Subgroup Decomposability of Income‐Related Inequality of Health, with an Application to Australia

Erreygers, Guido; Kessels, Roselinde; Chen, Linkun; Clarke, Philip

doi:10.1111/1475-4932.12373

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…Table in Appendix B provides summary statistics for the variables. Similar as in Erreygers et al (), the values for the indices R b and L b equal 0.0657 and 0.0144, respectively.…”

Section: Empirical Applicationsupporting

confidence: 72%

“…Although the weighting function of index L looks very similar to that of index R , the two indices are not identical (see Erreygers, Kessels, Chen & Clarke, ). A striking difference is that the pivotal position of the rank‐dependent index (meaning the position for which the weighting function equals zero and changes sign) corresponds to the median income level, whereas that of the level‐dependent index is constituted by the mean income level.…”

Section: Indices Of Socioeconomic Inequality Of Healthmentioning

confidence: 99%

“…Our sample contains 14,728 individuals. The empirical work in Erreygers et al () has shown that this is a fairly large and representative sample, which does not require sample weights in the calculations. The composition of our sample is described in Table in Appendix B.…”

Section: Empirical Applicationmentioning

confidence: 99%

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A direct regression approach to decomposing socioeconomic inequality of health

Kessels

Erreygers

2019

Health Economics

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Summary 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.

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“…Table in Appendix B provides summary statistics for the variables. Similar as in Erreygers et al (), the values for the indices R b and L b equal 0.0657 and 0.0144, respectively.…”

Section: Empirical Applicationsupporting

confidence: 72%

Section: Indices Of Socioeconomic Inequality Of Healthmentioning

confidence: 99%

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A direct regression approach to decomposing socioeconomic inequality of health

Kessels

Erreygers

2019

Health Economics

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“…However, this is a first step in exploring subgroup inequalities. A natural extension of this work would be to conduct subgroup decompositions to estimate how inequalities are explained into between- and within-group inequality, 26 which would provide more elements for a better understanding of health care inequalities in the country.…”

Section: Discussionmentioning

confidence: 99%

Socioeconomic inequalities in health care utilization in Paraguay: Description of trends from 1999 to 2018

Capurro

2022

J Health Serv Res Policy

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Objective Paraguay’s health care system is characterized by segmented provision and low public spending, with limited coverage and asymmetries in terms of access and quality of care. The present study provides national estimates of income-related inequality in health care utilization and trends in the country over the past two decades. Methods Using data from the Paraguayan Permanent Household Survey, we estimated socioeconomic inequality in health care use during the period 1999–2018. We used poverty-to-income ratio as the socioeconomic stratifier and defined health care use as having reported a health problem and subsequent health care use in the last 90 days before interview. Inequality was summarized by rank- and level-based versions of the Concentration Index for binary outcomes. Results Inequalities affecting those with lower incomes were present in all years assessed, although the magnitude of these inequalities declined over time. Inequality as expressed by the rank-based index decreased from 0.209 (95%CI 0.164; 0.253) in 1999 to 0.032 (95%CI -0.010; 0.075) in 2018. The level-based index decreased from 0.076 (95%CI -0.029; 0.182) in 1999 to 0.024 (0.002; 0.045) in 2018. Trends in both indices were generally stable from 1999 to 2009, with a noticeable decrease in 2010. The sharpest decreases relative to the 1999 baseline were observed in the period 2010–2018, reflecting changes in health care use and income distribution. Stratification by area, sex and older people suggest similar trends within subgroups. Conclusions Decreases in inequality coincide temporally with increments in public health expenditure, removal of user fees in public health care facilities and the expansion of conditional cash-transfer programmes. Future research should disentangle the role of each of these policies in explaining the trends described.

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“…As far as Australia is concerned, several studies have used survey data to estimate the social gradient of health by means of the concentration index and to compare Australia to other countries [17][18][19]. Various decomposition techniques have been applied in order to come to a better understanding of the underlying mechanisms [7,19,20]. For other countries, more complex econometric methods have been tried.…”

Section: The Income-health Relationshipmentioning

confidence: 99%

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

Kessels

Hoornweg

Bui

et al. 2020

Int J Equity Health

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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.

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Subgroup Decomposability of Income‐Related Inequality of Health, with an Application to Australia

Cited by 8 publications

References 40 publications

A direct regression approach to decomposing socioeconomic inequality of health

A direct regression approach to decomposing socioeconomic inequality of health

Socioeconomic inequalities in health care utilization in Paraguay: Description of trends from 1999 to 2018

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

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