Abstract
The paper develops and illustrates a new multivariate approach to analysing inequity in health care. We measure multiple inequity in health care relating to multiple equity-relevant variables – including income, gender, ethnicity, rurality, insurance status and others – and decompose the contribution of each variable to multiple inequity. Our approach encompasses the standard bivariate approach as a special case in which there is only one equity-relevant variable, such as income. We illustrate through an application to physician visits in Brazil, using data from the Health and Health Care Supplement of the Brazilian National Household Sample Survey, comprising 391,868 individuals in the year 2008. We find that health insurance coverage and urban location both contribute more to multiple inequity than income. We hope this approach will help researchers and analysts shed light on the comparative size and importance of the many different inequities in health care of interest to decision makers, rather than focus narrowly on income-related inequity.
Highlights
In the wake of the global universal health coverage movement, the issue of equity in health care is high on policy agendas in low, middle and high-income countries (Evans and Etienne, 2010; WHO, 2013)
We present our results using a concentration index, which is widely used in the literature and decomposable; allowing findings about multiple inequity to be compared with findings about income-related inequality and compared between studies and settings
The measurement of equity in health care remains dominated by a bivariate approach that focuses only on one equity-relevant variable at a time – typically income or socioeconomic status
Summary
In the wake of the global universal health coverage movement, the issue of equity in health care is high on policy agendas in low, middle and high-income countries (Evans and Etienne, 2010; WHO, 2013). During the 1990s and 2000s, research on inequity in health and health care focused on “bivariate” measures of unfair inequality based on the relationship between two main variables of interest: a health variable and a single equity-relevant variable of concern to policy makers, most frequently income or socio-economic status (Yukiko Asada et al, 2014; Mackenbach and Kunst, 1997; Adam Wagstaff et al, 1991). Influential was the work of the European Ecuity project team, who developed a powerful suite of bivariate measures based around the concentration curve – the natural extension of the univariate Lorenz curve to encompass the bivariate case – and disseminated these methods world-wide in training materials sponsored by the World Bank (O'Donnell et al, 2008). 2005, 2011; A. Wagstaff and Watanabe, 2003)
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