POVERTY & HEALTH by paulbourne01

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									Poverty & Health




     Paul Andrew Bourne
Poverty & Health




                   i
Poverty & Health



                    Paul A. Bourne
         Socio-Medical Research Institute




         Socio-Medical Research Institute
                      Kingston, Jamaica




                                        ii
©Paul A. Bourne, 2011

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


National Library of Jamaica Cataloguing Data




Poverty and Health



Includes index

ISBN

Bourne, Paul Andrew


All rights reserved. Published, 2011

Cover designed by Paul Andrew Bourne




Socio-Medical Research Institute
Kingston, Jamaica, West Indies




                                               iii
To Kimani & Kerron
     This one is yours




                    iv
Contents
Preface                                                                                         vii

Acknowledgement                                                                                 vii
CHAPTER 1: Overview of Poverty and Health                                                         1

CHAPTER 2: Poverty, Unemployment, Illness, Health Insurance and Health-care Seeking
       Behaviour in Jamaica: A Multivariate Analysis                                             24

CHAPTER 3: Modelling social determinants of self-evaluated health of poor older people in a
       middle-income developing nation                                                      51

CHAPTER 4: Disparities in self-rated health, health care utilization, illness, chronic illness and
       other socioeconomic characteristics of the Insured and Uninsured                          76

CHAPTER 5: Self-evaluated health and health conditions of rural residents in a middle-income
       nation                                                                              108


CHAPTER 6: Determinants of self-reported health conditions of people in the lower
        socioeconomic strata, Jamaica                                                          135

CHAPTER 7: The uninsured ill in a developing nation                                            160

CHAPTER 8: Self-reported health and medical care-seeking behaviour of
     uninsured Jamaicans                                                                       191


CHAPTER 9: Health Inequality in Jamaica, 1988-2007                                             215

CHAPTER 10: Impact of poverty, not seeking medical care, unemployment, inflation, self-
     reported illness, and health insurance on mortality in Jamaica                     249
CHAPTER 11: Health Disparities and the Social Context of Health Disparity between
     the Poorest and Wealthiest quintiles in a Developing Country                              284

CHAPTER 12: Is income a stronger determinant of self-rated health status
     than other socioeconomic and psychological factors?                                       312


CHAPTER 13: Health insurance coverage in Jamaica: Multivariate analyses using two cross-
     sectional survey data for 2002 and 2007                                             339

                                                                                                  v
CHAPTER 14: Retesting and refining theories on the association between illness,
     chronic illness and poverty: Are there other disparities?                       369


CHAPTER 15: Health of females in Jamaica: using two cross-sectional surveys          393


CHAPTER 16: Childhood Health in Jamaica: changing patterns in health conditions of
     children 0-14 years                                                             412


CHAPTER 17: Inflation, Public Health Care and Utilization in Jamaica                 439

CHAPTER 18: Health status and Medical Care-Seeking Behaviour of the poorest 20% in
     Jamaica                                                                         482

CHAPTER 19: Self-rated health and health conditions of married and unmarried men
     in Jamaica                                                                      510


CHAPTER 20: Self-evaluated health of married people in Jamaica                       537




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Preface
The issue of poverty is well discussed and this discourse is well documented in the literature.
The World Health Organization found that 80% of chronic illnesses were in low and middle
income countries, 60% of global mortality is caused by chronic illness, 65-70% of people who
mentioned that they were unable to afford medication claimed that they were unable to afford it,
suggesting that poverty retards the quality and productive of human capital, and that poverty
accounts for some of the premature mortality.

In Jamaica, rural poverty is twice more than urban poverty (PIOJ & STATIN, 2007). Despite
this a more social inequalities in Jamaica, the issue of poverty has never been comprehensively
study in a single volume. Poverty and Health is an introductory examination of matters
surrounding the two phenomena, with emphasis on Jamaicans and sub-populations.




Acknowledgement
Like many books that have been written prior to this one, I am also indebted to many people
have contributed differently and some invariably to the completion of this project. I would like to
extend my sincere gratitude to them. These persons are 1) Ms. Neva South-Bourne for her advice
in penning my ideas, 2) Mrs. Evadney Bourne, my wife, for support, understanding and patience
when things were difficult and surmountable at times, 3) all my co-writers, 4) God, for his
wisdom, 5) the Data Bank in Sir Arthur Lewis Institute of Social and Economic Studies, the
University of the West Indies, Mona, Jamaica for making the dataset available for use in this
study, and 6) all my associates (including best friends) whose love, support and encouragement
provided the impetus that I drew from to complete this project. I would also like to single out the
different journals that gave me the permission to reproduce some of the chapters – including
North American North of Medical Sciences, and Current Research in Social Sciences.




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Poverty & Health




                   viii
                                                                         Chapter 1
Overview of Poverty and Health


The WHO (2005) opined that 80% of chronic illnesses were in low and middle income countries,

suggesting that illness interfaces with poverty and other socio-economic challenges. Poverty

does not only impact on illness, it causes pre-mature deaths, lower quality of life, lower life and

unhealthy life expectancy, low development and other social ills such as crime, high pregnancy

rates, and social degradation of the community. According to Bourne, Beckford and McGrowder

(inprint), there is a positive correlation between poverty and unemployment; poverty and illness;

and crime and unemployment. Embedded in those findings are the challenges of living in

poverty, and the perpetual nature of poverty and illness, illness and poverty, poverty and

unemployment, economic deprivation and psychological frustration of poor families. Sen (1979)

encapsulated this well when he forwarded that low levels of unemployment in the economy is

associated with higher levels of capabilities. This highlights the economic challenge of

unemployment and equally explains the labour incapacitation on account of high levels of

unemployment, which goes back to the WHO’s perspective that chronic illnesses are more

experienced by low-to-middle income peoples. According to WHO (2005), 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.


       Jamaica is among those countries classified as a developing nation (or low income).

Hence, the challenges which were stated earlier also influence the quality of life of some people


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within the society. In 1988, Jamaica’s unemployment rate was 18.9% and 2 decades later (2007),

this fell by 67.2% (to 6.2%) which indicates close to full-employment (PIOJ & STATIN 2007).

This significant reduction in unemployment rates cannot be the only indicator used to evaluate

the socio-economic status of Jamaica and for a hasty conclusion to be drawn that the quality of

life of Jamaicans is better in 2007 compared to 1988. In 1988, inflation rate in Jamaica was 8.8%

and this increased by over 90%, suggesting that the economic cost of living of Jamaicans was

substantially higher than twenty years earlier. Importantly to note that the inflation rate in 2007

(16.8%) increased by 194.7% over 2006. This then explains why in 2007, the number of

Jamaicans seeking medical care fell to 66% over 70% in the previous year; while self-reported

was 15.5%, the highest in the 20-year period.


       In Jamaica, rural poverty is twice more than urban poverty (PIOJ & STATIN, 2007).

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

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

Hence, when poverty is coupled with unemployment, chronic illnesses and ageing, it creates

socio-economic challenges that can result in pre-mature death.


       In an article titled ‘Poverty and household size’, Lanjouw and Ravallion (1995) argue that

there is substantial evidence to show that a strong negative association exists between household

size and consumption per person in developing countries. This postulation highlights the how it

is that household size is probabilistically low in relation to access to post-secondary education.

From Lanjouw and Ravallion work (on Pakistan data), the poor who are predominantly from

large household size in developing countries will not be able to spend needed financial resources

on education despite its offerings because a large proportion of their income (i.e. consumption)



                                                                                                   2
must be spent on food, water, cooking utensils, firewood (i.e. fuel), clothing and housing. A part

of Lanjouw and Ravallion’s work spoke to the association that exist in poor household, which is

that they tend to have larger families.


       Buhmann, et al (1988), by means of cross-country information from a Luxembourg

Income Study data base on 10 developed countries, and Coulter, et al (1992), using the United

Kingdom Family Expenditure Survey data, both find associative relationship between inequality

and poverty estimates within the context of household size and consumption. A study conducted

by Meenakshi and Ray (2000) on 68,102 households in rural India, concurs with the findings of

previous studies that an inverse relationship exists between household size and consumption.

With the robustness of household size of the poor and the degree of material deprivation, they

are then less likely to access secondary and so post-secondary education. This is primarily due

to incapacitation and not ability or intellectual capacity of the people. It, therefore, can be

construed from studies that poverty may be caused from household size, which the influences

access to post-secondary education.


       One of the components of poverty is high rates of unemployment and so it is highly

probabilistic that the poor will reside within a particular geo-political zone due to financial

constraints. The poor are more likely to live in low-income areas, slums, dilapidated building,

within poor socio-economic surroundings, and in violent prone areas (Joseph Rowntree

Foundation, 2000, p. 3 - 5). Statistics for Jamaica revealed that unemployment is highest among

the poor and so is the lowest level of education.


       According to the WHO (2005) 80% of chronic diseases occur in low and middle income

countries (p 4). The WHO stated that “In reality, low and middle income countries are at the


                                                                                                3
centre of both old and new public health challenges” (WHO, 2005, p. 9). The high risk of death

in low income countries is owing to food insecurity, low water quality, low sanitation coupled

with inaccess to financial resources. Poverty makes it insurmountable for poor people to respond

to illness unless health care services are free. Hence, the people who are poor will suffer even

more so from chronic diseases. The WHO captures this aptly “...People who are already poor are

the most likely to suffer financially from chronic diseases, which often deepen poverty and

damage long term economic prospects” (WHO, 2005, p. 11). Again 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.


       Jamaicans enjoy high life expectancy (over 75 years for men and 77 years for women) in

spite of a relative low per capita Gross Domestic Product (GDP per capita), high crime rates, and

the fact that the country is among the developing nations. In a recently concluded study by

Bourne, McGrowder and Beckford (in print), a moderate positive correlation was found between

illness and unemployment, illness and poverty and poverty and private health insurance

coverage. Powell, Bourne and Waller (2007), using probability sampling cross-sectional survey

of 1,338 Jamaicans, found that the poor (ie lower class) has the lowest self-evaluated health

status, with the middle class reported the greatest health status. “In Jamaica 59% of people with

chronic diseases experienced financial difficulties because of their illness...”(WHO, 2005, p. 66).

This goes to the previous findings that argued about the negative association between poverty

and education, and poverty and tertiary education; and the positive correlation between poverty

and illness. Poverty is not only eroding people’s standard of living (ie economic wellbeing), it is

directly associated with increased chronic and non-chronic illness and the challenges of ones

inability to access health care services. This can be seen in a finding of WHO (2005), which


                                                                                                 4
indicated that in 2000, 65-70% of people who mentioned that they were unable to afford

medication claimed that they were unable to afford it, suggesting that poverty retards the quality

and productive of human capital.


Health Issues in particular Caribbean territories


In an article published by Caribbean Food and Nutrition Institute, the prevalence rate of diabetes

mellitus affecting Jamaicans is higher than in North American and “many European countries”.

(Callender 2000, 67).     Diabetes Mellitus is not the only challenge faced by patients, but

McCarthy (2000) argues that approximately 30% to 60% of diabetics also suffer from

depression, which is a psychiatric illness. Such a situation further complicates the woes of the

elderly as they seek to balance other psycho-sociological conditions with the diabetes and

hypertension as well as the stress that is frequently associated with the illness.


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

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

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

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

in March 2000. They found that there is a positive association between diabetic and hypertensive

patients - 50% of individuals with diabetes had a history of hypertension (Callender 2000, 67).

Prior to those scholars’ work, Eldemire (1995) finds that 34.8% of new cases of diabetes and

39.6% of hypertension were associated to senior citizens (i.e. ages 60 and over).




                                                                                                5
Asthma


Asthma is a chronic inflammation of the bronchial tubes that causes swelling and narrowing of

the airways. The result is difficulty breathing, the bronchial narrowing is usually either totally or

partially reversible with treatments. The muscles around the bronchial tubes tighten, causing the

airways to narrow. This is known as bronchospasm or bronchoconstriction. Mucus is produced

within the bronchial tubes further restricting air flow


       Bronchial tubes that are chronically inflamed may become overly sensitive to allergens or

irritants; the airways may become "stressed out" and remain in a state of heightened sensitivity.

This is called "bronchial hyper-reactivity" however, it is clear those asthmatics and allergic

individuals, without apparent asthma have a greater degree of bronchial hyper-reactivity than

non-asthmatic and non-allergic persons.


       In sensitive individuals, the bronchial tubes are more likely to swell and constrict when

exposed to triggers such as pollen, tobacco smoke, or exercise. Amongst asthmatics, some may

have mild bronchial spasms and no symptoms while others may have severe bronchial spasms

and chronic symptoms.


       Prevalence rates for asthma world-wide are said to be rising on average by 50% every

decade. According to the World Health Organization, asthma is now a serious public health

problem with over 100 million sufferers worldwide. World-wide, deaths from this condition have

been reported to have reached over 180,000 annually. In the United States alone, the number of

asthmatics has leapt by over 60% since the early 1980s and deaths have doubled to 5,000 a year.

The economic costs associated with asthma are estimated to exceed those of TB and HIV/AIDS


                                                                                                   6
combined world wide. The prevalence of asthma in the Caribbean is among the highest in the

world and the disease is associated with an unacceptable high morbidity and mortality.


       Asthma affects people differently. Each individual is unique in their degree of reactivity

to environmental triggers. This naturally influences the type and dose of medication prescribed,

which may vary from one individual to another. The normal bronchial tubes allow rapid passage

of air in and out of the lungs to ensure that the levels of oxygen and carbon dioxide remain

constant in the bloodstream. The outer walls of the bronchial tubes are surrounded by smooth

muscles that contract and relax automatically with each breath. This allows the required amount

of air to enter and exit the lungs to achieve the normal exchange of gases. The contraction and

relaxation of the bronchial smooth muscles are controlled by two different nervous systems that

work in harmony to keep the airways open


       The lining of the bronchial tubes contains cells that are intended to protect the bronchial

mucosa from microbes, allergens, and irritants inhaled, which can cause the bronchial tissue to

swell. These inflammatory cells play an important role in allergic reactions and their presence

causes the bronchial tissue to be a major unit for an allergic inflammatory process.


       Asthma causes a narrowing of the airways, which interferes with the normal movement

of air in and out of the lungs; it involves only the bronchial tubes and does not affect the air sacs

or the lung tissue. The narrowing that occurs in asthma is caused by three major factors:

Inflammation; Bronchospasm, and Hyper-reactivity.


       The first and most important factor causing narrowing of the bronchial tubes is

inflammation. The bronchial tubes become red, irritated, and swollen. The inflammation



                                                                                                   7
increases the thickness of the walls of the bronchial tubes and results in a narrower passageway

for the flow of air. It occurs in response to an allergen or irritant and results from the action of

chemicals such as histamines and others. The inflamed tissues produce excessive amounts of

"sticky" mucus into the tubes which can clump together block the smaller airways.


       During an attack of asthma, the muscles around the bronchial tubes tighten and cause the

air way to narrow further; this is called bronchospasm.


       The inflamed and constricted airways become highly sensitive, or reactive to triggers

such as allergens, irritants, and infections. Exposure to these triggers may result in progressively

more inflammation and narrowing. The combination of these three factors results in difficulty

with breathing out and as a result, the air needs to be forcefully exhaled to overcome the

narrowing, thereby causing a wheezing sound. Individuals with asthma frequently cough in an

attempt to expel the thick mucus plugs. As a result of the reduced air flow less oxygen reaches

the bloodstream and if very severe, carbon dioxide accumulates in the blood. The severity of an

asthma attack depends on how many agents activated the symptoms and how sensitive the lungs

are to them.


Diabetes


Diabetes is a disease in which the body does not produce or properly use insulin it is

characterized by abnormally high levels of sugar in the blood. When the amount of glucose in

the blood increases, e.g., after a meal, it triggers the release of the hormone insulin from the

pancreas. Insulin stimulates muscle and fat cells to remove glucose from the blood and




                                                                                                  8
stimulates the liver to metabolize glucose, causing the blood sugar level to decrease to normal

levels.


           In people with diabetes, blood sugar levels remain high. This may be due to insulin not

being produced at all, is not made at sufficient levels, or is not as effective as it should be. The

most common forms of diabetes are type 1, reported to be approximately 5% of all diabetics and

an autoimmune disorder, and type 2, which is associated with obesity and represents

approximately 95 % of all diabetics.


           Type 1 diabetes, most commonly occurs in children and is a result of the body's immune

system attacking and destroying the beta cells of the pancreas rendering it unable to produce

insulin. The trigger for this autoimmune attack is not clearly understood, but results in the end of

insulin production.


           In contrast, type 2 diabetes is a condition in which a resistance to the effects of insulin or

a defect in insulin secretion comes about. It commonly occurs in adults who are obese. There are

many risk factors that contribute to the high blood sugar levels in these individuals but an

important factor is the body's resistance to insulin, basically ignoring its insulin secretions. A

second factor is the falling production of insulin by the beta cells of the pancreas; an individual

with type 2 diabetes may have a combination of deficient secretion and deficient action of

insulin.


           The World Health Organization estimates that more than 180 million people worldwide

have diabetes and this number is likely to more than double by 2030. Almost 80% of diabetes

deaths occur in low and middle-income countries, about half of diabetes deaths occur in people



                                                                                                       9
under the age of 70 years; 55% of diabetes deaths are in women. WHO projects those diabetes

deaths will increase by more than 50% in the next 10 years without urgent action. Most notably,

diabetes deaths are projected to increase by over 80% in upper-middle income countries between

2006 and 2015.


       The driving force behind the high prevalence of diabetes is said to be the rise of obesity

in the population. It can be difficult to maintain a healthy weight as there is a combination of

unhealthy foods and a sedentary lifestyle which is in contrast to years ago, when people were

more active and unhealthy fast foods were not as abundant. As a result, many persons are obese

and poverty is increasing the risk as finance is limited


       Type 2 diabetes is common in people who eat too much fat and carbohydrate, too little

fibre, and get too little exercise. In contrast, people who live in areas that have not adopted a

western lifestyle tend not to get type 2 diabetes, regardless of their genetic risk. Obesity is a

strong risk factor for type 2 diabetes and is most risky for young people and for people who have

been obese for a long time.


Glaucoma


Glaucoma is a common eye condition in which the fluid pressure inside the eyes rises because of

slowed fluid drainage from the eye. If untreated, it may damage the optic nerve and other parts of

the eye, causing the loss of vision or even blindness.


       The optic nerve is the major nerve of vision; it receives light from the retina and transmits

impulses to the brain which is perceived as vision. Glaucoma is characterized by a pattern of

progressive damage to the optic nerve that usually begins with a loss of side vision. If glaucoma

                                                                                                 10
is not diagnosed and treated early, it can progress to loss of central vision and blindness. The

elderly and people with family histories of the disease are at greatest risk. There are no

symptoms in the early stage. Often, by the time the patient notices vision loss; glaucoma can be

halted but not reversed.


       Glaucoma is usually, but not always, associated with elevated pressure in the eye called

intraocular pressure. Generally, it is this elevated eye pressure that leads to damage of the optic

nerve. In some cases, glaucoma may occur in the presence of normal eye pressure. This form of

glaucoma is believed to be caused by poor regulation of blood flow to the optic nerve.


       There are several different types of glaucoma, including open-angle glaucoma and acute

angle-closure glaucoma. Open-angle glaucoma is the common adult-onset type. Acute angle-

closure glaucoma is a less common form but one that can rapidly impair vision.


       The treatment of glaucoma may include medication, surgery, or laser surgery. Eye drops

or pills alone can usually control glaucoma, although they cannot cure it. Some drugs are

designed to reduce pressure by slowing the flow of fluid into the eye, while others help to

improve fluid drainage. Surgery to help fluid escape from the eye was once extensively used, but

except for laser surgery, it is now reserved for the most difficult cases. In laser surgery for

glaucoma, a laser beam of light is focused on the part of the anterior chamber where the fluid

leaves the eye. This results in a series of changes, making it easier for fluid to exit.


Hypertension


High blood pressure or hypertension is a term used to refer to high pressure in the arteries. High

blood pressure does not mean excessive emotional tension, although emotional tension and stress

                                                                                                11
can temporarily increase blood pressure. Normal blood pressure is below 120/80; blood pressure

between 120/80 and 139/89 is called "pre-hypertension", and a constant blood pressure of 140/90

or above is considered as high blood pressure.


       The numerator or the systolic blood pressure corresponds to the pressure in the arteries as

the heart contracts and pumps blood into the arteries. The denominator or the diastolic pressure

represents the pressure in the arteries as the heart relaxes after the contraction. The diastolic

pressure reflects the lowest pressure to which the arteries are exposed.


       An elevation of the systolic and or diastolic blood pressure increases the risk of

developing cardiac disease, renal disease, atherosclerosis, arteriosclerosis, eye damage, and

stroke. These complications of hypertension are often referred to as end-organ damage because

damage to these organs is the end result of chronic high blood pressure. For this reason, the

diagnosis of high blood pressure is important so efforts can be made to normalize blood pressure

and prevent complications.


       Previously, it was thought that rises in diastolic blood pressure were a more important

risk factor than systolic elevations, but it is now known that in people 50 years or older systolic

hypertension represents a greater risk. Reports are that many persons are living with undiagnosed

hypertension making it a major public health problem.


Poverty depicts deprivation of material resources, inadequacies to access some goods and

services, lower nutritional intakes, lower capabilities and education, high unemployment,

unhealthy diet, and lower health status (World Bank, 2006; WHO 2005; Younger, 2002; Sen,

1979). Hence, it is not surprising that chronic illnesses are greater in lower and middle



                                                                                                12
income countries than in the developed nations. Importantly, in any discussion on poverty is

its direct effect on income, wealth, productive, employment and the family. Despite one’s

wealth, chronic illness can deplete this in a short time. If illness can cause poverty, what

happens to the poor who are interfacing with many chronic dysfunctions. The WHO (2005)

offered some explanation for poverty and chronic illness, when it opined that this can result

in premature mortality.

        The current study examines poverty and illness, illness on a family and the

challenges of an urban poor woman and the challenges of intervention mechanism to alleviate

the psychosocial challenges that the family faced owing to poverty and chronic illnesses. The

impact of illness on an urban poor family is extensive, far reaching, catastrophic, dehabilating

and can result in premature death. Illness coupled with poverty as well as being a woman is a

hallmark for psychosocial challenges for public health practitioners, health care workers,

community aides, social workers, and intervention specialists. Poverty increases the risk

factors of illnesses and can further erode the economic livelihood of the person, family,

community, society and country.

        Illness reduces an individual ability to carry out his/her function, and therefore

retards employment status. When chronic illnesses become severe and long lasting, the

economic burden of this can destroy a family and this does not include the psychosocial

challenges of reduced daily activities of the individual. When illness influences the head of

household, this is always problematic for the individual and by extension the other family

members. If the illness results in unemployment, there is a greater probability that other

family members will be incapacitated of educational and other goods and services. This




                                                                                                   13
oftentimes results in an association between activities inability and socio-economic burden

on the family (Elmstahl et al., 1996).

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

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

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

(2000) and Steingo (2000) 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 (Callender 2000, 67; Steingo    2000, 75). Those diseases are not only lifestyle

causing, they are expensive to treat especially if they become severe. Hence, health insurance

coverage is sought in keeping with the probability of illness but it remains beyond the reach of

the poverty stricken.


       From the findings of a cross-sectional study conducted by Powell, Bourne and Waller

(2007) of some 1,338 Jamaicans, 19.0% of respondents perceived their economic wellbeing to be

‘very bad’. In addition, when they were asked, “Does your salary and the total of your family’s

salary allow you to satisfactorily cover your needs”, 57.4% of them felt that this “does not

cover” their expenses (Powell, Bourne and Waller 2007,      29). Added to this out of a maximum

score of 10, those in the lower class scored 5.9 for how do they ‘feel about the state of their

health’ compared to a score of 6.6 for those in the upper class and a score of 6.7 for the middle

class. This again speaks to the difficulty that is faced by poor Jamaicans, and when this is

accompanied by illness, unemployment, low education and poverty in the extended, this makes it

significantly more difficulty for the family member who is the head of the household.




                                                                                                14
        In this study the client was one in a family that experienced poverty, and the introduction

of chronic illness of the person’s mother meant that she had to rearrange her life in keeping with

aiding her parent to cope with the illness. In order to care for her parent, she had to leave her job.

The state of unemployment coupled with chronic illness of her parent eventually eroded the

client’s financial basis. The client was thrust into the status of the head of the household, and this

meant that she had to provide for the family socially, economical and psychological.


    When the client became ill in addition to being unemployed, meant that she was now force to

care for a family in poverty while being ill, unemployment, and psychosocially challenged by her

new reality. Illness further sank the client into more poverty, deprivation, further inability to

take care of the extended family and herself and this resulted in a period of depression. The

challenges for this family compounded when young household members began to be abusive,

disrespectful, disobedient, unsympathetic and preceded in the dismemberment of the family

structure.


        This family saw household members becoming involved with ‘gunmen’, men and

spouses as a medium of intent to change their economic base. Household members removed

from the family home, but instead of changing the cycle of poverty that they grew up in, fell prey

to it. Those younger siblings of the client’s parents began having    children and this was such

that some of them have eight children for eight different fathers, and these children became

       the responsibility of the unhealthy client. Despite the unselfishessness of the client to

hold the family together and to offer other members an opportunity to learn from her mistake as

well as to provide for them, the failure to accomplish such mammoth tasks have seemingly

created psychosocial challenges for the client as well as the extended family.



                                                                                                   15
       While the intervention provides some relief for the client, and that some aspects will be

forthcoming, the researchers fear that continuous socio-economic interventions by students of the

Master in Public Health (MPH) will create a dependency syndrome in this family. The

researchers are cognizant that poverty is difficult to break (read Dereck Gordon, 1987), but there

is another side to this reality as poverty could breathe complacency, dependency and deepen

itself. It is germane that while we seek to alleviate the sufferings of poor people, including

children and women, our efforts should not be misunderstood by members in the society that the

University of the West Indies is here to aid them for their actions (or inactions) that result in their

current state. Chronic illness can erode economic livelihood, health, family structure and the

interpersonal relationship between and among family members. An occasional intervention is

difficult to make a substantial impact on a poor family’s socioeconomic position and this is made

more difficult when a family member is suffering from a chronic illness and there is wide spread

family unemployment.


       The US Information Service in speaking of the issue of poverty uses the United Nations

report of 1996 to argue that:


       … the quality of people's lives cannot be measured by income alone. It says that while
       Pakistan has had enviable economic growth, 61 percent of the population there lacks the
       health, education and nourishment needed to climb out of poverty. Argentina's income is
       among the highest in the developing world, but 20 percent of its rural population live in
       financial poverty and 29 percent lack access to safe water (US Information Service,
       1996).
       From the US Information Service’s monograph, the poor is unable to access education,

and some writers argue that this is not to any doings of their own. In its monograph, the US

Information Service has not afford a perspective on the levels of education to which poor is

unable to access. The researcher believes that this is even more difficult the higher one climbs on


                                                                                                    16
the education rung. This is even supported by US Information Service’s citation of the UN

report that:


        To reduce inequality while promoting growth, the report suggests that
        national authorities need to give more attention to human development,
        poverty    reduction,  and    employment      policies, especially for women;
        expand access to land and credit; boost investment in and access to
        education and health…(US Information Service, 1996).

        There is a convergence in principle that access to education reduces poverty. Academics,

researchers, non-governmental and governmental institutions are saying that access to quality

education is the hallmark of poverty alleviation. According to the US Department of State,


        Food security and alleviating hunger hinge, among other things, on defining property
        rights for small-scale farmers, on technology, and on providing social safety nets to the
        most vulnerable groups, says U.S. Secretary of Agriculture Ann Veneman. Cato Institute
        economist Ian Vásquez also highlights the property rights issue, as well as the
        correlation of economic freedom with poverty reduction (US Department of State, 2002).

        From the perspective of the US Department of State, poverty alleviation will only be

accomplished by addressing not only ‘food security’ but on ‘economic freedom’. Such a state in

the social setting of the poor must come from access to quality and higher education. Poverty

reduction, therefore, does not rest with the provision of food to the poor or to poor countries, as

this will not go to destroy the economic livelihood of farmers and other institutions within the

recipient country. The issue can only be address from a multidimensional approach which

includes the provision and access of education to all peoples within the country. By provide

access to quality education, the poor is given an opportunity to gain financial independence.

This seemingly simplistic approach holds the key to financial freedom, hunger eradication,

opportunities, plethora of choices and social harmony. Another aspect that is hidden in the food

insecurity is the nutritional deficiencies, and the direct association between poverty and illness,

unemployment and crime, unemployment and poverty and unemployment and illness.

                                                                                                17
       In ‘They cry ‘respect’: Urban violence and poverty in Jamaica’, Horace Levy, a senior

lecturer in the department of Sociology, Psychology and Social Work at the University of the

West Indies, Mona, in his research, finds that there is a relationship between unemployment and

crime. He says that “along with people from other areas they point to a direct link between

unemployment and crime.” (Levy, 2001, p.10). Despite the qualitative methodology that he uses

to acquire data for his findings, Levy’s findings provide a basis, upon which an understanding

may be had of the importance of financial independence, violence and crime, unemployment and

poverty. From Levy’s study, chief among the characteristics of youth involvement in gangs is

“parents not educated”. It is clearly from Levy’s study, that the poor experience a high rate of

non-school attendance because of in affordability. With such a setting, the ability to transform

their lives is high improbable as they lack the financial resources, and their human capital is

rather low making their labour cost low, and this explain the high degree of unemployment or

involvement in menial work or ‘hustling’. Lipton and Litchfield (2001) forward an explanation

for setting above. They say that “One of the main conclusions from Lipton (1998) is that higher

levels of resources are associated with lower levels of poverty” (Lipton & Litchfield, 2001, p.3).


       Access to tertiary education is a difficult option for the poor. Based on studies, education

is a vehicle in the socio-economic mobility (development) (Nie, et al., 1972) to which if can be

access by the poor will transform their social-environment (Barr, 2005). Poverty prevents

economic freedom and choice, and so despite ones willingness, this circumvents many realities

of their experience. The poor is held in the vicious cycle of continuous poverty. The Inter-

American Development Bank in highlighting the social conditions of the poor says, “Who are

the poor? They are likely to be less educated and to work in the informal sector” (Inter-American

Development Bank, 1998, p. 12). One writer forwards a perspective that converges with that of

                                                                                                18
the Inter-American Development Bank that access to higher education is the most basic

ingredient in the reduction of poverty (NetAid, 2005). NetAid asks the question ‘Why is

education key to ending global poverty?’


                                            Illiterate adults tend to be poor (Younger, 2002 p.98)

               Poverty is correlated with adults’ educational level: 66 percent of illiterate are
               adults poor,…64 percent of adults who did not graduate for primary school are
               poor, …22 percent of secondary school graduates are poor (Younger, 2002, p.
               100)

       From Younger’s findings, an underline principle of poverty is illiteracy and how it affects

the adult age cohorts. With such a finding, poverty directly affects the quality of the labour

stock. The situation emphasizes how access to tertiary level education is inversely related to the

adult poor as those who can access post-secondary education; only 22 percent of secondary

graduands are poor. Embedded within this finding is how increase in age of the poor will

inversely relate to accessing post-secondary level education, and the low probability of the poor

accessing post-secondary education.


ABOUT THIS BOOK


       Poverty is essentially the lack of means to live. At the heart of any consideration of
       poverty lies the issue of what is needed to live “a decent life” and, more fundamentally,
       what it is to be human (Senate Community Affairs References Committee, 2004, p.5).

       The existence of so many poor people in the capitals of the Caribbean has transformed
       those cities into large slums with here and there pockets of the upper and middle classes.
       …The poor, having been exploited over and over again by the elite, now live in large
       numbers in the cities and share the facilities developed earlier to meet chiefly the needs
       of the urban middle and upper classes (Languerre, 1990, p.6)

       The author believes that poverty erodes lives – including health, empowerment,

opportunities, cognitive skills, and what it means to be human. With the aforementioned matters,



                                                                                               19
the interconnectivity among poverty education, empowerment, opportunities, cognitive skills and

health cannot be denied. However, there was never a book which examines different aspect of

interrelation between poverty and health, particularly among Jamaicans using cross-sectional

data. This book is an introduction of poverty and health, and the topics are selected in keeping

with the general theme.


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                                                                                           23
                                                                         Chapter 2


Poverty, Unemployment, Illness, Health Insurance and Health-care Seeking
Behaviour in Jamaica: A Multivariate Analysis


Using approximately two decades of statistical data (1988-2007) on poverty, unemployment,
self-reported illness (or dysfunctions), health insurance coverage, and health seeking behaviour
for Jamaicans, the current study seeks to model the social determinants of 1) self-reported health
conditions, as well as the medical predictor of 2) health seeking behaviour, 3) poverty, 4)
unemployment, 5) health insurance coverage. The data for the study were published by the
Statistical Institute of Jamaica, Bank of Jamaica, and the Planning Institute of Jamaica and the
Statistical Institute of Jamaica. The data were analyzed using ordinary least square; and the level
of significance was 5%. The findings revealed that the social determinants of health care-
seeking behaviour were prevalence of poverty and health insurance with a biological variable -
illness. Health insurance coverage was the significant predictor of health care-seeking behaviour
of Jamaica (beta = 0.504, 95% CI = 0.563 – 1.525). Illness was found to be negatively correlated
to health care-seeking behaviour and the same was found for poverty. The determinants that
explain illness in Jamaica were poverty (beta = -527, 95% CI = -0.230-0.093); rate of growth in
GDP at constant prices (beta = 0.877, 95% CI = 0.787-1.109); health care seeking behaviour
(beta = -0.544, 95%CI = -0.235-0.106). Illness (beta = -0.432, 95%CI = 0.464-0.851); medical
care-seeking behaviour (beta = 0.466, 95%CI =0.136-0.314) and unemployment (beta = -0.604,
95% CI = -11.557-6.523) were found to be significant predictors of health insurance coverage.
The significant non-medical predictors of unemployment rate in Jamaica were poverty (beta =
0.195, 95% CI = -0.001-0.013); medical care-seeking behaviour (beta = 0.328, 95% CI = 0.002-
0.019) and health insurance coverage (beta = -0.881, 95% CI=-0.075-0.042) and a medical
predictor (illness – beta = 0.496, 0.034-0.067). The social predictors of poverty were
unemployment (beta = 0.411, 95% CI = 5.843-20.353) and medical care-seeking behaviour (-
0.575, 95% CI = -0.825-0.357) and a medical predictor (illness – beta = -0.237, 95% CI = -
1.358-0.176). The challenges for public health practitioners is address health, deprivation,
material inadequacies and poor sanitation conditions and poor water quality of the poor because
of their the direct association between those social variables and poor health. The current work
re-ashes those realities; but also highlight the health challenges of wealthy and wealthiest 20% in
the nation. Public health practitioner therefore can only focus on the poor and unemployed, as
the wealthy’s unhealthy lifestyle choices are eroding some of the benefits associated with income
and wealth.




                                                                                                24
Introduction

Life expectancy is among the indicators of health of an individual, a society, or a nation.

Statistics from the United Nations (2002) revealed that life expectancy at birth in the Caribbean

(for 2000-2005) was 68.1 years; 70.4 years in Latin America and the Caribbean; 78.5 years in

Western Europe; 81.5 years in Japan; 78.2 years in United Kingdom and Northern Ireland; 77.5

years in the United States; 75.6 years in the more developed nations and 75.7 years for Jamaica.

Using life expectancy as a measure of health, the health status of Jamaicans is comparably

equivalent to that in developed nations such as Japan and the United States. According to the

World Health Organization (1948), health is more than the mere absence of diseases or infirmity;

it includes the state of complete physical, social and psychological wellbeing, suggesting that

any study of health must include social, economic and psychological determinants as well as

biological variables. This demands an expansion of using life expectancy to assess health status.

Life expectancy which is computed from mortality data places emphasis on death and so this

justifies why the WHO argued for healthy life expectancy or disability free health as this is more

in keeping with the multi-dimensional tenets of health. Hence, only using life expectancy to

evaluate health of a people, society or nation is insufficient as WHO argued that the 21st

century’s focus is on superior quality of life and not the number of lived years.


       Health demand is needed because people want to live longer and experience a good

quality of life. Good health is desired by all for the very reasons that were aforementioned and

accounts for health demand. Good health is more than experiencing the absence of ill-health; it is

a state of harmony of the mind, body and the socio-physical environment. It contributes to virile

labour force, the creation of wealth for an individual and a nation, and accounts a happier person;


                                                                                                25
optimistic behaviour; high self-esteem and an individual who is more satisfied with life

(Hutchinson et al., 2004; Diener, 1984; Wilson, 1967). It is this desire to live, be happy, enjoy

life and live well that at a signal of unhealthiness some people and for others a degree of severity

is needed that will see them seeking medical care (Rosenstock, 1966; Strecher & Rosenstock,

1997).


         Strecher & Rosenstock (1997) attributed the willingness to seek medical care based on

perceived severity of the illness as an issue found in black men. Approximately 90 percentage of

the Jamaican population are blacks and close to 50 percent are men and so Strecher &

Rosenstock argument could provide some explanation of the low medical care-seeking behaviour

of Jamaican men. Statistics from the Jamaica Ministry of Health showed that most of the health

care-seekers were females (Jamaica Ministry of health, 2006) except in cases of injuries.

According to the Planning Institute of Jamaica and the Statistical Institute of Jamaica (2008),

66% of Jamaicans sought medical care in 2007. Of this figure, 68.1% was females compared to

62.8% males. Males did not only seek less health care than females, they bought less medical

(70.8% males, 75% females) as well as holding less health insurance coverage (20.1%)

compared to females (22.2%).


         Culturally, Jamaicans men do not seek medical care because it is interpreted as feminine,

weak and such a reality prevents them from seeking medical care, which was also found outside

of Jamaica (Doyal, 2000). The low responsiveness of men than women to health care treatment

was also the case in Zambia (Stekelenburg et al., 2005); Kenya (Taff & Chepngeno, 2005); and

Uganda (Lawson, 2004). A study done in Chekaria, Bangladesh, by (Future          Health Systems Research


Programme Consortium (2007)   found that more females reported an illness; but more men (58.2%)

sought medical care than females (40.9%) which contradicts the other aforementioned studies.

                                                                                                    26
Despite the preponderance of studies that have established that more females seek medical care

than men, in Jamaica, the mean number of days spent receiving curative care is more for males

than for females which was also the case in Zambia (Stekelenburg et al. 2005). For males,

medical care is acceptable when there is severity of a health condition.


       Medical care therefore, will always be sought by humans in an effort to live without the

severity of illness as this affects their employment status; family life; wealth; social

relationships; happiness, and harmony with themselves (Akande & Owoyemi, 2009). Health

which is a multidimensional construct (WHO, 1948) means that human will seek health-care

services not only at the onset of diseases (dysfunctions, illness or symptoms) but it has socio-

cultural influences such as income level; education; distance; cost and quality of care; perception

of ill-health and its severity; and life satisfaction. It is the need to protect the mechanism of the

body with the socio-physical milieu that dictates the demand for health care services; but this is

still guided by culture (Hausmann-Muela et al. 1998; Caldwell, 1993) and the interpretation of

the health condition(s) as well as what is happen inside the body.


       It is well established in health literature that poverty influences health (WHO, 2005;

Marmot, 2002; Wooden & Headey, 2004). According to the WHO (2005), 80% of the deaths

from chronic diseases occur in low-to-middle income countries and that 60% of global mortality

is caused by chronic illness. This indicates that a proportion of mortality that is occurring in the

developing nations is resulting in premature deaths. Poverty is more than income deprivation; it

includes material and social deprivation of resources (Marmot, 2002). Sen (1979) encapsulated

this well when he forwarded that low levels of unemployment in the economy is associated with

higher levels of incapability.



                                                                                                  27
       Poverty means therefore the inability to have good nutrition; proper water and food

supply; quality physical environment; quality education and choices. Education is a vehicle for

the socio-economic mobility (Nie, et al., 1972) to which continues to elude the poor and accounts

for their inability to transform the social-economic environment (Barr, 2005). Poverty prevents

economic freedom and choice, and so despite ones willingness, this circumvents many realities.

The poor is held in the vicious cycle of continuous poverty, and on the onset of health conditions

poverty could extend to the family. The Inter-American Development Bank in highlighting the

social conditions of the poor says, “Who are the poor? They are likely to be less educated and to

work in the informal sector” (Inter-American Development Bank, 1998, p. 12) and so with less

access to social and material resources, they are highly like to face premature death without

assistance from the wider society or the government. One writer forwards a perspective that

converged with that of the Inter-American Development Bank that access to higher education is

the most basic ingredient in the reduction of poverty (Net Aid, 2005).


       Poverty also influences many of the other social determinants of health such as income,

education, employment status (WHO, 2008; Kelly et al. 2007; Marmot, 2003) as well as

biological conditions. Money is need in health care services, and so its non-access affects the

treatment of care that the poor are likely to receive. A study by Lee & Kim (2003) found that

existing illness and ‘new health event’ were significantly correlated with the diminution of the

wealth of elderly in United States. Therefore, the awareness of ill-health is not a sufficient

driving force for people to seek medical care as they must balance the perception of severity with

the affordability of health care. It is this fact that explains why purchase health insurance

coverage allows the increase of health care demand.




                                                                                               28
       Health insurance which reduces the cost of medical care is possessed less by the poor,

and this is apart of the social deprivation which aids their poor health compared to the wealthy.

Another aspect to the reality of health insurance in particular to Jamaica is that is primarily a

private good. It is substantially offered to the employed, and within this context and the fact that

the poor are substantially in the informal sector, they are therefore greatly removed from

accessing this product and by extension health care-services. Prior to 2007, health insurance was

totally a private good in Jamaica. Such an arrangement and the fact that 21 percent of Jamaicans

had health insurance coverage in the same period; it means that health care service costs were

substantially out-of pocket payment for many Jamaicans. Hence, faced with the choice of

spending on food and health care, many people delay medical care to their detriment, and this is

even more complex for the poor who have less income in the first instance. Income deprivation

therefore, influences health insurance coverage, nutrition, timing in seeking medical care, illness

and further complicate the precipitous fall between poverty, illness and deeper poverty. While

the wealthy and the middle class experience the same typology of health conditions like the poor,

they are more ability to afford medical care than the poor.


       In 2007, statistics on self-reported illness in Jamaica was 15.5%: 15% for those in the

poorest 20%, 14.5% of the poor; 15.8% of the middle income categorization; 16.1% and 16% of

the wealth and wealthiest income quintiles, respectively (PIOJ & STATIN, 2007).

Concomitantly, arthritis was the highest among the poorest 20% (12.9% compared to the

national average of 8.8%). Eleven percent of the poorest 20% had asthma which was the highest

for all the income quintiles and the nation (8.7%). Fifty-one percent of those in poorest 20%

indicated that they did not seek medical care owing to inafffordability. Continuing, the statistics

revealed only 6.6% of the poorest 20% had health insurance coverage compared to 12% of the


                                                                                                 29
poor; 18% of the middle class and 22.7% of the wealthy and 43.5% of the wealthiest 20%. The

greatest percentage of Jamaican reporting a recurring illness was in the poorest 20% (58.7%

compared to 47.9% in the poor; 51.5% in the middle class; 47.8% in the wealthy and 53.6% in

the wealthiest 20%) and this cohort sought the least medical care (54.3% compared to 62.9% in

of the poor; 67.7% of the middle class; 68.7% of the wealthy and 73.5% of the wealthiest 20%).

The poorest 20% reported the most mean days in illness (11.3 days) compared to the poor (11.1

days); 9.7 days for the middle class; 9.6 days for the wealthy and 8.1 days for the wealthiest

20%.


       Health statistics in the Caribbean and in particular Jamaica continue to overemphasize

mortality, morbidity, or biomedical factors (Ministry of Health, 2007; PIOJ & STATIN, 2007;

STATIN, 2008), and although these are critical to the planning process and social development,

more research is needed in regard to the social determinants of health. An extensive review of

the health literature for the Caribbean in particular Jamaica found no study that has sought to

model the relationship between unemployment, illness, health care-seeking behaviour and

poverty. Hence this study will examine five hypotheses: (1) health care-seeking behaviour of

Jamaicans can be determined by social determinants (poverty and health insurance) and by a

biological factor (illness); (2) Self-reported illness can be predicted by social determinants

(poverty, health insurance; medical care-seeking behaviour; and rate of growth in GDP); (3)

Health insurance coverage can be determined by social variables (poverty, unemployment and

medical care-seeking behaviour) and a biological variable (illness); (4) Unemployment can be

predicted by social factors (poverty, health insurance coverage; and medical care-seeking

behaviour) and a biological factor (illness) and (5) Poverty is determined by social factors

(medical care-seeking behaviour and unemployment) and a biological factor (illness). The


                                                                                            30
purpose of the research is to aid public health practitioners with health research literature and

findings that can be used to inform policy decisions.


Method

DESIGN AND METHODS


The current study used two decades (1988-2007) of data which is published by the Planning

Institute of Jamaica, and the Planning Institute of Jamaica and Statistical Institute. The statistical

data was provided by the Jamaica Survey of Living Conditions (JSLC). The Survey is an

adaptation of the World Bank’s Living Standard Measurement Study (LSMS) household survey,

with some modifications as the JSLC (survey) focuses and emphasizes policy impacts. Since,

1988, the Statistical Institute of Jamaica in collaboration with the Planning Institute of Jamaica

has been conducting annual studies of living conditions of Jamaicans. The survey design is that

of a multi-topic household survey including a section on health, consumption, education, house,

anthropometric measurements and immunization data for all children 0-59 months, and

demographic variables.


       Each survey year was drawn using stratified random sampling. This design was a two-

stage stratified random sampling design where there was a Primary Sampling Unit (PSU) and a

selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which

constitutes of a minimum of 100 dwellings in rural areas and 150 in urban areas. An ED is an

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

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

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




                                                                                                   31
dwelling was compiled, which in turn provided the sampling frame for the labour force. The

survey is weighted in order to present the population of Jamaica.


        The survey is carried out with a self-administered questionnaire by trained interviewers to

responsible household members. Participations are asked to recall specific and detailed

consumption patterns over the last 30 days of the survey period as well as their health care

expenditure. The basic structure of the questionnaire has remained the same over the years with

inclusive of social safety net, crime and victimization, physical environment, remittances and

other components as modules at different survey periods. For this study, data used were on

unemployment, health insurance coverage, poverty, health care-seeking behaviour, illness and

utilization.


        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). Multiple regressions

were used to examine the relationship between the dependent variable and some predisposed

independent (explanatory) variables. Models will be established for (i) health care-seeking

behaviour; (ii) illness; (iii) health insurance; (iv) unemployment; and (v) poverty. The results

were presented using unstandardized B-coefficients, beta value, and confidence interval (95%

CI).


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

multicollinearity) existed between variables. Based on Cohen & Holliday [36] correlation can be

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

exclude (or allow) a variable in the model as well as the p < 0.05. For the model, tolerances less

than 0.2 were excluded as these indicate high multicollinearity. Multicollinearity is a relationship


                                                                                                 32
between two or more explanatory variables (Mamingi, 2005:49). Hence, this study did not use

highly collinear variables as the estimators lose precision and therefore are difficult to interpret.


Models


The use of multivariate analysis is well established in health literature to examine many socio-

economic and biological factors which simultaneously influence health, wellbeing or health

conditions (Grossman, 1972; Smith & Kington, 1997; Hambleton et al., 2005; Bourne, 2009;

Bourne & McGrowder, 2009). The current study will employ multivariate analyses in the study

of health and medical care seeking behaviour of Jamaicans. The use of this approach is better

than bivariate analyses as many variables can be tested simultaneously for their impact (if any)

on a dependent variable.

         Scholars like Grossman (1972), Smith & Kingston (1997), Hambleton et al. (2005),

Kashdan (2004), Yi & Vaupel [40], the World Health Organization pilot work a 100-question

quality of life survey (WHOQOL) (Murphy & Murphy, 2006) and Diener (1984) have both used

and argued that self-reported health status can be used to evaluate health status instead of

objective health status measurement. Other scholars, on the other hand, employed self-reported

health conditions to operationalize health of individual [30]. Embedded in the works of those

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

assessing health.




                                                                                                    33
       The current study will examine the social determinants (i) and biological determinants of

health care-seeking behaviour, (ii) poverty, (iii) unemployment, (iv) health insurance, (v) health

conditions of Jamaicans. Using multivariate analysis, this research will seek to model five

functions for the previously stated areas:



       HSBt = f(Pt, It,HIt,lnUt, GDPt, εt)         t = 1,2,3, ….,20              [1]

       It = f(Pt, GDPt,HIt,lnUt, HSBt, εt)         t = 1,2,3, ….,20              [2]

       HIpop = f(Pt, It,GDPt,lnUt, HSBt, εt) t = 1,2,3, ….,20                     [3]

       lnUt = f(Pt, It,HIt,HSBt, GDPt, εt)        t = 1,2,3, ….,20               [4]

       Pt = f(HSBt, It,HIt,lnUt, GDPt, εt)        t = 1,2,3, ….,20               [5]



       Where

       HSBt = health care-seeking behaviour (in %);

       Pt = prevalence of poverty (in %)

       It = self-reported illness (in %)

       HIt = health insurance coverage (in %)

       lnUt = logged unemployment rate (in %)

       GDPt = Rate of growth of Gross Domestic Product (GDP) at constant prices

       εt = error term

       t = time index




                                                                                               34
Results



Using 2 decades of data, the respective models below were based on p value < 0.05, and no

multicollinearity. Ordinary least square (OLS) was used to model each equation from the data.

The OLS estimations lead to the results shown below:




Health care-seeking behaviour:

HSBt = α + β1Pt + β2It + β3HIt + εt                    t = 1,2,3, ….,20         [1.1]

Self-reported illness:

It = α + β1Pt + β2GDPt +β3HSBt + εt                    t = 1,2,3, ….,20         [2.1]

Health Insurance coverage:

HIt = α + β1lnUt + β2It + β3HSBt + εt                  t = 1,2,3, ….,20         [3.1]

Log-lin:

lnUt = α + β1Pt + β2It + β3HSBt + β4HIt + εt           t = 1,2,3, ….,20         [4.1]
      (α + β1P + β2I + β3HSB + β4HI + ε )
Ut = e        t     t       t      t t                 t = 1,2,3, ….,20        [4.2]

Poverty:

Pt = α +, β1It + β2HSBt + β3lnUt + ε)                  t = 1,2,3, ….,20        [5.1]



Poverty and unemployment are highly corrected, and so both variables cannot be used in as

estimators as the same time. The correlation between poverty and unemployment was 0.707.




                                                                                            35
Predictors



The predictive functions with their different explanatory variables are presented below. The

explanatory power of each model are [1.2] r-squared of 0.735 (see Table 2.1); [2.2] r-squared of

0.853 (see Table 2.2); [3.2] r-squared of 0.870 (see Table 2.3); [4.3] r-squared of 0.811 and [5.1]

with a r-squared of 0.688 (Table 2.5).


Health care-seeking behaviour:

HŜBt = 65.86 – 0.305Pt – 1.100It + 1.044HIt                  t = 1,2,3, ….,20          [1.2]



Self-reported illness:

It = 23.09 – 0.161Pt + 0.948GDPt – 0.171HSBt                    t = 1,2,3, ….,20       [2.2]



Health Insurance coverage:

ĤIt = 12.836 – 90.40lnUt + 0.657It + 0.225HSBt                  t = 1,2,3, ….,20        [3.2]



Unemployment:

Ût = e(1.677 + 0.006Pt + 0.050It + 0.011HSBt   - 0.059HI )
                                                        t      t = 1,2,3, ….,20       [4.3]



Poverty:

Pt = 36.106 – 0.737It – 0.591HSBt + 13.098lnUt                    t = 1,2,3, ….,20     [5.1]



                                                                                                36
Discussion
Many studies that have examined social determinants of health have established that income,

poverty, illness, health care-seeking behaviour, health insurance and unemployment are

significantly correlated to health status (Marmot, 2002; Hambleton et al. 2005; Grossman, 1972;

Smith & Kington, 1997; Kelly et al., 2007; Marmot, 2008; WHO, 2008; Bourne, 2008a, 2008b,

2009; Bourne & Beckford, 2009). Those research conducted in the Caribbean on health

(Hambleton et al. 2005; Bourne, 2008a, 2008b, 2009; Bourne & McGrowder, 2009; Asnani, et

al., 2008; Hutchinson et al., 2004; Morgan, 2005) or the Americas (PAHO, 2001) have never

used national data on poverty, illness, unemployment, health care-seeking behaviour and health

insurance coverage in order to examine how those phenomena correlate. The current research is

not like the others in the Caribbean in particular Jamaica that used national cross-sectional

survey data to model health determinants of the individual, or other health related phenomena

from an individual perspective. In this paper, national figures were used for health care-seeking

behaviour, health insurance coverage, poverty, illness, unemployment and rate of growth of GDP

in order to model the type and degree of particular social determinants and medical factor

influence on each model.


       The current work went further than its predecessors as it found that with all other things

being held constant, 66 out of every 100 Jamaicans are likely to seek medical care. Poverty was

found to significant corrected with health care-seeking behaviour which Marmot (2002) opined

that this was owing to inafffordability. Poverty being a negative predictor of health care-seeking

behaviour within the context of low nutrition, poor quality milieu and low access to medical care

services justifies the greater probability of this cohort have chronic illness (WHO, 2005) and

experience premature death than the middle class or the wealthy in the same population. Hence,


                                                                                               37
the poor delay seeking medical care services, which contradicts studies by Okolo (1988) and

Akande & Owoyemi (2009) that found no correlation between income levels and delay in

seeking health care. Although those studies did not identify income levels as affecting waiting

time in selecting care, inafffordability of health care does affect the poor’s delay time in seeking

care and this is accounted in this study. In 2007, the poorest 20% sought the least medical care

and had the longest mean number of days spent in illness, suggesting that poverty influence on

health care seeking behaviour in Jamaica is not surprising and does further increase the health

expenditure of the nation when they do seek health care.


       Another important finding of the study is the direct correlation between health insurance

and health care-seeking behaviour. The costs of health care are such that out-of pocket

expenditure may ruin an individual wealth, create unemployment, reduce income, and further

deepens ill-health. Those realities are among reasons for people to purchase health insurance

coverage. Hence, health insurance allows the individual to seek as against delay care because

he/she is cognizant that the out-of pocket expenditure for health care costs will be lower than if

he/she did not have the product. This study found that health insurance coverage was the most

significant predictor of health care-seeking behaviour of Jamaicans, which concur with other

studies that showed that health insurance was a good indicator of health care demand (Geisler et

al. 2006; Schoen et al, 2000; Kasper et al, 2000). With 7 out of every 100 Jamaicans who are

classified in the poorest 20% having health insurance coverage in 2007, it is not surprising that

they have the lower demand for health care services. Again, this is again highlighting the

importance of social determinants in health care-seeking and providing an understanding of why

WHO (2005) forward the perspective on premature death of poor. Hence health insurance




                                                                                                 38
coverage does not only influence health care-seeking behaviour, it also reduces wellbeing of the

individual and the family, and the timing of health care.


       Studies have shown that there is positive correlation between illness and health care-

seeking behaviour; but this research found the contrary. This appears contradicting to the

argument that people will seek care in order to protect life and sustain life; but this is theoretical

true but why is this not the reality in Jamaica. Statistics for the last 2-decades in Jamaica revealed

that poor sent the least on medical expenditure; attended medical care the least; substantially

more of them reported that they were unable to seek medical care owing to inafffordability; had

the least health insurance coverage; reported a high probability of illness and so would explain

an aspect to this negative correlation. Another interesting explanation is embedded in the culture.

For men, health care-seeking behaviour is a signal of weakness and frequency of visits for

medical care is cultural interpreted as feminine. Boys are socialized to be strong, macho, and not

display sign of weakness (Chevannes, 2001) indicating an explanation for their low utilization of

medical care services. While the same rigidity does not hold true for girls, females (or women),

socio-cultural, there is a high unwillingness to seek more care than less care.


       Reporting illness therefore, does not symbolize more demand for medical care services.

There is a tendency for Jamaicans in particular men to seek health care on the first sign of

severity, indicating the psyche to delay care which is keeping with another study on black men in

United States (Strecher & Rosenstock, 1997). In 1989, 17 out of every 100 Jamaicans reported at

least one illness and in 2007, with a large population, the number of people reporting illness was

15 out of every 100. The media, internet, and more public health programmes are accounting for

the increase awareness and reporting of ill-health; but this has not translated into willingness to

seek medical care.

                                                                                                   39
        According to Marmot (2002), income affords choices in foods, nutrition, medical care

and better quality physical environment and therefore justifies the direct association between

itself and health. This study contradicts the findings of Marmot, as it found a positive correlation

between income (measures by rate of change GDP per capita) and illness. Studies have agreed

with Marmot, there is a direct correlation between income and health (UNDP 2006; Roos et al.

2004; Case 2001: Kawachi et at 1997; Smith & Kington 1997). One researcher went as far as to

say that income buy health (Sen, 1998). There is fallacious aspect to type of reason as it assumes

that health can be purchased, health can be transferred from medical practitioners to their clients

on demand; income comes with better choices, and that poor health can be reversed when we

want.


        A survey conducted by Diener, Sandvik, Seidlitz and Diener (1993), in Diener (1984),

stated that correlation between income and subjective wellbeing was small in most countries.

According to Diener (1984, 11), “…, there is a mixed pattern of evidence regarding the effects of

income on SWB [subjective wellbeing]”. Benzeval, Judge and Shouls (2001) study concur with

Diener that income is associated with health status. Benzeval et al went further as their research

revealed that a strong negative correlation exists between increasing income and poor health,

which concurs with this study. Furthermore, from a study, it was found that people from the

bottom 25 percent of the income distribution self-reported poorer subjective health by 2.4 times

than people in the wealthiest 20% (Benzeval et al., 2001). Like Benzeval et al. (2001), this study

highlights that lack of income and wealth for the poor is a disadvantage; but it is also a

disadvantage for the wealthiest 20%. Embedded in this finding is an unhealthy lifestyle practice

of the wealthy which erodes the merits of income to purchase quality food and water; nutrition

and its ability to secure good physical environment.


                                                                                                 40
       Two economists studying the ‘impact of wealth and income on subjective wellbeing and

ill-being’ found that employed people had a higher life and financial satisfaction than their

unemployed counterparts. Using linear regression analysis, they found that the employed had a

coefficient of 0.77 in life satisfaction compared to unemployed -3.00; and in the case of financial

satisfaction it was 5.52 to -11.52 respectively (Wooden & Headey, 2003, 16). Despite that

finding, it should be noted that the adjusted R2 for all the explanatory variables was 8.3% and

20.8% for life satisfaction and financial satisfaction respectively. The current research

established an association between poverty and health care-seeking, and unemployment and

poverty. The correlation between unemployment and poverty was a strong one indicating that

unemployment fuels more poverty and embodied in this reality is the psychological trauma of

job separation for the employed and material deprivation for the unemployed and poor. With

persistent unemployment comes the socio-cultural challenges of this experience, and this further

widens the gaps in the inability of the poor to seek medical care along with the unemployed

reality of recognizing and reporting ill-health but the difficulty of demanding care for the

negative health. Milstein & Smith (2006) has found the high cost of medical care is resulting in

some Americans seeking care abroad, as the current cost of health care is such they some them

have become pauper by their illnesses. While some Americans are able to go overseas for

medical care, the uninsured, poor, unemployed, self-employed on become ill is faced with the

choice of delaying care which can become fatal for them (Kaiser Commission on Medicaid and

the Uninsured, 2004), which is also the case in Jamaica.


Conclusion


The challenges for public health practitioners is address health, deprivation, material

inadequacies and poor sanitation conditions and poor water quality of the poor because of their

                                                                                                41
the direct association between those social variables and poor health. The current work re-ashes

those realities; but also highlight the health challenges of wealthy and wealthiest 20% in the

nation. Public health practitioner therefore can only focus on the poor and unemployed, as the

wealthy’s unhealthy lifestyle choices are eroding some of the benefits associated with income

and wealth.


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                                                                                             45
Table 2.1. OLS Results: Health Care-Seeking Behaviour and Social and Biological Variables,
1988-2007
                                       Coefficients
                                                Std.     Beta
 Variable                              B        Error             t statistic        95% CI

   Constant                           65.860     5.434               12.121     54.928 - 76.791***

   Prevalence of Poverty              -0.305     0.114   -0.313      -2.679       -0.534 - 0.076**

   Illness                            -1.100     0.234   -0.349      -4.698      -1.571 - 0.629***

   Health Insurance                    1.044     0.239   0.504        4.367       0.563 - 1.525***

Mean dependent variable = 58.06
R = 0.857
R2 =0.735
Adjusted R2 = 0.717
RSS = 796.173
F statistic = 48.590, p < 0.001
*p < 0.05, **p < 0.01, ***p < 0.001




                                                                                                     46
Table 2.2. OLS Results: Self-reported Illness and Social Variables, 1988-2007

                                       Coefficients               t statistic
 Variable                                       Std.     Beta
                                       B        Error                                95% CI

   Constant                           23.088     2.193              10.527      18.665 - 27.511***

   Prevalence of Poverty              -0.161     0.034   -0.527      -4.773      -0.230 - 0.093***

   Rate of Growth of GDP at
                                       0.948     0.080    0.877     11.872        0.787 - 1.109***
   constant prices

   Health Insurance                    0.121     0.070    0.185       1.730         -0.020 - .0262

   Seeking Medical Care               -0.171     0.032   -0.544      -5.316      -0.235 - 0.106***
Mean dependent variable = 12.41
R = 0.924
R2 =0.853
RSS = 46.919
Adjusted R2 = 0.839
F statistic = 62.377, p < 0.001
*P < 0.05, **P < 0.01, ***P < 0.001




                                                                                               47
Table 2.3. OLS Results: Health Insurance Coverage and Social and Biological Variables, 1988-
2007

    Variable                           Coefficients                                95% CI
                                                Std.     Beta
                                       B        Error             t statistic

   Constant                           12.836     4.608                 2.786    3.561 - 22.111**

   Prevalence of Poverty              -0.029     0.045   -0.062       -0.648      -0.119 - 0.061

   Illness                             0.657     0.096   0.432         6.826    0.464 - 0.851***

   Seeking Medical Care                0.225     0.044   0.466         5.076    0.136 - 0.314***

   Ln unemployment                                                                     -11.557 -
                                      -9.040     1.250   -0.604       -7.230
                                                                                       6.523***
Mean dependent variable = 12.36
R = 0.933
R2 =0.870
Adjusted R2 = 0.859
RSS = 98.622
F statistic = 77.144, p < 0.001
*P < 0.05, **P < 0.01, ***P < 0.001




                                                                                               48
Table 2.4. OLS Results: Logged Unemployment and Illness and Social Variables, 1988-2007

                                      Coefficients                                95% CI
    Variable                                   Std.     Beta    t statistic
                                      B        Error

   Constant                           1.677     0.317               5.295         1.040 - 2.315

   Prevalence of Poverty              0.006     0.004   0.195       1.747     -0.001 - 0.013***

   Illness                            0.050     0.008   0.496       6.185     0.034 - 0.067***

   Seeking Medical Care               0.011     0.004   0.328       2.528        0.002 -0.019*

   Health Insurance                   -0.059    0.008 -0.881       -7.230     -0.075 - 0.042***
Mean dependent variable = 2.33
R = 0.900
R2 =0.811
Adjusted R2 = 0.798
RSS = 0.64
F statistic = 49.267, p < 0.001
*P < 0.05, **P < 0.01, ***P < 0.001




                                                                                                  49
Table 2.5. OLS Results: Poverty and Illness and Social Variables, 1988-2007
    Variable                           Coefficients
                                                Std.     Beta
                                       B        Error             t statistic       95% CI

   Constant                           36.106    14.088                2.563        7.765 - 64.447

   Illness                            -0.767     0.294   -0.237      -2.611       -1.358 - 0.176*

   Seeking Medical Care               -0.591     0.116   -0.575      -5.079     -0.825 - 0.357***

   Log unemployment                   13.098     3.606   0.411        3.632     5.843 - 20.353**
Mean dependent variable = 22.79
R = 0.830
R2 =0.688
Adjusted R2 = 0.669
RSS = 1073.53
F statistic = 34.619, p < 0.001
*P < 0.05, **P < 0.01, ***P < 0.001




                                                                                                    50
                                                                         Chapter 3
Modelling social determinants of self-evaluated health of poor older people in
a middle-income developing nation


Over the last 2 decades (1988-2007), poverty in Jamaica has fallen by 67.5%, and this is within
the context of a 194.7% increase in inflation for 2007 over 2006. It does not abate there, as
Jamaicans are reporting more health conditions in a 4-week period (15.5% in 2007) and at the
same time this corresponds to a decline in the percentage of people seeking medical care. Older
people’s health status is of increasing concern, given the high rates of prostate cancer,
genitourinary disorders, hypertension, diabetes mellitus and the presence of risk factors such as
smoking. Yet, there is a dearth of studies on the health status of older people in the two poor
quintiles. This study examined (1) the health status of those elderly Jamaicans who were in the
two poor quintiles, and (2) factors that are associated with their health status. A sample of 1,149
elderly respondents, with an average age of 72.6 years (SD=8.7 years) were extracted from a
total survey of 25,018 Jamaicans. The initial survey sample was selected from a stratified
probability sampling frame of Jamaicans. An administered questionnaire was used to collect the
data. Descriptive statistics were used to examine background information on the sample, and
stepwise logistic regression was used to ascertain the factors which are associated with health
status. The health status of older poor people was influenced by 6 factors, and those factors
accounted for 26.6% of the variability in health status: Health insurance coverage (OR=13.90;
95% CI: 7.98-24.19), age of respondents (OR=7.98; 95% CI: 1.02-1.06), and secondary level
education (OR=1.82; 95% CI: 1.35-2.45). Males are less likely to report good health status than
females (OR=0.56; 95% CI: 0.42-0.75). Older people in Jamaica do not purchase health
insurance coverage as a preventative measure but as a curative measure. Health insurance
coverage in this study does not indicate good health but is a proxy of poor health status. The
demand of the health services in Jamaica in the future must be geared towards a particular age
cohort and certain health conditions, and not only to the general population, as the social
determinants which give rise to inequities are not the same, even among the same age cohort.



1. INTRODUCTION


Factors determining the poor health status of the elderly in Jamaica can be viewed from the

perspective of a socio-medical dichotomy. Such factors include poverty (resulting in one’s


                                                                                                51
inability to access loans, quality education and health care), lifestyle (e.g. smoking, sedentary

habits, sexual and dietary practices and physical inactivity), resulting in prostate cancer,

genitourinary disorders, hypertension, diabetes mellitus and premature death. In 2005, the World

Health Organization began a thrust in examining the social determinants of health, and despite

that reality there is a lack of literature in this regard on the elderly poor people in Jamaica. These

parameters were explored in the current research by using a sample of 1,149 elderly poor

Jamaicans.


       The findings of this paper reveal that the cost of medical care is positively correlated with

health conditions, and that economic constraints account for the decline in the elderly seeking

medical care. Older people in Jamaica do not purchase health insurance coverage as a

preventative measure but as a curative measure. Health insurance coverage in this study does not

indicate good health, but on the contrary, it is a proxy of poor health status. It is also noted that

income is positively correlated with a higher standard of living and life expectancy. In support of

this claim, studies have shown that life expectancy in many developing countries [1], in

particular the Caribbean (Barbados, Guadeloupe, Jamaica, Martinique, Trinidad and Tobago) has

exceeded 70 years, and they are now experiencing between 8-10% of their population living to

60+ years old. Life expectancy, which is a good indicator of the health status of a populace, is

higher in countries with high GDP per capita. This means that income is able to purchase better

quality products [2], and indirectly affects the length of years lived by people. GDP per capita is

used as an objective valuation of standard of living [3-12]. While a country’s GDP per capita

may be low, life expectancy is high because health care is free for the population. Despite this

fact, material living standards undoubtedly affect the health status and wellbeing of people, as

well as the level of females’ educational attainment [6] and the nutrition intake of the poor. On


                                                                                                   52
the other hand, when there is economic growth, the society has more to spend on nutrition, health

care, better physical milieu, better quality food, safer sanitation and education.


        Good health is, therefore, linked to economic growth, something which is established in a

plethora of studies by economists. Developing countries (a term synonymous with poverty) do

not only constitute low levels of democracy, civil unrest, corruption [13], high mortality and

crude birth rates, but one must also include nutritional deficiency [14]. The WHO in 1998 put

forward the position that 20% of the population in developing countries do not have access to

enough food to meet their basic needs and provide vital nutrients for survival.


        In the Caribbean, and in particular Jamaica, poverty is typical, and many of the ills that

affect other developing nations outside of this region are the same. The poor in this society are

facing insurmountable challenges in buying the necessary health care. In 2007, between 51 and

53% of those in the poor quintiles in Jamaica sought medical care, compared to 61-68 % of those

in the middle-to-wealthiest quintiles. When those who had reported that they were ill were asked

why they had not sought medical care, 51% of those in the poorest quintile indicated that they

‘could not afford it’, with 36.7% of those in the poor quintile giving the same response, and the

percentage declines as the wealth of the person increases to the wealthiest quintile (7.7% of those

in the wealthiest quintile).


        Over the last 2 decades (1988-2007), poverty in Jamaica has fallen by 67.5% and this is

in the context of a 194.7% increase in inflation for 2007 over 2006. Jamaicans are reporting more

health status in a 4-week period (15.5% in 2007) and at the same time this is associated with a

decline in the percentage of people seeking medical care. Older people’s health status is of

increasing concern, given the high rates of prostate cancer, genitourinary disorders, hypertension,


                                                                                                53
diabetes mellitus and the presence of risk factors such as smoking in earlier life. Yet, there is a

dearth of studies on the health status of older people in the two poor quintiles.


        Works which have examined the social determinants of health have used data for the

population [2,3], but none emerged from a literature research using data for poor old people. This

study examined (1) the health status of those elderly Jamaicans who were in the two poor

quintiles, and (2) factors that are associated with their health status.


2. MATERIALS AND METHODS

2.1 Sample


A sample of 1,149 elderly respondents was extracted from a larger survey of 25,018 Jamaicans.

The sample was based on being 60+ years old, and being classified in the two poorest income

categorizations. The initial survey sample (n = 25, 018) was across the 14 parishes, and was

conducted between June and October 2002. The sample (n=25,018 or 6,976 households out of a

planned 9,656 households) was drawn using a stratified random sampling technique. This design

was a two-stage stratified random sampling design, where there was a Primary Sampling Unit

(PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District

(ED), which constitutes of a minimum of 100 dwellings in rural areas and 150 in urban zones.

An ED is an independent geographic unit that shares a common boundary. This means that the

country was grouped into strata of equal size based on dwellings (EDs). Based on the PSUs, a

listing of all the dwellings was made, and this became the sampling frame from which a Master

Sample of dwellings was compiled, and which provided the frame for the labour force. The

survey adopted was the same design as that of the labour force, and it was weighted to represent

the population of the country.

                                                                                                54
       The survey was a joint collaboration between the Planning Institute of Jamaica and the

Statistical Institute of Jamaica. The data were collected by a comprehensive administered

questionnaire, which was primarily completed by heads of households for all household

members.      The questionnaire was adapted from the World Bank’s Living Standards

Measurement Study (LSMS) household surveys, and was modified by the Statistical Institute of

Jamaica with a narrower focus, to reflect policy impacts as well. The instrument assessed: (i) the

general health of all household members; (ii) social welfare; (iii) housing quality; (iv) household

expenditure and consumption; (v) poverty and coping strategies, (vi) crime and victimization,

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

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

demographic questions.


Data were stored and retrieved in SPSS for Windows, version 16.0 (SPSS Inc; Chicago, IL,

USA). The current study is explanatory in nature. Descriptive statistics were presented to

provide background information on the sampled population. Following the provision of the

aforementioned demographic characteristics of the sub-sample, chi-square analyses were used to

test the statistical association between some variables, t-test statistics and analysis of variance

(i.e. ANOVA) were also used to examine the association between a metric dependent variable

and either a dichotomous variable or non-dichotomous variable respectively. Logistic regression

was used to examine the statistical association between a single dichotomous dependent variable

and a number of metric or other variables (Empirical Model). The logistic regression was used

because in order to test the association between a single dichotomous dependent variable and a

number of explanatory factors simultaneously, it was the best available technique. A p-value <

0.05 (two-tailed) was selected to indicate statistical significance in this study. Where collinearity


                                                                                                  55
existed (r > 0.7), variables were entered independently into the model to determine those that

should be retained during the final model construction. To derive accurate tests of statistical

significance, SUDDAN statistical software was used (Research Triangle Institute, Research

Triangle Park, NC), and this was adjusted for the survey’s complex sampling design.


2.2 Measure


Social determinants. These denote the conditions under which people are born, grow, live, work
and age, including the health system.


Crowding. This is the total number of persons living in a room with a particular household.
                   , where is each person in the household and r is the number of rooms
excluding kitchen, bathroom and verandah.
Age: This is a continuous variable in years, ranging from 15 to 99 years.


Old/Aged/Elderly. An individual who has celebrated his/her 60th birthday or beyond.


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

a breadwinner and/or family member, loss of property, having been made redundant, failure to

meet household and other obligations.


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

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

insurance coverage, and 0 for not reporting ownership of private health insurance coverage.


Gender: Gender is a social construct which speaks to the roles that males and females perform

in a society. This variable is a dummy variable, 1 if male and 0 if otherwise.




                                                                                                56
Health conditions: The report of having had an ailment, injury or illness in the last four weeks,

which was the survey period. This variable is a binary measure, where 1=self-reported health

status or illnesses, and 0=otherwise (not reporting an illness, injured or dysfunctions).


Poverty: In this study, the definition of poverty is the same as that used to estimate poverty in

Jamaica. It is established from the basis of a poverty line. In order to compute the per capita

poverty line in each geographical area (Kingston Metropolitan Area, Other Towns and Rural

Areas), the cost of living for a basket of goods is divided by an average family of five. The

basket of goods is established by the Ministry of Health based on the normal nutrients of the

average family. Based on a per capita approach, there are five per capita income quintiles, with

the poorest being below the poverty line (quintile 1) and the wealthiest being in quintile 5.


Elderly, Aged or Old persons. Using the same definition offered by the United Nations in the

Report of the World Assembly on Ageing, July 26-August 6, 1982 in Vienna, that the elderly are

persons who are 60+ years old.


Older-poor (elderly-poor, aged-poor). All aged persons below and just above the poverty line

(quintiles 1 & 2) in Jamaica.


3. RESULTS

3.1 Demographic characteristics of sample


Consistent with the demographic characteristics of the ageing population, the sample was 1,149

of which there were 45% males (N=517) compared to 55% females (N=632). The mean age of

the sample was 72.6 years (SD=8.7 years). Most of the sample were married (40%, N=452),

50.5% (N=580) of the sample were in the poorest 20% of per capita income quintile, 95%


                                                                                                57
(N=1,087) were not receiving retirement income; those who were heads of households (98.3%,

N=1,129), those who had at most primary education (65.2%, N=700) and those who did not have

health insurance coverage (86.0%, N=973) (Table 3.1 ).


        Thirty-seven percent (37.2%) of the sample indicated having had an illness in the last 4-

week period. Approximately 64% of the respondents indicated that they sought health care for

their health conditions. When the respondents were asked if they had visited a health practitioner

for any other reason during the last 12 months, 57.1% reported yes and 30.3% reported going for

‘regular checkups’. Of those who indicated yes, 37.2% visited public health care institutions, and

18.7% went to private clinics, compared to 5.7% who claimed that they attended both health care

facilities. The typologies of illness included colds (1.4%), diabetes mellitus (5.7%), hypertension

(42.9%) and arthritis (31.4%), while 18.6% did not specify their health condition(s). Only 2% of

the respondents had health insurance coverage; 61% purchased the prescribed medication; and

81.8% of those who indicated having not bought their medication reported that they could not

afford it.


        The median number of days for how long an illness lasted was 7 days, with a median

medical expenditure of US $7.85 (US $1.00 = Ja. $50.97).


3.2 Bivariate Correlation of Health Status and Age Cohort


Of the 1,149 sample respondents for this study, 98.8% (N=1,135) were used for the statistical

correlation between health status and gender. Of the 1,135 respondents, there were 688 young-

old, 327 old-old and 120 oldest-old poor Jamaicans. There was a correlation between the two

above-mentioned variables – χ2 (df=2) = 22.863, p-value < 0.001. On an average, 46% of the

aged-poor (N=523) reported that they had at least one illness/injury in the survey period. The

                                                                                                58
most health status was reported by the oldest-old poor (59.2%, N=71), 52.9% (N=173) and the

least by the young-old (40.6%, N=279). Embedded in these findings is that for every 1 young-

old poor who indicated that he/she had an illness/injury, there are 1.5 oldest-old and 1.3 old-old

poor.




3.3 Multivariate Analysis


The results of the multiple logistic regression model (in Table 3.2), were statistically significant

[Model χ2 (df=18) = 229.47; -2Log likelihood = 1130.37; p-value < 0.001]. Table 3.2 showed

that 26.6% of the variances in the health status of older people in Jamaica were accounted for by

the independent variables used in the multiple logistic regressions. The mold revealed that there

were 6 statistically significant factors that determined health conditions. These predictors are age

(OR=1.04, 95% CI=1.02-1.06), health insurance coverage (OR=13.90, 95% CI=7.98-24.19),

physical environment (OR=1.42, 95% CI=1.06-1.89), cost of medical care (OR=1.00, 95%

CI=1.00-1.00), secondary level education (OR=1.82, 95% CI=1.35-2.45) with reference to

primary and below education, and gender of respondents (OR=0.56, 95% CI=0.42-0.75).

Controlling for the effect of other variables, the average likelihood of reporting illness/injury in a

4-week reference period declined by 17 times for those who had dysfunctions.

        The model had statistically significant predictor power (Model χ2 (df=18) = 229.47; -

Homer and Lemeshow goodness of fit χ2= 3.739, P=0.880), and correctly classified 70% of the

sample (correctly classified 55.4% of those with dysfunctions and 82.3% of those without

dysfunctions) (Table 3.2). The logistic regression model can be written as: Log (probability of

dysfunctions/probability of not reporting dysfunctions) = -4.185 + 0.039 (Age) + 2.632 (Health



                                                                                                   59
Insurance coverage, 1= yes, 0=no) + 0.348 (Physical Environment, 1=yes, 0=no) + 0.000 (Cost

of Medical Care) + 0.598 (Secondary level education=1, 0=primary and below) – 0.581 (Sex).




4. DISCUSSION



People are living longer [15], which means that on average the elderly are living 15-20 years

after retirement. Demographic ageing at the micro and macro levels implies a demand for certain

services such as geriatric care. In addition to preventative care, there will be a need for particular

equipment and products (i.e. wheelchairs, walkers etc.). Then there are future preparations for

pension and labour force changes, along with the social and economic costs associated with

ageing, as well as the policy based research to better plan for the reality of these age groups. The

World Health Organization (WHO), in explaining the ‘problems’ that are likely to occur because

of population ageing, argues that the 21st Century will not be easy for policy makers as it is

pivotal in the preparation process to postpone ailments and disabilities, and the challenge of

providing a particular standard of health for the populace [16]. What constitutes population

ageing? Some demographers have put forward the benchmark of 8-10% as an indicator of

population ageing [17]. Within the construct of Gavrilov and Heuveline’s perspective, the

Jamaican population began experiencing this significant population ageing as of 1975 (using 60+

years for ageing) or 2001 (if ageing is 65+ years). The issue of population ageing will double

come 2050, irrespective of the chronological definition of ageing, but what about the elderly

poor health conditions?

       Let us examine the disparity between long life and quality of lived years. Ali, Christian &

Chung [18] who are medical doctors, cite the case of a 74 year-old man who had epilepsy, and

                                                                                                   60
presented the findings in the West Indian Medical Journal. They write that “Elderly patients are

frequently afflicted with paroxysmal impairments of consciousness, because they frequently

have chronic medical disorders such as diabetes mellitus and hypertension, and can also be on

many medications….Many elderly patients may have more than one cause for this symptom”

[18].


        The case presented by the medical doctors emphasizes the point we have been arguing

that long life does not imply quality of lived years. Although the case study cited here does not

constitute a general perspective on all the elderly, other quantitative studies have concurred with

Ali, Christian and Chung’s general findings. Scientists agree that biological ageing means

degeneration of the human body, and such a reality means that longer life will not mean quality

years. Population ageing is going to be a socioeconomic, psychological and political challenge

today, tomorrow and in the future of developing countries and nations like Jamaica. This

reinforces the position postulated by the WHO that healthy life expectancy [19] is where we

ought to be going, as the new thrust is not living longer but how many of those years are lived

without dysfunctions. Within the context of healthy life expectancy, studies that will be used to

guide policy are those that incorporate many determinants, and not only biological conditions

[20-25]. But none of those studies examined poor old people. Hambleton [20] and Bourne [23-

25] are Caribbean scholars who have researched social determinants using the population of the

poor, and this gap to date in the literature needs to be addressed, as the elderly constitute a

vulnerable group, and the poor elderly group is even more vulnerable. Any policy which seeks to

reduce poverty must take into account the poor elderly.


        ‘Ageing in poverty’ implies that persons remain in their local environments with the

ability to live in their own home - wherever that might be - for as long as confidently and

                                                                                                61
comfortably possible. It inherently includes not having to move from one's current residence in

order to secure the necessary support services in response to changing needs. The ageing of

Caribbean populations has been accompanied by a shift to chronic non-communicable diseases

as major causes of morbidity. While overall national trends have been reported, examination of

local patterns of morbidity are increasingly important, as they have implications for the services

to be provided, the mix of human resources, and the maintenance of health and functional status

that facilitate ageing in place.


        Research has shown that crowding is strongly correlated with the wellbeing of the elderly

(ages 60+ years) [23]; however this phenomenon, which is synonymous with poverty, does not

influence the health status of poor elderly Jamaicans. Embedded in this finding is the fact that

older people, in particular those in poor quintiles, interpret people around not as a negative force

but as good social networking and interaction. What, then, influences their health conditions?


        Poverty speaks to a particular environment; Pacione [26] showed that one’s physical

environment affects one’s quality of life, and other scholars have agreed with this finding. The

current study concurs with Pacione and others, in that the physical milieu is positively correlated

with health conditions. Although Michael Pacione’s work was on the general population,

Bourne’s works [23, 24] examined the elderly population (ages 60+ years) and found a negative

association between physical environment and wellbeing, and this study concurred with that of

the aforementioned researcher on the correlation between physical environment and health

conditions. In this study, an important finding is to refine the correlation.


        Health insurance coverage is among the many indicators of the health-seeking behaviour

of a populace. For the poor elderly, it is the most significant predictor of health conditions. The


                                                                                                 62
correlation is a strong positive one, indicating that health insurance coverage is a good proxy for

more ill-health than good health. The current research found that those elderly poor who owned

health insurance were 14 times more likely to report dysfunctions (or injuries) than those who

did not. Health insurance is, therefore, a cost reducer for those who are aware that they are ill,

and it is not in demand as a preventative measure. Arising from this fact is the role played by the

costs of medical and curative care. Health is influenced by more than disease-causing pathogens.

[27]


       The cost of medical care is positively correlated with health conditions, suggesting that

the more dysfunctions (or injuries) that the elderly poor report, the more they are likely to spend

on medical care. The elderly poor are prevented from seeking preventative care as against

curative care. The latest data published by the Planning Institute of Jamaica and the Statistical

Institute of Jamaica[28] showed that 37.3% of elderly people are at least poor, with 20.6% falling

in the poorest quintile. This further explains the rationale for the reduction in the demand for

medical care within the context of a precipitous increase in inflation in 2007 over 2006 (194%).

With the steady rise in the cost of health care, as well as the increase in general food and non-

alcoholic beverage prices in Jamaica, coupled with the fact that illness in older age requires care,

the elderly poor are facing increasingly difficult times. The severity of the economic situation

has seen a dramatic increase in the number of Jamaicans not seeking medical care for

illness/injury. Although there is a decline in the general population seeking medical care (66%),

more of the elderly do seek health care (72.3%) and this is owing to recurrent chronic illness

which was shown to affect 74.2% of them28. Illnesses/injuries are precipitously affecting the

elderly, and the data showed that self-reported illness for the elderly was 2.3 times more (36.6%)

than in the general population (15.5%) [28]. In 2007, the elderly poor who constitute 38% of the


                                                                                                 63
poor-to-poorest in the population are mostly household heads (67.3%) and often unemployed,

and within this context they must provide for their own health needs and those of their family,

despite the harsh economic challenges and increased cost of health care.


       In 2002, 12.9% of Jamaicans were unable to afford medical care, and approximately 4

years later, the figure had risen by 162.8% to 33.9% in 2007. This is within the context of a

26.3% decline in poverty for the same period. Generally poverty has been falling over the last 2

decades in Jamaica, and inflation has fluctuated, justifying the increased amount spent on food

and beverages [28], and the corresponding reduction in health care expenditure. In Jamaica

remittances, which subsidize income for many households, have fallen by 7.7% and the

reduction is 33% for those in the poor-to-the-poorest income quintiles. If the cost of medical care

is positively correlated with the health status of the elderly poor, then can it be said that the poor

elderly have more ill-health within the context of biological ageing and lowered access to

employment income? Marmot [2] opined that there is a direct association between income and

poor health, and this further helps us to understand the embedded health challenge of the elderly

poor, as they must meet the increasing costs of medical care, cost of living, lower income,

illnesses and severity of health conditions. On examining the health statistics for 2007 [28], the

indication was that 50.8% of those in the poorest income quintile were unable to afford to seek

medical care, and the figure was 36.7% of those in the poor quintile. In order to understand the

severity of the situation regarding the aged-poor people in Jamaica, let us analyze the

aforementioned within the context of the aged-poor. The official statistical publication for

Jamaica for 2007 [28] showed that 20.6% percent of the elderly people are in the poorest quintile

and 17.7% in the poor quintile which means that a little over half of the aged-poorest in Jamaica

(10.4%) were unable to afford medical care, and 6.5% of the aged-poor had financial difficulty


                                                                                                   64
affording medical care expenditure. One of the choices that must be made by the aged-poor in

Jamaica is a switch from the formal medical care service to utilizing home remedies and over-

the-counter medications, instead of visiting their personal physicians or health care facilities.


       Since 1988 when the Jamaican authorities began collecting data on self-reported health

conditions, men have been reporting less health status than women [28]. The reporting of less

illness does not mean that men are healthier than women, as the same statistical report [28]

shows that women seek more medical care than men. Morbidity data for the sexes in Jamaica is

typical, as in Mexico City, Havana and Santiago-Chile at least 60% of females compared to 50%

of males aged 60+ years old reported fair-to-poor health [29]. Continuing, Buenos Aires,

Montevideo and Bridgetown-Barbados had twice the figures of the aforementioned geo-political

zones [29]. This is in keeping with women’s protective role of self, and their willingness to have

a regard for their future health status accounts for a higher health status and not a lower one,

although they report more dysfunctions than men. If life expectancy were to be used to proxy

good health status, females are healthier than men given that they outlive them by 6 years in

Jamaica and 8 years in the world. Furthermore, in 2000-2005, life expectancy for men was 69.5

years and 74.7 years for women, and come 2045-2050 they both would have gained an additional

2 and one-quarter years more to their life span. The equal and constant rate of change in the life

expectancy of both sexes in Jamaica highlights the fact that men do not enjoy better overall

health status than their female counterparts. More years of life for both sexes means that the life

course opens itself to coronary heart disease, stroke and diabetes mellitus, and so morbidity must

be examined in this discourse.


       Studies done by the Ministry of Health reveal that of the five leading causes of mortality

in Jamaica, which are malignant neoplasm, heart disease, diabetes mellitus, homicide and

                                                                                                    65
cerebrovascular diseases [30], more men die from more of the aforementioned conditions than

women. Malignant neoplasms are 39% greater for men than women; cerebrovascular diseases

are 14% higher for females than males; heart disease was 71.2 per 100, 000 for men and 66.1 per

100,000 for women; and diabetes mellitus was 64% more for females than males [30]. The

greater vulnerability of men to particular mortality than women is typical across Latin America

and the Caribbean [29], pointing to gender bias (that is feminization) in visits to health care

facilities, which are embedded in the life expectancy rates and visits to health care institutions.

The matter of reporting less health status, once again, does not imply a healthier person, as health

is not on a continuum, with ill-health on one extreme and good health on the other. Health is

more in keeping with cyclical flow, and changes over the life course with time, experiences and

socio-physical environmental conditions. Hence, asking about ill-health is not a good proxy for

health status, as in 2007 a group of Caribbean scholars conducted a national representative

prevalence survey of some 1,338 Jamaicans, and found that those who indicated themselves to be

of the lower class had the least self-reported health status [13].


       The discipline of gerontology – scientific inquiry into the biological, psychological, and

social aspects of ageing - has shown that ageing is not necessarily without increased health

conditions; it is natural for aged people to complain and die more of dysfunctions than other age

cohorts [31, 32] and that is directly related to their basal metabolic rate [33] and the nature of the

life course of the aged [34]. Here functional ageing is an explanation for the image of ageing,

and it can be measured by normal physical changes, diminished short-term memory, reduced

skin elasticity and a decline in aerobic capacity. It is well established in the research literature

that age is directly correlated with health status for the elderly, and in this study the finding

concurs with the literature. The current research shows that age is the second most significant


                                                                                                   66
predictor of health status for the elderly poor, and explains why the disparity in poor health in

Latin and America and the Caribbean is higher for older persons than younger people [29].

Population ageing is synonymous with more disability and more non-communicable diseases

such as malignant neoplasms, hypertension, diabetes, and heart diseases than younger ages.

Donald Bogue [35] noted that health problems increase with ageing, and that one’s health issues

intensify with ageing. Therefore, an unhealthy lifestyle – tobacco consumption, physical

inactivity, unprotected sex, and unhealthy diet - over the life course will affect the elderly in

latter life, and the declining health of the elderly poor is the same within the sub-categories of the

elderly – young-old, old-old and oldest old.


       Issues of the elderly cannot be discussed without an examination of area of residence.

This study found no correlation between the aged-poor’s health status and area of residence.

Using data since 1989 (from various issues of the Jamaica Survey of Living Conditions),

population ageing is biased by gender as well as by specific area of residence. Over the last

decade (1997-2007), the number of elderly Jamaicans living in rural areas has declined from

54.3% to 46.6% (a rate of 14.1%). For the same period, the rate of increase of the aged populace

in the Kingston Metropolitan Area (100% cities) was 19.5%, down from 27.2% (in 1997) while

the increase in the aged population over the same period in Other Towns was 12.9% over 18.5%

in 1997. Regarding the prevalence of poverty for the region (2007), rural poverty was 3.8 times

more than that in Other Towns, and 2.5 times more than that in the Kingston Metropolitan Area.

Despite the compounding economic challenges of poverty coupled with ageing, the poor-elderly

in Jamaica do not experience a difference in their health status owing to area of residence. Here

the health issues of the aged poor are independent of their area of residence, suggesting that in

the population the poor are age-residence insensitive. This contradicts research literature on the


                                                                                                   67
health status of the elderly which has shown a correlation between the aged and their areas of

residence [23,24,48], indicating that the physical characteristics of the aged poor are the same in

different areas of residence, and therefore do not account for any poor health, disability,

functional inability or psychological conditions.


       Like the WHO [36], the researcher believes that although ageing is a biological

phenomenon, it cannot be due only to biological conditions, as ageing relates to bio-psycho-

social [20, 25, 37-49] and environmental conditions [23-26], since people – biological organisms

– must operate in a socio-physical milieu throughout their life span, and this demands an

expansion of biological conditions in the ageing discourse. The very nature of gerontology must

coalesce biopsychosocial and environmental conditions in assessing ageing and the health of the

aged, which are in keeping with the WHO’s Constitution of 1948, and this has also been

established in many Caribbean scholarships [20,23-25,42-49]. Within the context of the above-

mentioned challenges for elderly people, when this is coupled with poverty which affects 10.2%

of elderly Jamaicans (N=29,794) in 2007, it intensifies the challenges experienced by elderly

people. With the increased cost of food and non-alcoholic beverages, fuel and household

supplies, housing and household operational expenses, the health status of the older-poor will

continue to deteriorate, as they will not be able to afford health care services. The decline in

medical care-seeking behaviour of Jamaicans speaks to the challenges of older people and the

rise in instances of switching to alternative medicine. This is further intensified by poverty; and

rural poverty, which is more severe than that found in urban areas [50], will further compound

the challenges of the health status of the aged populace. Older people who are poor must operate

within the same biopsychosocial and physical environment during their lifetimes as other

persons.


                                                                                                68
       Even among the WHO commissioned studies [51-53], as well as other studies on the

social determinants of health [2,3, 20-25], the population of the poor elderly were not examined.

Likewise in the Caribbean, scholars have examined the social determinants of the population or

the elderly population, with poverty being an independent variable [20, 23-25]. Any policy that

seeks to address the health status of the elderly poor must take into consideration, or concentrate

and/or rely on, not only the population in general, but the cohort of the elderly in particular. The

experiences and demands of the elderly are not the same as the general population, and the

current study shows that social determinants of health are somewhat different for the general

elderly population and the poor elderly cohort. The WHO [51] opined that the social

determinants of health for the most part account for the health inequities between and within

nations, which substantiates the differences that emerged between the elderly in other studies

[20, 23-24] and the current study of the poor elderly. These findings are far-reaching, and can be

used to guide policy and research. The elderly-poor in Jamaica are experiencing ‘health poverty’

which cannot be alleviated by unresearched policies or research policies on the general

population, but by the elderly cohorts in particular.


5. Conclusion

       In summary, the number of elderly persons who reported health conditions in Jamaica is

3 times more than that for the nation (i.e. 12.6%), suggesting that health care expenditure for

Jamaicans is substantially used to address health care needs for the aged population. With the

number of elderly come 2025 estimated to be 14.5% over 10.9% for 2007, health care

expenditure will be primarily absorbed in caring for this age cohort. Public health practitioners

must begin programmes to deal with this pending reality. Ageing is a process which denotes that

the high number of health conditions affecting the elderly would have started earlier, based on

                                                                                                 69
some of the decisions that they undertook (or did not) leading up to their current age. Hence,

there is a need to have a public health campaign geared towards the promotion of healthy

lifestyle practices for ages close to sixty years, in conjunction with one for children and for the

working-age population. The programme should target check-ups, preventative care, signs of the

onset of particular health conditions, and the distinction between ill health and good health care

practices. The demand of the health services in Jamaica in the future must be geared towards a

particular age cohort and certain health conditions, and not only to the general population, as the

social determinants which give rise to inequities are not the same even among the same age

cohort.


6. Disclosure

The author reports no conflict of interest for this study.


7. Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica
Survey of Living Conditions, none of the errors in this paper should be ascribed to the Planning
Institute of Jamaica or the Statistical Institute of Jamaica, but to the researcher.




8. Acknowledgement

The dataset for this study was made available from the databank of SALISES (Sir Arthur Lewis
Economic Institute), Faculty of Social Sciences, the University of the West Indies, Mona,
Jamaica and for this the researcher is indebted and greater appreciate this gesture.




                                                                                                70
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                                                                                          73
Table 3.1: Socio-demographic characteristics of sample

Description                                        N               Percent

Gender
   Male                                           517               45.0
   Female                                         632               55.0

Marital status
  Married                                         452               40.0
  Never married                                   357               31.6
  Divorced                                         10                0.9
  Separated                                        22                1.9
   Widowed                                        290               25.6

Per capita Income quintile
   Poorest                                        580               50.5
   Poor                                           569               49.5

Retirement Income
  No                                              1087              95.0
  Yes                                              57               5.0

Household head
  No                                               20                1.7
  Yes                                             1129              98.3

Health Insurance coverage
 No                                               973               86.0
 Yes                                              158               14.0

Educational Level
Primary and below                                 700               65.2
Secondary                                         363               33.8
Tertiary                                           10                0.9

Age                                   72.63 years (SD=8.7 years)
Total Medical Care Expenditure         $1,067.64 (SD=$2,000.00)
Per capita consumption                $30,998.07 (SD=$9,833.00)
US $1.00 = JA$50.97




                                                                             74
Table 3.2: Logistic Regression: Socio-demographic correlates of health status of poor older
people in Jamaica, N=1,033
                                                              OR             95.0% C.I.
 Variable
   Age                                                          1.04            1.02 - 1.06***
   Retirement income                                            0.75               0.38 - 1.49
   Per capita consumption                                       1.00               1.00 - 1.02

   Separated, divorced or widowed                               1.07               0.74 - 1.55
   Married                                                      1.11               0.77 - 1.58
   Never married (reference group)                              1.00

   Health insurance                                            13.90          7.98 - 24.19***
   Environment                                                  1.42             1.06 - 1.89*
   Household head                                               3.34             0.37 - 30.01
   Cost of medical care                                         1.00            1.00 - 1.05**

   Secondary                                                    1.82           1.35 - 2.45***
   Tertiary                                                     0.43              0.07 - 2.63
   Primary and below (reference group)                          1.00

   Semi-urban                                                   0.78               0.51 - 1.19
   Urban areas                                                  0.86               0.50 - 1.49
   Rural areas (reference group)                                1.00

   Sex                                                          0.56            0.42 - .75***
   Living arrangement                                           1.20              0.77 - 1.88
   Crowding                                                     0.89              0.78 - 1.02
   Crime index                                                  1.00              0.98 - 1.03
   Positive affective                                           0.96              0.90 - 1.01
Model Chi-square (df =18) = 229.47, p-value < 0.0001
-2Log likelihood = 1130.37;
Nagelkerke R-square = 0.266
Hosmer and Lemeshow test P = 0.880
*P < 0.05, **P < 0.01, ***P < 0.001




                                                                                                 75
                                                                         Chapter 4

Disparities in self-rated health, health care utilization, illness, chronic illness
and other socioeconomic 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, 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 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%), 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 will seek less care, and this will further
increase mortality among those cohorts.


Introduction

This study examines the self-rated health status, health care utilization, income distribution, and

health insurance status of Jamaicans, and the disparity between the insured and uninsured. It also

models self-rated health status, health care utilization, income distribution, and how these differ

between the insured and uninsured. The current findings revealed that 20.2% of Jamaicans had


                                                                                                76
health insurance coverage, suggesting that a large percentage of the population is 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 on 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’s health care regime. 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 demands for health care, as well as their health choices, are based on

affordability. Affordability of health care is assisted by health insurance coverage as the

provisions of care offered by governmental policies mean that the public health care system will

be required to meet the needs of many people. Those people will be mostly children, the elderly

and those who belong to other vulnerable groups.

       The public health care system in many societies often involves long queues, extended

waiting times, frustrated patients and poor people who are dependent on the service. In order to

circumvent the public health care system, people purchase health insurance policies as a means



                                                                                                77
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], are embedded in financial deprivation.

       The WHO stated that “In reality, low and middle income countries are at the centre of

both old and new public health challenges” [7]. The high risk of death in low-income countries

is owing to food insecurity, low water quality and low sanitation coupled with inadequate access

to financial resources [11, 13]. Poverty makes it impossible for poor people to respond to illness



                                                                                                78
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.



Methods and material


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

                                                                                               79
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

                                                                                              80
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%.




                                                                                                   81
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 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).

       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).



                                                                                             82
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

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



                                                                                                83
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-test = - 23.1, P < 0.0001). The mean age of some with chronic illnesses

was 62.3 years (SD = 16.2) compared to 29.3 years (SD = 26.1) for those with non-chronic

illnesses. Furthermore, the mean age of insured respondents with chronic illnesses was 63.8



                                                                                              84
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 4.1 examines information on crowding index, total annual food expenditure,

annual non-food expenditure, income, age, time in household, length of marriage, length of

illness and number of visits made to medical practitioner by health insurance status.

       Self-rated health status, health care seeking behaviour, illness, educational level, social

class, area of residence, and health conditions, and health care utilization by health insurance

status are presented in Table 4.2.

       Table 4.3 presents information on the age cohort of respondents by diagnosed health

conditions. A significant statistical association was found between the two variables χ2 = 436.8,

P < 0.0001.

       Table 4.4 examines illness by age of respondents controlled by health insurance status.

There was a significant statistical relationship between illness and age of respondents, but none

between the uninsured and insured, P = 0.410.

       Table 4.5 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-statistic = 99.9, P < 0.0001). Those with colds, 19.2 years (SD = 23.9);

diarrhoea, 30.3 years (SD = 31.4); asthma, 22.9 years (SD = 22.1); diabetes mellitus, 60.9 years

(SD = 16.0); hypertension, 62.5 years (SD = 16.8); arthritis, 64.3 years (SD = 14.5), and other

conditions, 38.3 years (SD = 25.3).




                                                                                                85
Analytic Models

Nine variables (see Table 4.6), account for 32.8% of the variance in moderate-to-very good self-

rated health status of Jamaicans The variables are medical expenditure, health insurance status,

area of residence, household head, age, crowding index, total food expenditure, health care

utilization and illness. Self-reported illnesses accounted for 62.2% of the explained variability of

moderate-to-very good health status.

       Table 4.7 shows information on the explanatory factors of self-reported illnesses. Seven

factors accounted for 66.5% of the variability in self-reported illnesses. Ninety-two percent of

the variability in self-reported illnesses was accounted for by health care utilization (health care-

seeking behaviour).

       Three variables emerged as statistically significant correlates of health care utilization.

They accounted for 71.9% of the variance in health care utilization. Most of the variability can

be explained by self-reported illnesses (71.2%, Table 4.8).

       Self-reported diagnosed chronic illnesses can be explained by 5 variables (gender, marital

status, health insurance status, age and length of illness), and they accounted for 27.7% of the

variance in self-reported diagnosed chronic illness (Table 4.9).

       Sixty-two percent of the variability in income can be explained by crowding index, social

class, household head, health insurance status, self-rated health status, health care utilization,

area of residence and marital status. Most of the variability in income can be explained by social

class (Table 4.10).

       Table 4.11 presents information on the explanatory variables which account for health

insurance coverage. Six variables emerged as significant determinants of health insurance

coverage (age, income, chronic illness, health care utilization, marital status and upper socio-



                                                                                                  86
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 4weeks, 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 cohort did not have health insurance

coverage. Insured respondents were 1.5 times more likely to rate their health as moderate-to-very

good compared to the uninsured, and they were 1.9 times more likely to seek more medical care,

1.6 times more likely to report having chronic illnesses, and more likely to have greater income

than the uninsured. Illness is a strong predictor of why Jamaicans seek medical care (R2 = 71.2%

of 71.9%), and health insurance coverage accounted for less than half a percent of the variance in

health care utilization. However, health care utilization is a strong predictor of self-reported

illness, but it was weaker than illness in explaining health care utilization (61.1% of 66.5%).

Public health insurance was most common among those with chronic illnesses (76%) compared

to 44% private health coverage, whereas 38% had no coverage at all. The income of those in the




                                                                                               87
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 report more chronic illnesses than

those in the wealthy class, it found that 86 out of every 100 uninsured respondents indicated poor

health status.

        Health insurance coverage provides valuable economic relief for chronically ill

respondents, as this allows them to access needed health care. Like Hafner-Eaton’s research [2],

this paper found that health insurance status was the third most powerful predictor of health care

utilization. Forty-nine to every 100 chronically ill persons use the public health care facilities.

The uninsured ill are therefore less likely to demand health care, and this economic burden of

health care is going to be the responsibility of either the state, the individual or the family. The

difficulty here is that the uninsured are more likely to be in the lower-to-middle class, of working

age or children, experiencing more acute illness; 38 out of every 100 chronically ill individuals

are in the lower class, and these provide a comprehensive understanding of the insured and

uninsured that will allow for explanations in health disparities between the socio-economic strata

and sexes. With 43 out of every 100 people in the lower socio-economic strata self-reporting

being diagnosed with chronic illness, health insurance coverage, public health systems and other

policy interventions aid in their health, and health care utilization.



                                                                                                 88
       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

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



                                                                                                   89
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) inafffordability, (2) type of employment (mostly part-time,

seasonal, low paid and uninsured positions) which makes it too difficult for them to be holders of

health insurance, and this retards the switch from public-to-private health care utilization.

Recently a study conducted by Bourne and Eldemire-Shearer [21] found that 74% of those in the

poorest income quintile utilized public hospitals compared to 58% of those in the second poor

quintile and 31% of those in the wealthiest 20%. Then, if public health is privatized and becomes



                                                                                                   90
increasingly more expensive for recipients, the socio-economically disadvantaged population

(poor, 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, payment of sick leave, mortality, lowered life expectancy and cost of care

for children, orphanages and the elderly who become the responsibility of the state. Therefore the

opportunity cost of a reduced public health care budget is the economic cost of the

aforementioned issues, and goes to the explanation of premature mortality in a society.

       The chronically ill, in particular, benefit from health insurance coverage, not because of

the reduced cost of health care, but the increased health care utilization that results from health

coverage. From the findings of Hafner-Eaton’s work [2], the chronically ill in the United States

were 1.5 times more likely to seek medical care, and while this is about the same for Jamaicans,

health insurance is responsible for their health care utilization and not the condition or illness.

According to Andrulis [22], “Any truly successful, long-term solution to the health problems of

the nation will require attention at many points, especially for low-income populations who have



                                                                                                91
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 cost does not mean equal opportunity to access health care, nor do they

eliminate the barriers to such access, neither does it 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, increased poverty, premature mortality and lower

life expectancy of the population.

       Increases in diseases (acute and chronic) are largely owing to the lifestyle practices of

people. Lifestyle practices are voluntary lifestyle choices and practices [24]. The poor are less

educated, more likely to be unemployed, undernourished, deprived of financial resources, and

their voluntary actions will be directly related to survival and not diet, nutrition, exercise or other

healthy lifestyle choices. Lifestyle choices such as diet, proper nutrition, and sanitation and safe

drinking water are costly, and they are choices which, often because of poverty, some people

cannot afford to make. It follows therefore that those in the lower socio-economic strata will

voluntarily make unhealthy choices because they are cheaper. Poverty therefore handicaps

people, and predetermines unhealthy lifestyle choices, which further accounts for greater



                                                                                                    92
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

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


                                                                                               93
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.


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Table 4.1. Crowding, expenditure, income, age, and other characteristics by health insurance
status
                                             Health insurance status                     P
Characteristics                         Non-insured           Insured
                                         mean ± SD          mean ± SD
Crowding index                                4.9 ± 2.6              4.1±2.1  t = 10.32, < 0.0001
Total annual food expenditure1        3476.09±2129.97 3948.12±2257.97         t = - 6.81, < 0.0001
Annual non-food expenditure1          3772.91±3332.50 6339.40±5597.60 t = - 21.33, < 0.0001
       1
Income                                7703.62±5620.94 12374.89±9713.00 t = - 22.75, < 0.0001
Age (in year)                                28.7±21.4           35.0 ±22.7   t = - 9.40, < 0.0001
Time in household (in years)                  11.7±1.6             11.8±1.3        t = - 1.62, 0.104
Length of marriage                           16.9±14.3            18.3±13.8        t = - 1.55, 0.122
Length of illness                            14.7±51.1            14.1±36.2      t = - 0.217, 0.828
No. of visits to medical practitioner          1.4±1.0               1.5±1.2     t = - 0.659, 0.511
1
    Expenditures and income are quoted in USD (Ja. $80.47 = US $1.00 at the time of the survey)




                                                                                                  97
Table 4.2. 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)



                                                                                                                                    98
Table 4.3. 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




                                                                                                                        99
Table 4.4. 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




                                                                                             100
Table 4.5. 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
                       2                          2                      2
                      χ = 317.5, P < 0.0001      χ = 234.5, P < 0.0001 χ = 73.6, P < 0.0001




                                                                                          101
Table 4.6. Logistic regression: Explanatory variables of self-rated moderate-to-very good health
 Explanatory variable                     Coefficient   Std. error   Odds ratio   95.0% C.I.
                                                                                                 R2

 Average medical expenditure                    0.000       0.000        1.00*      1.00 -1.00   0.003

 Health insurance coverage (1= insured)         0.410       0.181        1.51*     1.06 - 2.15   0.005

 Urban                                          0.496       0.180       1.64**     1.15 - 2.34   0.007
  Other                                         0.462       0.197        1.59*     1.08 - 2.34   0.006
 †Rural                                                                   1.00

 Household head                                 0.376       0.154        1.46*     1.08 - 1.97   0.004

 Age                                           -0.046       0.004      0.96***     0.95 - 0.96   0.081

 Crowding index                                -0.156       0.035      0.86***     0.80 - 0.92   0.010

 Total food expenditure                         0.000       0.000      1.00***     1.00 - 1.00   0.003

 Health care seeking (1=yes)                   -0.671       0.211       0.51**     0.34 - 0.77   0.005

 Illness                                       -1.418       0.212      0.24***     0.16 - 0.37   0.204
Model fit χ2 = 574.37, P < 0.0001
-2LL = 1477.76
Nagelkerke R2 = 0.328
†Reference group
***P < 0.0001, **P < 0.01, *P < 0.05




                                                                                                         102
Table 4.7. 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




                                                                                                           103
Table 4.8. 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




                                                                                                       104
Table 4.9. Logistic regression: Explanatory variables of self-reported diagnosed chronic illness


 Explanatory variable                          Coefficient    Std error    Odds ratio    95.0% C.I.
                                                                                                        R2
 Male                                                -1.037        0.205       0.36***    0.24 - 0.53   0.048

 Married                                             0.425         0.199         1.53*    1.04 - 2.26   0.012
 †Never married                                                                   1.00

 Health insurance coverage (1= insured)              0.454         0.220         1.58*    1.02 - 2.42   0.008

 Age                                                 0.047         0.005       1.05***    1.04 - 1.06   0.201

 Logged Length of illness                            0.125         0.059         1.13*    1.01 - 1.27   0.008

Model fit χ2 = 136.32, P < 0.0001
-2LL = 673.09
Hosmer and Lemeshow goodness of fit χ2 = 15.96, P = 0.04
Nagelkerke R2 = 0.277
†Reference group
***P < 0.0001, **P < 0.01, *P < 0.05




                                                                                                          105
Table 4.10. 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




                                                                                                        106
Table 4.11. Logistic regression: Explanatory variables of health insurance status (1= insured)

 Explanatory variable          Coefficient   Std. error   Odds ratio   95.0% C.I.       R2

 Age                                 0.014       0.006         1.01*      1.00 - 1.03        0.040

 Income                              0.000       0.000       1.00***      1.00 - 1.00        0.082

 Chronic condition                   0.563       0.210         1.7**      1.16 - 2.65        0.013

 Health care seeking (1=yes)         0.463       0.211         1.59*      1.05 - 2.40        0.010

 Married                             0.647       0.192        1.91**      1.31 - 2.79        0.024
 †Never married

 Upper class                         0.841       0.227       3.46***      1.49 - 3.62        0.025
 †Lower class

Model fit χ2 = 95.7, P < 0.0001
-2LL = 686.09
Hosmer and Lemeshow goodness of fit χ2 = 5.08, P =0.75
Nagelkerke R2 = 0.194
†Reference group
***P < 0.0001, **P < 0.01, *P < 0.05




                                                                                                     107
                                                                         Chapter 5
Self-evaluated health and health conditions of rural residents in a middle-
income nation


In Jamaica, in 1989, the national poverty rate was 30.5% and this exponentially fell by 208.1% in
2007, but in the latter year, rural poverty was 4 times more than peri-urban and 3 times more
than urban poverty rate. Yet there is no study on health status and health conditions in order to
examine changes among rural residents. The present study aims to (1) examine epidemiological
shifts in typology of health conditions in rural Jamaicans, (2) determine correlates and estimates
of self-evaluated health status of rural residents, (3) determine correlates and estimates of self-
evaluated health conditions of rural residents and (4) assist policy makers in understanding how
intervention programmes can be structure to address some of the identified inequalities among
rural residents in Jamaica. The current study extracted samples of 15,260 and 3,322 rural
residents from two national cross-sectional surveys. 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 and a selection of dwellings from the primary units. In 2002, 14%
of respondents indicated having an illness in the 4-week period of the survey compared in 17%
in 2007. For 2002, there are 12 determinants of health: 11 social and 1 psychological
determinants. In 2007, there were 7 determinants of health: 6 social and 1 biological variables.
The determinants accounted for 22.6% of the explanatory power of the health model for 2002
and 44.7% for 2007. Sixty-eight percentage points of the health status model can be accounted
for by self-reported illness (i.e. R squared = 30.4%). With the exponential increase in diabetes
mellitus and health inequalities that exists today in rural Jamaica, public health and other policy
makers need to use multidimensional intervention strategy to address those inequalities.



Introduction

The health of a population is critical to all forms of development. This is a justifiable rationale

for governments’ investment in health care and the health system. Despite governments in Latin

America and the Caribbean increased investment in health since the 1980 [1], there are still many

inequities in health among and within their nations [2]. This is evident in the health disparities

indicators as well as the social determinants of health [3-6]. The advancement in technology and

medical sciences have not abated the disparities in infant mortality, poverty, health service

utilization, and health differentials among Latin America and Caribbean nations as well as

                                                                                               108
among the social hierarchies. Casas et al. [4] cited that the improvements in health in the region

are not in keeping with the region’s economic development rates and the same can be said

between the wealthy and the poor. In Jamaica, which is an English-speaking country in the

region, in 1989 the national poverty rate was 30.5% and this exponentially fell by 208.1% in

2007, but in the latter year, rural poverty was 4 times more than peri-urban and 3 times more

than urban poverty rate [9].


       Statistics from the WHO for 2007 showed that both life expectancy and healthy life

expectancy at birth was at least 4 years more for females than males [7]. Many empirical studies

have found that rural residents had lower health status and/or more health conditions, greater

levels of poverty and lower levels of education compared to their urban counterparts [8-18], and

these are also the case in Jamaica [19]. Those disparities speak to socio-economic and health

inequalities in many states. Although there is empirical evidence which revealed that health

inequalities and inequities do exist between rural and urban residences as well as among social

hierarchies and between the sexes in Latin America and the Caribbean in particular Jamaica,

only few studies were found that have examined the health status of rural people in the region

[14, 19-28]. The different researches in the region on rural health have not investigated

epidemiological transition of health conditions in the rural areas, and in order to tackle the

identified health disparities and inequalities, intervention techniques must be based on analytic

research on the cohort and not a general understanding of the nation.


       Inequity and/or inequalities in health can only be addressed in the region if they are

understood through research within each nation, and that policy makers cannot rely on finding of

studies outside of the region or their countries in order to effectively remedy the challenges that

they face. The relationship between poverty and ill health is empirically established, but the

                                                                                               109
focus of the region since the 1980s has been poverty reduction and while this has been

materializing, the health disparities are still evident today [3]. Embedded in the literature

therefore are income maldistribution, working conditions and health outcome inequalities, health

determinants inequalities, lower material wellbeing and poverty direct influence on health.

Poverty also indirectly influences health service utilization, quality of received care and healthy

life expectancy. With poverty been substantially a rural phenomenon, investment in health in

rural areas require an understanding of the health and changes occurring in health conditions

among the residents. It follows therefore that a research for a nation with area of residence

between an explanatory variable does not provide a comprehensive insight into many of the

issues that are embodied in a particular municipality (or area of residence). For decades (since

the 1980s), Jamaican statistical agencies have been collected data on health status of the people

and these are used to guide policies, but with disproportionately more people in rural areas in

poverty and poverty influences inequalities and/or inequities in a group, then this is rationale for

the research of rural Jamaicans.


       The WHO [8] opined that 80% of chronic illnesses were in low and middle income

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

Poverty does not only impact on illness, it causes pre-mature deaths, lower quality of life, lower

life and unhealthy life expectancy, low development and other social ills such as crime, high

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

[15], there is a positive correlation between poverty and unemployment; poverty and illness; and

crime and unemployment. Embedded in those findings are the challenges of living in poverty,

and the perpetual nature of poverty and illness, illness and poverty, poverty and unemployment,

economic deprivation and psychological frustration of poor families. Sen [18] encapsulated this

                                                                                                110
well when he forwarded that low levels of unemployment in the economy is associated with

higher levels of capabilities. This highlights the economic challenge of unemployment and

equally explains the labour incapacitation on account of high levels of unemployment, which

goes back to the WHO’s perspective that chronic illnesses are more experienced by low-to-

middle income peoples. According to WHO [8], 60% of global mortality is caused by chronic

illness, and this should be understood within the context that four-fifths of chronic dysfunctions

are in low-to-middle income countries.


       Within the aforementioned findings, area of residence in particular rural area is too much

of an important variable to be treated as an explanatory concept. The health outcome inequalities

will be decline merely by investing in the health sector of the general population. Montgomery

[17] opined that urban causes of mortality and disability provide understanding into urban-rural

health differentials. The paper provides answers some of urban health disparities in developing

countries and compares those situations with those faced by rural residents. Montgomery’s

findings [17] were generally on developing countries and while it does give some insights to the

urban-rural health inequalities, it cannot be used to formulate policies or intervention strategies

specifically for Jamaica. The rationale embedded in this argument is the fact that not all

developing countries are at the same socio-economic stage of development, and therefore

requires research for any chosen intervention techniques that they decide to utilize to effect

health changes. Concurrent investment in health is critical to economic development [29]; once

again this has not result in removal of health inequalities in Latin America and the Caribbean in

particular Jamaica [3-5]. Therefore more research is needed to understand the health outcome in

rural zones in order to the health disparity gaps in the region and within political states. The

present study aims to (1) examine epidemiological shifts in typology of health conditions in rural

                                                                                               111
Jamaicans, (2) determine correlates and estimates of self-evaluated health status of rural

residents, (3) determine correlates and estimates of self-evaluated health conditions of rural

residents and (4) assist policy makers in understanding how intervention programmes can be

structure to address some of the identified inequalities among rural residents in Jamaica.


Materials and Method

The current study extracted samples of 15,260 and 3,322 rural residents from two surveys

collected jointly by the Planning Institute of Jamaica and the Statistical Institute of Jamaica for

2002 and 2007 respectively [30,31]. The method of selection of the sample from each survey

was solely based on rural residence. The survey (Jamaica Survey of Living Conditions) was

begun in 1989 to collect data from Jamaicans in order to assess policies of the government. Each

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

is critical within the nation. In 2002, the foci were on 1) social safety net and 2) crime and

victimization; and for 2007, there was no focus. The sample for the earlier survey was 25,018

respondents and for the latter, it was 6,783 respondents.

       The survey was drawn using stratified random sampling. This design was a two-stage

stratified random sampling design where there was a Primary Sampling Unit (PSU) and a

selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which

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

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

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

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



                                                                                               112
of the Labor Force Survey (i.e., LFS) was selected for the JSLC [30, 31]. The sample was

weighted to reflect the population of the nation.

       The JSLC 2007 [30] was conducted in May and August of that year, while the JSLC

2002 was administered between July and October of that year. The researchers chose this survey

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

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

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

The questionnaire was modeled from the World Bank’s Living Standards Measurement Study

(LSMS) household survey. There are some modifications to the LSMS, as JSLC is more focused

on policy impacts. The questionnaire covered areas such as socio-demographic variables such as

education; daily expenses (for past 7-days), food and other consumption expenditures, inventory

of durable goods, health variables, crime and victimization, social safety net, and anthropometry.

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

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

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

26.2% and 27.7%. The non-response includes refusals and rejected cases in data cleaning.

Measurement

Dependent variable


Self-reported illness (or self-reported dysfunction): The question was asked: “Is this a diagnosed

recurring illness?” The answering options are: Yes, Influenza; Yes, Diarrhoea; Yes, Respiratory

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

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

logistic regression. This was used to indicate health status (i.e. dependent variable) for 2002.


                                                                                                   113
Self-rated health status: is measured using people’s self-rate of their overall health status [32],

which ranges from excellent to poor health status. The question that was asked in survey was

“How is your health in general?” And the options were very good; good; fair; poor and very

poor. For the purpose of the model in this study, self-rated health was coded as a binary variable

(1= good and fair, 0 = Otherwise) [33-38]. The binary good health status was used as the

dependent variable for 2007.


Covariates


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


Social hierarchy: This variable was measured based on income quintile: The upper classes were

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

was those in lower quintiles (i.e. quintiles 1 and 2).


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

pharmacist being visited in the last 4 weeks?’ with there being two options Yes or No. Medical

care-seeking behaviour therefore was coded as a binary measure where 1= Yes and 0 =

otherwise.


Crowding is the total number of individuals in the household divided by the number of rooms

(excluding kitchen, verandah and bathroom). Age is a continuous variable in years.


Sex. This is a binary variable where 1= male and 0 = otherwise.


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

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

home or with whom one is able to network, 0 = otherwise).
                                                                                               114
Psychological conditions are the psychological state of an individual, and this is subdivided into

positive and negative affective psychological conditions [39, 40]. Positive affective

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

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

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

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


Statistical Analysis

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

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

the association between non-metric variables, t-test and an Analysis of Variance (ANOVA) were

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

categorical variables. The level of significance used in this research was 5% (i.e. 95% confidence

interval).

Results

Demographic


Table 5.1 examines the demographic characteristics of the samples for 2002 and 2007. The

samples were 15,260 and 3,322 rural respondents for 2002 and 2007 respectively. The findings

revealed that 96.3% of the sample for 2002 respondents to the question ‘Have you had any

illness in the past 4-weeks and the rate was 97% for 2007. In 2002, 14% of those who responded

to the question of illness claimed yes compared to 17% in 2007. When the respondents were

asked to state the experienced health conditions, in 2002, 1.3% answered compared to 14.8% in

2007. Self-reported health conditions showed that exponential increases in influenza and


                                                                                              115
respiratory conditions in 2007 over 2002. Hypertensive and arthritic cases fell by 44.1% and

75.7% respectively, while diabetes mellitus increased by 150% over the studied period.


       Eight-one percentage points of sample claimed to have at least a good health status and

6% at least poor health. Of those who indicated at least good health, 37% stated very good (or

excellent) health compared to 1.1% who claimed very poor health of those who indicated at least

poor health status.


       When respondents were asked ‘Why did you not seek medical care for your illness?’ in

2002, 23.2% stated could not afford it; 41.3% was not ill enough and 22.2% used home remedy.

For 2007, 17.4% claimed that they were unable to afford it, 43.3% was not ill enough and 16.8%

stated used home remedy.




                                                                                           116
Table 5.1. Demographic characteristics, 2002 and 2007
                                                            2002                  2007
Variable                                                n           %         n          %
Sex
  Male                                                   7,727       50.6     1,654       49.8
  Female                                                 7,524       49.3     1,668       50.2
Marital status
  Married                                                2,460       25.6       513       24.1
  Never married                                          6,436       66.6     1,462       68.7
  Divorced                                                  56        0.6        22        1.0
  Separated                                                104        1.1        20        0.9
  Widowed                                                  610        6.3       112        5.3
Social hierarchy
  Lower                                                  7,298       47.8     1,828       55.0
  Middle                                                 3,169       20.8       650       19.6
  Wealthy                                                4,791       31.4       844       25.4
Self-reported illness
 Yes                                                     1,987       13.5       536       16.6
 No                                                     12,713       86.5     2,688       83.4
Self-reported health conditions
Acute
 Influenza                                                   1          0.5       80      16.3
 Diarrhoea                                                   4          2.1       19       3.9
 Respiratory diseases                                        6          3.1       51      10.4
Chronic
 Diabetes mellitus                                          10        5.2          64     13.0
 Hypertension                                               82       42.9         118     24.0
 Arthritis                                                  48       25.1          30      6.1
Other                                                       40       20.9         130     26.4
Medical care-seeking behaviour
 Yes                                                     1,302       63.8         349     63.3
  No                                                       740       36.4         202     36.7
Medical care utilization
 Public hospitals                                           499      39.1         127     37.2
 Private hospitals                                           80       6.3           8      2.3
 Public health care centres                                 285      22.3          76     22.3
 Private health care centres                                528      41.3         158     46.3
Health insurance coverage
 Yes                                                     1,036         7.0      464        14.5
  No                                                    13,714        93.0    2,715        85.5
Age Median, in years, range)                                  23 (0 to 99)        25 (0 to 99)
Length of illness, in days, Median (range)                     7 (0 to 90)          7 (0 to 99)




                                                                                           117
Bivariate analyses


Table 5.2 presents self-reported health conditions by sex, age, health care-seeking behaviour, and

length of illness of sample. Females were more likely to indicated suffering from the different

health conditions than males except for respiratory diseases. Of those who stated a particular

health conditions, those with chronic illness such as hypertension and arthritis were more likely

to send more time suffering from the diseases.




                                                                                              118
Table 5.2: Self-reported health conditions by particular social variables
                                                                        Health conditions
                                                 Acute conditions                                        Chronic
Variable                               Influenza Diarrhoea     Respiratory Diabetes                   Hyperte Arthritis    Other         P
                                                                             mellitus                 nsion

                                                                                     2002
Sex (%)                                                                                                                                0.045
  Male                                        0.0          25.0            83.3               20.0        30.5     20.8        35.0
  Female                                   100.0           75.0            16.7               80.0        69.5     79.2        65.0
Total                                           1              4              6                 10          82       48          40
Age - in years- Mean (SD)              80.0 (0.0)      1.8 (1.7)    14.0 (24.6)        63.7 (13.2)        68.7     68.4        56.0 < 0.0001
                                                                                                        (13.7)   (12.60      (23.4)
Health care-seeking behaviour
  Yes (%)                                     0.0          75.0             100.0              88.9       79.3      83.3       65.0         0.05
Total                                          10            14                  6                9         82        48         40
Length of illness –in days –                3 (0)          4 (2)            11 (5)          12 (11)    16 (11)   18 (11)    19 (12)     0.045
Mean (SD)
                                                                                     2007
Sex (%)                                                                                                                               <0.0001
  Male                                       42.5          36.8            56.9               20.3        27.1      46.7       43.1
  Female                                     57.5          63.2            43.1               79.7        72.9      53.3       56.9
Total                                          80            19              51                 64         118        30        130
Age - in years- Mean (SD)                    19.5   20.1 (28.5)     24.3 (23.8)        56.5 (17.4)        64.0      68.3       36.0   <0.0001
                                           (24.8)                                                       (17.1)    (12.0)     (25.0)
Health care-seeking behaviour                                                                                                         < 0.0001
  Yes (%)                                   41.3           52.6           62.7               75.0         64.4     46.7        70.5
Total n                                       80             19             51                 64          118       30         129
Length of illness –in days –                8 (6)          5 (2)      42 (172)           76 (135)          104      112    57 (188)     0.004
Mean (SD)                                                                                                (239)    (217)




                                                                                                                                      119
Table 5.3 examines health care-seeking behaviour by sex, self-reported illness, health coverage,

social hierarchy, educational levels, age and length of illness for 2002 and 2007. Based on Table

5.3, the mean age of someone who sought medical care is greater than someone who does not.

There is no significant statistical association between medical care-seeking behaviour and self-

reported illness, but there is a relationship between length of illness and medical care-seeking

behaviour.




                                                                                             120
Table 5.3. Health care-seeking behaviour by sex, self-reported illness, health coverage, social hierarchy, education, age and length of
illness, 2002 and 2007
                                                                       2002                                       2007
Variable                                                 Health care-seeking behaviour               Health care-seeking behaviour
                                                        Yes               No            P             Yes             No           P
                                                  N (%)             N (%)                        N (%)          N (%)
Sex                                                                                      0.011                                   0.112
   Male                                                511 (39.2)      333 (45.0)                   134 (38.4)        89 (44.1)
   Female                                              791 (60.8)      407 (55.0)                   215 (61.6)      113 (55.9)
Self-reported illness                                                                    0.360                                   0.130
   Yes                                               1261 (97.0))      713 (96.6)                   335 (96.3)       199 (98.5)
    No                                                    39 (3.0)       25 (3.4)                     13 (3.7)           3 (1.5)
Health insurance coverage                                                                0.197                                   0.013
   Yes                                                    89 (6.9)       40 (5.4)                   270 (77.4)       173 (86.1)
    No                                                1210 (93.1)      700 (94.6)                    79 (22.6)        28 (13.9)
Social hierarchy                                                                      <0.0001                                    0.104
    Lower                                              545 (41.9)      363 (49.1)                   167 (47.9)       115 (56.9)
    Middle                                             248 (19.0)      157 (21.2)                    79 (22.6)        41 (20.3)
    Wealthy                                            509 (39.1)      220 (29.7)                   103 (29.5)        46 (22.8)
Educational level                                                                     <0.0001                                    0.623
    Primary or below                                   402 (40.5)      208 (41.5)                   336 (96.3)      191 (94.6)
    Secondary                                          569 (57.4)      279 (55.7)                     11 (3.2)           9 (4.5)
    Tertiary                                              21 (2.1)       14 (2.8)                      2 (0.6)           2 (1.0)
Age Mean (SD) – in years                               46.4 (27.4)    40.4 (28.3)     <0.0001      43.5 (27.5)     37.9 (146.8) 0.025
Length of illness Mean (SD) – in days                     12 (11)          10 (9)     <0.0001           7 (20)           5 (15)    0.01




                                                                                                                                   121
Multivariate analyses


Table 5.4 represents information on social and psychological determinants of health of rural

residents for 2002 and 2007. Based on Table 5.4, in 2002, there are 12 determinants of health: 11

social and 1 psychological determinants. On the other hand, in 2007, there were 7 determinants

of health: 6 social and 1 biological variables. The determinants accounted for 22.6% of the

explanatory power of the health model for 2002 and 44.7% for 2007. Sixty-eight percentage

points of the health status model can be accounted for by self-reported illness (i.e. R squared =

30.4%).




                                                                                             122
Table 5.4. Stepwise Logistic regression: Social and psychological determinants of self-evaluated health, 2002 and 2007
                                                       2002                                        2007
Explanatory variables:              Coefficient    Std.    Odds     95% CI      Coefficient Std.        Odds     95% CI
                                                  Error    ratio                               Error ratio
Income                                    0.000    0.000 1.00       1.00-1.00          0.000 0.000       1.00 1.00-1.00
Age                                      -0.044    0.002 0.96       0.93-0.96         -0.052 0.004       0.95 0.94-0.96
Middle                                      NS        NS     NS           NS           0.321 0.196       1.38 0.94-2.02
Wealthy                                  -0.311    0.090 0.73       0.61-0.88            NS      NS       NS          NS
†Lower                                                      1.00                                         1.00
Total Durable good                        0.058    0.013 1.06       1.03-1.09            NS      NS       NS          NS
Separated, divorced or widowed           -0.367    0.109 0.69       0.56-0.86            NS      NS       NS          NS
Married                                  -0.307    0.077 0.74       0.63-0.86            NS      NS       NS          NS
†Never married                                              1.00                         NS      NS       NS          NS
Tertiary                                 -0.175    0.065 0.84       0.72-0.98            NS      NS       NS          NS
†Primary or below                                           1.00
Social support                           -0.229    0.070 0.80       0.70-0.90           NID     NID      NID         NID
Male                                      0.803    0.011 2.23       1.95-2.56          0.563 0.134       1.76 1.35-2.28
Negative affective conditions            -0.062    0.037 0.94       0.92-0.96           NID     NID      NID         NID
Number of females in household            0.123    0.025 1.13       1.05-1.22           NID     NID      NID         NID
Number of children in household           0.056    0.006 1.06       1.01-1.11           NID     NID      NID         NID
Length of illness                        -0.039    0.193 0.96       0.95-0.97            NS      NS       NS          NS
Crowding                                    NS        NS     NS           NS          -0.081 0.029       0.92 0.87-0.98
Medical care-seeking = yes                  NS        NS     NS           NS           -1.01    0.26     0.36 0.21-0.60
Self-reported illness                                                                 -2.225    0.15     0.11 0.08-0.15
-LL                                                  6,381.3                                      1,562.6
n                                                     12,666                                       2,817
Nagelkerke R square                                   0.226                                        0.447
χ2                                                    1220.5                                       670.0
NS – not significant (P > 0.05)
NID – not in dataset and/or could not be measured based on the available data




                                                                                                                           123
Table 5.5 shows the contribution of each explanatory variable to the model for 2002 and 2007.

Based on Table 5.5, of the social and psychological determinants of health, age explains more

the variability in health than another other determinant. Income contributed at most 0.2% to

health of respondents. Using the not reporting an illness to measure health of rural respondents,

age accounted for 77% of the health; but when self-reported health status is used to measure

health, age accounted for only 11.5%.


Table 5.5. Stepwise Logistic regression: R-squared for Social and psychological determinants of
self-evaluated health, 2002 and 2007
                                                                  2002         2007
Explanatory variables:                                          R squared R squared
Income                                                                 0.1           0.2
Age                                                                   17.4          11.5
Middle                                                                 NS            0.4
Wealthy                                                                0.1           NS
Total Durable good                                                     0.2           NS
Separated, divorced or widowed                                         0.1           NS
Married                                                                0.2           NS
Tertiary                                                               0.1           NS
Social support                                                         0.2           NS
Male                                                                   2.2           1.2
Negative affective conditions                                          0.4         NID
Number of females in household                                         0.5         NID
Number of children in household                                        0.1         NID
Length of illness                                                      1.0           NS
Crowding                                                               NS            0.2
Medical care-seeking = yes                                             NS            0.8
Self-reported illness                                                               30.4
NS – not significant (P > 0.05)
NID – not in dataset




                                                                                             124
Discussion

The current health status of rural respondents was good (i.e. 81 out of every 100), but 17 out of

every 100 had an illness. Inspite of reporting an illness, the present study found that 36 out of

every 100 ill respondents had not sought medical care. Of those who did not utilize medical care

although they indicated an illness, at least 41% claimed financial inadequacies and in 2007, 17%

used home remedy. The results revealed that rural respondents have a conceptualization of

illness and the fact that medical care outside of the home should be utilized based on length of

illness and not mere ailments. Concurrently, illness accounts for most of current health status

which emphasizes the dominant of the biomedical perspective in viewing health and health care

in rural Jamaica. While self-reported chronic health conditions fell by over 41% in 2007 over

2002, the percent of those who reported acute conditions increased by over 436%. Of the

increased cases of acute conditions, respiratory diseases accounted for 235% while influenza

accounted for 3160% increase over 2002. Although overall self-reported chronic health

conditions see a decline for 2007 over 2002, diabetes mellitus was the only condition that

showed an increase in the study (i.e. 150%). Interestingly, the current findings showed that

107.1% more rural residents were covered by health insurance in 2007 over 2000, but this was

corresponding to a minimal reduction in those seek medical care. The number of rural residents

who were classified into the lower (i.e. working) class increased by 15.1% and a 19.1% of those

in the wealthy class. With income being positively correlated with good health, an increase in the

number of people the lower class highlights reduction in health for 2007. Males continue to

report better health status than females, but this fell from 2.3 times more in 2002 to 1.8 times in

2007, which suggests that the reduction in income is substantially influence the quality of life of

rural males.

                                                                                               125
       The current findings concur with the literature that showed that severity of illness (or

length of illness), age, and health coverage are positively related to medical care seeking

behaviour than illness [41-43]. Statistics from national cross-sectional surveys in Jamaica since

1989 [9] revealed that females were approximately more likely to report an illness and utilize

medical care than males. When the absolute figures from the surveys were cross-tabulation, it

was found that the statistical association which existed in 2002 disappeared in 2007. This is not

atypical to Jamaica as a qualitative study in Pakistan on street children found that boys would

attend formal health care are more likely to attend based on severity of illness and if it affects

their economic livelihood [41]. Another study conducted in Anyigba, North-Central, Nigeria

found that [42] found that 85 out of every 100 respondents waited for less than a week after the

onset of illness to seek medical, and that 57 out of every 100 indicated that they would recover

without treatment. In this research it was revealed that 43 out of every 100 rural residents

indicated that they were not ill enough which suggests that they would recover in time.


       Health care facilities in Jamaica are primarily operated by females, and with the

perception in the culture that males must be masculine, which include exhibiting strength, power

and avoiding weakness, this is a justification of the rationale for severity of illness account for

medical care-seeking behaviour as against actual illness [41-43]. Dunlop et al’s finding which

found that females utilize health care facilities more than males [44] partially concurs with this

research that found this to be the case in 2002. In 2002, 1.6 times more females sought medical

care than males, but the study found that there was no significant association between sex and

medical care-seeking behaviour for 2007. The explanation of this is embodied in the two things,

(1) income, (2) inflation and (3) the increased number of people who were classified into the

lower class.


                                                                                               126
       Income is positively correlated with social hierarchy, health, and employment status [16,

45-50]. Income which is among the social determinants of health, is directly associated with

health through material wellbeing, but it is also associated with occupational and social

hierarchies. The poor receives less of the income than the middle and wealthy classes, which

denotes that an increased in the number of people in the lower class, income will be reduced and

so will health status. It should be noted here that poverty which affect health, is exponential

greater in rural Jamaica and that there are more females in rural household. The health care-

seeking disparity which is diminished can be explained by the inflation over the study. In 2007,

inflation increased by 194% over 2006 [20] and coupled with the lower income, rural

respondents in particular females who are more likely to unemployment, owns less material

resources and increasingly are becoming single parents [9], would justify the narrowing of the

health care-seeking gap that existed in 2002.Williams et al. [42] found that medical care-seeking

behaviour did not differ significant between the sexes, which is in keeping with the situation for

2007 in this study.


       The WHO [8] found that poverty is associated with increased health conditions.

Empirical evidence existed that showed the poverty is related to low levels of choices, income,

access to health care services, and opportunities, which is highlighted in this study. Latin

America and the Caribbean governments have increased investment in health care and in the

2006, the Jamaican government introduced the removal of public health care utilization fees for

children (0 to 18 years) and expanded the a drug for the elderly programme to all people who

suffer from particular chronic illnesses. While these undoubtedly increase the health outcomes

which would have been lower if those opportunities were not present, health inequalities still

exist among rural residents.


                                                                                              127
       With all the investment in health from decentralization of the health care system, drug for

the elderly programmes, removal of health care user fees to health public care interventions,

there is a rise in acute health conditions in particular influenza and respiratory diseases. The good

news is the reduction in chronic health conditions. This good news is nothing to celebrate as

diabetes mellitus has increased exponentially in the last one half decade. The reduction in

number of hypertensive and arthritic cases correspond to lowered ages in reporting having those

illnesses. The mean age of reporting hypertension has declined by 5 years (to 64 years) and 7.2

years (to 56.5 years). Furthermore, Morrison [51] postulated that hypertension and diabetes are

now twin problems in the Caribbean and although the current study has shown a reduction in

self-reported hypertensive people in rural Jamaica, 24 out of every 100 health conditions were

accounted for by hypertension. Diabetes mellitus accounted for 13 out of every 100 health

conditions, which speaks to a future health rural problem. Another researcher found that 50% of

people with diabetes had a history of hypertension, and this future highlights a health challenge

for policy makers and public health practitioners. The lowered ages of reporting particular

chronic illnesses indicate that rural residents will be living longer with those conditions and this

measure increase burden on the health care system in the future.


       A critical issue which emerged from this study is the value that rural residents ascribed to

illness in determining their health status. There is a strong negative statistical correlation between

self-reported illness and good health status. The findings indicated that 68% of the explanatory

power of good health status can be accounted for by illness. This is not atypical as a research by

Hambleton et al. on Barbadian elderly found that illness accounted for 88.0% of health status. It

can be extrapolated from those findings that (1) the older one gets, he/she places more emphasis

on illness in the evaluation of health status, (2) the relationship between illness and health


                                                                                                  128
appears to more causal than an associative one, (3) the biomedical approach to measuring health

still predominates people’s perception, and (4) the culture which fashions the conceptualization

of health is influences health care-seeking. Those issues are principally among the reasons that

care is curative and not preventative in Jamaica and this is captured in the finding which showed

that health care-seeking behaviour is negatively correlated with good health. Rural respondents

who seek medical care are 64% less likely to report good health status, indicating embedded

cultural dominance of the biomedical approach in the conceptualization of health. The

dominance of the biomedical approach to the study of health in Jamaica is even high among

medical researchers as a study conducted in 2007/08 examined medical history; health care-

seeking behaviour; health (i.e. diseases, medication consumption), mental health, sexual

practices, dietary habits; lifestyle (i.e. violence and injury; smoking, narcotic and alcohol

behaviour), community and home milieu, suggesting the greater weight on health from the

perspective of illness, its treatment and measureable outcome as against people’s assessment of

their health status [53]. Another limitation of the ‘Jamaica Health and Lifestyle Survey II’ was

the omission of area of residence disaggregation of the collected though limited health data. The

current study bridges this gap, and goes further by using self-assessed heath status in addition to

self-rated health, health care-seeking behaviour and provide other pertinent health matters on

rural Jamaicans.


Conclusion

Health inequalities in rural Jamaica still exist today. The current study found that in the future

health care institutions will be called to invest more in the health system in order to address the

health challenges of increased diabetes mellitus as well as respiratory diseases. On the other

hand, despite investments in health by governments, progress in technology, public health

                                                                                               129
services, increased levels of education and income since the last century, decision makers, public

health practitioners and other health care providers need to recognize that increased life

expectancy and lowered infant mortality rates have not addressed the challenges of in the health

of rural population in Jamaica. General financial investment in health to control communicable

diseases that are particularly detrimental for children such as diarrhoea and respiratory diseases

are on the increase in rural areas, which means that the level of economic development since the

20th Century does not provide answers to the differences in health outcomes within a country.

The identified health disparities in rural Jamaica denote that investment in health and health

intervention strategies are not effectively addressing the health inequalities which are underlying

in the health statistics. This means that the health inequalities in those areas in Jamaica will fuel

future public health challenges for the societies, as well as increase the economic burden of

health care system. The analyses provided in the current study clearly highlight the need for

thinking that will incorporate the health realities of rural population in the agenda of policy

makers.


The way forward

The present work highlights the lingering dominance of the biomedical perspective that

influences health and health care in rural Jamaica. Hence the way forward for government and

policy makers including health care practitioners as well as public health educators in order to

reduce health inequalities is a multi-dimensional approach to health and health care as the current

mechanism is working. The researcher is proposing (1) mobile clinics, (2) community and house

visits from medical practitioners, (3) restructuring health care facilities to reflect a new

preventative thrust, (4) introduced preventative care approach as a subject in all schools, (5) that

the focus should not only be on the extreme of income poverty and health care access, but on

                                                                                                 130
opportunities, empowerment, security of poor and rural residents, (6) there is a need for a social

security network that nutritious foods to rural residents, and (7) there is a need for the

modification to the way public health programmes are fashioned and operated as well as a

widening and new definition of the boundaries of public health intervention. These new

mechanisms will be costly, but a reorganization of expenditure means that some of the money

spent for curative care will be reduce as preventative care is the focal point and not curative

health treatment. Another important thing which is needed is research on the value system of

rural residents and this should be done using a longitudinal study in order to provide information

for health care intervention strategies.


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                                                                          Chapter 6
Determinants of self-reported health conditions of people in the lower
socioeconomic strata, Jamaica


The current study examines self-reported health conditions of those in the lower socioeconomic
strata in Jamaica by building a model that captures factors that determine their health conditions.
This study comprised 9,931 respondents who are in lower income quintiles. This was extracted
from a nationally stratified survey of 25, 018 Jamaicans. Rural poor were more likely to report
health conditions than those who resided in other towns. Eight factors explain 39.6% health
conditions of poor Jamaicans - age (OR = 1.04, 95% CI = 1.04–1.05), sex (OR = 0.46, 95% CI =
0.39–0.56), negative affective psychological conditions (OR = 1.07, 95% CI = 1.04–1.10),
average income per household (OR = 1.00, 95% CI = 1.00–1.00), and educational level (OR =
1.44, 95% CI = 1.15–1.81). The findings are far reaching and can be used to effectively
established public health programmes.




Introduction


Poverty is a phenomenon that affects developed as well as developing societies [1-5], but more

widespread in the latter nations. The issue of poverty is much a reality as household crowding,

income inequality, health inequality, social development, demographic transition, gender

inequality, fertility, mortality, and mistrust as well as economic growth or healthy life

expectancy. It influences people in many ways such as in-affordability of resources that can

improve or retards socioeconomic development, nutrition, sanitation, and quality socio-

physiological milieu, premature deaths and unhealthy life expectancy. Owing to incapacitation

that poverty causes, it creates a ‘bad’ environment that accounts for a certain health status and in

the process facilitates ill-health [6]. An extensive review of literature has shown that a ‘poor’

milieu contributes to ill-health [6-15], suggesting that insufficient money accommodates health


                                                                                                135
hazards as the poor may not have money to spend on quality health care involving specialized

surgery or preventative treatments. In 2002, statistics for Jamaica reveals that 50.4 per cent of the

total consumption expenditure of those in the poorest quintile was spent on food and beverage,

compared to 38.1 per cent for those in the wealthiest quintiles.


       In a paper titled Poverty and Health, Murray [16] argued that there is a clear interrelation

between poverty and health. She noted that financial inadequacy prevents an individual from

accessing – food and good nutrition, potable water, proper sanitation, medicinal care,

preventative care, adequate housing, knowledge of health practices – and attendance at particular

educational institutions among other things, which concurs with previous research [2, 9-12, 15].

The issue of resource insufficiency affects the ability and capacity of the poor from accessing the

quality of goods and services comparable to the rich who are better able to enjoy quality of life/

wellbeing. The World Health Organization (WHO) [10] found that 80% of chronic illnesses

were in low and middle income countries, and 60% of global mortality is caused by chronic

illness, thus suggesting that illness interfaces with poverty and explain premature mortality

owing to material deprivation.


       The WHO stated that “In reality, low and middle income countries are at the centre of

both old and new public health challenges” [10]. The high risk of death in low income countries

is owing to food insecurity, low water quality, low sanitation coupled with inaccess to financial

resources. Poverty makes it insurmountable for poor people to respond to illness unless health

care services are free. Hence, the people who are poor will suffer from chronic diseases. The

WHO captures this aptly “...People who are already poor are the most likely to suffer financially

from chronic diseases, which often deepen poverty and damage long term economic prospects”

[10]. Clearly, material deprivation, lack of or limited opportunities high levels of incapacities,

                                                                                                 136
unemployment excess, dietary deficiency, and psychological frustration among the poor, seems

to be more life threatening among developing than developed nations.


       Based on statistics (Jamaica Survey of Living Conditions, (JSLC)), in 2006, 35.2 per cent

of those in the poorest quintiles indicated that they were not able to seek health care services

because of in-affordability compared to 16.7 per cent of those in the wealthiest quintile (Table

6.1). In the same year, the statistics revealed that 71 per cent of the poorest Jamaicans attended

public health care services compared to 21.7% wealthiest Jamaicans whereas 73.9 per cent of the

wealthiest visited private health care facilities compared to 24.3 per cent of the poorest

Jamaicans. The reality is the poor spend on food, beverages, fuel, household supplies and

household expenses (54 per cent in 2006) compared to those in the wealthiest quintiles (50 per

cent). The reverse side of this reality means that the poor has less income to be used for health

care, and for spending on safer milieus.


       It is argued that the environment has aided the increase of illnesses such as malaria, El

Niňo, dengue fever, asthma and other respiratory diseases, and cholera [17]. Diseases like

malaria and dengue are mostly attributable to poor living conditions, which speaks to the

importance of a quality of life measurement that is comprehensive as against the biomedical

model, which is an end product model. This point is further embodied in reason for the

resurgence of tuberculosis throughout the world even in affluent societies, despite the

advancement of technology and medicine. Such matter is answered in the simple fact that poor

sanitation, overcrowding in homes, inadequate nutrition are accounting for the rise in this

ailment.




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       The Organization of Eastern Caribbean States [18] in one of their reports writes that,

“while material poverty affect a large number of households, the Report points to the impending

dangers of more widespread and subtle forms of poverty that include poor health, inadequate

levels of educational attainment; lack of economic assets or access to markets or jobs that could

create the unsafe physical environment; and various forms of social exclusion.” According to

Ranis and Stewart [19], healthier, better nourished, and more educated people contribute

significantly to economic growth than their otherwise counterparts. In an organization with

highly trainable staff, very low absenteeism (not much sick leave) contributes to productivity.


       Marmot [9] posits that poverty means the inability to obtain the essentials of life. This is

also reflected in a (i) ‘poor’ milieu; (ii) matter of low income which prevents them from

accessing certain resources; and (iii) problem of social inequality. Those realities justify the

inverse relationship found between the physical environment and health status [6-8]. Moreover,

poverty explains the inadequacy in terms of material conditions. These are expressed through

inability to afford basic needs, food, clothing, and particular quality environment. However

limited resources interfere with the ability to acquire the essentials. Poverty can be seen as

exclusion; the European Union defines the poor as persons whose resources (material culture and

social) are so limited as to exclude them from the minimum acceptable way of life in the member

state in which they live depending on benefits as equivalents as claiming social assistance”.


       Dalzell-Ward [20], commented that, “The deprivation of energy foods will result in

excessive fatigue which will in turn diminish social and work performance and interfere with

well-being.” This position clearly indicates a level of development. If an individual is unhealthy

this would likely add to less working hours, and by extension reduce production in an



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organization and the country. This brings to mind, the view point of Adam Smith, that matters of

this nature are an indication of reduced economic growth.


       If an individual is malnourished, this will adversely affect the health of the person, hence

the possibility of mental and physical underdevelopment, lowered productivity, increased

absenteeism, and reduce social and economic capital. Another school of thought concurred with

Dalzell-Ward [20], when she said:


       An extensive examination of the literature review has shown that poverty is directly

associated with health conditions, and is inversely related to health status, thus suggesting that

income is critical to health status of people. In all the reviews, the researcher came upon no study

in Jamaica that sought to explore determinants of self-reported health status of those in the lower

socioeconomic strata, the magnitude of those factors, and the probability of switching from

‘good’ health to ‘bad’ health. As a result, the current study examines determinants of self-

reported health conditions of people in lower socioeconomic strata of Jamaica. This was in an

effort to fill the gap in the present literature; and in the process provide public health

practitioners with what are the predictors of self-reported health status of those who are in the

poor quintile which will allow them to understand people’s perception as well as to address

health issues relating to the population that may be generated from the actions (or inactions) of

the poor.


Theoretical Framework

The theoretical framework that underpins the current study is modelled by Franc, Perronin and

Pierre [1]. Using a Health, Health Care and Insurance Survey (ESPS) that was carried out

between 1994 and 2004 in France, Franc, Perronnin, Pierre [1] sought to model health insurance

                                                                                                139
demand. The survey sampled 20,000 insured respondents, who will be making the transition to

retirement. They used probit analysis to estimate the probability of switching from private

insurance to public providers or vice versa. The switching behaviour was controlled for

sociodemographic characteristics in order to remove the effects that are not directly linked to the

nature of the contract. This is embodied in the mathematical formula, (Eq. (1) :


       P (Yi = 1 / Zi, Xi, Δri) = α + β. Zi + γ. Xi + δ. Δri + ε ……………………Eq. (1)



       where yi is a binary variable defined by yi = 1 if the individual switched and yi=0

otherwise, Zi represents the nature of the contract (compulsory, voluntary or individual); Xi and

Δri represent controls variables: Xi is a vector of individual characteristics (education, age of

retirement, retirement wave, type of provider, individual assessment of payments for specialist

care, public fund, public co-payment exemption and vital risk) and Δri represents the variation of

income: “same income”, “higher income after” and “lower income after”. The standard error ε is

assumed to follow a cumulative normal distribution.

       In the current work, we will modify model 1, Eq. (1), to reflect a set of tested variables

for Self-reported health conditions of Jamaicans who are the bottom quintiles – poorest and poor.

Thus, we will build a model, Eq. (2), which allows us to estimate the individual probability, ‘P’,

of switch from ‘good’ health to ‘bad’ health – Self-reported health conditions by the estimates of

the parameters (α and β):




                                                                                               140
         where Hi is a binary variable defined by Hi = 1 if the individual Self-reported his/her

health conditions and Hi=0 otherwise, Ai represents the age of individual i, Yi represents average

income per person per household, MRi represents marital status –of an individual who is

divorced, separated, or widowed with reference to someone who is single; or an individual i who

is married with referent to person i who is single – ARi represents area of residence –of an

individual who lived in Other Towns with reference to someone who dwelled in a Rural area; or

an individual i who resided in Kingston Metropolitan Area with referent to person i who dwelled

in Rural Area – EDi represents educational level –of an individual who has a secondary level of

education with reference to someone has primary and below education; or individual i who has

tertiary level education with referent to person i who has primary or below education – Xi is

gender of respondent i (with 1=male and 0=otherwise)- Ci is crime that is exposed to by person i;

and where               is the summation of psychological state of person k, where i represents

negative psychological state of person k and j denotes the positive psychological state of person

k, and     to   represent the coefficient of variable 1 to variable 7 with βi denotes the coefficient

for negative psychological state and βj represents the coefficient of the variable positive

psychological state.


Ergo the model that will be examined in the current study is expressed in the equation below;




                                                                                                 141
       The current work is concerned about (i) the probability of predicting a person reporting a

health condition, and (ii) the parameter of each factor and its interpretation. From Table 6.3, we

will substitute the parameter with their appropriate coefficients:




       It should be noted that MRi represents an individual who is married with referent to one

who was never married; ARi represents person I who dwelled in Other Towns with reference to

someone who resided in a rural area; EDi represent the educational level of an individual who

has had secondary or post-secondary education; where Xi=1 represents a male and 0=otherwise

and Pi represents a negative psychological conditions of person k.




A probability value of 0.998 of Self-reported health conditions of poor Jamaicans indicates that

we are 0.998 sure of what explains how the poor switched from no health conditions to at least

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one-health condition. The current study also seeks to examine the factors that determine ‘health

conditions’ and their parameters as well as understanding each influential factor on health

conditions.


Methods and Measures
Of the 9,931 respondents, who participated in this study, 48.3% were males and 51.7% females,

who are in the bottom quintiles of per capita population (50.1% below the poverty line and

49.9% poor). The current study is a constituent of a prevalence survey that was carried by the

Statistical Institute of Jamaica and the Planning Institute of Jamaica between June and October

2002 (Jamaica Survey of Living Conditions, JSLC). The JSLC is a variation of the World

Bank’s Living Standard Measurement Study (LSMS) household survey. The JSLC focuses on

and emphasizes policy impact which is the difference from the LSMS. Ever since 1988, the

Statistical Institute of Jamaica (STATIN) in partnership with the Planning Institute of Jamaica

(PIOJ) have been carrying out twelve-monthly surveys on living conditions of Jamaican

populace. The JSLC’s design is that of a multi-module household survey with modules on

health, consumption, education, house, anthropometric measurements and immunization data for

all children 0-59 months, and demographic variables. JSLC is carried out with a self-

administered questionnaire by trained interviewers to responsible to household members.

Participations are asked to recollect detailed information their consumption patterns over the last

30 days of the survey period as well as their health care expenditure in addition to the

aforementioned variables. A self-administered questionnaire was used to collect data from

Jamaicans on different facet of life. The fundamental structure of the instrument has remained

the same over the years with some differences yearly to include module on social safety net,

elderly, crime and victimization, physical environment, remittances and other components.


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Stratified random sample was used to drawn the respondents (N=25,018). The survey was

weighted in order that it represents the population of Jamaica.


Data were stored, retrieved, and analyzed using Statistical Package for Social Sciences (SPSS)

for Windows, Version 16.0 (SPSS Inc; Chicago, IL, USA). For the current study, descriptive

statistics was used to provide socio-demographic characteristics on the sampled population, and

logistic regression was used to establish a model that reflects the health conditions of poor

Jamaicans. The dependent variable is self-reported health conditions, with some predisposed

explanatory variables (Measure).


For 2003 to 2006, the Jamaica Surveys of Living Conditions did not collect data on the health

status of Jamaica. Data for 2008 to 2009 are not yet ready, at the time of writing this paper the

researcher was not given access to the 2007 survey data and so the researcher had to resort to

using 2002 survey data to conduct this research


Measures

Self-reported Health Conditions denote self-reported illnesses/injuries that were experienced by

the individual in the 4-week of the survey period: This is a dummy variable, where 1=self-

reported ailments, injuries or illnesses suffered in the last four weeks and 0 if did not report any

illness/injury.

Household crowding is the mean number of persons who dwelling in a household:



                  Where   represents each individual, and          is the summation of the all the

individuals with the household, and ‘i’ denotes first person to the last person, n, and       is the

summation of number of rooms in the house excluding kitchen, bathroom(s) and verandah.


                                                                                                144
Physical Environment: This is the number of responses from people who indicated suffering

landsides; property damage due to rains, flooding; soil erosion;

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

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

household and other obligations.

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

optimistic about the future and life generally.

Crime and victimization index (Crime Index) denotes the self-reported experiencing a crime or

being a victim of any form crimes and this include if these crimes or victimization are against an

individual family member of close associate.



                              Where ki represents the frequency with which an individual

                              witnessed or experience a crime, where I denotes 0, 1 and 2, in

which 0 indicates not witnessing or experiencing a crime, 1 means witnessing 1 to 2, and 2

symbolizes seeing 3 or more crimes. Ti denotes the degree of the different typologies of crime

witnessed or experienced by an individual (where j=1 …4, which 1=valuables stolen, 2=attacked

with or without a weapon, 3= threatened with a gun, and 4= sexually assaulted or raped. The

summation of the frequency of crime by the degree of the incident ranges from 0 and a

maximum of 51.


Findings: Demographic Characteristics of Sampled Population

The sampled population was 9,931 respondents, of which 48.3% (n=4,799) were males and

51.7% (N=5,129) females with mean age for the sample being 26.1 years (± 22.5 years).
                                                                                              145
Moreover, most of the population were never married (71.3%, N=4,020), had secondary and

post-secondary level education, (75.5%, N=3,885), did not report having a health condition

(84.9%, N=8,268), with marginally more respondents owing property (58%, N=3,927). In

addition, the average occupancy per room was 3 (± ≈ 2); Range =11 persons per room (Table

6.2).

        Of those who reported ill-health (15.1%), 1% indicated that this was cold; 4.2%

diahorrea; 6.3% diabetes mellitus; 36.5% hypertension; 29.2% arthritis and 22.9% did not

specify the health condition. Fifty-five percent indicated that they purchased the prescribed

medication compared to 3.3% who claimed that they did not buy the prescribed medication and

31.0% mentioned that none prescribed medication was required; and 5.1% purchased over the

counter medication.

        Of those who answered the question on health care-seeking behaviour in the last 12

months (n=9,679), 34.7% indicated that they sought medical care compared to 65.3% who did

not. Twenty percent (representing 22.2%) sought public medical care, 10.1% private and 2.4%

said both. In response to the reason for not seeking medical care, 30.4% reported that they were

unable to afford medical care costs and 70.4% claimed that they were unable to afford

medications. Only 2.7% of the sample had health care insurance. The median expenditure on

medical care was US $5.89 (US $1.00 = Ja. $50.97).



Findings: Multivariate Analysis

Using logistic regression, it was found that 85.2% of the data were corrected classified: 98.3% of

those reported not having a health condition and 33.8% of those who reported at least one health

condition (Table 6.3). This is an indication that the data is a good fit for the model, as 85% were


                                                                                               146
correctly classified. Moreover, using the principle of parsimony only those factors that were

statistically significant (p < 0.05; 95% Confidence Interval) will be used in the model and so

those are the only ones that will be referred to in this study.

       We have found that 8 factors explained 39.6% health conditions of poor Jamaicans

(Table 6.3). Of the 8 factors, the 5 most significant ones are age (OR = 1.04, 95% CI = 1.04 –

1.05), sex of respondents (OR = 0.46, 95% CI = 0.39 – 0.56), negative affective psychological

conditions (OR = 1.07, 95% CI = 1.04 – 1.10), average income per household (OR = 1.00, 95%

CI = 1.00 – 1.00), and educational level (OR = 1.44, 95% CI = 1.15 – 1.81) (Table 6.3).

Moreover, poor respondents have a lower Self-reported health conditions compared to their

female counterparts (OR=0.39, 95%CI: 0.39, 0.56). Furthermore, a male is one-half less likely to

report a health condition than a female. On the other hand, the old a respondent become he/she is

1.04 times (OR=1.04, 95%CI: 1.04, 1.05). Similarly, crime directly affects the Self-reported

health conditions of poor respondents. An individual who has been influenced by more crime

and victimization is 1.01 times more likely to report a health condition compared to another

individual who is influenced by less crime and victimization. This positive association was also

observed for income (OR=1.00, 95%CI: 1.00, 1.00), a married person with referent to a single

individual (OR=1.29, 95%CI: 1.04, 1.59) which was the same for those who indicated a negative

affective psychological condition (OR=1.07, 95%CI: 1.04, 1.10).


Discussion

The literature revealed that access to good health care is dependent on possessing money. [1, 2,

4, 9]. The findings of this study endorse the view point of the literature. Marmot [9] and Sen [2]

argued that income directly influences good health, and explains why the poor’s health is poor

and justified their high levels of health conditions and premature mortality. Unlike the other

                                                                                              147
studies, this paper found that income for the poor was positively correlated with health

conditions, suggesting that the more income that this cohorts receives, there is an increased

probability that good health status will be reduced. Studies have highlighted that poor people are

more likely to have ‘lower’ health status (dysfunctions), suggesting that more income should

revert more health conditions. However, in this study income did not reduce but was associated

with increased health conditions in poor Jamaicans. Embodied in this finding is the implied

relationship between greater income and risky lifestyle behaviour, which accounts for the

rationale of more income explaining more self-reported health conditions. The increased income

for this group is not spent on things that would account for better health status, but on clothing,

entertainment, furniture and unhealthy foods.


       The argument of the maldistribution of poor income on particular items can be

highlighted by some of the findings of the current work. This research found that 55 out of every

100 of the poor purchased the prescribed medication given to them by their medical

practitioners, and 35 out of every 100 sought medical care. Comparatively, 64 out of every 100

Jamaicans sought medical care for the same period (2002) and the figure was 75 out of every 100

for the population that purchased the prescribed medication. The current finding revealed that

only 3 out of every 100 were holders of health insurance coverage compared to 14 out of every

100 for the nation; while for this study 70 out of every 100 of those who did not purchase

medication indicated inaffordability compared to 50 out of every 100 for the population.

Embedded in this finding is the fact that low income and more of income at this low level is

unable to afford positive effects on health to the point that it reduces poor health status (or ill-

health). The fact is more income for the poor does not change their physical milieu, educational

attainment, sanitation, nutrition or the social environment and therefore more money will be


                                                                                                148
spent on things that will not transform their economic status as well as their mindset about good

lifestyle choices.


        More income for this cohort explains the disparity, in particular health conditions

compared to that of the population. The current study revealed that 15 out of every 100 poor

Jamaican reported at least one health condition compared to 13 out of every 100 for the nation.

Some health conditions such as diabetes mellitus and hypertension are as a result of lifestyle

practices, and 37 out of every 100 poor Jamaican reported hypertension compared to 22 out of

every 100 for the population, while for diabetes mellitus it was 6 out of every 100 poor compared

to 12 out of every 100 for the nation (in 2007). In 2002, twenty nine out of every 100 of the poor

compared to 9 out of every 100 for Jamaicans (in 2007).


        Health literature has extensively examined the role of the physical milieu on health,

landslides, flooding, and other ecological conditions. It is noted that these variables do affect the

self-reported health status of poor Jamaicans. What can explain this seemingly paradox is

maladjustment. When people live in a particular sociopolitical or physiological environment,

they become more acceptable of the physical surrounding and this climatization allows for the

readjustment (or maladjustment) of the mind to those external pollutants that may be health

causing agents; but they are not recognized within their experiences as such conditions. On the

other hand, the crime and victimization that is products of environmental health, impacts on the

wellbeing of inner-city residence.


        One of the significant findings of this paper is the crucibility of ageing to self-reported

health conditions of Jamaicans. It was pointed out that ageing is the single most influential

predictor of health conditions of poor Jamaicans, suggesting that ageing comes with its health


                                                                                                 149
conditions and is consonant with general research on ageing and health [7], [8], and [10]. The

rationale of the aged being the age cohort with the most use of prescription drug is as a result of

the ageing process. The role that pharmaceutical drugs play in this experience is postulated.

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

services were senior citizens [21, 22]. Among the many issues that the research reported on, are

the six major causes of morbidity and mortality identified by the Caribbean Epidemiology

Centre. These are of paramount importance to this discussion.


       Men are less likely to visit health services, and normally this is the last resort for many of

them. The literature as established that females seek more health care than their male

counterparts [7, 8], and this accounted for the greater incidence of them reporting health

conditions.   This does not indicate that they are less healthy than males. Using the life

expectancy, which is greater for females than males in many nations including Jamaica, longer

life is associated with better health status. One Organization, in seeking to explain the health

disparity between the gender, shares the view that the disparity between contracting many

diseases are said to be due to the gender constitution of an individual [24]. Others argued that

differences in death and illnesses are as a result of differential risks acquired from functions,

stress, lifestyles and ‘preventative health practices’ [25]. This suggests that many of the diseases

are based on social factors, as lifestyle practices (contributing to health status) may justify the

advantages that women enjoy compared to men.


       Females, on the other hand, have a high propensity than males to experience certain

conditions such as depression, osteoporosis and osteoarthritis [24, 26]. Herzog [26] noted that

“…it appears that older women are more likely to be impaired by their health problems, while

older men are more likely to die from them.” A study that was conducted by Schoen et al. [27]

                                                                                                 150
on a group of adolescents reveals something different from that which was reported by WHO.

The authors found that males are more likely than females to feel stressed ‘overwhelmed’ or

‘depressed’, and they attributed this to limitedness of men’s social nerks. Schoen and colleagues

when further to argue that men can use denial, distraction, alcoholism and other social strategies

to conceal their illness or disabilities, which is supported by other scholars as the rationale for

women outliving their male counterparts[28-31]. Courtney and colleagues [32] like the World

Health Organization attributed this biomedical condition to difference between the genders based

on hormonal differentiations, social nerks and support, and cultural and lifestyle practices. This

is captured in the life expectancy that reflects a greater value for females than males.


       Among the justifications for the differential between life expectancy, gender is linked

with the health consciousness of women and their approach to preventative care. Unlike women,

worldwide men has a reluctance to ‘seek health-care’ compared to their female counterpart. It

follows in truth that women have bought themselves additional years in their younger years, and

it is a practice that they continue throughout their life- time which makes the gap in age

differential what it is – which is approximately a 4-year difference in Jamaica.


       Studies have shown that males benefit from their involvement in marriage where the

insistence of their wives regarding the practice of healthy lifestyle is adhered to [7, 8, 33], 34].

Smith and Waitzman [35] pointed out that wives dissuade their husbands from risky behaviours

such as the use of alcohol and drugs, bad eating habits and the maintaining of a strict medical

regimen. . Ergo, the current study has concurred with the literature that married people have

greater reporting health conditions compared to the other union status, which aids in the

explaining of them seeking more health-care services compared to the other marital statuses. In

Smith and Waitzman’s [35] literature review, they added that men’s gains from marriage was

                                                                                                151
greater than that of women [36].


         The question of education on matters of health and well-being has raised cause for

concern. Education is a well established factor of health status and/or mortality [7, 8, 20, 37-43];

and this was also the case in this study. This study disagreed not with education being the

predictor of health status, but the level of education that determines health status. All the studies

that were examined on the relationship between educational level and health status show that

higher than lower level of education accounts for the disparity in health status [45-50]. In the

current study, we found that there is no disparity in the health condition of someone with tertiary

level education and that of primary level education. However, there is disparity between primary

and secondary level education. The study also found, that for the poor, those with secondary

level education were less likely to report unfavourable health conditions than those with primary

education. Does this have anything to do with the psychological state of the poor at the primary

level?


         One of the realities of poverty is its deprivation with sociopolitical and ecological

conditions, and this accounts for a particular mindset of those who continuously have to live in

this reality. This study is not putting forward a perspective that the poor has the worse mindset;

instead, it is highlighting the position of continued deprivation which results from

maldistribution of money, as well as money not being spent on those materials that contribute to

positive lifestyle. It was noted in the literature that 50.4 per cent of the total consumption of

those in the poorest 20% was spent on food and beverage compared to 38.1 per cent for those in

the wealthiest quintiles [44]. If one were to include fuel and household supplies as well as

housing and household expenses, this would account for 68.3 per cent of the total consumption

of those in the poorest quintiles compared to only 10.3 per cent being spent on education. In this

                                                                                                 152
study, it is noted that self-reported health conditions are directly associated with negative

affective psychological state of the individual, indicating how the socio-environment create a

worsened state for the poor. This further goes to the inverse impact of negative affective

condition on the wellbeing and the direct influence of positive affective conditions on wellbeing

[7, 8], which adds credence to the position that the health-care treatment of poor must not hold

constant their socioeconomic and political environments as these are important in determining

health status of this cohort of people.


Conclusion

       In summary, public health practitioners in wanting to change some of the behavioural

practices of poor people in regard to their health must understanding the factors that determine

their health status and the quality of life enjoyed (or not), as this provides answers to some of the

issues in relation to action (or inactions) of those individuals. It should be understood also that

the milieu of the poor is a creation of income inequality, maldistribution of resources, lack of or

limited opportunities. Therefore, in using infant mortality, life expectancy and mortality to proxy

the health of a poor population, this would be omitting the psychological state that is a product of

the deprivation. Life expectancy speaks of length of life and omits healthy life expectancy; thus,

health education must incorporate for those realties with the findings of research if they are to

effectively address some of the health concerns of the poor. It should be noted here that any

inactions by government or public health specialists will result in increased health expenditure

by the state to treat the poor when they seek medical care.


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                                                                                        156
Table 6.1. Reason for Not Seeking Health Care and Public/Private Health Care Utilization by
Income Quintile

                          Could not afford it                          Public                     Private

Quintile
Poorest 20%               35.2                                         71.0                           24.3
2                         19.2                                         51.1                           42.3
3                         19.5                                         50.6                           42.9
4                         14.6                                         27.5                           65.4
5                         16.7                                         21.7                           73.9
Source: Planning Institute of Jamaica and the Statistical Institute of Jamaica. Jamaica Survey of Living conditions,
2006. PIOJ and STATIN; 2007.




                                                                                                               157
Table 6.2. Socio-demographic Characteristics of Sample, n = 9,931


         Details                                  Number                 Percent

Gender
         Male                                     4,799                  48.3
         Female                                   5,129                  51.7

Marital Status
       Married                                    1,255                  21.7
       Never married                              4,020                  71.3
       Divorced                                       19                 0.3
       Separated                                      51                  0.9
       Widowed                                      326                  5.8

Self-reported Health Conditions
        None                                      8,268                  84.9
        At least one condition                    1,475                  15.1

Educational Level
       Primary and below education                1,197                  23.3
       Secondary and post-secondary               3,885                  75.5
       Tertiary                                      66                   1.3

Property (i.e. land) ownership
       No                                         2,849                  42.0
       Yes                                        3,927                  58.0

Crowding                            2.6 ±1.6 person per room; Range = 11, 1 to 12 persons

Age                                        26.1 ± 22.5 years; Range = 99 years, 0 to 99 yrs.




                                                                                               158
Table 6.3. Logistic Regression: Self-reported Health Conditions of those in the lower
socioeconomic strata by some explanatory variable
                                                       Odds Ratio               95.0% C.I.


      Age                                                      1.04        1.04 – 1.05***
      Loneliness                                               0.88           0.69 – 1.13
      Averaged income                                          1.00         1.00 – 1.00**
      Physical environment                                     1.04           0.87 – 1.25

      Separated, Divorced, & Widowed                           1.19           0.87 – 1.63
      Married                                                  1.29
                                                                              1.04 -1.59*
      Referent group (never married)                           1.00

      Other towns                                              0.67         0.52 – 0.87**
      Urban areas                                              0.89           0.64 – 1.22
      Referent group (rural)                                   1.00

      Secondary                                                1.44         1.15 – 1.81**
      Tertiary                                                 1.25           0.54 – 2.87
      Referent group (primary or below)                        1.00

      Social Support                                           1.17           0.98 – 1.40
      Crowding                                                 0.98           0.91 – 1.06
      Negative affective                                       1.07        1.05 – 1.10***
      Positive affective                                       1.00           0.96 – 1.03
      Health insurance                                         8.23                 0.00 -
      Retirement income                                        1.26           0.92 – 1.71
      Sex (1= male)                                            0.46        0.39 – 0.56***
      Crime index                                              1.01         1.00 – 1.02**
    Nagelkerke R-squared = 0.396
    -2 Log Likelihood = 3,389.1
    Model χ2 (df = 19) = 1,365.5, P = 0.001
    *P < 0.05, **P < 0.01, ***P < 0.001




                                                                                             159
                                                                           Chapter 7
The uninsured ill in a developing nation


Empirical studies have used a piecemeal approach to the examination of health, health care-
seeking, uninsured people and the health status of those who are chronically ill, but no study
emerged in an extensive literature search, on the developing nations, and in particular Latin
America and the Caribbean, that has investigated health and health care-seeking behaviour
among uninsured ill people in a single research. The current study aims to narrow this divide by
investigating health, self-reported diagnosed health conditions, and health care-seeking
behaviour among uninsured ill Jamaicans, and to model factors which account for their
moderate-to-very good health status as well as health care-seeking behaviour. The current study
utilises cross-sectional survey data on Jamaicans which was collected in 2007. The survey is a
modification of the World Bank’s Living Standard Household Survey. This work extracted a
sample of 736 respondents who indicated that they were ill and uninsured from a sample of
6,783 respondents. Logistic regression analyses examined 1) the relationship between moderate-
to-very good health status and some socio-demographic, economic and biological variables; as
well as 2) a correlation between medical care-seeking behaviour and some socio-demographic,
economic and biological variables. Sixty out of every 100 uninsured ill Jamaicans were females;
43 out of every 100 were poor; 59 out of every 100 uninsured ill persons dwelled in rural areas; 1
of every 2 utilised public health care facilities, two-thirds had chronic health conditions, and 22
out of every 100 reported at least poor health. Moderate-to-very good health status was
correlated with age (OR = 0.97, 95% CI = 0.95-0.98); male (OR = 0.60, 95% CI = 0.37-0.97);
middle class (OR = 0.45, 95% CI = 0.21-0.95); logged income (OR = 2.87, 95% CI = 1.50-5.49);
area of residence (Other Town – OR = 2.33, 95^% CI = 1.19-4.54; Urban – OR = 2.01, 95% CI =
1.11-3.62), and health care-seeking behaviour (OR = 0.45, 95% CI = 0.27-0.74). Sixty-one of
every 100 uninsured respondents with ill health sought medical care. Medical care-seeking
behaviour was significantly related to chronic illness (OR = 2.25, 95%CI = 1.31-3.88); age (OR
= 1.03, 95%CI = 1.01-1.04); crowding (OR = 1.12, 1.01-1.24); income (OR = 1.00, 95% CI =
1.00-1.00); and married people (OR = 0.48, 95% CI = 0.28-0.82). Uninsured ill Jamaicans who
resided in rural areas had the lowest moderate-to-very good health status, but there was no
difference in health care-seeking behaviour based on the geographical location of residence.
Despite the fact that there is health insurance coverage available for those who are chronically ill
and elderly in Jamaica, there are still many such people who are without health insurance
coverage. The task of public health specialists and policy makers is to fashion public education
and interventions that will address many of the realities which emerged in this research.


Introduction
In all cultures, people desire good health and long life. Ill-health, therefore, is a challenge to the

aim of healthy life expectancy, and is the rationale for investments in health options such as


                                                                                                  160
exercise, diet, nutrition, science and technology, medical consultation and/or health care

utilisation. All living organisms will experience ill-health as well as good health over their life

courses; and when ill-health threatens the quality and length of life, it becomes the justification

for humans’ willingness to rectify, address and possibly postpone illnesses. Ill-health (i.e. illness,

sickness or ailment) threatens existence, productivity, development, the individual and the wider

society, and because of that humans demand the best health care options. Demand for health care

must be paid for by (1) a combination of health insurance coverage and out-of-pocket payment,

(2) the state, (3) out-of-pocket payments or (4) relatives, associates and/or family members. Ill-

health can be a burden to the individual, family, community and the nation, and it is a probability

against which people and the society seek to protect themselves. All illnesses require some

typology of treatment, and while this does not necessarily have to be a traditional medical

practitioner, curing illness means that the individual must forego consuming something in order

to restore his/her good health.


        Some illnesses such as the common cold may not require a trained medical practitioner to

cure, but often the individual will be required to spend money on over-the-counter medications,

use a home remedy or utilise non-traditional healers in the quest to restore his/her former healthy

state. There are other illnesses such as diabetes mellitus, heart disease, kidney problems,

hypertension, HIV/AIDS, sexually transmitted infections, and other chronic and non-

communicable diseases, which require the attention of traditional medical experts to address

their cure.


        The traditional medical practitioners require payment in the form of cash and/or health

insurance coverage. Because individuals desire to restore their health, they are expected to



                                                                                                  161
provide payment for health care, which for particular health conditions can be exorbitantly high.

It is this reality which may result in premature mortality if the state does not provide health care

coverage for those who are economically challenged and/or vulnerable. The World Health

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

countries, suggesting that illness interfaces with poverty. The WHO continued that 60% of

global mortality was caused by chronic illness, and this should be understood within the context

that four-fifths of chronic dysfunctions are in low-to-middle income countries [1]. It also

postulated that “In reality, low and middle income countries are at the centre of both old and new

public health challenges” [1]. Embedded in the realities outlined by the WHO are the incapacity

of the poor, the association between poverty and illness, between poverty and premature

mortality, poverty and human suffering, and poverty and future retardation of economic growth,

and the fact that health insurance provides some cushion against this, for the individual and for

society. Other studies have equally found that there is a significant statistical relationship

between poverty and illness [2-4] and poverty and chronic illness, [5] which means that illness

can make the vulnerable less likely to survive and the wealthy become poor.


       The high risk of mortality in developing countries is owing to food insecurity, low water

quality and low sanitation coupled with inadequate access to material resources. Poverty makes

it an insurmountable hurdle for poor people to effectively address illness unless health care

services are free. Hence, those in the lower socioeconomic class will be expected to have poorer

health, as they are crippled by their material deprivation and low health options. The WHO

captures this aptly “... People who are already poor are the most likely to suffer financially from

chronic diseases, which often deepen poverty and damage long term economic prospects”. [1]

Among the challenges for people living in poverty is access to health insurance coverage. Such a

                                                                                                162
possibility means that the burden of health care is an out-of-pocket payment that cannot be

provided by the poor, and this will eliminate life in the process. Cass et al. [6] found that infant

mortality in Peru for those in the poorest quintile (i.e. poorest 20%) was almost 5 times more

than for those in the wealthiest quintile (i.e. wealthiest 20%). This indicates the extent of the

health challenge of the poor, and the role that the lack of health insurance and income play in the

demise of individuals and even their children.


       Another research paper revealed that life expectancy between the poorest 20% and the

wealthiest 20% was 6.3 years, and this figure rose to 14.3 years for disability-free life

expectancy, [7] suggesting that access and lack of access to resources explain health and healthy

life expectancy in and among the social classes in a society. Grossman [8] found a positive

correlation between income and health status, indicating that money makes a difference in

health, health care-seeking behaviour, physical milieu and health care coverage. Smith and

Kington, [9] on the other hand, went further than Grossman when they postulated that money

buys health. This viewpoint is somewhat deceptive, as money provides access to good physical

milieu, the best health care options, nutrition, dietary choices and health information which are

not readily available to the poor, but it does not buy health. Health is not a commodity for sale,

and so it cannot be purchased, but money allows for access to better health choices and by

extension can change health outcomes. Those issues could be the intent of Smith and Kington,

when they say that money buys health, and they further exemplify the challenges if an individual

does not have access to it.


       Material deprivation is such that the poor will be far from concerned with health

insurance coverage, proper diet and nutrition, health care choices, but more with survivability.



                                                                                                163
This denotes that they will be living on the margins of survivability and the decision to purchase

health insurance will be the opportunity cost of food, clothing, shelter, minimal education and

health options. Within the context of material and widespread health deprivation for those in the

lower socioeconomic strata, the state must play a role in aiding improvements in the healthy life

expectancy of those therein. It is through this avenue that public health must act in order to fulfill

the aim of the state in improving the quality of life of all residents in the nation.


        Public health uses information from within and outside the society to improve the health

and quality of people’s lives, and this requires continuous research findings. According to the

WHO, “In Jamaica 59% of people with chronic diseases experience financial difficulties because

of their illness...” Hence, poverty and illness, poverty and chronic illness, and poverty and low

access to material resources are well established in research literature, but a dearth of

information existed in Latin America and the Caribbean, and in particular Jamaica, on the sick

and uninsured. Can we assume that they are all poor people, and use this to plan for them in a

developing nation? An extensive review of the literature in developing nations, and in particular

Latin America and the Caribbean, did not produce a single study that has examined health, and

health care-seeking behaviour among uninsured ill people. The current study aims to narrow this

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

behaviour, at the same time examining who are the unhealthy and uninsured, and modelling

factors which account for the moderate-to-very good health status of uninsured ill Jamaicans, in

order to provide public health specialists with pertinent information that can be used to address

some of the challenges within the society.




                                                                                                  164
Methods and material


Data


The current study utilised the latest cross-sectional survey data in Jamaica to examine health,

self-reported diagnosed health conditions and health care-seeking behaviour, and to model

factors which account for the moderate-to-very good health status of unhealthy and uninsured

Jamaicans. The Jamaica Survey of Living Conditions (JSLC) began collecting data from

Jamaicans in 1988 and the latest dataset available is for 2007. The JSLC is a modification of the

World Bank’s Living Standard Household Survey [10, 11]. This work extracted a sample of 736

respondents who indicated that they were ill and not insured, from a sample of 6,783 respondents

[12]. The cross-sectional survey was conducted between May and August 2002 in the

14 parishes across Jamaica, and included 6,783 respondents of all ages. The JSLC used a

stratified random probability sampling technique to draw the original sample of respondents,

with a non-response rate of 26.2%. The sample was weighted to reflect the population.


       The design was a two-stage stratified random sampling design where there was a Primary

Sampling Unit (PSU) and a selection of dwellings from the primary units. The PSU is an

Enumeration District (ED), which constitutes of a minimum of 100 dwellings in rural areas and

150 in urban areas. An ED is an independent geographical unit that shares a common boundary.

This means that the country was grouped into strata of equal size based on dwellings (EDs).

Pursuant to the PSUs, a listing of all the dwellings was made, and this became the sampling

frame from which a Master Sample of dwellings was compiled, which in turn provided the

sampling frame for the labour force. One third of the 2007 Labour Force Survey (i.e. LFS) was

selected for the survey.

                                                                                             165
Study instrument


The JSLC used an administered questionnaire where respondents were asked to recall detailed

information on particular activities. The questionnaire was modelled on the World Bank’s Living

Standards Measurement Study (LSMS) household survey. The questionnaire covered

demographic variables, health, education, daily expenses, non-food consumption expenditure,

and other variables. Interviewers were trained to collect the data from household members.


Statistical methods

Descriptive statistics were used to provide socio-demographic characteristics of the sample. Chi-

square analyses were used to examine the association between non-metric variables. Analysis of

variance was used to test the statistical significance of a metric and non-dichotomous variable.

Logistic regression analyses examined 1) the relationship between good health status and some

socio-demographic, economic and biological variables; as well as 2) a correlation between

medical care-seeking behaviour and some socio-demographic, economic and biological

variables. The statistical package SPSS 16.0 was used for the analysis. A p-value less than 5%

(2-tailed) was used to indicate statistical significance.


       The correlation matrix was examined in order to ascertain if autocorrelation and/or

multicollinearity existed between variables. Based on Cohen and Holliday [13] correlation can

be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. Any variable that had

at least moderate (r > 0.6) was re-examined in order to address multicollinearity and/or

autocorrelation between or among the independent variables [14-16]. Another approach in

addressing collinearity (r > 0.6) was to independently enter variables in the model to determine

which one should be retained during the final model construction. The method for retaining or


                                                                                             166
excluding a variable from the model was based on its contribution to the predictive power of the

model and its goodness of fit [17]. Wald statistics were used to determine the magnitude (or

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

Odds Ratio (OR) for the interpreting of each significant variable.


Measurement

Health status is a binary measure where 1= moderate-to-very good health; 0= otherwise which is

determined from “Generally, how do you feel about your health”? Answers to this question were

analyzed on a Likert scale ranging from excellent to poor. Medical care-seeking behaviour was

taken from the question ‘Has a health care practitioner, healer, or pharmacist been visited in the

last 4 weeks?’ with there being two options: Yes or No. Medical care-seeking behaviour

therefore was coded as a binary measure where 1=Yes and 0= otherwise. Crowding is the total

number of individuals in the household divided by the number of rooms (excluding kitchen,

verandah and bathroom). Sex: This is a binary variable where 1= male and 0= otherwise. Age is

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

group is a non-binary measure: children (aged less than 15 years); young adults (ages 15 to 30

years); other-aged adults (ages 31 to 59 years); young elderly (ages 60 to 74 years); old elderly

(ages 75 to 84 years) and oldest elderly (ages 85 years and older). Social hierarchy: This variable

was measured based on income quintile: The upper classes were those in the wealthy quintiles

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

(quintiles 1 and 2).


Chronic illnesses: These are ailments or diseases that are prolonged, not likely to be resolved

spontaneously, and are infrequently cured.



                                                                                               167
Inequity denotes differences that are unnecessary and avoidable, but are also thought to be unfair

and unjust, and these are adjudged based on the context of the customs operating in the society in

general.


Equity in health means (1) equal access to care for equal needs, (2) equal access to utilisation for

equal needs, and (3) equal quality of care for all in the society.


Inequalities in health mean patterns of socioeconomic disparities in health outcome which are

systematic, avoidable and important within a country.


Model


The multivariate model used in this study is in keeping with wanting to capture the multi-

dimensional concept of health and the health care-seeking behaviour of uninsured ill people.

Utilising logistic regression on secondary cross-sectional data, the present study modelled

moderate-to-very good health status and the health care-seeking behaviour of uninsured ill

Jamaicans. Using a p-value of less than 0.05 to indicate statistical significance, each model

reflects only those variables that are statistically significant.


Health Model


Hit = f(Ait, Xi, SSit, lnYit, ARit, HSBit, εit) ………………………………. [1]


Health Care-seeking Behaviour Model


Hit = f(Ait, CIit, Hit, lnYit, CRit, MSit, εit) ………………………………. [2]



                                                                                                168
        Where Hti is current moderate-to-very good health status of uninsured ill person i in time

period t; Ai is age (in years) of person i in time period t; Xi is gender of person i; SSit is social

class of person i in time period t; lnYit is logged income of person i in time period t; ARit is area

of residence in time period time t; HSBit is health care-seeking behaviour in time period t; CRi is

crowding in the household of person i in time period t; CIit is chronic illness of person i in time

period t; MSit is marital status of person i in time period t; εit is residual error of person i - in time

period t.


Results

Table 7.1 presents information on the demographic characteristics of the sample. The sample

was 736 respondents (i.e. 10.85% of the initial survey) who indicated that they were both sick

and uninsured, and of which 40.5% were males. Concurringly, of the sample 95.4% had at most

primary level education and 0.8% had tertiary level education. Children constituted 28.7% of the

sample; young adults, 10.2%; other adults, 31.3%; young-old, 16.4%; old-old, 10.5%; and

oldest-old, 3.0%. The median age was 42.0 years (range = 0 – 99 years). The median total annual

expenditure was USD 5,689.89 (range = USD 261.56 – 32,780.78; US$ 1.00 = J$ 80.47 - at the

time of the survey). The number of visits made to medical practitioner(s) was 1.4 ± 1.0), while

the amount of time spent in private care facilities was 3.0 ± 2.8 compared to 5.2 ± 5.0 for public

care facilities). The mean cost of public medical care was USD 4.44 ± USD 16.14 compared to

USD 13.64 ± USD 28.22 for private medical expenditure.

        Of those who utilised public health care facilities, 22.9% of them purchased the

prescribed medication compared to 78.8% who visited private health care facilities.




                                                                                                     169
         Table 7.2 highlights information on health care-seeking behaviour, health care utilisation,

self-reported illness and area of residence by social hierarchy. Based on Table 7.2, there were

significant statistical associations between (1) health care-seeking behaviour and social

hierarchy; (2) public health care centre utilisation and social hierarchy, and (3) private health

care centre utilisation and social hierarchy.

         Table 7.3 highlights information on monthly food expenditure, per capita consumption,

length of illness, number of visits made to health practitioners, medical expenditure and self-

reported diagnosed illness by area of residence. Based on Table 7.3, there were significant

statistical associations between (1) monthly food expenditure and area of residence and (2) per

capita consumption and area of residence – P < 0.05. However, there were no significant

statistical relationships between the other variables and area of residence – P > 0.05.

         There was a statistical association between health care-seeking behaviour and age group

of respondents – χ2 = 11.1, P = 0.048. As uninsured ill people become older, they are more likely

to seek medical care: Children, 54.8%; old-adults, 54.8%; other-age adults, 64.0; young-old,

63.3%; old-old, 73.3%; and oldest old, 66.7%.

         There was a statistical relationship between having chronic illness and being the

household head – χ2 = 63.3, P < 0.0001. Almost 55% of those with chronic illnesses were

household heads, compared to 22.4% who did not have chronic illness but were household

heads.

         A significant statistical association existed between sex and having chronic illness - χ2 =

4.7, P < 0.031. More females had chronic illness (69.8%) than males (61.7%).

         There was a significant statistical association between health status and typology of

illnesses (i.e. acute and chronic conditions) - χ2 = 62.3, P < 0.0001. Thirty-seven percent of



                                                                                                170
those with chronic illnesses reported at least poor health status compared to 12.2% of those with

acute conditions. On the other hand, 61.1% of those with acute conditions reported at least good

health status compared to 31.3% of those with chronic conditions.

        A statistical difference was found between the mean income of those in the different

social hierarchies – F statistic = 277.50, P < 0.0001. The mean income for those in the poorest

20% was USD 666.07 ± 175.40 followed by the second poor, USD 1,090.68 ± 132.14; middle

class, USD 1,489.69 ± 169.07; second wealthy, USD 2,131.55 ± 254.49 and the wealthiest 20%,

USD 4,201.39 ± 235.26.

Multivariate analysis

        Table 7.5 shows variables which are correlated (or not) with the moderate-to-very good

health status of uninsured ill respondents. Seven variables emerged as significantly associated

with moderate-to-very good health status – Model χ2 = 83.70, P < 0.001, -2 Log likelihood =

482.9 – and they accounted for 23% of the variability in health status. The model is a good fit

for the data - Hosmer and Lemeshow goodness of fit χ2= 3.72, P = 0.88.

        Table 7.6 presents information on variables and self-reported health care seeking

behaviour of uninsured respondents. Six variables emerged as significant statistical correlates of

self-reported health care-seeking behaviour - Model χ2 = 47.9, P < 0.001, -2 Log likelihood =

486.1. The model is a good fit for the data - Hosmer and Lemeshow goodness of fit χ2= 8.11, P =

0.62.

Discussion

The current research used a sample of respondents who indicated both experiencing ill-health

and having no health insurance coverage. Of the sample of respondents (i.e. n = 736), 60 out of

every 100 were females, 43 out of every 100 were poor, 35 out of every 100 were in the upper


                                                                                              171
social class, 59 out of every 100 dwelled in rural areas, 3 out of every 100 had been injured

during the last 4 weeks, 61 out of every 100 sought medical care, 50 out of every 100 utilised

public health care, two-thirds reported being diagnosed with a chronic illness, 31 out of every

100 were elderly, and 29 out of every 100 were children. Those in the lower socioeconomic

class were more likely to dwell in rural areas. Those in the poorest 20% were more likely to use

public health centres, and the wealthiest 20% were more likely to utilise private health care

centres. Fifty-four percent of those in the poorest 20% sought medical care in the last 4 weeks

compared to 72% of those in the wealthiest 20%. Concurringly, of the sample, 78.4% indicated

at least fair health status. Moderate-to-very good health status was explained by age, sex, social

class, income, area of residence and health care-seeking behaviour. Rural residents had the least

moderate-to-very good health status among uninsured ill Jamaicans. People who dwelled in

Other Towns were 2.3 times more likely to indicate moderate-to-very good health compared to

those in rural areas, and those in urban areas were 2.0 times more likely to claim moderate-to-

very good health status. Those who indicated having a chronic illness were 37% less likely to

report moderate-to-very good health. In addition, the present sample represents 70% of those

who indicated having an illness in Jamaica for 2007.

       Statistics from the Planning Institute of Jamaica and the Statistical Institute of Jamaica

[10] showed that 15.5% of Jamaicans reported ill-health in 2007. Within the context of the

current findings and that of PIOJ and STATIN, it computes that 71% of those who were

experiencing illness were without health insurance coverage. Given that 50% of those who

claimed to be experiencing ill-health utilised the public health care system and the fact that two-

thirds of the illnesses were chronic conditions (3 females for every 2 males were uninsured and

ill, and 6 out of every 10 uninsured ill people were of the dependent age cohort - less than 15



                                                                                               172
years or 60+ years), the public health care sector in Jamaica needs to recognize the impending

challenges of uninsured unhealthy people.

       Van Agt et al. [5] found that the chronically ill were more likely to be poor, a statement

with which this study concurs. In this paper, 43.2% of the chronically ill were poor (25.2% of

poorest 20%) compared to 35.2% of the upper class (15.3% of the wealthiest 20%). This study

went further than Van Agt et al.’s work, as the chronically ill were more likely to be elderly

(42.5% of the chronically ill were 60+ years), to seek more medical care, were more likely to

utilise public health facilities, more likely to live in rural areas (59.1%), more likely to be

household heads (54.8%) and more likely to be females (63%). Clearly the poor are highly

vulnerable to chronic illness [1, 5] and material deprivation [4], which accounts for more of them

not having health insurance coverage while suffering from ill-health. Hence, those who are

uninsured and ill must interface with chronic health conditions as well as income deprivation.

       Income is well established in the health literature as being associated with health [4, 8, 9],

and this explains the fact that those in the lower socioeconomic class have poorer health than

those in the upper class [18, 19]. This paper found that uninsured ill people with more income

are 2.9 times more likely to report moderate-to-very good health status, and they are also more

likely to seek medical care. The challenge for those in the lower class is more than lower health

status; it is also being deprived of the health care that they need. Statistics revealed that poverty

in Jamaica is substantially a rural phenomenon (prevalence of poverty in rural areas, 15.3%;

semi-urban poverty, 4.0%; urban poverty 6.2%) [10]. This study highlights that those who are ill

and uninsured are likely to dwell in rural zones, explaining how financial deprivation accounts

for lower ownership of health insurance coverage, the worst health being found among those in

rural areas compared to city dwellers. Using per capita consumption to measure income in this



                                                                                                 173
study, it was revealed that urban residents had 1.7 times more income than rural residents, and

that semi-urban residents had 1.3 times more income than rural dwellers, suggesting that the

health disparities between the geographical dwellers is explained by this income inequity. It is

therefore this access to more income that accommodates the greater health status of the urban

and semi-urban respondents, compared to the rural dwellers, and it highlights a real need to

correct income inequality among the socioeconomic groups in the nation. A study by Stronks et

al. [20] found an interrelationship between income, health and employment status, which further

argues for greater health for urban and semi-urban dwellers, as rural residents are more likely to

be seasonally employed, self-employed or have low-income employment.

       While income is related to better health status, which is also the case among uninsured ill

people, concurring with the literature on a population [8, 9, 20], the great health disparity

between the different social classes is more related to income than place of residence. Such a

finding provides clarification for a study done by Vila et al. [21] which stated that great health

disparities in the city of Milwaukee were associated with area of residence by different social

hierarchy. Income has a greater influence on better health than area of residence, and it even

correlates with health care-seeking behaviour among the uninsured ill, unlike area of residence.

Money matters in the health of uninsured Jamaicans as well as the general populace, as it offers a

better explanation for peoples’ choices, accounting for the greater health of those who are able to

choose, than their place of residence. Lack of access to money, therefore, in any geographical

locality, explains health and material deprivation. Hence, it is not the fact of being in a rural area

that accounts for poor health, but material and other deprivations are greater in rural areas, a

factor which provides an understanding for the massive health disparity between them and city

residents.



                                                                                                  174
       Poverty is associated with premature mortality, and the current research provides some

explanation for this established fact. This paper is on uninsured ill Jamaicans, and the findings

highlighted that 54% of those in the poorest 20% visited a health care practitioner, 58% of the

poor compared to 65% of the second wealthy and 72% of the wealthiest 20%. While the affluent

class has access to material and other resources to address health concerns, the poor are not as

privileged as the upper class. This research found that 70.1% of those in the poorest 20% had at

least one chronic health condition, the second poor, 61.2%; the second wealthy, 72.7% and the

wealthiest 20%, 68.7%, which means that non-utilisation of medical care is likely to lead to

complications and possible premature mortality. The WHO had stated that 60% of global

mortality is caused by chronic illness, but clearly poverty, non-treatment of chronic illnesses and

cultural practices are all a part of the rationale for mortality, and not merely the condition.

       Although those who suffer from chronic conditions in Jamaica are able to access public

health insurance which can reduce out-of-pocket payments for treatment and medication, clearly

the culture prevents some people from accessing this facility. This work showed that a large

percentage of uninsured ill people dwelled in rural areas, where poverty was 2.5 times more than

urban poverty and 3.8% more than semi-urban poverty, arguing for the role of the culture in

preventing them from accessing assistance from the state. With this preponderance of

unwillingness on the part of poor and rural residents to access health insurance, accompanied by

their low demand for health services compared to the wealthy, the inference is that many of them

will seek health services based only on severity of illness. Chronic illnesses are such that non-

medical practitioners should not interpret when conditions are serious and warrant health care

assistance. It is this culture underpinning that accounts for the premature mortality and not the

poverty or illness, as those with chronic health conditions in Jamaica are able to access public



                                                                                                  175
health care despite their reluctance to access public health insurance coverage. With not having

health insurance coverage, poverty and illness are likely to become a burden to individuals and

family, and when those social agents are unable to assist with the costing of medical treatment, it

will then become the responsibility of the state.

       This paper did not examine nutrition and health, but a study by Khetarpal and Kochar

[22] found a statistical relationship between nutrition and health in rural women, which offers

some explanation for the great health disparities in geographical areas of residence. Another

study by Foster [23] on low-income rural areas concurs with Khetarpal and Kochar [22] that

nutrition accounts for health or ill-health, as the body requires particular nutrients. It can be

extrapolated from the aforementioned studies, to that of the current one, that great disparities in

health status among the different geographical areas in Jamaica can be explained by the

nutritional intake (or lack of intake) based on where people dwell in this nation. There is a

question which must be addressed in order to provide some explanation for the seemingly low

nutritional intake of rural uninsured residents: Are rural residents less likely to intake the

required nutrients compared to residents in other geographical areas in Jamaica? The answer is

clearly yes as more of the uninsured ill Jamaicans are poor, and this means that they will be less

concerned about the required nutrient intake than food consumption and mere survivability.

Poverty is therefore more a factor in insurance, illness, lower health status and health care-

seeking behaviour than the geographical area of residence, but what about the general health

status of the uninsured ill, and is it lower than that of the population of Jamaica?

       Almost 78 out of every 100 uninsured ill Jamaicans claimed to have at least good health

status. A study by Bourne [24] found that 82 in every 100 Jamaicans reported at least good

health status, which is greater than that for the uninsured ill people. Furthermore, 3.3 times more



                                                                                               176
Jamaicans indicated very good health compared to the uninsured ill Jamaicans. The health

disparities were not only between the good and very good health status of Jamaicans and

uninsured ill Jamaicans, but were also evident for poor health status. Comparatively, 4.4 times

more uninsured ill Jamaicans claimed at least poor health as compared to the general population

(i.e. 4.9%), and 3 times more uninsured chronically ill Jamaicans reported at least poor health

status compared to those with acute health conditions. The current study concurs with (1) Reed

and Tu’s work [25] that uninsured chronically ill people in America reported lower health status

(or worse health) and (2) Bourne and McGrowder [26] which stated that 25.3% of chronically ill

Jamaicans reported at least poor health. Reed and Tu went on to state that the majority of

uninsured people with chronic illnesses delay health care utilisation owing to cost, which

explains an aspect of this study, that although 43.2% of the uninsured ill people were living in

poverty (i.e. poorest 20% and second poor income quintile), 39% did not seek medical care.

       Faced with poverty, no health insurance coverage and chronic illness, uninsured ill

Jamaicans are highly likely to face all kinds of life challenges such as material deprivation,

dietary and nutritional deficiencies, high risk of health complications, high out-of-pocket medical

bills, disruptions in family life, future vulnerabilities and premature mortality. When this burden

becomes untenable for the individual, family and wider community, it will then become the

responsibility of the state [27]. This justifies the need to expand public health insurance to

protect the poor, the chronically ill and the vulnerable in a society [28], as chronic illness can

erode the economic livelihood of an individual and therefore delay needed health care [29]. One

study stated that uninsured households are one illness away from financial catastrophe [30],

indicating that if a household was already in poverty this will become the burden of the state or

may lead to premature mortality, as the individual will be unable to access needed health care



                                                                                               177
owing to his/her inability to afford medical care. This implies that poverty encapsulates

powerlessness, physical weakness, illness, chronic illness, premature mortality, lack of

productive assets, emotional distress, constricted freedom and future impoverishment due to the

aforementioned conditions, if they are not addressed by policy makers.

       While impoverishment in urban areas is highly visible in the form of squalor, dilapidated

edifices, zinc fencing, improper sanitation, squatting and violence, rural poverty is less easily

identifiable and may be overlooked by the naked eye. Clearly, using health disparities between

area of residence and the socioeconomic strata, rural poverty in Jamaica is showing signs of

depleting the human capital more than urban poverty. According to Harpham and Reichenheim

[31], on the disaggregating of rural and urban health indicators, the latter ‘appear’ to have better

health status. This study dispels the notion of ‘appearance’ and goes to the reality of the health

differential using self-reported health among urban, semi-urban and rural uninsured ill

Jamaicans. The discipline of public health cannot only use external findings to carry out its

mandate, or divorce itself from the realities which emerge from the current study; poverty is

destroying the human capabilities and resilience of the Jamaican people and more so in the case

of rural uninsured ill people. Because poverty is strongly associated with illness, and illness can

result in poverty [32-34], those who are presently uninsured, ill and poor are highly vulnerable to

ill-health and premature mortality, which argues for an immediate health campaign to address the

challenges among the socioeconomic strata and area of residence, as these were not alleviated

with the introduction of the National Health Fund – NHF [35].

       The NHF is a statutory company which was established by the NHF Act (2003) with a

Chairman and Board of Management appointed by the Minister of Health. It was established in

2003 to provide direct assistance to patients with chronic conditions, to purchase drugs and fund



                                                                                                178
support to private and public companies for approved projects [35]. The NHF is a social health

insurance which is geared towards alleviating out-of-pocket payments for medication for those

who suffer from chronic illnesses. Fourteen chronic illnesses are covered by the NHF, with

respect to pharmaceutical benefits in direct assistance to ill individuals. The chronic health

conditions that are covered by the NHF are hypertension, diabetes mellitus, breast cancer,

prostate cancer, glaucoma, arthritis, asthma, high cholesterol, rheumatic heart disease, major

depression, epilepsy, psychosis, ischemia and vascular diseases. The NHF became operational in

August 2003, and has undoubtedly aided many chronically ill, non-poor and poor Jamaicans.

With all the investment, the NHF has not failed to have a major coverage of chronically ill

respondents using the Fund. The individuals are mostly rural residents, poor, under 60 years of

age, and female. Such a reality speaks to the administrative and operational failure of the NHF

to improve the lives of its intended population owing to the centralization of its operations in

Kingston, which is an urban area in Jamaica. The verdict is in, that merely instituting an agency

to carry out a particular task (which is to distribute benefits evenly across the socioeconomic

strata, area of residence and sex) will not provide solutions to the inequalities and inequities in

health between the particular groups in Jamaica. This study concurs with one in Finland [36]

showing that the poor are more vulnerable to illnesses, and research conducted in the United

Kingdom [37] found that those in the lower socioeconomic stratum were more likely to die

prematurely than those in the upper income groups. Embedded in those findings is the fact that

any equitable distribution of NHF benefits to those in the different socioeconomic strata will

show further unfairness and injustices in the health outcomes which already exist, owing to

income inequalities.

Conclusion


                                                                                               179
Two-thirds of uninsured ill Jamaicans are chronically ill. The uninsured ill are mostly within the

dependent age cohort (children and elderly), they are female and are rural respondents who are

generally poor people. With one half of the uninsured ill respondents utilising the public health

care system, and only 2 in every 10 of them purchasing medications, there are serious future

challenges for public health in Jamaica. There is an inverse relationship between the health status

of uninsured ill Jamaicans and those in socioeconomic strata. The findings of this study highlight

the likely challenge of the state in assisting uninsured ill Jamaicans. Despite the fact that health

insurance coverage is freely accessible to those who are chronically ill in Jamaica, there are still

many such people who are without health insurance coverage, and some are not even seeking

medical care. Another reality which emerged from this paper is that although health care

utilisation is free in Jamaica for children 18 years and younger, 45 out of every 100 of those

uninsured and ill did not seek medical care, emphasizing people’s interpretation of illnesses that

require medical attention, and how this retards health care demand. The task of public health

specialists and policy makers, therefore, is to fashion public education and intervention

programmes that will address many of the realities which emerged in this research. The great

health disparity between the lower socioeconomic strata and those in the upper strata, as well as

those who reside in rural areas, cannot be left to resolve itself, as clearly it has not happened in

the past and the situation cannot be allowed to continue indefinitely in the future.

The Way Forward

The variations in health status and health care-seeking behaviour within and between the

socioeconomic strata who are uninsured ill people, clearly present information that reveals public

health concerns, and highlights many challenges which are still unresolved in Jamaica. The

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



                                                                                                180
respondents, and this should be the subject of some future study, as it would provide needed

information about these individuals. Despite the investments in health, the health sector and

poverty alleviation programmes in Latin America and the Caribbean, there is still a need to study

the heterogeneity in health outcome between the socioeconomic strata and area of residence, as

health disparity between and within countries is still great and not in keeping with health

inequality eradication in the region. Another unresolved issue stemming from the present

research is how much of the cognitive dimension explains the health differential between the

socioeconomic strata and the area of residence. In order to understand how to address policy

intervention and health education programmes for people in Jamaica, studies need to examine the

breadth and scope of cognitive dimensions in explaining health inequalities. This will allow

public health technocrats to understand why 70.3% of those who were ill in Jamaica in 2007 did

not have health insurance, and some of the chronically ill people, despite having access to public

health insurance, did not possess such insurance, and did not seek medical care. A critical issue

which needs to be addressed in the future is the structure of the National Health Fund (the NHF

is accessible to, and provides public health insurance coverage for, those experiencing chronic

illnesses). Barrett and Lalta [32] wrote that “The National Health Fund dealt with these issues by

treating the non-poor and the poor as part of the same target beneficiary. Survey data and health

officials indicate that the poor suffer as much from chronic diseases as the rich, but are less likely

to seek treatment, or are only able to pay for part of their prescription drugs by reducing out-of-

pocket payment …” This study is 4 years after the operational establishment of the NHF, and

new findings are coming in, which show that the NHF cannot treat different socioeconomic

strata in the same way, neither can it deal equitably with those who reside in different

geographical areas.    The health disparities will not be addressed by merely offering equal



                                                                                                  181
benefits to all within the context of the current findings, as these will only perpetuate health

inequalities and inequities. The NHF therefore needs to be restructured in order to provide

definitions based on socioeconomic class and area of residence, so as to effectively alleviate

some of the challenges which emerged from this research.

Conflict of interest
The author has no conflict of interest to report.

Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica
Survey of Living Conditions, 2007, none of the errors that are within this paper should be
ascribed to the Planning Institute of Jamaica or the Statistical Institute of Jamaica as they are not
theirs, but are instead owing to the researcher.



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30. Cook K, Dranove D, Sfekas A. Does major illness cause financial catastrophe? Health
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31. Harpam T, Reichenmeim M. Urbanisation and health. In: Lankinen KS, Bergstrom S,
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   Oxford: MacMillan; 1994: pp. 85-94.
32. Wagstaff A Poverty, equity, and health: Some research findings. In: Equity and health:
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    Occasional publication No. 8, Washington DC, US; 2001: pp.56-60.

33. Bourne PA. Impact of poverty, not seeking medical care, unemployment, inflation, self-
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                                                                                          184
Table 7.1. Demographic characteristic of sample, n=736
Characteristic                                           n         %
Sex
  Male                                                       298       40.5
  Female                                                     438       59.5
Marital status
  Married                                                    161       30.8
  Never married                                              276       52.8
  Divorced                                                    14        2.7
  Separated                                                   10        1.9
  Widowed                                                     62       11.9
Social hierarchy
  Poorest 20%                                                170       23.1
  Second poor                                                146       19.8
  Middle                                                     165       22.4
  Second wealthy                                             142       19.3
  Wealthiest 20%                                             111       15.4
Area of residence
  Urban                                                      176       23.9
  Semi-urban                                                 128       17.4
  Rural                                                      432       58.7
Injury in last 4-weeks
  Yes                                                         23        3.1
  No                                                         712       96.9
Self-reported diagnosed illness
 Acute conditions
    Influenza                                                124       18.6
    Diarrhoea                                                 26        3.9
    Asthma                                                    73       10.9
 Chronic conditions
   Diabetes mellitus                                          69       10.3
   Hypertension                                              147       22.0
   Arthritis                                                  40        6.8
   Other                                                     189       28.3
Health care-seeking behaviour
  Yes                                                        446       61.4
   No                                                        280       38.6
Health care utilization
   Public hospital (yes)                                     146       29.9
   Private hospital (yes)                                     27        5.5
   Public health care centres (yes)                           96       19.6
   Private health care centres (yes)                         212       43.4
   Other (yes)                                                 8        1.6
Purchased medication
   Yes                                                       411       58.6

                                                                              185
Table 7.2. Health care-seeking behaviour, health care utilization, self-reported illness and area of
residence by social hierarchy
                                                    Social hierarchy
                               Poorest     Second         Middle        Second     Wealthiest
Characteristic                   20%         poor                       wealthy         20%
                                                                                                  P
                                n (%)       n (%)          n (%)         n (%)         n (%)
Health care-seeking                                                                               0.046
behaviour
   Yes                       90(54.2)    86(59.7)     98(60.1)         91(65.0)    81(71.7)
    No                       76(45.8)    58(40.3)     65(39.9)         49(35.0)    32(28.3)
Health care
utilization
  Public hospitals                                                                                0.337
   Yes                       32(37.2)    30(35.3)     35(35.7)         30(33.7)    19(23.5)
    No                       54(62.8)    55(64.7)     63(64.3)         59(66.3)    62(76.5)
  Private hospitals                                                                               0.451
   Yes                         5(5.7)      5(5.9)       3(3.1)           6(6.7)      8(9.9)
    No                       83(94.3)    80(94.1)     95(96.9)         83(93.3)    73(90.1)
  Public health care                                                                              0.016
centres
   Yes                       29(33.3)    21(24.7)     21(21.4)         15(17.0)    10(12.3)
    No                       59(67.0)    64(75.3)     77(78.6)         73(83.0)    71(87.7)
  Private health care                                                                             0.001
centres
   Yes                       28(31.5)    35(41.2)     49(50.0)         52(58.4)    48(59.3)
    No                       61(68.5)    50(58.8)     49(50.0)         37(41.6)    33(40.7)
  Self-reported diagnosed                                                                         0.200
illness
 Acute conditions
    Influenza                24(15.0)    25(19.1)     34(22.7)         26(20.3)    15(15.2)
    Diarrhoea                  3(1.9)      9(6.9)       7(4.7)           3(2.3)      4(4.0)
    Asthma                   21(13.1)    17(13.0)     17(11.3)           6(4.7)    12(12.1)
 Chronic conditions
   Diabetes mellitus          15(9.4)    15(11.5)       9(6.0)         15(11.7)    15(15.2)
   Hypertension              38(23.8)    23(17.6)     37(24.7)         27(21.1)    22(22.2)
   Arthritis                  15(9.4)      8(6.1)       7(4.7)           6(4.7)      4(4.0)
   Other                     44(27.5)    34(26.0)     39(26.0)         45(35.2)    27(27.3)
Area of residence                                                                               <0.0001
   Urban                     19(11.2)  21(14.4)  35(21.2)    42(29.6)              59(52.2)
   Semi-urban                  16(9.4) 25(17.1)  30(18.2)    36(25.4)              21(18.6)
   Rural                    135(79.4) 100(68.5) 100(60.6)    64(45.1)              33(29.2)
Length of illness (i.e.     10.6±11.6 12.9±22.7 11.1±15.9 31.5±116.3              14.9±21.8       0.006
in days) mean± SD




                                                                                                   186
Table 7.3. Monthly food expenditure, per capita consumption, length of illness, number of visits
made to health practitioner, medical expenditure and self-reported diagnosed illness by area of
residence
                                                    Area of residence
Characteristic                         Urban           Semi-urban            Rural              P
                                       n (%)              n (%)              n (%)
†Monthly food expenditure          280.71±192.00 277.45±162.97           237.07±145.59          0.002
mean ± standard deviation
Per capita consumption            2425.23±1992.1 1923.62±1241.6 1441.30±1179.8 < 0.0001
mean ± standard deviation                         8                  0                  5
Length of illness in day                 9.5±19.1          13.5±23.0           17.7±65.4        0.256
mean ± standard deviation
Number of visits made to                   1.4±0.7            1.4±1.3             1.4±1.0       0.927
health care practitioner in
last 4-weeks
mean ± standard deviation
†Medical expenditure
    Public                              3.47±7.07         4.72±16.51         4.78±18.65         0.787
    mean ± standard deviation
    Private                           13.58±13.21        15.38±15.60        13.14±35.37         0.851
    mean ± standard deviation
  Self-reported diagnosed                                                                       0.162
illness
  Acute conditions
    Influenza                             19(12.3)           34(28.8)            17(17.9)
    Diarrhoea                                3(1.9)             4(3.4)            19(4.8)
    Asthma                                21(13.6)              9(7.6)           43(10.9)
  Chronic conditions
    Diabetes mellitus                     16(10.4)           13(11.0)            40(10.1)
    Hypertension                          37(24.0)           24(20.3)            86(21.7)
    Arthritis                              10(6.5)              6(5.1)            24(6.1)
    Other                                 48(31.2)           28(23.7)          113(28.5)
†Quoted in USD (USD 1.00 = Ja. $ 80.47 at the time of the survey)




                                                                                              187
Table 7.4. Self-reported diagnosed health conditions of uninsured ill respondents by age cohort
                                                                           Age cohort
Characteristic                       Children      Young adults        Other-aged      Young old     Old-old    Oldest-old      P
                                                                         adults
                                       n (%)           n (%)             n (%)           n (%)       n (%)        n (%)
  Self-reported diagnosed                                                                                                    < 0.0001
illness
  Acute conditions
    Influenza                           83(45.6)        10(15.6)             19(9.1)        6(5.1)     6(8.1)       0(0.0)
    Diarrhoea                            13(7.1)          2(3.1)              6(2.9)        2(1.7)     2(2.7)       1(4.5)
    Asthma                              42(23.1)        11(17.2)             13(6.2)        4(3.4)     2(2.7)       1(4.5)
  Chronic conditions
    Diabetes mellitus                     1(0.5)          2(3.1)            32(15.3)      21(17.9)   10(13.5)      3(13.6)
    Hypertension                          0(0.0)          4(6.3)            55(26.3)      41(35.0)   36(48.6)     11(50.0)
    Arthritis                             0(0.0)          0(0.0)             12(5.7)      18(15.4)    9(12.2)       1(4.5)
    Other                               43(23.6)        35(54.7)            72(34.4)      25(21.4)    9(12.2)      5(22.7)




                                                                                                                                 188
Table 7.5: Logistic regression: Variables of moderate-to-very good health status of uninsured ill
respondents

                                          Coefficien      Std.   Wald                               95.0% C.I.
   Variable                                   t           Error statistic         Odds ratio
 Age                                         -0.033       0.008 18.605              0.967***         0.95 - 0.98

 Average Medical Expenditure                    0.000      0.000       1.668               1.00      1.00 - 1.00

 Male                                          -0.511      0.244       4.374             0.60*       0.37 - 0.97

 Middle Class                                  -0.807      0.387       4.345             0.45*       0.21 - 0.95
 Upper class                                   -1.029      0.553       3.465              0.36       0.12 - 1.06
 †Lower class                                                                             1.00

 Married                                        0.140      0.278       0.253               1.15      0.67 - 1.98

 Divorced, separated or
                                               -0.421      0.349       1.455               0.66      0.33 - 1.30
 widowed
 †Never married                                                                            1.00

 Logged Income                                  1.053      0.332     10.063             2.87**       1.50 - 5.49

 Urban area                                     0.696      0.300       5.365             2.01*       1.11 - 3.62
  Other town                                    0.844      0.342       6.092             2.33*       1.19 - 4.54
 †Rural area                                                                              1.00

 Head household                                 0.218      0.250       0.761               1.24      0.76 - 2.03

 Dummy health care-seekers                     -0.803      0.255       9.882            0.45**       0.27 - 0.74

 Chronic illness                               -0.456      0.351       1.696             0.63*       0.32 - 0.86

Model χ2 (12) = 83.70, P < 0.001
-2 Log likelihood = 482.96
Nagelkerke R2 = 0.23
Hosmer and Lemeshow goodness of fit χ2= 3.72, P = 0.88
Overall correct classification = 75.1%
Correct classification of cases of self-rated moderate-to-very good health status = 93.4%
Correct classification of cases of not self-rated not moderate-to-very good health status = 26.5%
†Reference group
*** P < 0.0001, **P < 01, *P < 0.05




                                                                                                                 189
Table 7.6: Logistic regression: Variables of self-reported health care-seekers of uninsured ill
respondents
                                                         Std.        Wald
 Variable                               Coefficient      Error      statistic Odds ratio   95% CI
 Chronic illness                             0.812       0.277         8.609     2.25**    1.31 - 3.88

 Age                                           0.024      0.008        9.593      1.03**    1.01 - 1.04

 Moderate-to-very good health                 -0.857      0.281        9.274      0.42**    0.24 - 0.74

 Secondary education                           1.117      0.762        2.148        3.06   0.69 - 13.60

 Tertiary education                            1.278      1.222        1.094        3.59   0.33 - 39.42
 †Primary and below
                                                                                    1.00
 education

 Male                                         -0.358      0.244        2.154        0.70    0.43 - 1.13

 Crowding                                      0.114      0.053        4.694       1.12*    1.01 -1.24

 Logged income                                 0.000      0.000        4.138       1.00*    1.00 - 1.00

 Length of illness                             0.000      0.002        0.013        1.00    1.00 - 1.00

 Married                                      -0.733      0.274        7.181      0.48**    0.28 - 0.82
 Divorced, separated, or
                                              -0.692      0.384        3.248        0.50    0.24 - 1.06
 widowed
 †Never married                                                                     1.00

 Urban area                                    0.171      0.286        0.359        1.19    0.68 - 2.08
  Other town                                  -0.336      0.302        1.238        0.72    0.41 - 1.29
 †Rural area                                                                        1.00
Model χ2 (13) = 47.85, P < 0.001
-2 Log likelihood = 486.1
Nagelkerke R2 = 0.15
Hosmer and Lemeshow goodness of fit χ2= 8.11, P = 0.62
Overall correct classification = 69.0%
Correct classification of cases of self-reported health care-seekers = 89.4%
Correct classification of cases of self-reported health care-nonseekers = 32.2%
†Reference group
*** P < 0.0001, **P < 01, *P < 0.05




                                                                                                     190
                                                                        Chapter 8

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

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


Introduction

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

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

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

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

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

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

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

Beckford [2], there is a positive correlation between poverty and unemployment; poverty and
                                                                                              191
illness; and crime and unemployment. Sen [3] encapsulated this well when he put forward the

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

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

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

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

individual and the family.


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

                                                                                              192
       In Jamaica, rural poverty is twice (15.3%) that of urban poverty (6.2%). [4] This may

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

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

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

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

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

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

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

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

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

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

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

people pay for medical care out of their pockets.


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

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

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

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

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

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

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

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

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

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

                                                                                                193
behaviour would be lower for rural than urban residents. While Call & Ziegenfuss’ perspectives

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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


Methods and materials
Data

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

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

6,782 Jamaicans [13-15]. The cross-sectional survey was conducted between May and August

2007 from the 14 parishes across Jamaica and included 6,782 people of all ages [16]. The JSLC


                                                                                                194
used stratified random probability sampling technique to draw the original sample of

respondents, with a non-response rate of 26.2%. The sample was weighted to reflect the

population. [13-15]


Study instrument


The JSLC used an administered questionnaire where respondents were asked to recall detailed

information on particular activities. The questionnaire was modelled on the World Bank’s Living

Standards Measurement Study (LSMS) household survey. There are some modifications to the

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

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

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

used in data analysis.


Statistical methods

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

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

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

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

status and some socio-demographic, economic and biological variables; as well as 2) a

correlation between medical care-seeking behaviour and some socio-demographic, economic and

biological variables.       The statistical package SPSS for Windows version 16.0 (SPSS Inc;

Chicago, IL, USA) was used to analyze the data. A p-value less than 5% was used to indicate

statistical significance.




                                                                                             195
         The correlation matrix was examined in order to ascertain if autocorrelation and/or

multicollinearity existed between variables. Based on Cohen and Holliday [17] correlation can

be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0. The approach in

addressing collinearity (r > 0.6) was to independently enter variables in the model to determine

which one should be retained during the final model construction. The method of retaining or

excluding a variable from the model was based on the variables’ contribution to the predictive

power of the model and its goodness of fit. [18-24] Wald statistics were used to determine the

magnitude (or contribution) of each statistically significant variable in comparison with the

others, and the Odds Ratio (OR) for the interpreting of each significant variable.


Models


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

[1]) and medical care seeking behaviour of Jamaicans (Equation [2]). The use of this approach is

better than bivariate analyses as many variables can be tested simultaneously for their impact (if

any) on a dependent variable.

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

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

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

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

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

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

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

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



                                                                                                196
       Where MCSBi is medical care-seeking behaviour of individual i is a function of PHt (ie

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

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

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

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

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

       residual error).

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

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

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

Measures

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

status is a dummy variable, where 1 = reporting an ailment or dysfunction or illness in the last

4 weeks, which was the survey period; 0 if there were no self-reported ailments, injuries or

illnesses. [11, 12, 25] While self-reported ill-health is not an ideal indicator of actual health

conditions because people may under-report, it is still an accurate proxy of ill-health and

mortality. [26, 27] Health status is a binary measure where 1=good to excellent health; 0=

otherwise which is determined from “Generally, how do you feel about your health”? Answers

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

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

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

through 84 years) and oldest-old (ages 85+ years). Medical care-seeking behaviour was taken

from the question ‘Has a health care practitioner, healer , or pharmacist been visited in the last 4



                                                                                                197
weeks?’ with there being two options Yes or No. Medical care-seeking behaviour therefore was

coded as a binary measure where 1=Yes and 0= otherwise.


Results

Socio-demographic characteristics of sample

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

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

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

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

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

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

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

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

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

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

(14.8% in the wealthiest group).

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

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

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

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

       Table 8.1 revealed that significantly more rural residents were poor (58.7%) compared to

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

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

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

                                                                                            198
       A cross-tabulation between health status and area of residence revealed a statistical

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

indicated poor health status (6.3%) than semi-urban (3.3%) and urban (3.9%) (see Table 8.1).

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

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

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

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

than the 12% for males (Table 8.2). Of the diagnosed recurring illness, approximately twice as

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

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

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

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

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

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

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

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

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

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

did not have an illness (Table 8.3).


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

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

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




                                                                                             199
illness increased to 14.7% for other aged adults; 33.3% of young old; 49.7% of old-old and

51.2% of oldest-old.

Multivariate Analysis

      Table 8.4 examines variables that seek to explain the good health status of insured

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

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

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

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

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

46.6% were cases with poor health status.

       Table 8.5 presents information on variables that determine (or not) the medical care-

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

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

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

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

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

Discussion

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

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

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

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

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




                                                                                                200
       The current study found that approximately one-half of Jamaicans who do not have

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

and the wealthy.




                                                                                            201
       In 2007, the government of Jamaica introduced national health insurance coverage for

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

social security coverage commissioned by the Jamaican government eliminates health insurance

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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



                                                                                                  202
100 were hypertensive; 9 out of 100 diabetic and 6 out of 100 arthritic compared to the

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

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

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

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

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

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

populace (41 out of every 100).

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

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

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

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

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

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

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

classes.

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

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

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

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

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

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

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



                                                                                                 203
risky lifestyle practices that reduce the advantage of income. While income can buy access to

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

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

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

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

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

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

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

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

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

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

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

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

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

respondents indicated that they had good health status.

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

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

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

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

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

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

from 49.2% in 2002 to 38.2% in 2004 [43]. Eldemire [44] found that 34.8% of new cases of

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



                                                                                                 204
Bourne, McGrowder, & Crawford [39] found that the poor health status of people 60 to 64 years

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

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

increased ailments and ageing of the Jamaican elderly.

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

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

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

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

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

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

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

Ziegenfuss.

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

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

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

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

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

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

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

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

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

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

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



                                                                                                 205
in the Caribbean, in particular Jamaica, which models the health care-seeking behaviour of

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

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

need further research.

Limitation of study

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

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

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

could have been critical to the study.

Conclusion

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

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

Conflict of interest

There is no conflict of interest to report.

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Table 8.1: Socio-demographic characteristics of sample
                                                      Area of residence                    P
Variable                                  Urban         Semi-urban           Rural
                                           n (%)            n (%)            n (%)
Sex                                                                                        0.284
  Male                                    662 (47.4)       544 (49.9)      1354 (49.9)
  Female                                  735 (52.6)       547 (50.1)      1361 (50.1)
Social class                                                                              < 0.001
  Poor                                    370 (26.5)       381 (34.9)      1594 (58.7)
  Middle                                  294 (21.0)       245 (22.5)       546 (20.1)
  Upper                                   733 (52.5)       465 (42.6)       575 (21.2)
Age group                                                                                  0.002
   Children                               418 (29.9)       334 (30.6)       961 (35.4)
   Young adults                           411 (29.4)      306 928.0)        646 (23.8)
   Other aged adults                      416 (29.8)       344 (31.5)       803 (29.6)
   Young old                                 93 (6.7)         72 (6.6)       199 (7.3)
   Old-old                                   48 (3.4)         27 (2.5)        82 (3.0)
   Oldest-old                                11 (0.8)          8 (0.7)        24 (0.9)
Health status                                                                             < 0.001
    Good                                1137 (81.7)        956 (87.6)      2202 (81.6)
    Fair                                  201 (14.4)          99 (9.1)      329 (12.2)
    Poor                                     54 (3.9)         36 (3.3)       169 (6.3)
Education                                                                                 < 0.001
   No formal                              841 (60.4)       687 (63.1)      1599 (59.1)
   Basic                                  174 (12.5)       118 (10.8)       362 (13.4)
   Primary/preparatory                    168 (12.1)       158 (14.5)       429 (15.8)
   Secondary/High                         166 (11.9)       111 (10.2)       300 (11.1)
   Tertiary                                  43 (3.1)         14 (1.3)        17 (0.6)
Marital status                                                                             0.012
   Married                                177 (18.3)       132 (17.5)       382 (21.9)
   Never married                          721 (74.5)       562 (74.6)      1245 (71.4)
   Divorced                                  18 (1.9)         17 (2.3)        15 (0.9)
   Separated                                  5 (0.5)          8 (1.1)        20 (1.1)
   Widowed                                   47 (4.9)         34 (4.5)        82 (4.7)
Self-reported illness                                                                      0.001
    Yes                                   176 (12.7)       128 (11.8)       432 (15.9)
     No                                 1215 (87.30        958 (88.2)      2280 (84.1)
Medical care-seeking behaviour                                                             0.375
     Yes                                  120 (66.3)        78 (59.5)        270 (60.9)
      No                                   61 (33.7)        53 (40.5)        173 (39.1)
Number of visits to medical            1.4 days (SD          1.4 days     1.4 days (SD     0.846
facilities                                     = 0.7)      (SD= 1.3)             = 1.0)




                                                                                            210
Table 8.2: Sociodemographic characteristic by Sex
Variable                                                         Sex                     P
                                                       Male              Female
Self-reported illness                                                                   < 0.001
     Yes                                               298 (11.7)         438 (16.6)
      No                                              2256 (88.3)        2197 (83.4)
Diagnosed Self-reported illness                                                         < 0.001
      Cold                                              56 (18.1)          69 (15.6)
      Diarrhoea                                          11 (3.6)           15 (3.4)
      Asthma                                            35 (11.3)           39 (8.8)
      Diabetes mellitus                                  19 (6.1)          50 (11.3)
      Hypertension                                      39 (12.6)         109 (24.6)
      Arthritis                                          20 (6.5)           21 (4.7)
      Other (unspecified)                               85 (27.5)         105 (23.7)
      No                                                44 (14.2)           35 (7.9)
Medical care-seeking behaviour                                                           0.101
   Yes                                                 182 (58.5)         286 (64.4)
    No                                                 129 (41.5)         158 (35.6)
Purchase medication                                                                      0.251
   Prescribed medicine                                 170 (56.9)         259 (60.1)
   Partial prescription                                    3 (1.0)           13 (3.0)
   Prescribed/over the counter                             9 (3.0)           15 (3.5)
   Over counter                                           20 (6.7)           25 (5.8)
   Prescribed/did not buy                                  9 (3.0)           17 (3.9)
   None prescribed required                              88 (29.4)        102 (23.7)
Number of visits to medical facilities Mean (SD)    1.3 days (0.7)     1.4 days (1.1)    0.252




                                                                                             211
Table 8.3. Health status by Self-reported dysfunction

                                        Self-reported Dysfunction

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

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


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


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

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




                                                                                         212
Table 8.4.        Ordinary Logistic Regression: Correlates of Good Health Status of Uninsured
Jamaicans
                                                                         Wald       Odds
 Variable                                  Coefficient     Std Error    statistic   ratio       95.0% C.I.
  Age                                           -0.049         0.004      191.667     0.95   0.95 -0.96***
  Logged consumption per capita                  0.000         0.000       11.692     1.00   1.00 - 1.00**
  Self reported illness                         -2.168         0.121      323.527     0.11   0.09 -0.15***

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

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

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

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




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Table 8.5. Ordinary Logistic Regression: Correlates of Medical Care-Seeking Behaviour of
Uninsured Jamaicans

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

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

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

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

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

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

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

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




                                                                                                          214
                                                                         Chapter 9

Health Inequality in Jamaica, 1988-2007


In Jamaica, mortality for men is not only greater than that of women as indicated by the life
expectancy but of the five leading causes of death (malignant neoplasms; cerebrovascular
disease; heart disease; diabetes mellitus and homicides), the rates for men were greater in four of
the five categories (malignant neoplasms; cerebrovascular; heart disease and homicides). Despite
these realities, men seek less medical care than women while staying longer in hospitals for
curative care. Hence, this study examines medical seeking behaviour, self-reported ill-health, and
sex differential in medical seeking health in nation. The current research used secondary data.
The data were extracted from the Jamaica Survey of Living Conditions (JSLC) on medical care
seeking behaviour, self-reported illness (or ill-health) and the sex composition of those who
reported ill-health. The JSLC was born out of the World Bank’s Living Standard Survey. Data
were also taken from the Ministry of Health’s Annual Report, which provided statistics on actual
percentage of Jamaicans who visited public hospitals. The current study used 19 years of
published data extracted from the JSLC (1988-2007). Scatter diagrams and best fitted lines were
used to examine correlations and trends. Over a 2-decade period, 1988 to 2007, only a small
percentage of Jamaicans reported ill-health (between 9 to 19 %) and 15.5% in 2007, which is an
increase of 3.3% over the previous year. Despite this low figure, increasingly more men sought
medical care over the study period (41.1%) compared to women (29%). Nevertheless, health
care seeking behaviour is still sex bias – 68.1% of women and 62.8% of men who reported
health conditions. For men, more of medical care seeking behaviour is explained by ill-health (r-
squared=35.4%) than women (r-squared 8.8%). This study is one of the first to examine and
provide some explanation on sex differentials in health care behaviour and self-reported
illness/injury in Jamaica. We found that while more men who report ill-health have been seeking
medical care, the gap between the sexes in regard health seeking behaviour has been narrowing.


Introduction



Globally, in 1950-1955, life expectancy for women was 47.9 years compared to 45.2 years for

men. One-half of a century later, the disparity increased to 4.2 years (68.1 years for women and

63.9 years for men). For the Caribbean, in the same aforementioned period, life expectancy was

53.5 years and 50.8 years for women and men respectively; and 50 years later, the disparity


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increased to 5.5 years (70.9 years for women and 65.4 years for men) which was greater than that

for the world. Life expectancy which is an indicator of mortality and morbidity are also a

measure for health status; and speaks to the quality of labour for the society. Although there are

some morbidity that are not life threatening, health literature showed that healthy life is not

equivalent to lived years. The World Health Organization being aware of this disparity

developed the DALE (ie disability adjusted life expectancy) to discount life expectancy for the

time lost due to illness. Based on this information, statistics revealed that developing countries

lost 9 years of life expectancy owing to unhealthy years (or illness); and this is still within the

cultural context of men’s unwillingness to seeking medical care. While this provides a general

framework for the rationale of the disparity in life expectancy of the sexes, it does not afford us a

comprehensive understanding of health inequality.


           There has always been a health differential between the sexes in Jamaica [1] Dating

back to 1880, which was the first time that life expectancy data was recorded for men and

women in the island, women outlived men. Statistics for Jamaica showed that for the period

1880 and 1882, women lived approximately 3 years more than men and 122 years later (2002-

2004), they outlived them by 6 years, which is an additional 3 years. Globally, women live

longer than men by 8 years which is 2 years more than that of the life expectancy sex differential

in Jamaica. Women are not only living longer than their male counterparts, but they are enjoying

greater quality of life[2] A study of 3,009 older people done in 2007 in Jamaica[3] revealed that

elderly women had a higher quality of life (3.3 ± 2.2) than men (2.8 ± 1.8; p value = 0.001),

which concurred with the earlier work done by the WHO in 1998. But, studies that have

examined well-being have shown that men experienced a greater economic wellbeing than




                                                                                                 216
women[4], despite not having a higher subjective wellbeing. What is explaining this health

differential between the sexes?


       Life expectancy which is calculated using mortality data indicate that men are

experiencing particular pathogen causing diseases which are accounting for the greater increase

in mortality and lower life expectancy than women. An epidemiological profile of selected health

conditions and services in Jamaica for 1990-2002 was conducted by the Health Promotion and

Protection Division, Ministry of Health in 2005 which indicated that malignant neoplasm was the

leading cause of death in Jamaica. It was 39% greater for men than women. The second leading

cause of death, cerebrovascular disease, was 14% higher for men than women; heart diseases rate

was 71.2 per 100,000 for men and 66.1 per 100,000 for women, and diabetes mellitus was

greater for women than men. The statistics revealed that mortality caused by diabetes mellitus

was 64% higher for women than men.


       Jamaica is not unique in regard to i) women outliving men, ii) particular morality is

greater for men than women, and ii) some of the leading causes and death are sex specific[2] The

issue of higher mortality differential between the sexes at older ages begins with boys suffering

more illnesses and injuries than girls[5] The World Health Organization (WHO) offered a potent

finding that age-and sex differential in mortality dates back to 1955[2] This indicates that higher

mortality in the world’s population tend to favour men, and justifies the longer life that they live

compared to men.


       In demography, life expectancy is used to measure health. But this approach fails to

capture health as one can be alive but enjoy optimum health – living with varying levels of

morbidity. There is an argument that morbidity is accounted for in mortality, and this so.


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However, some dysfunctions are not death causing, and so quality of life (health) will be lower

with these health conditions. It is owing to this reality that the World Health Organization

(WHO) introduced what is known as healthy life expectancy which discounts life expectancy by

morbidity.


       Healthy Life Expectancy

       One of the drawbacks to the use of life expectancy is its absence to capture ‘hale’ years

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

the number of years of life yet to be lived by an individual, assuming that he/she subscribes to

the same mortality patterns of the group. The emphasis of this approach is on length of life and

not on the quality of those years lived. Hence changes in life expectancy are primary due to

mortality movements, and imply changes in external conditions of the socio-biological

environment. These changes include the components of public health, the physical milieu, and

technological/medical advancement. With all the aforementioned conditions that have improved

over the last century, increased life expectancy in the world is not surprising to scholars. One

way of evaluating population ageing in the world or in any geopolitical space is ‘life

expectancy’. Today, it should come as no surprise to people that many developing nations have

been experiencing increased gains in additional years of life for members with its population in

comparison to 20th century.


       Associated with ageing are high probability of increased dysfunctions and the

unavoidable degeneration of the body. This explains why it is germane to analyze healthy life

expectancy and not merely life expectancy. Healthy life expectancy is defined as the number

of years that an individual is expected to live in ‘good’ health. Technological advancement is

able to prolong life, but it is not able to remove morbidity and its deterioration in quality of lived

                                                                                                  218
years of the individual. Thus, while life expectancy in the Caribbean is increasing and that this is

in keeping with the rest of the world, there is a simultaneous increase in chronic diseases and

resurgence of infectious disease. This reality highlights the disparity between quantity of years

lived and the quality of those lived years because of sociopsychological conditions- such as

loneliness, bereavement, social support (or the lack of), low self-esteem, and low self-

actualization and so on.


        In evaluating health or wellbeing, we must seek to examine more than just the number of

years that an individual is likely to survive as we should be concerned about the quality of those

years. Even though, life expectancy is an indicator of health, the new focus is on healthy life

expectancy. Based on the Healthy People 2010, the new thrust is on increasing quality of years

of life. In attempting to capture ‘quality of years lived’, in 1999, the WHO introduced an

approach that allows us to evaluate this, by the ‘disability adjusted life expectancy’ (DALE)[6]

DALE does not only use length of years to indicate health and wellbeing status of an individual

or a nation, but incorporate the number of years lived without disabilities.


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

It uses the number of years lived as its principal component. This is referred to as ‘full health’.

In addition, the number of years of ill-health is weighted based on severity as another component

in the equation. This is then subtracted from the expected overall life expectancy to give what is

referred to as years of hale life. Embedded in this approach is the adjustment of years lived in

‘ill-health’.


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

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


                                                                                                219
the WHO are the plethora of dysfunctions and the devastating effects of some tropical diseases

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

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

more developed nations. [6] This is in keeping with a more holistic approach to the measure of

health and wellbeing with which this study seeks to capture. By using the biopsychosocial

model in the evaluation of wellbeing of aged Jamaicans, we will begin to understand factors that

are likely to influence the quality of lived years of the elderly, and not be satisfied with the

increased length of life of the populace. Looking at the life expectancy data for Jamaica, the

figure is 74.1 years for both sexes[6] but by using healthy life expectancy it is 65.1 years[6] Here

life expectancy has been increasing at a faster rate than ‘healthy life expectancy’. Therefore,

Jamaicans are expected to spend some 9 years of their life in ‘poor health’.


       In summary, the use of life expectancy to measure health is inadequate and so morbidity

must be taken into consideration. When life expectancy is discounted by morbidity, it provides

an account of the healthy life expectancy of an individual. Hence, the use of life expectancy to

indicate health for men and women is equally insufficient in health analysis. It is evident from

statistics on life expectancy and particular diseases causing mortality that men are experiencing a

lower health status, and what accounts for this reality? Within the context of the aforementioned

issues, and the fact that medical health care seeking has increased from 54.6% in 1989 to 66.0%

in 2007 and that there is a decline of 5.7% over 2006 (Table 9.1), is this offering some

explanation the sex differential in health status? Although less Jamaicans are seeking medical

care of those who reported illnesses, 27.1% more Jamaicans reported dysfunctions (Table 9.1),

suggesting that there is greater health differential between the sexes. Hence, for this study,

medical seeking behaviour, self-reported ill-health, and sex differential in medical seeking health


                                                                                                220
care and self-reported ill-health will be examine to provide a better understanding of the healthy

life expectancy of the sexes in Jamaica.


Materials and Method


The current research used secondary data. The data constitute statistics from the Planning

Institute of Jamaica and the Statistical Institute of Jamaica (in Jamaica Survey of Living

Conditions, JSLC) and Ministry of Health Jamaica (MOH). The data were extracted from the

JSLC on medical care seeking behaviour, self-reported illness (or ill-health) and the sex

composition of those who reported ill-health. The Ministry of Health’s Annual Report provided

data on actual percentage of Jamaicans who visited public hospitals, which was contrasted by the

JSLC’s self-reported visits to public hospitals in order to further examine the sex differentials on

subjective ill-health.


        This study used 19 years of published data extracted from the JSLC (1988-2007). The

JSLC was born out of the World Bank’s Living Standard Survey. The JSLC began in 1988 when

the Planning Institute of Jamaica (PIOJ) in collaboration with the Statistical Institute of Jamaica

(STATIN) adopted with some modifications of the World Bank's Living Standards Measurement

Study (LSMS) household surveys. The JSLC has its focus on policy implications of government

programmes, and so each year a different module is included, evaluating a particular programme.

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

information on particular activities. The questionnaire covers demographic variables, health,

immunization of children 0 to 59 months, education, daily expenses, non-food consumption

expenditure, housing conditions, inventory of durable goods, and social assistance. Interviewers

are trained to collect the data, which is in preparation of the household members. The survey is


                                                                                                221
usually conducted between April and July annually. Furthermore, the instrument is posted on the

World Bank’s site to provide information on the typologies of question and the

(http://www.worldbank.org/html/prdph/lsms/country/jm/docs/JAM04.pdf).


       Ministry of Health is the body which is constituted by statutes to regulate all health

institutions in the country. The Ministry of Health (MOH) collects statistics on health, health

services, health utilization, health related matters, and carry out health mandate of the

government. MOH has decentralized its operations. The island is sub-divided in four regions

(South-East; North-East; Western, and Southern), which emerged owing to the passage of the

National Health Service Act of 1997. Each region operates as a semi-autonomous regional body

under the general directs of the central Ministry of Health, which is subject to the directions of

the Minister of Health. The central Ministry of Health collates all the data sent it by the four

health authorities in country. Therefore, data revealed in the Annual Reported of the Ministry of

Health, Jamaica, reflect actual accounts of the health matters in the country.


       Scatter diagrams and best fitted lines were used to examine correlations between different

variables, and percentages were also utilized to evaluate events over two decade (1988-2007).


Measure


Sex is the biological composition of being men or women.


Sex differential is the disparity between self-reported ill-health of male or female.


Medical Care Seeking Behaviour denotes the proportion of self-reported cases of visits for

seeking medical care of those who indicated ill-health.




                                                                                              222
Self-reported Illness is the percentage of people who have reported cases of dysfunctions (ill-

health or health conditions) as indicated by a respondent in a 4-week reference period.


Poverty is measured using the poverty line. The poverty line estimate is particular attainable

consumption expenditure in excess of a minimum necessary level of expenditure on a

representative bundle of necessary goods and services valued at germane prices. (JSLC 2008)


Results


Some scholars may want to believe that the use of subjective data on health (self-reported ill-

health) cannot be used to proxy health as it is not a good estimate of actual health status. In

order to remove this myth, the researcher will examine the actual figures provided by the

Ministry of Health on visits to public health care facilities and those garnered by the Jamaica

Survey of Living Conditions (JSLC). The JSLC is an annual probability sampled survey which

collects data from Jamaicans based on their recollection of events (self-reported). Based on

Table 9.4, self-reported health as indicated by the JSLC is a good proxy of visits. The data

revealed that in 1997, the difference between Jamaicans recall of events and those actually

happened as recorded by the Ministry of Health was marginally different (1%). Some 7 years

later (2004), the difference between same phenomena was 6.1% suggesting that subjective

assessment of health is a good proxy for actual health. It is within this context, that the researcher

will examine self-reported health data from JSLC to understanding health differential between

the sexes in Jamaica.


          During the periods of the greatest double digits inflation in history of Jamaica (early

1990s) (Table 9.2) in particular inflationary rates that were in excess of 25% (1990-1995),

Jamaicans reported the lowest percentages in ill-health (health conditions). Moreover, in 1991

                                                                                                  223
when inflation was at it peaks, the prevalence of poverty stood at its highest (44.6%), and the

data showed that self-reported illnesses were 13.7%. This figure was the fifth highest self-

reported ill-health in an 18 year period (1989-2007). In the unprecedented inflation of 1991

(80.2%), less men sought medical care (12.0%) over 1990 (16.35) compared to 15.0% in 1991

and 20.3% in 1990. In 1990, it was the first time in the history the of nation that inflation rose to

in excess of 20% and self-reported illness reached its maximum of 18.3%, and medical care

seeking behaviour was at its lowest (38.6%).


       In addition, in 1990, both sexes sought the most medical care (Table 9.3). Two years

later (1992), inflation rate fell by 49.9% (to 40.2%) over 1991 which explains the rationale for

the 24.0% decrease in prevalence of poverty; self-reported ill-health declined by 22.6%,

ownership of health insurance increased so to were people seeking medical care and the private

health care utilization. The irony here is that 17.5% less men reported accessing medical care for

their ill-health and 24.7% less women. This indicates that more of those people who did not

report ill-health visited private health care facilities for medical care. In 1993, inflation declined

further by 25.1%; poverty saw a reduction of 28.0%; self-reported health conditions increased by

13.2%; health insurance coverage increased by 12.2%; number of people seeking medical care

increased by 1.8%. In that same period, the number of women who sought care was 3.8 times

more (19.5%) than men (5.1%). Hence, high inflation was reducing visits for medical care and

another matter which emerged from the data during that period, that those who attending public

hospitals began reducing their visits while private hospital users, increased utilization (Table

9.2). There is a paradox post-2005 as inflation increased by an unprecedented 194.7% in 2007

over 2006 and this explains a corresponding decline in the number of persons who sought




                                                                                                  224
medical care (by 5.7%). Nevertheless, the number of men who visited health care facilities

increased in the period by 21.2% and the number of women was 1.24 times more than men.


        The data show that in the last 17 years, women place more emphasis on their health than

men. Between 1988 and 2007, it was only on one occasion that men have indicated having

sought more medical care than women (in 1997) (Table 9.3). The difference between men

seeking medical care and that of women was 0.7%. If health seeking behaviour is a proxy for

preventative care, then it would appear that they were more health conscious. This is the not the

case as in the same period, then spent more days receiving care (mean of 11 days) compared to

10 for women. Hence, this increased in health seeking behaviour was owing to curative and

preventative care. Nevertheless, over the studied period, severity of care for both sexes has been

reality the same. Using mean number of days men received care for illness/injury, the difference

is minute, suggesting that severity of illness between the sexes in Jamaica is the same.


        Another interesting finding that emerged from the data is the narrowing of the gap

between public health care utilization and private health care utilization in the nations,

suggesting that costing of living is accounting for more visits to public care facilities. Embedded

in those findings is the affordability in people’s decision to seek medical care. This indicates that

there are some other conditions that are interfacing with men’s and women’s decision to visit

health care facilities for care outside of prices (inflation).


Results: Bivariate Analyses


Percentage of People Seeking Medical Care by Percentage of People reporting Illness


On examination of Figure 9.1, it was revealed that a negative correlation exists between number

of people who sought medical care and percentage of people who reported ill-health. This

                                                                                                 225
indicates that as more people report health conditions, less of them are likely to seek medical

care. Furthermore, 16.3% of the variability in people seeking medical care can be explained by

illness, suggesting that ill-health is not a good reason for Jamaicans visiting health care

practitioners. On further investigation of people seeking medical care and self-reported

illness/injury, data (Tables 2,3) revealed that on the occasion when the highest percentage of

illnesses were reported, the least number of person sought care for those conditions. This irony

was equally the case for men (16.3%) as well as women (20.3%) (Table 9.3).


Percentage of People Seeking Medical Care by Prevalence of Poverty


On examination of a scatter diagram; it was observed that there is a negative correlation between

the percentage of people seeking medical care and prevalence of poverty. The best fit line

revealed that 57.6% of why people seek health care in Jamaica is determined by poverty (Figure

9.2). Hence, people are highly likely to visit health care facilities in periods of low poverty and

vice versa. This indicates that medical care is not simply about ill-health, it is equally determined

by affordability, suggesting that people will switch to home care in periods of increased poverty.

Irrespective of this knowledge, is there is sex disparity in regard to seeking medical care and

reporting illness?




Percentage of Men Seeking Medical Care by Percentage of Men reporting Illness


Generally 16.3% of why Jamaicans visit health care facilities in search of care is owing to their

health conditions. However, for men, 35.4% of why they sought medical care was due to ill-

health as 35.4% of the variability in men seeking medical care can be explained by medical care.

On decomposing the data, when the least percentage of men sought medical care assistance

                                                                                                 226
(37.9%), the most percentage of them reported illness (16.3%) (Table 9.3). Furthermore, when

the lowest percentage of men reported ill-health (health conditions/injuries) (7.4%), this was in

60% of those seeking more medical care. However, in 1999 and 2004, low self-reported illness

was correlated with relatively high health seeking behaviour.


Percentage of Women Seeking Medical Care by Percentage of Women reporting Illness


Health (medical) care seeking behaviour of women is lowly correlated with self-reported illness

(injury) (Figure 9.3). The scatter plot revealed that generally, the more women reported health

conditions the less likely they are to seeking medical care. Some 8.8% of the variability in

medial care seeking behaviour of this cohort can be explained by a change in self-reported health

conditions. Self-reported illness of women accounted for 54% less of the explanatory reason for

seeking medical care compared to that of the both sexes (16.3%), suggesting that women’s health

care behaviour is driven by other factors than ill-health. There are some similarities between

health care seeking behaviour and self-reported illness of both sexes as when women reported

the least percentage of health care seeking behaviour, this was corresponding to the most

reported health conditions (Table 9.3). Furthermore, when the least percentage of ill-health was

reported, this earmarked 59th percentage of the highest seeking medical care behaviour of

women. These were also the case for men.


Deconstruction the Self-Reported Health Status of Jamaicans by Sex, 1989-2006


Over the last 2 decades (1988-to-2007), a small proportion of Jamaicans have reported illness (or

dysfunction) (Table 9.5). This has been has high as 168 per 1,000 (in 1989) to a low of 88 per

1,000 (in 1997), and the figure was 155 per 1,000 in 2006 (Table 9.5). On deconstruction the

population self-reported health status, it was revealed that women continue to report more health


                                                                                             227
conditions than men. In 1989, there 123 women (or women) who reported health conditions to

100 men (or men), and in 2004, the ratio was as high as 153 women per 100 men. This indicates

that 53% more women reported health conditions than men in the latter year and there was an

increase of 30% more women reporting dysfunctions over the 2 decades. Over the studied

period, in 1992, the disparity in self-reported health conditions between men and women was

very close of which there were 114 women to 100 men as it relates to self-reported health

conditions. On the other hand, over the last decade (1997-to-2006), the disparity was 136 or 153

women per 100 men, and in the last 2 years the value has been relatively stable (136 or 137

women per 100 men).


       Percentage of People Seeking Medical Care by Percentage with Health Insurance


       Health Insurance is one indicator of people’s intent to access care. On examination of the

data (Table 9.2), only a small percentage of Jamaicans in 2007 had health insurance (21.1%).

This meant that more people who will become ill would need to meet their medical expenses out

of savings, current income and assistance from social support agent(s). Table 9.2 revealed in

8.6% of Jamaica had health insurance coverage during the period when the inflation rate was at

its peak (80%) and when it fell to 40.2%, health insurance coverage increased by only 0.4%.

Further investigation of health seeking behaviour and health insurance coverage showed that the

ownership of health insurance was positively related to health seeking behaviour. A bivariate

correlation between the two aforementioned factors revealed that 56.1% of the variability in

people seeking medical care was as a result of ownership of health insurance (Figure 9.5).



Ownership of Health Insurance and Prevalence of Poverty



                                                                                             228
Poverty does not only mean ones inability to purchase consumption items, but also non-

consumption items such as health insurance. On examining a scatter diagram with a best fit line

to establish any correlation between the two aforementioned variables, it was observed that a

moderately strong correlation existed (R-squared = 0.597) – Figure 9.5. This means that 60% of

the variability in ownership of health insurance can be accounted for by prevalence of poverty,

suggesting that poor is less likely to have health insurance coverage.

Discussion

In the conclusion of the health chapter in one of the JSLC’s reports [8] it reads “Sex differentials

with respect to self-reported illness and health seeking behaviours need to be investigated.” This

is the rationale for this study, to provide an assessment of differences in subjective health and

medical seeking behaviour of men and women in Jamaica.

           Globally, regionally and in particular Jamaica, women seek more health care than men [1,
2, 4-7]
          This is not alarming as it commences from at childhood. In 1998, one health organization

wrote that girls are less likely to be injured and have broken bones compared to boys [2], which

continue during the life span. So when the mortality rates show a higher rate for men than

women [2, 6], this is just a continuation of early socialization. Health, therefore, is sex bias. One of

the rationales for the emphasis on health care by women is reason for male’s abstinence, the

culture. Within many cultures, men are not to display any form of weakness which includes ill-

health. This culturalization has embedded in boys and avoidance of speak of illness/injury and

the image of ill-health is negative and is primarily feministic in nature. This is not limited to

Jamaica or African descendent societies as it is equally the case in European geopolitical zones

such as Norway [9]

           Many cultures view (image) of health is the absence of diseases and this is sometimes

                                                                                                   229
linked to cure of the gods or moral rationale, suggesting that ill-health is a weakened biological

state. Men who are culturalized to be strong and macho must now balance ill-health within a

plural culture. The 21 st century has seen the exponential increase in life expectancy of men

compared to women in nineteenth and earlier centuries, but what about high mortality for this

group. There has always been feminization of life expectancy in Jamaica since 1880 (Table 9.1)

and the disparity in life expectancy has double from 1880-1882 to 2002-2004 from 3 years to 6

years respectively. Life expectancy which is an indicator of health does not only speak of longer

life, there are also some cultural changes that account for this increased life span, the social

milieu.

          Despite the advancement in medical technology, men continue to outnumber women in

particular mortality rates.   These include heart disease and neoplasm to name a few non-

communicable diseases. Heart disease and neoplasms are caused through either lifestyle

behaviour or heredity, and the former explains more of heart disease than the latter. Globally, the

fact that women outlived men by 8 years and in Jamaica by 6 years, lifestyle behaviour

undoubtedly is explaining the higher morbidity in heart diseases of men.

          Life style behaviour is expressed in health seeking behaviour, the purchase of health

insurance, preventative care and not curative care. Jamaica women continue to seek more care

than men, and this concurs with the finding so other studies [2, 3, 10, 11] Women do not take their

health for granted as society labels them with the nurturing role of children as well as ascribe

them softer tasks. This means that health and ill-health are interpreted and viewed from within

the perspective of personal experiences, and expectations. It is through the socialization process

which is carried out by mothers (women) that ill-health and health will be defined which

accounts for ones expectation and some percentage of how the world is viewed and interpreted


                                                                                               230
by people. In a qualitative study that was done in Nairobi slums, the authors found a strong

correlation between severity of illness and health seeking behaviour of children[12] These

children do not seek care of themselves, but are taken for medical care by their mothers. Another

study on street children (ages 5 to 13 years), who take themselves, like the Nairobi study

attended health care institutions for care dependent on i) severity of illness and ii) if it stops their

economic livelihood[13] Eight percentage of the sampled population of the latter study (in

Pakistan) were boys (men). This speaks to the image of health as viewed by men, and when care

is sought by them.

       Ill-health, therefore, based on the image of health seen through the lens of men is weak,

breaches machoism and borders on the fringes of feminism. Within the homophobis world,

despite the gradual reduction of the degree in some societies, men (or boys) do not want to be

labeled weak, homosexual or effeminate. Hence, there is dialectic here as men want to live which

means that they must address ill-health and at the same time they must appear to be macho. Men

are less likely to both report ill-healths as well as seek medical care because of its image and

social labels that they may ascribe to them by society. Women also play a part in this process as

they grown their boys to be strong, ‘tough’, and that they should not show weakness. Ill-health

is a weakness (or negative health), and so women on seeing men visiting health practitioners

especially if this is frequent construe this as weak, but his is not ascribe to a female for doing the

same thing.

       Medical care seeking behaviour is, therefore, construed as indicating ill-health (curative

care) and not preventative care for men. Chevannes[14] wanting to explain how men are as they

are, opined that early socialization played a critical role in shaping men’s masculinity, image of

self and interpretation of the world around them. The image of health as viewed as far back as


                                                                                                   231
prehistoric society is that of sickness, a curse, a plague, a weakness and a state of biological

incapacitation. Men who are culturalized to be strong cannot afford to be seen as weak or

incapacitate by their peers or the opposite sex as the society removes the acclaim of greater,

power and prestige from any such male. This means that men must now report and display less

signs of ill-health (weakness), and the only time that illness must be shown is in times of severity

which is close to death.

         Jamaican men displaying low medical care seeking behaviour as cultural underpinnings,

and so does their unhealthy lifestyle practices. Unhealthy lifestyle is undoubtedly explaining

high mortality of men than women. This dates back to prehistoric society, when men must

hunters, heroes, warriors and fierce to defend themselves, their tribes and women. Such events

meant that they would take more risk than women, and this has continued during the centuries.

Although vast amount of information are available on health and health treatment, men continue

to indulge in risky behaviour which accounts for their high morbidity and mortality in some

conditions.    The literature speaks to 80% of injuries and between 30-40% of cases with

cardiovascular conditions and diabetes mellitus could have been prevented by lifestyle practices
[5]
      This explains much of the health conditions and increased in reported ill-health and medical

care seeking behaviour. What is the role of education in health differential in the sexes?

         Education which is well established has directly correlated with better health [15-22] does

not remove early culturalization by family, peer groups and religious affiliations. The general

education level of the Jamaican populace has been improving since the last 3-decade, but this

does mean the remove of the sex bias health image or stigma of weakness associated with illness.

In 1989, 54.6% of Jamaicans sought care for ill-health and in 2007 that figure has increased by

9.9% (to 60%). In the same period that rate of increase for women was 29.0% compared to


                                                                                                232
41.1% for men. Nevertheless, in 2007, for every 100 men that sought care for ill-health, 108

women sought medical care. Although, we cannot divorce health from the social milieu, more

men are seeking medical care for illness and this accounts for the faded difference between the

mean numbers of days spent for care in both sexes. The 21 st century has aided men in their

recognition for the need to seek medical care for ill-health, in spite of traditional cosmologies [23,
24]



       In contemporary societies, illness for men is not tied to health conditions such as

neoplasm, heart disease, hypertension, mellitus diabetes and stroke, but is synonymous to

sexuality which is a legacy of their socialization[14, 23-29] A medical doctor ascribes to the 21st

century, sex roles that are tied to sex (biological category). This means that being male is linked

to being the stronger sex, fertile, and sexual prowess. Society has not removed from its men that

sex stereotype, and so the image of health for them is substantially tied to sexuality. Men,

therefore, do not see themselves as ill, unless they are impotent. Culturally, because impotency

and infertility are a curse, men will not openly speak about those matters or/and other heath

conditions. Again, male means strength, sexual potency, and these are all at the other end of the

pendulum of ill-health. This explains the reason for the lower purchase of individual health

insurance as this symbolizes weakness or preparation of some negative conditions. In spite of

this reality, over the last one-half of a decade, there has been an increase in health insurance

coverage and health seeking behaviour of both sexes. As of 2007, 2.1% more women had health

insurance coverage than men (20.1%), which was more than the national average of 21.1%.

Again this speaks to the differences in image of health held sexes and how their decision is based

on this view. Health insurance is a component of lifestyle practices justify the advantages that

women enjoy compared in men concerning health status. This is also reinforced in the fact (in


                                                                                                  233
2007) that for every 133 women who indicated that they were unable to afford to seek medical

care 100 men [1], showing that men are naturally, owing to their culturalization, unwilling to seek

medical care and this is evident in their lifestyle practices, purchase of health insurance,

reporting ill-health and visits to health care institution for preventative and curative care [1, 5]

        According to one scholars income buys health [30], which has some merit. The merit to

this argument is linked to the fact that income affords one the ability and option to purchase

better foods, medical care, a particularly good physical environment that are all positively

correlated with good health[3, 15-18] There is a negative side to affluence and income, as it afford

particular lifestyle that retard good health. Income affords one the lifestyle to purchase cigar,

tobacco, speedy cars, and in the process remove the disadvantage of low income or poverty. In a

study done by a group Caribbean scholars of 1,338 Jamaicans (ages 15 to 99 years), they found

that greatest subjective psychosocial wellbeing was had by the middle class followed by the

wealth and lastly by the poor [31]

        Embedded in the income and health debate, is the difficulty of the poor in seeking

medical care (curative and preventative care). This study has shown that there is a moderately

strong correlation between seeking medical care and prevalence of poverty, suggesting that poor

men are even less likely to seek care than those in the middle to upper class. When poverty is

coalescing with the cultural biases and image of health, men are likely to suffer more as they

must balance ill-health which is a weakness with in affordability. The issue of affordability is

seen in the percentage of those in the poorest quintile with health insurance in 2007 (6.6%)

compared to 12% in quintile 2, 18% in quintile 3, 22.7% in quintile 4 and 43.4% of those in the

wealthiest quintile. Embedded in this disparity is the poor’s inability to plan for the eventuality

of ill-health coupled with deplorable reality of the physical environment. This physical


                                                                                                       234
                                                 [32]
environment is such to account for ill-health       , and when poor nutrition is added to this

situation the poor will become even more ill.

Concluding Comments

In summary, illness is still seen and interpreted by Jamaicans as punishment and negative health,

and this explains their low self-reported health conditions and health care seeking behaviour.

Men who are product of the society must abide within the image of its dictates, which justifies

their unwillingness to seek medical care, report illness, purchase health insurance coverage and

create an image of weakness. In spite of this reality, men have become more involved that

women in seeking medical care over the last 17 years. This means that the society is becoming

increasingly more cognizant that ill-health is more than negative health or is simply equivalent to

weakness, female or less macho men. Although men are substantially driven by health conditions

to seek medical care than women, they are becoming more involved in health care treatment.

Recommendation

Further efforts are needed to eliminate more of the barriers of the negative image of health and

the use of medical services for ill-health in Jamaica. Medical practitioners, health care workers,

social workers and researchers must integrate the image of men in their treatment, and create an

atmosphere which is conducive to health care for men. A single prevalence study is needed to

ascertain the influence of each of the identified variables in this study and others in order to

understand the role of poverty, health insurance, ill-health, on the health seeking behaviour of

Jamaicans, the media, education as well as confounding variables such as sex, age, religiosity,

area of residence and subjective social class. In addition, a study is necessary to ascertain

whether the increased in self-reported health is owing to unemployment, and how much of ill-

health is accounted for by psychological conditions.

                                                                                               235
References
   1. Planning Institute of Jamaica (PIOJ), and the Statistical Institute of Jamaica (STATIN),
       1990-2008. Jamaica Survey of Living Conditions, 1989-2007. Kingston: PIOJ, STATIN.
   2. World Health Organization, (WHO), 1998. The World Health Report 1998: Life in the
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   3. Bourne, PA., 2008. Medical Sociology: Modelling Well-being for elderly People in
      Jamaica. West Indian Med J, 57(6):596-04
   4. Rudkin, L., 1993. Sex differences in economic wellbeing among the elderly of Java.
      Demography, 30:209-226.
   5. The Health Promotion and Protection Division, Ministry of Health Jamaica (MOH),
       2005. Epidemiology Profile of Selected Health Conditions and Services in Jamaica,
       1990-2002. MOH.
   6. WHO, 2000. WHO Issues New Healthy Life Expectancy Rankings: Japan Number One
       in New ‘Healthy Life’ System. WHO.
   7. Statistical Institute of Jamaica, (STATIN), 2007. Demographic Statistics, 2006.
       STATIN.
   8. WHO, 2003. Healthy life expectancy. Washington DC: WHO.
   9. Planning Institute of Jamaica (PIOJ), and the Statistical Institute of Jamaica (STATIN).
       2000. Jamaica Survey of Living Conditions, 1999. PIOJ, STATIN.
   10. Kaasa, K. 1998. Loneliness in old age: Psychosocial and health predictors. Norwegian
       Journal of Epidemiology, 8:195-201.
   11. Hutchinson, G., Simeon, DT., Bain, BC., Wyatt, GE., Tucker, MB., and E LeFranc, 2004.
       Social and health determinants of wellbeing and life satisfaction in Jamaica. International
       Journal of Social Psychiatry, 50 (1):43-53.
   12. Hambleton, IR., Clarke, K., Broome, Hl., Fraser, HS., Brathwaite, F., and AJ.
       Hennis, 2005. Historical and current predictors of self-reported health status
       among elderly persons in Barbados. Revista Panamericana de salud Pύblic,
       17(5-6):342-353.
   13. Taff, N., and G. Chepngeno, 2005. Determinants of health care seeking for childhood
       illness in Nairobi slums. Tropical Medicine and International Health, 10:240-245.
   14. Ali, M., and A. de Muynck, 2002. Illness incidence and health seeking behaviour among
       street children in Rawlpindi and Islamabad, Pakistan – a qualitative study. Child Care,
       Health & Development, 31:525-532.
   15. Chevannes, B., 2001. Learning to be a man: Culture, socialization and sex identity in five
       Caribbean communities. The University of the West Indies Press.
   16. Bourne, P., 2007. Using the biopsychosocial model to evaluate the wellbeing of the
       Jamaican elderly. West Indian Medical J, 56: (suppl 3), 39-40.
   17. Bourne, PA., 2008. Health Determinants: Using Secondary Data to Model Predictors of
       Well-being of Jamaicans. West Indian Medical J, 57(5):476-481.
   18. Longest BB, Jr, 2002. Health Policymaking in the United States, 3rd ed. Health
       Administration Press.
   19. Brannon, L., and J. Feist, 2007. Health psychology. An introduction to behavior and
       health 6th ed. Thomson Wadsworth.
   20. Grossman, M., 1972. The demand for health- a theoretical and empirical
       investigation. National Bureau of Economic Research.

                                                                                              236
21. Smith, JP., and R. Kington, 1997. Demographic and economic correlates of health in old
    age. Demography; 34:159-170.
22. Ross, CE., and J. Mirowsky, 1999. Refining the association between education and
    health: The effects of quantity, credential, and selectivity. Demography; 36:445-460.
23. Freedman, VA., and LG. Martin, 1999. The role of education in explaining and
    forecasting trends in functional limitations among older Americans. Demography,
    36:461-473.
24. Meryn, S., 2004. Sex Quo Vadis: 21 the first female century: The Journal of Men’s health
    & sex, 1: 3-5.
25. Spector, RE., 2004. Cultural diversity in health and illness, 6th ed. New Jersey.
26. Barrow, Christine. 1998. Caribbean Sex Ideologies: Introduction and Overview. In
    Caribbean Portraits: essays on Sex Ideologies and Identities, Ed., Christine, B, Ian Randle
    Publishers, pp: xi-xxxviii.
27. Chevannes, B., 1999. What we sow and what we reap – problems in the cultivation of
    male identity in Jamaica. Grace, Kennedy Foundation.
28. Brown, J., and B. Chevannes,1998. Why man stay so – ties the Heifer and loose the bull:
    an examination of sex socialization in the Caribbean. University of the West Indies.
29. Bailey, W., (ed), 1998. Sex and the family in the Caribbean. Institute of Social and
    Economic Research.
30. Marmot, M., 2003.The influence of Income on Health: Views of an Epidemiologist:
    Does money really matter? Or is it a maker for something else? Health Affairs, 21:31-46.
31. Powell, LA., Bourne, P., and L. Waller, 2007. Probing Jamaica’s Political Culture: Main
    Trends in the July-August 2006 Leadership and Governance Survey, volume 1. Centre
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    Indies.
32. Pacione, M., 2006. Urban environmental quality and human wellbeing –a social
    geographical perspective. Landscape and Urban Planning, 65:19-30.




                                                                                           237
Table 9.1: Life Expectancy at Birth of Jamaicans by Sex: 1880-2004

                           Average Expected Years of Life at Birth
Period:
                           Man                        Woman

1880-1882                  37.02                      39.80

1890-1892                  36.74                      38.30

1910-1912                  39.04                      41.41

1920-1922                  35.89                      38.20

1945-1947                  51.25                      54.58

1950-1952                  55.73                      58.89

1959-1961                  62.65                      66.63

1969-1970                  66.70                      70.20

1979-1981                  69.03                      72.37

1989-1991                  69.97                      72.64

1999-2001                  70.94                      75.58

2002-2004                  71.26                      77.07
Sources: Demographic Statistics (1972-2006) in Bourne, P. Determinants of well-being of the
Jamaican Elderly. Unpublished thesis, The University of the West Indies, Mona Campus; 2007.




                                                                                         238
Table 9.2: Inflation, Public-Private Health Care Service Utilization, Incidence of Poverty, Illness and Prevalence of Population with
Health Insurance (in per cent), 1988-2007

Year                  Inflation       Public              Private                      Prevalence         Illness           Health               Seeking
                      Mean
                                      Utilization         Utilization        of poverty                         Insurance                 Medical Care Days of
                                                                                                                Coverage                               Illness


1988                8.8               NI                  NI                 NI                  NI                 NI                    NI           NI
1989               17.2               42.0                54.0               30.5                16.8               8.2                   54.6         11.4
1990               29.8               39.4                60.6               28.4                18.3               9.0                   38.6         10.1
1991               80.2               35.6                57.7               44.6                13.7               8.6                   47.7         10.2
1992               40.2               28.5                63.4               33.9                10.6               9.0                   50.9         10.8
1993               30.1               30.9                63.8               24.4                12.0               10.1                  51.8         10.4
1994               26.8               28.8                66.7               22.8                12.9               8.8                   51.4         10.4
1995               25.6               27.2                66.4               27.5                9.8                9.7                   58.9         10.7
1996               15.8               31.8                63.6               26.1                10.7               9.8                   54.9         10.0
1997               9.2                32.1                58.8               19.9                9.7                12.6                  59.6         9.9
1998               7.9                37.9                57.3               15.9                8.8                12.1                  60.8         11.0
1999               6.8                37.9                57.1               16.9                10.1               12.1                  68.4         11.0
2000               6.1                40.8                53.6               18.9                14.2               14.0                  60.7         9.0
2001               8.8                38.7                54.8               16.9                13.4               13.9                  63.5         10.0
2002               7.2                57.8                42.7               19.7                12.6               13.5                  64.1         10.0
2003               13.8               NI                  NI                 NI                  NI                 NI                    NI           NI
2004               13.7               46.3                46.4               16.9                11.4               19.2                  65.1         10.0
2005               12.6               NI                  NI                 NI                  NI                 NI                    NI           NI
2006               5.7                41.3                52.8               14.3                12.2               18.4                  70.0          9.8
2007               16.8               40.5                51.9               9.9                 15.5               21.2                  66.0         9.9
Source: Bank of Jamaica, Statistical Digest, Jamaica Survey of Living Conditions, Economic and Social Survey of Jamaica, various issues
Note: Inflation is measured point-to-point at the end of each year (December to December), based on Consumer Price Index (CPI)

NI – No Information Available



                                                                                                                                                                 239
 Table 9.3: Seeking Medical Care, Self-reported illness, and Sex composition of those who report illness
and Seek Medical Care in Jamaica (in percentage), 1988-2007

                                                         Reporting Reporting     Mean    Mean
                                  Seeking     Seeking     Illness-  Illness-      Days    Days
          Seeking       Health    Medical     Medical       Men     Women           Of      Of
          Medical     Insurance    Care -      Care -                            Illness Illness
  Year     Care       Coverage     Men        Women                               Men Women
  1988      NI            NI         NI          NI          NI          NI         NI      NI
  1989     54.6           8.2       44.7        52.8        15.0        18.5      10.6    11.1
  1990     38.6           9.0       37.9        39.2        16.3        20.3      10.2    10.2
  1991     47.7           8.6       48.5        47.4        12.1        15.0      10.0    10.3
  1992     50.9           9.0       49.0        52.5         9.9        11.3      10.7    10.9
  1993     51.8          10.1       48.0        54.7        10.4        13.5      10.7    10.1
  1994     51.4           8.8       49.0        53.4        11.6        14.3      10.3    10.4
  1995     58.9           9.7       59.0        58.9         8.3        11.3      10.6    10.7
  1996     54.9           9.8       50.5        58.5         9.7        11.8      10.0    11.0
  1997     59.6          12.6       60.0        59.3         8.5        10.9      11.0    10.0
  1998     60.8          12.1       57.8        62.8         7.4        10.1      11.0    11.0
  1999     68.4          12.1       64.2        71.1         8.1        12.2      11.0    11.0
  2000     60.7          14.0       57.4        63.2        12.4        16.8       9.0     9.0
  2001     63.5          13.9       56.3        68.2        10.8        15.9        9       10
  2002     64.1          13.5       62.1        65.3        10.4        14.6      10.0    10.0
  2003      NI            NI         NI          NI          NI          NI         NI      NI
  2004     65.1          19.2       64.2        65.7         8.9        13.6       11.0    10.0
  2005      NI            NI         NI          NI          NI          NI         NI      NI
  2006     70.0          18.4       71.7        68.8        10.3        14.1       9.7    10.0
  2007     66.0          21.2       62.8        68.1        13.1        17.8      10.6     9.3

 Source: Jamaica Survey of Living Conditions, various issues

 NI - No Information was available




                                                                                                     240
           Table 9.4: Public Health Care Visits (using the JSLC, data) and Actual Health Care Visits (using
                                                          Ministry of Health Jamaica, data), 1997 and 2004

                                       Public Health Care Visits in Jamaica

Year                           Actual Visits, MOH1             Self-reported Visits, JSLC
                               %                               %
1997                           33.1                            32.1



2004                           52.9*                           46.8

Source: Ministry of Health Jamaica and the Jamaica Survey of Living Conditions (JSLC)

1
    The Percentages of Actual visits were computed by author

*Preliminary data were used to calculate this percentage




                                                                                                       241
    Table 9.5: Self-reported Health Status per 1,000 by Population, Men and Women; Sex-Ratio of Self-
             reported Health Status, and Female to Male Ratio of Self-reported Health Status, 1989-2006
  Year       Self-reported Health Status per
                          1,000                   Male-to-Female       Female-to-Male ratio
                                                    ratio of Self-    of Self-reported Health
          Population          Men      Women      reported Health              Status
                                                        Status


    1989            168           150           185                      81                  123
    1990            183           163           203                      80                  125
    1991            137           121           150                      81                  124
    1992            106            99           113                      88                  114
    1993            120           104           135                      77                  130
    1994            129           116           143                      81                  123
    1995             98            83           113                      73                  136
    1996            107            97           118                      82                  122
    1997             97            85           109                      78                  128
    1998             88            74           101                      73                  136
    1999            101            81           122                      66                  151
    2000            142           124           168                      74                  135
    2001            134           108           159                      68                  147
    2002            126           104           146                      71                  140
    2003              -             -             -                       -                    -
    2004            114            89           136                      65                  153
    2005              -             -             -                       -                    -
    2006            122           103           141                      73                  137
    2007            155           131           178                      74                  136
Computed by Paul Andrew Bourne from Jamaica Survey of Living Conditions from various years




                                                                                                   242
                         70.00




                         60.00
  Seeking Medical Care




                         50.00




                         40.00



                                                                                  R Sq Linear = 0.163



                         30.00


                                 8.00   10.00   12.00       14.00        16.00   18.00           20.00
                                                        Illness/Injury

Figure 9.1: Percentage of Men Seeking Medical Care by Percentage of Men reporting Illness




                                                                                                         243
                         70.00




                         60.00
  Seeking Medical Care




                         50.00




                         40.00



                                                                         R Sq Linear = 0.576



                         30.00


                                 10.00   20.00            30.00            40.00
                                         Prevalence of Poverty


Figure 9.2: Percentage of People Seeking Medical Care by Prevalence of Poverty




                                                                                               244
                                         70.00
  Health Care Seeking Behaviour of Men




                                         60.00




                                         50.00




                                                                                R Sq Linear = 0.354
                                         40.00




                                                 7.50   10.00         12.50           15.00           17.50
                                                        Self-reported Health of Men

Figure 9.3: Percentage of Men Seeking Medical Care by Percentage of Men reporting Illness




                                                                                                              245
                                           70.00
  Health Care Seeking Behaviour of Women




                                           60.00




                                           50.00




                                           40.00                                                    R Sq Linear = 0.088




                                                   10.00   12.00     14.00     16.00      18.00    20.00           22.00
                                                                   Self-reported Health of Women


Figure 9.4: Percentage of Women Seeking Medical Care by Percentage of Women reporting Illness




                                                                                                                           246
                         70.00




                         60.00
  Seeking Medical Care




                         50.00




                         40.00



                                                                           R Sq Linear = 0.561



                         30.00


                                 9.00   12.00         15.00        18.00           21.00
                                                Health Insurance


Figure 9.5: Percentage of people Seeking Medical Care by Percentage with Health Insurance




                                                                                                 247
                   21.00




                   18.00
Health Insurance




                   15.00




                   12.00




                                                                                       R Sq Linear = 0.597
                    9.00




                                   10.00             20.00             30.00               40.00
                                                    Prevalence of Poverty


                     Figure 9.6: Ownership of Health Insurance and Prevalence of Poverty




                                                                                                             248
                                                                               Chapter 10

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


An extensive review of the literature revealed that no study exists that has examined poverty, not seeking
medical care, inflation, self-reported illness, and mortality in Jamaica. The current study will bridge the
gap by providing an investigation of exactly these things. Using two decades’ worth of data (1988-2007),
the current study used three sets of secondary data published by the (1) Planning Institute of Jamaica and
the Statistical Institute of Jamaica (Jamaica Survey of Living Conditions) (2) the Statistical Institute of
Jamaica (Demographic Statistics) and (3) the Bank of Jamaica (Economic Report). Scatter diagrams were
used to examine correlations between the particular dependent and independent variables. For the current
study, a number of hypotheses were tested to provide an explanation on mortality in Jamaica. The average
percentage of Jamaicans not seeking medical care over the last 2 decades was 41.9%; and that figure has
been steadily declining over the last 5 years. In 1990, the percentage of Jamaicans who did not seek
medical care was 61.4%, and this fell to 52.3% in 1991; 49.1% in 1992 and 48.2% the proceeding year.
In the early 1990s (1990-1994), the percentage of Jamaicans not seeking medical care despite illness was
close to 50%, but in the latter part of the decade, the figure was in the region of 30% and was as low as
31.6% in 1999. In 2006, the percentage of Jamaicans not seeking medical care despite being ill was 30%,
which increased by 4% the following year. Concomitantly, poverty fell by 3.1 times over the 2 decades to
9.9% in 2007, while inflation increased by 1.9 times. Self-reported illness was 15.5% in 2007 with
mortality averaging 15,776 per year over the 2 decades. There is a significant statistical correlation
between not seeking medical-care and prevalence of poverty (r = 0.759, p< 0.05), as well as a statistical
correlation between not seeking medical care and unemployment; but the association is a non-linear one.
The relationship between mortality and unemployment was an uncertain one, with there being no clear
linear or non-linear correlation. The findings revealed that there is a strong direct association between not
seeking medical care and inflation rate (r = 0.752). A strong negative statistical correlation was found
between mortality and prevalence of poverty (r=0.717). There is a non-linear statistical association
between not seeking medical care and illness/injury. s: Not seeking medical care is not a good indicator of
premature mortality; but when the percentage of those not seeking medical care is above 55%, then
premature mortality becomes evident. While this study cannot confirm a clear rate of premature mortality,
there are some indications that this occurs beyond a certain level of not seeking care for illness.




                                                                                                         249
Introduction

Health (medical) care-seeking behaviour of people is not only an indicator of their willingness to preserve

life but it is crucial to personal, societal and national development. The health of an individual affects all

areas of his/her life and extends to the family, community, society and the nation. The cost of ill health is

not only borne by the individual, but the entire society. Ill health means less time on the job, lowered

production and productivity, reduced Gross Domestic Product and savings, high health care expenditure,

and the switching of expenditure from education and other social development to health care, and all this

can further increase poverty for an individual or his/her family. Health therefore holds a key to social and

economic development. Hence, long life must be supported by a healthy individual or population. It is this

interrelationship among health, life expectancy, and social and economic development that accounts for a

demand in health care services.



Life expectancy is computed from mortality data, and so healthy life expectancy means the delaying of

mortality. Mortality statistics provide an insight into morbidity patterns as well as the health of a person

or a population. These statistics also provide a basis upon which we can estimate the burden of premature

deaths [1, 2]; lifestyle practices; and health care-seeking behaviour [3]. The Caribbean is experiencing a

health transition which accounts for a reduction in fertility and mortality, and the changing pattern of

diseases from communicable to non-communicable diseases as the leading cause of death [2, 4]. The

Caribbean is not atypical in regards to the aforementioned pattern [1] it is argued that 80% of chronic

disease deaths occur in low-to-middle income countries, and that this has a serious influence on the causes

of premature mortality.

       Statistics from the Planning Institute of Jamaica and the Statistical Institute of Jamaica published

in the Jamaica Survey of Living Conditions [5] revealed that in 2007, 15.5% of Jamaicans reported an

illness/injury compared with 9.7% in 1997. Of the 15.5% of Jamaicans who reported health conditions,

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66% of them sought medical care. Of those who sought health care, 40.5% went to public facilities

compared to 51.9% who attended private health care facilities. The typologies of diseases were asthma

(8.7%), diabetes mellitus (12%), hypertension (22.4%), and arthritis (8.8%). Concomitantly, 33.9% of

Jamaicans who did not seek care reported that they were unable to afford it; 30.2% mentioned that they

preferred home remedies and 6.0% remarked that they had no time. According to Fraser [6], the

prevalence of hypertension in the Caribbean was 28% and 55% for those over 25 years and 40 years

respectively. This explains Fraser’s call for an aggressive management drive to address the prevention of

those health conditions, which was equally echoed by other scholars [7, 8].


       Morrison [9] titled an article “Diabetes and hypertension: Twin Trouble” in which he established

that diabetes mellitus and hypertension have now become two problems for Jamaicans as well as in the

Caribbean in general. This situation was equally collaborated by Callender [10] at the 6th International

Diabetes and Hypertension Conference, which was held in Jamaica in March 2000. They found that there

is a positive association between diabetic and hypertensive patients – 50% of individuals with diabetes

had a history of hypertension [10]. Prior to those scholars’ work, Eldemire [11] found that 34.8% of new

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

A national study of 958 Jamaicans found that 18% of women had hypertension compared to 8% of men;

4.8% of women with diabetes compared to 3.3% of men [4]; and an earlier study by Forrester et al [8] had

found that 19.3% of African-Jamaican females reported hypertension compared to 13.0% of African-

Jamaican males.

       When the WHO [1] found that some deaths are premature, they also learned that the reasons for

this lie in care-seeking behaviour; time of treatment; identification of illness; poverty; inaccessibility;

unhealthy lifestyle practices; and physical inactivity. According to the WHO [1], one-half of all chronic

diseases occur prematurely in people who are below the age of 70 years compared to one-quarter of those

younger than 60 years. The organisation also reported that 80% of premature heart disease, stroke and

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diabetes mellitus could have been prevented from happening. Can premature deaths be prevented?

       Embedded in WHO publication is the relationship between poverty and illness, poverty and

chronic diseases and poverty and premature death. Marmot [12] explained that income is positively

associated with better health, and that poverty means poor nutrition, inadequate physical milieu, and poor

water and food supply which account for increased ill-health in this cohort. Like Marmot [12], Sen

[13,14] argued that poverty denotes reduced capability as this retards choices, freedom, educational

access, proper nutrition, and therefore explains not only chronic diseases but also employability, health

insurance coverage, and medical care-seeking behaviour. Statistics from the Planning Institute of Jamaica

and the Statistical Institute of Jamaica [5] revealed that those below the poverty line sought the least

medical care: 51.7% for those below the poverty line, 52.7% for those just above the poverty line, 61.2%

for those in the middle income categorization, 61.8% in the wealthy income category and 67.6% of those

in the wealthiest income cohort. Concomitantly, the poorest income category had the highest reported

illness (85.4%) compared to 85.1%; 79.6%: 67.5%; and 74.3% for poor, middle class, wealthy and

extremely wealthy income categories respectively [5].

       The poor not only seek less medical care – and this offers some more explanation for their

increased probability of contracting chronic illness and other mortality causing morbidities – but they are

also the least likely group to purchase health insurance coverage. Poverty means low access to material

and other social resources. In 2007, statistics on Jamaica revealed that 2.2% of those below the poverty

line had health insurance coverage compared to 10.1% of those just above the poverty line; 15.9% of the

middle class; 20.9% of the wealthy and 37.7% of the wealthiest income category [5]. This finding

highlights the reality of the poor: that in order for them to access health care, they must supply the

substantial funds out of pocket, or the state must pay for it. With the probability that people living in

poverty are the least likely to find out-of-pocket money to utilize on health care, premature mortality

indeed will be greater for this cohort than other income cohorts.

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       Poverty therefore erodes the good health status of a populace and further deepens individual and

national poverty while creating a public health concern for the society. Inflation is a persistent upward

movement in prices. It erodes the socio-economic choices of people within a society. Inflation increases

the prices of goods and services and a part of this consequence is the cost of health care. In 2007, the

annual rate of inflation on food and non-alcoholic beverages was 24.7% compared to 3.4% on health care

cost (Table 10.1), while it was 16.8% for the nation. The rate of the increase of inflation for 2007 over

2006 was 194.7%. With increases in food prices comes the upward price movement in other goods and

services and this removes the willingness of people to prioritise the pursuit of medical care over food. The

information above highlights the interconnectedness between poverty, unemployment, ill-health, not

seeking medical care, health insurance coverage and mortality. In spite of this reality, extensive review of

the literature has not found a study that has examined the aforementioned variables in a single research.

The current study will bridge the gap by providing an investigation of poverty, not seeking medical care,

illness, health insurance coverage, inflation and mortality in Jamaica.

       Using two decades’ worth of data (1988-2007), the current work will examine 10 hypotheses and

provide an extensive account for mortality, not seeking medical care, illness, health insurance coverage

and unemployment patterns in Jamaica in an attempt to provide research literature for future public health

planning and a better understanding of mortality and premature mortality in Jamaica. The hypotheses are

1) there is a statistical correlation between not seeking medical care and poverty; 2) there is a statistical

association between not seeking medical care and unemployment; 3) there is a statistical association

between poverty and unemployment; 4) there is a statistical relationship between poverty and inflation; 5)

there is a statistical association between not seeking medical care and illness; 6) there is a statistical

association between not seeking medical care and health insurance coverage; 7) there is a statistical

association between mortality and poverty; 8) there is a statistical relationship between mortality and

unemployment, 9) there is a statistical relationship between mortality and not seeking medical care, and

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10) there is a significant statistical association between not seeking medical care and inflation.

       The aim of this study was to examine the impact of poverty, not seeking medical care,

unemployment, inflation, self-reported illness, and health insurance coverage on mortality in Jamaica in

order to provide public health practitioners and health promotion specialists with research findings on

those matters in Jamaica.

       The current findings revealed significant statistical correlation between not seeking medical-care

and 1) prevalence of poverty (r = 0.759, p< 0.05); 2) unemployment; 3) inflation (r = 0.752); 4) illness; 5)

health insurance coverage; and mortality. There is a positive correlation between the prevalence of

poverty and unemployment (r = 0.69), with 48% of poverty able to be explained by unemployment. A

strong positive statistical correlation was found between poverty and inflation (r = 0.856), as 73.2% of

poverty can be explained by inflation. A strong negative statistical correlation was found between

mortality and prevalence of poverty (r=0.717), with 51.4% of the variance in mortality being explained by

poverty. The relationship between mortality and unemployment was an uncertain one, with there being no

clear linear or non-linear correlations. Linear associations were found between most of the

aforementioned variables; however, non-linear correlations were found between 1) mortality and not

seeking-medical care; 2) mortality and unemployment; 3) not seeking medical care and health insurance

coverage; not seeking medical care and illness; and 4) not seeking medical care and unemployment.


Materials and Methods

       Using two decades’ worth of data (1988-2007), the current study used three sets of secondary data

published by the 1) Planning Institute of Jamaica and the Statistical Institute of Jamaica (Jamaica Survey

of Living Conditions); 2) the Statistical Institute of Jamaica (Demographic Statistics); and 3) the Bank of

Jamaica (Economic Report). The years selected for this paper were selected due to the availability of data

on health care-seeking behaviour and illness.



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         Health care-seeking behaviour, poverty and illness data were taken from the Jamaica Survey of

Living Conditions. The Jamaica Survey of Living Conditions (JSLC) is conducted jointly by the Planning

Institute of Jamaica and the Statistical Institute of Jamaica. Its purpose is to collect data on living

standards of Jamaicans. The JSLC used a detailed questionnaire to collect data from respondents between

April and October each year. A self-administered questionnaire was used to collect the data which were

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

modelled from the World Bank’s Living Standards Measurement Study (LSMS) household survey. There

are some modifications to the LSMS, as the JSLC is more focused on policy impacts. The questionnaire

covered areas such as socio-demographic, economic and health variables. The non-response rate for the

survey was 26.2%.

         The survey was drawn using stratified random sampling. This design was a two-stage stratified

random sampling design where there was a Primary Sampling Unit (PSU) and a selection of dwellings

from the primary units. The PSU is an Enumeration District (ED), which constitutes a minimum of 100

dwellings in rural areas and 150 in urban areas. An ED is an independent geographic unit that shares a

common boundary. This means that the country was grouped into strata of equal size based on dwellings

(EDs). Based on the PSUs, a listing of all the dwellings was made, and this became the sampling frame

from which a Master Sample of dwelling was compiled, which in turn provided the sampling frame for

the labour force. One third of the Labour Force Survey (i.e., LFS) was selected for the survey. The sample

was weighted to reflect the population of the nation. Furthermore, the instrument is posted on the World

Bank’s       site    to     provide      information      on      the     typologies      of     question

(http://www.worldbank.org/html/prdph/lsms/country/jm/docs/JAM04.pdf).


         Unemployment data were taken from the publication of the Labour Force Survey of Jamaica

(conducted by the STATIN).



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       Mortality data were taken from the publication of the demographic statistics. Although a medical

certificate of death is used to indicate mortality, data from the Registrar General Department (RGD) were

cleaned, modified and validated by the Statistical Institute of Jamaica [15]. Using a study that was

conducted in 1999 which showed that there was an under-registration of deaths in RGD’s figures, the

STATIN developed a methodology that accounted for complete mortality.

       For the period 1998-2001, STATIN subtracted the number of deaths as reported by the police

(deaths from external causes) from the RGD’s record on external deaths. The difference was added to the

mortality data set. Secondly, on investigation of the infant mortality (ages below 1 year), STATIN found

that 80.25 per cent of the deaths occurred in the year in question and 19.75 percent occurred in the

previous year. This was taken into consideration with the RGD’s figures in order to account for all deaths

occurring in the year in question. For a more detailed explanation of this methodology, readers can

consult Demographic Statistics [15].

           Information is not available on those who are ill but not seeking medical care. As a result, this

information was computed by subtracting the percentage reported as seeking medical care each year from

100.

       The aforementioned data will be used to provide background information on the study.

Descriptive statistics and percentages will be presented on mortality, seeking medical care for the

population, and males and females.


       Scatter diagrams were used to examine correlations between the particular dependent and

independent variables. For the current study, a number of hypotheses were tested to provide a better

understanding of the correlation among mortality, poverty, unemployment, self-reported illness, health

insurance and inflation in Jamaica. Four hypotheses will be tested in this study: (1) there is a statistical

correlation between not seeking medical care and poverty; (2) there is a statistical association between not

seeking medical care and unemployment; (3) there is a statistical association between poverty and

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unemployment; (4) there is a statistical relationship between poverty and inflation; (5) there is a statistical

association between not seeking medical care and illness; (6) there is a statistical association between not

seeking medical care and health insurance coverage; (7) there is a statistical association between mortality

and poverty; (8) there is a statistical relationship between mortality and unemployment, (9) there is a

statistical relationship between mortality and not seeking medical care, and (10) there is a significant

statistical association between not seeking medical care and inflation.


Measures

Inflation: This is measured as the per cent increase in prices from December to December of each year.

Not seeking medical care: This variable is the difference between those who reported seeking medical

care owing to illness/injury (which is expressed as a per cent) and 100 per cent.


Medical care-seeking behaviour: This is the total number of people who reported seeking medical care

(i.e., health care practitioner, healer, pharmacist, nurse, etc.) (expressed in per cent).


Poverty is categorized into two major headings: (1) absolute and (2) relative poverty [13]. Absolute

poverty denotes the lack of particular social necessities caused by “limited material resource” in which to

function – affordability of meeting basic needs, such as adequate nutrition, clothing and housing. Relative

poverty, on the other hand, speaks to the individuals’ low financial resources (money or income) or other

material resources relative to other people. The Senate says that “relative poverty is defined not in terms

of a lack of sufficient resources to meet basic needs, but rather as lacking the resources required to

participate in the lifestyle and consumption patterns enjoyed by others in the society” [16].



The Senate Community Affairs Reference Committee (SCARC) ascribes Professor Ronald Henderson the

developer of the “poverty line”. “…he developed his ‘poverty line’ which was originally set equal to the

minimum wage plus child endowment in Melbourne in 1966” [16]. Within this measurement approach,

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poverty becomes a relative phenomenon instead of an absolutism technique. The SCARC [16] says that

“the aggregate money value of the poverty gap indicates the minimum financial cost of raising all poor

families to the poverty line” [16]. The concept of the poverty line is used in Jamaica to evaluate poverty.

In 2007, the poverty line for a household of five was $302,696.07 compared to $281,009.93 in 2006 [5].


Results

On average, over the studied period, the percentage of Jamaicans not seeking medical care was 41.9%.

The number of Jamaicans not seeking medical care has been steadily declining, which indicates that

health care seekers have been increasing over the past two decades (Figure 10.1; Table 10.2). In 1990, the

highest percentage of Jamaicans who did not seek medical care was 61.4% and this fell to 52.3% in 1991;

49.1% in 1992 and 48.2% the proceeding year. The percentages in the early 1990s (1990-1994) show that

the percentage of Jamaicans not seeking medical care was close to 50% and in the latter part of the

decade, the figure was in the region of 30%, and as low as 31.6% in 1999. In 2006, the percentage of

Jamaicans not seeking medical care despite being ill was 30% and this increased by 4% the following

year.

        Figure 10.1 showed that not seeking medical care (which is derived by subtracting medical care-

seeking behaviour from 100%) can be fitted with a straight line. Furthermore, not seeking medical care

has been steadily declining. However, mortality is best fitted with a non-linear curve. It was found that

mortality was falling up until 1990 where it reached the minimum, and then began rising at an increasing

rate up until 2002, and finally an ever-growing decline set in post-2005 (Fig. 2).

        Based on the findings (Table 10.2), Jamaicans have a preference for private health care utilization.

During the 1990s (1994-1995), the disparity between private and public health care utilization was

approximately 40%, and the divide has continued to narrow after that period. In 2007, the disparity was

11%, which represents a 28% narrowing of the gap between both utilizations.



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       Concomitantly, during the latter part of the 1980s to early 1990s, inflation began mounting so

much so that it peaked at 80.2% in 1991(Table 10.2). While inflation was rising, there were fluctuations

between poverty and self-reported illness/injury. Continuing, when inflation was at its highest (80.2%),

poverty was also at its peak (44.6%), unemployment was close to the peak (15.3%) (Table 10.3) and so

was the percentage of those not seeking medical care (52.3%). Inflation increased by 194% in 2007 over

2006 and during that period, health insurance coverage was at its highest (21.2%); medical care-seeking

behaviour fell by 4%, self-reported illness increased by 3% (to 15.5%) and 4% more Jamaicans did not

seek medical care.


       Table 10.3 revealed that average mortality over the two-decade period was 15,966 people. In

1999, that figure was 18,200 people with a low of 13,200 people in 1992. Correspondingly, over the two

decades there was one occasion where men sought more medical care than women (2006), with the

general trend in the data being that men are less likely to report illness/injury. In 2007, the findings

revealed that the mean number of days spent in medical care by men was marginally more (10.6 days)

compared to women (9.3 days); but that generally the difference is minimal (Table 10.3).



Not seeking medical care

There is a significant statistical correlation between not seeking medical care and prevalence of poverty

(r=0.759, p<0.05). The association therefore is a strong positive one, where 57.6% of the variance in not

seeking medical care can be explained by 1% change poverty (Fig. 3).

       There is a statistical correlation between not seeking medical care and unemployment; but the

association is a non-linear one (Fig. 4). The findings revealed that there is a direct correlation between

not seeking medical care and unemployment between 7.5% and 15% after which it begins to fall. At 15%

of unemployment (not clear) not seeking medical care is at its maximum. Then post that rate, the rate of

not seeking medical care falls precipitously.

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The findings revealed that there is a strong direct association between not seeking medical care and

inflation rate (r=0.752). Continuing, 56.5% of the variance in not seeking medical care can be explained

by a 1% change in inflation rate.


       There is a non-linear statistical association between not seeking medical care and illness/injury

(Fig. 5). The findings revealed that when the rate of illness/injury is more than 9% and less than 14%, the

rate of not seeking medical care falls at a decreasing rate, and after 15% the rate rises significantly.

       Figure 10.6 revealed a statistical association between not seeking medical care and health

insurance coverage, but the relationship is a non-linear one. It was found that between 8 to 18%, the

correlation is an inverse one and after 18% it becomes a direct one. Hence, the more people have health

insurance coverage, the less likely it is that they will not seek medical care and this correlation reverses

beyond 18% of coverage.



       There is a statistical relationship between mortality and not seeking medical care. Based on Figure

10.6, the correlation is best fitted with a non-linear curve than a linear one. Hence, the association does

not have the same gradient throughout the curve. It follows that after 35% of not seeking medical care, the

rate of change in mortality was decreasing and after 55% of not seeking medical care, the rate begins to

mount at an increasing rate.


Poverty, Unemployment, Inflation and Mortality

There is a positive correlation between prevalence of poverty and unemployment (r=0.69), with 48% of

poverty being able to be explained by unemployment (Fig. 7).



A strong positive statistical correlation was found between poverty and inflation (r=0.856), as 73.2% of

poverty can be explained by inflation (Fig. 8).


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A strong negative statistical correlation was found between mortality and prevalence of poverty (r=0.717),

with 51.4% of the variance in mortality being able to be explained by poverty.



The relationship between mortality and unemployment was an uncertain one, with there being no clear

linear or non-linear correlation (Fig. 10).


Discussion

Murray [18] found that there is a clear interrelation between poverty and health. She noted that financial

inadequacy prevents an individual from accessing (food and good nutrition, potable water, proper

sanitation, medicinal care, preventative care, adequate housing, knowledge of health practices) and

attendance (of particular educational institutions among other things), which was in agreement with

Marmot and Sen’s perspectives. Marmot [12] opined that poverty reduced an individual’s socio-economic

and political choices and like Sen [13], he saw this phenomenon as a retardation of human capabilities.

They believed that poverty accounted for much of the low educational outcome as well as accounting for

poor nutrition, low water quality, and poor physical environment, which is not surprising when the poor

experience increased health conditions. Marmot [12] argued that money can buy health, as those who

have it are able to afford medical care treatment, able to purchase particular goods, able to create a good

physical milieu and by extension, able to experience a better health status than the poor. This argument is

not entirely correct as income cannot actually buy health, as health is not a commodity that can be

purchased. However, income can buy the treatment which is a precursor to better health status, and this is

what the wealthy has over the poor. Easterlin [17] argued that material resources have the capacity to

improve one’s choices, comfort level, state of happiness and leisure, and not that money can buy actual

health or happiness.




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        Poverty undoubtedly incapacitates those who live with it, which explains why the WHO [1]

argued that some of the mortality in this group will be premature. The current study found that there is a

strong direct correlation between not seeking medical care and poverty. With 57% of the reason

Jamaicans do not seek medical care being accounted for by poverty, it follows that some of the

morbidities that require medical care will be attended to with home remedies and non-medical healers, or

nothing at all, and by extension will result in premature deaths. This concurs with Murray’s work which

showed that poverty also leads to increased dangers to health; working environments of poorer people

often hold more environmental risks for illness and disability, and other environmental factors, such as

lack of access to clean water disproportionately affect poor families [18].


        The studies clearly show a relationship between persistent and prolonged poverty and health and

even mortality [18-20]. If poverty is an undisputable a primary cause of malnutrition [21], then access to

money plays a pivotal role in well-being. In order to grasp the severity of the issue of money, we need to

be brought into the recognition of poverty and health status. According to Bloom and Canning [22], “ill-

health” significantly affects poor people. This postulate further goes on to explain the higher probability

(5 times) of mortality of the poor than the rich [23].


        A survey conducted by Diener, Sandvik, Seidlitz and Diener [24], stated that the correlation

between income and subjective well-being was small in most countries. According to Diener [25],

“…there is a mixed pattern of evidence regarding the effects of income on SWB [subjective well-being]”.

Benzeval, Judge and Shouls’s [26] study concurred with Diener in that income is associated with health

status. Benzeval et al went further as their research revealed that a strong negative correlation exists

between increasing income and poor health. Furthermore, from a study, it was found that people from the

bottom 25 percent of the income distribution self-reported poorer subjective health by 2.4 times than

people in the fifth quintile [26].


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       The poor, like the wealthy or middle class, also want long life and a life full of satisfaction, but the

reality is, in order for them to spend money on education and health care, they must first cover food and

non-alcoholic beverage costs. In 2007, inflation on non-alcoholic beverages was 24.7%, which means that

the poor must now face the additional cost of survivability before venturing into health care treatment. In

2003 and 2006, health care costs were close to double digits, and in the latter year, the price increase was

greater than that for food and non-alcoholic beverages. With the poor experiencing material and income

inadequacies, inflation not only creates an economic hardship but a treatment care hindrance. This study

revealed that there is a strong positive statistical relationship between not seeking medical care and

inflation, which means that when inflation increased by 194% in 2007 over 2006, many poor Jamaicans

delayed medical care treatment to their very detriment. It should be noted here that during the

aforementioned period, the percentage of Jamaicans reporting health conditions increased to 15.5% (from

12.2% in 2006), suggesting that many poor people were not being treated for some of the chronic diseases

that they were experiencing on a daily basis.

       One of the ways many people afford health care is with health insurance coverage. Health

insurance coverage reduces out-of-pocket payment, and makes medical care more affordable for countless

non-wealthy people. To address the exponential increase in prices that took place in 2007 over 2006,

many Jamaicans purchased health insurance as the percentage of people holding health insurance

coverage stood at 21.2%, the highest in the nation’s 20 year history. Concomitantly, only 2.2% of those in

the poorest income categorization were holders of health insurance coverage, and 10.1% of those were

just above the poverty line, suggesting that health care treatment would be an out-of-pocket payment for

those individuals. With the typologies of diseases reported by Jamaicans being hypertension, diabetes

mellitus, asthma, and arthritis, health insurance coverage increases the probability of medical care

utilization and non-out-of-pocket expenditures on medication and health care treatment. The current

research revealed that health insurance coverage is positively correlated with not seeking medical care.

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However, the association is not a linear one. The association between not seeking medical care and health

insurance coverage is negatively related up to 15% and above 18% of Jamaicans holding health insurance

coverage, the association changes to a positive one. Embedded in this finding is the fact that buying more

health insurance coverage does not indicate a willingness to seek medical care treatment.

       The WHO [1] opined that poverty is associated with increased chronic diseases and premature

death, and their opinion is cemented by this work. The findings herein revealed that poverty is positively

correlated with lowered medical care-seeking behaviour, and it was also found that there is a negative

relationship between mortality and poverty. This denotes that more poverty does not equate to increased

death; instead the converse is true. The study showed that when mortality is high, poverty is less than

18% and that when poverty increased beyond 20%, mortality begins to decline and reaches its lowest

level when poverty is in excess of 40%. If poverty is not directly correlated with mortality, then is it

possible that there are premature deaths of the poor?

       Studies on mortality have shown that there is a high correlation between patterns of death and

health and/or life expectancy [27, 28], indicating that unattended health conditions could cause death.

According to Kimmel [29], 80% of deaths of people over 65 years are attributed to cardiovascular

diseases, blindness, hearing impairment, diabetes, heart conditions, high blood pressure, arthritis, and

rheumatism. While this study was on Jamaicans and not of a particular age cohort, the poor reported the

greatest percentage of health conditions and within the context of their lack of ability to afford health care

and their low response to seek medical treatment compared to other social classes, there should be some

cases of premature mortality associated with low health care-seeking behaviour.

       An interesting finding of the current study was observed as an association was found between

mortality and not seeking medical care, and that it was a non-linear one. On disaggregating the data, it

was found that there were some extraordinary events which occurred that can justify the peak and tough

in the data. In 1988, hurricane Gilbert ravished Jamaica which would account more health care from

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physical and mental illness which would have occurred during and after the natural disaster. Another

extraordinary event which occurred was structural adjustment in the late 1980s which resulted in lowered

income, unemployment and people inability to afford many things including health care. This therefore

can explain the high non-health care-seeking between 1990 and 1991. Extraordinary ordinary events

included the malaria outbreak which occurred in Jamaica in 2006. Using available statistics on health care

seeking behaviour for 2004 and 2006, in 2006 health care seeking behaviour increased by 4.9% which

indicates people’s response to the disease outbreak. In 2006, the malaria outbreak accounted for the

increased mortality that is recorded in the data for 2007. But why was there not an increase in health care-

seeking behaviour in 2007? The answer lies in another extraordinary event, which was inflation. In 2007

over 2006, inflation rose by over 190%, which accounts for the reduction in seeking medical care. Food

and non-alcoholic beverages prices increased by 24.7% and health care costs increased by 3.4% which

would account for the switching from formal care to non-formal care (i.e. home remedy). This means that

the poor would have become poorer and those among them who are sick would be unable to afford

medical care. The increased non-seekers of medical care with some of them have chronic illness would

explain the increased mortality in 2007. Some of the deaths would be premature as they would have

resulted from people inability to seek medical care and be treated for the particular health conditions that

would have caused deaths. There is a direct correlation between poverty and not seeking medical care,

indicating that poverty coupled with the aforementioned extraordinary events resulted in more Jamaicans

not seeking medical care and justifies poverty role in mortality. In examining mortality and not seeking

medical care data for Jamaica, sex must be included within the discourse. Statistics for Jamaica in 2005

showed that there were 117 males who died for every 100 females, and this increased in 1998 to 115

males for every 100 females [15]. The current study provides some explanations for the disparity in

mortality data for the sexes. Males’ reluctance in seeking medical care accounts for a part of the mortality

disparity between the sexes. Their unwillingness in seeking medical care explains the rationale for them

                                                                                                        265
spending more time receiving care and also results in premature mortality. Premature mortality is another

of the explanations of the mortality disparity in between the sexes.


Conclusions

Not seeking medical care is influenced by inflation, poverty and unemployment. With the low probability

that the impoverished are likely to be holders of health insurance coverage in Jamaica, their out-of-pocket

payment for health care treatment will be higher resulting in a higher likelihood of them not seeking

medical care to their detriment. Not seeking medical care is not a good indicator of premature mortality;

but when the percentage of those not seeking medical care is above 55%, then premature mortality

becomes evident. While this study cannot confirm a clear rate of premature mortality, there are some

indications that this occurs beyond a certain level of not seeking care for illness.


Acknowledgement

The author would like to extend sincere gratitude to Ms. Neva South-Bourne who offered invaluable

assistance in editing the final draft of this manuscript.



References

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6. Fraser HS. Hypertension: The Silent Killer and the Deadly Quartet. Editorials. West Indian
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the experience with chronic cardiovascular diseases. British Medical Bulletin 1998;54:463-473.

9. Morrison E. Diabetes and hypertension: Twin trouble. Cajanus 2000;33:61-63.

10. Callender J. Lifestyle management in the hypertensive diabetic. Cajanus 2000;33:67-70.

11. Eldemire D. A situational analysis of the Jamaican elderly, 1992. Kingston: Planning
Institute of Jamaica; 1995.

12. Marmot M .The influence of Income on Health: Views of an Epidemiologist. Does money
really matter? Or is it a marker for something else? Health Affairs 2002; 21, pp.31-46.

13. Sen A. Poverty: An ordinal approach to measurement. Econometricia 1979;44, 219-231.

14. Sen A. Poverty and families: An essay in entitlement and deprivation. Oxford: Oxford
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15. Statistical Institute of Jamaica (STATIN). Demographic Statistics, 2007. Kingston:
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16. Senate Community Affairs Reference Committee. A hand up not a hand out: Renewing the
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2006,     from     http://www.aph.gov.au/Senate/committee/clac_ctte/completed_inquiries/2002-
04/poverty/report/report.pdf.

17. Easterlin RA. Building a better theory of well-being. Prepared for presentation at the
Conference “Paradoxes of Happiness in Economics”, University of Milano-Bicocca, March21-
23, 2003. http://www-rcf.usc.edu/~easterl/papers/BetterTheory.pdf (accessed April 4, 2009).

18. Murray S. Poverty and health. Canadian Medical Association J2006;174:923-923.

19. Menchik PL. Economic status as a determinant of mortality among black and white older
men: Does poverty kill? Population Studies 1993;47:427–436.

20. Zick CD, Ken RS. Marital transitions, poverty and gender differences in mortality. J of
Marriage and the Family 1991;53: 327–336.

21. Muller O, Krawinkel M. 2005. Malnutrition and health in developing countries. Canadian
Medical Association Journal 2005;173:279-286.

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22. Bloom DE, Canning D. The health and poverty of nations: From theory to practice. J of
Human Development 2003;4:47-72.

23. World Health Organization. World health report 1999. Geneva: WHO; 1999.

24. Diener E, Sandvik E, Seidlitz L, Diener M. The relationship between income and subjective
wellbeing: Relative or absolute? Social Indicator Research 1993;28:195-223.

25. Diener E. Subjective wellbeing. Psychological Bulletin 1984;95:542-575.

26. Benzeval M, Judge K, Shouls S. Understanding the relationship between income and health:
How much can be gleamed from cross-sectional data? Social policy and Administration. In
Benzeval M, Judge K. Income and health: the time dimension. Soc Sci and Medicine 2001;
52:1371-1390.

27. Horiuchi S, Wilmoth JR. Deceleration in the age pattern of mortality at older ages.
Demography 1998;35:391-412.

28. Gage TB. Population variation causes of death:        Level, gender, and period effects.
Demography 1994;31:271-296.

29. Kimmel DC. Adulthood and aging. New York: John Wiley and Son; 1974.




                                                                                         268
Table 10.1. Annual Inflation in Food and Non-Alcoholic beverages and Health Care Cost, 2003-
2007

                      Food and Non-Alcoholic beverage             Health Care Cost

2002                         7.8                                  5.2

2003                         10.0                                 9.7

2004                         13.7                                 6.4

2005                         11.7                                 7.5

2006                         5.0                                  9.7

2007                         24.7                                 3.4


Source: Planning Institute of Jamaica, Economic and Social Survey of Jamaica, various issues

Note: Inflation is measure using point-to-point at the end of the year (December to December).




                                                                                            269
Table 10.2. Inflation, Public-Private Health Care Service Utilization, Incidence of Poverty, Illness and Prevalence of Population with
Health Insurance (in per cent), 1988-2007

Year                  Inflation       Public              Private                      Prevalence         Illness           Health               Seeking
                      Mean
                                      Utilization         Utilization        of poverty                         Insurance                 Medical Care Days of
                                                                                                                Coverage                               Illness


1988                8.8               NI                  NI                 NI                  NI                 NI                    NI           NI
1989               17.2               42.0                54.0               30.5                16.8               8.2                   54.6         11.4
1990               29.8               39.4                60.6               28.4                18.3               9.0                   38.6         10.1
1991               80.2               35.6                57.7               44.6                13.7               8.6                   47.7         10.2
1992               40.2               28.5                63.4               33.9                10.6               9.0                   50.9         10.8
1993               30.1               30.9                63.8               24.4                12.0               10.1                  51.8         10.4
1994               26.8               28.8                66.7               22.8                12.9               8.8                   51.4         10.4
1995               25.6               27.2                66.4               27.5                9.8                9.7                   58.9         10.7
1996               15.8               31.8                63.6               26.1                10.7               9.8                   54.9         10.0
1997               9.2                32.1                58.8               19.9                9.7                12.6                  59.6         9.9
1998               7.9                37.9                57.3               15.9                8.8                12.1                  60.8         11.0
1999               6.8                37.9                57.1               16.9                10.1               12.1                  68.4         11.0
2000               6.1                40.8                53.6               18.9                14.2               14.0                  60.7         9.0
2001               8.8                38.7                54.8               16.9                13.4               13.9                  63.5         10.0
2002               7.2                57.8                42.7               19.7                12.6               13.5                  64.1         10.0
2003               13.8               NI                  NI                 19.1                NI                 NI                    NI           NI
2004               13.7               46.3                46.4               16.9                11.4               19.2                  65.1         10.0
2005               12.6               NI                  NI                 14.8                NI                 NI                    NI           NI
2006               5.7                41.3                52.8               14.3                12.2               18.4                  70.0          9.8
2007               16.8               40.5                51.9               9.9                 15.5               21.2                  66.0         9.9
Source: Bank of Jamaica, Statistical Digest, Jamaica Survey of Living Conditions, Economic and Social Survey of Jamaica, various issues
Note: Inflation is measured point-to-point at the end of each year (December to December), based on Consumer Price Index (CPI)

NI – No Information Available



                                                                                                                                                                 270
Table 10.3. Seeking Medical Care, Self-reported illness, and Gender composition of those who
report illness and Seek Medical Care in Jamaica (in percentage), 1988-2007

                                                                   Reporting Reporting   Mean    Mean
                                           Seeking       Seeking    Illness-  Illness-    Days    Days
                                           Medical       Medical      Men     Women         Of      Of
                                            Care -        Care -                         Illness Illness
  Year     Mortality     Unemployed         Men          Women                            Men Women
  1988     12,167.0         26.8              NI            NI        NI        NI          NI      NI
  1989     16400.0          18.0             44.7          52.8      15.0      18.5       10.6    11.1
  1990     14900.0          15.3             37.9          39.2      16.3      20.3       10.2    10.2
  1991     13300.0          15.3             48.5          47.4      12.1      15.0       10.0    10.3
  1992     13200.0           9.4             49.0          52.5       9.9      11.3       10.7    10.9
  1993     13900.0           9.5             48.0          54.7      10.4      13.5       10.7    10.1
  1994     13500.0          10.9             49.0          53.4      11.6      14.3       10.3    10.4
  1995     15400.0           9.6             59.0          58.9       8.3      11.3       10.6    10.7
  1996     15800.0          10.8             50.5          58.5       9.7      11.8       10.0    11.0
  1997     15100.0          10.6             60.0          59.3       8.5      10.9       11.0    10.0
  1998     17000.0          10.0             57.8          62.8       7.4      10.1       11.0    11.0
  1999     18200.0          10.0             64.2          71.1       8.1      12.2       11.0    11.0
  2000     17400.0          10.2             57.4          63.2      12.4      16.8        9.0     9.0
  2001     17800.0          10.3             56.3          68.2      10.8      15.9         9       10
  2002     17000.0          10.6             62.1          65.3      10.4      14.6       10.0    10.0
  2003     16699.0          17.6              NI            NI        NI        NI          NI      NI
  2004     16900.0           7.9             64.2          65.7       8.9      13.6        11.0    10.0
  2005     17552.0                            NI            NI        NI        NI          NI      NI
  2006     16300.0             7.0           71.7          68.8      10.3      14.1        9.7    10.0
  2007     17000.0             6.2           62.8          68.1      13.1      17.8       10.6     9.3

 Source: Jamaica Survey of Living Conditions, various issues

 NI - No Information was available




                                                                                               271
                                  70.00
   Not seek medical care (in %)




                                  60.00




                                  50.00




                                  40.00




                                  30.00

                                          1985.00   1990.00   1995.00          2000.00     2005.00   2010.00
                                                                        Year



            Figure 10.1. Not seeking medical care (in %) by Year



There is a linear pattern in percent of Jamaicans not seeking medical care (Figure 10.1)




                                                                                                          272
                           19000.00
                                                                                                   R Sq Linear = 0.43



                           18000.00
   Mortality (in people)




                           17000.00



                           16000.00



                           15000.00



                           14000.00
                                                                                                     R Sq Cubic =0.745



                           13000.00

                                      1985.00       1990.00          1995.00           2000.00   2005.00          2010.00
                                                                                Year


                       Figure 10.2. Annual Mortality (No. of people) in Years



Based on Figure 10.2, the annual number of Jamaicans who die is best fitted with a non-linear diagram.




                                                                                                                         273
                                    70.00
   Not seeking medical care (in%)




                                    60.00




                                    50.00




                                    40.00


                                                                                         R Sq Linear = 0.576



                                    30.00

                                            10.00      20.00            30.00               40.00
                                                    Prevalence of Poverty (in %)


Figure 10.3. Not Seeking Medical Care (in %) by Prevalence of poverty rate (in %)


There is a linear association between not seeking medical care (in %) and prevalence of poverty (in %) in Jamaica

(Figure 10.3). Furthermore, 58% of the variability in not seeking medical care (in %) can be explained by a 1%

change in prevalence of poverty (in %).




                                                                                                               274
   Not seeking medical care (in %)   70.00




                                     60.00




                                     50.00




                                     40.00


                                                                                                                   R Sq Cubic =0.581



                                     30.00

                                                       7.50            10.00             12.50            15.00             17.50
                                                                       Unemployment rate (in %)




                                             Figure 10.4. Not Seeking Medical Care (in %) by Unemployment rate (in %)



The statistical correlation between not seeking medical care (in %) and unemployment rate (in %) is not a linear one.
Based on Figure 10.4, it is best fitted with a non-linear cure.




                                                                                                                                       275
                                              70.00


            Not seeking medical care (in %)


                                              60.00




                                              50.00




                                              40.00


                                                                                                                           R Sq Cubic =0.365



                                              30.00

                                                        8.00          10.00         12.00          14.00          16.00   18.00         20.00
                                                                                         Illness/Injury (in %)



                                              Figure 10.5. Not Seeking Medical Care (in %) by Illness/Injury (in %)




Figure 10.5 revealed that statistical correlation between not seeking medical care (in %) and illness/injury (in %) is a
non-linear one.




                                                                                                                                     276
                                             19000.00


                                                                                                    R Sq Cubic =0.794
                                             18000.00
                  Mortality (No of people)



                                             17000.00



                                             16000.00



                                             15000.00



                                             14000.00


                                                                                                               R Sq Linear = 0.559
                                             13000.00

                                                        30.00          40.00              50.00              60.00                   70.00
                                                                          Not seeking medical care (in %)


                                        Figure 10.6. Mortality (No of people) by Not Seeking Medical Care (in %)



Based on Figure 10.6, the association between mortality (number of people that died) and not seeking medical care
(in %) can be best fitted with a non-linear curve.




                                                                                                                                     277
                               40.00
Prevalence of Poverty (in %)




                               30.00




                               20.00




                               10.00                                                          R Sq Linear = 0.48




                                       7.50      10.00             12.50              15.00             17.50
                                                 Unemployment rate (in %)
         Figure 10.7 Prevalence of poverty rate (in %) and Unemployment rate (in %)




                                                                                                                   278
Not seeking medical care (in %)   70.00




                                  60.00




                                  50.00




                                  40.00
                                                                                                           R Sq Quadratic =0.693




                                  30.00

                                               9.00              12.00              15.00              18.00              21.00
                                                               Health Insurance Coverage (in %)



                              Figure 10.8. Not Seeking Medical Care (in %) by Health Insurance Coverage (in %)



                              A non-linear relationship existed between not seeking medical care (in %) and health insurance coverage (in
                              %) (Figure 10.8).




                                                                                                                                      279
                           19000.00



                           18000.00
Mortality (No of people)




                           17000.00



                           16000.00



                           15000.00
                                                                                                                R Sq Linear = 0.514



                           14000.00



                           13000.00

                                                  10.00                 20.00                30.00                 40.00
                                                                 Prevalence of Poverty (in %)



                            Figure 10.9. Mortality (No. of people) by Prevalence of Poverty (in %)



                            Mortality (annual number of deaths) and prevalence of poverty (in %) is a linear one (Figure 10.9).




                                                                                                                                      280
                           19000.00



                           18000.00
Mortality (No of people)




                           17000.00



                           16000.00



                           15000.00



                           14000.00



                           13000.00

                                                   7.50            10.00            12.50             15.00            17.50
                                                                    Unemployment rate (in %)


                           Figure 10.10. Mortality (No. of people) by Unemployment rate (in %)




                           There is no clear pattern between mortality (number of people who die, annual) and unemployment
                           rate (in %) in Jamaica (Figure 10.10).




                                                                                                                       281
                               40.00
Prevalence of Poverty (in %)




                               30.00




                               20.00




                               10.00                                                                                 R Sq Linear = 0.732




                                        0.00              20.00             40.00              60.00             80.00             100.00
                                                                           Inflation rate (in %)

                                Figure 10.11. Prevalence of poverty rate (in %) by Inflation rate (in %)



                                       A strong statistical association existed between prevalence of poverty (in %) and inflation rate (in %)
                                       – R2 = 0.732 (Figure 10.11).




                                                                                                                                           282
                                     70.00



   Not seeking medical care (in %)

                                     60.00




                                     50.00




                                     40.00




                                     30.00

                                             0.00   20.00    40.00         60.00     80.00            100.00
                                                            Inflation rate (in %)

Figure 10.12. Not Seeking Medical care (in %) by Inflation rate (in %)



     There is a linear statistical correlation between not seeking medical care ( in %) and inflation rate
     (in %) in Jamaica. Fifty-seven percent of the variance in not seeking medical care (in %) can be
     explained by a 1% change in inflation rate (Figure 10.12)




                                                                                                     283
                                                                      Chapter 11

Health Disparities and the Social Context of Health Disparity between the
Poorest and Wealthiest quintiles in a Developing Country

Previous studies which have examined social determinants of health have done so for the
population or elderly, but none emerged which explore socio-biological determinants between
the poorest and wealthiest income quintile. Health disparity and socio-biological determinants of
the poorest quintile and the wealthiest quintile have never been examined for Jamaica. The
current study will bridge this gap in the literature by examining health status, illness, age at
which the lower and upper classes indicate having illness and particular illnesses, and parameters
that explain health status of the upper and lower quintiles in Jamaica as well as the social context
of the health disparity. A sample of 2,725 respondents from the wealthiest quintile and poorest
quintile was extracted from a cross-sectional survey of 6,783 respondents. Stepwise logistic
regression was used to determine the contribution of the significant socio-biological
determinants of the health status model. Health status is self-rated general health of respondents.
The wealthiest quintile reported good health status 62% more than for the poorest quintile. This
study is far-reaching and can be used to lessen the health disparities between and among social
hierarchies in Jamaica. Health policies in Jamaica must be adopted in order to address the social
determinants of health. They should aim at making the health system more effective in reaching
the poorest in the nation, especially those in rural areas.


Introduction

Since the 19th century, economic development in Latin American and the Caribbean has more

than doubled, but this has not eliminated social and health inequalities among and within nations.

Technology, public health, and economic development have all been associated with increased

life expectancy, improvements in living standards, lowered infant and general mortality, reduced

fertility and general health of the population, but this is not equally distributed among different

socio-economic groups within nations.1-8 Poverty in Latin America and the Caribbean (in

particular, Jamaica) has fallen by more than one-half since the 1980s, yet health status between

and among the upper and lower classes still show health differentials.1,2 Studies that have

examined health status of the wealthy and the lower classes clearly revealed that there are health

                                                                                                284
inequalities. Those health disparities are measured in life expectancy, health care utilization,

infant mortality, and health conditions.1-11 The World Health Organization (WHO)7 reports that

60% of global mortality is explained by chronic illness, and that 80% of chronic illness occur in

low-to-middle income nations. Connections between poverty and particular health outcomes

(such as healthy life expectancy, infant mortality, and health care utilization) are clearly present

in Latin America and the Caribbean.1,121

          Poverty and particular health outcomes are well established in the literature, as well as

poverty and health conditions (illnesses). The WHO estimates that more than 180 million people

worldwide have diabetes, and this number is likely to more than double by 2030.7 Almost 80%

of diabetes deaths occur in low and middle-income countries, about half of diabetes deaths occur

in people under the age of 70 years, and 55% of diabetes deaths are in women. The WHO

projects that those diabetes deaths will increase by more than 50% in the next 10 years without

urgent action. Most notably, diabetes deaths are projected to increase by over 80% in upper-

middle income countries between 2006 and 2015. Marmot8 argued that poverty explains lower

nutritional intake and poor physical milieu; like Sen,13 he also held that poverty is more than

material deprivation, as it also involves social, political, and economic deprivation and loss of

opportunities. The afore-mentioned deprivations can result in socio-economic burden not only

for the individual, but also for the family .14 One Caribbean researcher argued that while poverty

is difficult to break,15 it is also the case that poverty can breed dependency, and thus perpetuate

itself.




1
 In what follows, poverty denotes relative poverty. Relative poverty is deviation from country-specific
median income.



                                                                                                          285
           In 2007, statistics for Jamaica reveal that 59% of people in the Poorest quintile indicated

having an illness compared with 54% of those in the wealthiest quintile.16 Concurrently, those in

the Poorest quintile spent a longer time ill (mean = 11.3 days) than the wealthiest quintile (mean

= 8.1 days). The Poorest quintile is often unable to seek medical care at the onset of ill health.

The 2007 data showed the majority (51%) of the Poorest quintile explained that financial

difficulties were the reason they did not use health care services, while only a small proportion

(7%) of those in the wealthiest quintile reported this. In 2000, WHO found between 65% and

70% of people who mentioned that they did not fill their prescription claimed that they were

unable to afford it, suggesting that poverty retards the quality and productive nature of human

capital.     Poverty is therefore directly associated with lowered health care utilization and

medication in the Jamaica, as is generally true in developing nations.7
                                                                                  17
           According to the Pan-American Health Organization (PAHO),                   public hospitals

accounted for 95% of hospital-based care in Jamaica. The 2007 data showed that well over half

(65%) of the Poorest quintile in Jamaica utilized the public health care system, compared with

just one-quarter (25.6%) of those in the wealthiest quintile.16 In contrast, 68.4% of those in the

affluent class used private care, while 27.6% of those in the Poorest quintile did.15 With the

public system catering to a significant proportion of poor patients, poverty and illness place a

heavy burden on the public health care system.

           The objectives of the present research were to elucidate statistical significant differences

in illness, health status, age at illness, and explanatory parameters of health status of the

wealthiest and the poorest quintile as well as the social context of the health disparity between

the wealthiest and poorest quintile in the Jamaican population.




                                                                                                   286
Theoretical framework

       The multivariate model used in this study is a modification of Grossman’s work.18 Smith

& Kington’s19 work, which captures the multi-dimensional concept of health and health status.

Unlike Michael Grossman and Smith & Kington’s models, which used econometric analyses to

examine self-reported health status from data for a population, the current research uses the same

econometric analysis to explore factors which are associated with the health status of those in the

poorest quintile and those in the wealthiest quintile in Jamaica.



Methods

Sample. A sample of 2,725 respondents from the wealthiest quintile and the poorest quintile was

extracted from a cross-sectional survey of 6,783 respondents. Of the sample, 50.7% was in the

wealthiest quintile and 49.3% was in the poorest quintile. The survey, Jamaica Survey of Living

Conditions (JSLC), was collected jointly by the Planning Institute of Jamaica and the Statistical

Institute of Jamaica.20 The method of selection of the sample from each survey was solely based

on rural residence. The JSLC had begun to collect data from Jamaicans in 1989, in order to

assess policies of the government. Most years since 1989, the JSLC has added a new module in

order to examine some phenomenon that is critical within the nation. The current sample was

extracted from the 2007 dataset.

       The survey was drawn using stratified random sampling. This design was a two-stage

stratified random sampling design where there was a Primary Sampling Unit (PSU) and a

selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which

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 with other adjacent EDs. This



                                                                                               287
means that the country was grouped into strata of equal size based on dwellings (EDs). Based on

the PSUs, a listing of all the dwellings was made, and this became the sampling frame from

which a Master Sample of dwellings was compiled, which in turn provided the sampling frame

for the labor force. One third of the Labor Force Survey (LFS) was selected for the JSLC.20 The

sample was weighted to reflect the population of the nation.

       The JSLC 200720 was conducted in May and August of 2007. The researchers chose to

study the 2007 survey because it is the latest survey on the national population and because it has

data on self-reported health status of Jamaicans. An administered questionnaire was used to

collect the data, which were stored and analyzed using SPSS for Windows 16.0 (SPSS Inc;

Chicago, IL, USA). The questionnaire was modeled on 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 outgrowths. The questionnaire covered areas such as socio-

demographic variables (including education), daily expenses [for past seven 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 27.7%. The non-response rate includes refusals and cases rejected in data cleaning.

       Measurement. Dependent variable. Self-rated health status is people’s evaluation of

their general health. Self-rated health status is measured using people’s self-rating of their overall

health status,21 on a scale ranging from very good to very poor . The question that was asked in

survey was How is your health in general? The options were very good; good; fair; poor and

very poor. For the purpose of the model in this study, self-rated health was coded as a binary

variable (1= good, 0 = otherwise).21-28 The binary good health status was used as the dependent

variable.



                                                                                                  288
       Covariates. Self-reported illness is people’s reporting on the different typology of illness

that they have had in the last 4-week period. Self-reported illness (or self-reported dysfunction)

was measured by asking for any reported illness, Is this a diagnosed recurring illness? The

answering options were, Yes, Influenza; Yes, Diarrhoea; Yes, Respiratory diseases; Yes,

Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. A binary variable was later

created from this construct (1=no 0=otherwise) in order to be applied in the logistic regression.

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

birthday). Age groups were classified as children, young adults, middle aged-adults, young-old

(or young-elderly), old-old, and oldest-old: children – 0 to 14 years; young adults – 15 to 30

years; middle aged-adults – 31 to 59 years; young-old – 60 to 74 years; old-old―75 – 84 years;

and oldest-old – 85+ years.

       Social hierarchy (or social class) is the classification of people within the society based

on income categorization. Social hierarchy 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

lower classes were those in lowest quintiles (quintiles 1 and 2).

       Medical care-seeking behaviour is self-reported health care visits made to a medical

practitioner, healer, and/or pharmacist in the last 4-week period. Medical care-seeking behaviour

was taken from the question Has a health care practitioner, or pharmacist being visited in the

last 4 weeks? with there being two possible responses (Yes or No). Medical care-seeking

behaviour therefore was coded as a binary measure where 1= Yes and 0 = otherwise.

       Crowding is the number of people who reside in a room, and share the same household

with people in the same dwelling. Crowding is the total number of individuals in the household

divided by the number of rooms (excluding kitchen, verandah and bathroom).



                                                                                               289
       Sex is being male or female. This is a binary variable where 1= male and 0 = otherwise.

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

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

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

       Statistical analysis. Descriptive statistics such as mean, standard deviation (SD),

frequency and percentage were used to analyze the socio-demographic characteristics of the

sample. Chi-square was used to examine the association between non-metric variables, t-test and

an Analysis of Variance (ANOVA) were used to test the relationships between metric and/or

dichotomous and non-dichotomous categorical variables. Box-plots were used to examine what

was happening among age, self-reported illness, and social hierarchy as well as age, typology of

illness, and social hierarchy (Poorest quintile and wealthiest quintile). Multiple logistic

regression techniques were conducted to identify parameters and their estimates. Stepwise

logistic regression technique was used to determine the contribution of each significant

determinant to the model. A p-value less than 0.05 (two-tailed) was selected to indicate statistical

significance (95% confidence interval).

Results

       Demographic characteristics of the sample. The sample was 2,725 respondents: Of the

sample 50.7% was in the upper quintile, and 49.3% was in the lower quintile. Almost 50% of the

sample was male; 49% resided in rural areas, 32.0% in urban areas and 19% dwelled in peri-

urban areas (Table 11.1).

       Only 14.8% of the sample responded to the question, Have you sought care in the last

four weeks? Of those responding, 64.5% responded yes, and 62.8% indicated that they bought

prescribed medication. The general health status of the sample was good: 80.9% indicated at



                                                                                                290
least good health status, of which 36.5% stated very good health status. The median number of

visits to health care practitioners during the last 4 weeks was 1 (Range = 0 to 12). Six percentage

points of the sample mentioned poor health status, of which, 0.6% stated very poor. Over 50% of

the sample was 30 years old or younger (30.2% children); 31.9% middle aged-adults; and 12.6%

elderly (8.9% young-old, 2.8% old-old, and 0.9% oldest-old).

        Bivariate analyses. There is a significant statistical relationship between area of

residence and social hierarchy (Poorest quintile and wealthiest quintile) [χ2 (df=2) = 663.0, p <

0.0001]. Of the poorest quintile, 73.7% lived in rural areas compared with 25% of the wealthiest

quintile; 13.8% of the Poorest quintile dwelled in urban areas compared with 49.6% of those in

wealthiest quintile and 12.4% of the poorest resided in peri-urban areas compared with 25.5% of

the wealthiest quintile.

        A cross-tabulation between self-reported health conditions and social hierarchies found

no significant statistical association (p = 0.559).

        Table 11.2 shows age group by self-reported illness controlled by social hierarchy. A

significant statistical relationship was found between age group and self-reported illness, and this

did not change based on being in the Poorest quintile [χ2 (df = 5) = 134.2, p < .0001] or the

wealthiest quintile [χ2 (df = 5) = 107.4, p < .0001]. Based on Table 11.2, children whose parents

are in the poorest quintile are more likely to report an illness (28%) than those in the wealthiest

quintile (quintile).

PRODUCTION: INSERT TABLE 11.2 ABOUT HERE

        Figure 11.1 shows self-reported health conditions by social hierarchy controlled for by

age of respondents. Reading from left to right, the box plots are cold, diarrhoea, respiratory

diseases, diabetes mellitus, hypertension, arthritis and other (unspecified). Notice in Figure 11.1



                                                                                                291
that the mean ages of those with influenza and diarrhoea are substantially lower for those in

poorest quintile than for those in the wealthiest quintile.

PRODUCTION: INSERT FIGURE 11.1 ABOUT HERE

       A significant statistical difference was found between income of those in the poorest

quintile and those in the wealthiest quintile (t = -36.9, p < .001). The mean annual income of

those in the poorest quintile was US $ 4,067.80 (SD = US$ 2,227.90) compared with

US$14,933.83 (SD = US $10,564.10). At the time of the survey, the rate of exchange was US

$1.00 = Ja. 80.47. A statistical difference was found between the cost of medical expenditure of

those in the poorest quintile and those in the wealthiest quintile (t = - 4.5, p < .001). The mean

amount spent for medical expenditure by those in the poorest quintile was US $6.64 (SD =

US$9.56; per treatment) compared with US$19.66 (SD = US$26.64; per treatment) for those in

the wealthiest quintile during the past 4 weeks. However, there was no statistical difference

between the public medical expenditure of those in the poorest quintile and the wealthiest

quintile [t = -1.1, p = 0.202; US$ 1.96 (SD = US$2.43) and US$4.47 (SD = US$22.36),

respectively]. On the other hand, a statistical difference was found between mean total medical

expenditure of those in the poorest quintile and those in the wealthiest quintile [t = -4.4, p <

.0001; US $7.50 (SD = US$8.64) and US$22.11 (SD = US$33.91), respectively].

       No statistical difference was found between the mean number of visits made to medical

practitioners in the last four weeks by those in the poorest quintile and the wealthiest quintile[t =

-1.6, p = 0.100; 1.2 (SD = 0.5) visits by Poorest quintile and 1.4 (SD = 1.2) visits by wealthiest

quintile]. However a statistical relationship was found between health care-seeking behaviour

and social hierarchy [χ2 (df = 1) = 15.5, p < p.0001]. Fifty-five percentage points of those in the




                                                                                                 292
poorest quintile visit a health care practitioner in the last four weeks compared with 74% of those

in the wealthiest quintile.

          Multivariate analyses. Table 11.3 presents the socio-biological determinants of good

health status of people in the poorest quintile and the wealthiest quintile in Jamaica. Five socio-

biological determinants; (age of respondents, sex, social class, area of residence, and self-

reported illness) account for 36.8% of the variability in good health status. The model is a good

fit for the data (model χ2 = 504.51, p < .0001). Overall, 83.1% of the data were correctly

classified in the model: 95.4% of those in good health status and 42.4% of those in moderate-to-

very poor health status.

PRODUCTION: INSERT TABLE 11.3 ABOUT HERE

          Table 11.4 highlights the socio-biological determinants of good health status of people in

the poorest quintile in Jamaica. Three socio-biological determinants (age, sex, and self-reported

illness) account for 38.4% of the variance in good health status. The model is a good fit for the

data (model χ2 = 231.4, p < .0001). Overall, 80.2% of the data were correctly classified in the

model: 90.2% of those in good health status and 55.4% of those in moderate-to-very poor health

status.

PRODUCTION: INSERT TABLE 11.4 ABOUT HERE

          Table 11.5 examines the socio-biological determinants of good health status of people in

the wealthiest quintile in Jamaica. Four socio-biological determinants; (age of respondents,

marital status, area of residence, and self-reported illness) account for 34.6% of the variability in

good health status. The model is a good fit for the data (model χ2 = 260.56, p < .0001). Overall,

85.0% of the data were correctly classified in the model: 94.4% of those in good health status

and 45.9% of those in moderate-to-very poor health status.



                                                                                                 293
PRODUCTION: INSERT TABLE 11.5 ABOUT HERE

Limitations to the study

The current study has two fundamental limitations, which include the following: (1) the

subjectivity of self-reported data and biases, and (2) the type of data collection method (cross-

sectional survey). Biases are always present in self-reported health. But, studies have used

subjective health data despite its subjectivity.18,19,22,26-28 Furthermore, previous studies have

shown that self-reported health is a good measure of objective health indices such as functional
                           26,27
abilities and mortality.           Another limitation of this work is the data collection method, cross-

sectional survey. Cross-sectional survey data means that information cannot be used to predict

the future nor it is static over time.



Discussion

          The general health status of Jamaicans in the poorest quintile and wealthiest quintile was

good, with 81 out of every 100 indicating at least good health status. The current research

revealed that good health status for those in wealthiest quintile was greater than for those in the

poorest quintile. Eighty-five out of every 100 in the sample did not report an illness. Self-

reported illness for the poorest quintile and the wealthiest quintile accounted for at least 73.0% of

the variability in good health status, indicating the dominance of the biological condition in

health.

          Income disparity between the groups studied was appreciable: the wealthiest quintile had

incomes 3.7 as great as the incomes of those in the poorest quintile. In keeping with this, the

wealthiest quintile spent three times more on total medical expenditure than the poorest quintile

spent.



                                                                                                    294
       Overall, those in the poorest quintile indicated having illnesses at younger ages than those

in the wealthiest quintile. One of the health outcomes which showed a disparity was the mean

age at which the poorest quintile versus the wealthiest quintile reported being affected by

particular health conditions. Acute conditions (such as influenza, diarrhea, and respiratory

diseases) occurred in the Poorest quintile at younger ages, especially diarrhoea (which occurred

in the poorest quintile at younger than 20 years, but at older than 60 years for those in the

wealthiest quintile). Diabetes mellitus and other health conditions occurred at younger ages

among the poorest quintile compared with higher ages among wealthiest quintile. However,

hypertension occurred at younger ages among the wealthiest quintile and older ages among the

poorest quintile.

       Illness continues to dominate health services research despite the expanded conceptual

definition forwarded by the WHO in the preamble to its Constitution in 194829 as well as in

other work in this area.30-39 The studies just cited did not include parameter estimates for the

contribution of social determinants and the biological conditions, but this area was studied by
                    40
Hambleton et al.         with respect to Barbadian elderly. Hambleton, et al. found that illness

accounted for 88.0% of current health status. In the current research, self-reported illness

accounted for 73.1% of the variability in current good health status. This work goes further than

that of Hambleton et al., as it was found that self-reported illness accounted for 68.2% of the

explanatory power of good health status for those in the poorest quintile compared with 79.8%

for those in the wealthiest quintile. Ill-health therefore dominates the perception of wealthiest

quintile more than the poorest quintile, indicating the former social group would be more likely

to seek medical care in response to ill-health than the poorest quintile.




                                                                                               295
       The greater responsiveness of the wealthiest quintile towards ill-health is predictable,

given social determinants of health. There is a health disparity in Jamaica even among the

wealthiest quintile. Area of residence has been empirically established as a social determinant of

health,35-38 which is in keeping with the current findings. The wealthiest quintile who resided in

the peri-urban area was 72% more likely to record good health status than those in the rural

areas, and there was no statistical difference between the health status of those who resided in

urban and rural areas. Seventy-four out of every 100 of those in the poorest quintile dwelled in

rural areas, which are characterized by high unemployment and few opportunities for socio-

economic advancement. These considerations are relevant for understanding two central issues in

the study of health in developing nations: (1) urban-urban migration and (2) the direct influence

of poverty on health status. The findings in this study showed that wealthiest quintile is 1.62

times more likely to record good health than those in the poorest quintile. People in the

wealthiest quintile are least likely to live in rural areas (25 out of every 100); those in the poorest

quintile are most likely to dwell in rural areas (74 out of every 100). The national poverty rate in

1989 was 30.5%. Although this fell by 208.1% in 2007, rural poverty was four times greater than

urban poverty and three times greater than peri-urban poverty. Poverty is common in rural areas,

and the associated socio-economic deprivation increases physical illness as well as mental

illnesses, which account for some of the health disparity in Jamaica, including health disparities

among people within a single income group. Socio-economic deprivation and poverty, therefore,

account for health conditions among rural residents, and explain rural-urban migration as people

seek economic opportunities to better their living standard or quality of life.41

       It is not surprising that Jamaica has been experiencing increased urbanization since the

1980s. Urbanization in Latin America and the Caribbean is the second highest in the world.



                                                                                                   296
Statistics reveal that 10 out of every 13 people (78.3%) dwelled in cities in 2007.42,43 The

unemployment rate in 2007 in Jamaica was 65.4%,44 according to research for which the

participation rate (number of individuals who are working divided by the total number of

employed and the number of people who are actively seeking employment) was 55.4% for

women compared with 72.9% for men. Fifty-three percentage of women in the poorest quintile

were heads of households compared to 46.9% of men. The mean annual consumption for male

headed-households was US $2,188.03 compared with US $1,892.92 for female headed-

households. Migration is inevitable in such situation as people recognize the economic benefits

of migrating to cities. One scholar advocates what he calls the push-full theory of migration,41

opining that people are likely to migrate if they are pushed from their present location by the

attraction of the cities or other localities. This appears to explain low habitation in rural areas by

the wealthiest quintile.

       Income is not a social determinant of health in this study as found in the literature.

However, this study reveals the wealthiest quintile in Jamaica experiencing good health status

62% more than the poorest quintile; it is easy to infer an indirect influence of income on health

status. Marmot8 and Sen15 argued that income is associated with better and more opportunities,

more socio-political and material resources, better milieu and nutrition, low ignorance, and better

sanitation, all of which would account for the wealthiest quintile in Jamaica experiencing greater

good health status than the poorest quintile, even if income were not treated as a direct social

determinant of good health. Income also influences having less illnesses, and is associated with

higher infant mortality and premature birth among the poor than the wealthy.7,9,10,13,14 The

current findings highlight the fact that poor nutrition, sanitation, and milieu influence why




                                                                                                  297
children in the poorest quintile had influenza and diarrhoea at a younger age than those in the

wealthiest quintile.

       The governments of Jamaica over the decades have invested significantly in the health

care system and since 2005 have removed health care user fees for children under 18 years and

have provided assistance for those who have particular chronic diseases and the elderly in order

to obtain free medication.45 However, this clearly has not eliminated health disparities between

the poorest quintile and the wealthiest quintiles well as within each social hierarchy across

regions. When Jamaicans were asked Why did you not seek medical care? one-in-two

respondents indicated that he/she was unable to afford it. It does appear paradoxical that

although health care is heavily subsidized (free for children younger than 18 years as well as

medications for patients with particular chronic illness as well as the elderly), many respondents

reported being unable to utilize health care service owing to financial barriers. The answer lies in

three important issues (1) cost of transportation, (2) income, and (3) locality. With more than

50% of Jamaicans residing in rural areas with difficult terrain, health care is not readily

accessible in rural areas. This means that rural residents who are mostly seasonally employed,

and who have less disposable income and savings, are required to travel for miles in order to

access medical care from the formal health care system. Income plays a critical role in indirectly

influencing health and accounts for the health disparity between and among social hierarchies in

this study, which is in keeping with prior research.46,47

       Another, social determinant of good health of the poorest quintile and wealthiest quintiles

was gender. Males were 69% (OR = 1.69) more likely to record good health than females in both

the poorest quintile and the wealthiest quintile of the sample. Using self-reported illness to

measure health status, we find that in Jamaica since 1997 the sex ratio of illness (number of



                                                                                                298
males who reported an illness divided by the number of females who have illness) has been

1.3:1, 1.4:1, and 1.5:1, which indicates that self-reported health of males, has always been

greater than that of females. A part of the explanation for this disparity lies in socio-economic

inequalities between males and females in Caribbean societies. Caribbean males are the holders

of economic and social power, and more likely to be employed. The absence of health disparity

between the sexes in the wealthiest quintile may be due to heredity, shared access because of

marriage, and access to services owing to social standing. This explains why in the current

findings divorced, separated, or widowed wealthy respondents had lower (53%, OR = 0.474)

health status than never married wealthy respondents.

       Age is a social determinant which is empirically proven to be correlated with ill-health,

health or health status.28,35-40,   48-57
                                            The present study is in keeping with the literature

demonstrating that elderly people are more likely to report poor health.

       Only 55 out of every 100 of those in the poorest quintile regularly visited a health care

practitioner, compared with 74 out of every 100 in the wealthiest quintile. Although there was no

difference between the number of visits to health care practitioners made by the poorest quintile

and the wealthiest quintile, the latter spent three times more than the former on total medical

care. This exponential differential in medical care expenditure was due to the costs of private

health care. The poorest quintile, predominantly residents of rural areas, emerged as those

needing preventive services.

       The findings of this study provide an insight into the health disparity between the poorest

and the wealthiest quintile in Jamaica. Health policies in Jamaica must be adopted in order to

address the social determinants of health. They should aim at making the health system more

effective in reaching the poorest in the nation, especially those in rural areas as rural poverty



                                                                                              299
since 1990 continues to be at least twice than urban-poverty in Jamaica.16 Whether rural or

urban, poverty is associated with poorer nutritional intake, unsanitary conditions, poor water

quality, and socio-physical milieu as well as more illness and greater mortality. Thus, it should

not be surprising when the World Health Organization (WHO)7 states that 60% of global

mortality is explained by chronic illness, and that 80% of chronic illness occurs in low-to-middle

income nations.

        Although, poverty in Latin America and the Caribbean (in particular, Jamaica) has fallen

by more than one-half since the 1980s, yet health status between and among the wealthiest

quintile and poorest quintile still show health differentials. Studies that have examined the health

status of the wealthy and the lower classes clearly revealed that there are health inequalities, and

this is concurred by the current work. Does money matter in better health? The answer is a

resounding yes. The present research found that there is a strong inverse statistical association

between self-reported illness and good health status, suggesting that life expectancy would be

different between the studied populations. Hence, the health disparities which emerged from

these findings extend beyond life expectancy to health care utilization, infant mortality, and

health conditions.1-12 Poverty, therefore, robs a nation of its valuable human resources as well as

future human resources through greater infant mortality between the poorest and wealthiest

quintile.

        Using at least a decade and one-half data in Jamaica (1989-2007), Bourne and Beckford12

found a strong statistical correlation between poverty and illness. Bourne and Beckford’s study

goes to the crux of health disparities between the poorest and wealthiest quintile in Jamaica.

While the current work cannot generalize about other developing countries, or even those

countries in the Caribbean, it provides an insight into the effects of material and social



                                                                                                300
deprivation within a nation and among people of different socio-economic classes. Money

matters in health, illnesses, mortality, infant mortality, poor nutritional intake, and life

expectancy within and among nations.8 Thus, the gap that exists in life expectancy between

developed and developing nations10 is accounted for by the benefits of having access to more

material and other sources including money.

       The current research found that 79.8% of the variance in good health status of the

wealthiest quintile can be explained by a 1% change in illness compared to 68.2% of those in the

poorest quintile. Despite the expanded definition offered by the WHO in 1946 about health, the

present study shows that health status is still narrowed defined by many Jamaicans. This is not

limited to Jamaicans as in Hambleton, et al.’s work, using elderly Barbadians, they found that

67.5% of the variance in health status can be explained by illness.40 Thus, the definition of health

in both Jamaica and Barbados is similar, along with the dominance of illness in the

conceptualization of health. Apart from this difference, it can be extrapolated from the current

findings that, material deprivation among the poorest quintile has significant impact on their

good health and offers some understanding of the health disparities between the two social

hierarchies.

       In sum, investment in health has not eliminated the health disparities in Jamaica, between

and among social hierarchies. Relative poverty continues in Jamaica between the affluent

quintile and the poorest quintile and this income inequality accounts for health disparity between

the social classes. There is a huge health disparity between the two social hierarchies: The

wealthiest quintile records a good health status, 62% greater than those in the poorest quintile.

Those in the latter group had a good health status (1.5 times more than those in the former

group). Illness continues to dominate health status and this was more so for the wealthiest



                                                                                                301
quintile than for the poorest quintile. However, acute conditions occur at an earlier age among

the poorest quintile than among the wealthiest quintile. Furthermore, the poorest quintile

reported more illness than the wealthiest quintile.


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                                                                                         305
                                                    Poorest 20% (1) and Wealthiest 20% (5)
                                                1                                              5
                   100

                                        6,412

                               3,179                                                6,637
                   80           5,241
  Age (in years)




                   60

                         398
                                1,586                                                          4,630
                         898
                   40                                       2,921                              3,397   1,920



                                                                                               4,834   4,250

                   20
                                                3,549



                    0

                         COLD            DIABETES             OTHER         COLD            DIABETES     OTHER
                                                          Type of self-reported illness

Figure 11.1. Self-reported health conditions by social hierarchy controlled for by age of

respondents (95% CI). The illnesses are Influenza or cold; Diarrhoea; Respiratory diseases;

Diabetes; Hypertension; Arthritis; and Other.




                                                                                                                 306
Table 11.1. Demographic Characteristics of Sample, n = 2,725

Characteristic                                                   %

Sex

 Male                                                          49.9

 Female                                                        50.1

Area of residence

 Rural                                                         49.0

 Urban                                                         32.0

 Peri-urban                                                    19.0

Health insurance

 No                                                            75.3

 Yes                                                           24.7

Social hierarchy

 Poorest quintile                                              49.3

 Wealthiest quintile                                           50.7

Typology of illness

 Influenza                                                     13.7

 Diarrhoea                                                      2.2

 Respiratory                                                   12.4

 Diabetes mellitus                                             14.8

 Hypertension                                                  23.4

 Arthritis                                                      7.1

 Other                                                         26.4




                                                               307
Table 11.2. Age group by illness controlled by Social hierarchy

                                         Poorest quintile         Wealthiest quintile

Age group                              Self-reported illness      Self-reported illness

                                        Yes            No         Yes             No

Children                                27.5          42.7        19.5           19.2

Young adults                            11.9          26.2        13.2            29.1

Middle aged-adults                      26.4          22.6        33.2            42.7

Young old                               18.1           6.5        26.3            7.0

Old-old                                 13.5           1.4        6.3             1.3

Oldest-old                              2.6            0.5        1.5             0.8

Total                                   193           1119        205            1132




                                                                                          308
Table 11.3. Logistic regression: Explanatory variables of good health status of poorest quintile
and wealthiest quintile
                                                      Odds                    R2 = 0.368
 Explanatory variables        Coefficient     P       ratio       95% C.I.

        Young adults               0.409   0.013     1.506     1.092 - 2.076       0.004
        Young-old                 -1.385   0.000     0.250     0.177 - 0.355       0.046
        Old-old                   -1.381   0.000     0.251     0.137 - 0.462       0.016
        Oldest-old                -2.041   0.000     0.130     0.048 - 0.354       0.014
        †Children                                    1.000
        Male                       0.400 0.003       1.491     1.142 - 1.948       0.005

        Self-reported             -2.227 0.000       0.108     0.080 - 0.146       0.269
        illness
        Wealthiest                                                                 0.100
                                   0.480 0.000       1.615     1.236 - 2.111
        quintile
        Peri-urban                 0.430 0.017       1.538     1.078 - 2.193       0.004
        †Rural                                       1.000

Hosmer and Lemeshow goodness of fit χ2 (df=8) = 4.4, P = 0.82
-2LL = 1456.04
Nagelkerke R2 = 0.368
†Reference group




                                                                                            309
Table 11.4. Logistic regression: Explanatory variables of good health status of poorest quintile

                                                           Odds
 Explanatory variable          Coefficient        P        ratio       95.0% C.I. R2 = 0.384

        Young adults                 0.611    0.010       1.843      1.160 - 2.929        0.010
        Young-old                   -1.517    0.000       0.219      0.130 - 0.371        0.063
        Old-Old                     -1.285    0.002       0.277      0.122 - 0.627        0.018
        Oldest-old                  -3.065    0.005       0.047      0.005 - 0.400        0.021
        †Children                                         1.000
        Male                         0.523    0.011       1.687      1.128 - 2.522        0.010

        Self-reported                                                                     0.262
                                    -2.057    0.000       0.128      0.080 - 0.204
        illness



Hosmer and Lemeshow goodness of fit χ2 (df=5) = 1.0, P = 0.96
-2LL = 658.23
Nagelkerke R2 = 0.384
†Reference group




                                                                                               310
Table 11.5. Logistic regression: Explanatory variables of good health status of wealthiest quintile

                                                                      Odds
 Explanatory variable                    Coefficient           P      ratio                     R2 = 0.346
                                                                                95.0% C.I.
        Young-old                             -1.143       0.000      0.319   0.196 - 0.518           0.033
        Old-old                               -1.385       0.003      0.250   0.099 - 0.630           0.009
        Oldest-old                            -1.377       0.040      0.252   0.068 - 0.938           0.005
        †Children                                                     1.000

        Self-reported illness                 -2.416       0.000      0.089   0.060 - 0.134           0.276
        Divorced, separated or                                                                        0.016
                                              -0.747       0.011      0.474   0.266 - 0.844
        widowed
        †Never married                                                1.000
        Peri-urban                             0.544       0.017      1.722   1.103 - 2.688           0.007
        †Rural                                                        1.000


Hosmer and Lemeshow goodness of fit χ2 (df=3) = 1.05, P = 0.79
-2LL = 793.29
Nagelkerke R2 = 0.346
†Reference group




                                                                                               311
                                                                    Chapter 12
Is income a stronger determinant of self-rated health status than other
socioeconomic and psychological factors?


Income is directly and indirectly related to health, and some scholars have gone as far as to
conclude that it is able to buy health. This speaks to the crucible nature of income. But is this
issue the same in Jamaica as other nations. And, does money really matter in determining health
status and what is it influence? This study will provide answers to those questions as well as
provide an understanding of what matters for Jamaicans. This will aid public health practitioners
to garner an in-depth understanding of perspective of Jamaicans concerning their self-rated
health status, and in the process provide insights into what matters and the extent of those
conditions. This study utilizes secondary survey data – i.e. Jamaica Survey of Living
Conditions, (JSLC) - collected from 25,018 Jamaicans. The survey was a nationally
representative cross-sectional one in which data was collected using stratified random sample,
during June-October 2002. There were 49.3% of males (n=12,332) and 50.7% females
(n=12,675) with a mean age of 28.8 ± 22.0 years. Data was stored and retrieved in the SPSS
16.0; descriptive statistics were used to provide pertinent information on the sampled population
and stepwise multiple regressions were used to examine the influence of income and other
variables on self-rated health status (or reported health status). We found that of the total
explained variation (12.5%), age accounted for 10.2% of the total explained variable of reported
health status, with gender accounted for just some 0.8%, negative psychological conditions and
fertility accounted for 0.3%, while income and consumption accounted for 0.1% each of the total
explanation of reported health status. We have found that income like educational level and
marital status contribute the least to health of Jamaicans compared to age, gender and negative
psychological conditions. Of importance in this study is the fact that age accounts for the most
in health status, with males reporting a greater health compared to their female counterparts.
This study did not examine the duration of extent of negative psychological conditions (i.e. loss
of family member, breadwinners, property et cetera) nor crime and victimization on health status,
and within the context significant of negative state, we recommend future research on those
conditions, using a longitudinal panel study.



Introduction

Traditionally health has been conceptualized, operationalized, and treated by health-care

practitioners and the general populace as the absence of diseases or dysfunctions [1-6]. This

                                                                                             312
dates back to the time of Galen in Ancient Rome (130 to 200 CE). But even before that in

prehistoric time (i.e. 10,000 BCE), people believed that health/or ill-health occurred when the

spirits entered the human body, which emphasizes the absence of some ailment or external agent

before health can be attained by people, while the Babylonians and Assyrians (1800 to 700 BCE)

believe that diseases was an indicator of God curse on humans, which again speaks to the

absence of dysfunctions as the yardstick for health measurement.


       The aforementioned perspective of health and by extension its care was in operation up to

late 1800s, and then the World Health Organization in the preamble to its Constitution in 1946

[7] widened this definition to incorporate social, psychological, and physiological conditions to

proxy health. Some scholars labeled the approach to the study of health prior the WHO’s

definition as the biomedical model. The WHO’s conceptual framework of the health was new in

the early 1950’s and so many was aversive to its broadened perspective. However, George

Engel [1], [2], [3], [4], [5], a psychiatrist, coined what he termed the biopsychosocial model

which was in keeping to the WHO’s preamble of its Constitution.


       Engel’s biopsychosocial model incorporates biological, psychological and social

conditions in the study of health and its treatment of mental patient. He argued that the treatment

of patient with mental health conditions must begin with not with the outcome of the condition as

people are mind, body and social being. Hence, the display of the mental health condition is as a

result of all those elements. He had believed in this so much so that Engel incorporate this in the

curriculum of Rochester Medical School. One of the drawbacks to George Engel’s

biopsychosocial model which is the embodiment of the WHO’s definition of health is that

scholars argued that it was conceptual and as such too difficult to operationalized [8], [9] which



                                                                                               313
explained it initial low usage and acceptance by other scholars and practitioners, in the earlier

period.


          Although Bok [8] and Crisp [9] in the 21-century continue to argue that the WHO’s

conceptual definition is too broad, Michael Grossman in the 1970s [10], used econometric

analysis to model the same biopsychosocial model that had eluded scholars prior to that period.

Grossman showed that health is influenced by biological and social conditions. Post-1970s,

scholars like Smith and Kington [11]) have expanded on Michael Grossman’s model; and then in

2005 a group of researchers [12] conducting a study on behalf of PAHO examined health of

elderly Barbadians. Hambleton and colleagues’ study was on 1, 508 elderly Barbadians (ages 60

years and older) who were borned in 1939 or earlier. Their work found that current

socioeconomic factors accounted for 4.1% of the variability in reported health status, with

historical indicators accounted for 5.2%, lifestyle risk factors accounted for 7.1% and current

diseases explain 33.5% of the variance in health status. Income is a component of current

socioeconomic indictors explained the least variability in reported health status of elderly

Barbadians.


          Despite the contributions of many of the aforementioned scholars, there were

delimitations that were embodied in the various models. One of the delimitations of Ian

Hambleton et al.’s work was what proxy health status (i.e. self-rated of health) as well as those of

Michael Grossman’s model and the study of James Smith and Raynard Kington as they relate to

the operationalization of health status (i.e. functional limitation or self-rated dysfunctions).


          The works of the aforementioned scholars used health status (or conditions) to proxy

health, but it should be noted clearly here that the health according to the WHO is “a state of


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complete physical, mental and social well-being…” which indicates that the use of health status

to proxy health is one-dimensional. Brannon and Feist [6] forwarded that this was a negative

approach as it does not constitute what the terminology is, but what it is not. Simply put, the

‘absence of …’ denote a negative approach to the view of a phenomenon, and that broader

approach (i.e. positive) must be biopsychosocial. Hence, wellbeing is a referred terminology to

health as the former is broader and reflects the biopsychosocial approach to the study of health.


       For years, economists have being using gross nation (or domestic) product per capita (i.e.

income per capita) to measure quality of life (or wellbeing) of a populace; because there was a

belief that income is more reliable and less subjective than Self-rated measure to proxy a

variable. However, economists like Amartya Sen [13], [14] and Richard Easterlin [15], [16],

[17], [18] argued that any proxy of wellbeing (or quality of life, i.e. standard of life) must

incorporate objective as well as subjective indicators as human are multi-dimensional. This

argument has been long forward by psychological like [19], [20], [21] who have been using

happiness as a proxy of wellbeing. Such an approach has also been used by economists like

[15], [16], [17], [18], [22] to evaluate wellbeing.


       In 2007, a Caribbean scholar used a nationally representative stratified sample of 3,009

elderly Jamaicans (ages 60 years and older) and coined an operational definition of wellbeing

which includes health status and material resources, and used econometric analysis to model

determinants of wellbeing [23]. Bourne’s study revealed that 10 variables explained 40.1%

(adjusted R-squared) of the variability of wellbeing. He found that wellbeing of the sampled

population are influenced by psychological conditions (i.e. negative and positive affective

conditions); area of residence; crime and victimization; marital status; physical milieu;

educational level; Household crowding (i.e. average occupancy per room); cost of medical care;

                                                                                               315
ownership of property, and age of the respondent. Of the 10 determinants of wellbeing of the

elderly, the 5 most influential ones were (1) Household crowding (β= - 0.229); physical

environment (β= - 0.190); educational level (β= 0.173); area of residence (β= 0.164) and cost of

medical care (β = 0.148). (See also, [24]).


       Despite the alternative paradigm of health (i.e. the biopsychosocial model) which has

been use for some time now and an important issue forwarded by Brannon and Feist that

“Throughout the 20th century, adherence of the biomedical model allowed medicine to conquer

or control many of the diseases that once ravaged humanity. When chronic illnesses began to

replace infectious diseases as leading causes of death, question began to arise about the adequacy

of the biomedical model” (Brannon and Feist, 2007, p. 10)6, the predominant paradigm of health

and health-care in Jamaica by the general public and health practitioners is still the old

biomedical model. This mode has been abandon in America since the 20th century. Given that

the general public in Jamaica continues to use the biomedical model, researchers and public

health practitioners must understand what people mean by health and what its determinants are.

Currently no study exists that examine the role of income on health status to which this study

will bridge this gap. General prices on the world market have been increasing for some time

now, and within the present context of income increases not being more than single digit while

the Bank of Jamaica Governor (Mr. Derick Latibeaudiere) recent announcement that inflation is

expected to rise by some 16%, we must understand the role of income on health status. The

reason is simple as nation has made significant improvement in health as a part of this is due to

increases in income; and so income must be examined as real income has been falling and will

continue to decrease if world prices continue to increase.




                                                                                              316
       If we were to operationalize health using Self-rated health status, how important is

income as a determinant of Self-rated health status? And what are the most important of all the

determinants? Although the use of income (GDP per capita) is a relatively old indicator of

wellbeing its relevance is still applicable in contemporary societies [25]. The study used data

from 20 European countries, with a representative sample of 36, 424 people. Among the findings

of these scholars is that wealth is directly associated with subjective well-being. It should be

noted that income still affords one to purchase the best preventative and curative care. In

addition, the ownership of durable goods can be use as a form of investment in older years with

which the aged is able to use to acquire earnings [26].


       A survey conducted by Diener, Sandvik, Seidlitz and Diener (1993)27, in Diener [19],

stated that correlation between income and subjective wellbeing was small in most countries.

According to Diener (1984, 11)19, “…, there is a mixed pattern of evidence regarding the effects

of income on SWB [subjective wellbeing]”. Benzeval, Judge and Shouls [28] study concur with

Diener that income is associated with health status. Benzeval et al went further as their research

revealed that a strong negative correlation exists between increasing income and poor health.

Furthermore, from that study, it was found that people from the bottom 25 percent of the income

distribution self-rated poorer subjective health by 2.4 times than people in the fifth quintile [28].

In another study, of a nationally representative sample of 1,338 Jamaicans, Powell, Bourne and

Waller [29] found that those who classified themselves as being in the lower class had the least

mean score when asked ‘if they feel secure about the state of their health statuses with those in

middle having the highest mean score. What does this say about the role of income on health

status as well as other variables?




                                                                                                 317
318
Theoretical Framework


       The overarching theoretical framework that will be adopted in this study is an

econometric model that was developed by Grossman [10], and modified by Smith and Kington

[11], which reads:


       Ht = ƒ (Ht-1, Go, Bt, MCt, ED) ……………………..………………………………… (1)


In which the Ht – current health in time period t, stock of health (Ht-1) in previous period, Bt   –


smoking and excessive drinking, and good personal health behaviours (including exercise – Go),

MCt,- use of medical care, education of each family member (ED), and all sources of household

income (including current income)- (see Smith and Kington 1997, 159-160). Grossman’s model

further expanded upon by Smith and Kington to include socioeconomic variables (see Equation

2).


       Ht = H* (Ht-1, Pmc, Po, ED, Et, Rt, At, Go) …. ……………………………………..… (2)


       Eq. (2) expresses current health status Ht as a function of stock of health (Ht-1), price of

medical care Pmc, the price of other inputs Po, education of each family member (ED), all sources

of household income (Et), family background or genetic endowments (Go), retirement related

income (Rt ), asset income (At,).


       In the current study, we will examine Eqn (3)


       Ht = (Pmc, ED, Y, Rt, At, Gi , Ai , MR, AR, lnCR, PA, F, EN, lnC)………………… (3)


       Eq. (2) expresses current health status Ht as a function of price of medical care Pmc,

education of individual (ED), individual income (i.e. proxy by total expenditure on goods and

services) (Y), gender of the individual (Gi), retirement related income (Rt ),ownership of assets at

                                                                                                319
the current period (At,), age of the individual, Ai , marital status, MR; area of residence, AR;

logged Household crowding (proxy by average occupancy per room), lnCR; psychological

conditions, PA; fertility (proxy by the number of children 14 years and older), F; and the

physical environment, EN; logged of consumption, lnC. The price of medical care because in

excess of 90% of the cases were missing, this variable was deleted from the initial model. Ergo,

the revised model is Eqn. [4.0]:


        Ht = (ED, Y, Rt, At, Gi , Ai , MR, AR, lnCR, PA, F, EN, lnC )…………………..… (4.0)


        Ht = (ED, Y, Rt, At, Gi , Ai , MR, AR, lnCR, PA, F, lnC)…...………………....… (4.1)


Using econometric analysis and based on the principle of parsimony, only those variables that

are statistically significant (i.e. P< 0.05) will be left in the final model. Hence, factors of self-

rated health status, using the principle of parsimony, the final model is Eqn. (4.2) – (see Table

12.2)


        Ht = (Y, Rt, At, Gi , Ai , MR, AR, PA, F, lnC )……………………….…….…..… (4.2)


Method and Measure


        This study utilizes secondary survey data – Jamaica Survey of Living Conditions (JSLC)

- collected from 25,018 Jamaicans (i.e. 6, 976 households).           The survey is a nationally

representative cross-sectional one in which data was collected using way of an administered

questionnaire during June-October 2002 having done a stratified random sample of the

population. Data was stored and retrieved in the SPSS 16.0; descriptive statistics were used to

provide pertinent information on the sampled population and stepwise multiple regressions were

used to examine the influence of income and other variables on Self-rated health status (or


                                                                                                 320
reported health status). Where collinearity existed (r > 0.7), variables were entered independently

into the model to determine those that should be retained during the final model construction. To

derive accurate tests of statistical significance, we used SUDDAN statistical software (Research

Triangle Institute, Research Triangle Park, NC), and this adjusted for the survey’s complex

sampling design.

       Health conditions: It is the reported of having an ailment, injury or illness in the last four

weeks, which was the survey period.

       Health status:    This variable is operationalized using reverse of health conditions,

suggesting that more health conditions denote lower health status and vice versa.

       Household crowding: This is the average number of persons living in a room.

       Physical Environment: This is the number of responses from people who indicated

suffering landsides; property damage due to rains, flooding; soil erosion;

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

having loss a breadwinner and/or family member, loss of property, made redundancy, failure to

meet household and other obligations.

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

hopeful, optimistic about the future and life generally.



       Results: Demographic characteristics of the sampled population


       Of the sampled population of 25, 018 people, 61.0% (n=15,260) lived in Rural Areas,

25.6% (n=6,401) dwelled in Other Towns and 13.4% (n=3,357) resided in Kingston

Metropolitan Area. There were 49.3% (n=12,332) males and 50.7% (n=12,675) females, with

the mean age of the sample being 28 years and 10 months ± 21 years and 11 months. Of the


                                                                                                 321
population sampled (n=25,018), 64.3% responded to the question of union status (n=16,076), of

which two-thirds indicated that they were never married (n=10,813, 67%), one-quarter reported

that they were married (n=4,050), approximately 6% mentioned widowed (n=905), separated

1.2% (n=185) with less than 1% (n=123) being divorced (see Table 12.1).


       Of the population sampled (n=25,018), 97.4% indicated a response for self-rated health

status (n=24,369). Of those who responded to this question, approximately 84% reported that

they have no health condition over the last 4-weeks, with about 10% indicated 2 health

conditions and 5% said 1 health condition (i.e. ailment, dysfunction or diseases) (see Table 12.1),

suggesting that a small per cent of the Jamaican population reported being affected by an

injury/illness or dysfunction, (16%). Moreover, when a cross tabulation was done between self-

rated health conditions and gender of respondents, we found that 4.5% more female reported

being affected by at least one ailment/injuries compared to their male counterparts, (14.2%) –

χ2(1)=88.97, P=0.001.


       Furthermore, deconstructing the sampled population’s psychological state revealed some

interesting findings as Jamaicans had a moderate positive psychological state (mean = 3.5 (out of

6) ± 2.3); with there being a low negative affective condition (mean = 4.7 (out of 17) ± 3.4).


Results: Multivariate Analysis (i.e. hypothesis testing of Eqn [4.1]):


                                        Insert Table 12.2 here


       Further examination of Eqn. (4.2) revealed some interesting results as 12.5% of the

variability in reported health status   can be explained by the variables identified in Model 4 (i.e.

Eqn. (4.2) – (see Table 12.2).      Using stepwise approach in multiple regressions, each of the

factors identified in Eqn. (4.2) was disaggregated so as to examine each contribution to the total

                                                                                                 322
explained variation (see Table 12.3). We found that of the total explained variation (12.5%), age

accounted for 10.2% of the total explained variable of reported health status, with gender

accounted for just some 0.8%, negative psychological conditions and fertility accounted for

0.3%, while income, consumption and area of residence accounted for 0.1% each of the total

explanation of reported health status.    Ergo, income plays a minimal role on reported health

status (see Table 12.3). We went further to refine the association between income and self-rated

health by using the per capita population quintile of the population, and found that approximately

20% of those in the rich quintile reported suffering from at least one health condition compared

to 15.% of those in the poorest quintile, with 16% of those in the 4th quintile. (see Table 12.4)


                                       Insert Table 12.3 here


                                       Insert Table 12.4 here


         Further examination of area of residence revealed that Jamaicans who dwelled in Other

Towns have a greater reported health status with reference to rural dwellers (B = 0.066, P=

0.001). However is this no statistical difference between the reported health status of rural

residence with referent to dwellers in Kingston Metropolitan Areas (i.e. P= 0.053). (See Table

12.2).


         There was a similarity between area of residence and marital status; as there was no

difference of reported health status of married Jamaicans with referent to other marital status of

Jamaicans (P> 0.05). On the other hand, there is a statistical difference between the self-rated

health status of Jamaicans who are divorced, separated and widowed with referent to other

Jamaicans. We found that Jamaicans who indicated being divorced, separated or widowed had a

lower health status with referent to other marital status (B = - 0.153, P= 0.001 < 0.05).

                                                                                                    323
       Among the surprising findings of this study are that (1) physical environment, (2)

Household crowding (proxy by logged average occupancy per room), (3) retirement income (i.e.

NIS) and (4) education were not factors of self-rated health status of Jamaicans. Embedded in

this finding is the fact that education does not directly influence health status, but it does so

through purchases power (i.e. consumption or income) as there is weak direct statistical

association between education (i.e. this was tertiary level education with referent to below

secondary level education) and income ( bivariate correlation = 0.229, P0.001) and the relation

was even weaker when we examine secondary level education with referent to primary and

below educational levels (bivariate correlation = -0.046, ρ-value=0.001). Continuing, household

crowding in a dwell does not affect health status directly; but it indirectly affects it through

fertility. This study has shown that fertility positively influences health status, and that one of

the by-products of household crowding is expressed with the number of children that are in

household. It should be noted here that household crowding is directly associated with fertility,

and while Household crowding directly does not influence health status it affects it through

spending power. We must explain here that operational definition of fertility for this study was

the number of children that the household head has who are older than 14 years, and the

dependency of this factor on the household would be less and equally justify the positive

relationship between that factor and health status as the children will be source of assistance.

Furthermore, the non-statistical association between retirement income (i.e. NIS) and reported

health status may appear contradictory, but there is an explanation for this result. It should be

noted that in Jamaica, within the context of the high prices and lower social programme,

retirement income in particular National Insurance Scheme (i.e. NIS) is exorbitantly small and so

its impact on health.



                                                                                               324
       The self-rated health status of Jamaican males is higher than that of their female

counterparts (see Table 12.2), because they report less health conditions. We refined this by way

of a cross tabulation, we found that 18.7% of female reported at least one health condition

comparing to 14.2% of males, suggesting that health status of latter group is higher than the

former group (see Table 12.5). This goes the crux of gender bias utilization of health services in

the island. Not only do female report more health conditions than males, and this fosters the

explanation why they also seek more care than their male counterparts; because of their

recognition that there some ill-health is presenting itself.


       Delimitations of Study


       This study is taken from cross-sectional secondary survey data for 2002, and so this is a

limitation as people’s perspective may have changed between then and now, 2008. One of the

rationale for the utilization of this data set is because it represents the first time in the history of

data gathering that the two premier data gathering Institutes would have collected information

from 25, 018 Jamaicans. In addition to the aforementioned issues, secondly, the dataset was also

used because of the plethora of variables that were collected in that single survey was also a first.

Despite those delimitations, study provides us with invaluable insights into perspectives of

Jamaicans on reported health status as well as relative importance of some sociodemographic,

economic and psychological factors on health status. And it applicable to use the results to

generalized about the population because the sample was random stratified probability sampling.


       Discussion and Summary


       In this study we will not rehash the discourse of usefulness of subjective measure in the

valuation of health as this is well established by many scholars [13], [14], [15], [16], [17], [18],


                                                                                                   325
[19], [20]. However, we will note here that the when people report on their health, they draw on

a plethora of experience in order to establish current, past and future experiences as well as all

their expectations. Hence, embedded in self-rated health status are a series of information not

only about perspectives of people but also about the role of particular factors in influencing

reported health status, and how patient care should be managed from here onwards.


       Many people believe that income is a crucible factor explaining health conditions.

Moreover, poor people are not able to afford particular physical environment and this justify

their lower quality of life than those in the middle class or the bourgeoisie. And that poorer

health conditions are directly associated with low quality environment [30]. Pacione [30] noted

that “people in developed countries have come to realize that quality of life is not necessarily a

simple function of material wealth”, which has been concurred by other scholars. Studies have

shown that income accounts for a small portion of the explanation of health status [12], [19],

[20], [28] this was not different in this study. Smith and Kington [11] went on further to say that

“good health is an outcome that people desire, and higher income enables them to purchase more

of it” (p. 160), but this does not mean that it is automatic with higher income as this study has

concluded that income plays a role in reported health status. However, its contribution to health

status is far less than findings of other studies [33], [36]. Powell, Bourne and Waller’s work [29]

highlights this fact, they found that the middle class Jamaicans had the greatest self-rated health

status followed by upper class and least was recorded for by the poor.


       This study has answered the question on the influence of income on reported health

conditions, not in the indirect way taken by Powell, Bourne and Waller. In addition to income’s

influence on health status, what about demographic disparities in health status? The findings (see

Table 12.2) revealed that males indicated a higher reported health status compared to their

                                                                                               326
female counterparts, but this should be examined for an understanding. As the aforementioned

issue does not mean that males have a higher health status than females as culturally males do

not willing report health conditions (i.e. diseases, dysfunctions or ailments) because this is

construed as weak, which is embedded in the cosmology of the culture. We are not postulating

that there is no gender disparity in health status of Jamaicans, but the argument is that men

culturally report less health conditions [31] because of socialization and the interpretations that it

yield if they were to do. There is a paradox in gender and health status, as the women want their

men to be healthy, but at the same time they do not want them visiting health practitioners often

as they is construe to the wider community that their men are weak, ‘girlish’. This goes to the

reason for the gender biasness in the utilization of health services as documented by the Ministry

of Health in Jamaica [37]. On the other hand, a study conducted by Elizabeth Ward and her

colleagues [38] of some 6,107 Jamaicans who suffered violent injuries revealed that majority of

the cases were men, 62.2%. Thus a part of the disparity that arises here is the measure health

status – self-rated health conditions- neither can we use utilization of health services as neither

the former nor the latter proxy the health status of the population, suggesting that a new approach

is required that will be a closer proxy for the Jamaican reality.


       Is there an age disparity in health status of Jamaicans? This answer is expected as

biological composition of human account for what is inevitable as while an organism ages, its

health conditions automatically increase. The Planning Institute of Jamaica [31] wrote that “The

elderly accounts for approximately 60 per cent of admissions for chronic diseases” (p. 23.13).

Ergo, it is not surprising health status of this study is positive related to health conditions as

people age, they biological degenerates with ageing [32]. Ageing does not imply biological

degeneration; it also means the ownership of more material possession than those who are their


                                                                                                  327
younger counterparts. We found that there is a positive relation between asset ownership and

health condition, but that this is less impacting than income.


       Fertility that is by-product of ageing contributes more to reported health status than

income or asset ownership. The direct association between fertility and self-rated health status

may appear ironic; however, we will provide some explain for this statistical association.

Children in developing countries such as Jamaica, China and others are seen as economic

investments for families transcending out of poverty to economic advancement. This perspective

is not only a construction, but it now has health benefits as is provided by this study. Children

are investment in many ways as they provide financial assistance to parents, but they are also

asset in making themselves available to take their parent to doctors for patient care, which

enhances their (parents) health status.


       It is established by scholars like Edward Diener (1984, 2000)19, 20, Bourne (2007a)23, 24

and others that psychological conditions affect the subjective wellbeing of individuals. In this

study, we found that disaggregating the psychological conditions of an individual show that

negative affective conditions is of most influence than even income.             Positive affective

conditions, on the other hand, influence on health status is minimal compared to that of negative

affective conditions. In this study we cannot deny the importance of demographic factors such

as age, gender, fertility, and marital status. Consumption is negatively related to health status,

this means that people who spend more are less healthy, which was also concurred by the finding

that a direct association exists between per capita population quintile and self-rated health status

– 20% of those in the richest quintile reported have had a least one health condition compared to

15% of those in the poorest quintile (see Table 12.4).



                                                                                                328
       We can say that reported health status seems to be more driven by age, gender, negative

affective conditions, and fertility compared to income and consumption. Income’s contribution

to reported health status was very low (i.e. 0.1% of the explained variance, which is 12.5%); and

notwithstanding the argument that income can buy health, its influence is secondary to age and

other sociodemographic indicators. Area of residence’s contribution to health status is equally

low, and Jamaicans who dwelled in Kingston Metropolitan Area does not enjoy a greater

reported health status with referent to their rural area counterparts. On the other hand, being

negative (i.e. negative affective psychological conditions) influences reported health status more

than income and third to age, and gender. Moreover, being positive (i.e. positive affective

psychological conditions) plays a minimal role to changes in self-rated health status.


       Michael Marmot [33] argued that education is a better indicator than income for health

status; but this was not the case in this study. We found that formal education does not influence

reported health status, which contradicts the literature [10], [12], [23], [24]; nevertheless, it has

an indirect effect through earnings, ownership of assets, positive psychological conditions,

retirement income and fertility. In addition, gender role differentiation was evident as illustrated

in this study as finding show that males have a greater reported health status compared to their

female counterparts. Health status is primarily driven by sociopsychological and demographic

conditions, with income playing a secondary role to other aforementioned factors. This study did

not examine the duration of extent of negative psychological conditions (i.e. loss of family

member, breadwinners, property et cetera) nor crime and victimization on health status, and

within the context significant of negative state, we recommend future research on those

conditions, using a longitudinal panel study.




                                                                                                 329
       Public health specialists must be guided by research and not unsubstantiated cosmologies

as a basic for their epistemological thinking, suggesting that health education and promotion

must rely on research findings. In the general schema of Jamaicans, self-rated health status is

more a phenomenon of age, gender, negative affective psychological conditions, and fertility

than income. Income does matter in self-rated health status; and other studies have narrowed this

proposition that poor people have a lower health status than their rich counterparts [33], but what

emerge from this study is the secondary role income plays in determining health status. Our

study has contravenes that poor people have the lowest health status as the people in the richest

quintile reported the most health conditions (see Table 12.4). This should come as no surprise as

lifestyle practices of those in the richest income quintile explain this disparity in lf-rated health,

which is not mitigated by the poor physical environment of those who are poor or their financial

disadvantage state. Moreover, this study has disproved the perspective that there is a strong

statistical association self-rated health status and income[11], [28], [34], [35], [36] and has

equally concurred with other studies that that have established that there is a weak association

between the two aforementioned variables [12], [27]. It is within these findings that we

recommend that health practitioners need to refocus on those factors that are critical

determinants of health of the particular population as well as the cultural undertones within the

society when seeking to institute, locate, design, implement and frame health promotion, health

education and health care policies and use general cosmologies, which are not geopolitically

specific. The use of utilization of health services [38] to design health policies or health

education programme will be misleading in societies that exhibit similar ‘machoistic’

culturalization like Jamaica as only research will contextual people’s perspectives and not




                                                                                                  330
hunches. Muir Gray [39] opined that one of the challenges in the provision of health care is the

‘delay in implementation of research finding’, suggesting the value of research in pubic heath.


       Nations that share similar socioeconomic and political characteristics like Jamaica, their

health policies and health education must not be devoted to income and income base conditions

because those factors are not the most crucible determinants of self-rated health status. The

rationale for this is simple as the objective of health service, health education, and health

promotion is to improve the health status of a population and this is feasible if in the planning

process those factors that determine health status of that population are included in the planning

model. As without the full knowledge of all the causes and effect and the perception of the

population about those determinants of their health status, health planning and public health

measures would have missed the mark in health service delivery that are likely to reduce ill-

health and produce healthy life expectancy in a population.


       In sum, in this study we do not claim to provide all the answers; but it can provide public

health practitioners with more insights into the perspectives of people in Jamaica and how they

view their health and the role that particular factors in determining their health status. This we

anticipate will foster not only a better understanding of self-rated health status, but how health

care programmes must be tailored to address the concerns of people.



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Table 12.1. Demographic characteristic of sample


Of the sampled population of 25, 018 people, 61.0% (n=15,260) lived in Rural Areas, 25.6%
(n=6,401) dwelled in Other Towns and 13.4% (n=3,357) resided in Kingston Metropolitan Area.
There were 49.3% (n=12,332) males and 50.7% (n=12,675) females, with the mean age of the
sample being 28 years and 10 months ± 21 years and 11 months. Of the population sampled
(n=25,018), 64.3% responded to the question of union status (n=16,076), of which two-thirds
indicated that they were never married (n=10,813, 67%), one-quarter reported that they were
married (n=4,050), approximately 6% mentioned widowed (n=905), separated 1.2% (n=185)
with less than 1% (n=123) being divorced




                                                                                       335
Table 12.2: Self-rated Health Status and Some Explanatory Variables
                                                         Unstandardized        Standardized
                                                          Coefficients         Coefficients      Sig.              CI ((%%)
  Variables
                                                         B        Std. Error      Beta                     Lower Bound    Upper Bound
 (Constant)                                                .406         .160                        .011           .092           .720
 Age                                                      -.012         .001             -.269      .000          -.013          -.011
 Income                                                    .000         .000             -.041      .001           .000           .000
 Retirement Income – National Insurance Scheme,
                                                          -.041         .043             -.008      .340          -.124           .043
 NIS
 Retirement Income -Pension                               -.111         .026             -.037      .000          -.161          -.061
 marstatus1                                               -.153         .028             -.053      .000          -.208          -.098
 marstatus2                                               -.019         .017             -.011      .255          -.051           .014
 Dwellers - Other Towns                                    .066         .016              .037      .000           .035           .097
 Dwellers – Kingston Metropolitan Area
                                                           .039         .020             .017       .056          -.001           .078
 Referent group (Rural Area)

 Physical Environment                                      .027        .014               .016      .053           .000           .054
 Secondary Education                                      -.002        .020              -.001      .923          -.040           .036
 Tertiary Education
                                                          .036         .034              .011       .283          -.030           .102
 Referent group (Primary and below education)


 lnHousehold crowding                                      .014        .014               .013      .302          -.013           .041
 Dummy Gender (1= male)                                    .145        .013               .092      .000           .119           .170
 Psychological Condition 1 - Negative Affective           -.015        .002              -.064      .000          -.019          -.011
 Psychological Condition 2 - Positive Affective            .011        .003               .034      .000           .005           .017
 Fertility                                                 .019        .005               .042      .000           .009           .030
 Asset Ownership of durable goods                          .018        .003               .072      .000           .013           .024
 lnConsumption                                            -.040        .014              -.037      .005          -.069          -.012
N= 25,018
F statistic = 105.43, P= 0.001
R = 0.356
R-squared = 0.127
Adjusted R-squared = 0.125




                                                                                                                                         336
Table 12.3: The joint influence of prediction groups on Self-rated health status among 8, 809 Jamaicans
participants, using variations explained (in %) by each model in the study sociodemographic, psychological and
income predictors of Self-rated health status
                           Adjusted R
     R         R Square     Square


      0.319        0.102         0.102 Age
                                       Age + Dummy Gender
      0.331        0.110         0.110
                                         Age + Dummy Gender + Negative Affective
      0.337        0.114         0.113
                                         Age + Dummy Gender + Negative Affective + Fertility
      0.341        0.116         0.116
      0.344        0.119         0.118 Age o+ Dummy Gender + Negative Affective+ Fertility + Marstatus1
                                       Age + Dummy Gender + Negative Affective + Fertility + marstatus1 +
      0.346        0.120         0.120 Asset Ownership of durable goods

                                       Age + Dummy gender + Negative Affective + Fertility + Marstatus1 +
      0.349        0.122         0.121 Asset Ownership of durable goods
                                       + Income
                                         Age + Dummy Gender + Negative Affective + Fertility + Marstatus1+
      0.351        0.123         0.123
                                         Asset Ownership of durable goods + Income + Retirement Income
                                       Age + Dummy Gender + Negative Affective + Fertility + Marstatus1 +
                                       Asset Ownership of durable goods+ Income + Retirement Income+
      0.353        0.124         0.124
                                       Area_Residene2

                                       Age + Dummy Gender +Negative Affective + Fertility + Marstatus1+
                                       Asset Ownership of durable goods + Income + Retirement Income +
      0.354        0.125         0.124
                                       Area_Residene2 + Positive Affective

                                       Age + Dummy Gender+ Negative Affective + Fertility + Marstatus1 +
                                       Asset Ownership of durable goods + Income + Retirement Income +
      0.355        0.126         0.125
                                       Area_Residene2 + Positive Affective + lnConsumption




                                                                                                                 337
Table 12.4: Bivariate relationship between Self-rated Health Status and Per capita Population Quintile, N=24,369

                                                               Per Capita Population Quintile
                                             1=Very                                   4=Middle
 Details                                      Poor        2=Poor    3=Low Middle        Middle       5=Affluent
                                              Class        Class         class          Class          Class
                         Good and
                         beyond
                                                4132          4136            4104           4069          3920
                                               84.8%         84.9%           84.3%          83.5%         80.2%
 Self-rated Health
 Status
                         Poor                    739           736             762            806           965
                                               15.2%         15.1%           15.7%          16.5%         19.8%


 Total                                           4871         4872             4866          4875           4885


χ2(5)=53.24, P=0.001, contingency coefficient = 0.047




                                                                                                              338
                                                                      Chapter 13

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

Health insurance is established as an indicator of health care-seeking behaviour. Despite this
reality, no study existed in Jamaica that examines those factors that determine private health
insurance coverage. This study bridges the gap in the literature as it seeks to determine correlates
of private health insurance coverage. The aim of this study is to understand those who possess
Health insurance coverage in Jamaica so as to aid public health policy formulation. This study
used two secondary cross-sectional data from the Jamaica Survey of Living Conditions (JSLC).
The JSLC was commissioned by the PIOJ and the Statistical Institute of Jamaica (STATIN) in
1988. The surveys were taken from a national cross-sectional survey of 25 018 respondents (for
2002) and 6,782 people (for 2007) from the 14 parishes across Jamaica. The JSLC is a self-
administered questionnaire where respondents are asked to recall detailed information on
particular activities. The questionnaire was modelled from the World Bank’s Living Standards
Measurement Study (LSMS) household survey. There are some modifications to the LSMS, as
JSLC is more focused on policy impacts. The surveys used stratified random probability
sampling technique to draw the original sample of respondents. Descriptive statistics were used
to provide background information on the sample, and logistic regression was to determine
predictors of private health insurance coverage. Health insurance coverage can be predicted by
socio-demographic factors (such as area of residence; education, marital status, social support,
social class, gender, age), and economic (consumption and income). The findings revealed some
similarities and dissimilarities between data for 2002 and 2007. Area of residence, consumption,
educational level, marital status, income and social support were determinants over the two
periods. Asset ownership was a factor in 2002 but not in 2007. For 2007, age, gender and social
class were factors and not for 2002. A dissimilarity in this study was with social support. It was
found that in 2002, social support was negatively correlated with Health insurance coverage and
this shifts to a positive correlate in 2007. In 2002, age and gender were not associated with
Health insurance coverage but these became significant predictors in 2007. Interestingly, poor
health status is not correlated with private health insurance coverage. More health insurance
coverage is owned by urban than by other town or rural residents. Health insurance coverage is
more structured for employed people who are in the private or public sectors more within urban
and other towns than rural areas indicating that rural residents, who are faced high poverty and
self-employment, will be more likely in continuing their choice in home remedy or non-
traditional medicine in order to address their ill-health. Health which is strongly correlated with
income means that poor individuals, families, societies, nations, will be less healthy and will
need assistance in the form of health insurance to be able to reduce mortality.




                                                                                                339
Introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

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

of the comprehensive phenomenon.


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

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

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

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

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

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

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


                                                                                                  340
particular lifestyle practices account for pre-mature mortality for many of them. Material

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

social exclusion, perceived lack of control, limited access to good-quality health care,

constrained choices and physical inactivity account for higher levels of dysfunctions. According

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

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

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

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

publication.


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

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

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

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

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

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

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

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

and the recurring nature of some ailments can deplete people’s income and wealth to the point of

poverty. It is this reality that accounts for health insurance coverage. Health insurance coverage

is a by-product for people because it is demanded for lower treatment costing when illnesses

occur. Therefore, health insurance coverage not only lowers treatment cost of illnesses but also

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




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

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

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

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

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

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

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

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

probability of illness.


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

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

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

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

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

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

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

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

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

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

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

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

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




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

that drives health insurance coverage; but something else.


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

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

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

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

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

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

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


Methods


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

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

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

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

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

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

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

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

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

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

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

& STATIN, 2008).


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

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

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

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

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

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

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

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


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

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

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

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

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

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

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


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

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

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

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

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

be low (weak) - from 0 to 0.39; moderate – 0.4-0.69, and strong – 0.7-1.0 (see also, Cohen &

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



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addition, variables were excluded from the model if they had in excess of 20% of the cases

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


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

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

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

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

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

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

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

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

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

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

only those predictors.


Models


The current study will employ multivariate analyses in the study of health and medical care

seeking behaviour of Jamaicans. The use of this approach is better than bivariate analyses as

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

HIt=f(Ht, Ai, Gi, HHi, ARi, lnC, ∑Di, EDi, MRi, Si, HTi, lnY, CRi, MCt, SSi, Ti , CIi, Pi, Eni, HSB,
εi )                                                                                           (1)

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

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

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

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

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

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

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

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

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

      The final models that were derived from the general Equation (1) that can be used to

predict Health insurance coverage of Jamaicans are Equation (2) and Equation (3):

      HIt(Jamaicans, 2002) =f(ARi, lnC, EDi, MRi, lnY, SSi, ∑Di, HSB, εi)                    (2)

      HIt(Jamaicans, 2007) =f(ARi, lnC, EDi, MRi, lnY, SSi, Ai, Gi, Si, HSB, εi)              (3)


Measures


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

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

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

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

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

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

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

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

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

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

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

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

about the future and life generally. Negative affective psychological condition is number of
                                                                                                    346
responses from a person on having lost a breadwinner and/or family member, having lost

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

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

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

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

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

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

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

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

years.


Results

Demographic characteristic and bivariate analyses

In 2002 the sample was 25,018 respondents: 12,332 males (49.3%) and 12,675 females (50.7%).
In 2007 the sample was 6,782 respondents with there being marginally more females (51.3%)
than males (48.7%) (Table 13.1). The findings in Table 13.1 revealed that urbanization was taken
place in 2002, there were 13.4% of respondents living in urban zones and this shifted to 29.5% in
2007. The percentage of Jamaicans dwelling in rural areas declined from 61% in 2002 to 49.0%
in 2007. In 2002, 12.5% of respondents indicated that they had an illness in the 4-week survey
period and this increased by 2.4% in 2007. Sixty-four percent of respondents reported having
visited a health care facility (including a healer), and this increased to 66% in 2007. The social
class categorization of Jamaicans remained relatively the same over the studied period; and the
percentage of respondents who had health insurance coverage increased from 11.0% in 2002 to
20.2% in 2007. The mean number of visits made to health care institutions (including healers)
declined from 1.7 days (SD=1.4 days) to 1.4 days (SD=1.1 days). On the other hand, crowding
increased by 135% in 2007 over 2002; and medical care expenditure also increased by 29.1%
over the period (Table 13.1).


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       Based on Table 13.2, the mean annual income of respondents in 2002 was Ja $331,488.32

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

$128,742.65). On disaggregating income by area of residence, it was revealed that there was

significant statistical difference between income of respondents and their area of residents. On

average, urban respondents received 1.6 times more income than rural residents in 2007 and this

was similar in 2002 (approximately 1.5 times more). The disparity in income between urban and

other town respondents was lower (in 2007 – 1.1 times more and this was the same in 2002) than

that between urban and rural dwellers.

       A cross-tabulation between health status and self-reported illness revealed a significant
statistical correlations - χ2(df = 2) = 1,289.23, p < 0.001 (Table 13.3). Table 13.3 revealed that
an individual who reported poor health status was 9.3 times more likely to have an illness than
those stating a dysfunction. On the other hand, an individual who reported good health status was
2.0 more likely not to report an illness than those reporting at least one ailment. Based on Table
13.3, more males (85.4%) reported good health status than females (79.2%) - (χ2(df = 2) =
44.666, p < 0.001) - and the converse was true for poor health status, with 5.5% of females
compared to 4.2% of males.
       Based on Table 13.4, there was a change in pattern of 5-leading recurring illnesses in

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

followed by cold (19.9%); unspecified ailments (18.1%); diabetes mellitus (11.6%) and asthma

(9.6%). However in 2007, the leading prevalence of self-reported ailments shifted to unspecified

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

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

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

= 3,063) and in 2007 (2 χ2(df = 1) = 40.916, p < 0.001, n= 999) (Table 13.4). Table 13.4 showed

that diabetes mellitus and hypertension were significant more among for females than males and



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that arthritis, unspecified illnesses, asthma diarrhoea and cold were more prevalent among males

than females.

         Table 13.5 showed that there was a significant statistical correlation between medical

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

1) = 3.004, p < 0.048). In 2002 data revealed that more females sought medical care (66%) than

males (60.7%); and this was the case in 2007: 67.6% for females and 62.3% for males (Table

13.5).

         In 2007, there was a significant statistical correlation between health care-seeking

behaviour of Jamaicans and health insurance coverage (χ2(df = 1) = 16.712, p < 0.001). The

association was a very weak one (r = 0.128). However, the findings revealed that 76.2% (n =

189) of people with private health insurance visited a health care practitioner compared to 62.0%

(n = 468) those who do not have health insurance coverage.

Multivariate analyses

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

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

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

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

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

0.54, 95% CI = 0.36 - 0.80) (Table 13.7).

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

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

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

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



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13.7). The model (Table 13.7) can explain 44.7% of the variability in Health insurance coverage

of Jamaicans (for 2002).

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

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

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

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

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

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

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

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

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

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

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

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

coverage.

Discussion

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

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

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

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

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

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

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



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

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

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

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

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

health insurance because this socio-economic support is present.

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

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

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

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

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

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

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

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

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

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

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

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

2006.

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

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

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

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



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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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



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

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

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

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

Gaymu 2002, p. 905).

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

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

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

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

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

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

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

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

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

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

9.3 days) (PIOJ & STATIN, 2008).

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

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

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

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

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

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

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



                                                                                              353
2007, the disparity in self-reported hypertension fell to 1.7 times and increased to 2 times for

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

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

for every 100 females in 2007.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

other goods, which accounts for the positive correlation between income and private health

insurance coverage. This is also in keeping with Brown et al.’s study (2008) which had income


                                                                                                354
as a predictor of health care-seeking behaviour. The current research went further than Brown et

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

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

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

coverage than the poor.

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

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

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

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

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

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

ownership of private health insurance, narrowed in 2007.

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

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

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

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

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

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

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

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

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

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




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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

association between chronic conditions and functional limitation – which include difficulty

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



                                                                                                356
reiterating the need to seek medical care owing to ageing, and justifying the positive correlation

between age and health insurance coverage in this study.

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

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

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

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

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

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

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

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

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

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

Conclusion

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

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

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

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

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

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

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

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

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

traditional health care services.



                                                                                                  357
       In Jamaica, the employment typology in area of residents is different and contributes to

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

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

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

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

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

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

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

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

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

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

and rural poor.


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                                                                                       361
Table 13.1. Demographic characteristic of samples: 2002 and 2007


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




                                                                                                        362
Table 13.2. Income, Crowding, Age, by Area of residence: 2002 and 2007
Characteristic                 Year      Category       Mean          SD            p-value
Income Ja$                     2002†     Urban          $440,451.50 $521,519.38     < 0.001
                                         Other towns $385,625.70 $276,644.12
                                         Rural          $284,810.20 $231,540.04
                                         Total          $331,488.32 $304,040.77
                               2007†† Urban             $865,674.20 $673,512.10     < 0.001
                                         Other towns $771,300.50 $597,582.65
                                         Rural          $551,633.70 $389,765.68
                                         Total          $691,560.45 $128,742.65
Crowding                       2002      Urban          2.0 persons 1.4 persons     > 0.05
                                         Other towns 2.0 persons 1.4 persons
                                         Rural          2.0 persons 1.4 persons
                                         Total          2 persons     1.4 persons
                               2007      Urban          4.3 persons 2.4 persons     < 0.001
                                         Other towns 4.6 persons 2.3 persons
                                         Rural          5.0 persons 2.5 persons
                                         Total          4.7 persons 2.5 persons
Age                            2002                     28.2 yrs      22.0 yrs
                               2007                     29.9 yrs      21.8 yrs

No of visits to health care    2002                    1.7 days      1.4 days
facilities
                               2007                    1.4 days      1.1 days
Medical expenditure            2002†                   $1,144.14     $2,946.02
                               2007††                  $1,477.07     $4,711.15
†Ja $40.97 = US $1.00
††Ja $80.47 = US $1.00




                                                                                        363
Table 13.3. Health status by self-reported illness, and gender: 2007

Characteristic                        Category             Health status (%)          Total
                                                 Good       Fair            Poor
Self-reported dysfunction1            0             89.1          8.7           2.2    5569
                                      ≥1            42.8         36.8          20.4     976
                                      Total        5381          845            319    6545

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




                                                                                          364
Table 13.4. Self-reported diagnosed recurring illness by gender and years (2002, 2007)

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

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

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

      Total    610    83         294       356       661            209       553    297   306
                                                                                           3

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

      Femal    13.4 2.7          8.0       15.4      24.8           5.4       22.1   8.2   597
      e

      Total    149    27         95        123       206            56        234    109   999




                                                                                           365
Table 13.5. Medical Care-Seeking Behaviour by Gender, 2002, 2007

                                                   2002                   2007
Medical Care-Seeking Behaviour

                                            Male          Female   Male          Female
Yes1                                        60.7           66.0    62.3           67.6

No2                                         39.3           34.0    37.7           32.4

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




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Table 13.6. Health insurance coverage by Area of Residence, 2007
                                          Area of Residence

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

  Private Coverage
                                    19.2         15.1              7.1    12.4

  Public Coverage
                                    8.7           7.0              7.4     7.7

Total                               1939         1401          3177       6517
χ2(df = 4) = 184.347, p < 0.001, n = 6,517




                                                                                  367
Table 13.7. Logistic Regression: Predictors of Private Health Coverage in Jamaica
Characteristic                                            2002                       2007
                                                   OR              95% CI     OR            95% CI
Age                                                1.00           0.98-1.02   1.01     1.01-1.02***
Log consumption                                    1.90          1.12-3.23*   1.00        1.00-1.00*
Log income                                         1.71          1.02-2.87*   1.00     1.00-1.00***
Log medical expenditure                            0.99           0.81-1.21   1.00         1.00-1.00
Household head                                     4.61          0.21-99.16   1.03         0.86-1.23
Medical care seeking behaviour                     0.88           0.42-1.83   1.65        1.07-2.41*
Sex
Male                                               0.88           0.60-1.30   0.81          0.69-0.95*
Marital status
Separated, divorced or widowed                     1.38         0.49-3.88     1.19         0.87-1.64
Married                                            3.91     2.47-6.20***      1.84     1.52-2.22***
†Never married                                     1.00                       1.00
Area of residence
Urban                                              2.24      1.23-4.09**      1.30          1.08-1.57*
Other towns                                        1.19        0.75-1.89      1.11           0.90-1.36
†Rural                                             1.00                       1.00
Education
Secondary                                          2.97      1.46-6.00**      1.45          1.09-1.92*
Tertiary                                           18.8    8.11-43.43***
†Primary or below                                  1.00                       1.00
House tenure: owned                                1.76           0.16-19.4
Social class
Middle                                             0.88           0.32-2.41   0.96           0.63-1.46
Upper                                              1.88          0.68-5.24*   1.61          1.04-2.49*
†Lower                                             1.00                       1.00
Social support                                     0.54      0.36-0.80**      1.33          1.04-1.70*
Health status
Good health                                        0.93           0.56-1.53   1.05           0.84-1.31
Durable goods index (excluding land)               1.09          1.01-1.17*
Physical environment                               0.78           0.48-1.27
Crime index                                        1.01           0.99-1.03
Asset ownership (ie land or property)              0.79           0.51-1.22
Psychological condition
Negative affective conditions                      0.96           0.91-1.02
Log crowding                                       1.33           0.88-2.02   1.07           0.98-1.16
Social welfare                                                                0.79           0.52-1.20
Time spent in health care facilities
Public                                             0.96           0.79-1.20   1.00           1.00-1.00
Private                                            1.43           0.02-85.3   1.00           1.00-1.00
Illness                                            4.01          0.44-36.43   1.14           0.90-1.43
Injury                                             0.68           0.36-1.75   1.12           0.57-2.20
N                                                                    25,007                      6,565
Chi2                                                                  451.3                      590.1
Nagelkerke R2                                                          0.45                       0.49
LR                                                                    776.4                    4,126.8
*P< 0.05, **P< 0.01, ***P< 0.001




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                                                                      Chapter 14

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 indexes such as life expectancy,
infant mortality and general morality. This study (1) examined subjective indexes such as self-
reported illness and self-reported to measure health, (2) retested the theories that chronic
illnesses are more likely to greater 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 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. The
cross-sectional survey was conducted between May and August 2002 from the 14 parishes across
Jamaica and included 25,018 people of all ages. The statistical packages 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 illness
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 the 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 has continued to this day. The information provided in this
research has far reaching implications, and be used to guide policies, framed interventions and
focus 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 showed that those in the lower socioeconomic

status are less healthier than those who in the wealthy socioeconomic groups [9,10]. A study by

van Agt et al. [8] found that poverty was greater among the chronically ill that non-chronically ill


                                                                                                369
people, 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 greater mortality. According to 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; (3)

and other socio-political incapacity [3, 8, 11]. Sen [11] encapsulated this well when he opined

that high levels of unemployment in the economy is associated with higher levels of capabilities,

suggesting money and other incapacities of those who are likely to be unemployed in the society.

The poor is therefore more likely to be unemployed, ill, suffering from more chronic illness,

have insufficient money, low level of educational attainment, have greater percent of infant and

other mortality and live inadequate physical environment than those in the wealthy social

hierarchies.


       Using objective indexes such as infant mortality and life expectancy to measure health of

a population, studies on the Latin America and the Caribbean concurs 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 become poor and that a greater percent of these people do not have health insurance

coverage [2]. One study highlighted 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 is illness is longstanding, and the Director of the Pan American

Health Organization in 2001 wrote that it is still evident in contemporary societies [14]. He


                                                                                                  370
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 level of illness (i.e.

psychopathology).


       Clearly is well established for centuries that poverty is associated with illness, and that it

affects those therein by the constricting their capacity which further affects their health. The poor

have lower access to money and other resources than the wealthy, and are also deprived in the

future from good health outcome. A study by Mayer et al. [15] provided evident that there is a

strong relationship between health and future economic growth, suggesting that current poverty

contract future health and economic prosperity. Mayer et al.’s work provide pertinent insight into

the retardation of poverty, but also gives an understanding of how poverty affects health,

production, productivity and it being 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 like other nations. If poverty is associated with unemployment, not seeking medical

care, and not seeking medical care is related with illness, it appears to be a non-issue to retest the

established theory of poverty and illness and poverty and chronic illnesses in Jamaica.




                                                                                                  371
       All of the aforementioned studies that have examined poverty and illness have not used

self-reported data to test the poverty and illness, and poverty and chronic illness phenomena. The

aims of the current study to investigate (1) poverty and illness, (2) poverty and chronic illness,

and (3) other sociodemographic characteristics in order to provide understanding of existing

disparities as well as concur 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 policy guidelines for Jamaica. The cross-sectional

survey was conducted between May and August 2002 from 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 for 26.2%. The

sample was weighted to reflect the population.


Study instrument


The JSLC used 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. The questionnaire covers

demographic variables, health, education, daily expenses, non-food consumption expenditure,

and other variables. Interviewers are trained to collect the data from household members.




                                                                                              372
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 statistical significance of a metric and non-dichotomous variable.

Logistic regression analyses examined the 1) 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 packages 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. To retain or exclude a variable

from the model, this was based on the variables’ contribution to the predictive power of the

model and its goodness of fit. 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.


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,

                                                                                                373
injuries or illnesses [29-31]. 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 [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 in a Likert scale matter ranging from excellent to poor. Medical care-seeking

behaviour was taken from the question ‘Has a health care practitioner, header, or pharmacist

being visited in the last 4 weeks?’ with there being two options Yes or No. Medical care-seeking

behaviour therefore was coded as a binary measure where 1=Yes and 0= otherwise.


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 denote 0, 1 and 2, in

which 0 indicates not witnessing or experiencing a crime, 1 means witnessing 1 to 2, and 2

symbolizes seeing 3 or more crimes. Tj denotes the degree of the different typologies of crime

witnessed or experienced by an individual (where j = 1…4, which 1 = valuables stolen, 2 =

attacked with or without a weapon, 3 = threatened with a gun, and 4 = sexually assaulted or




                                                                                               374
raped. The summation of the frequency of crime by the degree of the incident ranges from 0 and

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-weeks, 63.9%; primary or below level education, 20.9; secondary level education,

73.1%, and mean age of 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 14.1 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 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: Mean age of rural residents was 29.1 years (± 22.6 years); semi-

urban’s residents mean age was 27.9 years (± 21.0) and the mean age of urban dwellers was 29.1

years (± 21.0 years). Concurringly, the mean number of visits to health care practitioner in the


                                                                                              375
last 4-weeks was 1.7 (± 1.4). The was a significant statistical difference between the mean

number of visits to health care practitioner and area of residence: Mean number of visits by rural

residents was 1.6 (± 1.2) compared to 1.6 (± 1.2) for semi-urban dwellers and 2.0 (± 2.5) for

urban dwellers.     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 crime and

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 14.2 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 residences,

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 14.3 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 mean length of illness among the social hierarchy – F

statistic = 2.536, P = 0.038. This difference was accounted for by the poorest 20% and the

wealthy (P = 0.049) and the poorest 20% and the wealthiest 20% (P = 0.049). Likewise the

statistical difference between the mean number of visits made to medical practitioner(s) and



                                                                                                  376
social hierarchy were accounted for by poorest 20% and wealthy (P = 0.011) and 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, arthritis; and other chronic illness (unspecified) was 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 illness 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 compared to 78.9% who do not have chronic illness that 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 those 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.




                                                                                              377
Multivariate analyses

Table 14.4 shows information on particular variables and their correlation (or not) with self-

reported illness. Of the 17 variables identified from the literature and available in for this study, 5

emerged as being statistical 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

statistical significant correlates accounted for 14.8% of the variability in self-reported illness.



Table 14.5 examines social hierarchy and sex and their influence (or not) on self-reported

chronic illness. One sex emerged as being statistical significant correlates of self-reported

chronic illness in Jamaica - Model χ2 (3) =6.42, P < 0.001.

Discussion

The current study revealed that 13 in every 100 Jamaican 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 that 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 illness 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 the male diabetics. Poverty

was substantially a rural phenomenon (75%), and almost 14% of rural residents indicated an

illness compared to semi-urban (11%) and urban dwellers (11%). The disparity did not cease



                                                                                                      378
there as rural residents had the least percent of people with tertiary level education, had 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 indexes such as life expectancy, infant mortality and

general mortality, it is well established that poverty is associated with illness and those with

more chronic illness are more likely to be poor. The current study, using self-reported illnesses,

has concurred with the literature that poor reported more illness and are more likely to have more

chronic illness than those in the upper class. This study, however, found than 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 the 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, and divorced people.

       Lillard and Panis [39] contradicted many of the traditional findings that married people

are healthier and reported 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 that enter marriage at an early age, which suggest that these

men do so because of health reasons [39]. This then would support the current research of

married people indicating more chronic illness than non-married people. Concurringly, married



                                                                                                 379
people do not report more illness, but more chronic illness 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 (ie. 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 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 poor inability to (1) access higher level

education; (2) access greater and the best health care treatment; (3) poor physical milieu; (4)

higher levels of infant mortality; (5) poor material conditions; (6) clean water and nutrition; and

(7) social position deprivation. It follows that poverty incapitates the individual and this extends

into the future if he/she is not assisted by external sources. Does money really makes 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 utilise public health facilities (including hospitals) compared to 28 out of

every 100 of those in the wealthiest 20%.


       Poverty therefore retards people’s health care choices, expenditure on medication, and by

extension healthy life expectancy. The current study found that 35 out of every 100 respondents

in the poorest 20% indicated that the reason why they have not visited a health care practitioner


                                                                                                380
was owing to insufficient funds compared to 9 out of every 100 of those in the wealthiest 20%.

Furthermore findings from the present research found that people who spent 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 seek medical care compared to the wealthy. This

insufficient financial resource is hampering 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] 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 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 illnesses can lead to poverty and future constraint on capabilities and limiting opportunities

to create a better life for themselves.


        If those in poorest 20% group experienced illness and visited medical practitioner more

than those in the upper class, it follows that poverty is explaining (1) most of prevalence of

illness, (2) severity of illness, and (3) more chronic illness, then money therefore does matter in

health, and offers an explanation of how chronic illness can result in poverty and how pauperism

leads to increased morbidity and premature mortality. An understanding of poverty in Jamaica as

well as a comprehensively knowledge of relationship between poverty and illness as well as the

other health inequalities, will aid physicians in understanding the reasons for the

disproportionately more poor visiting them and having particular chronic illnesses. Health is also


                                                                                                381
a social phenomenon, and so physicians need training in 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 of

money provides some insights into the plight of those therein. It is critical to understand this

group and where they live like Kiefer said that poverty was “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 which is nutritional deficiency, as some people belief that so as long as

they have some to eat or a ‘full tummy’ is enough to delay 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

nutritional value in food intake. Household in low-income groups are substantially 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 an underlying fact that reported illnesses among those in the lower

socioeconomic group may be understated figures as their image of ill-health is hampered by

nutritional deficiency. Diet and nutrition are important ingredient in good health [45], but do

residents of low-income rural area as well as low-income urban areas know that deficient intake


                                                                                                382
of calcium, iron, magnesium, zinc, folate, vitamin A, vitamin B6, vitamin C is responsible for

some of their illness. 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 indexes such as life expectancy, infant

mortality and 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 indexes of the past. While those people in the poor social

hierarchies were more likely to report more illness and self-reported chronic illness than those in

the wealthy group, there is no difference between those in the poor group and the middle class.

       Those with chronic illness 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

will have hypertension, and the least probable to utilise health care facilities than the upper class.

In sum, subjective index such as self-reported illness or self-reported diagnosed health conditions

can be used to measure health as the traditional infant mortality, general mortality and life

expectancy. Poverty indeed is still continuing to influence ill-health, and those with chronic

illness are more probable to be poor than in the upper class, but that other demographic

characteristics provide more information on the poor and those with chronic illnesses.

       In sum, much investment has been made on health and these clearly have not reduced the

inequalities and disparities between and among the difference social groups in Jamaica. It means

                                                                                                    383
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 be used to guide policies,

framed interventions and focus future research in Jamaica.

The way forward

Subjective indexes 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 need that will address issues in

rural areas, gender, income inequalities, and the health disparities between public and private

health care services offerings to the all. Another area which must be addressed is the nutritional

deficiencies between and among the social hierarchies and area of residences. A national dietary

survey is needed in order to provide evidence for policy intervention as well as the role of

identified social problems and their influence on mental health. Concurringly, future research is

needed to examine the harmful effects of mental health on the accumulation of negative life

events on people 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 perceptive of the

individuals (i.e. a survey research). This would provide pertinent information as to whether those

people who are poor perceived themselves to receiving the worst health, and to devise a method

that will objective assess service deliver to the social group in order to address this if it is


                                                                                               384
contributing to the lower health outcomes. Researchers need to treat poverty as a illness and not

a cause of illness, which would allow for a new shift in the study of poverty and this thereby

could provide more answers to health practitioners and policy makers.

Conflict of interest

The author has no conflict of interest to report.

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Table 14.1: 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)




                                                                                                                388
Table 14.2. 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

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Table 14.3. 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




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Table 14.4. 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 (17) =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




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Table 14.5. 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 (3) =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




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                                                                      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

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

and physiological wellbeing [3].


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       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.


       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

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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.


       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);


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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

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

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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).


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-

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

associations between a metric and non-metric variable. The level