Major strength of the present study is that we were able to examine impact of high-level facility use on fi- nancial status empirically and potential mitigation effect of national health insurance in it. Another strength is that our study design allows us to compare the rural and urban area based on the comparably selected samples. On the other hand, a few limitations of this study should not be overlooked. First, there was substantial level of missing information in variables related the healthcare utilization in Quoc Oai dataset. Supposedly, this might have not distorted the result considering a similar preva- lence of catastrophic expenditure between the present and previous studies. However, we still cannot be free from concern about bias. Second, the self-reported na- ture of the data can cause measurement error in our sample. Although medical expenditure information was obtained from the receipt of service utilization if it was available, it still cannot rule out potential biases. Third, since the study design is cross-sectional, interpretation should be limited only to the association rather than causality. Third, the data for our study were drawn only from a single district each from northern and southern regions, and are therefore not fully representative of Vietnam. Therefore, care should be given for generaliz- ing the result to other parts of the country.
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The three methods applied in evaluating the afford- ability of rare diseases and orphan drugs all have cer- tain limitations. The annual per capital income method uses residents’ annual income as indicator in calculation. However, the huge gap between the rich population and the poor makes average income an in- effective index to describe the affordability condition of the general public. With current data, we can spe- cify the evaluation into five different groups by their income level, but this is still not satisfying in reflect- ing the conditions of some rich groups and the poor. As for catastrophic expenditure method, it uses the threshold 40 % defined by WHO standards. As treat- ment is usually more expensive for rare diseases, the threshold can be lifted accordingly. For calculation in both catastrophic expenditure and impoverishment expenditure method, the distribution curves of urban and rural annual per capita disposable income. When fitting the income curve, we assume that for each in- come group, the income is in linear distribution, which may lead to an overestimated affordability for some low income groups and an underestimated affordability for some high income groups .
TB patients incur high costs for diagnosis and treatment despite the free TB care offered in most settings in China. A recent study analyzed the high costs among multi-drug resistant TB patients in China . This study aimed to estimate the costs associated with and analyze the extent of CHE for TB care in China. It is widely agreed that catastrophic health care expenditure occurs when OOP payments for care force a household to reduce expenditure on basic necessities over an extended period of time . However, there is still no consensus on the formal definition of CHE. Some researchers define CHE as the total health expenditure exceeding a threshold (varying from 5–20 %) of house- hold annual income [3, 5, 28, 29]. Others argue that a measure of the ‘capacity to pay’ (effective income) would better reflect purchasing power than total household in- come, and define CHE as health payment exceeding a threshold (usually 40 %) of effective income remaining after basic necessities have been met [3, 30]. Many researchers have used household non-food expenditure as a proxy measure for household effective income [9, 30]. In this study, we used two common measures: OOP payments exceeding 10 % of household annual income and OOP payments equaling or exceeding 40 % of household non- food expenditure. Even though both of the definitions are
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This descriptive-analytical examination of the Iranian health reform in the years 2014-2016, re- garding the trends of distribution of health pay- ments in 2010-2016, and the methods introduced by WHO (19). The data were collected from the source files of households’ income-expenditure surveys in the years 2010-2016, presented by the Iranian Statistical Center, ISC. The whole data sample size in urban and rural areas are shown in Table 1. The questionnaire of the surveys and the method of collecting data in the ISC have been adapted by the Classification of Individual Con- sumption by Purpose (COIOP) that provide data with international comparisons.
studies. Letters to the editors, presentations at conferences, and case reports were excluded. To assess the quality, two authors evaluated the articles according to Newcastle–Ottawa Scale (NOS) (26). In the first phase, articles with non- relevant titles on the subject of the study were ex- cluded. In the second phase, the abstract and the full text of articles were reviewed. Computer soft- ware for reference management (Endnote X6) was used for organizing and recognizing the duplica- tions. The collected data were summarized in pre- viously designed extraction tables. To estimate the proportion of catastrophic expenditure a quantita- tive meta-analysis method was done by computer software (CMA: 2-Comprehensive Meta-analysis). Forest plot with a 95% confidence interval was used to estimate the overall proportion of cata- strophic expenditure. Random effect was used to perform meta-analyses. I 2 was used to evaluate
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Nevertheless, there were some limitations associated with our study and with using the Korea Health Panel data. First, this study was cross-sectional in design; thus, possible inverse causality between chronic diseases and catastrophic expenditure are not reflected. Second, the Health Panel relies on data collection from a sample of the population. Therefore, in order to accurately measure the rate of catastrophic medical expenditure for the population as a whole, weighted values must be applied. However, in our research it was not possible to use weighted values. Third, the Korea Health Panel data for 2008 were collected in biannual segments; however, the medical expenditures reflected in the data do not fall exactly within 2008. We combined the two periods of medical expenses, making the assumption that the sum reflected the medical costs incurred in 2008 fairly accur- ately; nevertheless, there is the possibility that we underes- timated catastrophic medical expenditures. Fourth, the health care fees and income reported in the 2008 Korea Health Panel data were drawn from different time periods. Reported income was recorded from 2007, while medical costs were surveyed in 2008. Accordingly, we assumed that the previous year’s income represented the household Table 4 Associations between chronic diseases and
Our findings show that the NCMS provide some finan- cial protection for TB patients. We also find that there is a certain reduction in intensity indicators including MG, MPG, PG, NPG and MPPG. This finding is consistent with other studies conducted in Anhui, Chongqing, Qing- hai, Shandong and Ningxia of China, and in Gujarat of India for health expenditure on other diseases [22, 23, 31]. However, this protection is only limited and the expenses for TB care after reimbursement still pose a threat to some households. Our study finds that 46.7 % of the households still experience CHE and 35.4 % are still below the poverty line even after reimbursement. It implies that there is a big gap between actual protection level and the ideal goal of the policy design for the NCMS. Over the last decade, China has made great strides in NCMS coverage . But we still need to try to achieve the goal of universal coverage. Table 3 NCMS impact on intensity of catastrophic expenditure for TB care, China, 2012
households spent over 10% of their budget on outpatient treatment, compared to 4.2% for inpatient care. The in- cidence of catastrophic expenditure at corresponding thresholds is much higher when OOP payments are expressed as a proportion of non-food budget. This in- crease reflects the greater share of resources spent of food items in Kenya, which is typical of spending pat- terns in low-income countries. For total OOP payments, 5.6% of households reported payments greater than 40% of total expenditure; this proportion doubled, when the threshold was set relative to share of non-food expend- iture. Xu et al. estimated catastrophic spending among Kenyan households using data from a similar survey conducted in 2003 . They found that overall 4.1 per cent of households faced catastrophic health expend- iture. About 5.8% and 6.1% of households incurred health care costs over 40% of non-food budget for out- patient and inpatient services respectively. While it is not always possible to directly compare findings due to methodological differences, results presented in this paper suggest that the burden of OOP payments for in- patient care might be decreasing, while that of out- patient care is on the increase. This downward trend in the proportion of households facing catastrophic costs due to inpatient care should be interpreted with caution. It is known that inpatient care is much more expensive than outpatient and it is possible that households might have failed to seek admission due to affordability barriers (particularly the poor). Also, there is a tendency to over- estimate annual spending on OOP payments when health costs are scaled to annual estimates. The timing of household surveys also has important implications for levels of self reported illness, treatment seeking patterns and cost burdens [5,27].
referred to as Model 1A in Table 1). A consistent set of variables emerge as significantly impacting the odds of CHE across cut-off levels, though the exact magnitude of effect does show some variation. Kisumu (Obunga and Nyalenda) slum residents were less likely to experi- ence CHE. We found that an increase in the number of working adults in the household reduced the odds of CHE. Having two or more working adults in the house- hold reduced the likelihood of catastrophic expenditure by at least 1.2 times (1/0.82). Also, households with a main income earner older than 55 years were at least 1.56 times more likely to experience CHE. The average number of years a household had lived in the slum ap- pears to increase the risk of CHE. While the magnitude is small (coefficient = 1.02), it is significant across most models suggesting either a deterioration of health with time spent in the slums or a reduction in the resources available for utilizing health care services. Interestingly, we found that enrolment in an informal social safety net (such as membership in merry-go-round) reduced the risk of catastrophic spending. Households with a mem- ber enrolled in a safety net were 1.59 (1/0.63) times less likely to incur CHE.
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PMJAY aims to build on the base provided by Rashtriya Swasthya Bima Yojana (RSBY), the national PFHI scheme implemented by many states during 2008 to 2018. While RSBY had a vertical cover of INR 30,000 (around 400 USD) annual sum assured, PMJAY has a much bigger cover of INR 500,000 (around 7000 USD) per family [11, 32, 33]. Some of the evaluations of RSBY have suggested that the limited sum covered could be a factor in its inability to protect from catastrophic expenditure [18, 19, 21]. Since a 17 fold increase in vertical cover is the main change in PFHI design brought in by PMJAY, the relevant policy question is whether larger vertical cover can im- prove financial protection for hospital care under PFHI. An evaluation of state-specific PFHI programmes that have implemented a vertical cover larger than RSBY can help answer this question. Three states in Southern India - Andhra Pradesh, Karnataka and Tamil Nadu which were pioneers in India in initiating PFHI programmes of their own. They differed from RSBY in terms of having a verti- cal cover around five times bigger than what RSBY offered . Further, there are differences in benefit-packages and implementation arrangements across PFHI schemes in different states in India. This suggests a need to examine performance of PFHI state by state [10, 21, 22].
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on a household as compared with other illnesses. Added to this medical burden is the high burden of non- medical expenditure in RTI which is nearly similar to the average medical expenditure for hospitalisation due to any illness in India . Thus, these data suggest that a large proportion of those suffering RTI are incurring a double burden of high medical and non-medical expenses, thereby making households quite vulnerable to catastrophic OOP in RTI. Transportation, food and phone expenses were the major items in the non- medical expenditure category with the cost of vehicle damage the next important category of expenditure. It is possible that the catastrophic expenditure due to RTI is underestimated in these findings as it is likely that some patients would have continued to incur RTI expenditure beyond the follow-up period of this study. However, it is important to note that as these expenditure figures for RTI cases include costs 6 months beyond the crash they are more complete as compared with costs incurred only during the hospital admission/visit. On the other hand, since incomes are often underreported in household sur- veys, our catastrophic expenditures could be an overesti- mate for this reason. The absence of overall household expenditure data is a limitation of our study.
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. Food costs include total household expenditures in line with providing food, in addition to the monetary value of prepared and consumed food in households. However, the cost of fast foods and outdoor foods (hotels and restaurants) and money which has been spent on cigarettes, tobacco, alcohol and other similar expenses have not been taken into account. According to the theory of the World Health Organization, if expenditures on health exceed more than 40 percent of payment capacity, it would be considered as catastrophic expenditure.
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While during 2000–2007 access to care for the poor has improved slightly, the share of households that face cata- strophic health expenditure have seemingly increased. In 2007 the share of households incurring catastrophic health expenditure reported by Xu et all , based on the analysis of the Household Budget Survey 1999, was 2.8% which was close to the mean figure for 89 countries ana- lyzed by the authors. Our estimate of 11.7% of popula- tion based on the 2007 Health Utilization and Expenditure Survey puts Georgia on the top of the list – as having one of the most unprotected health care financing systems, along with other transition countries (Azerbaijan, Ukraine, Vietnam and Cambodia) that feature a similarly high rate. However, we think such international compari- sons bear inherent limitations. Our study primarily focused on questioning health care utilization and expenditure, while most surveys used in the papers were either Living Standard Measurement Studies, or household budget surveys or household income and expenditure sur- veys that did not specifically look at health care utilization and expenditure. Consequently, a recall bias in non- health care surveys may underestimate spending levels on health, while our survey focused on health, possibly ren- dered higher estimates. The same situation is observed in other countries, e.g. in Azerbaijan the household budget surveys in 1995 and 2006 showed almost three times lower health expenditures than specially designed health utilization and expenditure surveys . It was also the case when we compared HUES health expenditure esti- mates with HIS from 2007 . Nevertheless, the share of household that face catastrophic health spending is high in Georgia and calls for policy solutions. Consequently, monitoring the rate over time, while using the same HUES survey tool, will allow the Government to observe changes in the future if they occur. Finally, Georgia has improved its FFC index, which was estimated at 0.68 in 2004  and according to our survey findings stands at 0.82 for 2007. This figure will also serve as a baseline to assess the impact of the planned health sector reforms in future.
Since the seminal work by Xu et al. , a large body of literature on the incidence of CHE has emerged and de- veloped. However, in a systematic review of literature  several gaps were identified, namely the scarcity of up- to-date analysis and a bias of the literature towards mid- dle-income countries. Few studies analysed trends over time and not many developed equity analyses (and most equity analyses correspond to the calculation of CHE by expenditure or income quintiles). Hence, we aim to con- tribute to the literature by analysing the evolution of CHE in a high income country over a decade (from 2005 to 2015). By following exactly the same methodological steps in the analysis of the three surveys, this study pro- vides assurance of the comparability of results. More- over, our aim is to focus not only on incidence figures but also on distributional aspects which have been less explored. In the latter case, we consider income-related inequalities in CHE but we also analyse the distribution of CHE across geographic areas and across family types. We further look at the distribution of the health expend- iture of families incurring CHE across types of healthcare.
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To determine the appropriate cut off point, the data of the two aforementioned equations were analyzed and the number of households driven below the line of poverty due to health expenditure, regarded as the gold standard in this study, was calculated. To measure the compatibility of the thresholds in the two approaches with the number of households driven below the line of poverty, Kappa coefficient, calculated using the following formula, was employed (Table 1). Kappa coefficient ranges from 0 to 100 and values close to 100 indicate appropriateness. Stata version 11 was used for data analysis.
The most pronounced finding relates to the association between the poverty status of the household and the likelihood of incurring catastrophic health expenditure after accounting for the household’s health care seeking position. The results from the selection equation indicate that poor households are much less likely to seek health care than non-poor households for all threshold levels, which is consistent with the hypothesis that poor households may not seek health care due to affordability concerns. However, the results also suggest that poor households are less likely to experience catastrophic health expenditure as compared to non-poor households even after accounting for the potential selection problem. This finding may reflect a particular aspect of the Turkish health care system related to the fact that it is commonly accepted that patients receive a better quality service in private health care facilities (Savas et al., 2002). Before the health reforms, patients using private health care were paying for services out-of-pocket, even if they had health insurance. After the health reforms, however, access to private facilities was improved. Although using private facilities still requires paying an extra charge imposed by the private provider, this charge was reduced by the reforms. 13 It can be argued that this improvement in access to private health care particularly benefited the non-poor segment of the population who can afford to pay the extra charge imposed by the private provider. As Wagstaff and Lindelow (2008) argue, it is possible for health insurance to create demand inducement, and this demand increase may result in high levels of out-of-pocket health expenditure. It is possible, therefore, that the improvements in access to private health facilities have increased the demand among non- poor households who prefer private health care to public health care. This increase in demand
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hold. We do however recognise that that for some house- holds, even expenditure on medicines can be catastrophic. At SEWA, patients who had crossed the upper limit in the first admission may not have submitted a second claim, knowing that they would not be reimbursed. This further compounds our underestimate. As we did not have infor- mation on the individual household incomes of the patients at ACCORD, we had to use survey data as a proxy for calculating the CHE. So our calculation of CHE in ACCORD is actually the proportion of the median house- hold income. This naturally would result in an underesti- mation of the incidence of CHE in the poor, while there would be an overestimation among the better off. This disparity would depend a lot on the variation of the income within the community. But, as most adivasi households at ACCORD are relatively homogenous in their poverty, we feel that this factor should not affect the results significantly.
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Bangladesh has made significant progress between 2005 and 2010 by reducing the poverty rate from 40.0% to 31.5% (BBS, 2007, 2011). The study of van Doorslaer et al., (2006) found that OOP contributed to poverty by 3.8% in 2000, and this study observed a corresponding rate of 3.5% in 2010 (van Doorslaer et al., 2006). This implies that while poverty in general reduced by a higher rate (from 40.0% to 31.5%), we observed just a slight reduction in poverty attributed to OOP for healthcare. Healthcare financing methods of Bangladesh should concentrate on finding alternative financing methods than OOP for reducing the probability of CHE and consequently poverty. Pre-payment mechanisms, like social health insurance, which are often recommended by international organizations, e.g., the WHO and the World Bank, should be applied in Bangladesh as a remedy for reducing financial risk and poverty attributed to OOP healthcare spending (WHO, 2010). The Government of Bangladesh developed the healthcare financing strategy for addressing social protection in order to achieve universal health coverage (MoHFW, 2012). The strategy recognized the impact of OOP healthcare financing mechanism on financial risk and poverty and consequently recommended reduction of OOP as a share of total health expenditure from 64% to 32% in 20 year period (2012-2032) and applying prepayment mechanisms, like social health insurance, tax funding, community-based health insurance, more. Findings from this study would be supportive to the healthcare financing strategy of the Government for monitoring the progression towards universal health coverage in Bangladesh.
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associated with OOP health expenditure. Expenditure increased with age, with the age group of 5–14 years having the lowest spending. There was less expenditure among single people in all areas of residence. Direct payments were higher in the richest quintile 5 than in the poorest quintile (Q1) in all areas of residence; Abid- jan 13,300 XOF (23.74 USD) in Q1 versus 28,950.7 XOF (51.67 USD) in Q5, urban area 8327.5 XOF (14.86 USD) versus 22,292.1 XOF (39.79 USD) and rural area 10,048 XOF (17.93 USD) versus 22,410 XOF (40 USD). The OOP health expenditure increased from the poor living con- ditions to the better living condition [mean for score 0: rural = 13,600 XOF (24.27 USD) and urban = 13,518.6 XOF (24.13 USD), mean for score 3: rural = 14,428.9 XOF (25.75 USD), mean for score 4: urban = 32,741.7 XOF (58.44 USD)], except in Abidjan where a higher value observed for score 0 was not statistically different. Sex, level of education and household size did not influence OOP health expenditure. By comparing the three areas of residence, OOP health expenditure was highest in Abid- jan in most of the variables e.g. sex, age, marital status, household size, insurance or financial aid for treatment expenses (Additional file 1).
Several characteristics of the household’s head and the household were included in the analysis as potential confounders: age, gender, marital status, level of education health insurance of the household head and his perception of one's social class in five ways. We included too households size, presence (none, at least 1) of children under 5 years of age, adults aged 65 years or older and chronic disease in the household. We classified households into settlement (urban, rural, Abidjan the economic capital), wealth quintiles by using per capita consumption expenditure and into wealth index of commodities. This index was constructed by adding the availability of portable drinking water system, adequate sanitary installation, electricity as a source of lighting, and gas as a source of energy. Adequate sanitary installation obey