Although some studies have focused on the factors that are likely to be associated with the risk of catastrophic health expenditure, only a limited number of empirical analyses have been conducted. Therefore, little is known about which segments of the population are most at risk of experiencing catastrophic health expenditure. In general, catastrophic health expenditure is associated with poverty or low income, unemployment, low levels of insurance coverage and having disabled, chronically ill or aging household members. Wyszewianski (1986), for example, found that ageing, unemployment and poverty were the most important risk factors in the US for incurring catastrophic health expenditure. Similarly, in an earlier analysis of the US health system, Berki (1986) stated that poverty and not having health insurance coverage were among the risk factors associated with catastrophic expenditure on health care.
O’Donnell et al. (2005) investigated sources of variation in the incidence of catastrophic expenditure on health care across six Asian countries using household surveys. They used a 10% threshold level of total household expenditure following Pradhan and Prescott (2002) and Ranson (2002). They compared the estimation results of a standard probit model to one which considered the endogeneity of total household expenditure. They argued that total expenditure could be endogenous since households generally use a range of strategies, such as borrowing, using savings or selling assets, to meet health care costs and do not necessarily choose to cut other types of consumption within a fixed, single period income constraint. Different indicators of access to savings and credit, such as land holdings or land size, were used as instruments for total expenditure. It was assumed that such access affected the household’s total expenditure but was not correlated with the out-of-pocket budget share. The reason for choosing such measures of access as instruments was that, for a given initial income, households with easy access to credit have more capacity to extend the household budget to cover unexpected health expenditure. The authors found that the probability of incurring catastrophic
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health expenditure increases with total household expenditure (‘richer’ households incurred such expenditure more than poor households) when endogeneity was not controlled for. However, when endogeneity was allowed for by including instruments, the positive coefficient of total expenditure changed from being statistically significant to being statistically insignificant which implied that endogeneity led to a strong upward bias in the estimation results indicating the impact of the household income on its out- of-pocket expenditure share. They also found that having a highly educated household head, insurance coverage and living in an urban area were all inversely associated with the probability of incurring catastrophic health expenditure.
The study by O’Donnell et al. (2005) is important in terms of introducing a new methodology addressing a widely discussed dimension of catastrophic health expenditure. However, the methodology is arguably somewhat controversial because the instruments used in the analysis are potentially problematic in terms of their liquidity. They are large assets to sell in order to cover health care costs. Furthermore, inter-temporal adjustment to health care costs has a potential to extend its burden over time and may lead to greater debt in the future. In order to analyse the role of financial coping strategies, it seems that using longitudinal data is an interesting avenue to pursue.
Although as mentioned above, an important conceptual challenge should be again highlighted at this point. As Kawabata et al. (2002) emphasise, it is possible and common that the highest proportion of catastrophic health expenditure is not always experienced by the lowest income group. The reason is that ‘catastrophic health expenditure’ can only be experienced if the household seeks health care and expenditure occurs (Hatt, 2006). Poor households usually experience a delay in meeting their health care needs due to their economic situation. In this case, catastrophic health expenditure indicators are subject to potential selection bias. Medical care seeking behaviour is not accounted for in most of the analyses and this measurement problem is accepted as a limitation and the analysis is conducted with the available household survey data. However, this problem has a critical importance in terms of financial accessibility to health care (McIntyre et al, 2006). Thus, ignoring this dimension of catastrophic health expenditure arguably does not provide a complete picture of the issue of financial protection.
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Pradhan and Prescott (2002) used a simulation model to construct a distribution of needed health expenditure using household survey data for Indonesia which provides household health care expenditure as well as health care utilisation information. Catastrophic health expenditure was defined as out-of-pocket health expenditure exceeding 10% of the household’s total expenditure. The distribution of catastrophic health expenditure by expenditure quintiles indicated progressive trends, which implies that richer households are more likely to spend 10% of their income on health care as compared to poor households in Indonesia. It was claimed that it is not possible to investigate directly whether the poor households suffer disproportionally from catastrophic health expenditure from the household survey data. Therefore, they used observed health service utilisation and the expenditure pattern of the middle-income group and randomly applied this group’s pattern to the rest of sample. Then, the obtained and actual expenditure of the household were compared to each other to obtain the stochastic distribution of ‘needed’ health care. The reason for choosing the middle income quintile was justified as it is a starting point to obtain information for underutilisation by the poor households and overutilisation by the rich households (Hatt, 2006). The age and sex composition of households was used as a proxy for health status which identifies ‘needed’ health expenditure.
Pradhan and Prescott (2002) also highlighted the relationship between equity and financial protection. Besides financial protection, ensuring equity in access to health care is also one of the most important objectives of health policy. They noted that equity requires a subsidy for low-cost primary care that would generate benefit for a large proportion of the population, whereas financial protection focuses on subsidies for the smaller proportion of the population who are more likely to incur high health care costs and impoverishment. The results of their simulation analysis indicated that subsidising inpatient care would result in the greatest decrease in the proportion of households with catastrophic health expenditure while subsidising outpatient care would provide benefits particularly for the very poor segment of the population. They concluded that if the aim is to provide financial protection for poor households, the free inpatient regime is not the preferred regime for Indonesia. One limitation of their study is that they only allowed the prices of health care to change and treated all the utilisation rates as fixed and, in this case, increasing utilisation rates arising from subsidising outpatient care could not be identified (Hatt, 2006). On the other hand, Pradhan and Prescott (2002)
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attempted to shed light on the differences in the utilisation of health care between the poor and the rich households which can be regarded as an important contribution to the literature.
The relationship between health insurance and its effect on out-of-pocket health expenditure is also a widely analysed issue in the existing literature (Sepehri et al., 2006). It is expected that insurance coverage provides financial protection from catastrophic health expenditure. However, it is also possible for health insurance to create demand inducement, in the case of, for example, small benefit packages or inadequate insurance coverage, and this demand increase may result in high levels of out-of-pocket health expenditure (Wagstaff and Lindelow, 2008). In some countries, insurance packages cover only some of the total costs, requiring households to pay a co- payment. If the total cost is quite high relative to the budget, even a 20% co-payment can be classified as catastrophic health expenditure. As Kawabata et al. (2002) state, under insurance coverage, catastrophic health expenditure may not simply go away if the benefit package does not cover most of the health expenditure.
In addition, the study by Foster (1994) is considered as one of the leading studies in the existing literature, which emphasises studying the consequences of illness in terms of productivity. Foster (1994) noted that in the health-productivity literature the effect of illness is more important than the effect of nutrition because, in contrast to the case of nutrition over which households arguably have relatively direct control, illness has an unexpected nature, which households may have little control over. In this context, there is also a growing literature on indirect costs, which focus on productive time losses for the ill individual and for other household members. However, most of household surveys do not include indirect costs and, thus, studies generally include only direct costs due to data unavailability and methodological challenges (McIntyre et al., 2006). In this respect, most of the studies that include both direct and indirect costs emphasised that indirect costs are generally more than direct costs (see, for example, McIntyre et al., 2006 for a review of studies carried out in low and middle income countries; Sauerborn
et al. 1996 for Burkina Faso; Koopmenschap and Rutten, 1994 for eight different
countries and Gertler and Gruber, 2002 for Indonesia).
On the other hand, in addition to hospitalisation costs, fees and medicine costs, direct costs include transportation costs, costs of nutrition (e.g. special food for a sick member
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of the household) and accommodation costs. According to Attanayake et al. (2000), transport costs for ill household members may also impose a considerable burden on household’s budget and may constitute 20% of total direct costs (McIntyre et al., 2006). Analysis focusing on the economic consequences of out-of-pocket health expenditure including other components of direct costs is, however, limited due to data availability. In the case of Turkey, there are only a few studies focusing on the factors which are associated with catastrophic health expenditure risk. However, there have been important policy changes in the health area since 2003 which may have important effects on health expenditure in Turkey. Yardim et al., (2009) investigated the level of catastrophic health expenditure and identified the factors associated with catastrophic health expenditure risks in Turkey. The HBS for 2006 and the methods introduced by Xu et al., (2003) were used. The results of the logistic regression analysis indicated that the health insurance coverage of the household head and living in an urban area were closely related to the catastrophic status of households. There are, however, two main limitations of this study; firstly, they estimated the model only for 2006 and used only one threshold level (40% of non-food expenditure). Hence, it can be argued that the time dimension and sensitivity checks of the results were ignored. Therefore, policy implications drawn from these results could be potentially misleading. Secondly, they overlooked the problems arising from the distribution of catastrophic expenditure across income quintiles and the selection problem in terms of the difference in the treatment seeking position of the poor and the rich households were not taken into account. In another study focusing on Turkey, Sulku and Bernard (2009) examined the role of the health insurance system in terms of providing adequate financial protection against high out-of-pocket health expenditure in the population aged less than 65 years using Turkey’s 2002/2003 National Household Health Expenditure Survey. They found that 19% of the non-elderly population were living in households whose health expenditure exceeds 10% of their income. For poor households, 23% of the non-elderly population were living in households whose expenditure on health care is more than 20% of their income.
Kisa et al. (2009), on the other hand, investigated the delayed use of health care services among the urban poor in Turkey. They conducted a field study among the 92 poorest households in the Etimesgut region in Ankara in order to collect information about
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health service delays among the poor as well as the factors associated with those delays. Household members were asked whether they had experienced difficulty in paying for health care services and the results indicated that about 63.3% of poor households did not seek health care due to inability to pay and 17.4% of poor households reported that they had experienced extreme financial difficulty when attempting to pay for health care. They concluded that overall; two out of three poor households had delayed or not sought health care because they thought they could not afford it. The results of their study suggest that the medical care seeking behaviour of poor households is an important problem in Turkey. Thus, difficulties in access to health care among the poor should be one of the primary goals of health policy in Turkey. In this context, this chapter extends the analysis of the determinants of catastrophic health expenditure in Turkey by controlling for potential selection bias in terms of treatment seeking behaviour.
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2.4 DATA AND METHODOLOGY