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

ABSTRACT: 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.

SUBMITTER: Kessels R

PROVIDER: S-EPMC7310143 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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

Kessels Roselinde R Hoornweg Anne A Thanh Bui Thi Kim TK Erreygers Guido G

International journal for equity in health 20200622 1

<h4>Background</h4>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.<h4>Methods</h4>We adopt a distributional approach be ...[more]

PMID: 32571408

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Project description:BackgroundGeneral practitioners (GPs) are among the most important resources of healthcare system and public health is considerably influenced by the function of this group. Income inequality among GPs considerably affects the motivation and performance of this group. The present study aims to examine the income inequality among Iranian GPs in order to provide the necessary evidence for health human resource policy.MethodsIn this cross-sectional study, the distribution of income and wage inequality among GPs was investigated using income quintiles. We also used the Dagum's model to analyze the inequality between different groups of GPs through the decomposition of the Gini coefficient. Moreover, a regression model was used to determine the effective factors on GPs' income.ResultsThe results of this study indicated that income and wages of GPs in the highest quintile were eight times more than those of doctors at the lowest quintile. Regression estimates showed that factors such as gender, practice setting, and activity as the family physician (P < 0.001) were effective on income of GPs; and also male and self-employed GPs had significantly more wage (P < 0.001). Total Gini coefficient of GPs' income and wage were estimated at 0.403 and 0.412, respectively. Highest monthly income was found in GPs with 16-20 years practice experience ($8358) based on Purchasing Power Parity (PPP), male ($8339 PPP), and self-employed GPs ($8134 PPP) subgroup. However, the female ($5389 PPP) and single ($5438 PPP) GPs had the lowest income. Population share; income/wage share; income/wage mean; Gini coefficient; and within, between and overlap decomposed components of Gini coefficient are also reported for each GPs subgroups.ConclusionsWe found significant inequalities in income and wages among Iranian GPs. Adjustment of income based on working hours indicated that one of the most common causes of income inequality among GPs in Iran was different workloads among different groups. Since the motivation and function of physicians can be influenced by income inequality, policymakers in the health system should consider factors increasing such inequalities.

Dataset Information

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

Publications

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

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