Essay: DETERMINANTS OF OUT-OF-POCKET HEALTH EXPENDITURE IN KENYA.

CHAPTER ONE
1.0. INTRODUCTION
This section serves to discuss the background to the study, state and define the problem that the proposal is attempting to address, purposes of the study, state the aims and objective of the research work, justification.
BACK GROUND TO THE STUDY
Health sector is one of the key sectors in any economy; this is so because for any healthy nation there is productivity which will have a ripple effect in terms of economic growth. In most developing countries, Kenya being one health forms a background for economic development and economic growth. However, health financing in these countries is devastating both at the government level and the household level which leads to inefficiency and ineffectiveness of the health sector. According to ( WHO 2010) the objectives of health financing is to make funding available, as well as to set the right financial incentives to providers, to ensure that all individuals have access to effective public health and personal health care. Kenya’s healthcare financing has developed over the years into a mixed healthcare financing and comprises of the following main components: general tax financing, NHIF, employer self-funded schemes, community based health financing, out of pocket health spending, development partners and non-governmental organizations (NGOs) and private health insurance.
Kenya as a developing nation, majority of its citizens still do not have access to affordable healthcare and end up paying for health services at the point of consumption. The topic of household health expenditure globally have been a subject of discussion quite for some time now, this is so mainly because of the rising health expenditure and thus the need to know the proportion of household contribution especially in Africa. The most basic form of financing healthcare in sub Saharan Africa ( SSA) is out-of —pocket and basically this where a fee is charged to cover all or part of the cost of the service provided and it majorly takes two forms: User fees for public sectors health services and Direct payment to private sector providers.
In 2001 African leaders met in Abuja and declared that AIDS is a state of emergency in the continent. During this meeting the member state, in which Kenya is a party adopted what is known as Abuja declaration and made commitment to increase health allocations to 15 percent of total government allocations. Although over the last decade the total health spending has increased, the budget allocation to the health sector has been less than the agreed pledge
According to Kenya national health’s account 2012/2013 total health expenditure was KSh 234 billion (US$2,743 million) , up from KSh 163 billion(US$2,155 million) in 2009/10. In 2012/13, the total spending on health accounted for 6.8percent of GDP, up from 5.4 percent in 2009/10.The government expenditure on health as a percentage of total government expenditure increased from 4.6 percent in 2009/10 to 6.1percent in 2012/13. Of the total health expenditure in 2012/13, current health expenditure (CHE) accounted for 93 percent of THE, compared with 96 percent in 2009/10.Capital expenditures increased from 4 percent of THE in 2009/10 to 7 percent in 2012/13. THE per capita increased from KSh 4,232 (US$56) in 2009/10 to KSh 5,680 (US$67)in 2012/13. THE per capita and the proportion of GDP spent on health have steadily increased since 2001/2002 estimates.
In addition revenues to finance healthcare in Kenya come from three major sources: the government, households, and donors (i.e., the rest of the world). The private sector is the major financier of healthcare in Kenya, contributing 40 percent of THE in 2012/13, up from 37 percent in 2009/10. The public contribution to THE was 34 percent in 2012/13, an increase of 17 percent over the 2009/10 estimates. Donors contributed approximately 26 percent of THE in 2012/13, down from nearly 35 percent in 2009/10.
1.2. PROBLEM STATEMENT
An affordable and efficient medical sector is of crucial importance towards achievement of overall economic development and improved living standards of consumers. Government’s budget allocation to the health sector has been declining as a result of Structural Adjustment Programs (SAPs) and has therefore failed to meet international benchmarks including the Abuja target of allocating 15% of government’s budget to the health sector. According to WHO (2011), only Rwanda and South Africa have attained the Abuja 15% target while the African Union Commission (2001) reports that six AU member states have met the 15% benchmark are; Rwanda(18.8%), Botswana (17.8%), Niger (17.8%), Malawi (17.1%), Zambia (16.4%), Burkina Faso (15.8%). In this regard out-of-pocket is an important source of revenue for financing the health sector in Kenya. Healthcare services are essential for every individual in the society hence an efficient, comprehensive and sustainable financing framework is necessary for quality healthcare services leading to increased welfare of individuals. . Majority of Kenyans pay for health services when need arises and sometimes at levels that can impoverish the family unit i,e one can sell family assets to meet health care cost. Kenya, like any other developing nation, is still at the early implementation phase of health insurance scheme. The cost of care has remained an all vital issue in Kenya. Out of pocket Health Expenditure is still very high and this is more so with the private for profit clinics and hospitals consuming the large chunks of OOP funds. Poverty is still very prevalent with 43.7% below poverty line and life expectancy at birth being 56 years in the phase of HIV /AIDS pandemic. According to World Health Organization report, households in developing nation spend half of their capacity to pay on health monthly. Therefore the main purpose of this study is to examine the determinants of OOP Health expenditure in Kenya.
RESEARCH OBJECTIVE.
To investigate the determinants of out-of-pocket health care expenditure in Kenya
JUSTIFICATION
The role of OOP towards financing of the health sector cannot be ignored and therefore an assessment of health care financing based on OOP and its determinants is of key importance in health system performance. Since Out-of-pocket healthcare expenditure is the case in most households, could this be a burden to the majority of the population who has lower income in Kenya? What are the other determinants of OOP Health expenditure in Kenya? The understanding of these questions will be a policy guideline, Hence, the need to study determinants of out-of-pocket healthcare expenditure in Kenya.
CHAPTER TWO:
LITERATURE REVIEW
The Kenyan health sector relies heavily on OOP expenditure (Health accounts 2005/2006). The sector is largely underfunded and health care contributions are regressive, that is, the poor contribute a larger portion of their income to healthcare than the rich (WHO 2010). Health expenditure risk is of considerable importance as it has a significant impact on a household’s or an individual’s consumption, saving and asset allocation. Researchers such as Kiplagat I. (2006) have attempted to establish the causal relationship between health insurance membership and health status. Other extensive studies have aimed at determining the impact of health insurance on health care utilization, cost of illness on households, social health insurance schemes; opportunities and sustainability potential, and market assessment of private prepaid schemes among other studies. (WHO 2010, Mclntypue et al 2006, Nguli M et al, and Deloitte Consulting limited 2001).
Sandra Hopkins (2010) using Cross-sectional health expenditure data was concerned with comparison of health expenditure aggregates and the contribution of the public and private sectors in a selection of 31 low, middle and high income countries. Hopkins noted that Low and middle income countries heavily relied on private funding especially household out-of-pocket payments while Public funding is more rife for funding of curative care than for funding of pharmaceuticals in all three categories of countries.
Kiplagat, et al (2013) in their paper determinants of choice of health insurance schemes in Kenya showed that wealth index, employment status, education level and household size are important determinants of health insurance ownership and choice, and that lack of awareness prevents many from enrolling in any form of health insurance scheme. They explored those determinants using a Multinomial logit model on the 2008-2009 Kenya, Demographic Health Survey (KDHS). According to D. Kimani, D Muthaka (2009) contributions of private health insurance are likely to be progressive as a financing mechanism for healthcare utilization, but often cream skim and fail to cover people with chronic conditions or the premiums are unaffordable thus people opt for OOP payments.
Ilesanmi Olowolabi (2014) used Raw data from 2004/05 Household income and expenditure surveys conducted by Kenya Bureau of Statistics to analyze determinants of household health expenditure which included age, education, gender, settlement, household size and total income. Further results were achieved by analyzing average household healthcare expenditure by determinants. Regression analysis was done using 95% confidence interval level. This study showed that Education and Settlement are the main significant determinants of Household healthcare expenditure using multiple regression analysis with P-value being significant at 5% error level. No other explanatory variable is significant at this level, not even at 0.1000.The elderly age group was found to have the highest expenditure at 141KShs compared with other age groups while the youngest age group has the second highest expenditure at 129.5 Kshs. However, age as a determinant is not found to be significant determinant using the regression analysis.
Mhere (2013) examined the determinants of health insurance participation in Gweru
Urban in Zimbabweans. Using a probit model he showed that the household head’s level of education, household income, age, family size, and chronic illnesses, are all Significant predictors of participation in health insurance schemes.
KeXu, et al (2006), used a log-linear model to explore the determinants of OOP spending given utilization of health services. The unit of analysis was the individual. Apart from area of residence whereby urban- rural difference was found to be an insignificant predictor of OOP spending, age, chronic health conditions and insurance membership were found to be key determinants of OOP spending. Empirical findings were that young children pay less OOP and those aged older than 65 pay more OOP, also people with chronic health conditions paid higher OOP. Although income was not controlled for in the regression models used, researchers expected those with higher incomes to pay higher OOP. Insurance membership was seen to increase OOP considerably, with the insured individuals paying more for health care services than the uninsured. A study done by WHO (2000) showed that health insurance is emerging as the most preferred form of health mechanism in developing countries like Kenya where OOP expenditures on health are significantly high and cost recovery strategies affect access and utilization of healthcare.
According to karimo and apere using Heckman selection two-step model, they Examined the determinant of out-of-pocket healthcare expenditure in the south-south geopolitical zone of Nigeria using the 2010 national harmonized living standard survey data, they concluded that state of residence, age of household head, family size, per capita consumption expenditure and adult equivalent weight together determines whether a person who falls sick will spend out-of-pocket for healthcare. Also, if a person falls sick and seeks healthcare service(s) age, age squared, household size, household size squared and per capita consumption expenditure proxy for per capita income. These are the factors that determine how much he/she spends out-of-pocket for healthcare, while aged people (those beyond 50 years of age) spend more out-of-pocket for healthcare households with more than 7 members have little to spare and so spend less out-of-pocket on healthcare. This reveals that healthcare is a normal good. The study therefore, recommends a comprehensive health insurance scheme irrespective of the state of residence for households in the zone.
Brinda, et al (2014), investigated the determinants of out-of-pocket health expenditure among adults population in the United Republic of Tanzania. They also investigated the prevalence and associated determinants of household catastrophic healthcare expenditure. Employing multiple generalized linear and logistic regression models they showed the major determinants of out-of-pocket healthcare expenditure to be age, gender, obesity, functional disability and visits to traditional healers. Further, large household size, household head’s occupation as a manual laborer, household member with chronic illness, domestic violence against women and traditional healer’s visits were associated with high catastrophic health expenditure in the United Republic of Tanzania.
Abolhallaje et al (2012) examined the determinants of unpredictable healthcare expenditure in
Iran. They analyzed the shares of households ‘expenditures on main groups of goods and services in urban and rural areas and in groups of deciles using data from households’ expenditure surveys. They growth of spending in nominal values within the year 2002-2008 was considerably high and the rate for out of pocket payments is nearly the same or greater than the rate for total health expenditure. Uzochukwu, and Uju, (2012) Using intensity and
incidence methods showed that 24% of Nigerian households incur catastrophic health expenditure and this was more prevalent among the richest income quintiles in Nigeria and as such has succeeded in changing the poverty situation of most households who were originally on or above the poverty line. The study recommended the need for social health insurance expansion in order to accommodate informal sector to achieve universal access to health services and financial protection of the poor and vulnerable.
RESEARCH METHODOLOGY
The study will utilize secondary data drawn from the 2007 Household Health Expenditure and Utilization Survey. The survey data covered Kenyan’s eight provinces and is a national representative sample survey consisting of 8,844 households where 6,072 were from rural and 2,772 were from urban . Data collection was done using the interview method where questions pertaining to out-of-pocket spending and a series of personal characteristics including age, gender, marital status, area of residence, highest level of education attained, occupation status and health related behaviors of the respondents were administered.
Theoretical framework
This study is based on human capital theory which states that individual invest in themselves through education, training and health to increase their earnings. This theory was advanced by Michael Grossman and he concluded that when people get old, their stock of health depreciates at a faster rate, so investment must occur at a faster rate; i.e., the old spend more on health care than the young. Additionally, higher incomes increase the consumption value of health thus health spending rises with wage and income. It is hypothesized that more educated people are more efficient at producing health. Spending on health care, then, will fall with education. This is further supported by theory of welfare economics which holds that individuals are the best judges of how to allocate available resources to maximize their welfare or benefit. However, the theory also raises the question about what is reasonable for individuals to sacrifice. In the case of health care, it is widely accepted that individuals should be protected from catastrophic health care costs and not be expected to endure impoverishment to cope with health costs.
Research by Ke Xu, et al (2006) in Kenya shows that there is a positive relationship between age and OOP spending whereby children under 5 years pay less OOP due to government policies for young children. Those aged older than 65 years pay higher OOP as a result of poor health. Individuals who have chronic health conditions pay high OOP due to their demand for health services. Females were also found to pay less OOP and this is attributed to government policies on reproductive health seen to be more effective in improving financial protection. In terms of geographical location, those living outside Nairobi generally had lower or equivalent OOP spending. However, people from North Eastern paid high OOP as a result of non- uniform user charges and poor access to financial protection against health care costs. The type of health provider is also seen to affect OOP spending in that visiting a private health facility results in higher OOP payments in contrast to visiting a public health provider where one pays less OOP. There are mixed findings on the relationship between OOP spending and health insurance status. Some researchers find a negative relationship between health insurance membership and OOP payments and conclude that health insurance membership does not currently offer significant financial protection and reform is needed. (Wagstaff et al 2007; Ke Xu, et al 2006). However, other researchers like Morrisey (2001) have argued that the key effect of having a health insurance cover is to reduce OOP spending.
Conceptual model
The fundamental aim of this paper is to establish the determinants of OOP expenditure. Factors expected to have an effect on consumers’ OOP spending include age, gender, occupation status, and insurance status, type of hospital visited and health status of the individuals. Therefore OOP spending given utilization of health services can be expressed as;
OOP = f (age, sex, geographical location, marital status, health insurance status, chronic health condition, type of health provider, health status, employment status)
Independent variables Dependent variable
The independent variables are assumed to jointly affect an individual’s OOP
MODEL SPECIFICATION
In this study, more than one predictor variable (age, gender, occupation status, and insurance status, type of hospital visited and health status) will be considered and thus a multiple linear regression model will be adopted. A multiple linear regression model (MLRM) to examine the socio-economic factors associated with OOP expenditure among health care consumers will be estimated.
The estimated model can be expressed as:
Y= β0 +β1X1 +β2X2+β3X3+β4X4+β5X5+β6X6+β7X7+β8X8+β9X9+ε
Where; Y – is out-of —pocket expenditure used as the dependent variable and the determinants as the independent or explanatory variable as shown in the regression equation above.
The βs’ are a vector of parameters associated with the independent variables whereas ε is the error term.
Variable Definition characteristics
X1-Insurance status It seeks to know if an individual is covered with a health insurance. Yes
No
Not stated
X2-Age How old an individual was at his/her last birthday 0-4
5-14
15-49
50-64
65+
X3-Sex Gender of the individual Male
Female
X4-Employment status An individual’s employment status Working
Seeking work
Homemakers
Student
X5-Chonic health condition Presence of a chronic illness Present
Not present
X6-Geographic location Area of residence Urban
Rural
X7-Health status How an individual rates their health status compared with others of his/her age Very good
Good
Satisfactory
Poor
Not stated
X8-Marital status An individual current marital status Never married
Married
Divorced
Widowed
X9-Type of health provider Type of health provider visited, public or private? Government hospital
Private hospital
BY
QALICHA MALICHA ROBA
REG NO. X53/78787/2015
UNIVERSITY OF NAIROBI
SCHOOL OF ECONOMICS
DETERMINANTS OF OUT-OF-POCKET HEALTH EXPENDITURE IN KENYA.
CHAPTER ONE
1.0. INTRODUCTION
This section serves to discuss the background to the study, state and define the problem that the proposal is attempting to address, purposes of the study, state the aims and objective of the research work, justification.
BACK GROUND TO THE STUDY
Health sector is one of the key sectors in any economy; this is so because for any healthy nation there is productivity which will have a ripple effect in terms of economic growth. In most developing countries, Kenya being one health forms a background for economic development and economic growth. However, health financing in these countries is devastating both at the government level and the household level which leads to inefficiency and ineffectiveness of the health sector. According to ( WHO 2010) the objectives of health financing is to make funding available, as well as to set the right financial incentives to providers, to ensure that all individuals have access to effective public health and personal health care. Kenya’s healthcare financing has developed over the years into a mixed healthcare financing and comprises of the following main components: general tax financing, NHIF, employer self-funded schemes, community based health financing, out of pocket health spending, development partners and non-governmental organizations (NGOs) and private health insurance.
Kenya as a developing nation, majority of its citizens still do not have access to affordable healthcare and end up paying for health services at the point of consumption. The topic of household health expenditure globally have been a subject of discussion quite for some time now, this is so mainly because of the rising health expenditure and thus the need to know the proportion of household contribution especially in Africa. The most basic form of financing healthcare in sub Saharan Africa ( SSA) is out-of —pocket and basically this where a fee is charged to cover all or part of the cost of the service provided and it majorly takes two forms: User fees for public sectors health services and Direct payment to private sector providers.
In 2001 African leaders met in Abuja and declared that AIDS is a state of emergency in the continent. During this meeting the member state, in which Kenya is a party adopted what is known as Abuja declaration and made commitment to increase health allocations to 15 percent of total government allocations. Although over the last decade the total health spending has increased, the budget allocation to the health sector has been less than the agreed pledge
According to Kenya national health’s account 2012/2013 total health expenditure was KSh 234 billion (US$2,743 million) , up from KSh 163 billion(US$2,155 million) in 2009/10. In 2012/13, the total spending on health accounted for 6.8percent of GDP, up from 5.4 percent in 2009/10.The government expenditure on health as a percentage of total government expenditure increased from 4.6 percent in 2009/10 to 6.1percent in 2012/13. Of the total health expenditure in 2012/13, current health expenditure (CHE) accounted for 93 percent of THE, compared with 96 percent in 2009/10.Capital expenditures increased from 4 percent of THE in 2009/10 to 7 percent in 2012/13. THE per capita increased from KSh 4,232 (US$56) in 2009/10 to KSh 5,680 (US$67)in 2012/13. THE per capita and the proportion of GDP spent on health have steadily increased since 2001/2002 estimates.
In addition revenues to finance healthcare in Kenya come from three major sources: the government, households, and donors (i.e., the rest of the world). The private sector is the major financier of healthcare in Kenya, contributing 40 percent of THE in 2012/13, up from 37 percent in 2009/10. The public contribution to THE was 34 percent in 2012/13, an increase of 17 percent over the 2009/10 estimates. Donors contributed approximately 26 percent of THE in 2012/13, down from nearly 35 percent in 2009/10.
1.2. PROBLEM STATEMENT
An affordable and efficient medical sector is of crucial importance towards achievement of overall economic development and improved living standards of consumers. Government’s budget allocation to the health sector has been declining as a result of Structural Adjustment Programs (SAPs) and has therefore failed to meet international benchmarks including the Abuja target of allocating 15% of government’s budget to the health sector. According to WHO (2011), only Rwanda and South Africa have attained the Abuja 15% target while the African Union Commission (2001) reports that six AU member states have met the 15% benchmark are; Rwanda(18.8%), Botswana (17.8%), Niger (17.8%), Malawi (17.1%), Zambia (16.4%), Burkina Faso (15.8%). In this regard out-of-pocket is an important source of revenue for financing the health sector in Kenya. Healthcare services are essential for every individual in the society hence an efficient, comprehensive and sustainable financing framework is necessary for quality healthcare services leading to increased welfare of individuals. . Majority of Kenyans pay for health services when need arises and sometimes at levels that can impoverish the family unit i,e one can sell family assets to meet health care cost. Kenya, like any other developing nation, is still at the early implementation phase of health insurance scheme. The cost of care has remained an all vital issue in Kenya. Out of pocket Health Expenditure is still very high and this is more so with the private for profit clinics and hospitals consuming the large chunks of OOP funds. Poverty is still very prevalent with 43.7% below poverty line and life expectancy at birth being 56 years in the phase of HIV /AIDS pandemic. According to World Health Organization report, households in developing nation spend half of their capacity to pay on health monthly. Therefore the main purpose of this study is to examine the determinants of OOP Health expenditure in Kenya.
RESEARCH OBJECTIVE.
To investigate the determinants of out-of-pocket health care expenditure in Kenya
JUSTIFICATION
The role of OOP towards financing of the health sector cannot be ignored and therefore an assessment of health care financing based on OOP and its determinants is of key importance in health system performance. Since Out-of-pocket healthcare expenditure is the case in most households, could this be a burden to the majority of the population who has lower income in Kenya? What are the other determinants of OOP Health expenditure in Kenya? The understanding of these questions will be a policy guideline, Hence, the need to study determinants of out-of-pocket healthcare expenditure in Kenya.
CHAPTER TWO:
LITERATURE REVIEW
The Kenyan health sector relies heavily on OOP expenditure (Health accounts 2005/2006). The sector is largely underfunded and health care contributions are regressive, that is, the poor contribute a larger portion of their income to healthcare than the rich (WHO 2010). Health expenditure risk is of considerable importance as it has a significant impact on a household’s or an individual’s consumption, saving and asset allocation. Researchers such as Kiplagat I. (2006) have attempted to establish the causal relationship between health insurance membership and health status. Other extensive studies have aimed at determining the impact of health insurance on health care utilization, cost of illness on households, social health insurance schemes; opportunities and sustainability potential, and market assessment of private prepaid schemes among other studies. (WHO 2010, Mclntypue et al 2006, Nguli M et al, and Deloitte Consulting limited 2001).
Sandra Hopkins (2010) using Cross-sectional health expenditure data was concerned with comparison of health expenditure aggregates and the contribution of the public and private sectors in a selection of 31 low, middle and high income countries. Hopkins noted that Low and middle income countries heavily relied on private funding especially household out-of-pocket payments while Public funding is more rife for funding of curative care than for funding of pharmaceuticals in all three categories of countries.
Kiplagat, et al (2013) in their paper determinants of choice of health insurance schemes in Kenya showed that wealth index, employment status, education level and household size are important determinants of health insurance ownership and choice, and that lack of awareness prevents many from enrolling in any form of health insurance scheme. They explored those determinants using a Multinomial logit model on the 2008-2009 Kenya, Demographic Health Survey (KDHS). According to D. Kimani, D Muthaka (2009) contributions of private health insurance are likely to be progressive as a financing mechanism for healthcare utilization, but often cream skim and fail to cover people with chronic conditions or the premiums are unaffordable thus people opt for OOP payments.
Ilesanmi Olowolabi (2014) used Raw data from 2004/05 Household income and expenditure surveys conducted by Kenya Bureau of Statistics to analyze determinants of household health expenditure which included age, education, gender, settlement, household size and total income. Further results were achieved by analyzing average household healthcare expenditure by determinants. Regression analysis was done using 95% confidence interval level. This study showed that Education and Settlement are the main significant determinants of Household healthcare expenditure using multiple regression analysis with P-value being significant at 5% error level. No other explanatory variable is significant at this level, not even at 0.1000.The elderly age group was found to have the highest expenditure at 141KShs compared with other age groups while the youngest age group has the second highest expenditure at 129.5 Kshs. However, age as a determinant is not found to be significant determinant using the regression analysis.
Mhere (2013) examined the determinants of health insurance participation in Gweru
Urban in Zimbabweans. Using a probit model he showed that the household head’s level of education, household income, age, family size, and chronic illnesses, are all Significant predictors of participation in health insurance schemes.
KeXu, et al (2006), used a log-linear model to explore the determinants of OOP spending given utilization of health services. The unit of analysis was the individual. Apart from area of residence whereby urban- rural difference was found to be an insignificant predictor of OOP spending, age, chronic health conditions and insurance membership were found to be key determinants of OOP spending. Empirical findings were that young children pay less OOP and those aged older than 65 pay more OOP, also people with chronic health conditions paid higher OOP. Although income was not controlled for in the regression models used, researchers expected those with higher incomes to pay higher OOP. Insurance membership was seen to increase OOP considerably, with the insured individuals paying more for health care services than the uninsured. A study done by WHO (2000) showed that health insurance is emerging as the most preferred form of health mechanism in developing countries like Kenya where OOP expenditures on health are significantly high and cost recovery strategies affect access and utilization of healthcare.
According to karimo and apere using Heckman selection two-step model, they Examined the determinant of out-of-pocket healthcare expenditure in the south-south geopolitical zone of Nigeria using the 2010 national harmonized living standard survey data, they concluded that state of residence, age of household head, family size, per capita consumption expenditure and adult equivalent weight together determines whether a person who falls sick will spend out-of-pocket for healthcare. Also, if a person falls sick and seeks healthcare service(s) age, age squared, household size, household size squared and per capita consumption expenditure proxy for per capita income. These are the factors that determine how much he/she spends out-of-pocket for healthcare, while aged people (those beyond 50 years of age) spend more out-of-pocket for healthcare households with more than 7 members have little to spare and so spend less out-of-pocket on healthcare. This reveals that healthcare is a normal good. The study therefore, recommends a comprehensive health insurance scheme irrespective of the state of residence for households in the zone.
Brinda, et al (2014), investigated the determinants of out-of-pocket health expenditure among adults population in the United Republic of Tanzania. They also investigated the prevalence and associated determinants of household catastrophic healthcare expenditure. Employing multiple generalized linear and logistic regression models they showed the major determinants of out-of-pocket healthcare expenditure to be age, gender, obesity, functional disability and visits to traditional healers. Further, large household size, household head’s occupation as a manual laborer, household member with chronic illness, domestic violence against women and traditional healer’s visits were associated with high catastrophic health expenditure in the United Republic of Tanzania.
Abolhallaje et al (2012) examined the determinants of unpredictable healthcare expenditure in
Iran. They analyzed the shares of households ‘expenditures on main groups of goods and services in urban and rural areas and in groups of deciles using data from households’ expenditure surveys. They growth of spending in nominal values within the year 2002-2008 was considerably high and the rate for out of pocket payments is nearly the same or greater than the rate for total health expenditure. Uzochukwu, and Uju, (2012) Using intensity and
incidence methods showed that 24% of Nigerian households incur catastrophic health expenditure and this was more prevalent among the richest income quintiles in Nigeria and as such has succeeded in changing the poverty situation of most households who were originally on or above the poverty line. The study recommended the need for social health insurance expansion in order to accommodate informal sector to achieve universal access to health services and financial protection of the poor and vulnerable.
RESEARCH METHODOLOGY
The study will utilize secondary data drawn from the 2007 Household Health Expenditure and Utilization Survey. The survey data covered Kenyan’s eight provinces and is a national representative sample survey consisting of 8,844 households where 6,072 were from rural and 2,772 were from urban . Data collection was done using the interview method where questions pertaining to out-of-pocket spending and a series of personal characteristics including age, gender, marital status, area of residence, highest level of education attained, occupation status and health related behaviors of the respondents were administered.
Theoretical framework
This study is based on human capital theory which states that individual invest in themselves through education, training and health to increase their earnings. This theory was advanced by Michael Grossman and he concluded that when people get old, their stock of health depreciates at a faster rate, so investment must occur at a faster rate; i.e., the old spend more on health care than the young. Additionally, higher incomes increase the consumption value of health thus health spending rises with wage and income. It is hypothesized that more educated people are more efficient at producing health. Spending on health care, then, will fall with education. This is further supported by theory of welfare economics which holds that individuals are the best judges of how to allocate available resources to maximize their welfare or benefit. However, the theory also raises the question about what is reasonable for individuals to sacrifice. In the case of health care, it is widely accepted that individuals should be protected from catastrophic health care costs and not be expected to endure impoverishment to cope with health costs.
Research by Ke Xu, et al (2006) in Kenya shows that there is a positive relationship between age and OOP spending whereby children under 5 years pay less OOP due to government policies for young children. Those aged older than 65 years pay higher OOP as a result of poor health. Individuals who have chronic health conditions pay high OOP due to their demand for health services. Females were also found to pay less OOP and this is attributed to government policies on reproductive health seen to be more effective in improving financial protection. In terms of geographical location, those living outside Nairobi generally had lower or equivalent OOP spending. However, people from North Eastern paid high OOP as a result of non- uniform user charges and poor access to financial protection against health care costs. The type of health provider is also seen to affect OOP spending in that visiting a private health facility results in higher OOP payments in contrast to visiting a public health provider where one pays less OOP. There are mixed findings on the relationship between OOP spending and health insurance status. Some researchers find a negative relationship between health insurance membership and OOP payments and conclude that health insurance membership does not currently offer significant financial protection and reform is needed. (Wagstaff et al 2007; Ke Xu, et al 2006). However, other researchers like Morrisey (2001) have argued that the key effect of having a health insurance cover is to reduce OOP spending.
Conceptual model
The fundamental aim of this paper is to establish the determinants of OOP expenditure. Factors expected to have an effect on consumers’ OOP spending include age, gender, occupation status, and insurance status, type of hospital visited and health status of the individuals. Therefore OOP spending given utilization of health services can be expressed as;
OOP = f (age, sex, geographical location, marital status, health insurance status, chronic health condition, type of health provider, health status, employment status)
Independent variables Dependent variable
The independent variables are assumed to jointly affect an individual’s OOP
MODEL SPECIFICATION
In this study, more than one predictor variable (age, gender, occupation status, and insurance status, type of hospital visited and health status) will be considered and thus a multiple linear regression model will be adopted. A multiple linear regression model (MLRM) to examine the socio-economic factors associated with OOP expenditure among health care consumers will be estimated.
The estimated model can be expressed as:
Y= β0 +β1X1 +β2X2+β3X3+β4X4+β5X5+β6X6+β7X7+β8X8+β9X9+ε
Where; Y – is out-of —pocket expenditure used as the dependent variable and the determinants as the independent or explanatory variable as shown in the regression equation above.
The βs’ are a vector of parameters associated with the independent variables whereas ε is the error term.
Variable Definition characteristics
X1-Insurance status It seeks to know if an individual is covered with a health insurance. Yes
No
Not stated
X2-Age How old an individual was at his/her last birthday 0-4
5-14
15-49
50-64
65+
X3-Sex Gender of the individual Male
Female
X4-Employment status An individual’s employment status Working
Seeking work
Homemakers
Student
X5-Chonic health condition Presence of a chronic illness Present
Not present
X6-Geographic location Area of residence Urban
Rural
X7-Health status How an individual rates their health status compared with others of his/her age Very good
Good
Satisfactory
Poor
Not stated
X8-Marital status An individual current marital status Never married
Married
Divorced
Widowed
X9-Type of health provider Type of health provider visited, public or private? Government hospital
Private hospital