An intersectionality framework for identifying relevant covariates in health equity research

Abstract

IntroductionHealth equity research uses impact evaluations to estimate the effectiveness of new interventions that aim to mitigate health inequities. Health inequities are influenced by many experiential factors and failure of research to account for such experiential factors and their potential interactions may jeopardize findings and lead to promoted methods that may unintentionally sustain or even worsen the targeted health inequity. Thus, it is imperative that health equity impact evaluations identify and include variables related to the circumstances, conditions, and experiences of the sample being studied in analyses. In this review, we promote intersectionality as a conceptual framework for brainstorming important yet often overlooked covariates in health equity related impact evaluations.MethodsWe briefly review and define concepts and terminology relevant to health equity, then detail four domains of experiential factors that often intersect in ways that may obscure findings: Biological, Social, Environmental, and Economic.ResultsWe provide examples of the framework’s application to lupus-related research and examples of covariates used in our own health equity impact evaluations with minority patients who have lupus.DiscussionApplying an intersectionality framework during covariate selection is an important component to actualizing precision prevention. While we do not provide an exhaustive list, our aim is to provide a springboard for brainstorming meaningful covariates for health equity evaluation that may further help unveil sustainable solutions to persisting health inequities.