THE POTENTIAL MARKET FOR PRIVATE LOW-COST HEALTH INSURANCE IN 4 AFRICAN CONTEXTS Lessons Learned from the Health Insurance Fund

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1 THE POTENTIAL MARKET FOR PRIVATE LOW-COST HEALTH INSURANCE IN 4 AFRICAN CONTEXTS Lessons Learned from the Health Insurance Fund Report for USAID November, 2011 Emily Gustafsson-Wright Alexander Boers Melinda Vigh Jacques van der Gaag 1 1 The authors wish to thank all AIID and AIGHD staff who contributed to data cleaning and preparation as well as the local teams who contributed to field work and data collection. Without them, our work would not be possible.

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3 Table of Contents Executive Summary Introduction Description of the Data: Household Surveys in 4 African Contexts The Datasets Summary Statistics Illness and Health Care Utilization Chronic Disease and Acute illness Health Care Utilization and Hospitalization Out-of-Pocket Expenditures for Health Willingness to Pay For Health Insurance Discussion and Conclusions Appendix References Table of Tables Table 1: Summary statistics by context Table 2: Self-reported prevalence of acute illness and chronic disease Table 3: Reasons for not consulting a medical practitioner for last acute illness Table 4: Self-reported utilization of health services for acute illness Table 5: Self-reported hospitalization rates Table 6: Self-reported utilization of health services for hospitalization Table 7: Average per capita out-of-pocket health expenditures for chronic and acute illness and hospitalization (for last treatment sought and including zero values) Table 8: Percentage of respondents willing to join insurance program by socioeconomic characteristics Table 9: Percentage of respondents willing to join insurance program by health status and expenditure last year Table 10: Mean WTP for low-cost health insurance by consumption quintile (per year) Table 11: Mean WTP as percentage of per capita consumption (per year) Table of Figures Figure 1: Percentage of respondents willing to pay the first bid (per year)

4 Executive Summary This report summarizes some of the main issues surrounding health care utilization and health insurance in 4 different contexts in (3 countries in) Africa in which an innovative model of subsidized low-cost health insurance is currently being implemented by the Health Insurance Fund and its implementing partner the PharmAccess Foundation. These programs not only help low and middle-income households bear the burden of health care costs, but they secure HIV/AIDS, malaria and tuberculosis treatment and simultaneously improve health care facilities in program areas. Data for the report are taken from the baseline household surveys that were implemented, for the purpose of examining the potential impacts of the programs, by the Amsterdam Institute for International Development (AIID) and the Centre for Poverty-related Communicable Diseases (CPCD) (now renamed the Amsterdam Institute for Global Health and Development AIGHD). These baseline household surveys provide a rich set of data pertaining to socioeconomic and biomedical characteristics. In this report, specifically, we examine the driving factors behind health status, health care utilization and out of pocket health expenditures, exploring such determinants as income, education, and employment status. In addition, we examine willingness to pay for health insurance comparing across socioeconomic groups and across different contexts in countries for which we have data. Because the surveys used are baseline surveys, this report is not meant to give a long-term view of the state of health in these contexts nor is it meant to provide findings on the impact of the Health Insurance Fund health insurance projects. We examine data taken from household interviews with individuals and their families in following four contexts: (1) market women in Lagos, Nigeria; (2) farm workers in Central Kwara State, Nigeria; (3) members of a micro-credit group in Dar es Salaam, Tanzania, and (4) dairy farmers in the area of Eldoret, Kenya. In our analysis we find that; (i) individuals and particularly the poor are underreporting illness, an indication that they are likely not receiving necessary health care; (ii) low and middle-income populations use public over private health facilities if they use any care at all, raising concerns of quality and an burden on the public system; and (iii) the poor spend less in absolute terms compared to the rich on health but more in relative terms, raising the potential to deepen poverty. (iv) over 90% in Tanzania, 79% in Kwara and 53% in Lagos were willing to join the hypothetical low-cost insurance programs proposed in the survey. (v) over 94% of those interviewed in Kwara indicated a higher reservation price for the insurance package than the current HIF Program cost of 300 Naira, and in Lagos, also about 94 percent of respondents were willing to pay at least the 800 Naira cost for the insurance package. 4

5 Our first three findings are consistent with earlier evidence from the literature in developing countries showing that the poor spend a disproportionate amount of their income on remedial health care while spending little on preventive care. Delays in accessing medical care also lead to costly inefficiencies. Evidence shows that these health expenditures can in turn exacerbate the prevalence and depth of poverty. Findings (iv) and (v) on willingness to join and pay for health insurance also support evidence from the literature in developing countries which is promising for the continued development of low-cost health insurance programs such as those implemented by the Health Insurance Fund. Further research about the impacts of health insurance on health status, health care utilization, and out-of-pocket expenditures on health will continue to enrich the debate about alternative mechanisms of health care financing and provision. This debate is imperative today as the epidemics of HIV/AIDS, tuberculosis and malaria as well as non-communicable diseases, are putting heavy demands on the health care sector, especially in Africa. 5

6 1. Introduction The scrutiny of existing and alternative mechanisms of health care provision and financing is increasingly important, especially for African countries where the epidemics of HIV/AIDS, tuberculosis and malaria as well as non-communicable diseases such as cardiovascular diseases are putting increased demands on the health care sector. The burden of ill health lies not only on the public sector but also very much on individuals themselves. Evidence shows that the poor spend disproportionate amounts of their income on remedial health care up to twice, as percentage of their income, what would be considered affordable in the United States (Dupas, 2011; Dupas and Robinson, 2009; Banerjee et al. 2009, Cunningham, 2009). Several factors lie behind these high expenditures. First, exposure to illness due to lack of proper water, sanitation and living conditions, leaves the poor more vulnerable to illness. Second, poor households spend little on preventive health care leading to higher costs for remedial care. Third, the poor almost universally lack health insurance making the costs of medical care high and often unexpected. Finally, delays in accessing medical care due to the inability to pay or large distances to healthcare facilities can lead to costly inefficiencies (Dupas, 2011). These health expenditures in turn exacerbate the prevalence and depth of poverty (van Doorslaer et al., 2006). In fact, evidence shows that over 150 million people suffer financial catastrophe every year due to out-of-pocket health expenditures (Ke et al., 2007). A debate is currently ongoing about the potential benefits of private voluntary and communitybased health insurance (CBHI) for low- and middle-income countries as a mechanism to finance healthcare. 2 Advocates of private health insurance point to the potential efficiency gains, the need to curb catastrophic out-of-pocket health expenditures for uninsured households, and the low quality of public health systems in many countries. Opponents of private insurance emphasize drawbacks related to adverse selection, moral hazard and escalating costs, cream skimming, and increasing inequality. Evidence to date shows mixed results. Some studies have found community-based health insurance to reduce out-of-pocket payments and incidence of catastrophic payments (Hsiao and Yip, 2008; Yip, Wang and Hsiao, 2008 and Wagstaff and Yu, 2007) while others found an increase in out-of-pocket payments due to increased consumption of more costly health services and self-selection of the sickest (Trivedi, 2002). This contrary finding doesn t necessarily point to a failure of health insurance. It may perhaps even be the opposite as it may indicate that those most likely to get ill are receiving the care they need, including preventive care. Among the leading organizations in developing a model of subsidized health insurance are the Dutch Health Insurance Fund and its implementing partner the PharmAccess Foundation. These organizations have supported low-cost (subsidized) private health insurance programs in 3 African countries since In conjunction with these programs, household surveys were implemented by the Amsterdam Institute for International Development (AIID) and the Centre for Poverty-related Communicable Diseases (CPCD) (now renamed the Amsterdam Institute for 2 Ekman, 2004; Sekhri and Savedoff, 2005; Wagstaff and Lindelow, 2008; van der Gaag and Stimac, 2008; Ekman et al,

7 Global Health and Development AIGHD) in each of the countries for the purpose of examining the potential impacts of the HIF Programs. Baseline household surveys, conducted before initiation of the programs, provide a rich set of data pertaining to socioeconomic and biomedical characteristics. This study, utilizing these baseline surveys, provides insight into some of the main issues surrounding health care utilization and health insurance in 4 different contexts in (3 countries in) Africa: market women in Lagos, Nigeria; farm workers in Central Kwara State, Nigeria; members of a micro-credit group in Dar es Salaam, Tanzania 3, and dairy farmers in the area of Eldoret, Kenya. (For simplification, hereafter we will refer to these four contexts as Lagos, Kwara, Tanzania and Kenya.) Specifically, we examine the driving factors behind health care utilization and out of pocket health expenditures on health, exploring such determinants as income, education, and illness. In addition, we examine willingness to pay for health insurance comparing across socioeconomic groups and across different contexts in countries for which we have data. Because the surveys used are baseline surveys, this report is not meant to give a long-term view of the state of health in these contexts but rather a snapshot in time. Nor is it meant to provide findings on the impact of the Health Insurance Fund health insurance projects in these contexts. Future studies will explore the impacts of health insurance on the variables we examine here. The remainder of the report is organized as follows. In section 2, we describe the survey data utilized in this report. In section 3, we present data on prevalence of illness and health care utilization in the four contexts. In section 4, we discuss how much individuals are spending out of pocket on health care. Section 5, presents unique data on willingness of individuals to pay for health insurance. Conclusions and discussion can be found in section Description of the Data: Household Surveys in 4 African Contexts A total of 4 baseline household surveys were conducted in 3 different African countries in conjunction with the Health Insurance Fund programs. Each of these surveys provides a rich database of information related to socioeconomic and biomedical characteristics. Each of the surveys is described in detail below. It is important to keep in mind that the four contexts studied in this report are quite diverse and that across the board generalizations should not be made. 4 In section 2.2 we describe some of the basic characteristics of the samples in each context. 3 The program was officially terminated in 2011 due to a lack of capacity of the local partners 4 In addition, it is important to note that these surveys are not representative of any of the countries as a whole. 7

8 2.1. The Datasets Nigeria Kwara (2009): A household survey was conducted among a representative sample of farm workers and their families in Kwara State, Nigeria totaling 1,462 households and 5,991 individuals. The socioeconomic portion of the survey contains data on households including education, health care expenditures and utilization, employment and income from work, health insurance including willingness to pay for health insurance, customer satisfaction with health care and health insurance, household assets, housing, other non-work sources of income, consumption expenditures (food and non-food), mortality, household financial decisions, health and financial activities and financial choice, and shocks to household welfare. It was followed by a bio-medical component, including a HIV-test for those aged 12 years and above. Lagos (2008): A household survey was conducted among a representative sample of market workers (women) and their families in Lagos, Nigeria including 1,979 households and 7,091 individuals. The socioeconomic portion of the survey contains data on households including education, information from market workers, market context and household decision-making, employment and income from work, income from household business/market activity, health insurance access and willingness to pay, household assets, housing, other non-work sources of income, consumption expenditures (food and non-food), household financial decisions, and health and financial activities/financial choice. It was followed by a bio-medical component which contains self-reported health status, questions related to demand for or utilization of health care and out-of-pocket expenditures on health, child immunization, and knowledge and practices related to health. In addition, the biomedical survey includes the analysis of blood samples to map the prevalence of main diseases, including HIV and established risk factors for cardiovascular diseases, in the target population and to describe and understand health related risk behavior. Tanzania (2010): The statistically representative sample (of the target group of members of a micro-credit group and their families) covers 678 households and 2,241 individuals in Dar es Salaam, Tanzania. The socioeconomic component of the survey contains detailed information for the entire household on education, employment, housing, businesses, microfinance, health insurance access and willingness to pay, customer satisfaction with health care, housing, household assets, non-work income, consumption, household financial decisions, and health and financial behavior and choice. The biomedical component contains self-reported health status, questions related to demand for or utilization of health care and out-of-pocket expenditures on health, child immunization, and knowledge and practices related to health. In addition, the biomedical survey includes anthropometric measurements and the analysis of blood samples to map the prevalence of main diseases, including HIV and established risk factors for cardiovascular diseases, in the target population and to describe and understand health related risk behavior. 8

9 Kenya (2011): The statistically representative sample (of the target group of dairy farmers and their families near El Doret, Kenya) of about 1200 households is divided between 800 households who would be offered the health insurance intervention (after the survey was completed) and 400 in a comparable (control) group who would be offered the health insurance at a later date. The socioeconomic component of the survey contains detailed information for the entire household on employment, housing, education, income, consumption, financial shocks, dairy farming, household assets, social networks, and credits and savings. The biomedical component contains self-reported health status, questions related to demand for or utilization of health care and out-of-pocket expenditures on health, child immunization, and knowledge and practices related to health. In addition, the biomedical survey includes anthropometric measurements and the analysis of blood samples to map the prevalence of main diseases, including HIV and established risk factors for cardiovascular diseases, in the target population and to describe and understand health related risk behavior Summary Statistics The four contexts in our study differ not only geographically but also in socioeconomic measures. Table 1 provides some basic statistics in order to better understand the differences and comparability between the four contexts. To begin with, the average household size is larger in the rural contexts (Kwara and Kenya) than in the urban contexts (Lagos and Tanzania). Average age is similar among all contexts, ranging from 23 to 27 years. The samples are equally representative between male and female. Household heads are more likely to be married in the two rural contexts, where more than 75% of household heads are married, compared to the urban contexts where this value is below 70%. The percentage of adults employed is quite high for three of the contexts (Lagos, Kwara and Tanzania) lying at around 80%. While in Kenya, less than 60% of adults are employed. Income in the survey households as measured by per capita consumption is also presented in table 1. In order to compare per capita consumption between the four contexts, the local currency values are converted into International Dollars 5 using Purchasing Power Parity. We observe that on average, individuals in the rural contexts (Kwara and Kenya) consume around half the amount of the urban contexts (Lagos and Tanzania). The two extremes are Tanzania, where average per capita consumption is 3123 PPP Dollars and Kwara, where it is only 1185 PPP Dollars. Average per capita consumption in the fifth (richest) quintile is nearly 9 times higher than per capita consumption in the first (poorest) quintile for Lagos. Kenya also demonstrates relatively high inequality with the consumption in the highest quintile equalling over 8 times that in the lowest 5 Prices of each context are deflated to the 14 th of April 2010 using their respective weighted CPI deflator. Local currencies are then converted to US Dollars using exchange rates obtained from for the 14 th of April The respective 2010 GDP PPP factor was then applied to the USD prices in order to arrive at comparable 2010 international dollars. Macroeconomic data was obtained from Note, because of differences within country, in particular in rural vs. urban areas, it is likely that the PPP values may not be fully accurate. These are best estimates however given available data. 9

10 quintile. These values are equal to over 5 and nearly 7 times for Tanzania and Kwara respectively. Table 1: Summary statistics by context Lagos Kwara Tanzania Kenya Sample size Average HH size Average age Gender (% female) 51.95% 51.87% 54.65% 49.69% % HH heads married 68.43% 76.35% 62.65% 78.37% % employed (18 and older) 77.36% 81.79% 78.55% 58.41% Average pc consumption (LCU) Average pc consumption (PPP$) Quintiles (PPP$ for 18 and older) Q Q Q Q Q Sources: Lagos Market Baseline Survey 2008 (AIID and CPCD); Kwara II Baseline Survey 2009 (AIID and CPCD); Tanzania PRIDE Baseline Survey 2010 (AIID and CPCD); Kenya Community Healthcare Plan (CHP) Baseline Survey 2011 (AIID and AIGHD) PPP rates used (calculation described in footnote 5) are : Lagos: 1 Naira = USD; Kwara II : 1 Naira = USD; Tanzania : 1 TZS = USD; Kenya: 1 KSH = USD Turning our attention to the educational achievement levels of adults 6 in the four contexts shown in table A1 in the appendix, individuals from Kwara appear to have the lowest level of education with over 60% of the adult population not having completed primary education. In fact, over 50% of the population reports never having attended an education facility. In Tanzania and Kenya, the majority of the adult sample has only completed primary education, while in Lagos, the majority of the sample has completed secondary education. The percentage of the respective samples that have completed tertiary education is also highest in Lagos, while it is lowest in Tanzania, the other urban context. As we can see from the data, the contexts differ not only in geographical dimensions but also in socioeconomic characteristics. In reading this report, it is important to keep this in mind while also considering the differences in demographic and historical development in these four contexts. For the purposes of examining the development of four innovative low-cost 6 Adult defined as an individual that is 18 years of age or older. 10

11 community based health insurance schemes, however, the comparison across contexts provides a useful starting point for a necessary dialogue. 3. Illness and Health Care Utilization This chapter of the study examines data on health status and care utilization within our four African contexts. We first focus on the self-reported prevalence of chronic diseases and acute illnesses examining these by consumption quintiles, highest education level completed and employment status. Next, we turn our attention to whether the health care utilized for acute illnesses as well as hospitalization occurs primarily in private, government or traditional health facilities, if treatment takes place at all. One would expect prevalence of illness to be higher among the poorest individuals and in the poorest contexts in our sample assuming that these individuals are living in relatively worse conditions and that they suffer from a lack of access to medical care. Nevertheless, consistent with the literature in the developing world, individual self-reported data from our four contexts show the opposite the rich report higher prevalence of acute and chronic illness (Strauss and Thomas, 1996; Deolalikar, 1997; Butler et al., 1987). While looking across countries however, findings are mixed Chronic Disease and Acute illness We begin by examining in table 2 the prevalence of chronic diseases 7 across the four contexts. Table A2 in the appendix shows chronic disease by socioeconomic characteristics. Prevalence is described as the percentage of the sample with at least one chronic disease. The pattern of prevalence of chronic disease is roughly consistent across three of the contexts (Lagos, Kwara and Tanzania), with the prevalence of chronic disease far higher in Kenya. The prevalence within the adult 8 population is between 9% and 12% within the three similar contexts (Lagos: 9%; Kwara: 11%; Tanzania: 12%) while being near 26% in Kenya. Notably, Lagos is the richest context while Kwara and Kenya are the poorest. Also, the higher prevalence in both Tanzania and Kenya could be due to the inclusion of more categories of chronic diseases within these surveys. As would be expected, the prevalence of chronic diseases in individuals younger than 18 years of age is substantially lower than in adults, with Tanzania having the highest prevalence of chronic diseases in this age group at near 5% while Kwara has the lowest reported prevalence at less than 1%. Due to chronic diseases being most prevalent in the adult population, we focus on this group of individuals in the analysis. The data show that in general, those in higher consumption quintiles, are more likely to report chronic disease; consistent with existing evidence from developing countries (ibid.). This finding is likely due to the fact that wealthy and better educated may have better information about 7 Chronic diseases included in the survey are: diabetes, hypertension, heart disease, asthma, epilepsy, sickle cell disease, severe allergies, HIV/Aids, musculo- skeletal disorders and physical disability. Additionally, the Tanzania and Kenya surveys include gastrointestinal problems, cancer and severe hearing disability. 8 Adult defined as an individual 18 years and older. 11

12 their health through personal knowledge or through better access to medical care. The variance in prevalence between quintiles differs depending on the specific context, with Tanzania exhibiting the largest variance while Kwara exhibits very low variance. An individual s employment status does have a consistent relationship with the prevalence of chronic diseases in adults across contexts, with the unemployed exhibiting lower prevalence in Lagos and Tanzania but higher prevalence in Kwara and Kenya. Table 2: Self-reported prevalence of acute illness and chronic disease Lagos Kwara Tanzania Kenya % % % % Acute illness Chronic Disease Sources: Lagos Market Baseline Survey 2008 (AIID and CPCD); Kwara II Baseline Survey 2009 (AIID and CPCD); Tanzania PRIDE Baseline Survey 2010 (AIID and CPCD); Kenya Community Healthcare Plan (CHP) Baseline Survey 2011 (AIID and AIGHD). *The percentage is the number of individuals ill out of those who answered the question. We now turn our attention to the prevalence of acute illnesses across the four separate contexts also in table 2 above. Table A3 in the appendix presents more detail. Prevalence is described as the occurrence of more than one acute illness over the past 12 months. For the Kenya context only, injuries are included within the definition of acute illnesses which may result in the average prevalence of acute illnesses for the whole sample being considerably higher there, at 76%, compared to the other three contexts. Prevalence is the lowest in Kwara with 22% followed by Lagos at 33% and Tanzania at 57% for the adult population. As would be expected, there is a less distinct difference between the prevalence in adults versus the prevalence in children when looking at acute illnesses compared to chronic diseases. In general, the data suggest that there is a higher prevalence of acute illnesses within the higher consumption quintiles. Again, it is difficult to interpret these results. As in the case with chronic illness, it could be that the wealthier have access to more information about their health and therefore are more likely to report an illness. We also see the same pattern as under chronic disease prevalence in terms of a correlation with employment status, where the unemployed have a lower prevalence in Lagos and Tanzania, but a higher prevalence in Kwara and Kenya. The data show that the poor are less likely to report illness than the rich. We found that for both chronic disease and acute illness, individuals in higher consumption quintiles are more likely to report illness, a finding that is consistent with the literature (ibid.). While one would expect higher reported illness among the poor due to living conditions and exposure, we expect that 12

13 our finding is probably due to the fact that the poor are underreporting their actual illness. One explanation is that the wealthy and better educated have better information about their health through personal knowledge or through better access to medical care. We are able to confirm our hypothesis with the unique biomedical data in these surveys. In tables A4 and A5 in the appendix, self-reported data as well as measured data for prevalence of diabetes and hypertension are presented. In all contexts, for both diabetes and hypertension, reported prevalence is much lower than actual measured prevalence. The difference is stark in the lower quintiles highlighting the issue of underreporting among the poor. Again, lack of preventive or regular access to care due to supply issues, inability to pay and/or no health insurance create a situation in which health costs eventually are inflated to unnecessary levels if care is sought at all Health Care Utilization and Hospitalization In the introduction we discussed the issue of failure to seek preventive care and delays in accessing medical care both of which ultimately lead to higher medical expenditures for the poor. In this section, we examine health care utilization in our four contexts. While we do not have information about preventive care, we do have self-reported health care utilization for acute illness and data on hospitalization. First, we look at whether or not a medical practitioner 9 was consulted for the last acute illness suffered. Overall, a very large proportion of the population consulted a medical practitioner for their last acute illness: about 90% of the population for Lagos, Kwara and Kenya, while being substantially higher (97%) for Tanzania which is promising. Children are also more likely to have consulted a medical practitioner for their last acute illness than adults. The difference between consumption quintiles for consulting a medical practitioner for the whole sample is small; there is no pattern suggesting that certain consumption quintiles are more likely than others to seek out a medical practitioner when suffering from an acute illness. It can however be observed that adult individuals that have achieved a higher level of education, consult with a medical practitioner more often than individuals with a lower levels of education which supports previous findings in developing countries that those with more education possess more knowledge about their health and are more likely to seek care. This is at least the pattern for Lagos, Kwara and Kenya. Also, for three of the contexts (Lagos, Kwara and Tanzania), employed adults seek out a medical practitioner more often than unemployed adults. This may be related to education, income or access to health insurance. We also examine the reasons behind care not being sought for acute illness. Table 3 shows that in the first three contexts: Lagos, Kwara and Tanzania; care was not sought because the illness was not considered serious enough. In Kenya however, where this category was not an option, the category with the highest percentage is the category of financial constraint. Over half of all 9 Medical practitioner excludes the likes of traditional healers, but includes professionals such as pharmacists. 13

14 individuals reporting an illness in Kenya did not seek care because of financial constraint. It is plausible that in the other contexts, the not serious enough category is also picking up some of the cases where financial constraint was the underlying reason. This data points to the importance of reducing the financial burden of health care so that care is sought when needed. In Kenya, 37% of individuals did not seek care because there was an issue with the facility highlighting the importance of ensuring access to good quality health facilities. Table 3: Reasons for not consulting a medical practitioner for last acute illness Percent Lagos Kwara Tanzania Kenya Percent Percent Percent Financial constraint 11.03% % % % 195 Time constraint 1.78% % % 12 Not serious enough 72.95% % % Issue with facility % % % 131 Other 14.23% % % % 12 Total % % % % 350 Sources: Lagos Market Baseline Survey 2008 (AIID and CPCD); Kwara II Baseline Survey 2009 (AIID and CPCD); Tanzania PRIDE Baseline Survey 2010 (AIID and CPCD); Kenya Community Healthcare Plan (CHP) Baseline Survey 2011 (AIID and AIGHD) We now take a closer look at self-reported health care utilization for acute illness, examining which type of medical care is utilized; whether it is government, private or traditional, when medical care is sought at all (see table 4). Utilization is defined as which type of medical practitioner was first consulted for the last acute illness suffered. The medical practitioners are classified as either government, private or traditional. In Nigeria (Lagos and Kwara) private health care was utilized the most. On the other hand, government health care seems to primarily be utilized in Tanzania and Kenya. While it is consistent with expectations that our sample in Kenya, a poorer rural context, uses government over private care; it is surprising that our sample in Tanzania, an urban and our wealthiest context, also shows a higher use of public facilities. Kenya also shows the highest rate of failure to seek care. On the contrary, in terms of failure to seek care, Kenya is followed by Lagos, our second wealthiest context. Traditional healers and medicine make up a small part of medical care for acute illness with the largest percentage taking place in Lagos. 14

15 Table 4: Self-reported utilization of health services for acute illness Utilization % Lagos Kwara Tanzania Kenya Utilization % Utilization % Utilization % None 9.08% % % % 541 Government provided 15.83% % % % 2672 Privately provided 71.05% % % % 969 Traditional 4.04% % % % 39 Total % % % % 4221 Sources: Lagos Market Baseline Survey 2008 (AIID and CPCD); Kwara II Baseline Survey 2009 (AIID and CPCD); Tanzania PRIDE Baseline Survey 2010 (AIID and CPCD); Kenya Community Healthcare Plan (CHP) Baseline Survey 2011 (AIID and AIGHD) Table 5 presents data on reported hospitalization for one night or more over the past 12 months. The average rates are fairly low at less than three percent for whole samples of Kwara, Tanzania and Lagos while being substantially higher for Lagos at over seven percent. Hospitalization is far higher for adults, about twice as large as for children in all contexts except for Kwara where it is four times as large. In Lagos and Kwara, individuals who are employed are more likely to report having been hospitalized while in Tanzania and Kwara, rates are higher for the unemployed. Table 5: Self-reported hospitalization rates Lagos Kwara Tanzania Kenya % % % % Younger than % 0.85% 1.65% 1.83% 18 and older 9.06% 3.35% 2.91% 3.69% Unemployed 6.93% 2.95% 5.08% 4.96% Employed 9.66% 3.44% 2.48% 2.99% Whole sample 7.37% 2.11% 2.41% 2.71% Sources: Lagos Market Baseline Survey 2008 (AIID and CPCD); Kwara II Baseline Survey 2009 (AIID and CPCD); Tanzania PRIDE Baseline Survey 2010 (AIID and CPCD); Kenya Community Healthcare Plan (CHP) Baseline Survey 2011 (AIID and AIGHD) Table A6 in the appendix shows more on the relationship between hospitalization and other socioeconomic characteristics. For the Kwara and Tanzanian contexts, it appears that adults in the higher consumption quintiles are hospitalized more often than adults in the lower consumption quintiles. This general trend is reversed for Lagos and Kenya, where adults in the poorer consumption quintiles exhibited higher rates of hospitalization than those in the richer consumption quintiles. It is unclear if the cause of this difference is related to presence of hospitals or financial means but analysis by type of facility provides more insight into this 15

16 disparity. In fact, the utilization mix between government and private healthcare facilities for hospitalization shows a similar pattern (as demonstrated in table 6) to utilization for acute illnesses just discussed. For the Lagos and Kwara contexts, the majority of hospitalization takes place in private hospitals, while in the Tanzania and Kenya contexts, the majority of hospitalization takes place in government hospitals. If there is better access to public hospitals in these two contexts, it is likely that individuals in the poorest quintiles will use those while the richest portion of the population will likely not use public facilities as the quality of these facilities is probably low. Table 6: Self-reported utilization of health services for hospitalization Utilization % Lagos Kwara Tanzania Kenya Utilization % Utilization % Utilization % Government provided 27.95% % % % 78 Privately provided 72.05% % % % 36 Total % % % % 114 Sources: Lagos Market Baseline Survey 2008 (AIID and CPCD); Kwara II Baseline Survey 2009 (AIID and CPCD); Tanzania PRIDE Baseline Survey 2010 (AIID and CPCD); Kenya Community Healthcare Plan (CHP) Baseline Survey 2011 (AIID and AIGHD) We conduct additional analysis of the likelihood of seeking care in a private health facility compared to a public healthcare facility for acute illnesses and hospitalization using a multivariate regression model (Table A7 and table A8 present the results from a probit model). The explanatory variables included in the model are household size, sex, sex of the household head, age, age squared, marital status, marital status of the household head, highest education level completed, the highest education level completed by the household head, employment status, employment status of the household head, log per capita consumption, log per capita consumption squared and whether a health insurance policy is present within the household. For acute illnesses, higher income (as measured by consumption) has a negative effect on private healthcare utilization in both Lagos and the Tanzanian context. Having a female household head decreases your likelihood of using private health care in Lagos, as is the case if the household head is married. In Kenya, being married increases the individual s likelihood of using private healthcare, as does having a higher level of education. This situation is reversed in Kwara, where better education seems to reduce the likelihood of private healthcare being utilized. The likelihood of using private healthcare over government healthcare for hospitalization declines with income in Lagos. This result is comparable to that of the previous regression on acute illness utilization in the private sector. In Kwara, the household being headed by a female 16

17 will reduce the likelihood of an individual spending a night in a private hospital vs. a public hospital, while being older will increase the chances of using a private hospital. Use of private over public facilities is likely due to quality differences. The choice between the two is highly dependent on ability to pay and access. This aspect is of great importance for the HIF as the program not only addresses the demand side of access to healthcare, but also the supply-side in that it improves the quality of healthcare facilities in program areas. 4. Out-of-Pocket Expenditures for Health Among other reasons, due to the almost universal lack of affordable risk-sharing mechanisms such as health insurance for low-income populations in developing countries, out-of-pocket health expenditures can be high and can have catastrophic impacts on poor households. This section of the report examines data on health expenditures for acute and chronic illness and hospitalization expenses in our four African contexts. 10 We first examine average out of pocket health care expenditures on chronic illness for the last 12 months an individual sought care (in table 7). 11 In all contexts, chronic illness appears to be the most costly type of illness. Individuals in Lagos spend the most on chronic healthcare and those in the Kenyan context, spend the least. Table 7: Average per capita out-of-pocket health expenditures for chronic and acute illness and hospitalization (for last treatment sought and including zero values) Lagos Kwara Tanzania Kenya Naira PPP$ Naira PPP$ TZS PPP$ KSH PPP$ Chronic Acute Hospitalization Sources: Lagos Market Baseline Survey 2008 (AIID and CPCD); Kwara II Baseline Survey 2009 (AIID and CPCD); Tanzania PRIDE Baseline Survey 2010 (AIID and CPCD); Kenya Community Healthcare Plan (CHP) Baseline Survey 2011 (AIID and AIGHD) PPP rates used (calculation described in footnote) are : Lagos: 1 Naira = USD; Kwara II : 1 Naira = USD; Tanzania : 1 TZS = USD; Kenya: 1 KSH = USD 10 The values are converted into International Dollars using Purchasing Power Parity allowing for a more precise comparison between the different contexts. The PPP $ was calculated by, based on the date of the survey, inflating or deflating the currency to one common date (in 2010), then converting all currencies to USD exchange rate on that given date and finally converting to PPP$ based on the USD/PPP$ exchange rate at that time. 11 It is important to note that we only have information on those who seek care for a chronic illness. Those who are unaware of their illness or who choose not to seek care are not considered. It is likely therefore that the amounts that we capture in the survey greatly underestimate what the actual costs for chronic disease would be if care was sought when necessary. 17

18 As shown in table A9, the richest consumption quintile in each context consistently spends the largest amount on chronic diseases on average in absolute terms. This value is more than double as large as in the quintile with the lowest average expenditure in Tanzania. In Lagos and Kwara expenditures are four times as large in the highest quintile relative to the lowest quintile and in Kenya they are over ten times as large. Across contexts, excluding Tanzania, unemployed adults spend more out-of-pocket on chronic diseases than adults that are employed. This difference is quite substantial for the two Nigerian contexts (Lagos and Kwara), while fairly small for Tanzania and Kenya. As seen in the previous section, this may be related to access to public health care facilities that cost less than the private facilities more often used in Lagos and Kwara. It may also be related to access to health insurance. Table 7 also shows average out of pocket health care expenditures on acute illness for the last time an individual sought care. As frequency is not taken into consideration, it is not possible to determine the exact total yearly costs of acute illness from these data but it is likely that each of the average expenditures would need to be multiplied by some amount making costs even higher than we report here. Again, Lagos demonstrates the highest out-of-pocket expenditures among the four contexts and Kenya the lowest. Examining expenditures on acute illness by consumption quintile in table A10, we observe that in general the middle quintiles, spend the lowest amount on acute illness across all contexts, while both the lower and higher consumption quintiles spend more. Health care expenditure for acute illnesses does not seem to be dependent on whether one is employment or unemployed for Tanzania. On the other hand, unemployed adults have a higher expenditure than employed adults in both Lagos, Kwara and Kenya. Out-of-pocket expenditures on hospitalization are shown in table 7 for Kwara and Lagos, the two contexts where data are available. Although the average total expenditure on hospitalization by children, adults and overall is similar between the two contexts, the difference in expenditures between the two contexts by socioeconomic characteristics is seen throughout table A11. First, taking a closer look at expenditure dependent on consumption quintiles, we see that in Lagos, the lowest expenditure is by Q2 while both the lower and higher quintiles spend more on hospitalization. On the other hand, in Kwara, the third consumption quintile has the second highest expenditure on hospitalization with the lower and higher quintiles having lower expenditure, other than Q5. In Lagos, the employed pay more on hospitalization than the unemployed, while in Kwara the opposite is true. The difference between employed and unemployed is nevertheless quite substantial in Kwara. Summarizing, we find in our sample of four contexts that expenditures are highest in the wealthiest contexts and where use of public health care facilities is lower. For chronic illness, we find that in all contexts, the richest spend more than the poor which may also be due to the use of private versus public facilities. Nevertheless, it is important to keep in mind that these expenditures are represented in absolute terms. When analyzing the portion of total household expenditures that health expenditures represent, the pattern is exactly the opposite. As seen in the literature, on average, the poorest spend a much larger percentage of their total 18

19 expenditures on health than the rich (Gustafsson-Wright et al. 2011). These expenditures, which can be considered catastrophic when they are over 40% of income (net of subsistence costs), can lead to financial ruin for an already poor or near poor family (Xu et al., 2003; van Doorslaer et al. 2006). Without health insurance, the poor are unprotected against the potential shocks of illness because as evidence shows, if possible in any way, may it be through the sale of assets, taking of loans or other coping mechanisms, the poor will attempt pay to save the life of a family member. 5. Willingness to Pay For Health Insurance One of the questions, on the side of the opponents of health insurance, is whether or not the poor would be willing to pay for health insurance even if they could afford it. This section of the study utilizes unique data on willingness to pay (WTP) for health insurance to examine this very question. In the surveys, a contingent valuation (CV) method was utilized to assess the WTP of adult individuals for health insurance. The method is based on presenting a description of a situation for which the individual would hypothetically pay. In the present case, the respondents are presented with an insurance card describing a potential insurance product. To elicit the maximum amount the respondents are willing to pay for the insurance product, a double bounded dichotomous choice elicitation method is used. This method involves asking the respondents whether they would be willing to pay a randomly assigned pre-specified amount for the insurance product instead of directly asking an open-ended question about the highest amount they are willing to pay. 12 Findings from the literature are promising. In a CV based study, Asenso-Okyere et al. (1997) found, in Ghana, that almost 64% of respondents were willing to pay about US$3.00 per month for a household of five for a National Health Insurance scheme aimed at the informal sector. In Ethiopia, a CV based study (Asfaw and von Braun, 2005) finds evidence supporting the feasibility of introducing community based health insurance schemes (CBHIS). Asgary et al examine willingness-to-pay for health insurance in rural Iran finding that households are willing to pay on average US$2.77 per month for health insurance. 13 While levels are not necessarily comparable across countries and differing products, this evidence demonstrates that individuals in a variety of low-income countries would be willing to pay for low-cost health insurance schemes. In Asfaw and von Braun (2004), the authors investigate the potential of such schemes to mitigate the impacts of health shocks due to economic reforms on poor rural households. Their findings suggest that such schemes indeed would be helpful in protecting the poor against shocks. 12 The initial bid is randomized to avoid the problem of initial bid bias. 13 The study doesn t consider per member WTP. 19

20 Willingness to pay for health insurance was surveyed in Lagos, Kwara and Tanzania but not in Kenya. 14 The insurance coverage of the households in the surveyed areas was very low in all three contexts at the time of the baseline survey: in Lagos 3.5% of the households reported that at least one household member had insurance before, while only 2.6% of the households had a member currently enrolled in an insurance scheme. In Kwara, a mere one percent of the households had had insurance before and the same amount was currently enrolled. The coverage of insurance was the highest in Tanzania, where 5% of the households reported having had insurance before. Individuals who reported being currently enrolled in any health insurance policy were excluded from the analysis. The WTP interviews were given to all household members above 18 years of age who were present in the household. The interviews were conducted in the following order: first, the interviewee was explained the basic concept of insurance; then s/he was asked whether s/he would be interested in enrolling in a health insurance and if not why. Next, the interviewee was presented with an insurance card describing a potential insurance product and s/he was asked for the number of relatives s/he would like to be covered by such a medical aid. If the individual wants to insure at least one person, s/he was asked whether s/he was willing to pay the first bid. Otherwise, no further question was asked. After a yes response the interviewee was presented with a higher second bid, while after a no response s/he was presented with a lower second bid. Finally, the interviewee was asked the highest amount s/he would be willing to pay for the insurance product phrased as an open-ended question. Below, the willingness to join, willingness to pay the first bid and the mean WTP are discussed. The willingness to join is defined as willing to enroll at least one family member in the offered health insurance scheme. Individuals who give inconsistent answers to the highest amount question conditional on their bid responses are excluded from the analysis. 15 In addition, a consistent highest amount of zero is considered as the individual is not willing to enroll in the insurance scheme because s/he is not willing to pay for it. The willingness to pay the first bid is analyzed for individuals willing to join the insurance. Four values of the starting bids were varied randomly across the respondents to reduce initial bid bias and obtain a better estimate of the demand for health insurance. The mean willingness to pay is calculated from the highest amount that respondents reported they were willing to pay for the health insurance package in response to the open-ended question. Only respondents willing to join the insurance program are included in the calculation. In all three contexts, the offered insurance packages are low-cost schemes covering relatively limited services. These include unlimited access to consultation with the general practitioner or a specialist, routine immunizations, HIV treatment, and limited access to prescribed medicines, ante-natal care and delivery. The basic package does not cover the costs of hospitalization or 14 Due to time and financial constraints it was not possible to include a willingness to pay module in the Kenya Survey. 15 The number of individuals excluded due to inconsistencies is 121 for Lagos and 75 for Kwara. In the Tanzania survey there are no inconsistent responses. 20

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