Catastrophic health payments: does the equivalence scale matter?

Revisiting a decade of inequality in healthcare financial burden in Cambodia, 2009-19: trends, determinants and decomposition

BACKGROUND

Out-of-pocket healthcare expenditure (OOPHE) without adequate social protection often translates to inequitable financial burden and utilization of services. Recent publications highlighted Cambodia's progress towards Universal Health Coverage (UHC) with reduced incidence of catastrophic health expenditure (CHE) and improvements in its distribution. However, departing from standard CHE measurement methods suggests a different storyline on trends and inequality in the country.

OBJECTIVE

This study revisits the distribution and impact of OOPHE and its financial burden from 2009-19, employing alternative socio-economic and economic shock metrics. It also identifies determinants of the financial burden and evaluates inequality-contributing and -mitigating factors from 2014-19, including coping mechanisms, free healthcare, and OOPHE financing sources.

METHODS

Data from the Cambodian Socio-Economic Surveys of 2009, 2014, and 2019 were utilized. An alternative measure to CHE is proposed: Excessive financial burden (EFB). A household was considered under EFB when its OOPHE surpassed 10% or 25% of total consumption, excluding healthcare costs. A polychoric wealth index was used to rank households and measure EFB inequality using the Erreygers Concentration Index. Inequality shifts from 2014-19 were decomposed using the Recentered Influence Function regression followed by the Oaxaca-Blinder method. Determinants of financial burden levels were assessed through zero-inflated ordered logit regression.

RESULTS

Between 2009-19, EFB incidence increased from 10.95% to 17.92% at the 10% threshold, and from 4.41% to 7.29% at the 25% threshold. EFB was systematically concentrated among the poorest households, with inequality sharply rising over time, and nearly a quarter of the poorest households facing EFB at the 10% threshold. The main determinants of financial burden were geographic location, household size, age and education of household head, social health protection coverage, disease prevalence, hospitalization, and coping strategies. Urbanization, biased disease burdens, and preventive care were key in explaining the evolution of inequality.

CONCLUSION

More efforts are needed to expand social protection, but monitoring those through standard measures such as CHE has masked inequality and the burden of the poor. The financial burden across the population has risen and become more unequal over the past decade despite expansion and improvements in social health protection schemes. Health Equity funds have, to some extent, mitigated inequality over time. However, their slow expansion and the reduced reliance on coping strategies to finance OOPHE could not outbalance inequality.

Rural‒urban disparities in household catastrophic health expenditure in Bangladesh: a multivariate decomposition analysis

BACKGROUND

Rural‒urban disparity in catastrophic healthcare expenditure (CHE) is a well-documented challenge in low- and middle-income countries, including Bangladesh, limiting financial protection and hindering the achievement of the Universal Health Coverage target of the United Nations Sustainable Development Goals. However, the factors driving this divide remain poorly understood. Therefore, this study aims to identify the key determinants of the rural‒urban disparity in CHE incidence in Bangladesh and their changes over time.

METHODS

We used nationally representative data from the latest three rounds of the Bangladesh Household Income and Expenditure Survey (2005, 2010, and 2016). CHE incidence among households seeking healthcare was measured using the normative food, housing, and utilities method. To quantify covariate contributions to the rural‒urban CHE gap, we employed the Oaxaca-Blinder multivariate decomposition approach, adapted by Powers et al. for nonlinear response models.

RESULTS

CHE incidence among rural households increased persistently during the study period (2005: 24.85%, 2010: 25.74%, 2016: 27.91%) along with a significant (p-value ≤ 0.01) rural‒urban gap (2005: 9.74%-points, 2010: 13.94%-points, 2016: 12.90%-points). Despite declining over time, substantial proportions of CHE disparities (2005: 87.93%, 2010: 60.44%, 2016: 61.33%) are significantly (p-value ≤ 0.01) attributable to endowment differences between rural and urban households. The leading (three) covariate categories consistently contributing significantly (p-value ≤ 0.01) to the CHE gaps were composition disparities in the lowest consumption quintile (2005: 49.82%, 2010: 36.16%, 2016: 33.61%), highest consumption quintile (2005: 32.35%, 2010: 15.32%, 2016: 18.39%), and exclusive reliance on informal healthcare sources (2005: -36.46%, 2010: -10.17%, 2016: -12.58%). Distinctively, the presence of chronic illnesses in households emerged as a significant factor in 2016 (9.14%, p-value ≤ 0.01), superseding the contributions of composition differences in household heads with no education (4.40%, p-value ≤ 0.01) and secondary or higher education (7.44%, p-value ≤ 0.01), which were the fourth and fifth significant contributors in 2005 and 2010.

CONCLUSIONS

Rural‒urban differences in household economic status, educational attainment of household heads, and healthcare sources were the key contributors to the rural‒urban CHE disparity between 2005 and 2016 in Bangladesh, with chronic illness emerging as a significant factor in the latest period. Closing the rural‒urban CHE gap necessitates strategies that carefully address rural‒urban variations in the characteristics identified above.

Incidence and determinants of out-of-pocket health expenditure in Ethiopia 2012-16

This study assesses the incidence of catastrophic health expenditure (CHE) and identifies the significant factors that expose households to higher levels of out-of-pocket (OOP) health expenditure. Data from the fifth and the sixth Ethiopian National Health Accounts household surveys, which were conducted in 2012-13 and 2015-16, respectively, are used. The incidence of CHE is estimated using both the capacity-to-pay and the budget share approaches. To ensure the robustness of our findings, both unconditional and conditional quantile estimators are adopted as multivariate regression techniques to estimate the impact of socio-economic variables on the distribution of households' OOP expenditure. Our findings show that the incidence of CHE in Ethiopia ranges from 1.7% to 4.7% depending on the approach and the threshold adopted. Larger families, the unemployed, the extremely poor, those who seek care at private-owned providers and families with members affected by chronic illness face higher OOP expenditure. Hence, policy should target those with these identified socio-economic characteristics in the provision of financial risk protection such as fee waiver systems and subsidies.

Assessing progress towards universal health coverage in Cambodia: Evidence using survey data from 2009 to 2019

Over the past decades, many low- and middle-income countries have implemented health financing and system reforms to progress towards universal health coverage (UHC). In the case of Cambodia, out-of-pocket expenditure (OOPE) remains the main source of current health expenditure after several decades of reform, exposing households to financial risks when accessing healthcare and violating UHC's key tenet of financial protection. We use pre-pandemic data from the nationally representative Cambodia Socio-Economic Surveys of 2009 to 2019 to assess progress in financial protection to evaluate the reforms and obtain internationally comparable estimates. We find that following strong improvements in financial protection between 2009 and 2017, there was a reversal in the trend thereafter. The OOPE budget share rose, and the incidence of catastrophic spending and impoverishment increased in nearly all geographical and socioeconomic strata. For example, 17.7% of households experienced catastrophic health expenditure in 2019 at the threshold of 10% of total household consumption expenditure, and 3.9% of households were pushed into poverty by OOPE. The distribution of all financial protection indicators varied strongly across socioeconomic and geographical strata in all years. Fundamentally, the demonstrated trend reversal may jeopardize Cambodia's ability to progress towards UHC. To improve financial protection in the short term, there is a need to address the burden created by OOPE through targeted interventions to household groups that are most affected. In the medium term, our findings emphasize the importance of expanding health pre-payment schemes to currently uncovered vulnerable groups, specifically the near-poor. The government also needs to consider extending the scope of services covered and the range of providers to include the private sector under these schemes to reduce reliance on OOPE.

Crowding-Out Effect of Out-of-Pocket Health Expenditures on Consumption Among Households in Mongolia

BACKGROUND

High out-of-pocket (OOP) health expenditures are a common problem in developing countries. Studies rarely investigate the crowding-out effect of OOP health expenditures on other areas of household consumption. OOP health costs are a colossal burden on families and can lead to adjustments in other areas of consumption to cope with these costs.

METHODS

This cross-sectional study used self-reported household consumption data from the nationally representative Household Socioeconomic Survey (HSES), collected in 2018 by the National Statistical Office of Mongolia. We estimated a quadratic conditional Engel curves system to determine intrahousehold resource allocation among 12 consumption variables. The 3-stage least squared method was used to deal with heteroscedasticity and endogeneity problems to estimate the causal crowding-out effect of OOP.

RESULTS

The mean monthly OOP health expenditure per household was ₮64 673 (standard deviation [SD]=259 604), representing approximately 6.9% of total household expenditures. OOP health expenditures were associated with crowding out durables, communication, transportation, and rent, and with crowding in education and heating for all households. The crowding-out effect of ₮10 000 in OOP health expenditures was the largest for food (₮5149, 95% CI=-8582; -1695) and crowding-in effect was largest in heating (₮2691, 95% CI=737; 4649) in the lowest-income households. The effect of heating was more than 10 times greater than that in highest-income households (₮261, 95% CI=66; 454); in the highest-income households, food had a crowding-in effect (₮179, 95% CI=-445; 802) in absolute amounts. In terms of absolute amount, the crowding-out effect for food was up to 5 times greater in households without social health insurance (SHI) than in those with SHI.

CONCLUSION

Our findings suggest that Mongolia's OOP health expenses are associated with reduced essential expenditure on items such as durables, communication, transportation, rent, and food. The effect varies by household income level and SHI status, and the lowest-income families were most vulnerable. SHI in Mongolia may not protect households from large OOP health expenditures.

Sensitivity Analysis and Methodological Choices on Health-Related Impoverishment Estimates in Cambodia, 2009 to 2017

The measurement of health-expenditure related impoverishment as a proxy of financial risk protection is regularly used as an indicator of progress towards universal health coverage. However, the use of this indicator is greatly sensitive to analysts’ choices and data sources, making comparisons across time and countries challenging.

We report the results of a sensitivity analysis of critical methodological choices in estimating health-related financial impoverishment in Cambodia from 2009 to 2017. We include in our analysis: the construction and data sources for consumption aggregates and out-of-pocket health estimates; the use of international and national absolute and relative poverty thresholds (defined by share of household food consumption); and, time and regional price adjustment methods and index sources.

Marginal changes substantially affected estimates at the national and regional levels among households. In most cases, the choice of poverty thresholds and temporal and regional deflators had a significant effect. An increase of 0.01 USD in the average daily per capita poverty line resulted in relative increases in impoverished incidences of 2.90% to 2.62% for 2009, and 3.06% to 2.95% for 2014. From 2013 onwards, estimates for impoverishment in rural areas based on median food consumption were often significantly higher than estimates using official poverty lines.

The high sensitivity of the impoverishment indicator cautions against its use in assessing health-related financial hardship and protection, especially with low and absolute poverty lines. In the context of low- and middle-income countries, assessing financial hardship in relative terms by using measures such as catastrophic health expenditure, complemented with research on coping strategies and their socio-economic effects on households, may be more conducive to policymaking goals and progress toward achieving universal health coverage.

A dominance approach to analyze the incidence of catastrophic health expenditures in Iran

Financial protection is a health system goal for all countries. Assessing progress on this relies on measuring the incidence of catastrophic health expenditures (proportion of the population whose out-of-pocket (OOP) payments for health surpass a certain threshold of household resources). Standard approaches rely on selective thresholds, however this masks varying intensities of financial hardship and poses a measurement challenge as incidence is sensitive to the choice of the threshold. We address this problem by applying the dominance approach, which tests differences in catastrophic incidence curves over a continuous range of thresholds. Iran is an interesting country for empirical application of the dominance approach given its historically high reliance on OOP payments to finance its health system and its commitment to improving financial protection through several national health policies over the last two decades. Using data from annual Household Income and Expenditure Surveys from 2005 to 2017 (sample size: 26,851-39,088 households), incidence was analyzed following this novel approach. Distribution of incidence across socio-economic status was also analyzed by estimating concentration indices and across health services or products by estimating average shares of each item. Results showed that over time catastrophic health expenditures increased for thresholds lower than 25% and decreased for thresholds higher than 35%. Catastrophic health expenditures were more equally distributed across income levels at lower thresholds, becoming concentrated amongst the rich as the threshold rose. Medicines represented the largest share of catastrophic spending for the poorest; medicines, dentistry, inpatient and ancillary services for the richest. This is the first study to apply dominance methods to analyze catastrophic health expenditures in a country over time. The analysis provides a nuanced picture of who incurs catastrophic health expenditures, to what extent hardship is experienced and what were the drivers of these expenditures - thus providing a better basis for policy responses.

The Basic vs. Ability-to-Pay Approach: Evidence From China's Critical Illness Insurance on Whether Different Measurements of Catastrophic Health Expenditure Matter

Alleviating catastrophic health expenditure (CHE) is one of the vital objectives of health systems, as defined by the World Health Organization. However, no consensus has yet been reached on the measurement of CHE. With the aim of further relieving the adverse effects of CHE and alleviating the problem of illness-caused poverty, the Critical Illness Insurance (CII) program has been operational in China since 2012. In order to verify whether the different measurements of CHE matter under China's CII program, we compare the two-layer CII models built by using the basic approach and the ability-to-pay (ATP) approach at a range of thresholds. Exploiting the latest China family panel studies dataset, we demonstrate that the basic approach is more effective in relieving CHE for all insured households, while the ATP approach works better in reducing the severity of CHE in households facing it. These findings have meaningful implications for policymaking. The CII program should be promoted widely as a supplement to the current Social Basic Medical Insurance system. To improve the CII program's effectiveness, it should be based on the basic approach, and the threshold used to measure CHE should be determined by the goal pursued by the program.

Catastrophic health expenditures arising from out-of-pocket payments: Evidence from South African income and expenditure surveys

This study examines catastrophic health expenditures and the potential for such payments to impoverish South African households. The analysis applies three different catastrophic expenditure measurements, and we apply them across four South African Income and Expenditure Surveys. Since households have limited resources, they are also limited in their capacity to purchase health care. Thus, if a household devotes a large share of that capacity to health care, it may not be able to cover other necessary expenses, which could be catastrophic. The measurements differ in their definition of household capacity. Despite the differences in measurements, and, therefore, results, we find limited incidence of health care expenditure catastrophe, although larger shares of capacity are being devoted to health care in more recent years. In line with the finding that catastrophe is rare, we find that very few households are subsequently impoverished, because of health care costs.

Incidence, socio-economic inequalities and determinants of catastrophic health expenditure and impoverishment for diabetes care in South Africa: a study at two public hospitals in Tshwane

BACKGROUND

Direct out of pocket (OOP) payments for healthcare may cause financial hardship. For diabetic patients who require frequent visits to health centres, this is of concern as OOP payments may limit access to healthcare. This study assesses the incidence, socio-economic inequalities and determinants of catastrophic health expenditure and impoverishment amongst diabetic patients in South Africa.

METHODS

Data were taken from a cross-sectional survey conducted in 2017 at two public hospitals in Tshwane, South Africa (N = 396). Healthcare costs and transport costs related to diabetes care were classified as catastrophic if they exceeded the 10% threshold of household's capacity to pay (WHO standard method) or if they exceeded a variable threshold of total household expenditure (Ataguba method). Erreygers concentration indices (CIs) were used to assess socio-economic inequalities. A multivariate logistic regression was applied to identify the determinants of catastrophic health expenditure and impoverishment.

RESULTS

Transport costs contributed to over 50% of total healthcare costs. The incidence of catastrophic health expenditure was 25% when measured at a 10% threshold of capacity to pay and 13% when measured at a variable threshold of total household expenditure. Depending on the method used, the incidence of impoverishment varied from 2 to 4% and the concentration index for catastrophic health expenditure varied from - 0.2299 to - 0.1026. When measured at a 10% threshold of capacity to pay factors associated with catastrophic health expenditure were being female (Odds Ratio 1.73; Standard Error 0.51), being within the 3rd (0.49; 0.20), 4th (0.31; 0.15) and 5th wealth quintile (0.30; 0.17). When measured using a variable threshold of total household expenditure factors associated with catastrophic health expenditure were not having children (3.35; 1.82) and the 4th wealth quintile (0.32; 0.21).

CONCLUSION

Financial protection of diabetic patients in public hospitals is limited. This observation suggests that health financing interventions amongst diabetic patients should target the poor and poor women in particular. There is also a need for targeted interventions to improve access to healthcare facilities for diabetic patients and to reduce the financial impact of transport costs when seeking healthcare. This is particularly important for the achievement of universal health coverage in South Africa.

Disaggregating catastrophic health expenditure by disease area: cross-country estimates based on the World Health Surveys

BACKGROUND

Financial risk protection (FRP) is a key objective of national health systems and a core pillar of universal health coverage (UHC). Yet, little is known about the disease-specific distribution of catastrophic health expenditure (CHE) at the national level.

METHODS

Using the World Health Surveys (WHS) from 39 countries, we quantified CHE, or household health spending that surpasses 40% of capacity-to-pay by key disease areas. We restricted our analysis to households in which the respondent used health care in the last 30 days and categorized CHE into disease areas included as WHS response options: maternal and child health (MCH); high fever, severe diarrhea, or cough; heart disease; asthma; injury; surgery; and other. We compared disease-specific CHE estimates by income, pooled funding as a share of total health expenditure, share of the population affected by the different diseases, and poverty status.

RESULTS

Across countries, an average of 45.1% of CHE cases could not be tied to a specific cause; 37.6% (95% UI 35.4-39.9%) of CHE cases were associated with high fever, severe cough, or diarrhea; 3.9% (3.0-4.9%) with MCH; and 4.1% (3.3-4.9%) with heart disease. Injuries constituted 5.2% (4.2-6.4%) of CHE cases. The distribution of CHE varied substantially by national income. A 10% increase in heart disease prevalence was associated with a 1.9% (1.3-2.4%) increase in heart disease CHE, an association stronger than any other disease area.

CONCLUSIONS

Our approach is comparable, comprehensive, and empirically based and highlights how financial risk protection may not be aligned with disease burden. Disease-specific CHE estimates can illuminate how health systems can target reform to best protect households from financial risk.