Evidence map›Paper›PMID 34869821›Full record

ReviewSSM - population health2021

An investigation of quantitative methods for assessing intersectionality in health research: A systematic review.

Alice Guan, Marilyn Thomas, Eric Vittinghoff, Lisa Bowleg, Christina Mangurian, Paul Wesson

Abstract readReview
In one paragraph

Review in SSM - population health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

63 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  20. Kinlessness and end-of-life care quality: does race and ethnicity matter?The journals of gerontology. Series B, Psychological sciences and social sciences · 2025
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3 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Alice GuanDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Marilyn ThomasDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Eric VittinghoffDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Lisa BowlegDepartment of Psychological and Brain Sciences, The George Washington University, Washington, DC, USA.
Christina MangurianDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Paul WessonDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.

Funding

Ujima Mentoring ProgramP30MH062246 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MALLORY O JOHNSON · 2001 to 2026
$57.4M
Training Program for Scientists Conducting Research to Reduce HIV Health DisparitR25MH067127 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Emily A Arnold, Torsten Brian Neilands · 2003 to 2026
$5.4M
The science for the last mile: Enhanced epidemiologic surveillance to accelerate HIV eliminationK01AI145572 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI WESSON, PAUL DOUGLAS · 2020 to 2024
$679k
A lifecourse approach to evaluating the effect of structural stigma on cancer risk factors among sexual minoritiesF31CA257351 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GUAN, ALICE · 2021 to 2023
$114k
NCI NIH HHS F31 CA257351NIAID NIH HHS K01 AI145572NIMH NIH HHS P30 MH062246NIMH NIH HHS R25 MH067127
6 · The paper itself

Abstract

Intersectionality is a theoretical framework that investigates how interlocking systems of power and oppression at the societal level influence the lived experiences of historically and socially marginalized groups. Currently, there are no consistent or widely adopted quantitative methods to investigate research questions informed by intersectionality theory. The objective of this systematic review is to describe the current landscape of quantitative methods used to assess intersectionality and to provide recommendations on analytic best practices for future research. We searched PubMed, EMBASE, and the Web of Science in December 2019 to identify studies using analytic quantitative intersectionality approaches published up to December 2019 (PROSPERO CRD42020162686). To be included in the study, articles had to: (1) be empirical research, (2) use a quantitative statistical method, (3) be published in English, and (4) incorporate intersectionality. Our initial search yielded 1889 articles. After screening by title/abstract, methods, and full text review, our final analytic sample included 153 papers. Eight unique classes of quantitative methods were identified, with the majority of studies employing regression with an interaction term. We additionally identified several methods which appear to be at odds with the key tenets of intersectionality. As quantitative intersectionality continues to expand, careful attention is needed to avoid the dilution of the core tenets. Specifically, emphasis on social power is needed as methods continue to be adopted and developed. Additionally, clear explanation of the selection of statistical approaches is needed and, when using regression with interaction terms, researchers should opt for use of the additive scale. Finally, use of methods that are potentially at odds with the tenets of intersectionality should be avoided.

Indexed as

EpidemiologyIntersectionalityResearch methodsStatisticsSystematic review

Identifiers

PMID34869821
PMCPMC8626832

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.