Equity in healthcare is an overarching goal of many healthcare systems around the world. Empirical studies of equity in healthcare utilisation primarily rely on the horizontal inequity (HI) approach which measures unequal utilisation of healthcare services by socioeconomic status (SES) for equal medical need. The HI method examines, quantifies, and explains inequity which is based on regression analysis, the concentration index, and the decomposition technique. However, this method is not beyond limitations and criticisms, and it has been subject to several methodological challenges in the past decade. This review presents a summary of the recent developments and debates on various methodological issues and their implications on the assessment of HI in healthcare utilisation. We discuss the key disputes centred on measurement scale of healthcare variables as well as the evolution of the decomposition technique. We also highlight the issues about the choice of variables as the indicator of SES in measuring inequity. This follows a discussion on the application of the longitudinal method and use of administrative data to quantify inequity. Future research could exploit the potential for health administrative data linked to social data to generate more comprehensive estimates of inequity across the healthcare continuum. This review would be helpful to guide future applied research to examine inequity in healthcare utilisation.
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In the case of diabetes, “The frequency of A1C testing should depend on the clinical situation, the treatment regimen, and the clinician’s judgment” .
It is important to note that there are different approaches that have been developed to measure inequality and inequity in healthcare. We focus on the most widely used approach based on the concentration curve and the concentration index; a summary of alternative approaches can be found in Allin et al.  and Hernández-Quevedo and Papanicolas .
The four assumptions are:
(1) The determinants of health do not determine rank (rank ignorability). (2) The determinants of health do not determine the weighting function (weighting function ignorability). (3) Healthcare can be modelled as a linear function of variables X and an error term. (4) Exogeneity: the errors from the health regression have zero conditional mean.
There is no universally accepted method for determining equivalence scales. A wide range of equivalence scales exist, some of the other most commonly used scales include, the OECD equivalence scale, OECD-modified scale, and square root scale .
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We thank two anonymous referees for their insightful comments and reviews which have helped improving the quality of the paper.
Mohammad Habibullah Pulok acknowledges to receive generous Ph.D. stipends and scholarships from the Capital Markets Cooperative Research Centre (CMCRC), Australia, University of Technology Sydney (UTS), Australia, and the Australian Institute of Health and Welfare (AIHW). However, views and opinions expressed in this article are solely of the authors and do not necessarily represent the official position or policies of the funding agencies.
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This article is a part of the first author’s PhD thesis completed at University of Technology Sydney (UTS), Australia.
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Pulok, M.H., van Gool, K., Hajizadeh, M. et al. Measuring horizontal inequity in healthcare utilisation: a review of methodological developments and debates. Eur J Health Econ 21, 171–180 (2020). https://doi.org/10.1007/s10198-019-01118-2
- Healthcare utilisation
- Concentration index