Bulletin of the World Health Organization

Coping with out-of-pocket health payments: empirical evidence from 15 African countries

Adam Leive a, Ke Xu b

Introduction

The economic consequences of illness in developing countries have been the focus of increasing attention in recent years.1–3 Health shocks, defined as unpredictable illnesses that diminish health status, are among the most important factors associated with poverty in this context. Households facing health shocks are often affected by both the payments for medical treatment and the income loss from an inability to work. In the absence of panel data, recent research has focused on the financial burden of health payments across countries.4–7 When measuring financial protection from such payments, coping mechanisms provide important information on how households respond to health shocks and how payment may affect their future welfare; simply looking at the ratio of health spending to household expenditure can overstate the threat to consumption and the catastrophic consequences of health payments.8

Research from several studies suggests that households employ different strategies to cope with health shocks.9–11 In the short run, when medical bills exceed a household’s income, households may use savings, sell assets, borrow money from friends and family, or take out a loan using collateral. Families may also alter their labour allocation decisions; if a household head falls ill, family members previously not working may begin to do so to substitute for lost income and repay loans. Formal health insurance in developing countries is rare and many households also lack access to formal credit and savings arrangements.12 Correspondingly, much of the borrowing and saving by households is informal in nature and reliant on the social capital of communities.

Most studies to date have focused on the coping strategies employed in one particular country.13,14 While there is reason to believe that households in different contexts cope with health shocks differently,15,16 determining the existence of patterns across countries is conceivably of great interest.

The purpose of this paper is to explore how households in Africa cope with out-of-pocket health payments and how strategies differ between financing inpatient services and financing outpatient and routine care. Out-of-pocket payments for outpatient services or drugs, particularly among people with chronic conditions, could amount to a great deal of money and may be even more detrimental to households over the long-term; however, they differ from out-of-pocket payments for inpatient care, which can involve large sums of money in a short period of time. Inpatient expenses may also correspond to more unpredictable forms of illness that households may be poorly equipped to deal with. Our focus is on the short-term strategies used to cope with the cost of medical care. Since our dataset is cross-sectional and lacks exogenous measures of a health shock, such as a reduction in activities of daily living, we are unable to examine the full economic costs of illness. This would also include lost income from lower productivity and the resulting change in household consumption.

The Setting

Limited by data availability at the time the study was conducted, we included the following 15 African countries: Burkina Faso, Chad, the Congo, Cote d’Ivoire, Ethiopia, Ghana, Kenya, Malawi, Mali, Mauritania, Namibia, Senegal, Swaziland, Zambia and Zimbabwe. These countries vary in their levels of income, government and total health expenditure, extent of out-of-pocket payments for health financing and average life expectancy (Table 1). All are classified as low-income countries by The World Bank, with the exception of the Congo, Namibia and Swaziland, which are lower-middle income countries. Average life expectancy ranges from a low of 37 years in Zimbabwe to a high of 58 in Ghana. These 15 countries are geographically spread throughout the western, central, eastern and southern parts of sub-Saharan Africa.

However, the health systems of these countries are generally characterized by low government revenues, low government and total health spending and few risk-pooling mechanisms. In 2002, total health expenditure was less than 30 US dollars (US$) per capita except in Namibia (US$ 97), Swaziland (US$ 63) and Zimbabwe (US$ 151) according to World health statistics 2007.17 As a share of total health expenditure, out-of-pocket payments ranged from less than 6% in Namibia to over 60% in Cote d’Ivoire and Chad, with an average of about 40% for all 15 countries. Some, such as Burkina Faso, Ghana and Senegal, have a history of community health insurance. Such microinsurance schemes for health care are part of a larger umbrella of microfinance initiatives, including savings and credit instruments, that have a large degree of community involvement.18 Social health insurance exists in few African countries, such as Ghana, Kenya and the United Republic of Tanzania and only on a very small scale.

Methods

Data

The data were obtained from the World Health Survey conducted in 2002–2003, which was launched by WHO to provide valid, reliable and comparable information across countries regarding health status and health systems.1 The World Health Survey is cross-sectional and is based on a multi-stage clustered random sample of households designed to be nationally representative. The questionnaire is standardized across countries to facilitate international comparisons. Sample sizes ranged from 2754 in the Congo to 5276 in Malawi.

The survey collects a wide range of information on health status, health service utilization, health expenditures and household socioeconomic indicators. The household questionnaire is administered to the household member most knowledgeable about the health, employment and expenditures of the household. Household out-of-pocket payments for outpatient and routine expenses in local currency units were collected for a 4-week recall period. Household out-of-pocket payments for inpatient services were collected for both a 4-week and a one-year recall period.

With regard to coping strategies, the survey included questions on the means the household had employed to finance any out-of-pocket payments over the previous year. Such means included the following: (i) income; (ii) savings; (iii) reimbursement from an insurance plan; (iv) sale of assets; (v) borrowing from friends or family outside the household; (vi) borrowing from others; and (vii) other. However, information on the fraction of the out-of-pocket payments that was financed by each household by borrowing money or selling assets was unavailable.

Variables

The dependent variable was a binary variable representing the coping strategy used to finance out-of-pocket payments. We compared households selling assets and/or borrowing money to those that financed health care entirely from current income or savings. In our analysis, the dependent variable measuring coping behaviour was equal to 1 if a household sold assets, borrowed money, or did both to finance out-of-pocket payments during the year; it was equal to 0 if income or savings were used. The few households that used insurance were excluded from the analysis. To allow for comparisons across countries with different currencies, for each country we examined quintiles of total household spending on inpatient care among households where a hospitalization had occurred over the previous year. The lowest quintile of inpatient spending corresponds to level 1 and the highest corresponds to level 5. These households may also have incurred outpatient out-of-pocket spending during this period, but this information is unavailable in the survey and so is not included. We compared the coping strategies of these households to that of those whose health payments did not include hospitalization.

Control variables included socioeconomic indicators for the household and the household head. Since the survey did not contain detailed consumption or expenditure modules, including the amount of food purchased, home-produced, or received as a gift, household durables, etc., a household asset index was used as indicative of a household’s permanent income. Such an index had already been calculated in the survey data for each socioeconomic quintile using a variant of the hierarchical ordered probit (HOPIT) model.19 The index was divided into quintiles, which appear as explanatory variables. We included household size as defined by the survey – number of people living in the household – and a dummy variable for urban households. Three characteristics of the household head were included, specifically age (above or below 60 years), sex and schooling. Schooling was measured using three dummy variables representing the following: no primary education; completion of primary school; completion of secondary school or higher. Descriptive statistics are presented in Table 2.

Regression model

We estimated a simple logit model to explore the factors correlated with a greater likelihood of financing health care by selling assets, borrowing, or both rather than by using income, savings, or other sources. The model was run separately for each country using the same set of independent variables available from the survey. The analysis unit was the household. Only households that reported having spent on health in the previous year were included in the regression model. The reference categories are the poorest income quintile for income, no primary education for schooling of the household head and households with out-of-pocket spending that did not include a hospitalization for health expenditure. We report regression results of the likelihood of selling assets and borrowing money to finance health payments and the average partial effects for different levels of spending on inpatient care. The average partial effects were calculated by computing the partial effects for each observation and then taking the average across the sample. This corresponds to the relative probability of selling assets and borrowing money between households with a particular level of inpatient out-of-pocket expenditure and those whose out-of-pocket payments did not include a hospitalization.

Results

Descriptive results

In most countries, around 30% of all households financed out-of-pocket health expenditure by borrowing and selling assets (Table 2). About 50% of the households with a hospitalization in the previous year did so across countries, while the figure was less than 40% among those whose health services did not include hospitalization. Fig. 1 illustrates the percentage of households borrowing and selling assets by income quintile. In nearly all countries, fewer households in the richest quintile sold assets or borrowed money to cope with medical bills compared to lower quintiles. However, no clear differences were noted at intermediate income levels.

Fig. 1. Coping with health care expenditure through selling assets and borrowing, by household income level, in 15 African countries
Fig. 1. Coping with health care expenditure through selling assets and borrowing, by household income level, in 15 African countries

The utilization rate of inpatient services of any household member within the previous year was between 10% and 20% in most countries. This was lowest in Ethiopia at about 6% and highest in Mauritania at nearly 24% (Table 2). Monthly out-of-pocket payments on outpatient and inpatient services varied widely by country; however, out-of-pocket payments for inpatient care were greater than for outpatient or routine services in all but Zimbabwe and more than twice as large in seven countries (Fig. 2).

Fig. 2. Ratio of inpatient to outpatient household out-of-pocket (OOP) health payments (monthly) in 15 African countries
Fig. 2. Ratio of inpatient to outpatient household out-of-pocket (OOP) health payments (monthly) in 15 African countries

Regression results

Table 3 displays the results of the logit regressions. In general, higher inpatient spending was associated with a greater likelihood of borrowing and selling assets at the 5% significance level, except in Burkina Faso, Namibia and Swaziland. The probability was greater the higher the level of inpatient spending, as indicated by the average partial effects (Table 4). In 11 countries, households with the highest level of inpatient spending were at least 10% more likely to borrow and sell assets than those that made no out-of-pocket payments for inpatient care. The effect was greatest in the Congo, Ethiopia and Ghana, where households in the highest category of inpatient spending were 38%, 39% and 40% more likely to cope by selling assets and borrowing, respectively. The effect of lower levels of inpatient spending was not as strong.

Across household income quintiles, the results are consistent with the descriptive analysis. The richest households were almost always less likely to sell assets and borrow to finance health spending than the poorest households, after controlling for location, characteristics of the household head and type of household health spending. The results obtained were statistically significant at the 5% significance level in 9 of the 15 countries. However, there was no significant difference in household coping behaviours among the lowest three household income quintiles. There was also no significant difference between the rich and the poor in several countries (Cote d’Ivoire, Mauritania, Senegal), and income was positively correlated with borrowing and selling assets in Malawi.

In addition, in half the countries urban households were significantly less likely than rural ones to cope by borrowing and selling assets. Male-headed households were also less likely to borrow and sell assets in 11 countries, although the opposite was noted in households headed by someone over the age of 60.

Discussion

In interpreting the results and making international comparisons, it is important to recognize the limitations of this study. First, information on the amount each household borrowed or the value and type of assets sold would have allowed for more insightful analysis of the coping mechanisms used by households to finance out-of-pocket payments for health care.20 In the absence of such data, the study is largely limited to qualitative conclusions. Again, the analysis only captures the response to medical payment and not the full economic costs of an exogenous health shock. Moreover, a hospitalization may have occurred in combination with other idiosyncratic or common shocks, such as fluctuations in the weather and in commodity prices, that could have affected the coping strategies households used to finance medical care. Finally, households that were too poor to seek health care were not captured in the analysis.

Nevertheless, the study provides cross-country evidence that African households often turn to borrowing and selling assets to cope with medical bills. Households that incur spending for inpatient care, which is often unpredictable and sizeable, are more likely to do so than those whose health spending did not include hospitalization. The size and significance of this effect were generally more pronounced at higher levels of expenditure for inpatient care.

The likelihood of using credit and of selling assets may be less strongly correlated with household income if the major source of health financing in the country is out-of-pocket payments. This appears to be the case in Burkina Faso, the Congo, Cote d’Ivoire and Senegal, where out-of-pocket payments comprise over 50% of total health expenditure. Additionally, the poor in many of these countries often lack savings; however, this is not the case in Cote d’Ivoire, for example, where, perhaps surprisingly, nearly 80% of those living on less than US$ 1 a day have a savings account.21 This may help explain why differences in income were not associated with differences in coping behaviour in this country.

The use of the coping strategies described herein also depends on the ability of households to borrow and the availability of assets that can be sold. The former is linked not only to the financial capacity to repay a loan but also to the availability of social capital. Differences in the amount and types of social capital may be large between the richest and poorest in society but smaller between households in the middle- and lower-income strata.22 This may be one of the reasons we did not find a significant difference in the use of these coping strategies between lower income quintiles in nine countries. Moreover, while we have searched for patterns in behaviour among countries, it is also reasonable to believe that the precise mechanisms underlying coping strategies are likely to be context-specific both within and across countries. Although focusing on income shocks from drought and not illness, Fafchamps et al. (1998) and Kazianga and Udry (2006) found that in Burkina Faso livestock sales make up a small share of financing such shocks.23,24 This may explain why in our study the type and level of health spending in Burkina Faso showed no significant correlation with borrowing money and selling assets, although their research did find changes in grain stocks played an important role.

While this research has described the prevalence of different coping behaviours across countries, how well these mechanisms smooth consumption and to what extent they increase future vulnerability to shocks are key questions. Informal credit networks and microcredit schemes may help households maintain consumption levels in the face of idiosyncratic shocks. It may be possible to accumulate assets during good times and sell them if needed when illness strikes. Without formal insurance markets, such risk-coping strategies may help households smooth consumption, though perhaps not fully.25–27 However, the evidence from analysing health shocks using panel data finds that such coping strategies do not fully protect consumption.28–31 Several other studies have found that spending on food and education is sacrificed after illness.32–34

Introducing formal prepayment and risk-pooling to protect households, at least for large health shocks, is likely to be beneficial. Recently, Gertler et al. (2008) found that consumption is not protected from unexpected illness, but access to microfinance and lending programmes helps households self-insure consumption.35 Our results indicate that households with outpatient spending or relatively inexpensive hospitalizations finance the cost of treatment from current income and savings more often than households with hospital episodes requiring higher payments. Coverage for catastrophic inpatient expenses could offer sizeable gains. However, it would be important to investigate the degree to which this might crowd out informal risk-coping arrangements on a context-specific basis.

Even so, there could likely still be gains from some form of formal prepayment scheme if informal coping strategies increase household vulnerability to future shocks.36 Borrowing can be at high rates of interest; assets may be lumpy, in the sense that they must be accumulated in large, discrete amounts rather than small increments, and depleting them may sacrifice future income; and withdrawing children from school can reduce their human capital. It is therefore important to examine both the type of coping strategy used and the change in consumption, since smooth consumption might still reflect a costly situation for households.37

While formal prepayment including a comprehensive benefit package for health care might remain limited in these African countries for some time, coverage for catastrophic inpatient expenses may offer financial protection for many households. However, achieving this key policy objective is probably far from enough to prevent poverty caused by ill health. Income lost from an inability to work may be larger than the payment for health services with longer-lasting consequences. An overall social protection network could be beneficial to support poverty reduction in African countries. ■

Acknowledgements

We thank David Evans and Guy Carrin for their valuable comments and suggestions in the earlier stage of the study and are grateful to two anonymous referees for helpful comments. At the time of this research, Adam Leive was working at WHO. The views expressed here are those of the authors and do not necessarily represent those of the WHO, the International Monetary Fund or its Board of Executive Directors, or of the countries they represent.

Competing interests: None declared.

See http://www.who.int/healthinfo/survey/en/ for a detailed description of the survey.

References

Affiliations

  • International Monetary Fund, 700 19th Street NW, Washington, DC 20431, United States of America.
  • World Health Organization, Geneva, Switzerland.
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