Evidence map›Paper›PMID 30770131›Full record

ArticleSocial science & medicine (1982)2019

Intersectional decomposition analysis with differential exposure, effects, and construct.

John W Jackson, Tyler J VanderWeele

Abstract read
In one paragraph

Article in Social science & medicine (1982), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 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

44 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Review
  4. Causal Inference in Health Disparities Research.Annual review of public health · 2026
    Review
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  6. Observational
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  10. Observational
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  15. Day-to-day associations of intersectional minority stressors with sleep health in sexual and gender minority people of color.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2024
    Article
  16. Article
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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

2 authors.

John W JacksonDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Electronic address: john.jackson@jhu.edu.
Tyler J VanderWeeleDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Funding

Theory and methods for mediation and interactionR01CA222147 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI TCHETGEN TCHETGEN, ERIC JOEL, VANDERWEELE, TYLER · 2018 to 2022
$2.1M
Systems Science for Translational Health Disparities ResearchK01HL145320 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI JACKSON, JOHN WILLIAM · 2019 to 2023
$832k
NCI NIH HHS R01 CA222147NHLBI NIH HHS K01 HL145320NIMH NIH HHS L30 MH111012
6 · The paper itself

Abstract

In recent years a wide array of proposals for bringing intersectional perspectives into quantitative studies of health disparities have appeared, from studies of interaction, predictive discrimination, to mediation. Bauer and Scheim, in a companion set of articles, extend these proposals by developing new attribution-blind measures of perceived discrimination and using VanderWeele's 3-way decomposition to quantify its contribution to disparities through differential exposure and differential effects (sometimes called differential vulnerability or susceptibility). In this commentary, after providing an overview of causal inference interpretations with social characteristics, we provide a broad overview of old and new decomposition methods in the social sciences literature and contrast their strengths and weaknesses for studying intersectional inequalities. We then examine how different forms of differential effects can be expressed within these decompositions and discuss their utility for the purpose of informing interventions for reducing disparities. Last, we discuss the tension in social sciences research when prominent explanatory variables represent constructs that are only defined or exist for certain marginalized populations and may not neatly fit within the decomposition methods framework. Through these discussions, we aim to provide greater conceptual clarity for applied researchers who are interested in using decomposition methods and other approaches to advance intersectional equity.

Indexed as

Health Status DisparitiesSocial TheoryHumansPsychometricsVulnerable PopulationsDecompositionDisparitiesEquityInequalitiesIntersectionalityMediationOaxaca-blinderVulnerability

Identifiers

PMID30770131
PMCPMC6886261

What OpenQuestion holds

Textmetadata
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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.