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Equity-informative methods of health services research

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JournalJournal of Health Organization and Management
DateAccepted/In press - 18 Feb 2021
DateE-pub ahead of print - 2 Jul 2021
DatePublished (current) - 8 Oct 2021
Issue number6
Volume35
Number of pages17
Early online date2/07/21
Original languageEnglish

Abstract

Purpose: We review quantitative methods for analysing the equity impacts of health care and public health interventions: who benefits most and who bears the largest burdens (opportunity costs)? Mainstream health services research focuses on effectiveness and efficiency but decision makers also need information about equity. Design/methodology/approach: We review equity-informative methods of quantitative data analysis in three core areas of health services research: effectiveness analysis, cost-effectiveness analysis and performance measurement. An appendix includes further readings and resources. Findings: Researchers seeking to analyse health equity impacts now have a practical and flexible set of methods at their disposal which builds on the standard health services research toolkit. Some of the more advanced methods require specialised skills, but basic equity-informative methods can be used by any health services researcher with appropriate skills in the three core areas. Originality/value: We hope that this review will raise awareness of equity-informative methods of health services research and facilitate their entry into the mainstream so that health policymakers are routinely presented with information about who gains and who loses from their decisions.

Bibliographical note

Funding Information:
This is independent research by the University of York funded by the Wellcome Trust (Grant No. 205427/Z/16/Z). Work developing some of the methods described was funded by the National Institute for Health Research (SRF-2013-06-015).

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    Research areas

  • Conditional average treatment effects, Cost/benefit analysis, Distributional cost-effectiveness analysis, Equity, Inequality, Quality improvement, Quality indicators, Quasi experimental designs, Randomised controlled trials, Small-area analysis, Socioeconomic factors, Subgroup analysis

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