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An intersectionality framework for identifying relevant covariates in health equity research

Bibliographic Data

ID22082978
AuthorsAndrew Simkus (0000-0002-8791-3843, KDH Research & Communication (United States), corresponding author), Kristen D Holtz (0000-0001-8985-9943, KDH Research & Communication (United States)), Eric C Twombly (Manchester Metropolitan University)
Year2024
Volume12
Pages1286121-1286121
Publication date2024-03-14
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2024.1286121
PMID38560446
OpenAlexW4392814464
LanguageEN
Citations received1
References cited59

Introduction: Health 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. Methods: We 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. Results: We 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. Discussion: Applying 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

Brainstorming · Business · Conceptual framework · Economics · Experiential learning · Health equity · Intersectionality · Political science · Psychological intervention · Public economics · Public health · Social science · Sociology · Terminology · Health disparities and outcomes · Health Policy Implementation Science · Medicine · Nursing · Psychology · Systemic Lupus Erythematosus Research · Marketing

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Unique citing works1
Citations per year0,5
Citation span2024 - 2024 (1)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 1
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