must be equally cognizant of this population ageing

migration phenomenon in order to effectively address the needs of the cohort within their new

place of abode. There are no gender differences in the urbanization of this cohort in Jamaica.

However, approximately 50% of elderly still reside in rural areas (50.8% men and 48% women).



                                                155
       Another interesting finding of the current study is the preponderance of women to men in

the 80+ age cohort. The sex ratio for this cohort was 59 men to every 100 women indicating a

greater mortality of men at older ages than women. Concomitantly, the expansion of public

health insurance for elderly Jamaica has seen an exponential increase in the number of 80+ aged

Jamaican accessing the service; but most of the cohort is yet to subscribe for this free

programme. Again this emphasize the need for a national public health campaign by the National

Health Fund to inform senior citizens about the public assistance available to reduce their out of

pocket cost for medical care.


       In Jamaica, the elderly poorest 20% to the wealthiest 20% has the same access to health

insurance coverage as this is free for all persons 60+ years. In spite of this reality, the wealthiest

20% recorded the greatest health insurance coverage (44%) compared to 21% of the poorest

20%; 33% of the poor; 28% of the middle class and 22% of the wealthy. The issue here is not

access or inaffordability as is the case in other Latin Americans and Caribbean states [42]; but

willingness to access such facilities owing to culturization. The loftiness in the culture explains

the rationale for the greater percentage of Jamaicans using private health care facilities because

in 2007, 52% of Jamaicans used private health care facilities compared 41% using the free public

healthcare facilities. The elderly would be more set in their ways, and so the willingness to

request and seek assistance from stranger in particular an outsider will be offensive. This

unwillingness may explain the reluctance of the poorest 20% to subscribing for the free health

insurance coverage compared to the wealthiest 20%.


       Health care-seekers are not likely to respond greater than the change in particular

individual attributes because health care-seeking behaviour is an inelastic commodity. An crucial

finding is the participants’ irresponsiveness to changes in health insurance coverage or more total

                                                156
expenditure. It is this fact that explains why health insurance coverage increased by over 180%

and this results in a 64% change in health care-seeking behaviour, with all other things being

held constant. Likewise, a 167% change in total annual expenditure result in a 64% change in

health care-seeking behaviour with all other things being held constant.


Conclusion

In summary, money continues to explain greater health status for the wealthiest 20% of 80+ age

cohort in Jamaica. One of the ironies in this study is the fact that the poorest 20% recorded the

second highest health status, indicating that this social class enjoys a greater health status than

the wealthy, middle and poor cohorts. Money therefore makes a difference for the wealthiest and

not the wealthy or middle class that are 80+ years old. This contradicts the general perspective

that poverty is the cause of ill-health [42] as wealthy and middle classes recorded greater poor

health status that the poorest 20% of 80+ age cohort. Simply put, the poorest 20% reported less

health conditions than the two aforementioned age cohorts because access to more financial

resources do no mean this will be expended on medical care. The current study highlights a

critical issue in that the health care-seeking behaviour of the elderly 80+ years is an inelastic

product. This inelasticity suggest that health care seeking behaviour is less responsive to self-

reported illness, health insurance and the amount of money that the individual is able to spend

because at this age people do not aspire for much more in their lives.


Conflict of interest
The authors have no conflict of interest to report.

Disclaimer



                                                157
The researchers would like to note that while this study used secondary data from the Jamaica
Survey of Living Conditions, none of the errors in this paper should be ascribed to the Planning
Institute of Jamaica or the Statistical Institute of Jamaica, but to the researchers.



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                                             161
Table 6.1. Sociodemographic characteristic of sample

Variable                                                          2002                 2007
                                                                 N= 566               N=159
Gender
   Male                                                           39.9                 37.1
   Female                                                         60.1                 62.9
Health care-seeking behaviour
   Yes                                                            68.2                 77.6
   No                                                             31.8                 22.4
Health insurance coverage
  Yes                                                             2.9                  29.0
  No                                                              97.1                 71.0
Area of residence
  Rural                                                           71.2                 49.1
  Semi-urban                                                      18.7                 20.8
  Urban                                                           10.1                 30.2
Self-reported illness
  Yes                                                             43.5                 48.7
  No                                                              56.5                 51.3
Diagnosed Health conditions
Acute:
  Cold                                                             2.8                  1.3
  Diarrhoea                                                        2.8                  3.9
  Asthma                                                            -                   1.3
Chronic:
  Diabetes mellitus (ie diabetes)                                 2.8                  18.2
  Hypertension                                                    47.8                 46.8
  Arthritis                                                       27.8                 6.5
  Other                                                           16.7                 18.2
  Not diagnosed                                                    -                   3.9
Income quintile
  Poorest 20%                                                      24                  18.9
  Poor                                                            20.1                 18.9
  Middle                                                          18.7                 29.6
  Wealthy                                                         18.2                 17.0
  Wealthiest 20%                                                  18.9                 15.7
Age Mean (SD)                                               85.4 yrs (4.6 yrs)   85.0 yrs (4.5 yrs)
Length of illness Median (Range)                            10 days (90 days)      7days (998)
Number of visits to health practitioner(s) median (range)        1.0 (7)              1.0 (4)
Health status
  Very good                                                        NI                  6.4
  Good                                                             NI                  18.5
  Moderate                                                         NI                  40.1
  Poor                                                             NI                  26.8
  Very poor                                                        NI                  8.3
Marital status
  Married                                                         23.3                 25.8
  Never married                                                   22.8                 19.4
  Divorced                                                        0.7                  2.6
  Separated                                                       1.8                  2.6
  Widowed                                                         47.9                 49.7



                                                     162
Table 6.2. Diagnosed health conditions by area of residence

Variable                               20021                             20072




Diagnosed Health          Rural     Semi-urban       Urban    Rural   Semi-urban   Urban
conditions

Cold                        3.2         0.0           0.0      0.0       6.3        0.0

Diarrhoea                   0.0        33.3           0.0      5.3       0.0        4.8

Asthma                       -           -             -       0.0       0.0        2.4

Diabetes                    3.2         0.0           0.0     21.1       25.0      14.3

Hypertension               48.8        66.7           0.0     47.4       25.0      54.8

Arthritis                  29.0         0.0          50.0      5.3       6.3        7.1

Other                      16.1         0.0          50.0     15.8       37.5      11.9

No                           -           -             -       5.3       0.0        4.8

  χ (df = 10) = 15.561, P > 0.05
 1 2


  χ (df = 14) = 13.607, P > 0.05
 2 2




                                               163
Table 6.3. Health status by area of residence

Variable                                20021                            20072




Health status              Rural     Semi-urban       Urban   Rural   Semi-urban   Urban

Very good                   NI           NI            NI      6.4       6.1        6.5

Good                        NI           NI            NI     31.9       18.2      10.4

Moderate                    NI           NI            NI     38.3       33.3      44.2

Poor                        NI           NI            NI     21.3       30.3      28.6

Very poor                   NI           NI            NI      2.1       12.1      10.4

1
 NI
 χ (df = 8) = 11.899, P > 0.05
2 2



NI No information available




                                                164
Table 6.4. Health status by self-reported illness, 2007

Health status                                                Self-reported illness
                                                          Yes                         No
Very good                                                  2.6                       10.0
Good                                                      11.8                       25.0
Moderate                                                  39.5                       40.0
Poor                                                      32.9                       21.3
Very poor                                                 13.2                        3.8
  χ (df = 4) = 13.036, P = 0.011, cc=0.278
 2 2




                                               165
Table 6.5. Health status by gender

Health status                                     Sex1
                                           Man           Woman
Very good                                   5.1            7.1
Good                                       16.9           19.4
Moderate                                   35.6           42.9
Poor                                       35.6           21.4
Very poor                                   6.8            9.2
  χ (df = 4) = 3.867, P > 0.05
 1 2




                                     166
Table 6.6. Health status by gender

Health status                                      Income quintile1
                       Poorest       Poor            Middle         Wealthy   Wealthiest
                        20%                                                     20%
Very good               10.0           6.7             0.0            3.7       17.4
Good                    13.3          10.0            23.4            7.4       39.1
Moderate                36.7          46.7            40.4           48.1       26.1
Poor                    36.7          23.3            27.7           25.9       17.4
Very poor                3.3          13.3             8.5           14.8        0.0
 χ (df = 16) = 26.716, P < 0.045, cc=0.381
1 2




                                             167
Table 6.7. Health status by health care-seeking behaviour
Health status                                         Health care-seeking behaviour1
                                                        Yes                     No
Very good                                                1.7                    5.9
Good                                                    11.9                   11.8
Moderate                                                35.6                   52.9
Poor                                                    35.6                   23.5
Very poor                                               15.3                    5.9


χ (df = 4) = 3.350, P > 0.05
1 2




                                            168
Table 6.8. Health status by health insurance coverage
Health status                                Health Insurance Coverage1
                       Private       Public, NHF        Public, Other      No
Very good                 0.0            12.0                0.0           5.5
Good                     60.0            28.0                0.0          17.3
Moderate                 20.0            48.0               53.3          38.2
Poor                     20.0             8.0               46.7          28.2
Very poor                 0.0             4.0                0.0          10.9
 χ (df = 4) = 21.913 P = 0.039, cc=0.352
1 2


Note: NHF – National Health Fund




                                            169
Table 6.9. Diagnosed health conditions by health care seeking behaviour
Variable                                20021                               20072

                             Health care-seeking behaviour       Health care-seeking behaviour




Diagnosed Health                 Yes                  No           Yes                 No
conditions

Cold                             6.0                  12.5          1.7                0.0

Diarrhoea                        3.6                  0.0           5.1                0.0

Asthma                            -                    -            1.7                0.0

Diabetes                         3.6                  0.0          18.6                17.6

Hypertension                     46.4                 50.0         42.4                64.7

Arthritis                        32.1                 12.5          5.1                11.8

Other                            14.3                 25.0         20.3                5.9

No                                -                    -            5.1                0.0

  χ (df = 5) = 5.381, P > 0.05
 1 2



  χ (df = 7) = 6.209, P > 0.05
 2 2




                                                170
Table 6.10. Health status by Annual total expenditure, 2007
                                             Descriptive statistics
                                                                                      95% Confidence Interval
 Health status                     Mean         Std. Deviation
                                                                      Std. Error
 Very good                       1,447,018.91    1,595,683.12            504,599.31     305,535.97 - 2,588,501.85
 Good                              651,694.11      561,405.68            104,250.42       438,146.81 - 865,241.41
 Moderate                          442,482.79      400,604.78             50,471.46       341,591.79 - 543,373.80
 Poor                              473,225.25      428,012.88             66,043.82       339,847.05 - 606,603.46
 Very poor                         502,309.96      214,315.07             59,440.31       372,800.66 - 631,819.26
 Total                             558,288.05      615,473.17             49,120.11       461,261.72 - 655,314.38

F statistic [5,152] = 7.134, P < 0.001

Values are quoted in Jamaican dollars (US $1.00 = Ja. $80.47, in 2007)




                                                                  171
Table 6.11. Self-reported health conditions by total expenditure, 2002 and 2007

                                                                  20021
                                                                                                95% Confidence Interval for
                                                                                                          Mean
                                                                    Std.                          Lower
Self-reported health conditions                    Mean           Deviation       Std. Error      Bound      Upper Bound
Cold                                              101,186.97                .               .              .                .
Diarrhoea                                         301,830.15                .               .              .                .
Diabetes mellitus                                 227,604.63                .               .              .                .
Hypertension                                      191,674.92      114,769.48       27,835.69     132,665.90     250,683.94
Arthritis                                         180,414.82      153,268.67       48,467.81      70,773.02     290,056.62
Unspecified                                       148,949.44       98,636.87       40,268.33      45,436.40     252,462.48
Total                                             182,970.56      119,752.91       19,958.82     142,452.01     223,489.11


                                                                  20072
  Cold                                            236,103.72                 .              .              .                .
  Diarrhoea                                       597,953.39        98,902.35      57,101.30     352,266.33       843,640.45
  Asthma                                           42,703.27                 .              .              .                .
  Diabetes mellitus                               568,441.75       417,728.90     111,642.74     327,252.26       809,631.23
  Hypertension                                    310,082.28       233,719.82      38,953.30     231,002.88       389,161.69
  Arthritis                                       188,747.40       123,903.88      55,411.50      34,900.41       342,594.40
  Unspecified                                     496,102.96       343,771.70      91,876.86     297,615.09       694,590.83
  No                                            1,103,454.01 1,320,420.17         762,344.94    2,176,651.5     4,383,559.56
  Total                                           420,692.91       398,170.68      45,375.76     330,319.25       511,066.57
1
 Values are quoted in Jamaican dollars (US $1.00 = Ja. $50.97, in 2002)
1
 F statistic [5, 30] = 0.396, P > 0.05
2
 Values are quoted in Jamaican dollars (US $1.00 = Ja. $80.47, in 2007)
2
 F statistic [7, 69] = 2.935, P = 0.009


                                                                 172
Table 6.12. Self-reported health conditions by medical care expenditure (public and private health care expenditure), 2002

                                                                Mean             Std. Deviation       Std. Error          95% Confidence Interval
                       Self-reported health conditions
                                                                Upper Bound      Lower Bound          Upper Bound         Lower Bound       Upper Bound
    Cost at Public     Cold
                                                                          0.00                    .                   .                 .                 .
    Health Facility1
                       Diarrhoea                                          0.00              0.00                   0.00            0.00               0.00
                       Asthma                                             0.00                 .                      .               .                  .
                       Diabetes mellitus                                120.00           145.68               46.07               15.79           224.21
                       Hypertension                                     997.50          3124.79              698.72             -464.95          2459.95
                       Arthritis                                        416.67           520.42              300.46             -876.12          1709.45
                       Unspecified                                      240.91           406.71              122.63              -32.32           514.14
                       No                                                33.33             57.73               33.33            -110.09             176.75
                       Total                                            493.14          1983.53              277.75              -64.74          1051.01
    Cost at Private    Cold
                                                                       1600.00                    .                   .                 .                 .
    Health Facility2
                       Diarrhoea                                       4300.00          3404.41             1965.54            -4157.03         12757.02
                       Asthma                                             0.00                .                   .                   .                .
                       Diabetes mellitus                               2855.55          7762.10             2587.37            -3110.93        8822.0378
                       Hypertension                                    2147.83          6564.29             1368.75             -690.78          4986.44
                       Arthritis                                        833.33          763.763              440.96            -1063.96          2730.62
                       Unspecified                                     2316.67         3151.291              909.70              314.43          4318.90
                       No                                              1650.00           919.24              650.00            -6609.03          9909.03
                       Total                                           2281.48          5482.01              746.01              785.18          3777.78
1
F statistic [7, 53] = 0.288, P = 0.955
2
F statistic [7, 46] = 0.119, P = 0.997




                                                                 173
CHAPTER 7

Socioeconomic correlates of self-evaluated health status of elderly
with diagnosed chronic medical conditions, Jamaica


The aim of the current study is to examine the health status of elderly Jamaicans in rural, peri-
urban and urban areas of residence in Jamaica, and to propose a model to predict the social
determinants of poor health status of elderly Jamaicans with at least one chronic disease. A sub-
sample of 287 respondents 60 years and older was extracted from a larger nationally cross-
sectional survey of 6783 respondents. The stratified multistage probability sampling technique
was used to draw the survey respondents. A self-administered questionnaire was used to collect
the data from the sample. Descriptive statistics were used to examine the demographic
characteristics of the sample; chi-square was used to investigate non-metric variables, and
logistic regression was the multivariate technique chosen to determine predictors of poor health
status. Approximately thirty six percent of the sample had poor health status. Majority (43.2%)
of the sample reported hypertension, 25.4% diabetes mellitus and 13.2% arthritis. Only 35.4% of
those who indicated that they had at least one chronic illness reported poor health status and
there was a statistical relation between health status and area of residence [χ2 (df = 4) = 11.569,
p = 0.021, n = 287]. Rural residents reported the highest poor health status (44.2%) compared
to other town (27.3%) and urban area dwellers (23.7%). The majority of the respondents in the
sample had good health, and those with poor health status were more likely to report having
hypertension followed by diabetes mellitus and arthritis. Poor health status was more prevalent
among those of lower economic status in rural areas who reported greater medical health care
expenditure. The prevalence of chronic diseases and levels of disability in older people can be
reduced with appropriate health promotion and strategies to prevent non-communicable
diseases.




Introduction

The Caribbean has been identified as the most rapidly ageing region of the world. Between 1960

and 1995, there was a 76.8% increase in the elderly population.1 Among its regional island

states, the average growth rate in the elderly population was approximately 5.3% for the 1995-

2000 period. The elderly as a percentage of total population was 4.3% in 1950 and is estimated

to reach about 15% by 2020.1 In Jamaica, a similar pattern has been observed with a clear and

rapidly rising trend in the elderly as a proportion of the population.2 By 2025 as much as 1 in 7

                                               174
persons will be elderly. Moreover, characterizing this pattern of increasing elderly is the

differential growth rates within the various sub-age groups over age 60, with the 75 years and

above age group expected to double moving from 2.8% currently to 4.0 % in 2025.3 Eldemire4

noted that the elderly in Jamaica represents 10% of the population, and that they were for the

most part mentally competent and physically independent. With a calculated life expectancy of

75.5 years5, the burden on the healthcare system can be expected to increase.

       The epidemiologic transition in the Caribbean over the last 40 years has produced an

epidemic of lifestyle-related chronic non-communicable diseases.6 Among these are obesity,

diabetes, and hypertension, along with such complications as stroke, heart disease, and

amputations.6 Cardiovascular disease is by far the leading cause of death at older ages in

developing countries, although the impact of communicable diseases remains considerable.7 One

comprehensive analysis attributes nearly 46 percent of all deaths among women aged 60 and

over in developing countries in the early 1990s to cardiovascular disease; the corresponding

figure for older men was 42 percent.7 Older people with diabetes are at particularly high risk for

heart disease, stroke, eye damage, kidney disease, limb amputation and depression. In the Survey

on Health and Well-Being of Elders (SABE), among those reporting diabetes, at least 60% also

reported visual problems with or without eye glasses. Among those reporting at least two chronic

diseases, 25% had symptoms of depression.8 Furthermore, SABE indicates that an average of

70% of women aged 60 years and older have at least one potentially disabling condition, such as

low vision, arthritis, or urinary incontinence.8

       In developed countries, the health and social status of the elderly has received a fair

amount of attention.9 With the Caribbean, some progress has been made in terms of research on

the elderly since Braithwaite10 when he noted that data on the Caribbean elderly were extremely



                                                   175
limited. With the continuing aging of the population in the Caribbean, gerontological research

has devoted increasing attention to those at very advanced ages11 and in recent years, there has

been increasing interest in issues relating to health of the elderly in the Caribbean. Patterns of

mortality at the most advanced ages are of interest in their own right, indicating variation in

health status and well-being among this group. Moreover, differences in mortality and trends in

them may give clues about the likelihood of a further extension of life expectancy.12

       Rural populations in Caribbean countries generally experience excessive deficiencies in

health care access, social services, and other goods and services needed for health living. Rural

residence has significantly influenced health care access and health status. Urban residents

consistently reported better health status than rural residents and greater satisfaction with their

health care.13 Rural residents are more often uninsured,14 and have greater distance to travel for

their health care needs,13 and are more often plagued by resource inaccessibility.15 A greater

proportion of people from the rural population in Jamaica reported having chronic illnesses, with

an even smaller population having insurance of any kind (7.6% in rural areas versus 25.0% in

urban areas).16 The aim of the study is to examine the health status of elderly Jamaicans in rural,

peri-urban and urban areas of residence. A model to predict the social determinants of poor

health status of elderly Jamaicans who have reported at least one chronic disease is proposed.


Methods
The current study used cross-sectional survey data collected by the Planning Institute of Jamaica

(PIOJ) and the Statistical Institute of Jamaica (STATIN)17 between May and August 2007. The

sample for this study was 287 individuals who indicated having being diagnosed with a chronic

illness and who are older than 60 years. The study was extracted from a larger nationally

representative cross-sectional survey of 6,783 Jamaicans. The survey was drawn using stratified


                                               176
random sampling. This design was a two-stage stratified random sampling design where there

was a Primary Sampling Unit (PSU) and a selection of dwellings from the primary units. The

PSU is an Enumeration District (ED), which constitutes of a minimum of 100 dwellings in rural

areas and 150 in urban areas. An ED is an independent geographic unit that shares a common

boundary. This means that the country was grouped into strata of equal size based on dwellings

(EDs). Based on the PSUs, a listing of all the dwellings was made, and this became the sampling

frame from which a Master Sample of dwelling was compiled, which in turn provided the

sampling frame for the labour force. One third of the 2007 Labour Force Survey (LFS) was

selected for the Jamaican Survey of Living Conditions (JSLC, 2007).17 The sample was weighted

to reflect the population of the nation.


       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 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 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, economic and health variables. The non-response rate

for the survey was 26.2%.


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

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

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

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

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

                                              177
some predisposed independent (explanatory) variables, because the dependent variable was a

binary one (health status: 1 if reported poor health status and 0 if otherwise).


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

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

Omnibus Test of Model and Hosmer and Lemeshow18 was used to examine goodness of fit of the

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

multicollinearity) existed between variables. Based on Cohen and Holliday19 correlation can be

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

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

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

Odds Ratio (OR) for the interpreting of each significant variable.


       Multivariate regression framework was utilized to assess the relative importance of

various demographic, socio-economic characteristics, physical environment and psychological

characteristics, in determining the health status of Jamaicans; and this has also been employed

outside of Jamaica. This approach allowed for the analysis of a number of variables

simultaneously. Secondly, the dependent variable is a binary dichotomous one and this statistic

technique has been utilized in the past to do similar studies. Having identified the determinants

of health status from previous studies, using logistic regression techniques, final models were

built for Jamaicans as well as for each of the geographical sub-regions (rural, peri-urban and

urban areas) and sex of respondents using only those predictors that independently predict the

outcome. A p-value of 0.05 was used to for all tests of significance.




                                                178
Model

The use of multivariate analysis in the study of health and subjective wellbeing (ie self-reported

health or happiness) is well established20,21 and this is equally the case in Jamaica and

Barbados.22,23 The current study will employ multivariate analyses in the study of health status of

old Jamaicans with diagnosed chronic medical conditions. The use of this approach is better than

bivariate analyses as many variables can be tested simultaneously for their impact (if any) on a

dependent variable.

The current study seeks to examine the social determinants of poor health status of old Jamaicans

who reported having at least one chronic medical condition (Equation [1]):

        H t = f(A i , G i , AR i , FC i , NFC i , MR i , S i , HIi , CR i , MC t , SA i , ε i )   [1]

where H t (self-rated current health status in time t) is a function of age of respondents, A i ; sex

of individual i, G i ; area of residence, AR i ; food consumption per person per household member,

FC i ; non-food consumption per person per household member, NFC i ; marital status of person i,

MR i ; social class of person i, S i ; health insurance coverage of person i, HIi ; crowding of

individual i, CR i ; medical expenditure of individual i in time period t, MC t ; social assistance of

individual i, SA i and an error term (ie. residual error).


Measure

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

Age group is a non-binary measure: young-old (ages 60 to 74 years); old-old (ages 75 to 84

years) and oldest-old (ages 85 years and older).


Elderly denotes the chronological age of 60 years and beyond. Self-reported illness (or self-

reported dysfunction): The question was asked: “Is this a diagnosed recurring illness?” The

answering options were: Yes, cold; Yes, diarrhoea; Yes, asthma; Yes, diabetes; Yes,

                                                             179
hypertension; Yes, arthritis; Yes, Other; and No. A binary variable was later created from this

construct (1 = yes, 0 = otherwise) in order to use in the logistic regression.


Health status: “How is your health in general?” And the options were very good; good; fair; poor

and very poor. For this study the construct was categorized into 3 groups with (i) good; (ii) fair,

and (iii) poor. A binary variable was later created from this variable (1 = good and fair 0 =

otherwise).


Social class: This variable was measured based on income quintile: The upper classes were those

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

quintiles (quintiles 1 and 2).


Results
Socio-demographic characteristics

The sample was 287 respondents (38.3% of men and 61.7% of women), with 57.1% young-old;

33.1% old-old and 9.8% oldest-old. Seventy percent of the sample was head of household;

35.5% had poor health status; 70.8% sought health care; 72.0% purchased the prescribed

medication; 33.2% had public health insurance coverage; 39.7% were poor; 26.5% lived in urban

areas, 19.1% in other towns and 54.4% in rural areas (Table 7.1). Majority (43.2%) of the sample

reported hypertension; 25.4% diabetes mellitus; 13.2% arthritis and 18.2% unspecified the type

of chronic illness that they were diagnosed with (Table 7.1). Approximately eighteen percent of

those who indicated that they did not seek care indicated that they could not afford it; 36.7%

indicated that they were not ill enough; 13.9% reported that they use home remedy.




                                                 180
Bivariate analyses


There was no statistical correlation between health status and self-reported dysfunction (χ2 =

1.810, p = 0.404, n= 286) (Table 7.2). Based on Table 7.2, only 35.4% of those who indicated

that they had at least one chronic medical condition reported poor health status. Table 7.3

revealed a statistical relation between health status and area of residence [χ2 (df = 4) = 11.569, p

= 0.021, n = 287]. Rural residents reported the highest poor health status (44.2%) compared to

other town (27.3%) and urban area residents (23.7%). On the other hand, greatest good health

status was reported by urban residents (21.1%), compared with other town (20.0%) and rural

area residents (14.1%) (Table 7.3). No statistical association was found between diagnosed

chronic medical condition and area of residence [χ2 (df = 6) = 10.455, p = 0.107, n = 287] (Table

7.4).



A statistical correlation was found between self-reported chronic medical condition and social

class [χ2 (df = 6) = 15.870, p = 0.014, n = 287]. The wealthy was most likely to have diabetes

mellitus (36.9%) while the poor (48.2%) and the middle class (51.6%) were mostly likely to

indicated hypertension. Approximately ten percent of the wealthy had arthritis compared to

12.9% of middle class and 16.7% of poor (Table 7.5).



The mean number of day reported to have illness was 71.6 days (SD = 185.1, 95% CI = 49.1 –

94.2 days). Urban dwellers reported the least number of days in illness (mean = 7.5 days,

SD=10.96, 95% CI = 4.7 – 10.2 days) compared to other town residents (mean = 98 days, SD =

216.4, 95% CI = 38.3 – 157.6 days) and rural residents (mean = 90.6 days, SD = 206.9, 95% CI

= 56.4 – 124.8 days) - F statistic [2,257] = 5.031, p = 0.006. This was similar for medical health


                                                181
care expenditure - F statistic [2,196] = 0.136, p = 0.001. The mean amount spent on medical care

for urban residents was US $21.85 compared to US $26.12 for other town residents and US

$26.81 for rural respondents. On the other hand, there was a statistical difference between annual

consumption expenditure and area of residence - F statistic [2,284] = 10.248, p < 0.001. The

mean annual amount spent by urban dwellers was US $8,711.95 than other town dwellers US

$7,388.90 and rural residents US $5,445.09 (Table 7.6).

Multivariate analyses

The socio-demographic determinants of poor health status of those who indicated being

diagnosed with chronic illness were sex of respondents (OR = 2.15, 95% CI = 1.009 – 4.578) and

food consumption (OR = 1.00, 95% CI = 1.00 – 1.00) (Table 7.7). Elderly men who revealed that

they were diagnosed with chronic illness were 2.15 times more likely to indicated poor health

than elderly women (Table 7.7).

Discussion
Self-reported health status has been widely used in censuses, surveys, and observational studies

and there is evidence suggesting that self-reported health is an indicator of general health with

good construct validity24 and is a respectably powerful predictor of mortality risks,25 disability26

and morbidity.27 The results of this study showed that the majority of those sampled reported

themselves to be experiencing good or fair health, while approximately one-third indicated poor

health. These results concur with those by other researchers from Dominica28 and Trinidad.29 In a

recent island wide survey of persons aged 65 years and older conducted in Trinidad in 2002,

44% reported their health as fairly good or good. In reviews of the literature, Benyamini &

Idler30 and Idler & Benyamini,25 showed that in most studies conducted since the 1980s, the

elderly people who self-rated their health as bad presented greater incidence of death than did

those who considered it to be excellent. Among elderly people, self-rated health may present
                                               182
greater sensitivity for men than for women. Since women live longer than men and experience

more years with diseases and incapacities, they tend to rate their health more negatively than do

men, but do not necessarily die because of this, over the short term. Thus, negative self-rated

health expressed by women may be more associated with quality of life. On the other hand, when

men rate their health negatively, they present a greater risk of succumbing to a fatal event.31


       There has been a general epidemiological shift from infectious to chronic diseases and

the elderly are one of the main at risk groups. In this study, just over one-third of the respondents

who reported poor health indicated that they had at least one chronic disease. This is less than the

80% reported in a study in Trinidad.29 The main chronic illnesses reported by the respondents in

this study were hypertension, diabetes and arthritis. This is in keeping with the study by Rawlins

et al31 and other Caribbean studies on this age group.32,33 Furthermore, a study conducted on

elderly Jamaicans showed that this age cohort was mainly affected by chronic non-

communicable diseases.34 The most common chronic diseases identified among the elderly in

Jamaica are hypertension, arthritis, diabetes, cardiovascular arrest, stroke and cancer. Patients in

the 60 and over age groups accounted for 37.2% and 41.1%, respectively, of new hypertensive

and diabetic cases.35 Some gender differences have been reported in respect of chronic illnesses

with women at greater risk for hypertension and men cardiovascular diseases.36 Furthermore, in

1991, cardiovascular diseases followed by diabetes mellitus and neoplasms were the diseases for

which Jamaicans 65 years older were most often hospitalized.37


       Data for the Caribbean showed that hypertension and arthritis are morbidities that

significantly affect both men and women.38 The current study revealed that hypertension was the

leading cause of illness among older and oldest elderly in Jamaica, followed by diabetes and

arthritis, which concurs somewhat with a past study39 that had hypertension as the leading cause

                                                183
of morbidity of the elderly, followed by arthritis and diabetes mellitus. In another reported study,

the most common chronic diseases identified among the elderly were hypertension, arthritis,

diabetes, cardiovascular arrest, stroke and cancer.35 Some gender differences have been reported

in respect of chronic illnesses with women at greater risk for hypertension and men

cardiovascular diseases.36 In a recent study by Bourne and colleagues, 1.4 percent more women

had diabetes mellitus than men and this was the same for hypertensive older and oldest elderly

Jamaicans. On the other hand, there were 1.6 times more old and oldest elderly Jamaican men

with self-reported arthritis than women.39 These chronic non-communicable diseases continue to

interface within the functional lives of the elderly, which means that they are indeed living

longer but are faced with lower levels of good health than young adults (ages 15 to 29 years) and

middle-aged adults (ages 30 to 59 years). According to the JSLC there has been significant

increase in illness/injury among older persons since 1997.40 Data from the 2002 survey indicate

that 34.6 percent of the elderly population surveyed, reported an illness or injury during the four-

week reference period.41


       Hypertension is one of the most important treatable causes of morbidity and mortality

and accounts for a large proportion of cardiovascular diseases in elderly in Jamaica.42 It is known

to be a major risk factor for the development of diabetic renal disease, and hyperglycaemia also

has a role in the development of diabetic nephropathy.43 Studies from developed countries have

reported prevalence of raised blood pressure among elderly to vary from 60% to 80%.44

Furthermore, diabetes mellitus is one of the leading causes of morbidity and mortality among

persons aged 65 and older.45 About 20% of persons in this age group are estimated to have

diabetes, with another 25% in pre-diabetic stages.46 Moreover, because diabetes can be

asymptomatic for many years, about 50% of older individuals with diabetes are thought to be


                                               184
undiagnosed.47 In Jamaica, diabetes-related deaths in 1994 had increased 147% over the 1980

level and represented the third leading cause of loss of years of potential life among women and

tenth among men.48 There is evidence that this is due to the low rates of awareness, treatment

and control among patients with hypertension and diabetes.49,50

       Rural populations generally experience excessive deficiencies in healthcare access, social

services and other goods and services needed for healthy living. Furthermore, 23% of people

from rural Jamaica who reported having a chronic medical condition were not actively engaged

in seeking health care because of affordability issues, compared with 9.4% from urban areas.

Urban residents consistently reported better health status than rural residents, and greater

satisfaction with their health care.51 There was a statistical correlation between good health status

and area of residence, or self-reported (chronic) recurring illness and age cohort. Furthermore,

the data showed that elderly Jamaicans who dwelled in rural area had the lowest self-reported

good health compared to those who resided in other towns and urban areas. Continuing, those

who resided in urban residence reported the greatest good health status. In 1997, statistics from

PIOJ and STATIN52 revealed that 54.3 percent of elderly (ages 60 years and over) lived in rural

areas. A study by Bourne39 showed that approximately 7 out of every 10 old and oldest elderly in

Jamaica lived in rural areas, compared to 6 out of 10 for those 60 years and older of the

population. In addition, 20 out of every 100 Jamaicans were below the poverty line, compared to

25 out of every 100 in rural Jamaica. Given that the elderly substantially lived in rural areas and

that poverty for this group was 10.2 percent,53 it is not surprising that the elderly in this area of

residence had a lower level of good health status than the urban elderly in Jamaica.

       The wealthiest in the society are expected to experience better health due to their

knowledge of health risks and their access to the resources necessary to avoid such risks and treat



                                                185
emerging health conditions.54 But with increasing wealth and development these has been an

increase in chronic disease as lifestyle changes have had a negative impact. The studies found

that there were large gaps between the mean amounts of money spend by urban residents

compared with their rural counterparts. Furthermore, the elderly who are wealthy were more

likely to have diabetes mellitus while the poor and the middle class were more likely to report

hypertension. This suggests the consumption patterns of the wealthy contribute to ill-health.

Thus whereas the poor become ill due to their inability to access their basic human rights, the

rich become ill as a result of their harmful consumption patterns. According to Sobal and

Stunkard,55 in developing societies there is a higher likelihood of obesity among men in higher

socioeconomic strata. These men are at increased risk of developing type 2 diabetes mellitus56

which is increasing in the adult population. Among the demographic correlates of health is the

cost of medical care. It is established in health literature that medical care20 and cost of medical

care21 are among the social determinants of health. In this study, rural residents significantly

spend more money on medical care compared to their urban counterparts. Furthermore, rural

residents reported the largest amount of days of illness compared to their counterparts living in

urban areas or other towns.


Conclusion


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

Jamaica puts the elderly at risk. Majority of the respondents in the sample had good or fair

health, and those with poor health status were more likely to report having hypertension followed

by diabetes mellitus and arthritis. Poor health status was more prevalent among those of lower

economic status in rural areas who reported the greatest number of sick days of illness and

medical health care expenditure. The prevalence of chronic diseases and levels of disability in

                                                186
older people can be reduced with appropriate health promotion and strategies to prevent non-

communicable diseases. This research provides valuable information on health status and the

non-communicable diseases which affect the elderly in Jamaica. These findings can assist health

care professionals to specifically and adequately address the health needs of the elderly in

Jamaica.




                                             187
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                                            193
Table 7.1: Socio-demographic characteristics of sample
Variable                                                 Frequency   Percent
Sex
  Men                                                         110       38.3
  Women                                                       177       61.7
Diagnosed chronic medical condition
  Diabetes mellitus                                            73       25.4
  Hypertension                                                124       43.2
  Arthritis                                                    38       13.2
 Other (unspecified)                                           52       18.2
Health care-seeking behavior
  Sought care                                                 201       70.8
  Did not seek care                                            83       29.2
Why didn’t you seek care
  Could not afford it                                          14       17.7
  Was not ill enough                                           29       36.7
  Preferred home remedies                                      11       13.9
  Didn’t have time to go                                        6        7.6
   Unspecified                                                 19       24.1
Purchased medication
  Prescribed medicine                                         198       72.0
  Partial prescription                                          8        2.9
  Prescribed/over the counter                                   6        2.2
  Over counter                                                  6        2.2
  Prescribed, but did not buy                                   9        3.3
  No                                                           48       17.4
Health insurance coverage
   Private                                                     23        8.0
   Public                                                      72       25.2
   No                                                         191       66.8
Health status
  Good                                                         49       17.1
  Fair                                                        136       47.4
  Poor                                                        102       35.5
Area of residence
   Urban                                                       76       26.5
  Other town                                                   55       19.1
  Rural                                                       156       54.4
Social class
  Poor                                                        114       39.7
  Middle                                                       62       21.6
  Wealthy                                                     111       38.7
Household head
  No                                                           85       29.6
  Yes                                                         202       70.2

                                            194
Table 7.2: Health status by self-reported dysfunction

                                            Self-reported Dysfunction
 Health status                                   No           Yes          Total
                                                n (%)        n (%)         n (%)

 Good                                               0 (0.0)    49 (17.2)    49 (17.1)


 Fair                                               0 (0.0)   135 (47.4)   135 (47.2)


 Poor                                           1 (100.0)     101 (35.4)   102 (35.7)

 Total                                            1             285         286

χ2 (df = 2) = 1.810, p = 0.404, n=286




                                              195
Table 7.3: Health status by area of residence


                                              Area of residence
                                                                              Total
 Health status                    Urban         Other town        Rural

 Good                             16 (21.1)        11 (20.0)      22 (14.1)    49 (17.1)


 Fair                             42 (55.3)        29 (52.7)      65 (41.7)   136 (47.4)


 Poor                             18 (23.7)        15 (27.3)      69 (44.2)   102 (35.5)

 Total                              76                55          156          287

χ2 (df = 4) = 11.569, p = 0.021, n=287




                                                196
Table 7.4: Diagnosed chronic medical condition by area of residence

                                                  Area of residence
                                                                                    Total
 Diagnosed chronic medical condition      Urban       Other town      Rural

Diabetes mellitus                         25 (32.9)     17 (30.9)     31 (19.9)    73 (25.4)



                                          25 (32.9)     22 (40.0)     77 (49.4)   124 (43.2)
Hypertension



                                           9 (11.8)        5 (9.1)    24 (15.4)    38 (13.2)
Arthritis



                                          17 (22.4)     11 (20.0)     24 (15.4)    52 (18.1)
Other (unspecified)

Total                                       76            55           156          287

χ2 (df = 6) = 10.455, p = 0.107, n=287




                                            197
Table 7.5: Self-reported chronic medical condition by social class

                                                        Social Class

 Self-reported chronic medical condition                  Middle       Upper
                                              Poor        class        class       Total

 Diabetes mellitus                          21 (18.4)     11(17.7) 41 (36.9)      73 (25.4)


 Hypertension                               55 (48.2) 32 (51.6) 37 (33.3)         124(43.2)


 Arthritis                                  19 (16.7)     8 (12.9)     11 (9.9)   38 (13.2)


 Other (unspecified)                        19 (16.7) 11 (17.7) 22 (19.8)         52 (18.1)

 Total                                            114           62         111         287

χ2 (df = 6) = 15.870, p = 0.014, n=287




                                               198
Table 7.6: Annual consumption expenditure, length of illness, total medical expenditure, public medical expenditure,
private medical expenditure by area of residence


                                      Area of
 Variable                            residence                                                        95% Confidence
                                                        N           Mean          Std. Deviation         Interval
 †Annual consumption                 Urban
                                                             76         8711.95         6761.20        716695 - 10256.95
 expenditure*
                                   Other Town                55         7388.90         5271.25         5963.88 - 8813.91
                                   Rural                    156         5445.09         4470.72         4738.01 - 6152.17
                                   Total                    287         6682.69         5485.63         6045.34 - 7320.03
 ††Length of illness               Urban                     64            7.45           10.96               4.72 - 10.19
                                   Other Town                53           97.98          216.44            38.32 - 157.64
                                   Rural                    143           90.55          206.90            56.35 - 124.76
                                   Total                    260           71.61          185.10             49.01 - 94.22
 †††Number of visit to health care Urban
                                                             55            1.65             1.58               1.23 - 2.08
 practitioner
                                   Other town                39            1.21             .61                1.01 - 1.40
                                   Rural                    101            1.42             .85                1.25 - 1.58
                                   Total                    195            1.44            1.08                1.29 - 1.59
 ††††Medical expenditure*          Urban                     57         1481.58         1988.75          953.89 - 2009.27
                                   Other town                39         1817.95         2377.57         1047.23 - 2588.67
                                   Rural                    103         1805.34         5154.02          798.04 - 2812.64
                                   Total                    199         1715.07         3988.73         1157.48 - 2272.67

† F statistic [2,284] = 10.248, p < 0.001
†† F statistic [2,257] = 5.031, p = 0.006
††† F statistic [2,192] = 2.057, p = 0.131
†††† F statistic [2,196] = 0.136, p = 0.001




                                                                  199
Table 7.7: Logistic regression: Predictors of poor health status of those diagnosed with chronic
medical condition

                                                                             Odds
 Variable                                                Std.   Wald         ratio
                                          Coefficient    Error statistic             95.0% C.I.
        Middle class                            0.647    0.527    1.507      1.909   0.680 - 5.360
        Upper class                             0.427    0.639    0.446      1.533   0.438 - 5.366
        †Poor

        Man
                                                0.765    0.386    3.937*     2.150   1.009 - 4.578

        Urban areas                            -0.314    0.439      0.512    0.730   0.309 - 1.727
        Other towns                            -0.449    0.466      0.931    0.638   0.256 - 1.589
        †rural areas

        Dummy social assistance                -0.112    0.461      0.059    0.894   0.362 - 2.207
        Crowding                                0.173    0.119      2.124    1.189   0.942 - 1.499
        Age                                     0.033    0.022      2.182    1.033   0.989 - 1.079

        Married                                 0.257    0.403      0.406    1.293   0.587 - 2.847
        Divorced, separated or
                                                0.629    0.461      1.858    1.875   0.759 - 4.628
        widowed
        †Never married

         Non-food consumption                    0.000 0.000         0.017   1.000   1.000 - 1.000
         Food consumption                        0.000 0.000 4.088*          1.000   1.000 - 1.000
         Dummy health insurance                  0.390 0.382         1.039   1.476   0.698 - 3.123
         Constant                               -1.152 1.604         0.516   0.316        -
χ (df = 13) = 20.249, p < 0.001; n = 285
 2

-2 Log likelihood = 238.17
Nagelkerke R2 =0.115
Hosmer and Lemeshow goodness of fit χ2=7.565, P = 0.477
Overall correct classification = 83.5%
Correct classification of cases of self-rated poor health status = 99.2%
Correct classification of cases of self-rated good health status = 6.3%
†Reference group
*p < 0.05, **p < 0.01, ***p < 0.001




                                                200
CHAPTER 8

Child Health Disparities in an English-Speaking Caribbean nation:
Using parents’ views from a national survey



Paul Andrew Bourne , Cynthia Grace Francis & Elaine Edwards




Previous studies in the English-Speaking Caribbean, and in particular Jamaica, have used a
piecemeal approach to the study of child health, and none emerged that has modelled good
health status while evaluating other areas of health. The current study seeks to evaluate the
general health of children from the perspective of their parents’ views in an English-Speaking
Caribbean nation as well as the typology of dysfunctions, health disparities, social determinants
of self-evaluated health of children, and provide policy formulators as well as health researchers
with pertinent information that can be used to formulate health intervention programmes and
guide the focus of future research. A sample of 2,642 children (≤ 18 years) was used for this
analysis. The data were taken from the 2002 Jamaica Survey of Living Conditions (JSLC).
Stratified probability sample was used to collect the data. The JSLC used an administered
questionnaire to detail recall information on particular activities from parents. The
questionnaire was modelled from the World Bank’s Living Standards Measurement Study
(LSMS) household survey. Multivariate models were used to establish statistical association
between good health status and social determinants, health seeking behaviour, and length of
illness. Eleven percent of the sample reported an illness in the last 4-weeks. Of those who
indicated an illness, 16.5% claimed that their illness were non-diagnosed by medical
practitioners. Fifty-eight percent of those who indicated diagnosed illness had acute conditions
(34.7% influenza, 4.5% diarrhoea and 19.2% respiratory diseases), 2% chronic diseases (i.e.
diabetes mellitus) and 24.1% unspecified conditions. Six explanatory determinants were found
that explain good health status: age (OR = 0.95, 95% CI = 0.90-1.00); health care-seeking
behaviour (OR = 0.29, 95% CI = 0.15-0.56); middle class (OR = 5.00, 95% CI = 1.75-14.28);
length of illness (OR = 1.00, 95% CI = 1.00-1.00); medical expenditure (OR = 1.00-1.00) and
area of residence (urban – OR = 2.75, 95% CI = 1.36 – 5.57; peri-urban – OR = 3.37, 95% CI
= 1.42 – 7.99). Although health indicators such as life expectancy, infant mortality, illnesses,
and nutrition as well as socio-economic determinants such as poverty and education have
improved exponentially in Jamaica as well as in the wider Latin America and the Caribbean,
child health disparities still exist in Jamaica. The findings are far reaching, provide more



                                              201
information than objective indices, and can be used to aid policy formulation and guide future
research.



Introduction


In 1946, the World Health Organisation1 (WHO) joined the discussion on health which resulted

in a conceptual definition that expanded on the popular absence of diseases. The WHO theorized

that health must incorporate social, economic and psychological variables and not merely the

absence of diseases. This was documented in the preamble to its Constitution1 in 1948. Engel2-6

who was a physician later became involved in the discourse and added a conceptual model. He

opined that the treatment of mentally ill-patient must include the physical, social and

psychological conditions. He called this conceptual framework, a biopsychosocial model.

Despite the efforts of WHO and Engel to broaden the biomedical model (ie diseases causing

pathogens), scholars such as Bok7 argued that the WHO’s conceptual definition of health is too

broad and by extension elusive to operationally measure. He therefore cited that the difficulty

with measuring the WHO’s conceptual definition of health is such that it should not be used by

researchers. Bok’s perspective did not include a suggestion to replace this but speaks to the

dominance of traditional approach to the measurement of health. The traditional approaches such

as mortality, diagnosed illness and life expectancy have objectively measurable outcomes which

are among the rationales offered for justifications of their usages.


       Using mortality or morbidity to measure health is a narrow approach. This on the other

hand is on the opposite extreme of the health pendulum as health is more than not having

dysfunctions or death.8 Death is the outcome of some morbidities, accidents, injuries, suicide and

other conditions. Those aforementioned issues omit the role that social determinants play on
                                                202
people health. These social determinants include poverty, income, marital status, crime and

violence, culture, and much more.9-27 Poverty is empirically established as strongly correlated

with poor health.25-27 It affects the quality of the physical environment, nutrition, choices,

psychological state of the individual as well as socio-political choices. The deprivation which

results from poverty may influence ones physical illness, but there are social issues surrounding

poverty that may not result in injuries or even diseases. We can argue within the reality of

contemporary societies that all peoples have equal access to health and other material resources,

which would result in the same health outcome. If we assume this position, it would be highly

flawed as the WHO28 opined that 80% of chronic illnesses were in low and middle income

countries. This undoubtedly suggests 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. Using two

decades worth of data on Jamaica, Bourne29 found that there was a positive correlation between

poverty and unemployment; poverty and illness; and crime and unemployment as well as a

negative correlation between poverty and not seeking medical care.


       Illness therefore is an outcome of a plethora of conditions which include biological,

social, economic and psychological issues. Many studies in the English-Speaking Caribbean as

well as Cuba that have examined health status of children have substantially only examined

mortality, birth, morbidity and to a lesser extent nutrition.30-37 Those studies are once again

highlighting the strength of the biomedical model in contemporary Caribbean nations, and to a

lesser extent not recognize the value of the social determinants in health and health care. The

WHO and any other scholars have joined the discourse in the value of social determinants since


                                              203
the 2000 and this has seen many publications on the matter.16-19,21 Although the WHO opined

that health research and by extension health must include the social determinants,21

subconsciously the dominance of the biomedical approach is so engrained in psyche that in 2009

WHO published a document entitled ‘World Health Statistics’ and the social determinants were

omitted from the section on health indicators. The document examined mortality, morbidity,

typologies of dysfunctions, burden of diseases, immunization, sanitation, healthy life

expectancies, health expenditure, health care-utilization and omitted critical social determinants

such as poverty, marital status, education, and so on.


       Like WHO, Caribbean scholars are so focused on the objective health measures (such as

life expectancy, mortality and diagnosed morbidity) that their work lack policy invention

strategy that include critical social determinants. Humans are multi-dimensional animals,

suggesting that omitting social determinants are excluding critical tenets that can enhance policy

formulation in improving health and guide political actions.18 In 2007, poverty rates in rural

Jamaica was twice that of urban poverty39 and within the context of empirical findings the health

status of children in the former areas cannot be the same as those in the latter areas. Poverty

therefore affects the choices, physical environment, nutrients intakes, health care utilization, and

the quality of life of parents as well as their children. Having identified the weaknesses of many

of the previous studies and the role of social determinant in health and health intervention, the

current study will fill this gap by examining child health from the perspective of social

determinants (including area of residence). In addition to the identified weakness of many studies

that have examined health in children, the current study using Casas et al.’s40 work recognize that

health disparity in Latin America and the Caribbean is accounting for some of the inequalities in

health outcomes. Casas et al cited that the region demonstrated the greatest disparities in income


                                               204
and other social determinants, indicating a justification for the disparity in infant mortality

between poor and developed countries.26 The aims of the present work are to evaluate the general

health of children from the perspective of their parents’ views in an English-Speaking Caribbean

nation as well as the typology of dysfunctions, health disparities, social determinants of self-

evaluated health of children, and provide policy formulators as well as health researchers with

pertinent information that can be used to formulate health intervention programmes and guide

the focus of future research.


Materials and Methods

The Jamaica Survey of Living Conditions (JSLC) was commissioned by the Planning Institute of

Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN) in 1988.39 These two

organizations are responsible for planning, data collection and policy guideline for Jamaica, and

have been conducting the JSLC annually since 1989.39 The JSLC is a administered questionnaire

where respondents are asked to recall detailed information on particular activities. The

questionnaire was modelled from the World Bank’s Living Standards Measurement Study

(LSMS) household survey.41 There are some modifications to the LSMS, as JSLC is more

focused on policy impacts.        The questionnaire covers demographic variables, health,

immunization of children 0–59 months, education, daily expenses, non-food consumption

expenditure, housing conditions, inventory of durable goods and social assistance. Interviewers

are trained to collect the data from household members. The survey is conducted between April

and July annually. The current study extracted a sub-sample of 2,642 respondents 18 years and

below from a larger nationally cross-sectional survey of 6,782 Jamaicans. This study used the

dataset of the JSLC for 2007.42



                                              205
Measures



Table 8.1 shows the operational definitions of some of the explanatory variables used in this

study. An explanation of some of the variables in the model is provided here.


Statistical analysis


Statistical analyses were performed using the Statistical Packages for the Social Sciences v 16.0

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

deviation (SD), frequency and percentage were used to analyze the socio-demographic

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. Logistic regression examined the relationship

between the dichotomous binary dependent variable and some predisposed independent

(explanatory) variables (dependent variable was 1 if reported good health status and 0 if poor

health). A pvalue < 0.05 was selected to established statistical significance. The final model was

based on those variables that were statistically significant (p < 0.05). Categorical variables were

coded using the ‘dummy coding’ scheme.


       The predictive power of the model was tested using the ‘omnibus test of model’ and

Hosmer and Lemeshow’s43 technique was used to examine the model’s goodness of fit. The

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

collinearity) existed between variables. Cohen and Holliday44 stated that correlation can be

low/weak (0–0.39); moderate (0.4–0.69), or strong (0.7–1). This was used in the present study to

exclude (or allow) a variable. Where collinearity existed (r > 0.7), variables were entered

                                               206
independently into the model to determine those that should be retained during the final model

construction. The final decision on whether to retain was based on the variables’ contribution to

the predictive power of the model and its goodness of fit. Finally, forward stepwise technique in

logistic regression was used to identify variables as well as determine the magnitude (or

contribution) of each statistically significant variable, and the odds ratio (OR) for interpreting

each of the significant variable.


Results
Demographic Characteristic


The current study had a sample of 2, 642 respondents (ages 0 to 18 years): 50.9% males and

49.1% females. Forty-eight percentage of the sample was poor with 25% in the poorest 20%

compared to 33% in the wealthy social hierarchies (including 14% in the wealthiest 20%). Fifty-

two percent of the sample resided in rural areas compared to 28% in urban and 20% in peri-

urban areas. Eleven percent of the sample reported an illness in the last 4-weeks. Of those who

indicated an illness, 16.5% claimed that their illness were non-diagnosed by medical

practitioners. Self-reported diagnosed illness were 58.2% acute conditions (including 34.7%

influenza, 4.5% diarrhoea and 19.2% respiratory diseases), 1% chronic (i.e. diabetes mellitus)

and 24.1% unspecified conditions. Of the sample 11.1% answered the question “Have you

sought medical care in the last 4-weeks? Of those who responded to the medical care-seeking

question, 58.4% claimed yes. When the respondents were asked “Why did you not seek medical

care?” 17.8% said that they could not afford it, 50.8% was not ill enough and 19.5% used home

remedy. Concurrently, 91.4% of the sample indicated at least good self-evaluated health status

(including 45.1% excellent health status) with 0.2% claimed that their health status was very

poor.

                                               207
       There was a significant statistical difference between the mean age of respondents and

self-reported diagnosed health conditions – F statistic = 8.4, P < 0.0001. The mean age of child

being diagnosed with particular illness was 6.5 years (SD = 5.1; 95% CI = 5.5-7.1). The mean

age of children with particular health conditions in sample was 4.8 years (SD = 4.5, influenza);

3.5 years (SD = 2.7, diarrhoea); 7.4 years (SD = 4.4, respiratory disease); 12.3 years (SD = 5.9

diabetes mellitus) and 8.4 years (SD = 5.9; other – unspecified conditions).


       Table 8.2 highlights particular social, economic and biological variables by area of

residence. Three times more children in rural areas were from households in the poorest 20%

compared to urban area. Rural children were 3.3 times more likely to experience illness over a

longer period than urban children compared to 2 times more than peri-urban children. The

identified cases of chronic condition (i.e. diabetes mellitus) were a rural matter (1.8%).


       Table 8.3 shows self-reported diagnosed health conditions by particular demographic

characteristics. Rural children were highly likely to indicate most of the health conditions

compared to other children from other geographical zones. However, urban children were most

likely to be diagnosed with respiratory diseases (35.7%) compared to peri-urban children with

influenza (27.7%) and rural children with diarrhoea (92.3%). All the reported cases of diabetes

mellitus were from rural zones (100.0%).


       Table 8.4 presents information between health care-seeking behaviour and particular

demographic variables. A child who received medical care in the last 4 weeks was 1.8 times

more likely to have health insurance coverage and 3.9 times more likely to report poor health

status. No significant statistical association was found between health care-seeking behaviour




                                                208
and social hierarchy (P = 0.866), health care-seeking behaviour and age (P = 0.503) and health

care-seeking behaviour and sex of respondents (P = 0.356).


Multivariate analysis


       Table 8.5 highlights the explanatory social determinants of good health status of children

in Jamaica. Six explanatory determinants were found explain good health status: age (OR = 0.95,

95% CI = 0.90-1.00); health care-seeking behaviour (OR = 0.29, 95% CI = 0.15-0.56); middle

class (OR = 5.00, 95% CI = 1.75-14.28); length of illness (OR = 1.00, 95% CI = 1.00-1.00);

medical expenditure (OR = 1.00-1.00) and area of residence (urban – OR = 2.75, 95% CI = 1.36

– 5.57; peri-urban – OR = 3.37, 95% CI = 1.42 – 7.99). The data were also a good fit for the

model – model chi-square = 46.4, P < 0.0001.

Discussion

The current study highlighted that 89 out of every 100 children in Jamaica did not have an illness

in 4-week period of a survey. Instead of using diseases to measure health, 91 out of every 100

reported at least good health status (including 45 out of every 100 very good self-evaluated

health statuses). Using health conditions and mortality of children 0 – 18 years, the Pan

American Organization (PAHO) concluded that most of Jamaica’s children were in good

health.45 This finding is concurred by the current study, but this does not provide a holistic

understanding of the health disparities in child health in the nation. The current findings revealed

that 36 out of every 100 rural children were living in household in the poorest 20% compared to

14 out of every 100 in peri-urban households and 11 out of every 100 in urban households. Does

this account for any health disparity in child health in the country? Concurrently, the present

work showed that the length of illness experienced by child in rural households was 3.4 times


                                                209
more than for those in urban households and 1.9 times more than that for those in peri-urban

households. This health disparity that did not emerge in the PAHO’s findings or other studies

that have examined infant mortality or maternal deaths and/or births. The child health disparity

continues as the only cases of chronic illness (ie diabetes mellitus) were found in rural children.

Another notable health disparity was found in health insurance coverage of children in particular

households. The current research highlighted that 11 out of every 100 children in rural household

had health coverage compared to 22 out of every 100 in urban households and 16 in every 100 in

peri-urban dwellings. Health disparities were also observed between typology of illnesses and

social hierarchy in which children are in. Comparing the poorest 20% and the wealthiest 20%,

the findings revealed that none of the children in wealthiest 20% households had diabetes

mellitus compared to 1.8% children in households with the poorest 20%. Interestingly it was

noted that children in urban households were 2.8 times more likely to claim good health with

reference to rural children and ratio was 3.4 times more for peri-urban children with reference to

rural children.


       Chronic illness in Jamaica is clearly not limited to adults as the current study found that

those with diabetes mellitus were rural females. The mean ages of rural female children being

diagnosed with diabetes mellitus was 12.3 years, suggesting that chronic conditions begin to

early in rural females. The number of cases in diabetes mellitus was spread equally among the

poorest 20%, poor and the wealthy (33.3% respectively). This denotes that Jamaican females will

be living longer with chronic illness, and this has implications for policy intervention, health care

expenditure, public health care utilization and gerontological care in the future. An issue which is

embedded in the present study that begs for some clarification is the rationale why urban and

boys did not indicate any cases of diabetes.


                                                210
       Morrison46 in an article entitled ‘Diabetes and hypertension: Twin Trouble’ established

that diabetes mellitus and hypertension have now become two problems for Jamaicans and the

wider Caribbean. This situation was equally collaborated by Callender47 at the 6th International

Diabetes and Hypertension Conference. They opined that there was a positive association

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

hypertension, suggesting that those who current had diabetes mellitus are highly likely to

develop hypertension in the future. Embedded here is the immediate need to commence public

health campaign geared towards parents as well as children who currently have diabetes about

the likeliness of developing hypertension and how their lifestyle choices will become critical in

lowering this probability. Another issue which emerged from the data is the correlation between

health care-seeking behaviour and good health status. This work found that children who seek

care are 71% less likely to declare good health status.


       Since 1988 data published in the Jamaica Survey of Living Conditions39 has been

showing that females seek more medical care than males. The current study provides us with

some understanding of role of socialization in this health disparity. This research revealed that

there is no statistical association between health care-seeking behaviour and age of children as

well as health care-seeking behaviour and sex of children, suggesting that it is not early

socialization that accounts for males’ unwillingness to utilize health care services. Hence, this

study rules out the role of parents in accounting for males’ actions (or inactions) on health care-

seeking in Jamaica. This denotes that the peer group, school, and political agents are among the

socializing institutions responsible for males’ lower choice in medical care-seeking behaviour

compared to females.




                                                211
       One of the social determinants of health that is empirically established in health research

as influencing health is education.9-21 The current work concurs with the empirical findings as

children from middle class households were 5 times more likely to experience good self-

evaluated health status with reference to those in poor social hierarchies. It follows that health

disparity this current as highlighted by this study denotes that education (or the lack of) is

explaining more of the health disparity experienced by children instead of money. Health

inequalities among children of particular households in Jamaica is embedded in the educational

achievement (or lack) of their parents. In Jamaica, the educated class is more likely to be

teachers, doctors, nurses, public health practitioners and university graduants who are more

informed about many issues including health options than the poorer social classes and this is

translated into better health choices. A study on twins in USA found that more years in schooling

(i.e. education) was associated with healthier patterns of behaviour. It is this value that

accommodates for the higher health of the middle class over the poor and other social

hierarchies. The current study highlighted yet another health disparity which is difficult to

explain ‘Why the children in the wealthy-to-wealthiest social hierarchies do not have a better

good self-evaluated health compared to those in the poor-to-poorest households.


       This work revealed that children in poor and wealthy social hierarchies experience the

same good health status. A part of the explanation for the comparable quality of life between the

two aforementioned groups lies in the quality of public health care facilities and public health in

the country. With the government health care policy which has removed user fees from public

hospitals for children less than 18 years, access to health care is equally opened to all social

classes. Although access does not represents utilization, within the Jamaican context, children

who are taken to public health facilities are provided with a high level of care. Statistics from the


                                                212
Planning Institute of Jamaica and the Statistical Institute of Jamaica showed that private health

care is pro-wealthy and public pro-poor, suggesting that for the wealthy and children of those

social hierarchies to experience comparable self-evaluated health status, the quality of public

health care is high in Jamaica. Another issue which holds some of the justifications is public

health. It follows that in Jamaica, water supply, sewerage and food hygiene, and public education

are of a high quality. Even though there are substantial inequalities between the public health for

the wealthy compared to the poor, the physical environment, lower nutrition and diet are not

such that they erode the quality of public health care and general public health and these are

reducing some of the health outcome between the poor and the wealthy. Health therefore cannot

be bought as was forwarded by Smith & Kington15 it is supported by other social determinants

such as education, choices made by parents and health care system in the nation which when

coalesce produce healthier people. Health care can be purchased, but this does not translate into

better quality of life for those who are able to access those services. This is equally supporting

the perspectives of Casas et al.40 which forwarded that improvement in health in the Latin

America and the Caribbean do not correspond to the economic development levels or the

economic resources within countries as well as possessed by individuals.


       Addressing health disparities in children cannot omit the inequality between the lengths

of time spent by children in rural households compared to children of other households. The

current study revealed that there is no significant statistical relationship between self-reported

illness and area of residence, yet children within rural households were 3.4 times more likely to

experience longer time in illness compared to children of urban households and 1.9 times more

than those in peri-urban households. A part of the answer lies in the culture, operational

definition of health, choices on experiencing illness and health inequality among the parents


                                               213
within the different geographic areas. One of every two child who was ill was not taken to see a

health care practitioner because parents’ reported that they were not ill enough. This highlights

not only the cultural biases which are embedded in many parents and by extension Jamaicans

about when one should visit medical practitioners. From this bias another is the number of

parents who prefer to use home remedy as a first option instead of taking the child immediately

to the medical practitioner. For one in every 5 children, the parent used home remedy compared

to 9 in every 50 who claimed that affordability was the reasons for not taking the ill-child to a

medical doctor.


       Education in Jamaica is a pro-urban and pro-peri-urban phenomenon making children in

rural household more likely to have parents who are less educated compared to urban and peri-

urban counterparts. With more than fifty percentages of the Jamaican children residing with

parents of rural households, the benefits of education that include the decision to make healthier

choices because of information would be missing from those households. Many rural parents will

be taking decision on health care choices based on their socialization, which includes home

remedy and wait and see when a medical practitioner is needed by the ill-child. This delay of

rural parents to take their ill-children to medical practitioners initially offers an explanation for

them spending longer time with the illness as well as accounting for increased mortality among

these children. The health inequality that exists in Jamaica on the health status of children can be

explained more so by the retardation of culture, low education and tradition than on income.


       Although government policy has resulted in the removal of health care user fees for

children (0 – 18 years) in Jamaica, open access does not denote equality in access. Health care

institutions in urban and peri-urban areas are in easy access to residents, with this not being the

case for rural residents, addressing cost of care is not putting care in the hand of all. The terrain

                                                214
in rural Jamaica means that public health care choices are not easily accessed by some residents

and the distance is such that unless the conditions is severe many parents will prefer to treat the

child at home or use the traditional healer (i.e. untrained physician). It is this cultural belief that

retard many rural parents from purchasing health insurance coverage, and accounting for the

high number of cases with diarrhoea. Hence, the association between poverty and ill-health is

operating through education. Poverty leads to increased lower levels of education, and education

reduces poor health status.


        Using statistics for 2007 on Jamaica, 71% of poverty was in rural areas49. Poverty is not

only a rural phenomenon in Jamaica, but it also denotes material deprivation, social exclusion

nutritional deficiency, increases chronic diseases and premature mortality27, 50-53 Poverty means

that the individual will be unable to afford particular necessities, and good physical milieu, and

these deprivations will be such that food becomes important and the not dietary requirements.

The poor will eat (or eat sometimes), but their physical milieu will be low and survival becomes

so pronounced that choice in food is never the case. The nutritional deficiency will affect the

parents and moreso the foetus.54 According to Martin-Gronert and Ozanne54 fetal overgrowth

can transport glucose and other nutrients from the mother suffering from diabetes mellitus to the

unborn child, and means that the fetal intra-uterine milieu will become susceptible to chronic

diseases for the child in later life.


        Money therefore offers choices in a particular physical environment, social arrangement,

food selection and health demand that is not available to someone who does not have it.

According to Smith and Kington15, money buys health. This perspective assumes that health is a

transferrable product and clearly it is not, but money really open access to things. This is

justification for the lower health of those in the lower socioeconomic strata compared to those in

                                                 215
the upper income group. Van et al53 found that those with chronic health conditions were more

likely to be in the lower income group, and this is somewhat concurred by the current study.

Clearly, poverty, low education, poor physical environment, nutritional deficiency, puberty and

other sedentary and unhealthy lifestyle practices are justifying young rural female prevalence of

diabetes mellitus54,55. Here the health disparity in health outcomes of children in particular

socioeconomic strata and area of residence are clearly explained by social determinants of

health16-19 and justify a rationale for lifestyle behaviour modifications that are needed to bring

about greater responsibility of parents and children in Jamaica.


       According to Marvicsin56, type I diabetes has been increasing in Western industrialized

countries over the past 2 decades, and that its occurrence appears during puberty (ages 10 to 12

years). She stated that 1 in every 400 to 500 children and adolescents had type I diabetes in

United States. The present study revealed that 9 to every 500 children and adolescents had

diabetes in Jamaica, indicating the extent of this chronic illness in an English-Speaking

Caribbean nation. Statistics from Jamaica revealed that diabetes mellitus was almost 2 times

more for females than males49. Within the context of the aforementioned findings, it can be

extrapolated from the information that the rationale for no male-child having diabetes are

embedded in (1) this appears earlier for females, (2) puberty, (3) obesity, (4) insulin resistance

and (5) nutritional deficiency.


       The present findings highlight that females are more insulin resistant than males which is

concurring to research by Murphy et al.57 The early inception of females with diabetes are owing

to the fact that females enter puberty before males58 and that they are more likely to be

overweight than boy59 which increases their risk of having diabetes. Within the general setting,

there is a need for chronic diseases management in Jamaica so as to address the current and

                                               216
future challenges, which is only reinforcing a call made by Swaby et al in 200160. Another potent

issue which accounts of the wide health disparity in chronic illness between males and females is

owing to what Choudhary et al61 termed under-nutrition of girls (ages 10 – 12 years). The

findings of Choudhary et al’s work61 showed that 7 of every 10 adolescent females were under

weight (BMI < 18.5), which adds another dimension to the lifestyle management that is needed

for parents, children, and in particular females, in order to rectify some of the future health

problems which are accounting for lower health status of women compared to men in Jamaica.

Khetarpal and Kochar62 also argued that diet and nutrition among rural women affect morbidity

and clinical status of these women, which emphasize the importance of a normal balanced diet in

health and wellbeing and not the mere consumption of food. They also concur with previous

studies which found an association between income, individual preference, belief, cultural

traditions, physical milieu and morbidity in rural women. These all add a further dimension to

the present study about the role of money, cultural sociophysical milieu in the infection of

diseases and how these influence unhealthy (or healthy) lifestyle choices. The nutritional

deficiency in rural women in Jamaica account for the prevalence of diabetes in females 10-12

years as these individual become infected with this chronic condition on the premise that they

enter puberty before males, and that it is likely that rural males show later signs of infection after

18 years. Khetarpal and Kochar62 provide insights into the prevalence of diabetes in rural

females, when they found that 6 out of every 10 rural women (ages 25-45 years) in a rural

district in Yamunanagar (Haryana, India) were anemic owing to their low iron, B complex

vitamins and vitamin C intake. This information provides an understanding of the present

nutritional deficiency of rural women and that this is accounted for by material and income

deprivation, and how this transmitted to rural female children.



                                                217
Conclusions

In summary, although health indicators such as life expectancy, infant mortality, illnesses, and

nutrition as well as socio-economic determinants such as poverty and education have improved

exponentially in the Jamaica as well as in the Wider Latin America and the Caribbean, child

health disparities still exist within Jamaica. Among the findings that emerged which account for

these are: cultural biases, policy intervention, health care choices disparities and lack of

education. The very young age at which rural females were diagnosed with diabetes mellitus

speaks to unhealthy and sedentary lifestyle practices of their mothers during pregnancy, and how

this is affecting their female offspring. The findings are far reaching and can be used to aid

policy formulation and guide future research. Clearly there is a need for rural Jamaicans to

understand and ensure that they are having a balanced diet (with nuts, seeds, grains, vegetables

and fruits) as otherwise this will affect not only them, but their children. The challenge of this

being materialized will be linked to reduced material and income deprivations in rural Jamaica,

coupled with a health-building understanding of what you eat, how it affects your health and how

this influences morbidity in later life and increase the risk of chronic diseases in children.




Policy and Research Recommendations

In the 1990s, in seeking to lower health inequalities in the Jamaica, government policies focused

on poor people. The policies have been able to reduce poverty, but health inequality and child

health disparities are present in contemporary Jamaica. While poverty plays a role in the

immunology of an individual, the quality of the physical and social environments, lowered

access to material resources and utilization of particular health care services, many of the health

                                                 218
inequalities which government policies should have addressed since the 1990s are still evident in

child health. The issues of fairness in the distribution of health care choices and utilization are

not resulting in removal of child health disparities in Jamaica. The findings which emerged from

this work provide us with an understanding of some of the health disparities and clearly highlight

that policy focus needs an overhaul for the future. In order to reduce some of the child health

disparities in contemporary Jamaica, policy intervention must tackle education, cultural biases on

health, health care definitions, timing in seeking health care, focus on accessibility of rural

people to health care, and a direct intensive education campaign towards rural and indigenous

populations on their perception of illness as well as utilizing medical practitioners as the first

option. Another issue is an immediate and extensive health campaign on chronic disease. This

programme must to geared toward how to identify early symptoms of chronic illness in children,

where to seek care, how to live with chronic illness from in childhood onwards, preventative as

against curative care, and an intervention progrmme for public health practitioners. Public health

practitioners need to be sensitized on the earliest of children in particular rural female being

diagnosed with chronic illnesses which would see medical practitioners testing children on the

appearance of particular symptoms and not assume that they are too young. In order to reach

rural residents, a new approach is needed that will be established geared towards medical

practitioners. The new thrust must include taking medical practitioners to the residents such as in

schools, churches, house visits, mobile clinics, and remove the emphasis of tackling health of

poor people as this bottom up approach has not addressed the health inequalities. Consequently,

research should begin focusing on premature mortality in rural children, medical practitioners’

biases in working in rural areas, cultural and target rural residents in their communities in order

to understand their perception of health care and/or health care utilization as well as health



                                               219
outcome after the new intervention is implemented in rural areas. Another way forward for

researchers is to commence studying health disadvantage, health gaps and health gradients in

Jamaica with a policy implementation approach.




                                            220
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                                          224
Table 8.1: Operational definitions of particular variables
Variable           Operational definition         Coding
Self-evaluated     Parents’ evaluation of their 1= moderate-to-very good health status, 0 =
health status (or children’s health status.       otherwise
health status)     This is taken from the
                   question “In general,
                   would you say your health
                   is excellent, good,
                   moderate, poor or very
                   poor?”

Age group          Age group is classified into 1 = children ages < 5 years old
                   4 categories.                2 = children ages 5 – 9 years old
                                                3 = adolescents ages 10 – 14 years old
                                                4 = adolescents ages 15 – 18 years old
Crowding           Number of people who live Total number of people in household divided by
                   in a room                    total number of room excluding kitchen,
                                                bathroom and verandah
Social hierarchy   Income quintiles were used Low = poorest 20% to poor; middle = middle
                   to measure social class, and quintile and upper = wealthy to wealthiest 20%
                   these range from quintile 1
                   (poorest 20%) to 5
                   (wealthiest 20%)
Durable good       Items owned by household Summation of durable goods
                   members excluding
                   property (or land)
Health care-       Visits to pharmacies,        1=Visits to health care professionals,
seeking            medical practitioners,       0=otherwise
behaviour          nurses,
(health seeking
behaviour)
Income             Income is measured by
                   consumption
Self-reported      Have you had any illness
illness            or injury during the past
                   four weeks? For example,
                   have you had a cold,
                   diarrhoea, asthma,
                   diabetes, hypertension,
                   arthritis or other?




                                                  225
Table 8.2: Demographic characteristic of sample, n = 2, 642
                                                          Area of residence
Variable                                      Urban         Peri-Urban Rural            P
                                              (%)           (%)           (%)
Age group                                                                                0.112
   Children < 5 years old                           24.3           23.5         22.6
   Children 5 – 9 years old                         25.2           27.0         28.7
   Adolescents: 10 – 14 years old                   26.9           29.3         30.0
   Adolescents: 15 – 18 years old                   23.6           20.2         18.7
Health Seeking-behaviour                                                                 0.224
   No                                               66.7           57.7         54.8
   Yes                                              33.3           42.3         45.2
Health insurance coverage                                                              <0.0001
   No                                               77.8           83.6         88.9
   Yes                                              22.2           16.4         11.1
Self-reported illness                                                                    0.315
  None                                              10.4           10.0         12.1
  Yes                                               89.6           90.0         87.9
Self-reported diagnosed health conditions                                                0.002
 Acute
   Influenza                                        22.7           53.8         34.1
   Diarrhoea                                          0.0            1.9         7.3
   Respiratory diseases                             26.7           15.4         17.1
 Chronic
   Diabetes mellitus                                  0.0            0.0         1.8
   Other                                            25.3           15.4         26.2
Self-evaluated health status                                                           <0.0001
    Very good                                       42.3           47.1         45.7
    Good                                            46.8           49.6         44.7
    Moderate                                          9.4            2.9         6.7
    Poor                                              1.4            0.4         2.5
    Very poor                                         0.0            0.0         0.4
Sex                                                                                      0.684
   Male                                             49.1           50.7         51.1
   Female                                           50.9           49.3         48.9
Social hierarchy                                                                       <0.0001
   Poorest 20%                                      11.1           13.7         36.4
   Poor                                             14.5           23.1         27.0
   Middle                                           21.0           22.5         18.2
  Wealthy                                           26.6          23.6         12.7
  Wealthiest 20%                                    26.9          17.1          5.6
Length of illness mean (SD)                    7.7 (8.1)   13.6 (51.0)   25.8 (125)      0.045




                                             226
Table 8.3: Self-reported health conditions by particular social variables
                                                                             Health conditions
                                                                Acute conditions               Chronic       Other
Variable                                              Influenza Diarrhoea     Respiratory Diabetes                         P
                                                                                             mellitus
                                                          %          %             %               %            %
Sex                                                                                                                        0.112
  Male                                                     46.5             30.8       50.0           0.0        44.3
  Female                                                   53.5             69.2       50.0         100.0        55.7
Social hierarchy                                                                                                           0.352
  Poorest 20%                                              19.8             15.4       21.4          33.3        28.6
  Poor                                                     21.8             53.8       19.6          33.3        20.0
  Middle class                                             27.7             23.1       21.4           0.0        17.1
  Wealthy                                                  17.8              7.7       14.3          33.4        17.1
  Wealthiest 20%                                           12.9              0.0       23.2           0.0        17.1
Age group                                                                                                                <0.0001
  Children: ages less than 5 years                         59.4             69.2       30.4           0.0        31.4
  Children: 5 – 9 years                                    18.8             30.8       41.1           0.0        22.9
  Adolescents: 10 – 14 years                               17.8              0.0       19.6         100.0        22.9
  Adolescents: 15 – 18 years                                4.0              0.0        8.9           0.0        22.9
Health care-seeking behaviour                                                                                              0.002
  Yes                                                      41.0             53.8       64.3          66.7        65.7
  No                                                       59.0             46.2       35.7          33.3        34.3
Area of residence                                                                                                          0.004
 Urban                                                      16.8            0.0         35.7           0.0        27.1
 Peri-urban                                                 27.7            7.7         14.3           0.0        11.4
 Rural                                                      55.4           92.3         50.0        100.0         61.4
Number of visits to health care practitioner           1.2 (0.4)      1.1 (0.4)    1.4 (1.0)     1.0 (0.0)   1.3 (0.5)     0.393
Mean (SD)




                                                                   227
Table 8.4: Health care-seeking behaviour by particular social variables
                                                         Health care-seeking
Variable                                                 Yes             No            P
                                                          %              %
Age group                                                                             0.503
   Children < 5 years old                                     44.4             39.3
   Children 5 – 9 years old                                   28.1             25.4
   Adolescents: 10 – 14 years old                             17.0             23.8
   Adolescents: 15 – 18 years old                             10.5             11.5
Health insurance coverage                                                             0.027
   No                                                         76.6             86.9
   Yes                                                        23.4             13.1
Self-reported illness                                                                 0.138
  None                                                         3.5              0.8
  Yes                                                         96.5             99.2
Health conditions                                                                     0.012
 Acute
   Influenza                                                  31.1             53.6
   Diarrhoea                                                   5.3              5.5
   Respiratory diseases                                       27.3             18.2
 Chronic
   Diabetes mellitus                                           1.5              0.9
 Other (unspecified)                                          34.8             21.8

Self-evaluated health status                                                          0.006
   Very good                                                18.2               32.2
    Good                                                    45.3               45.5
    Moderate                                                22.9               19.0
    Poor                                                    12.9                3.3
    Very poor                                                0.6                0.0
Sex                                                                                   0.866
   Male                                                     48.5               47.5
   Female                                                   51.5               52.5
Social hierarchy                                                                      0.356
   Poorest 20%                                              18.1               24.6
   Poor                                                     19.9               23.8
   Middle                                                   22.8               23.0
  Wealthy                                                   20.5               16.4
  Wealthiest 20%                                            18.7               12.3




                                             228
Table 8.5: Logistic regression: Explanatory social determinants of good health status of children
                                                                    Odds                       R2
 Explanatory variable                        Std. Error     P       ratio      95% C.I.
 Age                                               0.027 0.034        0.95       0.90-1.00 0.019
 Health care-seeking behaviour                     0.331 0.000        0.29       0.15-0.56 0.032
 Middle class                                                                                 0.040
                                                   0.535 0.003        5.00      1.75-14.28
†Poor classes                                                                      1.00
 Length of illness                                          0.001   0.046   1.00      0.99-1.00   0.020
 Medical expenditure                                        0.000   0.044   1.00      1.00-1.00   0.026
 Urban area                                                 0.361   0.005   2.75      1.36-5.57   0.035
 Peri-urban area                                            0.440   0.006   3.37      1.42-7.99   0.024
†Rural area                                                                        1.00
Hosmer and Lemeshow goodness of fit χ2 = 6.8 (8), P = 0.6
-2LL = 305.3
Nagelkerke R2 =0.196
†Reference group




                                                    229
CHAPTER 9



Health Status of Patients with self-reported Chronic Diseases in
Jamaica


Paul A. Bourne & Donovan A. McGrowder


To examine the physical health status of Jamaicans in rural, peri-urban and urban areas of
residence. A model was used to determine the significant predictors of poor health status of
Jamaicans who reported being diagnosed with a chronic non-communicable disease. The
current study extracted a sub-sample of 714 people from a larger nationally representative
cross-sectional survey of 6,783 Jamaicans. A self-administered questionnaire was used to collect
the data from the sample. Descriptive statistics were used to examine the demographic
characteristics of the sample; chi-square was used to investigate non-metric variables, and
logistic regression was the multivariate technique chosen to determine predictors of poor health
status. Approximately one-quarter (25.3%) of the sample reported that they had poor health
status. Thirty-three percent of the sample indicated unspecified chronic diseases; 7.8% arthritis,
28.9% hypertension, 17.2% diabetes mellitus and 13.3% asthma. Asthma affected 47.2% of
children and 23.2% of young adults. Significant predictors of poor health status of Jamaicans
who reported being diagnosed with chronic diseases were age of respondents, area of residence
and inability to work. Majority of the respondents in the sample had good health, and adults
with poor health status were more likely to report having hypertension followed by diabetes
mellitus and arthritis, while asthma was the most prevalent among children. Improvement in
chronic disease control and health status can be achieved with improved patient education on
the importance of compliance, access to more effective medication and development of support
groups among chronic disease patients.




Introduction


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

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


                                               230
in the world and approximately 46% of the global burden of disease (1). The proportion of the

burden of non-communicable diseases is expected to increase to 57% by 2020. Furthermore,

there is increasing evidence that chronic disease risks begin in fetal life and continue into old age

(2). Adult chronic disease, therefore, reflects cumulative differential lifetime exposures to

damaging physical and social environments. Almost half of the total chronic disease deaths are

attributable to cardiovascular diseases; obesity and diabetes are also showing worrying trends,

not only because they already affect a large proportion of the population, but also because they

have started to appear earlier in life (2).

        The chronic disease problem is far from being limited to the developed regions of the

world. Contrary to widely held beliefs, developing countries are increasingly suffering from high

levels of public health problems related to chronic diseases. In five out of the six regions of the

World Health Organization (WHO), deaths caused by chronic diseases dominate the mortality

statistics (3) and there is evidence that 79% of deaths attributable to chronic diseases are

occurring in developing countries such as those in the Caribbean predominantly in middle-aged

men (3). Most Caribbean countries have experienced a health transition, with decreases in

fertility and mortality rates and changing disease patterns. Leading up to the mid 1990s, the

mortality pattern changed from deaths being mainly due to communicable diseases to them being

mainly due to non-communicable diseases (4, 5). More recently, these countries have also been

observing the re-emergence of ‘old’ communicable diseases and the emergence of new

communicable diseases, together with an increasing prominence of non-communicable diseases.

A study conducted in 10 Caribbean countries during 1975-1979, observed that the leading causes

of death were similar to those found in industrialized countries (6). Additionally, with 15-20%

and 20-25% of the adult population in English and Dutch-speaking Caribbean countries having



                                                231
diabetes and hypertension, respectively, these non-communicable diseases accounting for the

single largest expenditure in national drug budgets (7). Morbidity and mortality patterns in recent

decades in Caribbean countries such as Jamaica are largely explained by individual lifestyle

choices and social, cultural and economic determinants that affect these choices (8).

       Jamaica is the third largest island in the Caribbean, comprising of approximately 4,400

sq. miles or 10,991 square kilometers in area. Jamaica has a population of approximately 2.6

million and has undergone a significant demographic transition in the last 5 decades (9, 10).

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

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

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

to greater than 70 years in 2000 (11). As a result, the main causes of illness and death in Jamaica

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

non-communicable diseases (12). There is an increased prevalence of diet-related chronic non-

communicable diseases, such as cardio-vascular diseases, diabetes and obesity. Wilks, et al. (13)

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

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

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

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

males and females respectively (13). In a later study Wilks et al. found that the prevalence of

Type 2 diabetes mellitus was 9.8% among men, 15.7% among women and 13.4% overall (14).

Cooper et al. (15) found a prevalence of 17.1% for hypertension and 8.1% for non-insulin

dependent diabetes among the Jamaican population. There are indications that mortality and

morbidity due to chronic diseases are increasing, especially among persons over 45 years of age.



                                               232
       Chronic diseases such as heart disease, cancer and diabetes negatively affect the general

health status and quality of life of individuals (16) and there is the absence in the literature of

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

diseases. It is against this background that this study was undertaken. This study was, therefore

designed to explore any association between chronic non-communicable disease and health

status. The aim of the study was to examine the self-reported health status of Jamaicans in rural,

peri-urban and urban areas of residence. A model is used to predict the social determinants of

poor health status of Jamaicans who reported at least one chronic non-communicable disease.



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

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

survey of 6,783 Jamaicans (Jamaica Survey of Living Conditions, 2007) (17). The survey was

drawn using stratified random sampling. This design was a two-stage stratified random sampling

design where there was a Primary Sampling Unit (PSU) and a selection of dwellings from the

primary units. The PSU is an Enumeration District (ED), which constitutes a minimum of 100

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

shares a common boundary. This means that the country was grouped into strata of equal size

based on dwellings (EDs). Based on the PSUs, a listing of all the dwellings was made, and this

became the sampling frame from which a Master Sample of dwelling was compiled, which in

turn provided the sampling frame for the labour force. One third of the 2007 Labour Force

Survey (ie LFS) was selected for the survey (17). The sample was weighted to reflect the

population of the nation.



                                               233
       This study used JSLC 2007 (17) which was conducted by the Statistical Institute of

Jamaica (STATIN) and the Planning Institute of Jamaica (PIOJ) between May and August 2007.

The researchers chose this survey based on the fact that it is the latest survey on the national

population and that that it has data on self-reported health status of Jamaicans. A self-

administered questionnaire was used to collect the data, which were stored and analyzed using

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

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

some modifications to the LSMS, as JSLC is more focused on policy impacts. The questionnaire

covered areas such as socio-demographic, economic and health variables. The non-response rate

for the survey was 26.2%.


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

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

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

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

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

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

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


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

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

Omnibus Test of Model and Hosmer & Lemeshow (18) was used to examine goodness of fit of

the model. The correlation matrix was examined in order to ascertain whether autocorrelation (or

multicollinearity) existed between variables. Based on Cohen & Holliday (19) 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

                                                234
(or allow) a variable in the model. Wald statistics were used to determine the magnitude (or

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

Odds Ratio (OR) for the interpreting of each significant variable.


          Multivariate regression framework (20) was utilized to assess the relative importance of

various demographic, socio-economic characteristics, physical environment and psychological

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

employed outside of Jamaica (21, 22). This approach allowed for the analysis of a number of

variables simultaneously. Secondly, the dependent variable is a binary dichotomous one and this

statistic technique has been utilized in the past to do similar studies. Having identified the

determinants of health status from previous studies, using logistic regression techniques, final

models were built for Jamaicans as well as for each of the geographical sub-regions (rural, peri-

urban and urban areas of residence) and sex of respondents using only those predictors that

independently predict the outcome. A p-value of 0.05 was used to for all tests of significance.


Model

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

health or happiness) is well established (23) and this is equally the case in Jamaica and Barbados

(24, 25). The use of this approach is better than bivariate analyses as many variables can be

tested simultaneously for their impact (if any) on a dependent variable. The current study will

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

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

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

H t =f(A i , G i ,HH i , AR i , I t , J i, lnC, lnD i , ED i, MR i , S i , HIi , lnY, CR i , MC t , SA i , Ti , ε i )   [1]




                                                               235
where H t (ie self-rated current health status in time t) is a function of age of respondents, A i ; sex

of individual i, G i ; household head of individual i, HH i ; area of residence, AR i ; current self-

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

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

was unable to carry out normal activities, lnD i ; education level of individual i, ED i ; marital

status of person i, MR i ; social class of person i, S i ; health insurance coverage of person i, HIi ;

logged income, lnY; crowding of individual i, CR i ; medical expenditure of individual i in time

period t, MC t ; social assistance of individual i, SA i ; length of time living in current household by

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

The final models that were derived from the general Equation [1] that can be used to predict

health status of Jamaicans (Equation [2]); men (Equation [3]); women (Equation [4]); urban area

(Equation [5]); other towns (Equation [6]); and rural areas (Equation [7]).

H t = f(A i , AR i , lnDUt , ε i )                                       [2]


Measure

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

Age group is a non-binary measure: children (ages less than 15 years); young adults (ages 15 to

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

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


Self-reported illness (or self-reported dysfunction): The question was asked: “Is this a diagnosed

recurring illness?” The answering options are: Yes, Cold; Yes, Diarrhoea; Yes, Asthma; Yes,

Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. A binary variable was later

created from this construct (1 = yes, 0 = otherwise) in order to use in the logistic regression.



                                                   236
Self-reported health status: “How is your health in general?” And the options were very good;

good; fair; poor and very poor. For this study the construct was categorized into 3 groups – (i)

good; (ii) fair, and (iii) poor. A binary variable was later created from this variable (1 = good and

fair 0 = otherwise).


Social class: This variable was measured based on income quintile: The upper classes were those

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

quintiles (quintiles 1 and 2).


Results
Demographic characteristics of sample

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

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

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

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

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

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

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

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

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

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

3.9 persons in other towns (S.D. 2.41) and 3.6 persons in urban areas (S.D. 2.42) - F statistic [2,

711] = 6.642, p = 0.001.




                                                237
       Table 9.1 revealed that there is a statistical correlation between social class, self-

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

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

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

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

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

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

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

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

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

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


       Table 9.2 revealed that there is a statistical correlation between diagnosed chronic

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

illness for the younger ages and more so affecting children: 47.2% of children and 23.2% of

young adults (Table 9.2). The findings revealed as an individual ages, he/she was more likely to

report being diagnosed with hypertension: 0% of children; 8.7% of young adults; 31.7% of other

aged-adults; 35.7% of young old; 50.5% of old-elderly and 48.3% of oldest-elderly. Arthritis was

more likely to affect older ages than young ages: 0% of children; 1.4% of young adults; 7.1% of

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

other hand, as an individual age, he/she is more to be aware of the typology of chronic illness

that he/she has than at young ages (i.e. ages less than 31 years). Interestingly, 2.8% of children

had diabetes compared to 4.3% of young adults; 18.3% of other aged-adults; 28.7% of young

old; 19.6% of old-elderly and 17.2% of oldest-elderly.


                                               238
       Based on Table 9.3, no statistical correlation was found between diagnosed chronic

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

relationship was found between income, consumption, crowding and chronic disease (p < 0.5;

Table 9.4). Furthermore, there is a similarity across the afore-mentioned variable as asthma was

found to be associated with the most income, consumption and persons per room; and

unspecified chronic disease was the second leading reported dysfunction. Diabetes mellitus was

found to be the third leading reported chronic disease influencing people with more income and

consumption. While hypertension was the third most reporting chronic disease associated with

crowding, it was the fourth most reported dysfunction associated with income and consumption

expenditure.


Multivariate analyses

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

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

being diagnosed with chronic diseases (Table 9.5): age of respondents (OR = 1.029, 95% CI =

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

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

CI = 1.280 - 2.271).


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

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

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

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

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



                                                239
(age) -1.041(urban residents) -1.041 (other towns) + 0.537 (log duration unable to work).

Furthermore, the predictors accounted for 24% of the variability in poor health status (Table 9.5).


Discussion
Health is influenced by numerous other factors, particularly lifestyle, the amount of exercise and

nutrition. Chronic diseases strongly relate to behavioural risk factors such as smoking, physical

inactivity and unhealthy dietary patterns that tend to precede and exacerbate their traditional risk

factors, such as raised blood pressure, cholesterol and blood glucose levels. There is an

association between chronic disease and health status and the former has a significant negative

impact on the physical aspects of health (26). Self-reported health status has been widely used in

censuses, surveys, and observational studies and there is evidence suggesting that self-reported

health is an indicator of general health with good construct validity (27) and is a respectably

powerful predictor of mortality risks (28), disability (29) and morbidity (30). The results of this

study showed that the majority of those sampled reported to be experiencing at least good or fair

health, while approximately one-quarter indicated poor health. These results concur with those

by other researchers from Dominica (31) and Trinidad (32).

       The current study revealed that hypertension was the most common chronic disease

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

among the elderly, with just than one-half of the old-elderly and oldest-elderly being

hypertensive. In a study by Sargeant et al. (33), hypertension is more common among women

and the elderly in Jamaica. Studies from developed countries have reported prevalence of raised

blood pressure among elderly to vary from 60% to 80% (34). Hypertension is one of the most

important treatable causes of morbidity and mortality and accounts for a large proportion of

cardiovascular diseases in elderly in Jamaica (33). The age and sex adjusted prevalence in

                                                240
Jamaica is 24% (15) with somewhat higher levels in women than in men. The Jamaican Healthy

Lifestyle Survey Report 2000 (35) noted a prevalence of hypertension of 19.9% among males

and 21.7% among females; prevalence increased with age in both rural and urban populations

and in both sexes. Among persons known to be hypertensive, 42% were on treatment, and of this

group, 37.7% had been able to lower and maintain their blood pressure at 140/90 or less. In the

Caribbean and the USA, the higher prevalence of hypertension was associated with an increased

prevalence of obesity, especially in women, and with greater intake of dietary sodium (36, 37).

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

represents a significant burden on health services. Diabetes was the second leading cause of

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

third reporting that they have diabetes mellitus. The prevalence of diabetes mellitus is high in

Jamaica and the Caribbean and many patients have poor metabolic control (38). In Jamaica the

prevalence of diabetes among persons 25–74 years old is estimated to be 12% to 16% (14, 39-

40), but of which a third is unrecognized (39-40). There is also evidence that the diabetes

prevalence has increased (41). In the Jamaican Healthy Lifestyle Survey Report 2000 (35),

diabetes mellitus was found in 6.3% of males and 8.2% of females and there was a sharp

increase with age. Awareness of diabetes mellitus among those classified as diabetic by the

survey was 76.3%. Almost one-third of those classified as diabetic were not being treated, and

60% of those who reported being on medication did not have their condition controlled. The

average length of stay was 8.3 days for diabetes mellitus in 2002, compared to 6.3 days for all

conditions (35). Diabetes mellitus accounts for about 10% of mortality in Jamaica (42) and is

ranked fourth as the principal cause of death among Jamaicans during the period 1990 to 1994

(43). But the impact of diabetes mellitus on mortality is under-reported since the disease may



                                              241
contribute to mortality from such other conditions as cerebrovascular accidents and myocardial

infarctions (44). Furthermore, there is evidence that the high prevalence of diabetes in Jamaica is

due to the low rates of awareness, treatment and control among patients with diabetes mellitus

(38). Since having a chronic disease exacerbates individuals’ risk for other long-term diseases,

the diseases tend to occur in multiples. For example, diabetes patients are at two to four times

greater risk of heart disease than non-diabetics (45) which adversely affect the physical health

status of these patients.


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

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

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

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

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

these children are exposed (46). In this study, asthma was the predominant chronic diseases

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

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

occur in 20 per cent of school age children (47). In government hospitals in Jamaica, five per

cent of clinic visits are asthma related and 25 per cent of respiratory admissions to hospital are

due to asthma (48). Barnes and colleagues (49) studied asthmatic children in Barbados where

treatment was associated with use of inhalers, but no distinction between bronchodilators and

corticosteroids was made (50). Asthma is a significant cause of mortality in Jamaica, resulting in

a death rate of approximately 5 per 100 000 (51).

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

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


                                               242
inflammatory disorder that may affect many tissues and organs but particularly the joints, often

progressing to destruction of the articular cartilage and ankylosis of the joints (52, 53). Due to its

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

aspects of their lives and can contribute in these patients experiencing poor health. Rheumatoid

arthritis is the third chronic illness among the respondents in the study. In India, the prevalence

of rheumatoid arthritis (0.75%) is similar to that in the West (54). The rarity of rheumatoid

arthritis in rural Africa contrasts with the high prevalence of the disease in Jamaica, where over

2% of the adult population is affected (55). In a study in Latin America, rheumatoid arthritis was

the reason for seeking medical advice in 22% of rheumatology clinic patients (56). Quality of life

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

syndrome, whose depression and/or anxiety scores are high (57). Therefore, these patients should

be managed using a multidisciplinary approach including psychiatric support.

       In this study just over one-third of the respondents indicated an unspecified chronic

illness. The unspecified chronic diseases could be other chronic non-communicable disease such

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

15% of non-communicable diseases and 9% of total disease burden in 1990. Cancers of the

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

per 1,000 population, respectively. Prostate cancer was the number one form of cancer found in

men (58). In 2002 there were 3,769 public hospital discharge diagnoses (4% of total discharge

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

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

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

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



                                                243
organs; trachea, bronchus, and lungs; leukemia; and non-Hodgkin’s lymphoma, together

representing 56% of all cancers (59). The unspecified chronic illness may include HIV/AIDS, a

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

incidence of AIDS in 2000 was 352 per 1,000,000 population (60). In addition, the unspecified

chronic disease may include depression and there is evidence to suggest that depressive disorders

frequently accompany other chronic medical diseases. The 2000 Lifestyle Survey found

approximately 25 % depressive symptoms in the general population (35). Anderson et al. (61)

concluded that the presence of diabetes mellitus increases the risk of depression and studies have

shown that in persons diagnosed with diabetes mellitus the prevalence of depression range from

6.1% - 60.7% (62).

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

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

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

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

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

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

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

According to Sobal and Stunkard (63), in developing societies there is a higher likelihood of

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

developing type 2 diabetes mellitus (64) which is increasing in the adult population. Most of the

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

households (65). In Jamaica, female-headed households are poorer than those headed by males

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



                                              244
expenditure 10 times higher than female-headed households (66, 67). The 1999 data from

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

are males are less likely to seek health care (68). This under-representation of men in clinics in

rural communities is of great concern, as hypertension and diabetes are not gender-specific

conditions and could contribute to the poor health status of these men. The ‘silent’ nature of

these diseases has grave implications for the mortality and morbidity of middle-aged and elderly

Jamaican men.

       Poorer socioeconomic status co-segregates with obesity, even in diabetes (69), as do

physical inactivity, smoking, and low birth weight, which are all known risk factors for type 2

diabetes. The prevalence of chronic diseases varied with socioeconomic status with more persons

at the lower levels suffering from diabetes, hypertension and depression (70). A recent study

showed that persons of a lower socioeconomic status have significantly higher mean systolic

blood pressures and waist circumference when compared with those of high socioeconomic

standing (70). In Trinidad and Tobago, blood pressure and hypertension were negatively

associated with income and education in women, while no consistent associations were observed

in men (71). Chang et al. (72) found that in younger women in Latin America and the Caribbean,

greater education was associated with lower risk of stroke but there was no education-related

gradient in risk of myocardial infarction. Two Jamaican studies (73, 74), as well as a small study

from St Vincent (75), have provided some evidence for a positive association between blood

pressure and socioeconomic position in men. However, rural farmers who have traditional diets

and physical activity patterns generally show a lower prevalence of hypertension than their urban

contemporaries (76).




                                               245
Conclusion

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

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

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

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

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

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

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

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

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

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

lifestyle modifications that are necessary to bring about greater levels of control. We suggest that

further improvement in chronic disease control can be achieved with improved patient education

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

support groups among patients with chronic disease(s).




                                               246
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Table 9.1: Socio-demographic characteristics of sample
                                                  Area of residence
            Variable                 Urban            Other towns                         Rural         pvalue
                                     n (%)               n (%)                            n (%)
Sex                                                                                                      0.702
   Male                                  70 (35.0)          48 (39.7)                     144 (36.6)
   Female                             130 (65.0)            73 (60.3)                     249 (63.4)
Injury                                                                                                   0.347
   Yes                                      4 (2.0)            6 (5.0)                      13 (3.3)
  No                                   195 (98.0)          115 (95.0)                     380 (96.7)
Self-reported chronic illness                                                                            0.214
  Asthma                                 33 (16.5)            11 (9.1)                     51 (13.0)
  Diabetes                               32 (16.0)          27 (22.3)                      64 (16.3)
  Hypertension                           47 (23.5)          41 (33.9)                     118 (30.0)
  Arthritis                                16 (8.0)           10 (8.3)                      30 (7.6)
  Unspecified                            72 (36.0)          32 (26.4)                     130 (33.1)
Social class                                                                                            < 0.001
  Poor                                   36 (18.0)          32 (26.4)                     208 (52.9)
  Middle                                 30 (15.0)          27 (22.3)                      84 (21.4)
  Upper                                134 (67.0)           62 (51.3)                     101 (25.7)
Self-evaluated health status                                                                            < 0.001
  Good                                   77 (38.5)          38 (31.7)                     112 (28.6)
  Fair                                   93 (46.5)          61 (50.8)                     150 (38.4)
  Poor                                   30 (15.0)          21 (17.5)                     129 (33.0)
Household head                                                                                           0.082
  Yes                                  105 (52.5)           72 (59.5)                     189 (48.1)
  No                                    95 (47.5)           49 (40.5)                     204 (51.8)
Marital status
  Married                                60 (35.3)          34 (31.8)                     130 (39.8)     0.166
  Never married                          78 (45.9)          48 (44.9)                     144 (44.0)
  Divorced                                  7 (4.1)            6 (5.6)                       6 (1.8)
  Separated                                 4 (2.4)            4 (3.7)                       2 (0.6)
  Widowed                                21 (12.4)          15 (14.0)                      45 (13.8)
Educational level                                                                                        0.466
  No formal                            160 (80.0)          106 (87.6)                     319 (81.2)
  Basic                                    14 (7.0)            5 (4.1)                       33 (8.4)
  Primary/Preparatory                      12 (6.0)            5 (4.1)                       25 (6.4)
  Secondary/High                            9 (4.5)            4 (3.3)                       13 (3.3)
  Tertiary                                  5 (2.5)            1 (0.8)                        3 (0.8)
Age Mean (SD)                  47.5 yrs (25.07      53.36 yrs.                        48.7 (25.79        0.114
                               yrs)                 (23.61)                           yr)
†Annual Income Mean (SD)       US $10,312.41        US $8,218.05                      US $5,873.08      < 0.001
                               (US $9,059.70)       (US $7,653.84)                    (US 4,473.51)
Number of visits to health                1.4 (1.1)          1.5 (1.5)                      1.4 (1.1)    0.842
care practitioner Mean (SD)
†Annual Income is quoted in US $ (US$ 1.00 = Ja. $ 80.47 at the time of the survey)


                                                       255
Table 9.2: Diagnosed chronic recurring illness by age group


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

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


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

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

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

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




                                                                 256
Table 9.3: Diagnosed chronic illness by social class



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

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


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


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

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

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


 Total                                      276              141             297            714

χ2 (df = 8) = 13.882, p = 0.085




                                                  257
Table 9.4: Crowding, income and annual consumption expenditure by diagnosed chronic disease



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

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

 †††Annual                Asthma
 consumption                                                                95     655299.59      461384.09        47337.01    561310.85    749288.33
 expenditure*
                        Diabetes mellitus                       123 509264.21                     363216.62        32750.14    444432.04    574096.38
                        Hypertension                            206 500635.23                     450169.33        31364.78    438796.32    562474.15
                        Arthritis                                56 404152.14                     352416.40        47093.62    309774.41    498529.87
                        Other (unspecified)                     234 586512.66                     432316.66        28261.42    530832.07    642193.25
                        Total                                   714 543277.73                     429059.15        16057.14    511752.81    574802.65
†Crowding - F statistic [4, 709] = 7.778, p < 0.001
††Income - F statistic [4, 709] = 3.250, p = 0.012,
†††Annual consumption Expenditure - F statistic [4, 709] = 4.472, p = 0.001
*Income and Annual Consumption Expenditure were quoted in Jamaican dollars (Ja. $80.47 = US $1.00 at the time of the survey)


                                                                            258
Table 9.5: Logistic regression: Predictor of poor health status of patients who reported chronic
disease



                            Std.       Wald             Odds
 Predictors                 error     statistic         ratio     95.0% C.I.
  Age                        0.006   26.131***            1.029    1.018 - 1.040
  Urban areas                0.313    11.061**            0.353    0.191 - 0.652
  Other towns                0.372     7.582**            0.359    0.173 - 0.744

     Log duration unable
                            0.145    13.803***           1.711     1.289 - 2.271
     to work

   Constant                  0.338          4.331      0.494            -
χ (df = 4) =59.76.149, p < 0.001; n = 714)
 2

-2 Log likelihood = 332.325
Nagelkerke R2 =0.240
Hosmer and Lemeshow goodness of fit χ2=9.956, P = 0.268
Overall correct classification = 74.7%
Correct classification of cases of self-rated poor health status = 92.6%
Correct classification of cases of self-rated good health status = 31.6%
†Reference group – rural areas
*p < 0.05, **p < 0.01, ***p < 0.001




                                                  259
CHAPTER 10

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


Poverty is well established as being associated with illness and chronic illness. Studies which
have examined this phenomenon have done so using objective indices such as life expectancy,
infant mortality and general morality. This study (1) examined subjective indices such as self-
reported illness and self-reported health, (2) re-tested the theories that chronic illnesses are
more likely to be greater in number among the poor and that illnesses are positively correlated
with poverty, and (3) evaluated other social characteristics that account for the poverty-illness
theory. The current study used a secondary cross-sectional dataset from the Jamaica Survey of
Living Conditions (JSLC). The JSLC used an administered questionnaire where respondents
were asked to recall detailed information on particular activities. The questionnaire was
modelled on the World Bank’s Living Standards Measurement Study (LSMS) household survey.
The cross-sectional survey was conducted between May and August 2002 in the 14 parishes
across Jamaica and included 25,018 people of all ages. The statistical package SPSS 16.0 was
used for the analysis. A p-value less than 5% (2-tailed) was used to indicate statistical
significance. Those in the two wealthy social hierarchies were 18% less likely to report chronic
illnesses compared to those in the two poor social hierarchies. Males were 69% less likely to
report chronic illness compared to females as well as 56% less likely to indicate an illness. When
the chronic illnesses were disaggregated by sex of respondents, the prevalence rate of females
with hypertension was 2.2 times more than hypertensive males; 3.2 times more than male
arthritic patients, and 3.0 times more than male diabetics. Forty-five percent of those with
chronic illnesses were married. While poverty has declined in Jamaica since the 1990s, the
health disparity between the poor and the upper social hierarchy continues to this day. The
information provided in this research has far-reaching implications, and may be used to guide
policies, frame interventions and provide a focus for future research in Jamaica.



Introduction


Empirically there are many studies which have found and established a statistical association

between poverty and illness [1-8]. Some research has shown that those in the lower

socioeconomic status are less healthy than those in the wealthy socioeconomic groups [9, 10]. A

study by Van Agt et al. [8] found that poverty was greater among chronically ill people than the



                                              260
non-chronically ill, and the WHO [4] concurred with Van Agt et al. [8] when it opined that 80%

of chronic illnesses were in low and middle income countries. Poverty is not only associated with

illness and ill-health, but also higher rates of mortality. According to the WHO [4], 60% of

global mortality is caused by chronic illness, and this should be understood within the context

that four-fifths of chronic dysfunctions are in low-to-middle income countries. The rationales

given for the poverty and illness theory are (1) money (insufficient financial resources); (2)

medical expenditure; and (3) other types of socio-political incapacity [3, 8, 11]. Sen [11]

encapsulated this well when he opined that high levels of unemployment in the economy are

associated with higher levels of capabilities, pointing to money and other incapacities of those

who are likely to be unemployed in the society. The poor are therefore more likely to be

unemployed, to be ill, to suffer from more chronic illnesses, to have insufficient money, low

levels of educational attainment, to experience a greater percentage of infant and other mortality

and to live in an inadequate physical environment, compared to those in the wealthy social

hierarchies.


       Using objective indices such as infant mortality and life expectancy to measure the health

of a population, studies in Latin America and the Caribbean concur with the aforementioned

research. Cass et al. [12] found that infant mortality in Peru for those in the poorest quintile (i.e.

poorest 20%) was almost 5 times more than that for those in the wealthiest quintile (i.e.

wealthiest 20%). Another study revealed that out-of-pocket medical expenditure accounts for

some people becoming poor and that a greater percentage of these people do not have health

insurance coverage [2]. One study highlighted the fact that life expectancy between the poorest

20% and the wealthiest 20% was 6.3 years and this was 14.3 years for disability-free life

expectancy [13]. The relationship between poverty and illness is longstanding, and the Director


                                                261
of the Pan American Health Organization in 2001 wrote that it is still evident in contemporary

societies [14]. He however went further to state that poverty affects mental as well as physical

health, and concurs with the literature that those in the lower socioeconomic status have greater

levels of illnesses (i.e. psychopathology).


       It has been clearly understood and well-established for centuries that poverty is

associated with illness, and that it affects those individuals by constricting their capacity, which

further affects their health. The poor have less access to money and other resources than the

wealthy, and are also deprived of a good health outcome in the future. A study by Mayer et al.

[15] provided evidence that there is a strong relationship between health and future economic

growth, suggesting that current poverty contracts future health and economic prosperity. Mayer

et al.’s work provides pertinent insight into the retardation of poverty, but also gives an

understanding of how poverty affects health, production, productivity and how it poses a present

and future problem for public health policy makers. How is this of concern to public health

policy makers in Jamaica?


       A recent study conducted by Bourne [16] found that (1) moderate and direct correlation

between the prevalence of poverty (in %) and unemployment (R2 = 0.48); (2) direct association

existed between not seeking medical care (in %) and prevalence of poverty (in %) – R2 = 0.58;

(3) a strong statistical relationship between prevalence of poverty and mortality – R2 = 0.51; and

(4) a non-linear relationship between not seeking medical care and illness. From Bourne’s

findings, the challenges for public health specialists as well as policy makers are a reality in

Jamaica, as in other nations. If poverty is associated with unemployment and not seeking medical

care, and not seeking medical care is related to illness, it appears to be a non-issue to re-test the



                                                262
established theory of poverty and illness and poverty and chronic illness in Jamaica, but this is

not the case as there is self-reported illness may not give the same result as diagnosed illnesses.


       None of the aforementioned studies that have examined poverty and illness have used

self-reported data to test the poverty and illness, and poverty and chronic illness phenomena. The

aims of the current study are to investigate (1) poverty and self-reported illness, (2) poverty and

self-reported chronic illness, and (3) other socio-demographic characteristics, in order to provide

an understanding of existing disparities as well as to concur with, or refute, current theories.


Methods
Study population

The current study used a secondary cross-sectional dataset from the Jamaica Survey of Living

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

Statistical Institute of Jamaica (STATIN) for analysis [17-19]. These two organizations are

responsible for planning, data collection and formulating policy guidelines for Jamaica. The

cross-sectional survey was conducted between May and August 2002 in the 14 parishes across

Jamaica and included 25,018 people of all ages [20]. The JSLC used stratified random

probability sampling technique to draw the original sample of respondents, with a non-response

rate of 26.2%. The sample was weighted to reflect the population.


Study instrument


The JSLC used an administered questionnaire where respondents were asked to recall detailed

information on particular activities. The questionnaire was modelled on the World Bank’s Living

Standards Measurement Study (LSMS) household survey. The questionnaire covered




                                                263
demographic variables, health, education, daily expenses, non-food consumption expenditure

and other variables. Interviewers were trained to collect the data from household members.


Statistical methods

Descriptive statistics were used to provide socio-demographic characteristics of the sample. Chi-

square analyses were used to examine the association between non-metric variables. Analysis of

variance was used to test the statistical significance of a metric and non-dichotomous variable.

Logistic regression analyses examined 1) the relationship between good health status and some

socio-demographic, economic and biological variables; as well as 2) a correlation between

medical care-seeking behaviour and some socio-demographic, economic and biological

variables. The statistical package SPSS 16.0 was used for the analysis. A p-value less than 5%

(2-tailed) was used to indicate statistical significance.


        The correlation matrix was examined in order to ascertain if autocorrelation and/or

multicollinearity existed between variables. Based on Cohen and Holliday [21] correlation can

be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. Any variable that had

at least moderate (r > 0.6) was re-examined in order to address multicollinearity and/or

autocorrelation between or among the independent variables [22-28]. Another approach in

addressing collinearity (r > 0.6) was to independently enter variables in the model to determine

which one should be retained during the final model construction. The method of retaining or

excluding a variable from the model was based on the variables’ contribution to the predictive

power of the model and its goodness of fit. Wald statistics were used to determine the magnitude

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

Odds Ratio (OR) for the interpreting of each significant variable.



                                                 264
Measures

Self-reported illness status is a dummy variable, where 1 = reporting an ailment or dysfunction or

illness in the last 4 weeks, which was the survey period; 0 if there were no self-reported ailments,

injuries or illnesses [29-31]. While self-reported ill-health is not an ideal indicator of actual

health conditions because people may under-report, it is still an accurate proxy of ill-health and

mortality [32, 33]. Health status is a binary measure where 1=good to excellent health; 0=

otherwise which is determined from “Generally, how do you feel about your health?” Answers

for this question are on a Likert scale, ranging from excellent to poor. Medical care-seeking

behaviour was taken from the question “Has a health care practitioner, healer, or pharmacist been

visited in the last 4 weeks?” with there being two options: Yes or No. Medical care-seeking

behaviour therefore was coded as a binary measure where 1=Yes and 0= otherwise. Crowding is

the total number of individuals in the household divided by the number of rooms (excluding

kitchen, verandah and bathroom).


Sex: This is a binary variable where 1= male and 0 = otherwise.


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




                             where ki represents the frequency with which an individual

                             witnessed or experienced a crime, where i denotes 0, 1 and 2, in

which 0 indicates not witnessing or experiencing a crime, 1 means witnessing 1 to 2, and 2

symbolizes seeing 3 or more crimes. Tj denotes the degree of the different typologies of crime

witnessed or experienced by an individual (where j = 1…4, which 1 = valuables stolen, 2 =

                                               265
attacked with or without a weapon, 3 = threatened with a gun, and 4 = sexually assaulted or

raped. The summation of the frequency of crime by the degree of the incident ranges from 0 to a

maximum of 51.

Result

       The sample was 25,018 respondents: males, 49.3%; rural residents, 61%; semi-urban

residents, 25.6%; married, 16.2%; never married, 67.3%; divorced, 0.8%; separated, 1.2%;

widowed, 5.6%; self-reported illness, 12.5%; self-reported injury, 1.2%; health care seekers in

the last 4-week period, 63.9%; level of education primary or below, 20.9; secondary level

education, 73.1%, and the mean age of the sample was 28.8 years (SD = 22.0 years). The mean

number of people per room was 2.0 (SD = 1.4), and the mean number of crimes experienced

(including family members) was 2.1 (SD = 8.0).

       Table 10.1 presents information on demographic characteristics of the sample by area of

residence for 2002. There was a significant statistical association between social hierarchy and

area of residence – χ2 = 1739.98, P < 0.0001. Poverty (i.e. poorest 20%) was substantially a rural

phenomenon (74.9%) compared to semi-urban poverty (17.2%) and urban poverty (7.9%) - χ2 =

1739.98, P < 0.0001. Almost 14% of rural residents reported having an illness in the last 4 weeks

compared to semi-urban residents (10.9%) and urban residents (10.9%) - χ2 = 36.861, P <

0.0001. However, for 2002, no significant statistical relationship existed between self-reported

diagnosed health conditions and area of residents - χ2 = 12.62, P = 0.397.

       The mean age of the sample was 28.8 years (± 22.0 years), with there being a statistical

difference between the mean ages of respondents based on their area of residence – F-statistic [2,

24991] = 7.28, P < 0.0001: the mean age of rural residents was 29.1 years (± 22.6 years); that of

semi-urban residents was 27.9 years (± 21.0) and the mean age of urban dwellers was 29.1 years


                                               266
(± 21.0 years). Concurringly, the mean number of visits to health care practitioners in the last 4-

week period was 1.7 (± 1.4). There was a significant statistical difference between the mean

number of visits to health care practitioners and area of residence (F-statistic = 5.48, P = 0.004:

the mean number of visits by rural residents was 1.6 (± 1.2) compared and 2.0 (± 2.5) for urban

dwellers, but non between rural and semi-urban dwellers (1.6 ± 1.2). However, there was no

significant difference between mean medical expenditure and area of residence (mean public

health care expenditure was USD 9.05 ± USD 25.65 – F-statistic [2, 1126] = 0.577, P = 0.562;

and mean private health care expenditure was USD 24.40 ± USD 37.13 – F-statistic [2,935] =

0.577, P = 0.220).

       There was a significant statistical difference between crime and victimization and area of

residence - F-statistic [2, 24958] =28.604, P < 0.0001. The mean number of crimes and incidents

of victimization experienced by people in rural residents was 1.8 ± 7.7 compared to semi-urban

residents, 2.3 ± 8.0; and urban dwellers, 2.9 ± 9.3.

       Table 10.2 examines visits to health care facilities, health insurance coverage, educational

level and crime by social hierarchy.

       When self-reported illness and social hierarchy was disaggregated by area of residence,

the significant statistical relationship was explained by rural areas (χ2 = 30.92, P < 0.0001) and

not semi-urban (χ2 = 8.84, P = 0.065) and urban areas (χ2 = 1.74, P = 0.789).

       Table 10.3 presents information on self-reported injury, normally go if ill/injured, why

didn’t seek care for current illness, length of illness and number of visits to health practitioner by

social hierarchy. A statistical relationship existed between each of the variables (P < 0.0001). A

statistical difference existed between the mean length of the illness among the social hierarchy –

F statistic = 2.536, P = 0.038. This difference was accounted for by the poorest 20% and the



                                                267
wealthy (P = 0.049) and the poorest 20% and the wealthiest 20% (P = 0.049). Likewise the

statistical difference between the mean number of visits made to medical practitioner(s) and

social hierarchy were accounted for by the poorest 20% and wealthy (P = 0.011) and the poorest

20% and wealthiest 20%.

       The prevalence of chronic illness was 104 out of every 10,000 respondents. On

disaggregating the overall prevalence of chronic illness into the different typology of conditions

it was found that 5 out of every 10,000 respondents had diabetes mellitus; 50 out of every 10,000

had hypertension; 28 per 10,000 had arthritis; and other chronic illnesses (unspecified) accounted

for 21 per 10,000.

       Chronic illness was more a female phenomenon than for males- χ2 = 6.56, P = 0.013. The

prevalence rate of females with chronic illness was 144 per 10,000 compared to 62 per 10,000

for males. Furthermore, the prevalence rates of those with particular chronic illnesses by sex was

as follows: diabetes mellitus 2 per 10,000 for males and 7 per 10,000 for females; hypertension

32 per 10,000 for males and 69 per 10,000 for females; arthritis 13 per 10,000 for males and 42

per 10,000 for females and other chronic conditions, 15 per 10,000 for males and 27 per 10,000

for females. Seventy-two percent of those who indicated that they had a chronic illness sought

medical care in the last 4-week period, compared to 78.9% not suffering from a chronic illness

who sought medical attention - χ2 = 0.030, P = 0.562. Likewise no statistical association existed

between health insurance coverage and chronic illness - χ2 = 0.048, P = 0.649. Concurringly,

there was a significant statistical association between marital status and individuals with chronic

illness - χ2 = 12.708, P = 0.013. Of those who indicated that they had chronic illness, 44.9%

were married; 29.1% were never married; 0.4% divorced; 1.2% separated and 24.4% widowed.




                                               268
Multivariate analyses

Table 10.4 provides information on particular variables and their correlation (or not) with self-

reported illness. Of the 17 variables identified from the literature and available for this study, 5

emerged as being statistically significant correlates of self-reported illness of Jamaicans (i.e.

social hierarchy, medical expenditure, sex, age and income) - Model χ2 (17) =56.45, P < 0.001.

The statistically significant correlates accounted for 14.8% of the variability in self-reported

illness.



Table 10.5 examines social hierarchy and sex and their influence (or not) on self-reported

chronic illness. One sex emerged as being a statistically significant correlate of self-reported

chronic illness in Jamaica - Model χ2 (3) =6.42, P < 0.001.

Discussion

The current study revealed that 13 out of every 100 Jamaicans reported an illness in the 4-week

surveyed period. Concurringly, those in the two wealthy social hierarchies were 18% less likely

to report chronic illnesses compared to those in the two poor social hierarchies, and the former

group was 64% less likely to report an illness compared to the latter group. Males were 69% less

likely to report chronic illness compared to females, as well as 56% less likely to indicate an

illness. The prevalence rate of those with chronic illness was 104 per 10,000 respondents –

diabetes, 5 per 10,000; hypertension, 50 per 10,000; arthritis, 28 per 10,000 and other chronic

conditions, 21 per 10,000. When the chronic illnesses were disaggregated by sex of respondents,

the prevalence rate of females with hypertension was 2.2 times more than hypertensive males;

3.2 times more than male arthritic patients, and 3.0 times more than male diabetics. Poverty was

substantially a rural phenomenon (75%), and almost 14% of rural residents indicated an illness



                                               269
compared to semi-urban (11%) and urban dwellers (11%). The disparity did not cease there as

rural residents had the least percentage of people with tertiary level education, and the least per

capita consumption, which was 57.4% of consumption per capita of urban residents and 69.0%

of that consumption per capita of semi-urban people. On the contrary, those in the poorest 20%

self-reported fewer injuries (owing to work and care accidents, poisoning, and burns) than those

in the wealthiest 20%.

       For centuries, using objective indices such as life expectancy, infant mortality and

general mortality, it has been well established that poverty is associated with illness, and those

with more chronic illnesses are more likely to be poor. The current study, using self-reported

illnesses, has concurred with the literature that the poor report more illnesses and are more likely

to have more chronic illness than those in the upper class. This study, however, found that there

is no significant statistical correlation between self-reported illness or chronic illness of those in

the poor social hierarchies and those in the middle class. The current research does not concur

with the literature that married people are healthier than other marital cohorts [34-38] as the

findings showed no statistical association between marital status and self-reported illness.

However, the findings revealed that almost 45% of those with chronic illnesses were married

compared to those who were never married, widowed, separated or divorced.

       Lillard and Panis [39] contradicted many of the traditional findings, for instance that

married people are healthier and report less health conditions than non-married people. They

found that healthier men are less likely to be married; and secondly, that healthier married men

enter into unions later in life and that they do postpone remarriage. Conversely, Lillard and Panis

[39] revealed that it is unhealthy men who enter marriage at an early age, which suggests that

these men do so because of health reasons [39]. This then would support the current research of



                                                270
married people indicating more chronic illnesses than non-married people. Concurringly, married

people do not report more illnesses, but do report more chronic illnesses than non-married people

in this study.


        An interesting finding that emerged from this study is the low statistical relationship

between self-reported illness and self-reported injury (i.e. contingency coefficient = 0.11).

Furthermore 4.4% of those who indicated that they were ill had an injury in the last 4 weeks, and

of those who had an injury, 46.2% claimed they were ill. This denotes that few people

considered illness and injury and vice versa. Illnesses therefore is in keeping with acute and

chronic health conditions, and less so with injuries caused by accidents, burns, poisoning and

other such events.


        Marmot [3] asked the question “Does money matter for health? If so, why?” It is the lack

of money (i.e. insufficient money) that accounts for the inability of the poor to access (1) higher

level education; (2) greater and better, or the best, health care treatment; (3) a better physical

milieu; (4) lower levels of infant mortality; (5) better material conditions; (6) clean water and

nutrition; and (7) social position. It follows that poverty incapacitates the individual and this

extends into the future if he/she is not assisted by external sources. Does money really make a

difference in Jamaica? The answer is a resounding yes. Those in the poorest 20% spent on

average almost 3 times less than those in the wealthiest 20%, and the second poor spent 2 times

less than those in the wealthiest 20% on medical expenditure. Concurringly, 76 out of every 100

of those in the poorest 20% normally utilize public health facilities (including hospitals)

compared to 28 out of every 100 of those in the wealthiest 20%.




                                               271
Poverty therefore retards people’s health care choices, expenditure on medication, and by

extension healthy life expectancy. The current study found that 35 out of every 100 respondents

in the poorest 20% indicated that the reason why they have not visited a health care practitioner

was owing to insufficient funds, compared to 9 out of every 100 of those in the wealthiest 20%.

Furthermore, findings from the present research showed that people who spend more on medical

expenditure are 39% less likely to report an illness, suggesting that the poor are more likely to be

living with their health conditions without seeking medical care, compared to the wealthy. This

matter of insufficient financial resources hampers the healthy life expectancy of the poor, as well

as explaining the greater infant and general mortality among them than those in the upper class.

According to Grossman [40], Smith and Kington [41], there is a positive statistical association

between income and health, and income and demand for health, which further unfolds the

complexity of poverty and health. Corbett [42] argued that Edwin Chadwick, in the 1840s,

believed “that the primary cause of pauperism and misery was not poverty or rampant capitalism,

but filth.” This study is not arguing that the main cause of pauperism is ill-health, but it does

substantiate an association between poverty and illness and poverty and chronic illness. This

finding is contrary to the belief of Edwin Chadwick; insufficient money does account for some

amount of illness, and illness can lead to poverty and future constraints on capabilities, limiting

opportunities for the creation of a better life for themselves.


       If those in the poorest 20% group experienced illnesses and visited medical practitioners

more than those in the upper class, it follows that poverty explains (1) most of the prevalence of

illness, (2) the severity of the illness, and (3) more chronic illnesses. Money therefore does

matter in health, and offers an explanation of how chronic illness can result in poverty, and how

pauperism leads to increased morbidity and premature mortality. An understanding of poverty in


                                                 272
Jamaica as well as a comprehensive knowledge of the relationship between poverty and illness as

well as the other health inequalities, will aid physicians in understanding the reasons for the

disproportionately greater number of poor visiting them and having particular chronic illnesses.

Health is also a social phenomenon, and so physicians need training in the roles of social

determinants and their influence on health, as these are outside of the clinical laboratory, but

provide an understanding of those on the social margins of the health care system. Given that

illness is influenced by exposure to pathogens, the socio-physical milieu of the poor, coupled

with their incapacitation because of money, provides some insights into their plight. It is critical

to understand this group and where they live, as Kiefer said, and to see poverty “not as a simple

economic condition, but as a state of demoralization, where people lack all or most of the

minimum ingredients we accept as the basis of a decent life” [43] and we can also add the

justifications of their encounter with illness and particular health conditions such as tuberculosis,

HIV/AIDS, diarrhoea, respiratory tract infections, arthritis and malaria.


       Another issue is nutritional deficiency, as some people hold the belief that so long as they

have something to eat, or a ‘full tummy’, it is enough to prevent illness. The image of a ‘full

tummy’ is embedded in those in the lower socioeconomic class and not the upper class. It

follows therefore that households in lower socioeconomic group find it difficult to address

material, food and opportunity deprivation within the context of a social setting to pay special

attention to the nutritional value in food intake. Households in low-income groups are

substantially found in rural areas in Jamaica where a ‘full tummy’ is important and not the

nutritional intake of the food groups. According to Foster [44] “…a better-off individual who is

generally healthy may be more readily able to identify when he or she is ill than a poor

individual with low caloric intake.” Within Foster’s perspective lies the underlying fact that


                                                273
reported illnesses among those in the lower socioeconomic group may be understated figures, as

their image of ill-health is hampered by nutritional deficiency. Diet and nutrition are important

ingredients in good health [45], but do residents of low-income rural areas as well as low-income

urban areas know that a deficient intake of calcium, iron, magnesium, zinc, folate, vitamin A,

vitamin B 6 and vitamin C is responsible for some of their illnesses? And another aspect to this

discussion is their image of health, illness and the role that these play in influencing the collected

survey data on health, health conditions and health outcome from those in the lower

socioeconomic group.


Conclusion

For centuries researchers have been using objective indices such as life expectancy, infant

mortality and the general mortality of a population or sub-population to measure health, and

these have been used to establish a statistical association with poverty. Other scholars and

institutions have found a significant statistical relationship between diagnosed illness and

poverty, but this research has established that self-reported illness and self-reported diagnosed

health conditions can be used instead of the objective indices of the past. While those people in

poor social hierarchies were more likely to report more illnesses and self-reported chronic

illnesses than those in the wealthy group, there is no difference between those in the poor group

and the middle class.

       Those with chronic illnesses are not only more likely to be poor, they are married,

females, rural residents, less educated at the tertiary level, more likely to visit public hospitals,

most likely to have hypertension, and there is less probability that they will utilize health care

facilities than the upper class. In summary, subjective indices such as self-reported illness or self-

reported diagnosed health conditions can be used to measure health as the traditional infant

                                                274
mortality, general mortality and life expectancy. Poverty indeed still continues to influence ill-

health, and those with chronic illnesses are more likely to be poor than in the upper class, but

other demographic characteristics provide more information on the poor and those with chronic

illnesses.

        In summary, much investment has been made in health and this clearly has not reduced

the inequalities and disparities between and among the different social groups in Jamaica. It

means that merely mobilizing greater domestic resources for health will not address the

inequalities, as using national health aggregates do not provide a detailed understanding of the

disparities between and among groups. While poverty has declined in Jamaica since the 1990s,

the health disparity between the poor and the upper social hierarchy has continued to this day.

The information provided in this research has far-reaching implications, and can be used to guide

policies, frame interventions and provide a focus for future research in Jamaica.

The way forward

Subjective indices such as self-reported illness and self-reported chronic illness can be used to

measure ill-health and replace infant and general mortality in the study of health. The use of

national statistics does not provide a comprehensive understanding of the health disparity and

inequalities between and among the social groups in a society. In order to address some of the

health inequalities and disparities in society, programmes are needed that will address issues in

rural areas, gender, income inequalities, and the health disparities between public and private

health care services offered to the public. Another area which must be addressed is that of the

nutritional deficiencies between and among the social hierarchies and area of residences. A

national dietary survey is needed in order to provide evidence for policy intervention as well as

the role of identified social problems and their influence on mental health. Concurringly, future


                                               275
research is needed to examine the harmful effects of mental health on the accumulation of

people’s negative life events, and their effects on the crime problem in the Caribbean. Another

issue which must be investigated is the quality of care offered to the poor from the perspective of

the individual (i.e. a survey research). This would provide pertinent information as to whether

those people who are poor perceived themselves to be receiving the worst health, and to devise a

method that will objectively assess, service and deliver to the social group in order to address

this, if it is contributing to the lower health outcomes. Researchers need to treat poverty as an

illness and not a cause of illness, which would allow for a new shift in the study of poverty, and

this thereby could provide more answers to health practitioners and policy makers.

Conflict of interest
The author has no conflict of interest to report.




                                                    276
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                                         279
Table 10.1: Demographic characteristic of sample, 2002
                                                                           2002
                                                                     Area of residence                      P
Characteristic                                 Urban                  Semi-urban       Urban
                                               n (%)                  n (%)            n (%)
Sex                                                                                                     < 0.0001
 Male                                              7727(50.7)             3062(47.9)    1543(46.0)
 Female                                            7524(49.3)             3337(52.1)    1814(54.0)
Marital status                                                                                           < 0.0001
 Married                                           2460(25.5)             1115(26.9)     475(21.0)
 Never married                                     6436(66.6)             2758(66.5)    1619(71.6)
 Divorced                                             56(0.6)                41(1.0)       26(1.2)
 Separated                                           104(1.1)                49(1.2)       32(1.4)
 Widowed                                             610(6.3)               187(4.5)      108(4.8)
Self-reported diagnosed illness                                                                             0.397
 Acute conditions
  Influenza                                                 1(0.5)            0(0.0)           0(0.0)
  Diarrhoea                                                 4(2.1)            5(8.9)           0(0.0)
  Respiratory                                               6(3.1)            2(3.6)           1(3.1)
 Chronic conditions
  Diabetes mellitus                                     10(5.2)               1(1.8)         1(3.1)
  Hypertension                                         82(42.9)             29(51.8)       15(46.9)
  Arthritis                                            48(25.1)             13(23.2)        8(25.0)
  Other                                                40(20.9)              6(10.7)        7(21.9)
Health care-seeking behaviour                                                                               0.816
 Yes                                               1302(63.8)              436(63.4)     228(65.3)
  No                                                740(36.2)              252(36.6)     121(34.7)
Self-reported illness                                                                                    < 0.0001
  Yes                                              1987(13.5)              669(10.9)     354(10.9)
   No                                             12713(86.5)             5488(89.1)    2902(89.1)
Health insurance                                                                                         < 0.0001
   Yes                                              1036(7.0)             1023(16.5)     612(18.7)
    No                                            13714(93.0)             5178(83.5)    2654(81.3)
Social hierarchy                                                                                         < 0.0001
 Poorest 20%                                       3724(24.4)              858(13.4)     393(11.7)
 Poor                                              3574(23.4)              968(15.1)     414(12.3)
 Middle                                            3169(20.8)             1217(19.0)     598(17.8)
 Wealthy                                           2774(18.2)             1427(22.3)     822(24.5)
 Wealthiest 20%                                    2017(13.2)             1929(30.1)    1130(33.7)
Per capita consumption mean ±                      1181±1340              1771±1605     2129±2434
SD (in USD)
†USD 1.00 = Ja. $ 80.47 at the time of the survey) (2007)
††USD 1.00 = Ja. $50.97 (in 2002)




                                                              280
Table 10.2. Particular variable by social hierarchy, 2002
                                                                   Social hierarchy                          P
Characteristic                                    Poorest   Poor    Middle          Wealthy   Wealthiest
                                                  20%                                         20%
Sex                                                                                                          0.002
 Male                                        2454(49.3)  2345(47.3)  2440(49.0)  2482(49.4)  2611(51.4)
 Female                                      2520(50.7)  2609(52.7)  2542(51.0)  2540(50.6)  2464(48.6)
Marital status                                                                                             < 0.0001
 Married                                      569(21.1)   656(22.3)   742(23.3)   860(25.4)  1223(31.7)
 Never married                               1926(71.3)  2094(71.2)  2229(69.9)  2303(67.9)  2261(58.7)
 Divorced                                        14(0.5)      5(0.2)     16(0.5)     26(0.8)     62(1.6)
 Separated                                       30(1.1)     21(0.7)     30(0.9)     31(0.9)     73(1.9)
 Widowed                                       162(6.0)    164(5.6)    173(5.4)    172(5.1)    234(6.1)
Visits to health care institutions (for last
visit)
  Public hospitals                            166(49.3)   135(38.5)   164(42.7)   175(42.1)   137(30.6)    < 0.0001
  Private hospitals                              14(4.2)     29(8.3)     19(5.0)     40(9.7)   52(11.7)    < 0.0001
  Public health care centre                   107(31.7)   102(29.1)    75(19.6)    64(15.5)      34(7.6)   < 0.0001
  Private health care centre                   76(22.6)   120(34.1)   137(35.6)   176(42.2)   258(57.2)    < 0.0001
Health insurance ownership                                                                                 < 0.0001
   Yes                                           84(1.7)   172(3.6)    270(5.6)   655(13.5)  1490(30.7)
   No                                        4745(98.3)  4651(96.4)  4574(94.4)  4204(86.5)  3370(69.3)
Educational level                                                                                          < 0.0001
  Primary and below                           609(24.6)   588(22.0)   628(22.7)   604(20.1)   568(16.5)
  Secondary                                  1837(74.3)  2048(76.5)  2114(75.3)  2249(75.0)  2292(66.4)
   Tertiary                                      25(1.0)     41(1.5)     57(2.0)   146(4.9)   591(17.1)
Crime and victimization index mean ± SD        2.4±10.2     1.5±4.9     2.0±7.2     2.2±8.5     2.4±8.2
Age mean ± SD                                 25.5±22.7   26.8±22.2   28.3±21.9   29.6±21.3   33.8±20.9    < 0.0001
Crowding mean ± SD                              3.0±1.8     2.3±1.3     2.0±1.2     1.6±0.9     1.2±0.8    < 0.0001
Total medical expenditure mean ± SD (in 15.22±28.91 21.67±37.99 22.54±42.87 33.11±70.35 45.53±79.52        < 0.0001
USD)†
†USD 1.00 = Jamaican $50.97

                                                            281
Table 10.3. Self-reported injury, normally go if ill/injured, why didn’t seek care for current illness, length
of illness and number of visits to health practitioner by social hierarchy, 2002

                                                   Social hierarchy                             P
Characteristic            Poorest       Poor         Middle        Wealthy        Wealthiest
                          20%                                                     20%
Self-reported injury                                                                             < 0.0001
  No                      4811(99.1) 4815(99.1) 4801(98.9) 4806(98.7) 4797(98.2)
  Yes                        46(0.9)    43(0.9)    54(1.1)    61(1.3)    87(1.8)

Normal go it ill/injury                                                                          < 0.0001
  Public hospital       2252(46.4) 2004(41.3) 1786(36.8) 1449(29.7) 1049(21.5)
  Public health centre 1474(30.3) 1124(23.2) 854(17.6) 605(12.4)      315(6.5)
  Private hospital      1123(23.1) 1713(35.3) 2202(45.4) 2799(57.4) 3498(71.6)
  Pharmacy                   2(0.0)     0(0.0)     1(0.0)     3(0.1)     3(0.1)
  Other                      7(0.1)     8(0.2)   12(0.2)    17(0.3)    10(0.4)
Why didn’t seek care                                                                             < 0.0001
for current illness
  Could not afford it     72(35.1)   61(26.3)   47(21.3)   23(11.2)    19(8.6)
  Was not ill enough      59(28.8)   92(39.7) 111(50.2) 105(51.2)     97(43.9)
  Use home remedy         50(24.4)   43(18.5)   35(15.8)   47(22.9)   61(27.6)
  Did not have the time      2(1.0)     2(0.9)   10(4.5)      6(2.9)   14(6.3)
  Other (unspecified)     22(10.7)   34(14.7)    18(8.1)   24(11.7)   30(13.6)
Length of illness (in    11.5±10.4 10.8±10.0 10.4±10.9      9.8±9.7    9.9±9.7                      0.038
days) mean ± SD
Number of visits to        6.1±8.8    5.5±8.6    4.9±7.7    4.6±6.3    4.8±7.7                      0.007
health practitioner
mean ± SD




                                                     282
Table 10.4. Logistic regression: Self-reported illness by particular variables
                                            Std.                               Odds        95.0% C.I.
 Variable                  Coefficient     error       Wald          P         ratio
                                                      statistic                          Lower    Upper
Injury                            -0.20       0.32         0.40       0.53        0.82     0.44     1.52
 Health care-seeking               0.57       0.43         1.81       0.18        1.78     0.77     4.09
 Middle                           -0.80       0.51         2.49       0.12        0.45     0.17     1.21
 Two Wealthy quintiles            -1.03       0.51         4.02       0.04        0.36     0.13     0.98
 †Two poor quintiles                                                              1.00
 Logged medical
                                  -0.49       0.14       12.00        0.00        0.61     0.47         0.81
expenditure
 Durable goods                     0.01       0.07         0.01       0.91        1.01     0.88         1.16
 Separated, divorced or
                                   0.27       0.64         0.18       0.67        1.31     0.38         4.57
widowed
 Married                           0.08       0.42         0.03       0.86        1.08     0.47         2.47
†Never married                                                                    1.00
 Physical environment             -0.43       0.33         1.74       0.19        0.65     0.34      1.23
 Semi-urban                       -0.01       0.37         0.00       0.99        0.99     0.48      2.07
 Urban                             0.96       0.77         1.58       0.21        2.62     0.59     11.72
†Rural                                                                            1.00
 Secondary                        -0.33       0.44         0.55       0.46        0.72     0.31         1.71
 Tertiary                         -0.90       0.87         1.07       0.30        0.41     0.08         2.23
†Primary or below                                                                 1.00
 Sex                               0.81       0.32         6.54       0.01        0.44     0.24         0.83
 Crowding                         -0.15       0.16         0.88       0.35        0.86     0.63         1.18
 Age                               0.03       0.01         5.51       0.02        1.03     1.01         1.05
 Total expenditure                 0.00       0.00         3.54       0.06        1.00     1.00         1.00
Model χ2 =56.45, P < 0.001
-2 Log likelihood = 368.58
Nagelkerke R2 =0.148
Hosmer and Lemeshow goodness of fit χ2= 6.53, P = 0.59
Overall correct classification =97.1%
Correct classification of cases of self-rated illness =100.0%
Correct classification of cases of not self-rated ill =54.9%
†Reference group




                                                                283
Table 10.5. Logistic regression: Self-reported chronic illness by some variable

                                                     Std.                           Odds       95.0% C.I.
 Variable                          Coefficient       error        Wald       P      ratio
                                                                 statistic                   Lower    Upper
 Middle                                    -0.34        0.66          0.26   0.61     0.72     0.20     2.62

 Two wealthy quintiles                     -0.33        0.58          0.31   0.58     0.72     0.23         2.26
 †Two poor quintiles                                                                  1.00

 Sex                                       -1.16        0.49          5.75   0.02     0.31     0.12         0.81
Model χ2 =6.42, P < 0.001
-2 Log likelihood = 368.58
Nagelkerke R2 =0.06
Hosmer and Lemeshow goodness of fit χ2= 1.34, P = 0.854
Overall correct classification =93.2%
Correct classification of cases of self-rated illness =100.0%
Correct classification of cases of not self-rated ill =49.9%
†Reference group




                                                                284
CHAPTER 11


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



It is well established in health literature that most adolescents have good health, but this does not
mitigate the reality that there are some who are living with chronic and other health conditions. To
examine the demographic shifts in health conditions and the typology of health conditions experienced by
this age cohort. The current study extracted a sample of 5,229 and 1,394 adolescents aged 10-19 years
from two surveys collected jointly by the Planning Institute of Jamaica and the Statistical Institute of
Jamaica for 2002 and 2007 respectively. The survey was drawn using stratified random sampling. The
sample was weighted to reflect the population of the nation. Descriptive statistics and chi-square were
used in this study. The level of significance used in this research was 5% (i.e., 95% confidence interval).
In 2002, most of the respondents had colds (28.3%), and in 2007 this shifted to unspecified health
conditions (35.5%). The number of reported cases of arthritis in adolescents was 0.4% in 2002, which fell
by 100% in 2007. Increases were observed for: unspecified conditions, 42%; hypertension, 175%; and
diabetes mellitus, 700%. There is an immediate need for health promotion and education campaigns
geared towards the sensitization of adolescents about the rise in chronic illness and its challenges,
lifestyle practices, and willingness to seek care if particular symptoms are presently affecting them.




Introduction

Life expectancy and infant mortality are two of the indicators of the health status of a community, society,

nation, or population. This emphasizes the rationale behind extensive studies on those conditions, and a

possible paucity of research on adolescents’ health, except in the area of reproductive health. In Jamaica,

the life expectancy at birth, for 2005-2010, is 69.75 years for males and 74.95 years for females [1],

which is equally comparable to that of those peoples in developed countries [2]. On extensive review of

health literature in Jamaica, many studies have examined infant or general mortality [3-7], sexual lifestyle

and reproductive health in particular adolescents [8-13], and depression in adolescents [14-15] with an

emphasis still on life expectancy. Adolescent denotes an individual who is aged 10 to 19 years, and is

among a section of the young population who will be or is seeking employment, attending school, and by

                                                    285
extension, will form a critical part of the human development in the future. In 1991, the adolescent

population comprised 22.2% of the total population and this fell to 19.7% in 2007 (Table 11.1), indicating

that one-fifth of the nation’s population could be totally or partially dependent on family, relatives, or the

state for survivability.



The issue of survivability constitutes more than socio-economic assistance to the health status of this

group of people. Statistics from the Planning Institute of Jamaica and the Statistical Institute of Jamaica

[16] for 2007, revealed that most adolescents reported no illness/injury in the 4-week period of the JSLC,

23.9% had a recurring illness, 53.7% of those who were ill sought medical care, suggesting that the health

status of this cohort is relatively good. While this can be deduced from the statistics, there is no certainty

to this deduction. Using mortality data for adolescents in 1998, 1.86% of all mortality could be accounted

for by male adolescents compared to 1.19 for female adolescents, and this figure increased by 29% for

males and fell by 2.5% for females (Table 11.1).



Table 11.1 Adolescents Mortality and Live Birth by Sex (female < 20 yrs), 1998-2007.

In spite of the aforementioned disparity in mortality of the sexes for adolescents, the fact that 20% of all

births occur to this cohort, and on average 1.7% of all mortality is accounted for by this age cohort (Table

11.1), academics in Jamaica continue to be overindulgent in adolescents’ reproductive health research.

PAHO [17] however, noted that the health status of adolescents in Jamaica is good, which concurs with

PIOJ [18], and PIOJ and STATIN’s publications [16], and like Jamaican scholars, dedicated more time to

reproductive health and opined that 26% of all those who had injuries from violent acts were adolescents.

Using gunshot wounds to examine injuries of adolescents, disaggregating the figures revealed that more

adolescent females were injured and sought medical care than males (Tables 11.2, 11.3). Additionally, of

the fewer than 10% of adolescent Jamaicans who reported an illness or injury in 2007, 54% sought

medical care, which indicates that some illnesses or injuries are not associated with health care utilization.
                                                    286
The text “Health Issues in the Caribbean” contained eight articles on adolescents [19]. Of these, one

examined ‘The Health Impact of Injuries’ and another, ‘Injuries – The Broad Consequences’, again

indicating limitedness of studies on the general health status of this cohort. Injuries comprised only a

small percentage of poor health status and while depression is an aspect to the broad definition of health

according to the WHO [20], and can be used to proxy some aspect of health, a recently published study by

Bourne [21] was not significantly correlated with good health status of those who sought medical care in

Jamaica. A study by Bourne et al. [22], examining mortality and health status of elderly Jamaicans,

revealed that chronic health conditions were not correlated with age, which may argue for the non-

examination of general health conditions for adolescents. Studies have shown that the health status over

the life course is not constant [23-27], and Kuh and Ben-Shlomo [28] showed that as people age, the

probability of experiencing chronic diseases increase, and so understanding health conditions of the

elderly does not equate to comprehending health conditions or general health of adolescents.



According to Kuh and Ben-Shlomo [28], in the last two decades, the main concern of public health in

developed countries was chronic diseases, and while these accounted for 60% of mortality in developing

countries, and that 80% of chronic illness were in low-to-middle income countries [29], the reality is that

this expands beyond the elderly. In 2007, 40.2% of elderly Jamaicans indicated that they had an illness;

19.1% of those with diabetes mellitus were 65+ and 21% were 60-64 years. Of those with hypertension,

36.5% were 65+ and 33% were 60-64 years, and of those with arthritis, 18.6% were 65+ and 16.9% were

60-64 years [16]. Public health, therefore, cannot singly be about the health status of a particular group

over another, or reproductive health and injuries of a particular age-sex cohort to another, but a holistic

understanding of health status of people over the life course in order to formulate policies that are

embedded in research literature. There is indeed a paucity of health literature on the health status of

adolescents, health conditions, and the demographic shifts in health conditions of this age cohort in
                                                287
Jamaica. Depression and reproductive health are not comprehensive enough to provide a holistic

understanding of adolescents’ health status in spite of the low percentage of those who are experiencing

ill-health. This study aims to examine the demographic shifts in health conditions and the typology of

health conditions experienced by this age cohort.



Materials and Method

The current study extracted a sample of 5,229 and 1,394 adolescents aged 10-19 years 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

age (10-19 years). 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 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.


                                                    288
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. 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 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 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

Adolescent: an individual who is aged 10-19 years. Younger adolescent: an individual who is aged 10-14

years. Older adolescent: an individual who is aged 15-19 years.



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 an Analysis of Variance (ANOVA) was used to test the

relationships between metric and non-dichotomous categorical variables. The level of significance used in

this research was 5% (i.e., 95% confidence interval).




                                                     289
Results

Of the 5,229 adolescents (aged 10-19 years) sampled in 2002, one-half were males, 62.8% resided in rural

areas, 24.6% lived in semi-urban zones, and 12.6% dwelled in urban areas. The response rate for the

question “Have you ever had any illness in the past 4 weeks” was 95.9% (n=5,017). Two percent of those

who were asked the question “Are you pregnant” (n=1,569) remarked yes. Comparatively, of the 1,394

adolescents sampled in 2007, 48.2% was males, 51.1% resided in rural area, 28.3% lived in urban zones,

and 20.6 dwelled in semi-urban areas. Of the 96.1% (n=1,340) of respondents who were asked the

question “Have you ever had any illness in the past 4 weeks”, 6.6% reported yes. In 2002, 63.9% of the

sample sought medical care, 9.3% were covered by health insurance. Of those who had indicated an

illness, 28.3% were diagnosed with a cold, 5.1% diarrhea, 24.6% asthma, 0.4% diabetes mellitus, 0.4%

hypertension, 0.4% arthritis, 25.0% other, and 15.9% indicated that they were not diagnosed by a medical

practitioner or a health care worker. In 2007, 53.8% of the sample sought medical care, and 14.7% were

covered by health insurance (i.e., 9.3% private and 5.4% public coverage). Of those who had reported

suffering from an illness, 23.7% were diagnosed with a cold, 1.1% diarrhea, 17.2% asthma, 3.2% diabetes

mellitus, 1.1% hypertension, 35.5% other, and 18.3% indicated that they were not diagnosed by a medical

practitioner or a health care worker.




                                                  290
Figure 1 revealed that there was a shift in the typology of health conditions for 2007 over 2002.

In 2002, most of the respondents had colds (28.3%) and in 2007, this shifted to unspecified

health conditions (35.5%). The number of reported cases of arthritis in adolescents was 0.4% in

2002, which fell by 100% in 2007. The number of reported cases of diarrhea and colds fell and

notable increases for 2007 over 2002 were for unspecified conditions, 42%; hypertension, 175%;

and diabetes mellitus, 700%.




On further examination of the data, a cross tabulation with health insurance coverage,

educational level, and population income quintile by area of residents revealed a significant

statistical correlation (p < 0.05) (Table 11.4). In 2002, rural adolescents were the least covered

by health insurance and this remained the same in 2007. Concomitantly, the rural areas had the

least number of people in the wealthiest 20% compared to other geographical zones, with urban

areas recorded the most in the wealthiest 20%.



No significant statistical association was found between health conditions and area of residence

(P > 0.05). However, there is a shift in the typology of health conditions from colds and diarrhea

to other illness and to a lesser extent, hypertension. In 2002, 0.4% were reported as being

diagnosed with hypertension (from rural areas) and this increased 175% in 2007 (to 1.1%), and

this shifted to urban zones (Table 11.4). There is a shift to unclassified health conditions in 2007

over 2002, and this was across the 3 geographical areas in Jamaica. The number of adolescents



                                                 291
being diagnosed with asthma fell across the geographic zones except in semi-urban areas where a

marginal increase was noted, with the greatest movement being in rural areas (+54.2) followed

by urban (+24.7%) and a reduction of 7.4% in semi-urban areas. No arthritis was reported in

2007 compared to 0.4% in semi-urban areas in 2002.



Table 11.4 revealed that there was no significant difference between the lengths of time spent

receiving medical care by area of residence for both years. The number of urban adolescents

diagnosed with colds fell by more than 100% in 2007 over 2002 and while there was a reduction

of the same health condition for rural adolescents, this was not the case for the semi-urban

populace. However, based on Table 11.4, there was a 30.2% increase in the number of semi-

urban adolescents who were diagnosed with colds for 2007 over 2002.



In 2002, a significant statistical difference was found between those who sought and did not seek

medical care by the typology of health conditions - P < 0.05, χ2 (DF = 7) = 49.823, contingency

coefficient = 0.392. However, none was found for 2007 – P > 0.05 (Table 11.5). Based on Table

11.5, 3.1 times more adolescents who were diagnosed with a cold did not seek medical care

compared to those who did. For the chronic illnesses, except for arthritis, those ill respondents

sought medical care. With regards to the unspecified health conditions, 3.4 times more sought

health care compared to those who did not. For 2007, no statistical difference was observed for

health conditions and health care-seeking behavior of adolescents. Comparatively, the number of

adolescents seeking medical care for 2007 over 2002 fell for asthma patients, and likewise for

diarrhea and cold patients. However, there was a substantial increase in the number of




                                              292
adolescents both seeking and not seeking care for diabetes mellitus, and the those seeking care

for hypertension saw a drastic increase.



A cross-tabulation between health condition and health insurance coverage revealed that there

was a significant statistical correlation for 2002 [p < 0.05, χ2 (DF = 7) = 35.222, cc = 0.336] and

for 2007 [P < 0.05, χ2 (DF = 12) = 22.641, cc = 0.442] – Table 11.6. Based on Table 11.6, in

2002, most of those who were covered by health insurance had colds (24%) and asthma (52%);

and this shifted to diabetes mellitus (33%) and unspecified conditions in 2007. There was a

30.4% reduction in the number of adolescents covered by health insurance in 2007 over 2002

who reported having a cold (Table 11.6).



The cross-tabulation between health condition and cohort of adolescents revealed that there is no

significant statistical association (P > 0.05). Although no statistical correlation was identified by

Table 11.7, 27.6% of younger adolescents in 2002 reported unspecified illnesses compared to

48.6% of the older adolescents. Based on Table 11.7, the number of diabetic cases was zero for

older adolescents in both years, the number of reported diabetic cases for younger adolescents

increased by 766.67% (to 5.2%) for 2007 over 2002.



Using data for 2007, on investigation of hypertension with diabetes mellitus, it was found that a

statistical correlation existed between both conditions (χ2 (DF = 1) = 34.439, P <0.001).

Additionally, the study found that 23.5% of those with diabetes mellitus had hypertension.




                                                293
Discussion

Public health is not about collecting, addressing and formulating policies for an individual

patient as it must focus on diseases and conditions which influence health, and by so doing

address a large population [32]. Public health therefore must be guided by research on a

population, and the adolescent is one such sub-population. In Jamaica, this comprises about 20%

of the population indicating that by not having research information on this sub-population,

policies will be on a trial-and-error basis, which suggests that 1 in every 5 Jamaicans is not

understood. Concomitantly, health conditions are well researched in adolescents [33-36], but

there is no such study on this sub-population in Jamaica. The current study found that chronic

conditions such as diabetes mellitus and hypertension were diagnosed in adolescent Jamaicans.

In 2007, 3 out of every 100 adolescent Jamaicans had diabetes and 1 in every 100 had diabetes

mellitus. The diabetic cases were all found in younger adolescents (aged 10 to 14 years), while

the hypertensive cases were only found in older adolescents (aged 15 to 19 years). Interestingly,

in this study a shift in typology of health conditions was observed for 2007 over 2002. In the

former year, the leading health condition was colds (28 out of every 100) and this has shifted to

unspecified conditions in the latter year (36 out of every 100). Like colds, the proportion of cases

of asthma has fallen. However, a critical finding in this study was the drastic increase in the

percentage of samples with diabetes and hypertension. Although there is a shift towards chronic

non-communicable diseases in 2007 over 2002, the percentage of adolescents seeking medical

care fell by approximately 10%.



Hypertension is viewed as a silent killer [37] and like hypertension, diabetes mellitus is very high

in Jamaica [38], indicating that the adolescent will be exposed to chronic diseases management



                                                294
over the remainder of their lives. Morrison [39], in an article entitled ‘Diabetes and hypertension:

Twin Trouble’, established that diabetes mellitus and hypertension have now become two

problems for Jamaicans as well as countries in the wider Caribbean area. This situation was

equally correlated by Callender [40] at the 6th International Diabetes and Hypertension

Conference held in Jamaica in March 2000. Callender [40] found that there was a positive

association between diabetic and hypertensive patients—50% of individuals with diabetes had a

history of hypertension [40]. Prior to those scholars’ work, Eldermire [41] found that 34.8% of

new cases of diabetes and 39.6% of hypertension were associated with senior citizens (i.e., age

60 and over). Unlike the general populace and the elderly cohort, 24 out of every 100 adolescents

had hypertension and diabetes mellitus, indicating the importance of studying a sub-population

and not assuming that what holds for the general population or a particular sub-population is the

same for another sub-population.



Among the challenges associated with chronic conditions are 1) management, 2) cost, 3) impact

on the family, 4) influence on lifestyle behavior and 5) psychosocial challenges of those

conditions. An adolescent with hypertension or diabetes or both, impacts on the functional

capacity of people in the same age cohort. This is not atypical in Jamaica as the same thing

happens in America [35]. Chronic illnesses in adolescents interface with their schooling,

intellectual development, recreation, future employability, and occupational selection. And when

one is afflicted with both conditions (i.e., hypertension and diabetes mellitus), severity in those

conditions can result in poverty for the family and the individual, and requires non-out-of-pocket

assistance such as social welfare or health insurance coverage. Since 2007 in Jamaica, health

care coverage for children (0-18 years) is free and offers much assistance to those who are poor



                                               295
and suffering from health conditions, and by extension reduces the medical care out-of-pocket

payment for adolescents. In spite of this positive, chronic disease management is a socio-

economic and psychological burden not only for the adolescents but their families. Chronic

diseases are more than a public health concern, they account for a substantial percentage of

mortality each year and in the United States studies show that 10% of the adolescent population

(or 20 million) have some type of such condition [42, 43], and while the number of Jamaican

adolescents who are affected are substantially a smaller percentage than in America or even

Switzerland [44]—11.4% of girls and 9.6% of boys—the reality of Jamaican adolescents living

with chronic diseases do exist.



According to the WHO [29], it is estimated that 60% of all mortalities in 2005 were a result of

chronic illnesses. While injuries accounted for more deaths of adolescents in Jamaica than

chronic conditions, understanding general health condition is still as important as reproductive

health risk and infant mortality, as it could lead to premature mortality or could mean that the

cost of health care expenditures by the state could substantially increase if those with chronic

conditions live to become part of the elderly population (60+ years). Health denotes longevity,

and ill-health suggests that the quality of life of those affected will be lower than those with good

health. If ill adolescents are to live a long life, health care services should cater to their needs;

this postulate was equally uttered by Sawyer et al. [45]



There is another management epidemic which this study has unearthed, which is the shift in

health conditions to the unspecified classification. With 4 in every 10 Jamaican adolescents

having unspecified health conditions, and 5 in 10 older adolescents, this silent category could be



                                                296
a premature mortality group for this age cohort. Chronic diseases management therefore must

treat this unspecified category with urgency, as the fact that so many of the sample were in this

group, an understanding of its components will provide better modalities of responding to the

precarious health care needs of this silent group.



Conclusions

The general health status of adolescents (aged 10-19 years) in Jamaica is very good, with 93 out

of every 100 indicating that they have had no illness/injury in the survey period. In spite of the

small numbers who have ill-health, the most prevalent health condition in 2007 was in the

unspecified category, which is a shift away from colds in 2002. Interestingly, this study showed

an exponential increase in the number of diabetic adolescents in 2007 over 2002. In 2007, the

number of diabetic adolescents increased by 700% and hypertension increased by 175%,

indicating that this is a public health challenge. There is an immediate need for a health

promotion and education campaign geared towards the sensitization of adolescents about the rise

in chronic illness and its challenges, lifestyle practices, and willingness to seek care if particular

symptoms are presently affecting them.



Conflict of Interest

The author has no conflict of interest to report.



Acknowledgement

The author would like to extend his deepest appreciation to Ms. Neva South-Bourne for

proofreading the final manuscript.



                                                    297
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Figure 1. Health condition for 2002 and 2007




                                               301
Table 11.1. Adolescents Mortality and Live Birth by Sex, 1998-2007
                                                               Year
Variable         1999       2000           2001           2002         2003        2004   2005
Occurrence of 20.8          204            20.2           20.0         19.4        19.3   18.7
live      birth
(female < 20
yrs)

Mortality:
 Male            1.86       1.76          1.88           1.38          2.34        2.38   2.40
 Female          1.18       1.17          1.19           1.61          1.11        1.15   1.16
 Total           1.6        1.5           1.6            2.1           1.8         1.8    1.8

Source: Figures were computed by author from the Demographic Statistics for 2007




                                                     302
Table 11.2: Treatment for Gunshot wounds at the Accident and Emergency Depts. Of Public Hospitals by Gender
and Age cohort (in %): 1999-2002

Age cohort                                                Year
               1999                        2000                  2001                         2002
               Male Female                Male  Female            Male Female          Male Female

< 5 years       0.8      1.3              0.2     3.1              0.2     0.0              0.0      0.0

5-9 years       0.3      3.0              0.7     1.9              0.3     1.1              0.3      0.6

10-19 years     17.9     24.5             16.2    18.5             10.2    17.0             13.9     17.0

20-29 years     39.0     32.5             40.5    30.2             35.8    19.4             36.6     35.2

30-44 years     30.6     23.6             31.1    11.1             32.3    26.9             29.3     32.1

45-64 years     6.6      12.2             6.7     28.4             10.7    22.3             8.9      11.3

65+ years       3.5      3.0              2.3     11.1             6.7     12.7             8.8      3.6

Not unknown     1.4      0.0              2.2     1.2              3.8     0.7              2.3      0.6

Total %           100      100            100      100              100      100              100    100
Calculated by Paul A. Bourne from Annual Report, 2002 published by the Policy, Planning and Development
Division, Ministry of Health, Jamaica




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Table 11.3: Visitation to the Accident and Emergency Depts. Of Public Hospitals for attempted suicide by Gender
and Age cohort (in %): 2000-2002

Age cohort                                                  Year

                                   2000                     2001                       2002
                                  Male  Female               Male    Female           Male  Female

< 5 years                         0.0      0.0              1.0      0.0              1.0      0.9

5-9 years                         0.0      3.4              2.0      0.0              2.0      3.5

10-19 years                       19.0     39.3             13.0     49.4             13.0     38.3

20-29 years                       24.1     36.0             20.0     34.8             20.0     36.5

30-44 years                       34.5     13.5             13.0     6.7              13.0     17.4

45-64 years                       12.1     2.2              4.0      3.4              4.0      0.9

65+ years                         6.9      3.4              4.0      2.2              4.0      0.0

Not unknown                       3.4      2.2              0.0      3.4              1.7      2.6

Total %                            100    100              100      100               100     100
Calculated by Paul A. Bourne from Annual Report, 2002 published by the Policy, Planning and Development
Division, Ministry of Health, Jamaica




                                                      304
Table 11.4. Demographic characteristic of sample
                                                      2002                                2007
             Variable                  Urban         Semi-      Rural      Urban         Semi-     Rural
                                                     urban                               urban
Health Insurance coverage*
   No                                  87.2          86.3       93.1       80.6          86.4      87.4
   Yes, Public                         12.8          13.7       6.9        6.1           4.0       5.5
   Yes, Private                        -             -          -          13.3          9.6       7.0
Health conditions
   Cold                                25.0          28.8       28.7       11.1          37.5      26.0
   Diarrhoea                           3.1           8.8        3.7        0.0           0.0       2.0
   Asthma                              34.4          17.5       26.2       25.9          18.8      12.0
   Diabetes mellitus                   0.0           1.3        0.0        0.0           0.0       6.0
   Hypertension                        0.0           0.0        0.6        3.7           0.0       0.0
   Arthritis                           0.0           0.3        0.0        -             -         -
   Other                               25.0          21.3       26.8       40.7          31.3      34.0
   No                                  12.5          21.3       14.0       18.5          12.5      20.0
Pregnancy
  No                                   98.1          97.0       98.4
  Yes                                  1.9           3.0        1.6
Educational level*
  Primary and below                    4.3           3.3        4.7        36.6          48.6      46.1
  Secondary or high                    93.6          98.2       94.0       56.5          48.6      53.0
  University                           2.1           1.5        1.3        6.9           2.9       0.9
Population Income quintile*
  Poorest 20%                          13.7          15.0       25.7       12.2          12.9      33.0
  Poor                                 14.6          16.7       23.9       13.5          24.7      28.6
  Middle                               21.1          22.2       22.3       22.3          20.2      19.8
  Wealthy                              24.4          22.2       18.9       25.1          22.0      14.2
   Wealthiest 20%                      26.3          23.9       9.2        26.9          20.2      4.5
Length of illness – Mean (SD) in       7.19 (6.29)   7.86       7.8        5.96 (5.74)   28.38     33.4
days                                                 (7.46)     (8.14)                   (89.9)    (148)
No of visits - Mean (SD) in days       1.4 (1.3)     1.4(0.6)   1.5(1.0)   1.13          1.38      1.19
                                                                           (0.516)       (0.518)   (0.402)
Health care-seeking behaviour
 No                                    48.0          56.6       44.0       40.7          50.0      48.0
 Yes                                   52.0          43.4       56.0       59.3          50.0      52.0
Age Mean (SD) in years                 14.4 (2.91)   14.4       14.2       14.43 (2.7)   14.22     14.01
                                                     (2.85)     (2.85)                   (2.90     (2.7)
*P < 0.05




                                                     305
Table 11.5. Health conditions by medical care-seeking behaviour, 2002 and 2007
                                          2002*                                    2007

                       Do not seek             Sought medical       Do not seek       Sought medical
Health conditions      medical care            care                 medical care      care
                                %                      %
                                                                             %               %
Cold                           43.60                 14.20
                                                                           37.20            12.00
Diarrhoea                       4.50                  5.70
                                                                            0.00             2.00
Asthma                         19.50                 29.10
                                                                           18.60            16.00
Diabetes mellitus               0.00                  0.70
                                                                            2.30             4.00
Hypertension                    0.00                  0.70
                                                                            0.00             2.00
Arthritis                       0.80                  0.00
                                                                            0.00             0.00
Other                          11.30                 38.30
                                                                           32.60            38.00
 *P < 0.05, χ2 (df = 7) = 49.823, cc = 0.392




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Table 11.6. Health conditions by health insurance coverage

Health                             20021                                 20072
Condition
                       No health             Health          No health       Health insurance
                       insurance           insurance         insurance   Private          Public
                           %                   %                 %          %               %
Cold                      28.7                24.0              26.7      16.7              0.0


Diarrhoea                  5.6                  0.0             1.3        0.0             0.0

Asthma                    21.9                  52.0           17.3       16.7             16.7

Diabetes                   0.0                  4.0             1.3        0.0             33.3
mellitus

Hypertension               0.4                  0.0             1.3        0.0             0.0

Arthritis                  0.0                  4.0              -          -               -

Other                     27.1                  4.0            34.7       50.0             16.7
 1
  P < 0.05, χ2 (df = 7) = 35.222, cc = 0.336
 2
  P < 0.05, χ2 (df = 12) = 22.641, cc = 0.442




                                                       307
Table 11.7. Health condition by age cohort, 2002 and 2007
                                              20021                   20072
Health condition                         Adolescents               Adolescents
                                  Younger         Older       Younger    Older
                                  %               %           %          %
Cold                              29.0            27.2        31.0       11.4

Diarrhoea                          4.3           6.1          0.0        2.9

Asthma                             27.2          21.1         19.0       14.3

Diabetes mellitus                  0.6           0.0          5.2        0.0

Hypertension                       0.0           0.9          0.0        2.9

Arthritis                          0.0           0.9          -          -

Other                              22.2          28.9         27.6       48.6


1,2
  Not statistically significant (P > 0.05)




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CHAPTER 12

Determinants of quality of life of youths in an English-speaking
Caribbean nation

Studies on quality of life (QoL) on youths are limited and have not examined determinants of
QoL for this cohort. The current study seeks to examine the QoL of Jamaican youths and to build
a model that identifies factors that explain QoL. During the period June to August 2006, the
Centre of Leadership and Governance, Department of Government, at the University of the West
Indies, Mona Campus, conducted a stratified random probability survey of 1,338 respondents.
Data were collected using a 166-item questionnaire. Of the sampled population (N=1,338), the
proportion of those respondents age 18 to 25 years (i.e., youths) was 27% (N=364) and this
constitutes the sample for the current study. The data were stored and retrieved in the Statistical
Package for the Social Sciences (SPSS 12). Descriptive statistics were used to analyse the data,
and logistic regression was used to establish the model. The quality of life of Jamaican youths
was determined by 4 factors which explain 20% of the variability in quality of life. The parents’
economic wellbeing has the most influence on the quality of life of Jamaican youths (OR=1.348;
95% CI: 1.35, 3.04), followed by moderate religiosity (OR=3.594; 95% CI: 1.47, 8.82), the
extent of the welfarism of the state (OR=5.273; 95% CI: 1.04, 1.69) and gender (OR = 1.329,
95% CI = 1.04, 1.69). The current work has offered us an understanding of the determinants of
QoL of youths and how interventions can be planned for in the future.




Introduction

Studies on quality of life (QoL) of a population have substantially [1-12] focused on the elderly

[13-25]. Research in the Caribbean in particular has primarily examined QoL of the elderly [14,

16-26] and outside of that research, studies have also examined a population’s wellbeing [27-28],

QoL of sickle cell patients [28] and QoL of Jamaican women [30]. Scholars like Gayle and

Chevannes, among others [31, 32, 33, 34], have examined some aspect of the life of youths in

Caribbean, and in particular Jamaica, but they have done so from the perspective of qualitative

methodology. Those who have used quantitative methodology (i.e., survey research) like Lipps

et al. [35, 36], Anglin-Brown et al. [37], Anderson [38], and Bourne [39] have not examined the

general QoL of youths.


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       Having pursued a plethora of literature on QoL in Jamaica and wider English-speaking

Caribbean countries, it was realized that there is a lack of studies on the quality of life of youths

in general. Youths constitute a significant segment of the working age population (i.e.,

productive population – people ages 15 through 60 years) and equally important about this age

cohort is the fact that it represents the future of any nation. Therefore, we cannot deny the

importance of this age cohort to current and future development, health of the workforce and

population. QoL of this group must become crucial to clinicians, medical practitioners and policy

makers. It is within this context that this study seeks to fill the gap in the literature firstly by

examining the QoL of Jamaican youths and secondly by identifying factors that explain their

QoL.


       QoL is widely accepted by medical researchers and clinicians as an alternative paradigm

to dysfunction in the measurement of health and treatment of health-care customers (i.e.,

patients) [40-41]. The rationale for this paradigm is its maximization perspective [42-43]. Many

scholars including economists such as Sen [44-45], Easterlin [5-6], Stutzer & Frey [3], and Di

Tella, et al. [46] have proposed that QoL (or wellbeing) must incorporate subjective as well as

objective conditions. They contend that any construct which may be used to capture QoL (or

wellbeing) must be such that it embodies economic wellbeing (i.e., Gross Domestic Product per

capita growth) and emotional reactions to events as they are a part of the whole life of an

individual. This argument was also forwarded by psychologists such as Diener [11] and

Veenhoven [9], that justifies the use of happiness or self-reported overall QoL to assess

wellbeing [3, 5-6, 9, 11-12].


       In order to assess overall QoL of an individual, it is argued that the “best” approach was

to use a questionnaire that collects information on QoL [1, 47-50]. Kashdan [49] writes that the

                                                310
assessment of subjective wellbeing (or QoL) can be addressed with a questionnaire on happiness

which the aforementioned literature has outlined as the proposition of other scholars. Murphy &

Murphy [1] and Hutchinson et al. [28], on the other hand, believe that QoL assessment can be

done by way of self-reported satisfaction with life and subjective assessment of the life by the

individual. A part of this assessment was self-esteem; self-achievement (or actualizations) which

are embodied in Abraham Maslow’s 5-level Hierarchy of Needs. Pacione [50] opines, “The

simplest model states that satisfaction with life in general is a weighted sum of satisfactions with

different domains or aspects of life (for example, job satisfaction) and that, in turn, these domain

satisfactions are weighted sums of specific satisfiers and dissatisfiers…A more complex

formulation is the hierarchy of needs of model…”[50]. Cummins [47], on the other hand,

provides a contravening argument to the view of Pacione that needs must not be used as an

assessment of life’s quality of an individual. He argues that the drawback to the use of needs is

embedded in the fact that low degree of needs does not necessarily associate with QoL. Hence,

Cummins’s delimitations will not hold in the current study as the needs index are moderate or

high evaluations.


       The rationale for this study was embodied in three main issues. This age cohort is

vulnerable to specific risk (i.e., high crime and victimization, high teenage pregnancy, high

unemployment) just as children (ages 0 to 15 years) and the elderly (60 years and older) are, and

a research on the QoL of this age group will provide invaluable information about this group’s

wellbeing status. In order to establish a model that can simultaneously examine and provide

possible factors that influence QoL, this study uses econometric analysis—multivariate

analysis—which has been used by other scholars like Grossman and Smith & Kingston (see

Theoretical Framework) to do similar studies.


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Theoretical Framework

An econometric model was developed by Grossman [10] to evaluate the wellbeing of the world’s

population which is in keeping with the WHO’s definition of wellbeing [8]. Grossman’s model

reads [Eqn (1)]:


       H t = ƒ (H t-1 , G o , B t , MC t , ED) ……………………………………………………..(1)


       In which the H t (current health in time period t) is a function of stock of health in

previous period, H t-1 ; good personal health behaviours (including exercise), G o ; bad health

behaviour (i.e., smoking and excessive drinking), Bt ; use of medical care, MC t ; education of

each family member, ED. The Grossman model encapsulates biological conditions,

psychological and socioeconomic factors.


       According to Smith & Kington [8], using H t = f (H t-1 , P m G o , Bt , MC t ED, Ā t ,) to

conceptualize a theoretical framework for “stock of health” noted that health in period t, H t , is

the result of health preceding this period (H t-1) , medical care (MC t) , good personal health (G o) ,

the price of medical care (P m ), and bad health choices (Bt) , and a vector of family education

(ED), and all sources of household income (Ā t ). Embedded in this function is the wellbeing that

that individual enjoys (or does not enjoy) [8].


       This study was guided by econometric analysis. It is a modification of Grossman and

Smith & Kington’s works. Modifications were made to the previous works in keeping with the

culture, the literature and studied group. Another fundamental difference between the current

research and that of Grossman and Smith & Kington, is that it is area specific as it focuses

primarily on youths which make up a substantial proportion of the Jamaican population. For any

effective health education and private care to take place, this cohort’s general health status must


                                                  312
be explained by way of scientific inquiry. The proposed model that this research seeks to

evaluate is displayed below [Eqn. (2)]:


       QoLi = ƒ(REi , W i , RA i , PPIi , AR i , X i , SS i , C i , ES i , TIi , O i , A i , E i , ES i,, ε i )….……(2)


              Where QoLi of youths i is a function of religiosity, REi ; welfare index of youths i,
              W i ; race of youths i, RA i ; political participation index of person i, PPIi ; area of
              residence of youths i, AR i ; gender of youths i, X i ; subjective social class of youths
              i, SS i ; confidence in sociopolitical institution index of youths i, C i ; economic
              situation of youths i, ES i ; interpersonal trust of person i, TIi ; occupation of person i,
              O i ; age of person i, A i ; educational level of person i, Ei ; employment status of
              person i, ES i ; and an error term, ε i .

              Using empirical data QoL of sample can be determined by

       QoLi = ƒ (REi , W i , X i , ES i , ε i ) ……………………………………….…………....(3)

Method

During the months of July to August 2006, the Centre of Leadership and Governance,

Department of Government (CLG), The University of the West Indies, Mona Campus,

conducted a stratified probability sample of 1,338 respondents. The sampling design used for the

study was that used by the Statistical Institute of Jamaica. The survey was the first of its kind as

it collected data on Jamaica’s political culture. The themes ranged from democracy, civic culture,

trust and confidence, perception of wellbeing using Abraham Maslow’s 5-level Hierarchy of

Needs, preference for whether the private or public sector should be solving problems in the

economy, political participation and civic engagement, and finally, leadership, party, and

electoral preferences. Face-to-face interviews were used to collect the data on an instrument

which took about 90 minutes. The instrument consisted of 166 items that were taken from

Latinobarometer and Eurobarometer Cross-Cultural Survey, the American National Election

Studies Series, the Harvard/Washington Post Leadership Survey, the New Zealand Election



                                                        313
Surveys, the Cross-Cultural Variations in Distributive Justice Perception Survey and the Carl

Stone Surveys. The instrument was vetted by senior scholars and researchers as well as by

interviewers within the data divisions of the Statistical Institution of Jamaica (STATIN) and

Social Development Commission (SDC). After the vetting phase, the questionnaire was pretested

in a number of communities across the 14 parishes of Jamaica as well as among UWI faculty and

the student population. Modifications were made at a training symposium based on the

comments of the different interviewers and remarks of trained researchers. All the interviewers

employed by the CLG’s team were either data collectors by STATIN or SDC.


       Although the interviewers are trained data collectors, they were trained by the CLG team

for a 3-day period. Dr. Lloyd Waller (project manager of the CLG) was assigned to travelling

across the entire island as a verifier of the interviewers’ collection of the information from

Jamaicans. Furthermore, a part of this study was to examine Jamaicans’ QoL, and so questions

(needs, physiological needs, social needs, self-esteem and self-actualisation) were placed on the

instrument that examined respondents’ perceptions on Abraham Maslow’s 5-level Hierarchy of

Needs model. Prior to data entry, a data template was created by senior researcher in the

Department of Government, Dr. Alfred Lawrence Powell, who also trained and familiarized the

data-entry clerks with the instrument. The data were entered by trained data-entry clerks who are

employed in the Department of Sociology, Psychology and Social Work. Three different groups

independently entered the data and these were cross-reference by Paul Andrew Bourne, a

demographer and reviewed by Alfred L. Powell for accuracy. Both Bourne and Powell were

responsible for the cleaning and validation process of the entered data.


       Data were stored and retrieved in the Statistical Package for the Social Sciences (SPSS

16.0). The sampling error was ±3% at the 95% confidence level (i.e., CI). This was done to aid

                                               314
the external validity of the survey, as well as to enhance the associational and inferential

statistics. Cronbach alpha was used to test the internal reliability of QoL (i.e., QoL), which was a

5-item Likert scale question. The Cronbach alpha for QoL was 0.841. Descriptive statistics were

used to provide background information on the sample, t-test was used to examine the

association between quality of life and gender, analysis of variance (ANOVA) was used to test

whether a statistical correlation existed between quality of the thee age groups and logistic

regression (i.e., enter method) was used to establish the model. The predictive power of the

model was tested using Omnibus Test of Model, and Hosmer and Lemeshow [51] 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

[52] 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. Finally, Wald statistics

was used to determine the magnitude (or contribution) of each statistically significant variable in

comparison with the others, and the odds ratio (OR) for the interpretation of each significant

variable. The final model [Eqn. (3] was determined by those variables that were statistically

significant in Table 12.2 (P < 0.05).


       Of the sampled population (N=1,338), the proportion of those respondents aged 18 to 25

years was 27% (N=364) and this constitutes the sample for the current study. The overall

response rate for the survey was 95.7% and that of the youths was 96.4%. The power of the

study is 0.98 with an alpha of 0.05 to detect a 10% difference between people who were

classified in the reference group (i.e., low QoL) and those in the moderate to high group.




                                               315
Measure


QoL was defined as the overall self-reported life satisfaction of an individual. QoL is the

summation of the 5-item need of Abraham Maslow’s hierarchy. These items were safety needs,

physiological needs, social needs, self-esteem and self-actualisation. Each item was on a 10-

point Likert scale. Using Cronbach alpha for the five-item scale, reliability was 0.841 (or α =

84%). Hence;


QoL i = 1/5*∑Ni where i is each need (i.e., I = 1, 2, 3, 4, 5)

where the QoL index is: 0≤QoL i ≤10. The index valuations can be interpreted as low (where the

values can be interpreted as low (0 to 3), moderate (4 to 6) and high (7 to 10). For the purpose of

logistic regression, the dependent variable, QoL, was a dummy variable where 1 = moderate to

high QoL, 0 = otherwise or low.


Welfare Index, W, is the index of the extent of individual’s or government’s responsibility for

particular functions within a society. The functions vary from health care, employment and

retraining, adequate housing, child-care assistance, replacement income due to loss of job owing

to accident, retirement income, disability assistance, and educational assistance for tertiary

training. Each function is on a 10-point Likert scale, where 1, the lowest, refers to this function

being totally the individual’s responsibility and 10, the highest, refers to this function being

solely the government’s responsibility. Hence, the Welfare Index is the mean summation of the

15-question, 10-point Likert scale response. The minimum score is 1 and the maximum is 10.


Political Participation Index, PPI. This is the extent of someone’s involvement in conventional

(road blocks, demonstrations, protest, riots, etc.) or unconventional political activities (internet

blogging, etc). PPI = Σb i , b i ≥ 0, and b i represents each “yes” response to a question on political


                                                 316
behaviour, such as voting, involvement in protest which is given a value of 1 and a “no” was

given a value of 0≤PPI≤19.


Religiosity denotes the frequency with which an individual attends church, mosque, or

synagogue.


Results: Demographic characteristics of sample

Table 12.1 examines the demographic characteristics of the sample. Of the sampled population

(n=364), 96.4% responded to the question of gender (n=351). Of those who indicated a gender,

57.3% (n=201) were females. The mean age (SD) of the sample was 21.6 years (2.3 years). One-

half of the population had a QoL of 7.2 out of a total index of 10, with most respondents having a

QoL of 7.6 out of 10. Using Analysis of Variance (ANOVA), no statistical difference was found

between the mean wellbeing of youths, other adults and the elderly. The mean (SD) wellbeing of

youths (N=364, 29.9%) was 6.9 (1.7), for other adults (N= 810, 64.4%) 6.8 (1.8), and the elderly

(N=83, 6.6%) 7.0 ± 1.7, with F-test [3, 1259] = 0.699, P = 0.552.


Results: Multivariate Analysis

Table 12.3 presents a logistic regression of social and political variables on QoL of youths in

Jamaica. The final model [Eqn (3)] explained 20% of the variance in QoL of respondents who

were youths. Four variables account for the QoL of youths, and these factors are religiosity,

extent of the welfare index, gender, and economic situation of the youths’ parents.


Discussion and Conclusion


Studies in the literature have shown that age, education, race, social class, employment, and

occupation are statistically associated with QoL [5-6, 8, 10, 12]. However, these were not found

to be the case in the current research. Some of the aforementioned factors are not statistically

                                               317
significant in determining QoL of youths, although this is well-established in other cohorts. One-

quarter of youths are below the age of 20 years and may still be residing with parents, which can

indicate the reliance on parental or family support.

       Some of the important findings that emerged from the current research are the role of

parents’ economic situation on QoL of youths, the importance of moderate religiosity, and the

significance of the nation’s social security programmes on youths’ wellbeing. Embedded in those

findings are the capacities of parents and the nation to provide for this age cohort of Jamaicans as

well as the positive attributes of religiosity on wellbeing. It follows that a slowing of economic

growth and development of the nation will impact not only the ability and capacity of the nation,

but also the likelihood of youths becoming increasingly involved in criminality to subsidise for

the state’s lowering of social assistance to this age cohort.

       Religion is associated with wellbeing [53-57] as well as low mortality [58]. Religion is

seen as the opiate of the people from Karl Marx’s perspective, but theologians hypothesised that

religion is a coping mechanism against unhappiness and stress. According to Kart [59], religious

guidelines aid wellbeing through restrictive behavioural habits which are health risks such as

smoking, drinking of alcohol, and even diet.


       Scholars found that the relationship was even stronger for men than for women, and that

this association was influenced by denominational affiliation. Graham et al.’s study [57] found

that blood pressure for highly religious male heads of household in Evan County was low. The

findings of this research did not dissipate when checked for age, obesity, cigarette smoking, and

socioeconomic status. A study on the Mormons in Utah revealed that cancer rates were lower (by

80%) for those who adhered to Church doctrine [60, 61] than for those with weaker adherence.


       In a study of 147 volunteer Australian males between 18 and 83 years old, a study by

                                                 318
Jurkovic & Walker [55] found that the stress levels of non-religious were higher than those of

religious men. The researchers in constructing a contextual literature quoted many studies that

made a link between non-spirituality and “dryness”, which results in suicide. Even though

Jurkovic & Walker’s research was primarily on spiritual wellbeing, it provides a platform that

can be used in understanding linkages between psychological status of people and their general

wellbeing. In a study which looked at young adult women, the researchers found that spirituality

affects the physical wellbeing of its populace [62]. Edmondson et al. [62] did a study of 42

female college students of which 78.8 percent were Caucasian, 13.5 percent African-American,

5.8 percent Asian and 92 percent were non-smokers in which they established an association

between spirituality and wellbeing of female Caucasians and African-Americans.


       In examining the limitation of this study, this research, which is a cross-sectional study,

cannot be used the same way as an experimental design, which is to establish causality. In

addition, based on some of the findings presented in this study, questions have emerged that

must be addressed (such as, why male youths have higher QoL than female youths), but the

current study will not be able to provide all of the answers to those questions. Hence, the

researcher recommends that an ethnographic methodology be utilised to unearth the cultural

underpinnings that will explain everything further.

Conclusion

In sum, QoL of Jamaican youths is substantially determined by the household economic

situation, religiosity and the perceived reported extent of welfare state. The study revealed that a

youth who had moderate religiosity was 4 times more likely to have a greater QoL with reference

to one who reported low religiosity. Whereas a youth who reported high religiosity is 3 times

more likely to report a greater QoL with reference to a youth who reported low religiosity. The


                                               319
current work has offered us an understanding of possible causal factor of QoL of youths, but this

must be further studied using longitudinal study in order to establish with finality the causal

explanatory factors of QoL.


Conflict of interest


There is no conflict of interest to report.


Acknowledgement

The author would like to extend special accommodation to the Centre of Leadership and

Governance, Department of Government, University of the West Indies, Mona, Jamaica for

allowing him the privilege of utilising the dataset from which this study was possible, as well as

Dr. Donovan A. McGrowder for his suggestions in reviewing the manuscript.




                                               320
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                                           327
Table 12.1: Demographic characteristics of sample
Variable                                                  Number           %
Gender
  Male                                                         150             42.7
  Female                                                       201             57.3
Educational level
 Secondary and below                                           191             54.1
 Post-secondary [i.e., Vocational (or skills training)]         68             19.3
  Tertiary                                                      94             26.6
Subjective Social Class
  Lower                                                        186             53.0
  Middle                                                       154             43.9
  Upper                                                         11              3.1
Ethnic background
  African (i.e., Black)                                        274              76.3
  Mixed (i.e., Brown)                                           74              20.6
  European (i.e., white)                                         2               0.6
  Other (i.e., Chinese, etc.)                                    9               2.6
Age Mean (SD)                                                    21.6yrs. (± 2.3yrs)
Welfare Index Mean (SD)                                                  6.9 (± 1.4)
Political Participation Index Mean (SD)                                  2.1 (± 2.7)
Quality of LifeMean (SD)                                                 6.9 (± 1.7)




                                                328
Table 12.2: Quality of Life of youths by some explanatory variables
                                           Std.              Odds
                                           Error     P       ratio
 Variables
                                                                            95.0% C.I.
 High religiosity                                0.443    0.022   2.756    1.157-6.562
  Moderate religiosity                           0.458    0.005   3.594    1.465-8.815
 †Low religiosity                                                 1.000
  Welfare Index                                  0.124    0.022   1.329    1.043-1.694
  Dummy Race (1 = black)                         0.606    0.966   1.026    0.313-3.368
  Political Participation Index                  0.067    0.720   0.976    0.856-1.114
  Dummy area of residence
                                                 0.505    0.560   0.745    0.277-2.005
  (1= urban area)
 Male                                            0.348    0.045   2.011    1.016-3.980
 Middle class                                    0.358    0.720   0.879    0.436-1.774
 Upper class                                     0.971    0.326   2.595   0.387-17.394
 †Lower class                                                     1.000
  Confidence Index                               0.017    0.148   1.024    0.991-1.058
  Economic Situation Index                       0.207    0.001   2.022    1.348-3.035
  Dummy Trust (1= yes)                           0.359    0.118   1.753    0.867-3.543
  Occupation                                     0.461    0.340   0.644    0.261-1.590
  Age                                            0.083    0.495   0.945    0.803-1.112
  Dummy education (1= tertiary)                  0.401    0.973   1.013    0.461-2.226
  Dummy Employed                                 0.239    0.950   1.015    0.635-1.622
χ2 (df=16) =31.65, P = 0.011
-2Log likelihood = 225.62; Nagelkerke R-squared = 0.198




                                                    329
CHAPTER 13


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


Education provides choices, opportunities, access to resources and it is associated with an
increased likelihood of higher income. Does this holds true in developing nations like Jamaica,
and does the educated class experience greater self-rated health status than the uneducated
classes? The current study will identify the socio-demographic correlates of self-rated health
status of Jamaicans, examine the effects of these variables, explore self-rated health status and
self-reported diagnosed recurring illness among the educated and uneducated classes, compute
mean income among the different educational types, and determine whether a significant
statistical correlation exists between the different educational cohorts. The current study utilised
the data set of Jamaica Survey Living Conditions which is a cross-sectional survey. It is a
national probability survey, and data were collected across the 14 parishes of the island.
Stratified random sampling techniques were used to draw the sample. Self-rated health statuses
of respondents are correlated with age, income, crowding, sex, marital status, area of residence,
and self-reported illness (es) – χ2= 1,568.4, P < 0.001. Respondents with tertiary level
educations were most likely to be classified in the wealthiest 20% (53.4%) and there was no
significant statistical difference between their health status and the lower educated classes.
There is a need for a public health care campaign that is specifically geared towards the
educated classes as their educational achievement is not translating itself into better health care-
seeking behaviour and health status than the uneducated classes.



Introduction
Health is imperative for socio-economic and political development of people, a society and a

nation. It is within this context that a study of health is critical as it relates to the wider society.

Traditionally, the concept of health is measured using life expectancy, mortality, and diagnosed

illness. In the social sciences, researchers have used self-rated health status [1-9], and self-

reported illness [10-17] to measure health. Apart from those terminologies, other synonyms such

as self-assessed health, self-reported health, perceived health, self assessment of health, global


                                                 330
health status, and health status have all been used to speak about health. It follows from the

aforementioned perspective that all those terms imply the same measurement of health or health

status. Self-rated health status is among the subjective indexes used to measure health, and some

scholars argue that they are not a good assessment of health when it comes to life expectancy,

per capita income, or mortality [18-20].


       The subjective/objective indexes of measuring health emerged as scholars sought to

ensure that the measurement of health was a reliable and valid one. Some scholars opined that

the self-assessment of one’s health status was more comprehensive than objective assessment [3,

5, 21] as it included one’s health and general life satisfaction. Studies have shown that subjective

indexes are a good measurement for mortality [2, 22-24] and life expectancy [25]. Concurringly,

a recently conducted study by Bourne [25] found that self-assessed illness was not a good

measure of mortality; however, it was was very useful when it came to the subject of life

expectancy in Jamaica.


       The subjective indexes in measuring health open themselves up to systematic and

unsystematic biases [26]. People’s perception can be biased as they may inflate or deflate their

status in an interview or on a self-administered instrument (i.e., questionnaire). Another aspect of

bias in subjective evaluation of health is the matter of recall. It is well established in research

literature that as people age, their mental faculties decline [27-32], suggesting that some people

will have difficulties recalling experiences which happened in the past. Within the context of the

time recollection, bias can occur in subjective indexes. Kahneman [33] devised a procedure of

integrating and reducing the subjective biases when he found that instantaneous subjective

evaluations are more reliable than assessments of recollection of experiences. Contrary to

Kahmeman’s work, Bourne’s [25] results show that self-assessed health for a 4-week period is a

                                               331
good measure of life expectancy (objective index). In spite of the fact that subjective indexes are

a good measure of objective health, the former still contains biases, which Diener [34] opines

still have valid variance.


          It is well established in health research that there is a correlation between or among

different socio-demographic, psychological and economic variables [4, 6-17, 20] and self-rated

health status. The correlates include education, marital status, area of residence, education,

income, psychological conditions (i.e., positive and negative psychological affective conditions),

and other variables. Freedman & Martin [35], using data from 1984 and 1993’s panel survey of

Income and Program Participation, noted that there was an association between educational level

and physical functioning of people over 65 years. Another study by Koo, Rie & Park [36], using

multivariate regression, concluded that education was a predictor of increased subjective

wellbeing (t [2523] = 7.83, P<0.001], which means that education was more than associated

with health. Concomitantly, another research found that the number of years of school (i.e., the

Quantity Theory) was a crucial predictor of health status of an individual [37] which indicates

that tertiary level graduates are more likely to be healthier than non-tertiary level educated

people.


          While education provides choices, opportunities, access to resources and is associated

with increased likelihood of achieving a higher income, does it hold true in developing nations

like Jamaica that the educated class has greater self-rated health status than the uneducated

classes? A paucity of information (research literature) exists in Jamaica on the educated and

uneducated classes and their self-rated health status, self-reported illness(es), the areas in which

the educated and uneducated classes reside, health care-seeking behaviour among the different

educational classes and the self-rated health status of Jamaicans and its correlates.

                                                332
       The current study is important, as it uses a statistical technique which accommodates all

items in self-rated health status categories as opposed to dichotomising self-rated health.

Dichotomising self-rated health status in good and poor health means that some of the original

information will be lost; and this explains why some researchers argue for the maintenance of the

Likert nature of the measuring tool over dichotomisation [38-40]. Secondly, the study is

significant as it included more variables: (1) educational levels and area of residence, (2)

educational levels and health care-seeking behaviour, (3) health insurance coverage and

educational levels, (4) self-reported illness(es) and educational levels, (5) social standing and

educational levels. The objectives of the current study therefore are to (1) identify the socio-

demographic and economic correlates of self-rated health status of Jamaicans, (2) examine the

effects of these variables, (3) explore self-rated health status and self-reported diagnosed

recurring illness among the educated and uneducated classes, (4) calculate the mean age of

respondents in the different educational categories, (5) compute mean income among the

different educational types, and (6) determine whether a significant statistical correlation exists

between the different educational cohorts.


Materials and methods

Data


A joint survey on the living conditions of Jamaicans was conducted between May and August of

2007 by the Planning Institute of Jamaica (PIOJ) and the Statistical Institute of Jamaica

(STATIN) [41]. The survey is called the Jamaica Survey of Living Conditions (JSLC) which

began in 1988 and is now conducted annually. The JSLC is a modification of the World Bank’s

Living Standards Measurement Study (LSMS) which is a household survey [42]. The current


                                               333
study used the JSLC’s data set for 2007 in order to carry out the analyses of the data [43]. It had

a sample size of 6,783 respondents, with a non-response rate of 26.2%.


          The JSLC is a cross-sectional survey which used stratified random sampling techniques

to draw the sample. It is a national probability survey, and data was collected across the 14

parishes of the island. The design for the JSLC was a two-stage stratified random sampling

design where there was a Primary Sampling Unit (PSU) and a selection of dwellings from the

primary units. The PSU is an Enumeration District (ED), which constitutes a minimum of 100

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

shares a common boundary. This means that the country was grouped into strata of equal size

based on dwellings (EDs). Based on the PSUs, a listing of all the dwellings was made, and this

became the sampling frame from which a Master Sample of dwellings was compiled. This, in

turn, provided the sampling frame for the labour force. One third of the Labour Force Survey

(i.e. LFS) was selected for the JSLC. The sample was weighted to reflect the population of the

nation.


Instrument


A self-administered instrument (i.e., questionnaire) was used to collect the data from

respondents. The questionnaire covers socio-demographic variables such as education, age, and

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.


                                                334
Statistical Analyses


The Statistical Packages for the Social Sciences – SPSS-PC for Windows version 16.0 (SPSS

Inc; Chicago, IL, USA) – was used to store, retrieve and analyze the data. Descriptive statistics

such as median, mean, percentages, and standard deviation were used to provide background

information on the sample. Cross tabulations were used to examine non-metric dependent and

independent variables. Analysis of variance was used to evaluate a metric and a non-

dichotomous variable. Ordinal logistic regression was used to determine socio-demographic,

economic and biological correlates of health status of Jamaicans, and identify whether the

educated have a greater self-rated health status than uneducated respondents. A 95% confidence

interval was used to examine whether a variable is statistically significant or not.


       There was no selection criterion used for the current study. On the other hand, for the

model, the selection criteria were based on 1) the literature; 2) low correlations, and 3) non-

response rate. The correlation matrix was examined in order to ascertain if autocorrelation and/or

multicollinearity existed between variables. Based on Cohen and Holliday [44] and Cohen and

Cohen [45], low (weak) correlation ranges from 0.0 to 0.39, moderate – 0.4-0.69, and strong –

0.7-1.0. This was used to exclude (or allow) a variable in the model. Any correlation that had at

least a moderate value was excluded from the model in order to reduce multicollinearity and/or

autocorrelation between or among the independent variables [46-51]. Another approach in

addressing and/or reducing autocorrelation was to include in the model all variables that were

identified from the literature review with the exception of those where the percentage of missing

cases were in excess of 30%.




                                                335
          The current study used the ordinal nature of the dependent variable (self-rated health

status or self-rated health) which denotes that none of the original data will be lost as is the case

in dichotomising self-rated health. Ordered regression model is written as:


                                            , s = 1, …k,                                  (1)

          Where x is the vector of covariates with coefficient to be estimated, k is the number of

cut-points for the dependent variable, and α s , α l stand for the intercepts in the regression models.

Anderson [52] opined that ø 1 =1 and ø k, and that other constraints are possible. In the current

study, the researcher set ø 1 =1 and 0= ø1 < ø 2 < …< ø k =1 to correspond to the levels from very

good to very poor, and other levels of health are relative to “very good”. Based on Anderson’s

arguments, the monotone increase of ‘ø’s are dealt with by varying the sign for β. Within this

context, a positive estimation of coefficient denotes that those with this characteristic would be

negatively associated with good health status and those without would positively associated with

good health status (or self-rated health status). Simply put, positive estimation of coefficients

means poor health and negative estimation of coefficients denotes better self-reported health

status.


Measurement of variables


Dependent variable


Self-rated health status (i.e., self-rated health) was derived from the question, “Generally, how is

your health?” with the options being very good, good, fair (or moderate), poor, or very poor. The

ordinal nature of this variable was used as was the case in the literature [38-40].


Independent variables


                                                 336
Information on self-reported illness was derived from the question, “Have you had any illnesses

other than injury?” The examples given include cold, diarrhoea, asthma attack, hypertension,

arthritis, diabetes mellitus or other illness. A further question about illness asked, “(Have you

been ill) In the past four weeks?” The options were yes and no. This variable was re-coded as

binary value, 1 = yes and 0 = otherwise.


Information about self-reported diagnosed recurring illness was derived from the question, “Is

this a diagnosed recurring illness?” The options were: (1) yes, cold; (2) yes, diarrhoea; (3) yes,

asthma; (4) yes, diabetes mellitus; (5) yes, hypertension; (6) yes, arthritis; (7) yes, other; (8) no.


Information on medical care-seeking behaviour was taken from the question, “Has a health care

practitioner, healer, or pharmacist been visited in the last 4 weeks?” The options were yes or no.

Medical care-seeking behaviour therefore was coded as a binary measure where 1 = yes and 0 =

otherwise.


The term crowding refers to the average number of person(s) per room excluding the kitchen,

bathroom, and veranda (i.e., total number of people in household divided by the total number of

rooms excluding kitchen, bathroom and veranda).


Total annual expenditure was used to measure income.


Income quintile was used to measure social standing. The income quintiles ranged from poorest

20% to wealthiest 20%.


Results

Demographic characteristic of sample and bivariate analyses




                                                 337
The sample was 6,783 respondents: 48.7% males and 51.3% females. Eighty-two percent of

respondents rated their health status as at least good compared to 4.9% who rated it as poor.

Fifteen percent of respondents reported some form of illness within the last 4 weeks. Of those

who recorded an ailment, 89% reported that the dysfunction was a diagnosed recurring one. The

most frequently recurring illness was unspecified conditions (23.4%) followed by hypertension

(20.6%), cold (14.9%), diabetes mellitus (12.3%), and others (Table 13.1).

         The median age of the sample was 29.9 years (range = 99 years). The median annual

income was US $7,050.66 (rate in 2007: 1US$ = Ja$80.47; range = US $4,406.20), and median

crowding was 4.0 persons per room (range = 16 persons).

         A cross-tabulation between educational level and area of residence revealed a significant

statistical correlation – χ2(df = 40 = 78.02, P < 0.001 (Table 13.2). Based on Table 13.2, 0.8% of

rural respondents had tertiary level education and 5.4 times more urban residents had tertiary

level education compared to rural respondents.

         No significant statistical correlation existed between educational level and sex of

respondents – χ2 (df = 2) = 5.61, P > 0.05 (Table 13.3). Similarly, no significant statistical

association was found between purchased prescribed medication and educational levels of

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

         A significant statistical difference was found between mean age of respondents who are

at different educational levels – F statistic [2, 6589] = 214.64, P < 0.001. The mean age of

respondents with primary level of education and below was 32.0 years (SD = 22.6, 95% CI =

31.4-32.6) compared to 14.6 years (SD = 1.7, 95% CI = 14.5-14.8) for those with secondary

education level and 26.4 years (SD = 10.6, 95% CI = 24.6-28.2) for those with tertiary education

level.



                                                 338
          A cross-tabulation between self-reported illness and educational level revealed a

significant statistical association - χ2 (df = 2) = 61.33, P < 0.001. Respondents with primary

education level and below recorded the greatest percent of people with illness(es) (16.2%)

followed in descending order by tertiary level (9.2%) and secondary level respondents (5.4%).

The statistical correlation was a weak one – correlation coefficient = 0.10.

      A significant statistical correlation existed between self-reported diagnosed recurring

illness and educational level – χ2 (df = 14) = 42.56, P < 0.001 (Table 13.4). Respondents with

secondary level education (37.5%) had the highest percent of unspecified health conditions

followed in descending order by tertiary (33.3%) and primary level respondents (22.7%).

Hypertension was substantially a phenomenon occurring among those with primary education

level and below: 21.6%, compared to 8.3% of tertiary level individuals. Similarly, diabetes

mellitus (12.8%) was more prevalent among primary level respondents compared to 5.0% of

secondary level respondents. On the other hand, asthma was the greatest among tertiary level

respondents (33.3%) compared to secondary level (22.5%) and primary level respondents

(8.7%).

          Respondents with tertiary level education were most likely to be classified in the

wealthiest 20% (53.4%) compared to those with secondary education who were more likely to be

in the middle class and those with primary level education were either in the poorest 20%

(20.3%) or in the wealthiest 20% (20.3) (Table 13.4) – χ2 (df = 8) = 124.53, P < 0.001.

          Of the 20.2% of respondents who had health insurance coverage, tertiary level people

were more likely to have private coverage (35.9%) followed by primary or below (12.0%) and

secondary level individuals (11.6%) – χ2 (df = 4) = 76.95, P < 0.001 (Table 13.4).




                                                339
       Concurringly, a significant statistical difference existed between the mean age among the

different educational levels in which respondents were categorised (Table 13.4) – F statistic [2,

6589] = 214.6, P < 0.001: mean age for those with at most primary level education was 32.0

years (SD = 22.6) compared to a mean age of 26.4 years (SD = 10.6) for those with tertiary level

education. When educational level of respondents was disaggregated into no formal, basic, and

primary to tertiary, the mean age of respondents with no formal education was 42.7 years (SD =

18.0), 2.7 years (SD = 1.9) for basic school level respondents, and 9.0 years (SD = 2.2) for those

who have primary level education – F statistic [4,6587] = 2207.9, P < 0.001

Multivariate analysis

Self-rated health statuses of respondents are correlated with (1) age, (2) income, (3) crowding,

(4) sex, (5) marital status, (6) area of residence, and (7) self-reported illness(es) – χ2= 1,568.4, P

< 0.001; and that the data is a good fit for the model – LL = 9,218.0. The 7 socio-demographic

and economic correlates accounted for 33% of the variability in self-rated health status (Table

13.5). Based on the Table 13.5, the older the respondents get, the more likely they are to rate

their health status as poor and this was the same for crowding and for those who report an illness

(health condition). Urban residents are more likely to report poor self-rated health status than

rural residents. However, there was no statistical difference between self-rated health status for

rural and semi-urban residents. Married people are more likely to report better self-rated health

status than widowed people, people with more income are more likely to report better health

status, and males are more likely than females to report better health status. However, no

significant statistical difference was found between self-rated health status among the educated

and uneducated cohorts.




                                                340
Discussion
The current study concurs with the literature in that self-reported illness has the most influence

on self-rated health status of people [8]. In a study of elderly Barbadians (ages 60+ years),

Hambleton et al. [8] found that current illness accounted for 87.7% of the variance in self-rated

health status. In another study on married people in Jamaica, Bourne and Francis [53] found that

73% of self-reported illnesses explains the variability in self-reported health status. Embedded in

the current finding is whether self-rated health is examined on elderly or married people.

Current self-reported illnesses accounted for a critical proportion of self-rated health and can be

used to measure health. Within this context, self-reported illness is a good measure of self-rated

health, and this has been established by other studies [10-17, 25]. A recently conducted research

found that self-reported illness accounted for 54% (r-square) of the variance in life expectancy of

Jamaicans [25], and this increased to 63% for males. Subjective indexes such as self-rated health

and self-reported illness can be used to measure health, but the latter is a better measure and this

must be taken into consideration in the interpretation of findings using this measurement.


       The challenges noted by some researchers in using self-rated health are: (1) bias and (2)

the dichotomisation of the measure. While bias is synonymous with subjective assessment or

evaluation of any construct, the validity of using the measure is high. Diener [34] noted in 1984

that there are still some valid variances, which was validated in a recent study by Bourne [25].

Health literature has long established that subjective indexes such as self-rated health, happiness,

and life satisfaction are good measures of health as they are more comprehensive (including

social activities and relationships, psychological conditions, emotions, spirituality, life

satisfaction) while still incorporating the objective component [3, 21, 34]. This is justified by

studies that found strong statistical correlations between subjective health and objective indexes


                                               341
such as life expectancy [25] and mortality [2, 22-24]. It should be noted here that subjective

indexes (e.g., self-reported illness) and mortality are lowly correlated in Jamaica [25], which

suggests that health literature among regions has revealed different findings. This denotes that

the wholesale use of what is obtained in one nation cannot be applied to another without

understanding socio-demographic characteristics. However, Jamaica, like other nations, can use

subjective indexes to assess health status of its people and by extension its entire population.


       The issue of the dichotomisation of self-rated health, because some of the original values

will be lost, is now resolved by this study as self-rated health was dichotomised and findings

were similar to those who had dichotomised the dependent variable (i.e., self-rated health status).

What are the similarities and dissimilarities between the two statistical approaches in

operationalising subjective health?


       Studies in the Caribbean found that age, marital status, crowding, sex of respondents,

area of residence, income and illnesses were statistically correlated with subjective health [8, 10-

17, 53], which is validated by the current study. Even some non-Caribbean studies have found

the aforementioned variables to be statistically associated with subjective health [7, 9], indicating

that dichotomising self-rated health status does not fundamentally change most of the socio-

demographic, economic, and biological variables.


       Examining data on married people by way of dichotomising self-rated health status,

Bourne [25] found that men had a greater self-reported health status than women, and in the

current study (non-dichotomisation of self-rated health status), males had a higher health status

than females. On the other hand, in Bourne’s work [25], he found in descending order self-

reported illnesses, age, income and sex to be the only factors of self-reported good health while


                                                342
in the non-dichotomised study more variables accounted for health status. Nevertheless, ranking

of the correlates were similar in both studies as in the current. The factors in descending order

were self-reported illness, age, crowding, income, sex and the others, indicating the closeness of

the statistical approaches. Married people are a component of the general populace and they have

socio-demographic and economic experiences which differ from some unmarried people.


       The literature showed that income is strongly correlated with self-rated health. However,

in Jamaica this is clearly not the case. In Jamaica, income plays a secondary role to illness and

age and when self-rated health is non-dichotomised, it becomes an even weaker variable.

Although income affords one particular choices (or lack thereof), the educated class in Jamaica

received more income than uneducated classes, yet the former class is not healthier than the

latter. This finding is contrary to the literature that showed the association between higher

education and health [7-9]. Education influences social standing and income, but it does not

directly influence good health status in Jamaica. Concurringly, the current work found that

education is positively correlated with more health insurance coverage. However, health

insurance coverage is not significantly associated with better health status. Embedded here is the

fact that health insurance coverage in Jamaica is not an indicator of health care-seeking

behaviour but a product that is purchased for the eventuality of the onset of illness, as it will

lower out-of-pocket medical care expenditure.


       Education provides its recipients with knowledge, access to knowledge, access to income

and other empowerment, but it does not mean that the educated classes are more concerned about

their health, and this can be measured using health care-seeking behaviour and knowledge about

the illnesses that are affecting the individual. The current paper found that 25 out of every 100

educated Jamaicans are aware of their health condition(s), and this is greater than that for

                                                343
uneducated classes. Jamaicans with the least level of education were most cognizant of their

ailments and sought medical care just as much as did educated Jamaicans. Education, therefore,

does not denote empowerment to seek medical care, which is embedded in the culture, in

particular for men. Education is still unable to break the bondages of the perceptions of society

which purport that health is weakness, and that to display weakness as a man removes his

masculinity. This continues to shackle Jamaicans, particularly men, who still subscribe to the

traditional notion that illness is correlated to weakness and that men should not display

weakness. It is this cultural perspective that bars many men from visiting health care facilities,

except in cases of severe illness or if they are married [25]. Hence, mortality being greater for

men is not surprising [54] as many men will die prematurely because of the fact that they are

reluctant to visit health care institutions. This reluctance to seek medical care is not limited to

males. In 1988, when Jamaica began collecting data on the living conditions of its people,

females sought more medical care than males, but the disparity ranged between -2 to 6%. In

2007, 68% of females sought medical care compared to 63% of males, which means that higher

education, which is substantially a female phenomenon in Jamaica, is not fundamentally

improving the health status of females or even males.


       Educated Jamaicans are more likely to live in urban areas and those with primary

education levels or below are more likely to live in semi-urban zones. The current findings found

that semi-urban respondents were more likely to have better health status, although they are more

likely to have at most primary level education. In 2007, statistics revealed that 15.3% of

Jamaicans in rural areas were below the poverty line compared to 4% of semi-urban and 6.2% of

urban Jamaicans [41], indicating that poverty is more synonymous with rural areas, yet there is

no significant statistical difference between the self-rated health status of rural and urban


                                               344
Jamaicans. Income makes a difference in health, as those with more means can access more and

greater resources including health care, but clearly income beyond a certain amount is retarding

the health status of Jamaicans. This study cannot stipulate a baseline income that people should

receive in order to prevent a decline in health status. However, there is clearly a state of

contentment among the poor and very poor who were equally as healthy as the wealthy. The

health disparity between them and the educated showed no significant statistical difference and

this emphasises that wealth does not automatically transfer itself into health. Another issue which

is evident in the data is the variability in the measurement of health among the social classes, as

the poorest 20% reported less illness than the wealthiest 20% [41], yet the former group still

dwells in slums, inner-city neighbourhoods, and violent communities, and they have lower levels

of education. Despite Diener’s findings [34] that the variance is minimal, Bourne’s work showed

a strong association between subjective health (i.e., self-reported illness) and life expectancy – a

correlation coefficient between 50 and 60% for a single variable is strong. However, this

highlights that there are still some challenges embedded in the use of self-rated health status.


Conclusion

While the dichotomisation of self-rated health status loses some of the original data, when self-

rated health is non-dichotomised, socio-demographic and biological variables accounted for 33%

of the explanation of the variance and this was 44% using dichotomisation for married Jamaica,

suggesting dichotomisation of health status still holds some validity. Another critical finding that

emerged from the current work is that education is not improving the health status of Jamaicans.

However, it is correlated with better social standing and higher income. Income is significantly

associated with better health status and it played a secondary role to self-reported illness and age

of respondents. Education is associated with more health insurance coverage, but that health

                                                345
insurance coverage cannot be used to measure health care-seeking behaviour or measure better

health status of Jamaicans. In summary, there is a need for a public health care campaign that is

specifically geared towards the educated classes as their educational achievement is not

translating itself into better health care-seeking behaviour and health status than the uneducated

which suggests that societal pressures are barring Jamaicans from better health status choices.


Conflict of interest

The author has no conflict of interest to report.


Acknowledgement

Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica
Survey of Living Conditions, 2007, none of the errors that are within this paper should be
ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica, but rather to
the researcher.




                                                    346
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                                               349
Table 13.1. Demographic characteristic of sample, n=6,783
Characteristic                                                n      %
Sex
Male                                                        3303   48.7
Female                                                      3479   51.3
Marital status
Married                                                     1056   23.3
Never married                                               3136   69.2
Divorced                                                      77    1.7
Separated                                                     41    0.9
Widowed                                                      224    4.9
Social standing
Poorest 20%                                                 1343   19.8
Poor                                                        1354   20.0
Middle                                                      1351   19.9
Wealthy                                                     1352   19.9
Wealthiest 20%                                              1382   20.4
Area of residence
Urban                                                       2002   29.5
Semi-urban                                                  1458   21.5
Rural                                                       3322   49.0
Self-reported illness
Yes                                                          980   14.9
No                                                          5609   85.1
Self-reported diagnosed recurring illness
Cold                                                        149    14.9
Diarrhoea                                                    27     2.7
Asthma                                                       95     9.5
Diabetes mellitus                                           123    12.3
Hypertension                                                206    20.6
Arthritis                                                    56     5.6
Unspecified                                                 234    23.4
Not reported as diagnosed                                   109    10.9
Health care-seeking behaviour
Yes                                                         658    65.5
No                                                          347    34.5
Self-rated health status
Very good                                                   2430   37.0
Good                                                        2967   45.2
Moderate                                                     848   12.9
Poor                                                         270    4.1
Very poor                                                     50    0.8




                                            350
Table 13.2. Educational level by area of residence, n = 6,592
Characteristic                                              Area of residence       Total
Educational level                                Urban       Semi-urban Rural
                                                         %               %       %          %
Primary and below                                      84.8            89.0    88.0       87.3
Secondary                                              10.9             9.6    11.2       10.8
Tertiary                                                4.3             1.5     0.8        2.0
Total                                                 1952            1421    3219       6592
Chi-square (df = 4) = 78.02, P < 0.001, cc = 0.11




                                             351
Table 13.3. Education level by sex of respondents, n = 6,592

Characteristic                                                         Sex               Total
                                                          Male               Female
                                                           %                   %          %
Educational level
Primary and below                                              87.9              86.6          87.3
Secondary                                                      10.5              11.0          10.8
Tertiary                                                         1.6               2.4          2.0
Total                                                          3207              3385         6592
Chi-square (df = 2) = 5.61, P > 0.05




                                              352
Table 13.4. Self-reported diagnosed recurring illness and social standing by educational level
                                                          Educational Level             Total
Characteristic                                Primary or Secondary Tertiary
                                              below
                                                         %             %             %             %
Self-reported diagnosed recurring
illness1
Cold                                                  15.0          17.5            0.0         14.9
Diarrhoea                                               2.9           0.0           0.0           2.7
Asthma                                                  8.7         22.5          33.3            9.5
Diabetes mellitus                                     12.8            5.0           0.0         12.3
Hypertension                                          21.6            0.0           8.3         20.6
Arthritis                                               5.9           0.0           0.0           5.6
Unspecified condition                                 22.7          37.5          33.3          23.4
Not diagnosed                                         10.5          17.5          25.0          10.9
Total                                                  947             40           12           999
                                      2
Social standing (income quintile)
Poorest 20%                                           20.3          19.7            3.8         19.9
Poor                                                  20.0          21.7            7.6         20.0
Middle                                                19.4          24.5          16.0          19.9
Wealthy                                               19.9          20.3          19.1          19.9
Wealthiest 20%                                        20.3          13.7          53.4          20.2
Total                                                5752            709           131          6592
Health Insurance coverage3
No                                                    79.8          83.7          57.8          79.8
Private                                               12.0          11.6          35.9          12.5
Public                                                  8.1           4.6           6.3           7.7
Total                                                5682            689           128          6499
Age4 Mean (SD) in years                        32.0 (22.6) 14.6 (1.7) 26.4 (10.6) 30.0 (21.8)
Health care-seeking behaviour5
Yes                                                   65.7          60.0          66.7          65.5
No                                                    34.3          40.0          33.3          34.5
Total                                                  953             40           12          1005
Income6 Mean (SD) in US$7                         8,381.88     9,580.20     14,071.67       8,623.84
                                                (6,641.28) (7,712.81)        (9,31.10)    (6,874.54)
1
 Chi-square (df = 14) = 42.56, P < 0.001, cc=0.20
2
 Chi-square (df = 8) = 124.53, P < 0.001, cc=0.14
3
 Chi-square (df = 4) = 76.95, P < 0.001, cc=0.11
4
  F statistic [2,6589] = 214.6, P < 0.001
5
 Chi-square (df = 2) = 0.6, P > 0.05
6
  F statistic [2,6589] = 52.4, P < 0.001
7
 Rate in 2007:1US$= Ja$80.47




                                               353
Table 13.5. Ordinal logistic regression: Socio-demographic and biological differentials of self-
rated health status of Jamaicans
                                                      Std.                             95% CI
  Characteristic                            Estimate Error Wald        P        Upper          Lower
            Excellent self-rated health              0.0      0.0
            Good self-rated health (ø 1 )         0.540    0.345     2.456   0.117        -0.135        1.216
            Fair self-rated health (ø 2 )         3.504    0.625    31.465   0.000         2.279        4.728
            Poor self-rated (ø 3 )                5.935    0.985    36.327   0.000         4.005        7.865
            Very poor (ø4)                        8.659    1.425    36.909   0.000         5.865       11.452
            Age                                   0.045    0.008    34.055   0.000         0.030        0.060
            Income                          -3.79E-007     0.000    10.636   0.001   -6.06E-007    -1.51E-007
            Crowding                              0.083    0.025    11.130   0.001         0.034        0.132
            Primary or below                     -0.187    0.252     0.553   0.457        -0.681        0.307
            Secondary                             0.042    0.267     0.025   0.874        -0.481        0.566
            Tertiary (=0)
            Sex (female=0)                       -0.221    0.077     8.290   0.004       -0.372        -0.071
            Married                              -0.554    0.200     7.704   0.006       -0.945        -0.163
            Never married                        -0.352    0.192     3.342   0.068       -0.729         0.025
            Divorced                             -0.469    0.319     2.171   0.141       -1.094         0.155
            Separated                            -0.109    0.369     0.087   0.768       -0.832         0.615
            Widowed (=0)
            Poorest 20%                           0.203    0.163     1.554   0.213       -0.116        0.523
            Poor                                  0.013    0.140     0.009   0.925       -0.262        0.288
            Middle                                0.028    0.126     0.048   0.826       -0.219        0.274
            Wealthy                              -0.238    0.122     3.782   0.052       -0.477        0.002
            Wealthiest 20% (=0)
            Urban                                0.217     0.090     5.789   0.016        0.040        0.395
            Semi-urban                           0.008     0.085     0.008   0.927       -0.159        0.174
            Rural (=0)

            Private insurance                    -0.175    0.110     2.542   0.111       -0.389        0.040

            Public insurance                     0.026     0.149     0.032   0.859       -0.265        0.318

            Public insurance – other             0.387     0.209     3.433   0.064       -0.022        0.796
            No insurance coverage (=0)

            Illness                              2.377     0.401    35.152   0.000        1.591        3.163
Nagelkerke r-square = 0.33
Chi-square = 1,568.4, P < 0.001
LL = 9,218.0
n=4,433




                                                354
CHAPTER 14



An Epidemiological Transition of Health Conditions, and Health
Status of the Old-Old-To-Oldest-Old in Jamaica: A comparative
analysis


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. The current study utilized a sub-sample of approximately 4% from each
national cross-sectional survey that was conducted in 2002 and 2007. The sub-sample was 282
people ages 75+ years from the 6,783 respondents surveyed for 2007 and 1,069 people ages 75+
years from the 25,018 respondents surveyed for 2002. 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


                                               355
form of external intervention. Eldemire’s work revealed that 88.5 percent being physiologically

independent.


       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.



                                                356
       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

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


                                               357
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.


       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.




                                               358
       The survey was drawn using stratified random sampling. This design was a two-stage

stratified random sampling design where there was a Primary Sampling Unit (PSU) and a

selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which

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

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

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

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

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

third of the Labour Force Survey (i.e. LFS) was selected for the JSLC [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.




                                                359
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].


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).




                                                 360
Statistical Analysis


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

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

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

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

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

and a dichotomous categorical variable. The level of significance used in this research was 5%

(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 14.1).

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 14.1).


                                               361
       A cross tabulation between self-reported illness and sex revealed that there was no

significant statistical correlation between the two variables (Table 14.2). 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 14.4, 14.5). In spite of the aforementioned situation, the divorced sample

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 14.5). 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 14.6). 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 14.6).

       Based on Table 14.7, 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



                                                 362
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)

and 2007 (χ2 (df = 4) = 10.28, P < 0.05). Based on Figure 14.2, 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 14.8).

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 14.9 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).



                                                363
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 14.10).

       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 14.11). Based on Table 14.11, 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.




                                                364
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

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




                                               365
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

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



                                                 366
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

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



                                                367
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

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.




                                                368
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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.




                                              371
Table 14.1. 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          Ja $341.54 (±Ja.$1165.60)       Ja $368.89.54
Mean (SD)                                                              (±Ja.$1518.66)
Private health care expenditure               Ja. $1436.23               Ja. $1856.04
Mean (SD)                                    (±Ja.$2060.42)            (±Ja.$4347.78)




                                             372
Table 14.2. Self-reported illness by sex of respondents, 2002 and 2007


                                    20021                                  20072

Self-reported

illness                  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




                                               373
Table 14.3. Self-reported illness by marital status, 2002

                                                                    Marital status*

Self-reported illness   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




                                                              374
Table 14.4. Self-reported illness by marital status, 2007

                                                                    Marital status*

Self-reported illness   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




                                                              375
Table 14.5. Self-reported illness by Age cohort, 2002 and 2007


                                    20021                                     20072

Self-reported

illness                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




                                                                   376
Table 14.6. Mean age of oldest-old with particular health conditions



                                   20021                                   20072

Health

conditions                   Mean Age (±SD)                            Mean Age (±SD)

Cold                                            -                77.63             (±1.77)
                         80.00
Diarrhoea                                       -                85.00             (±9.66)
                         86.00
Asthma                                          -                81.00             (±5.20)
                          0.00
Diabetes mellitus                                                90.92             (±4.84)
                         79.50              (±2.50)
Hypertension                                                     81.21             (±4.95)
                         80.13              (±0.84)
Arthritis                                                        79.13             (±3.54)
                         79.32              (±0.69)
Other                                                            83.90             (±6.82)
                         81.64              (±1.75)
Total                    80.14              (±4.73)              82.75             (±4.50)


F statistic [7,134] = 2.085, P = 0.049




                                                                 377
Table 14.7. Diagnosed Health Conditions by Aged cohort


                                    20021                           20072

Diagnosed

Health                           Aged cohort                     Aged cohort

conditions                                  Oldest-Old                      Oldest-Old
                       Old-Old                           Old-Old
                                                %                               %
                          %                                %
Cold                                           0.0         7.2                 0.0
                          1.5
Diarrhoea                                      8.3         2.7                 3.2
                          0.0
Asthma                                         0.0         1.8                 3.2
                          0.0
Diabetes mellitus                                         11.1                 16.1
                          3.0                  0.0
Hypertension                                              44.1                 45.2
                         47.8                  58.3
Arthritis                                                 12.6                 6.5
                         35.8                  8.3
Other                                                     11.7                 22.6
                         11.9                  25.0
                                                           2.7                 3.2
                          0.0                  0.0
No

1 χ2 (df = 1) = 10.028, P =0.074

2 χ2 (df = 1) = 5.382 P =0.613


                                                         378
Table 14.8. Self-reported illness (in %) by health status.

                                                                         Health Status

                                                              Good            Fair         Poor

Self-reported illness                                         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




                                                                  379
Table 14.9. Health care-seeking behaviour and health status, 2007

                                                                       Health Status

                                                             Good           Fair         Poor

Health care-seeking behaviour                                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




                                                                380
Table 14.10. Health care-seeking behaviour by health status controlled for aged cohort


                                                             Health status

Aged cohort
                                                 Good            Fair            Bad       Total

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


  χ (df = 2) = 11.296, P =0.004, cc=0.305
1 2



  χ (df = 2) = 0.390, P =0.823
2 2




                                                                381
Table 14.11. Logistic regression on Good Health status by variables
                                                                                        Wald
 Variable                                            Coefficient           Std. Error   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
                                                            2.979             2.456        1.471      19.667             -
 Constant
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



                                                                             382
Figure 14.1. Diagnosed health conditions, 2002 and 2007


Figure 14.1 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.




                                                 383
Figure 14.2. Self-reported illness (in %) by Income Quintile, 2002 and 2007


Figure 14.2 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).




                                              384
CHAPTER 15



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. 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. 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



                                              385
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.



                                                386
       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.



                                                387
       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

                                                388
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).




                                               389
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.




                                                390
       Table 15.1 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 15.1. 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 15.1).


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 15.2). Based on Table 15.2, 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.

                                                391
       Based on Figure 15.1, 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 15.3). Based on Table 15.3, 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 15.4).



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.

                                                392
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-



                                                393
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



                                               394
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.




                                                 395
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American J of Medical Sciences. In print.




                                             399
Table 15.1. Sociodemographic characteristics of sample by area of residence, 2002 and 2007

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

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

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

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

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

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




                                                    400
Table 15.2. 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




                                                401
Figure 15.1. Mean scores for self-reported diagnosed health conditions, 2002 and 2007




                                                      402
Table 15.3. Self-rated health status by income quintile, 2007
                                                  Income Quintile
  Self-rated health
 status               Poorest 20%         2.00        3.00        4.00           Wealthiest 20%

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



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



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


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



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

 Total                         650          697            695            665               663

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




                                               403
Table 15.4. Self-reported diagnosed health condition by per capita income
                                                          Income Quintile
 Diagnosed health condition     Poorest 20%         2.00         3.00     4.00          Wealthiest 20%

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


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


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


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


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


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


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


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

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




                                                404
CHAPTER 16



Gender differences in self-assessed health of young adults in an
English-speaking Caribbean nation



Gender differences in self-assessed health in young adults (i.e. ages 15 – 44 years) are under-
studied in the English-speaking Caribbean. The aims of the current research were to (1) provide
the demographic characteristics of young adults; (2) examine the self-assessed health of young
adults; (3) identify social determinants that explained the good health status for young adults; (4)
determine the magnitude of each social determinant, and (5) determine gender differences in
self-assessed health. The current study extracted a sub-sample of 3,024 respondents from a larger
nationally cross-sectional survey of 6,782 Jamaicans. Statistical analyses were performed using
the Statistical Packages for the Social Sciences v 16.0. Descriptive statistics were used to provide
demographic information on the samples. Chi-square was used to examine the association
between non-metric variables, and an Analysis of Variance was used to test the relationships
between metric and non-dichotomous categorical variables. Logistic regression examined the
relationship between the dependent variable and some predisposed independent variables. One
percent of the sample reported injury and 8% reported illness. Self-reported diagnosed illnesses
were influenza (12.7%); diarrhoea (2.9%); respiratory disease (14.1%); diabetes mellitus (7.8%);
hypertension (7.8%); arthritis (2.9%) and unspecified conditions (41.2%). The mean length of
illness was 26.0 days (SD = 98.9. Nine social determinants and biological condition explained
19.2% of the variability of self-assessed health. The biological condition accounted for 78.1% of
the explanatory model. Injury accounts for a miniscule percentage of illness and so using it to
formulate intervention policies would lack depth to effectively address the health of this cohort.



Introduction

Gender differences in self-assessed health in young adults (i.e. ages 15 – 44 years) are under-

studied in the English-speaking Caribbean. Previous studies that have examined young adults

have focused on reproductive health; survivability; teenage pregnancy; substance use and abuse;

HIV/AIDS; injuries and the impact of injuries on health [1-7]. While studies on injuries have



                                               405
shown that young males 15 to 44 years are mostly affected by violent-injuries [6, 7], in Jamaica

statistics [8] have revealed that many of the deaths which occurred in this age group could be

accounted for by injuries. Injuries are among the reasons for ill-health and by extension do not

constitute a significant percentage of illness. Injuries accounted for most morbidities and/or

mortalities in the world [7], but this is not typical to Jamaica and undertaking studies on injuries

are germane but lack extensive coverage on health. Statistics on Jamaica showed that of the 10

leading causes of mortalities, in 2002 [8-10], were homicides and injuries, which were the 5th

and 10th causes of deaths respectively [10]. In 2004, statistics from the World Health

Organization (WHO) showed that injuries were the 4th leading cause of morality in Jamaica [11]

and in 2006, statistics from the Jamaican Ministry of Health [9] indicated that injuries were not

among the 5 leading cases of hospitalisation in Jamaica.


       Therefore, in Jamaica, policies would not have been formulated using general health

status research, but more so from data on injuries, reproductive health, survivability and

mortalities. Policy interventions on those issues are pertinent and cannot be neglected from the

general pursuit of health. Using general health status and health conditions would provide

invaluable insights from the individual’s perspective on those issues; which would add value to

addressing health concerns, for particular outcomes such as pregnancies, mortality, injuries or

crime, violence and victimization by young adults. A study by Hambleton et al. [12] identified

that illness constituted a significant percentage of the explanatory power of the self-assessed

health of older Barbadians (ages 60+ years) and while this provides some understanding on the

role of illness on the general health status which may be caused by injuries, the research

identified other factors (i.e. social determinants) that played roles in health status determination.




                                                 406
       Injuries therefore, do account for a percentage of ill-health, indicating that a study of their

typologies is imperative, but this cannot abate or replace a study on the general health of young

adults. An extensive revelation of health literature in the English-speaking Caribbean nations

found a lack of studies on the general health status of young adults. Empirical literature showed

that   any   study    on       health   must   coalesce    biological   and    social   determinants

[13],[14],[15],[16],[17],[18],[19],[20],[21],[22],[23],[24],[25], which is also lacking for young

adults. Recently, a study by Bourne [26] provided invaluable insights into the typology of health

conditions and the demographic shifts in these between 2002 and 2007. [Table/Fig 1], [Table/Fig

2] and [Table/Fig 3] highlight hospital utilisation for gunshot wounds and suicides and the victim

prolife of individuals in Jamaica in 2005. The data highlights the crime and hospital utilisation

profile, which indicates that health care utilisation and victims of crimes are substantially

between 14 and 45 years. The age group of 15 – 45 years does not only represent most of the

victims of crime, mortality and hospital utilization in Jamaica, but it also denotes the group

which constitutes arrest for major crimes [Table/Fig 4]. Some of the issues are social and do

affect mortality, but of importance are the persons of this group who are alive and fear being a

victim of violence, as well as those who reside in those communities in which such incidences

are perpetuated each day. In addition, their general health is also of concern, as well as those

members of this age group who are not likely victims owing to other social conditions such as

social hierarchy, area of residence or those who do not reside in inner-city communities. It is

within this context that the current study chose to examine the self-reported health of this group

in order to provide insights into the health of young adults and the social determinants that

explain their health status.




                                                407
       The aims of the current research are to (1) provide the demographic characteristics of

young adults; (2) examine       the self-assessed health of young adults; (3) identify social

determinants that explain the good health status for young adults; (4) determine the magnitude of

each social determinant, and (5) determine the gender differences in self-assessed health.


Materials and Methods

The Jamaica Survey of Living Conditions (JSLC) was commissioned by the Planning Institute of

Jamaica (PIOJ) and the Statistical Institute of Jamaica (STATIN) in 1988 [27]. These two

organizations are responsible for planning, data collection and policy guidelines for Jamaica and

have been conducting the JSLC annually since 1989 [28]. The JSLC is an administered

questionnaire where respondents are asked to recall detailed information on particular activities.

The questionnaire was modelled from the World Bank’s Living Standards Measurement Study

(LSMS) household survey [28]. There are some modifications to the LSMS, as JSLC is more

focused on policy impacts.         The questionnaire covers demographic variables, health,

immunization of children 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 the household members. The survey is conducted between

April and July annually. The current study extracted a sub-sample of 3,024 respondents (i.e.

ages15 - 44 years) from a larger nationally cross-sectional survey of 6,782 Jamaicans. This

study used the dataset of the JSLC for 2007 [29].


Measures


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

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

                                               408
4 weeks, which was the survey period; 0 (poor health) if there were no self-reported ailments,

injuries or illnesses. While self-reported ill-health is not an ideal indicator of actual health

conditions because people may underreport, it is still an accurate proxy of ill-health and

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

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

who visit one’s home or with whom one is able to network, 0 = otherwise). Psychological

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

negative affective psychological conditions. Positive affective psychological condition is the

number of responses with regard to being hopeful, optimistic about the future and life generally.

Negative affective psychological condition is number of responses from a person on having lost

a breadwinner and/or family member, having lost property, being made redundant or failing to

meet household and other obligations. Health status is a binary measure (1=good to excellent

health; 0= otherwise) which is determined from “Generally, how do you feel about your health”?

Answers for this question are in a Likert scale matter ranging from excellent to poor. Health

care-seeking behaviour is derived from the question: Have you visited a health care practitioner,

pharmacist or healer in the past four 4 weeks, with an option of yes or no. For the purpose of the

regression the responses were coded as 1=yes, 0=otherwise. Crowding is the total number of

individuals in the household divided by the number of rooms (excluding kitchen, verandah and

bathroom). Age is a continuous variable in years.


Statistical analysis


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

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



                                              409
deviation (SD), frequency and percentage were used to analyze the socio-demographic

characteristics of the sample. Chi-square was used to examine the association between non-

metric variables and an Analysis of Variance (ANOVA) was used to test the relationships

between metric and non-dichotomous categorical variables. Logistic regression examined the

relationship between the dependent variable and some predisposed independent (explanatory)

variables, because the dependent variable was a binary one (self-reported health status: 1 if

reported good health status and 0 if reported poor health status). The final model was based on

those variables that were statistically significant (p <0.05), and all other variables were removed

from the final model (p >0.05). Categorical variables were coded using the ‘dummy coding’

scheme or a reference category.


        The predictive power of the model was tested by using the ‘omnibus test of model’ and

Hosmer and Lemeshow’s [30] 3 technique was used to examine the model’s goodness of fit. The

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

collinearity) existed between the variables. Cohen and Holliday [31] stated that the correlation

can be low/weak (0–0.39); moderate (0.4–0.69), or strong (0.7–1). This was used in the present

study to exclude (or allow) a variable. Finally, the forward stepwise technique in logistic

regression was used to determine the magnitude (or contribution) of each statistically significant

variable in comparison with the others, and the odds ratio (OR) for interpreting each of the

significant variables.


Model


To study the relationship between self-assessed health status, social determinants, biological

conditions and welfare. Logistic regression was used to estimate the following regression model.

                                               410
The equation [1] described below, denotes the 20 social, SDHij, 3 welfare variables, Wij, and

biological condition, B i , of self-assessed health status (Hi ) and some standard error:



                                                     ,                                      [1]




Table 16.6 presents the results from the econometric exercise, which is captured in Equation [2].

The equation [2] therefore presents only those variables which are significantly correlated with

self-assessed health status of young adults:



                                                     ,                             [2]

       where:

       Hi       is the level of self-assessed health status of person i.

       SDH ij denotes the 9 statistically significant social determinants of person i.

Results

The sample was 3,024 respondents: 47.6% males and 52.4% males. The mean age of the sample

was 28.5 years (SD = 8.8 years). Thirty percent of the sample was single; 20.4% was common-

law; 13% was married; and 27.1% was in visiting unions. Thirty-six and three-tenth percent of

the sample was poor, with 17.1% in the poorest,20%, as compared to 44.1% in the wealthy social

hierarchies, of which 23.2% was in the wealthiest 20%. Forty-five and nine tenth percent of the

sample dwelt in rural areas, 22% in peri-urban areas and 32.1% in urban areas. Of the sample

population, with respect to the questions on injury and illness, 97.1% and 97% responded

respectively. Of those respondents, 1% claimed injury and 8% mentioned illness. When the

respondents were asked whether the illness was diagnosed and the typologies of the conditions,

                                                  411
100% stated that their health condition was diagnosed by a medical practitioner. The self-

reported diagnosed health conditions were influenza (12.7%); diarrhoea (2.9%); respiratory

disease (14.1%); diabetes mellitus (7.8%); hypertension (7.8%); arthritis (2.9%) and unspecified

conditions (41.2%). The mean length of illness was 26.0 days (SD = 98.9), with 1 visit made to a

health care practitioner in the last 4-weeks. When the respondents were asked if they had visited

a health care practitioner (including healer, pharmacist, nurse, and wife) in the last 4-weeks,

64.2% said yes. The health care institutions were public hospitals (34.8%); private hospitals

(7.0%); public health care centres (14%); and private health care centres (51.6%). Twenty

percent of the sample had health insurance coverage; 89.6% claimed at least good health

(including 42.2% very good self-assessed health) as compared to 1.9% who stated poor health

(including 0.3% very poor health).

        A cross-tabulation of health care-seeking behaviour and illness shows no significant

statistical association. Ninety-seven percent of those who sought medical care were ill as

compared to 94% of those who sought medical care in the last 4-weeks.

        A cross-tabulation between illness and age group revealed a significant statistical

association – χ2 = 39.4, P < 0.0001. [Table/Fig 16.1] provides the information on the age group

and the percentage of young adults who indicated that they had an illness in the last 4-weeks.

        No significant statistical association was found between the health care-seeking

population and their age group (P = 0.608): age 15 – 19 years, 60%; age 20 – 24 years, 53.1%;

age 25 – 29, 60.0%; age 30 – 34 years, 67.7%; age 35 – 39 years, 68.0% and age 40 – 44 years,

69.%.




                                               412
       No significant statistical relationship was found between health care-seeking behaviour

and social hierarchy (P = 0.339): poorest 20%, 51.1%; poor, 69.2%; middle class, 67.4%;

wealthy, 65.5%, and wealthiest 20%, 67.7%.

       There was a statistical difference between the age of the respondents who reported to be

having particular health conditions – F-test = 4.5, P < 0.001. The mean ages of the people having

particular health conditions were influenza,-29.3 years (SD = 9.2); diarrhoea- 32.2 years (SD =

8.7); respiratory illnesses- 30.3 years (SD = 9.6); diabetes mellitus- 37.3 years (SD = 5.9);

hypertension- 36.8 years (SD = 7.1) and others- 29.9 years (SD = 9.3).



       Fig 16.2 highlights the (%) of young adults who reported injury and illness, who dwelt

in a particular area of residence which was controlled for the sex of the respondents.

Table/Figure 16.2 showed that over 50% of those with illness and injury dwelt in rural areas.

However, there was no significant statistical relationship when illness and injury by the area of

residence was controlled by the sex of the respondents (illness – male χ2 = 2.6, P < 0.271 and

female χ2 = 2.3, P < 0.323; injury – male χ2 = 2.5, P < 0.292 and female χ2 = 0.93, P < 0.628).

       Fig 16.3 shows the sex composition of those people who utilised health care facilities in

Jamaica. Most young adult males utilised private hospitals (36.4%) as compared to females who

visited public health care centres (72.7%). The least percentage of females visited private

hospitals (63.6%) as compared to public health care centres for males (27.3%).




Multivariate analysis

Tables 6 representS the results from the econometric exercise: Of the 24 variables that were

tested in an initial model, 9 were social determinants and 1 was a biological variable. The


                                               413
biological variable (i.e. self-reported illness) accounted for 78.1% of the explanatory power of

the model (i.e. 15.3%), thus indicating that social determinants accounted for 21.9% of the self-

assessed health status of young adults.

Limitations of study

Health is a function of social, psychological, economical, biological and ecological factors.

Based on the multi-dimensional nature of health determinants, the present study used secondary

survey data and variables such as psychological, ecological and some social issues; issues such

as childhood health history, culture, belief and value system were omitted from the model. Those

omissions reduced the explanatory power of the current study, but provided a platform on which

future studies could be launched.



Discussion

In the present study, the prevalence of injury in Jamaica for young adults was 1% as compared to

8% for illness. A cross-tabulation between self-reported injury and self-reported illness showed a

significant statistical relationship. The association was a very weak one and the correlation

coefficient was 0.12 (or 12%). Forty-one of every 100 young adults who reported having an

injury, stated that they had an illness in the last four-weeks, indicating that less than one- half

percent of those with an injury had an illness. Concurrently, 2 times more young adult-females

sought medical care more than males. On the other hand, males were 2.3 times likely to record

injury, while females were 2 times more likely to have an illness in the last 4-weeks.

Furthermore, the odds ratio of recording better good self-assessed health status for males was 1.5

times more than that of females. Ignoring the gender differences in self-assessed health status,

medical care-seeking behaviour and injuries, the odds ratio of recording good health in married


                                               414
young adults was 1.6 times more than their single counterparts and this was similar for peri-

urban respondents with reference to rural young adults. On the other hand, a young adult who

sought medical care was 65% less likely to record good health; young adults with tertiary level

education were 47% more likely to record good health and those who spent more on medical

care (i.e. medical care-expenditure) were 1% less likely to have good self-assessed health status.



          Empirically, research has established that any investigation of health must coalesce

social,         psychological,         economical           and         biological         variables.

[12],[13],[14],[15],[16],[17],[18],[19],[20],[21],[22],[23][24],[25], [32],[33],[34],[35],[36],[37].

Hambleton et al. [12] went further when he disaggregated the contribution of biological and non-

medical conditions of self-assessed health status. They found that 87.7% of the explanatory

power of good health status of elderly Barbadians could be accounted for by their current illness.

The present study found that current illness accounted for 78.1%, which suggested that illness

accounted for less of the young adults’ health status than for elderly people. One of the

challenges in effectively comparing the aforementioned issues (which is embedded in the data),

is that the perception of people across different nations are not the same and this, as well as the

age component, could account for some aspects of the disparity. The present study has not only

highlighted the role that social determinants play in health status, but also that they play a greater

role in the health of younger adults than older people. Statistics seemingly shows that a large

percentage of young adults are victims of injuries, but the current findings indicate that these

represent only a small part of the ill-health of young adults. The small percentage of injuries

which are experienced by young adults denote that using injuries as a guide in health policy

intervention would be addressing an even smaller percentage of health status than illnesses. The



                                                415
aforementioned results       show that illness contributes more to health status than social

determinants, along with injuries. It is clear that despite the cultural and biological differences

rooted in both figures, current illness is a strong determinant of self-assessed health status in each

region and if health must combine social, biological, psychological and ecological determinants,

public health interventions that are using any one determinant in particular injuries would not be

addressing the health concerns of young adults. This empirical evidence concretizes the rationale

for social determinants in the discussion and research on health status as well as ill-health.



       The findings of the present study showed that the social determinants of young adults

constituted more explanation than for the elderly. Therefore, the usage of injuries and/or illness

to measure and guide public intervention denotes that1 in 5 of the health status of young adults

would have been unaddressed in this effort and as much as 9 out of 10 of injury statistics are

used in public policy interventions. Current social determinants of health for elderly Barbadians

accounted for 4.1% of health and historical determinants, suggesting the increased role of

biological determinants in the health process with ageing. Historical determinants which

included education, occupation, children, economic situation, childhood nutrition, childhood

health and diseases theoretically, is not a part of social determinants. Disaggregating social

determinants to ascertain a value for historical determinants to compare with Hambleton et al.’s

finding in this study, found that education was the only factor of those identified in the

Barbadian health status, and that education accounted for only 0.3% of the explanatory model in

this study. Therefore, within the limitations of the current study, meaningful comparisons using

disaggregated social determinants would be close to impossible, as the components are not

necessarily the same.



                                                416
       Inspite of the limitations of the current work, the study can effectively compare self-

assessed health status, as both studies collected this data from their population. The current study

which used the data of 2007 and Hambleton et al’s work which used data from December 1999

to June 2000, showed that the health of young adults was between 1.5 to 1.9 times more than that

of the elderly Barbadians. Although there are time differences which cannot be discounted for in

this study, there is emerging information about the reduction of health status with ageing. Ageing

is a nature event. Imagine purchasing a new car, taking this car home and locking it away in the

car porch under cover for 20 years; and on removing the covers, although the item was not used,

it would have aged.    Using the car, however, increases the deterioration or depreciation on the

human structure and therefore it accounts for illnesses, health care utilisation and lowered health

status. The issue of the car symbolizes the natural ageing and progressive depleted state of things

and this is similar in the case for humans. The current study revealed that as young people age,

the odds ratio (OR = 0.97) of indicating good health falls by 3% and using the aforementioned

statistics would mean that the odds ratio of good health for elderly people should fall. A study by

Bourne, McGrowder and Crawford [38] showed that the illnesses affecting elderly Jamaicans

was more chronic than acute as compared to the converse in this study. With the changes in the

typology of illnesses from acute to chronic conditions, the elderly’s health status must be lower

than that of young adults. Hence, although homicides accounted for more deaths of young adults

that elderly people, the health status of the former is still greater and this is due largely to the

lower risk of biological conditions. Again, the biology of an individual accounts for a greater

percentage of self-assessed health than external factors such as injuries from accidents. Injuries

from accidents affect 1 in every 100 young adults, making it’s effect on health status smaller



                                               417
than on self-reported illnesses which accounts for 8 in every 100 young adults.With biological

conditions accounting for more of the self-assessed health of older people, this supports lower

health status than young adults and greater health care participation for the former, as they seek

to address the ageing of the organism and the increased depreciation owing to old age.



       Gompertz’s law in Gavriolov and Gavrilova [39] shows that there is a fundamental

quantitative theory of ageing and mortality of certain species (the examples here are as follows –

humans, human lice, mice, fruit flies, and flour beetles). Gompertz’s law went further to

establish that human mortality increases twofold with every 8 years of an adult life, which means

that ageing increases in geometric progression. This phenomenon means that human mortality

increases with the age of the human adult, but that this becomes less progressive in advanced

ageing. Thus, biological ageing is a process where the human cells degenerate with years (i.e.

the cells die with increase in age), which is explored in evolutionary biology [40],[41],[42],[43].

But, studies have shown that using evolutionary theory for “late-life mortality plateaus”, fail

because of the arguable unrealistic sets of assumptions that the theory uses to establish itself

[44],[45],[46].



       Ageing therefore, denotes gradual deterioration in living organisms as well other non-

living items, which accounts for a demand in medical care. Medical seeking-behaviour could

indicate either preventative or curative care. The present study revealed that the odds ratio of the

good health of young adults in Jamaica declined by 65% for those who sought medical care.

Medical care for young adults therefore, is a good measure of curative than preventative care.

This also speaks of the cultural impact on health, through people’s conceptual perceptions of



                                               418
health; that is illness and so, care is sought for ill-health as against preventative care. The current

work revealed that 94 out of every 100 young adults who sought medical care were ill;

reinforcing the cultural perception of illness and the reason why young adults sought health-

care, is curative than preventative for this group.



       Illness in the current work is substantially a female phenomenon. Young adult females

were 2 times more likely to report an illness and this justified their greater probability to utilize

medical care seeking in order to address ill-health. These findings have a high degree of validity,

as statistics from the Ministry of Health (Jamaica) showed that females attended health care

institutions twice as much as men for curative care since 2000-2007 [9]. Since 1988, statistics

obtained from Jamaicans in national cross-sectional surveys revealed that females were

approximately more likely to report an illness and utilize medical care than males. This

reinforced the cultural biasness of illness and health care facilities. Health care facilities were

primarily governed by females for females and this added to the cultural handicap of males

attending public health care institutions on experiencing ill-health. The feminization of health

care facilities and the large percentage of people, in particular, females who visited public health

care institutions was another rationale for the use of private health care facilities by males. Males

on the other hand, would attend medical care facilities when ill-health would interface with their

economic livelihood and the severity was such that this was the only avenue. This is not typical

to Jamaica, as a qualitative study in Pakistan on street children, found that boys would attend

formal health care if it affected their economic livelihood and is their health conditions were

severe [47]. Another study conducted in Anyigba, North-Central, Nigeria, found that [48] found

that 85 out of every 100 respondents waited for less than a week after the onset of illness to seek



                                                 419
medical care and that 57 out of every 100 indicated that they would recover without treatment.



       A Caribbean anthropologist [49] stated that the macho socialisation of the Caribbean

male accounts for his unwillingness to seek medical care.             Caribbean males, including

Jamaicans, are socialised to be strong, do not show weakness and are involved in particular tasks

to exhibit their masculinity.   As a result, illness is considered to be a signal of weakness,

therefore accounting for the reasons why men are skeptical to visit medical institutions and often

wait   till the disease becomes severe.      It is sometimes so difficult for traditional medical

practioners to offer cure for such cases. This then offers an explanation for females living

longing than males. Although the current findings showed that the odds of recording good health

is 1.5 times greater for young adult males, this     owes to the reality that often   males do not

see themselves as ill, they visit medical practitioners less and justify the higher mortality among

them than females. The social determinants         of health, therefore, offer explanation for more

than biological issues. This means that health interventions geared toward improve the health of

young adults, in particular males, must extend beyond illness and severity of symptoms, which

concurs with a previous study by Williams et al. [50]. Unlike this study, Williams et al. [50]

found that medical care-seeking behaviour did not differ significantly between the sexes.        In

agreement with this study, Dunlop et al [51] found that African American men had few physician

contacts than non-Hispanic white women. The irresponsiveness of young adult males in seeking

health care is comparable to their female counterparts in Jamaica and this extends to even older

African American men.



       With the advancement in literacy and numeracy in the world since the 19th century,



                                               420
specifically among Jamaicans since 1960 (i.e. educational levels), empirical findings showed that

education was among the social determinants that influenced the health status [12-26]. Education

affects health directly and indirectly. A study on twins in USA found that more years in

schooling (i.e. education) was associated with healthier patterns of behaviour. [52], which is an

example of the direct impact of education on health.       Fujiwara’s and Kawachi’s [52] work on

increased schooling was associated with the reducing of the smoking habit and other such

unhealthy practices. The current study concurs with the literature as the odds ratio of the good

health status of young adults with tertiary level education are 1.5 times more than those with

primary or    a low education status. The indirect way that education affects health can be

measured by using social hierarchy. The present findings revealed that the middle class who are

the educated ones were 1.5 times more likely to report good health status and that wealth or

income was not correlated with good health status, or for that matter, the self-assessed health

status of wealthy social hierarchies did not differ from those in the poor social hierarchies.



       Empirical evidence existed that among the social determinants of health, is marital status.

Some research showed that married people are healthier than non-married people

[12],[13],[14],[15],[16],[17],[18],[19],[20],[21],[22],[23],[24],[25],[53],[54],[55],[56],[57],[58].

Koo, Rie and Park’s [54] findings revealed that being married was a ‘good’ cause for an increase

in psychological and subjective wellbeing in old age. Smith and Waitzman [55] offered the

explanation that wives could dissuade their husbands from particularly risky behaviours such as

the use of alcohol and drugs and could ensure that they maintain a strict medical regimen

coupled with proper eating habits [53], [56]. In an effort to contextualize the psychosocial and

biomedical health status of a particular marital status, one demography cited that the death of a



                                                421
spouse meant a closure to daily communication and shared activities, which sometimes translate

into depression that affect the wellbeing, more of the elderly who would have had investment

must in a partner [57]. They pointed to a paradox within this discourse as “…this is not observed

among men”. To provide a holistic base to the argument, the researcher would quote a sentence

from the findings of Delbés and Gaymu [57] study that reads “The widowed have a less positive

attitude towards life than married people, which is not an unexpected result” [57]. The present

study concurs with the literature that the health status of married young adults is greater than

those who are single, but that this was only explained by females. Those findings highlight the

value of marriage to females which commences at an early age and seemingly that the benefits of

marriage are not for males. This is clearly not the case as studied by Bourne [58], who by using

data on Jamaicans, found that the odds ratio of reported good health was 1.6 times more for

married males than their female counterparts.



Conclusion and policy recommendations

To sum up, the statistics for 2007 revealed that one in every two Jamaicans was 15-44 years old.

This speaks about the importance of a research on this age group. With the demographic reality

of young adults in the country, using injury to examine health is grossly inadequate, narrow and

fails to understand the matter of health. Health is more that illness as it incorporates social,

economic, psychological, ecological and biological determinants of health and merely the

absence of illness. While the biological determinants of the self-assessed health of young adults

predominate health determinants, injury accounts for a miniscule percentage of illness and so,

using injury to formulate intervention policies would be lacking in depth, to effectively address

the health of this cohort. Although the health of young adult Jamaicans is very good, there are



                                                422
many health disparities between the sexes, which are justifying inequities in the health outcomes

between males and females.

       The present study highlights some of the health disparities between the sexes and affords

research findings that can be used to refashion health policies and research focus in the future.

Health policies must utilize the wide spectrum of health determinants in order to address the

multi-dimensional nature of health. The use of injuries to measure and guide policies and

programmes because seemingly there are many young adults who are affected, is a misnomer

and does not capture the gamut of illness or even the health of this group of people.

       The identified health disparities are among the reasons for health inequities in health

outcome and should justify a call for a research and policy directions, which include

avoidabilities such as technical, financial and moral, as these would provide additional

explanations for health disparities, choices, inequity and/or inequalities in the health outcomes

among young adults.

       In the present study, the prevalence of injury in Jamaica for young adults was 1% as

compared to 8% for illness. A cross-tabulation between self-reported injury and self-reported

illness showed a significant statistical relationship. The association was a very weak one and the

correlation coefficient was 0.12 (or 12%). Forty-one of every 100 young adults who reported

having an injury, stated that they had an illness in the last four-weeks, indicating that less than

one- half percent of those with an injury had an illness. Concurrently, 2 times more young adult-

females sought medical care more than males. On the other hand, males were 2.3 times likely to

record injury, while females were 2 times more likely to have an illness in the last 4-weeks.

Furthermore, the odds ratio of recording better good self-assessed health status for males was 1.5

times more than that of females. Ignoring the gender differences in self-assessed health status,


                                               423
medical care-seeking behaviour and injuries, the odds ratio of recording good health in married

young adults was 1.6 times more than their single counterparts and this was similar for peri-

urban respondents with reference to rural young adults. On the other hand, a young adult who

sought medical care was 65% less likely to record good health; young adults with tertiary level

education were 47% more likely to record good health and those who spent more on medical

care (i.e. medical care-expenditure) were 1% less likely to have good self-assessed health status.



          Empirically, research has established that any investigation of health must coalesce

social,         psychological,         economical           and         biological         variables.

[12],[13],[14],[15],[16],[17],[18],[19],[20],[21],[22],[23][24],[25], [32],[33],[34],[35],[36],[37].

Hambleton et al. [12] went further when he disaggregated the contribution of biological and non-

medical conditions of self-assessed health status. They found that 87.7% of the explanatory

power of good health status of elderly Barbadians could be accounted for by their current illness.

The present study found that current illness accounted for 78.1%, which suggested that illness

accounted for less of the young adults’ health status than for elderly people. One of the

challenges in effectively comparing the aforementioned issues (which is embedded in the data),

is that the perception of people across different nations are not the same and this, as well as the

age component, could account for some aspects of the disparity. The present study has not only

highlighted the role that social determinants play in health status, but also that they play a greater

role in the health of younger adults than older people. Statistics seemingly shows that a large

percentage of young adults are victims of injuries, but the current findings indicate that these

represent only a small part of the ill-health of young adults. The small percentage of injuries

which are experienced by young adults denote that using injuries as a guide in health policy



                                                424
intervention would be addressing an even smaller percentage of health status than illnesses. The

aforementioned results       show that illness contributes more to health status than social

determinants, along with injuries. It is clear that despite the cultural and biological differences

rooted in both figures, current illness is a strong determinant of self-assessed health status in each

region and if health must combine social, biological, psychological and ecological determinants,

public health interventions that are using any one determinant in particular injuries would not be

addressing the health concerns of young adults. This empirical evidence concretizes the rationale

for social determinants in the discussion and research on health status as well as ill-health.



       The findings of the present study showed that the social determinants of young adults

constituted more explanation than for the elderly. Therefore, the usage of injuries and/or illness

to measure and guide public intervention denotes that1 in 5 of the health status of young adults

would have been unaddressed in this effort and as much as 9 out of 10 of injury statistics are

used in public policy interventions. Current social determinants of health for elderly Barbadians

accounted for 4.1% of health and historical determinants, suggesting the increased role of

biological determinants in the health process with ageing. Historical determinants which

included education, occupation, children, economic situation, childhood nutrition, childhood

health and diseases theoretically, is not a part of social determinants. Disaggregating social

determinants to ascertain a value for historical determinants to compare with Hambleton et al.’s

finding in this study, found that education was the only factor of those identified in the

Barbadian health status, and that education accounted for only 0.3% of the explanatory model in

this study. Therefore, within the limitations of the current study, meaningful comparisons using




                                                425
disaggregated social determinants would be close to impossible, as the components are not

necessarily the same.



       Inspite of the limitations of the current work, the study can effectively compare self-

assessed health status, as both studies collected this data from their population. The current study

which used the data of 2007 and Hambleton et al’s work which used data from December 1999

to June 2000, showed that the health of young adults was between 1.5 to 1.9 times more than that

of the elderly Barbadians. Although there are time differences which cannot be discounted for in

this study, there is emerging information about the reduction of health status with ageing. Ageing

is a nature event. Imagine purchasing a new car, taking this car home and locking it away in the

car porch under cover for 20 years; and on removing the covers, although the item was not used,

it would have aged.     Using the car, however, increases the deterioration or depreciation on the

human structure and therefore it accounts for illnesses, health care utilisation and lowered health

status. The issue of the car symbolizes the natural ageing and progressive depleted state of things

and this is similar in the case for humans. The current study revealed that as young people age,

the odds ratio (OR = 0.97) of indicating good health falls by 3% and using the aforementioned

statistics would mean that the odds ratio of good health for elderly people should fall. A study by

Bourne, McGrowder and Crawford [38] showed that the illnesses affecting elderly Jamaicans

was more chronic than acute as compared to the converse in this study. With the changes in the

typology of illnesses from acute to chronic conditions, the elderly’s health status must be lower

than that of young adults. Hence, although homicides accounted for more deaths of young adults

that elderly people, the health status of the former is still greater and this is due largely to the

lower risk of biological conditions. Again, the biology of an individual accounts for a greater



                                               426
percentage of self-assessed health than external factors such as injuries from accidents. Injuries

from accidents affect 1 in every 100 young adults, making it’s effect on health status smaller

than on self-reported illnesses which accounts for 8 in every 100 young adults.With biological

conditions accounting for more of the self-assessed health of older people, this supports lower

health status than young adults and greater health care participation for the former, as they seek

to address the ageing of the organism and the increased depreciation owing to old age.



       Gompertz’s law in Gavriolov and Gavrilova [39] shows that there is a fundamental

quantitative theory of ageing and mortality of certain species (the examples here are as follows –

humans, human lice, mice, fruit flies, and flour beetles). Gompertz’s law went further to

establish that human mortality increases twofold with every 8 years of an adult life, which means

that ageing increases in geometric progression. This phenomenon means that human mortality

increases with the age of the human adult, but that this becomes less progressive in advanced

ageing. Thus, biological ageing is a process where the human cells degenerate with years (i.e.

the cells die with increase in age), which is explored in evolutionary biology [40],[41],[42],[43].

But, studies have shown that using evolutionary theory for “late-life mortality plateaus”, fail

because of the arguable unrealistic sets of assumptions that the theory uses to establish itself

[44],[45],[46].



       Ageing therefore, denotes gradual deterioration in living organisms as well other non-

living items, which accounts for a demand in medical care. Medical seeking-behaviour could

indicate either preventative or curative care. The present study revealed that the odds ratio of the

good health of young adults in Jamaica declined by 65% for those who sought medical care.



                                               427
Medical care for young adults therefore, is a good measure of curative than preventative care.

This also speaks of the cultural impact on health, through people’s conceptual perceptions of

health; that is illness and so, care is sought for ill-health as against preventative care. The current

work revealed that 94 out of every 100 young adults who sought medical care were ill;

reinforcing the cultural perception of illness and the reason why young adults sought health-

care, is curative than preventative for this group.



       Illness in the current work is substantially a female phenomenon. Young adult females

were 2 times more likely to report an illness and this justified their greater probability to utilize

medical care seeking in order to address ill-health. These findings have a high degree of validity,

as statistics from the Ministry of Health (Jamaica) showed that females attended health care

institutions twice as much as men for curative care since 2000-2007 [9]. Since 1988, statistics

obtained from Jamaicans in national cross-sectional surveys revealed that females were

approximately more likely to report an illness and utilize medical care than males. This

reinforced the cultural biasness of illness and health care facilities. Health care facilities were

primarily governed by females for females and this added to the cultural handicap of males

attending public health care institutions on experiencing ill-health. The feminization of health

care facilities and the large percentage of people, in particular, females who visited public health

care institutions was another rationale for the use of private health care facilities by males. Males

on the other hand, would attend medical care facilities when ill-health would interface with their

economic livelihood and the severity was such that this was the only avenue. This is not typical

to Jamaica, as a qualitative study in Pakistan on street children, found that boys would attend

formal health care if it affected their economic livelihood and is their health conditions were



                                                 428
severe [47]. Another study conducted in Anyigba, North-Central, Nigeria, found that [48] found

that 85 out of every 100 respondents waited for less than a week after the onset of illness to seek

medical care and that 57 out of every 100 indicated that they would recover without treatment.



       A Caribbean anthropologist [49] stated that the macho socialisation of the Caribbean

male accounts for his unwillingness to seek medical care.             Caribbean males, including

Jamaicans, are socialised to be strong, do not show weakness and are involved in particular tasks

to exhibit their masculinity.   As a result, illness is considered to be a signal of weakness,

therefore accounting for the reasons why men are skeptical to visit medical institutions and often

wait   till the disease becomes severe.      It is sometimes so difficult for traditional medical

practioners to offer cure for such cases. This then offers an explanation for females living

longing than males. Although the current findings showed that the odds of recording good health

is 1.5 times greater for young adult males, this     owes to the reality that often   males do not

see themselves as ill, they visit medical practitioners less and justify the higher mortality among

them than females. The social determinants         of health, therefore, offer explanation for more

than biological issues. This means that health interventions geared toward improve the health of

young adults, in particular males, must extend beyond illness and severity of symptoms, which

concurs with a previous study by Williams et al. [50]. Unlike this study, Williams et al. [50]

found that medical care-seeking behaviour did not differ significantly between the sexes.        In

agreement with this study, Dunlop et al [51] found that African American men had few physician

contacts than non-Hispanic white women. The irresponsiveness of young adult males in seeking

health care is comparable to their female counterparts in Jamaica and this extends to even older

African American men.



                                               429
       With the advancement in literacy and numeracy in the world since the 19th century,

specifically among Jamaicans since 1960 (i.e. educational levels), empirical findings showed that

education was among the social determinants that influenced the health status [12-26]. Education

affects health directly and indirectly. A study on twins in USA found that more years in

schooling (i.e. education) was associated with healthier patterns of behaviour. [52], which is an

example of the direct impact of education on health.       Fujiwara’s and Kawachi’s [52] work on

increased schooling was associated with the reducing of the smoking habit and other such

unhealthy practices. The current study concurs with the literature as the odds ratio of the good

health status of young adults with tertiary level education are 1.5 times more than those with

primary or    a low education status. The indirect way that education affects health can be

measured by using social hierarchy. The present findings revealed that the middle class who are

the educated ones were 1.5 times more likely to report good health status and that wealth or

income was not correlated with good health status, or for that matter, the self-assessed health

status of wealthy social hierarchies did not differ from those in the poor social hierarchies.



       Empirical evidence existed that among the social determinants of health, is marital status.

Some research showed that married people are healthier than non-married people

[12],[13],[14],[15],[16],[17],[18],[19],[20],[21],[22],[23],[24],[25],[53],[54],[55],[56],[57],[58].

Koo, Rie and Park’s [54] findings revealed that being married was a ‘good’ cause for an increase

in psychological and subjective wellbeing in old age. Smith and Waitzman [55] offered the

explanation that wives could dissuade their husbands from particularly risky behaviours such as

the use of alcohol and drugs and could ensure that they maintain a strict medical regimen



                                                430
coupled with proper eating habits [53], [56]. In an effort to contextualize the psychosocial and

biomedical health status of a particular marital status, one demography cited that the death of a

spouse meant a closure to daily communication and shared activities, which sometimes translate

into depression that affect the wellbeing, more of the elderly who would have had investment

must in a partner [57]. They pointed to a paradox within this discourse as “…this is not observed

among men”. To provide a holistic base to the argument, the researcher would quote a sentence

from the findings of Delbés and Gaymu [57] study that reads “The widowed have a less positive

attitude towards life than married people, which is not an unexpected result” [57]. The present

study concurs with the literature that the health status of married young adults is greater than

those who are single, but that this was only explained by females. Those findings highlight the

value of marriage to females which commences at an early age and seemingly that the benefits of

marriage are not for males. This is clearly not the case as studied by Bourne [58], who by using

data on Jamaicans, found that the odds ratio of reported good health was 1.6 times more for

married males than their female counterparts.



Conclusion and policy recommendations

To sum up, the statistics for 2007 revealed that one in every two Jamaicans was 15-44 years old.

This speaks about the importance of a research on this age group. With the demographic reality

of young adults in the country, using injury to examine health is grossly inadequate, narrow and

fails to understand the matter of health. Health is more that illness as it incorporates social,

economic, psychological, ecological and biological determinants of health and merely the

absence of illness. While the biological determinants of the self-assessed health of young adults

predominate health determinants, injury accounts for a miniscule percentage of illness and so,



                                                431
using injury to formulate intervention policies would be lacking in depth, to effectively address

the health of this cohort. Although the health of young adult Jamaicans is very good, there are

many health disparities between the sexes, which are justifying inequities in the health outcomes

between males and females.

       The present study highlights some of the health disparities between the sexes and affords

research findings that can be used to refashion health policies and research focus in the future.

Health policies must utilize the wide spectrum of health determinants in order to address the

multi-dimensional nature of health. The use of injuries to measure and guide policies and

programmes because seemingly there are many young adults who are affected, is a misnomer

and does not capture the gamut of illness or even the health of this group of people.

       The identified health disparities are among the reasons for health inequities in health

outcome and should justify a call for a research and policy directions, which include

avoidabilities such as technical, financial and moral, as these would provide additional

explanations for health disparities, choices, inequity and/or inequalities in the health outcomes

among young adults.




                                               432
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                                        436
Figure 16.1: Illness (%) by age group




                                        437
Figure 16.2: Area of residence of those with Injury (%) and Illness (%) controlled for by sex




                                               438
Figure 16.3. Sex composition of those who attend health care facilities




                                               439
Table 16.1: Treatment for Gunshot wounds at the Accident and Emergency Depts. Of Public
Hospitals by Gender and Age cohort (in %): 1999-2002

Age cohort                                        Year
                1999               2000                  2001                   2002
            Male Female        Male Female               Male Female        Male Female

< 5 years      0.8    1.3          0.2    3.1             0.2    0.0          0.0    0.0

5-9 years      0.3    3.0          0.7    1.9             0.3    1.1          0.3    0.6

10-19 years    17.9   24.5         16.2   18.5            10.2   17.0         13.9   17.0

20-29 years    39.0   32.5         40.5   30.2            35.8   19.4         36.6   35.2

30-44 years    30.6   23.6         31.1   11.1            32.3   26.9         29.3   32.1

45-64 years    6.6    12.2         6.7    28.4            10.7   22.3         8.9    11.3

65+ years      3.5    3.0          2.3    11.1            6.7    12.7         8.8    3.6

Not unknown 1.4       0.0          2.2    1.2             3.8    0.7          2.3    0.6

Total %       100 100              100 100              100 100                100 100
Calculated by Paul A. Bourne from Annual Report, 2002 published by the Policy, Planning and
Development Division, Ministry of Health, Jamaica




                                            440
Table 16.2: Visitation to the Accident and Emergency Depts. Of Public Hospitals for attempted
suicide by Gender and Age cohort (in %): 2000-2002

Age cohort                                         Year

                              2000                 2001                  2002
                             Male Female           Male Female          Male Female

< 5 years                    0.0    0.0            1.0    0.0           1.0    0.9

5-9 years                    0.0    3.4            2.0    0.0           2.0    3.5

10-19 years                  19.0   39.3           13.0   49.4          13.0   38.3

20-29 years                  24.1   36.0           20.0   34.8          20.0   36.5

30-44 years                  34.5   13.5           13.0   6.7           13.0   17.4

45-64 years                  12.1   2.2            4.0    3.4           4.0    0.9

65+ years                    6.9    3.4            4.0    2.2           4.0    0.0

Not unknown                  3.4    2.2            0.0    3.4           1.7    2.6

Total %                     100 100               100 100              100 100
Calculated by Paul A. Bourne from Annual Report, 2002 published by the Policy, Planning and
Development Division, Ministry of Health, Jamaica




                                             441
Table 16.3: Victims of Major Crimes by Age Cohorts, 2005

                                                                              Age Group


                                                                                                                                             Carnal
                            Murder                      Shooting                  Robbery                    Breaking           Rape         Abuse
      Age Group
                    Male    Female       Total   Male   Female     Total   Male   Female      Total   Male   Female     Total   Female       Female

0-4                     2            4       6      3         1        4      0           0       0      0         0        0           3             3

5-9                     3            5       8      0         5        5      1           0       1      0         0        0          27         15

10-14                  10            8      18      4        11       15     16        11        27      0         6        6          212       223

15-19                 122        18        140    107        13      120     59        49       108      8        17       25          223       103

20-24                 268        23        291    212        30      242    162       115       277     52        75      127          122            0

25-29                 252        33        285    192        22      214    233       130       363     81       106      187          48             0

30-34                 223        22        245    161        16      177    198       112       310    114       115      229          28             0

35-39                 177        17        194    138        15      153    199       102       301    140       104      244          23             0

40-44                 139        12        151    107        15      122    171        77       248    116       107      223          17             0

45-49                  72        16         88     68         5       73    146        44       190     98        75      173          12             0

50-54                  46            9      55     46         8       54     98        32       130     75        47      122           7             0

55 & Over              81        16         97     50         6       56    152        66       218    171       100      271          16             0

Unknown                91            5      96    408         3      411     28           9      37     32        14       46           8             2

Total                1486       188       1674   1496       150    1646    1463       747     2210     887       766    1653           746       346

Total Reported                            1674                     1646                       2210                      1653           746       346
Source: Statistics department, Jamaica Constabulary Force



                                                                    442
Table 16.4: Age Group of Persons Arrested for Major Crimes for 2005
                                                                            Age Group
                       Murder                        Shooting                        Robbery                     Breaking                 Rape   C/Abuse

Age Group              Male     Female       Total   Male    Female       Total      Male   Female       Total   Male    Female   Total   Male   Male        Total

12-15                      6             1       7       4            0          4     10            0      10      54        0     54      12          11     98
16-20                    157             6     163     167            1     168       183            0     183     122        3    125      66          43    748

21-25                    235             8     243     239            1     240       214            1     215     129        3    132      68          52    950

26-30                    160             2     162     137            0     137       120            1     121     105        3    108      73          27    628
31-35                     85             1      86      74            0         74     71            1      72      93        2     95      48          26    401

36-40                     54             3      57      40            1         41     36            1      37      69        0     69      23          19    246
41-45                     15             0      15      12            0         12     13            0      13      44        1     45      18          12    115

46-50                      7             1       8       2            0          2      1            0       1      18        0     18      12           5     46

51-55                      5             1       6       0            0          0      2            0       2       2        0       2      3           2     15

56-60                      1             0       1       1            0          1      6            0       6       1        1       2      1           1     12

61& Over                   0             0       0       2            0          2      2            0       2       3        1       4      2           0     10
Unknown                   40             0      40      86            0         86     23            0      23      11        0     11      10           0    170

Total                    765         23        788     764            3     767       681            4     685     651       14    665     336       198     3439
Source: Statistics department, Jamaica Constabulary Force




                                                                          443
Table 16.5. Particular variables by sex of respondents
                                                                  Sex                   P
Variable                                             Male (%)       Female (%)
                                                     n = 1,439      n = 1,585
Injury                                                                                   0.037
   Yes                                                         1.4              0.6
   No                                                         98.6             99.6
Illness
   Yes                                                         5.3             10.5    < 0.0001
   No                                                         94.7             89.5
Self-assessed health status                                                            < 0.0001
   Very good                                                  44.8             39.8
   Good                                                       47.5             47.4
   Moderate                                                    6.3             10.4
   Poor                                                        1.1              2.2
   Very poor                                                   0.4              0.2
Health care-seeking behaviour                                                          < 0.0001
   Yes                                                         3.5              6.8
   No                                                         96.5             93.2
Household head                                                                         < 0.0001
   Yes                                                        34.1             73.0
   No                                                         65.9             27.0
Union status                                                                             0.103
   Married                                                    12.1             15.1
   Common-law                                                 19.7             21.0
   Visiting                                                   28.1             26.2
   Single                                                     30.8             28.9
   Not stated                                                  9.3              8.7
Self-reported diagnosed health condition                                                 0.289
   Acute: Influenza                                           12.9             12.7
        Diarrhoea                                              6.5              1.4
        Respiratory                                           16.1             13.4
   Chronic: Diabetes                                           6.5              8.5
        Hypertension                                          11.3             21.1
        Arthritis                                              1.6              3.5
   Other (unspecified)                                        45.2             39.4
Area of residence                                                                        0.756
   Urban                                                       32.2             31.9
   Peri-urban                                                  21.4             22.5
   Rural                                                       46.4             45.6
No. of visits to health care facilities Mean (SD)         1.2 (0.5)        1.5 (1.3)     0.144
Age Mean (SD)                                        28.4 yrs (8.8)   28.5 yrs (8.9)     0.746
Medical expenditure Mean (SD) in US $                16.67 (42.01)    16.42 (26.82)      0.971
†US$ 1.00 = Ja. $ 80.47



                                               444
Table 16.6. Logistic regression: Explanatory variables of good health status, n = 2, 832

 Explanatory variable                 Std.                                       P         R2
                                      Error      Odds ratio     95.0% C.I.
 Social determinants:
    Age                                  0.01            0.97      0.96-0.99   < 0.0001         0.004
    Crowding                             0.03            0.95      0.90-1.00      0.043         0.003
    Tertiary                             0.28            1.47      1.27-1.81      0.007         0.003
    †Primary                                             1.00
    Male                                 0.14            1.45      1.11-1.91      0.007         0.006
    MiddleClass                          0.18            1.45      1.02-2.07      0.041         0.003
    †Poor classes                                        1.00
    Married                              0.21            1.63      1.09-2.43      0.018         0.004
    †Single                                              1.00
    Other town                           0.18            1.61      1.12-2.30      0.009         0.005
    †Rural                                               1.00
    Medical expenditure                  0.00            0.99      0.99-1.00      0.017         0.006
    Health care- seeking                 0.29            0.35      0.20-0.62   < 0.0001         0.009
 Biological condition:
   Self-reported illness                 0.24            0.17      0.11-0.28   < 0.0001         0.153
Hosmer and Lemeshow goodness of fit χ2 = 4.4 (8), P = 0.82
-2LL = 1615.7
Nagelkerke R2 =0.196
†Reference group




                                                     445
CHAPTER 17



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. Cross-sectional data from the 2007 Jamaica Survey of
Living Conditions (JSLC) were used to analyze the information for this study. Statistics were
analyzed using the Statistical Package for the Social Sciences (SPSS) for Windows, Version 16.0.
Analytic models, using multiple logistic and linear regressions, were used to determine factors
which explain self-rated health status, health care utilization, and income distribution. 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

                                                446
between the insured and uninsured. The current findings revealed that 20.2% of Jamaicans had

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, is 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



                                               447
circumvent the public health care system, people purchase health insurance policies as a means

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



                                                448
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

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

                                               449
utilization, health insurance coverage, non-food consumption expenditure, housing conditions,

inventory of durable goods, social assistance, demographic characteristics and other issues [14].

The information is from the civilian and non-institutionalized population of Jamaica. It is a

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

survey [15].

       Overall, the response rate for the 2007 JSLC was 73.8%. Over 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

                                                 450
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

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%.




                                                451
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

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).




                                              452
       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

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



                                               453
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

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



                                               454
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.

       Table 17.1 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 17.2.

       Table 17.3 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 17.4 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 17.5 presents information on the age cohort by diagnosed health conditions, and

diagnosed health conditions controlled by health status.

       There is a statistical difference between the mean age of respondents and the typology of

self-reported illnesses (F = 99.9, P < 0.0001). Those with 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




                                                455
(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 17.6), 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

care utilization and illness. Self-reported illnesses accounted for 62.2% of the explained

variability of moderate-to-very good health status.

       Table 17.7 shows information on the explanatory factors of self-reported illnesses. Seven

factors accounted for 66.5% of the variability in self-reported illnesses. Ninety-two percent of

the variability in self-reported illnesses was accounted for by health care utilization (health care-

seeking behaviour).

       Three variables emerged as statistically significant correlates of health care utilization.

They accounted for 71.9% of the variance in health care utilization. Most of the variability can

be explained by self-reported illnesses (71.2%, Table 17.8).

       Self-reported diagnosed chronic illnesses can be explained by 5 variables (gender, marital

status, health insurance status, age and length of illness), and they accounted for 27.7% of the

variance in self-reported diagnosed chronic illness (Table 17.9).

       Sixty-two percent of the variability in income can be explained by crowding index, social

class, household head, health insurance status, self-rated health status, health care utilization,

area of residence and marital status. Most of the variability in income can be explained by social

class (Table 17.10).




                                                456
       Table 17.11 presents information on the explanatory variables which account for health

insurance coverage. Six variables emerged as significant determinants of health insurance

coverage (age, income, chronic illness, health care utilization, marital status and upper 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%).



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%).



                                               457
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,

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



                                                458
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

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



                                                 459
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,

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



                                                460
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.

          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,



                                                 461
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

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



                                                 462
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.

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




                                               463
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

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.




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, none of the errors in this paper should be ascribed to the Planning
Institute of Jamaica or the Statistical Institute of Jamaica, but to the researcher.

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 (Jamaica Survey of
Living Conditions) available for use in this study. In addition the aforementioned, the author
would also like to extend sincere appreciation to Samuel McDaniel, Ph.D (Harvard),
Biostatistician, Department of Mathematics, the University of the West Indies, who checked the
statistical accurateness in this manuscript, and made suggestions for its improvements.




                                                    464
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20. Lasser KE, Himmelstein DU, Woolhandler S. Access to care, health status, and health
    disparities in the United States and Canada: Results of a Cross-National Population-
    Based Survey. Am J Public Health 2006; 96:1300-1307.
21. Bourne PA, Eldemire-Shearer D. Public hospital health care utilization in Jamaica.
    Australian J of Basic and Applied Scie 2009; 3:3067-3080.
22. Andrulis DP. Access to care is the centerpiece in the elimination of socioeconomic
    disparities in health. Ann Intern Med 1998; 129:412-416.
23. Foster AD. Poverty and illness in low-income rural areas. The American Economic
    Review 1994; 84:216-220.
24. Barnekow-Bergkvist M, Hedberg GE, Janlert U, Jansson E. Health status and health
    behaviour in men and women at the age of 34 years. European J of Public Health 1998;
    8:179-182.




                                        466
Table 17.1. Crowding, expenditure, income, age, and other characteristics by health insurance
status
                                             Health insurance status                     P
Characteristics                         Non-insured           Insured
                                         mean ± SD          mean ± SD
Crowding index                                4.9 ± 2.6              4.1±2.1   t = 10.32, < 0.0001
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
1
Expenditures and income are quoted in USD (Ja. $80.47 = US $1.00 at the time of the survey)




                                                     467
Table 17.2. Health, health care seeking behaviour, illness and particular demographic characteristics by health insurance status
                                                                Health insurance status                                        P
Characteristic                                                 Coverage                             No coverage
                                     Private n (%) Public, NI Gold n (%) Other Public n (%) n (%)
Health conditions                                                                                                    χ2 = 42.62, P < 0.0001
    Acute and other                      53 (56.4)                  24 (34.8)            13 (24.5)     415 (61.7)
    Chronic                              41 (43.6)                  45 (65.2)            40 (75.5)     258 (38.3)
Health care seeking behaviour                                                                                        χ2 = 70.09, P < 0.0001
    No                                  724 (89.3)                283 (81.3)            118 (75.2) 4735 (91.0)
    Yes                                  87 (10.7)                  63 (18.2)            39 (24.8)      468 (9.0)
Illness                                                                                                              χ2 = 67.14, P < 0.0001
    No                                  699 (86.2)                272 (78.6)            101 (64.3) 4453 (85.8)
    Yes                                 112 (13.8)                  74 (21.4)            56 (35.7)     736 (14.2)
Education level                                                                                                      χ2 = 78.10, P < 0.0001
    Primary and below                   684 (84.4)                318 (92.2)            144 (91.7) 4536 (87.5)
    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)


                                                                 468
Table 17.3. Age cohort by diagnosed illness

                                                                 Diagnosed illness

                                 Acute condition                              Chronic condition
                                                                  Diabetes
                      Cold         Diarrhoea       Asthma         mellitus        Hypertension     Arthritis     Other        Total
 Age cohort
                      n (%)          n (%)         n (%)            n (%)            n (%)          n (%)        n (%)        n (%)


 Children            97 (65.1)       13 (48.2)     51 (53.7)           3 (2.4)          0 (0.0)       0 (0.0)    54 (23.1)   218 (24.5)


 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




                                                           469
Table 17.4. Illness by age of respondents controlled for health insurance status
                                                                  Age of respondents
Characteristic                                              Uninsured             Insured
                                                            Mean ± SD            Mean ± SD
Illness
   Acute condition
     Cold                                                        18.8 ± 23.5       21.0 ± 26.3
     Diarrhoea                                                   28.4 ± 30.3       31.8 ± 13.5
     Asthma                                                      21.0 ± 21.7       29.4 ± 22.9
  Chronic condition
     Diabetes mellitus                                           58.7 ± 16.1       63.8 ± 15.4
     Hypertension                                                62.1 ± 17.3       63.6 ± 15.7
     Arthritis                                                   64.0 ± 13.3       65.0 ± 18.7
 Other condition                                                 38.1 ± 25.0       39.2 ± 26.8
F statistic                                                73.1, P < 0.0001 23.3, P < 0.0001




                                              470
Table 17.5. Age cohort by diagnosed health condition, and health insurance status

                        Diagnosed health                   Diagnosed health condition
                            condition
Characteristic          Acute       Chronic         Acute     Chronic        Acute      Chronic
                                                       Uninsured                  Insured
                           n (%)        n (%)         n (%)      n (%)         n (%)       n (%)
Age cohort
Children              215 (42.6)       3 (0.8)   183 (44.1)      1 (0.4) 32 (35.6)    2 (1.6)
Young adults            75 (14.9)    10 (2.6)     58 (14.0)      6 (2.3) 17 (18.9)    4 (3.2)
Other aged-adults     131 (25.9) 137 (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
                      χ = 317.5, P < 0.0001
                       2
                                                 χ = 234.5, P < 0.0001 χ = 73.6, P < 0.0001
                                                  2                      2




                                              471
Table 17.6. Logistic regression: Explanatory variables of self-rated moderate-to-very good
health
 Explanatory variable                     Coefficient    Std. error   Odds ratio   95.0% C.I.
                                                                                                  R2

 Average medical expenditure                   0.000          0.000       1.00*      1.00 -1.00   0.003

 Health insurance coverage (1= insured)        0.410          0.181       1.51*     1.06 - 2.15   0.005

 Urban                                         0.496          0.180      1.64**     1.15 - 2.34   0.007
 Other                                         0.462          0.197       1.59*     1.08 - 2.34   0.006
 †Rural                                                                     1.00

 Household head                                0.376          0.154       1.46*     1.08 - 1.97   0.004

 Age                                           -0.046         0.004     0.96***     0.95 - 0.96   0.081

 Crowding index                                -0.156         0.035     0.86***     0.80 - 0.92   0.010

 Total food expenditure                        0.000          0.000     1.00***     1.00 - 1.00   0.003

 Health care seeking (1=yes)                   -0.671         0.211      0.51**     0.34 - 0.77   0.005

 Illness                                       -1.418         0.212     0.24***     0.16 - 0.37   0.204
Model fit χ2 = 574.37, P < 0.0001
-2LL = 1477.76
Nagelkerke R2 = 0.328
†Reference group
***P < 0.0001, **P < 0.01, *P < 0.05




                                                        472
Table 17.7. Logistic regression: Explanatory variables of self-reported illness
                                                            Std
 Explanatory variable                       Coefficient    Error    Odds ratio   95.0% C.I.        R2


 Average medical expenditure                       0.000    0.000        1.00*       1.00 - 1.00   0.001

 Male                                             -0.467    0.137       0.63**       0.48 - 0.82   0.003

 Married                                           0.527    0.146      1.69***       1.27 - 2.25   0.002

 Age                                               0.031    0.004      1.03***       1.02 - 1.04   0.037

 Total food expenditure                            0.000    0.000       1.00**       1.00 -1.00    0.002

 Self-rated moderate-to-excellent health          -1.429    0.213      0.24***       0.16 -0.36    0.009

 Health care seeking (1=yes)                       5.835    0.262    342.11***   204.71 -571.72    0.611
Model fit χ2 = 2197.09, P < 0.0001
-2LL = 1730.41
Hosmer and Lemeshow goodness of fit χ2 = 4.53, P = 0.81
Nagelkerke R2 = 0.665
†Reference group
***P < 0.0001, **P < 0.01, *P < 0.05




                                                     473
Table 17.8. Logistic regression: Explanatory variables of health care seeking behaviour

                                                                        Odds
 Explanatory variable                      Coefficient     Std error    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




                                                     474
Table 17.9. Logistic regression: Explanatory variables of self-reported diagnosed chronic illness


 Explanatory variable                          Coefficient   Std error    Odds ratio    95.0% C.I.
                                                                                                       R2
 Male                                               -1.037        0.205       0.36***    0.24 - 0.53   0.048

 Married                                             0.425        0.199         1.53*    1.04 - 2.26   0.012
 †Never married                                                                  1.00

 Health insurance coverage (1= insured)              0.454        0.220         1.58*    1.02 - 2.42   0.008

 Age                                                 0.047        0.005       1.05***    1.04 - 1.06   0.201

 Logged Length of illness                            0.125        0.059         1.13*    1.01 - 1.27   0.008

Model fit χ2 = 136.32, P < 0.0001
-2LL = 673.09
Hosmer and Lemeshow goodness of fit χ2 = 15.96, P = 0.04
Nagelkerke R2 = 0.277
†Reference group
***P < 0.0001, **P < 0.01, *P < 0.05




                                                     475
Table 17.10. Multiple regression: Explanatory variables of income
                                          Unstandardized
                                           Coefficients
 Explanatory variable                                             β                                R2
                                           B       Std. Error                     95% CI
 Constant                                 11.630        0.061                  11.511 - 11.750

 Crowding index                            0.206         0.008   0.625***        0.190 - 0.221   0.195

 Upper class                               1.265         0.052   0.649***        1.162 - 1.368   0.320

 Middle Class                              0.692         0.047   0.347***       0.599 - 0.784    0.133
 †Lower class

 Household head                           -0.181         0.038   -0.108***     -0.256 - -0.106   0.012

 Health insurance coverage (1= insured)    0.137         0.042    0.075**        0.054 - 0.220   0.007

 Self-rated good health status             0.165         0.040   0.094***       0.088 - 0.243    0.006

 Health care seeking (1=yes)               0.109         0.039    0.063**        0.033 - 0.185   0.003

 Urban                                     0.145         0.046    0.079**        0.055 - 0.235   0.002

 Other town                                0.130         0.049    0.063**        0.033 - 0.226   0.003
 †Rural area

 Married                                   0.075         0.038        0.044*     0.000 - 0.150   0.001
 †Never married

F = 144.15, P < 0.0001
R2 = 0.682
†Reference group
***P < 0.0001, **P < 0.01, *P < 0.05




                                                   476
Table 17.11. Logistic regression: Explanatory variables of health insurance status (1= insured)

 Explanatory variable          Coefficient   Std. er