Hardship financing, productivity loss, and the economic cost of illness and injury in Cambodia.

BACKGROUND: Financial risk protection is a core dimension of universal health coverage. Hardship financing, defined as borrowing and selling land or assets to pay for healthcare, is a measure of last recourse. Increasing indebtedness and high interest rates, particularly among unregulated money lenders, can lead to a vicious cycle of poverty and exacerbate inequity. METHODS: To inform efforts to improve Cambodia's social health protection system we analyze 2019-2020 Cambodia Socio-economic Survey data to assess hardship financing, illness and injury related productivity loss, and estimate related economic impacts. We apply two-stage Instrumental Variable multiple regression to address endogeneity relating to net income. In addition, we calculate a direct economic measure to facilitate the regular monitoring and reporting on the devastating burden of excessive out-of-pocket expenditure for policy makers. RESULTS: More than 98,500 households or 2.7% of the total population resorted to hardship financing over the past year. Factors significantly increasing risk are higher out-of-pocket healthcare expenditures, illness or injury related productivity loss, and spending of savings. The economic burden from annual lost productivity from illness or injury amounts to US$ 459.9 million or 1.7% of GDP. The estimated household economic cost related to hardship financing is US$ 250.8 million or 0.9% of GDP. CONCLUSIONS: Such losses can be mitigated with policy measures such as linking a catastrophic health coverage mechanism to the Health Equity Funds, capping interest rates on health-related loans, and using loan guarantees to incentivize microfinance institutions and banks to refinance health-related, high-interest loans from money lenders. These measures could strengthen social health protection by enhancing financial risk protection, mitigating vulnerability to the devastating economic effects of health shocks, and reducing inequities.

A distributional regression approach to income-related inequality of health in Australia.

BACKGROUND: Several studies have confirmed the existence of a significant positive relationship between income and health. Conventional regression techniques such as Ordinary Least Squares only help identify the effect of the covariates on the mean of the health variable. In this way, important information of the income-health relationship could be overlooked. As an alternative, we apply and compare unconventional regression techniques. METHODS: We adopt a distributional approach because we want to allow the effect of income on health to vary according to people's health status. We start by analysing the income-health relationship using a distributional regression model that falls into the GAMLSS (Generalized Additive Models for Location, Scale and Shape) framework. We assume a gamma distribution to model the health variable and specify the parameters of this distribution as linear functions of a set of explanatory variables. For comparison, we also adopt a quantile regression analysis. Based on predicted health quantiles, we use both a parametric and a non-parametric approach to estimate the lower tail of the health distribution. RESULTS: Our data come from Wave 13 of the Household, Income and Labour Dynamics in Australia (HILDA) survey, collected in 2013-2014. According to GAMLSS, we find that the risk of ending up in poor, fair or average health is lower for those who have relatively high incomes ($80,000) than for those who have relatively low incomes ($20,000), for both smokers and non-smokers. In relative terms, the risk-lowering effect of income appears to be the largest for those who are in poor health, again for both smokers and non-smokers. The results obtained on the basis of quantile regression are to a large extent comparable to those obtained by means of GAMLSS regression. CONCLUSIONS: Both distributional regression techniques point in the direction of a non-uniform effect of income on health, and are therefore promising complements to conventional regression techniques as far as the analysis of the income-health relationship is concerned.

A direct regression approach to decomposing socioeconomic inequality of health.

This paper presents a new regression-based decomposition of socioeconomic inequality of health that is more direct than other approaches. The method can be applied to both rank-dependent and level-dependent indicators of inequality. The response variable of our regression model is a simple reformulation of the measure of overall performance of an individual in the health and socioeconomic domains. Regression results are described in terms of marginal effects of the explanatory variables, but also in terms of their logworths or importance values. We illustrate our method, and compare it with alternatives, using Australian health and income data.

A dual Atkinson measure of socioeconomic inequality of health.

The Atkinson index of income inequality is based on a comparison of the average income with the equivalent income, where the equivalent income is defined as the level of income that, if given to everyone, would generate the same social welfare as the existing distribution of income. This paper explores the possibility of extending this approach to the measurement of socioeconomic inequality of health. It assumes a social evaluation function that depends upon two variables: socioeconomic status as well as health status. With a general form of this function, an Atkinson measure is derived, which gives exactly the same result when applied to the socioeconomic variable and when applied to the health variable. The paper examines the properties of the index and suggests various extensions.

Advancing universal health coverage in the COVID-19 era: an assessment of public health services technical efficiency and applied cost allocation in Cambodia.

BACKGROUND: Achieving universal health coverage (UHC) is a global priority and a keystone element of the 2030 Sustainable Development Goals. However, COVID-19 is causing serious impacts on tax revenue and many countries are facing constraints to new investment in health. To advance UHC progress, countries can also focus on improving health system technical efficiency to maximize the service outputs given the current health financing levels. METHODS: This study assesses Cambodia's public health services technical efficiency, unit costs, and utilization rates to quantify the extent to which current health financing can accommodate the expansion of social health protection coverage. This study employs Data Envelopment Analysis (DEA), truncated regression, and pioneers the application of DEA Aumann-Shapley applied cost allocation to the health sector, enabling unit cost estimation for the major social health insurance payment categories. RESULTS: Overall, for the public health system to be fully efficient output would need to increase by 34 and 73% for hospitals and health centers, respectively. We find public sector service quality, private sector providers, and non-discretionary financing to be statistically significant factors affecting technical efficiency. We estimate there is potential supply-side 'service space' to expand population coverage to an additional 4.69 million social health insurance beneficiaries with existing financing if the public health system were fully efficient. CONCLUSIONS: Public health service efficiency in Cambodia can be improved by increasing utilization of cost-effective services. This can be achieved by enrolling more beneficiaries into the social health insurance schemes with current supply-side financing levels. Other factors that can lead to increased efficiency are improving health service quality, regulating private sector providers, focusing on discretionary health financing, and incentivizing a referral system.

Defining and measuring health poverty.

Unlike other aspects of welfare (e.g. income), health has been relatively neglected when it comes to defining and measuring aspects of poverty. The aim of the paper is twofold: first we elaborate how the concept of 'health poverty' can be defined and measured, and second we apply the methodology to study health poverty in a variety of cases. The measurement of health poverty allows us to gain insights into different sorts of health deprivation in society as a whole, and in specific subgroups. We measure poverty by means of the widely adopted Foster-Greer-Thorbecke (FGT) class of indicators and apply this to three different health variables: cardiovascular risk, health status and life expectancy. Moreover, the FGT class is additively decomposable, making it possible to gauge the contribution of specific subgroups to overall poverty. We provide two applications of these methods. Firstly, we examine changes in the risk of cardiovascular disease (CVD) in the United States using two waves of the NHANES survey from 2005-06 and 2013-14 (n = 3,014 and 4,001 respectively) and use a threshold of 20% 10 year CVD risk to define health poverty. Overall our results indicate a slight decline in the proportion at high CVD risk between these periods. Secondly, we apply poverty measures to health status as measured by the SF-6D index and to empirically derived predictions of life expectancy and estimated using 24,820 individuals from the first 15 waves of the Australian HILDA survey. Trends in poverty over time are compared using several thresholds and decomposed by a variety of sub-groups. Measures of health poverty can be an important instrument for focusing the attention on those with the worst health, or highest risk, in a society and should be used more widely.

Correcting the concentration index.

In recent years attention has been drawn to several shortcomings of the Concentration Index, a frequently used indicator of the socioeconomic inequality of health. Some modifications have been suggested, but these are only partial remedies. This paper proposes a corrected version of the Concentration Index which is superior to the original Concentration Index and its variants, in the sense that it is a rank-dependent indicator which satisfies four key requirements (transfer, level independence, cardinal invariance, and mirror). The paper also shows how the corrected Concentration Index can be decomposed and generalized.

Can a single indicator measure both attainment and shortfall inequality?

When a distribution can be described either in terms of attainment or in terms of shortfall, the classical inequality measures appear to be one-sided. I show that it is possible to find indicators which simultaneously measure attainment and shortfall inequality. I derive one indicator belonging to the Gini family, and another belonging to Coefficient of Variation family. I also indicate how they are connected to other measures.

Socioeconomic Status and Health: A New Approach to the Measurement of Bivariate Inequality.

We suggest an alternative way to construct a family of indices of socioeconomic inequality of health. Our indices belong to the broad category of linear indices. In contrast to rank-dependent indices, which are defined in terms of the ranks of the socioeconomic variable and the levels of the health variable, our indices are based on the levels of both the socioeconomic and the health variable. We also indicate how the indices can be modified in order to introduce sensitivity to inequality in the socioeconomic distribution and to inequality in the health distribution. As an empirical illustration, we make a comparative study of the relation between income and well-being in 16 European countries using data from the Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 4.

Using administrative data to look at changes in the level and distribution of out-of-pocket medical expenditure: An example using Medicare data from Australia.

OBJECTIVES: Australia's universal health insurance system Medicare generates very large amounts of data on out-of-pocket expenditure (OOPE), but only highly aggregated statistics are routinely published. Our primary purpose is to develop indices from the Medicare administrative data to quantify changes in the level and distribution of OOPE on out-of-hospital medical services over time. METHODS: Data were obtained from the Australian Hypertension and Absolute Risk Study, which involved patients aged 55 years and over (n=2653). Socio-economic and clinical information was collected and linked to Medicare records over a five-year period from March 2008. The Fisher price and quantity indices were used to evaluate year-to-year changes in OOPE. The relative concentration index was used to evaluate the distribution of OOPE across socio-economic strata. RESULTS: Our price index indicates that overall OOPE were not rising faster than inflation, but there was considerable variation across different types of services (e.g. OOPE on professional attendances rose by 20% over a five-year period, while all other items fell by around 14%). Concentration indices, adjusted for demographic factors and clinical need, indicate that OOPE tends to be higher among those on higher incomes. CONCLUSIONS: A major challenge in utilizing large administrative data sets is to develop reliable and easily interpretable statistics for policy makers. Price, quantity and concentration indices represent statistics that move us beyond the average.

Structural equation modeling for decomposing rank-dependent indicators of socioeconomic inequality of health: an empirical study.

We present a flexible structural equation modeling (SEM) framework for the regression-based decomposition of rank-dependent indicators of socioeconomic inequality of health and compare it with simple ordinary least squares (OLS) regression. The SEM framework forms the basis for a proper use of the most prominent one- and two-dimensional decompositions and provides an argument for using the bivariate multiple regression model for two-dimensional decomposition. Within the SEM framework, the two-dimensional decomposition integrates the feedback mechanism between health and socioeconomic status and allows for different sets of determinants of these variables. We illustrate the SEM approach and its outperformance of OLS using data from the 2011 Ethiopian Demographic and Health Survey.

"Mirror, mirror, on the wall, who in this land is fairest of all?"--Distributional sensitivity in the measurement of socioeconomic inequality of health.

This paper explores four alternative indices for measuring health inequalities in a way that takes into account attitudes towards inequality. First, we revisit the extended concentration index which has been proposed to make it possible to introduce changes into the distributional value judgements implicit in the standard concentration index. Next, we suggest an alternative index based on a different weighting scheme. In contrast to the extended concentration index, this new index has the 'symmetry' property. We also show how these indices can be generalized so that they satisfy the 'mirror' property, which may be seen as a desirable property when dealing with bounded variables. We compare the different indices empirically for under-five mortality rates and the number of antenatal visits in developing countries.

Measuring socioeconomic inequality in health, health care and health financing by means of rank-dependent indices: a recipe for good practice.

The tools to be used and other choices to be made when measuring socioeconomic inequalities with rank-dependent inequality indices have recently been debated in this journal. This paper adds to this debate by stressing the importance of the measurement scale, by providing formal proofs of several issues in the debate, and by lifting the curtain on the confusing debate between adherents of absolute versus relative health differences. We end this paper with a 'matrix' that provides guidelines on the usefulness of several rank-dependent inequality indices under varying circumstances.