Quantitative methods for descriptive intersectional analysis with binary health outcomes

Mayuri Mahendran, Daniel Lizotte, Greta R. Bauer

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Intersectionality recognizes that in the context of sociohistorically shaped structural power relations, an individual's multiple social positions or identities (e.g., gender, ethnicity) can interact to affect health-related outcomes. Despite limited methodological guidance, intersectionality frameworks have increasingly been incorporated into epidemiological studies, both to describe health disparities and to examine their causes. This study aimed to advance methods in intersectional estimation of binary outcomes in descriptive health disparities research through evaluation of 7 potentially intersectional data analysis methods: cross-classification, regression with interactions, multilevel analysis of individual heterogeneity (MAIHDA), and decision trees (CART, CTree, CHAID, random forest). Accuracy of estimated intersection-specific outcome prevalence was evaluated across 192 intersections using simulated data scenarios. For comparison we included a non-intersectional main effects regression. We additionally assessed variable selection performance amongst decision trees. Example analyses using National Health and Nutrition Examination Study data illustrated differences in results between methods. At larger sample sizes, all methods except for CART performed better than non-intersectional main effects regression. In smaller samples, MAIHDA was the most accurate method but showed no advantage over main effects regression, while random forest, cross-classification, and saturated regression were the least accurate, and CTree and CHAID performed moderately well. CART performed poorly for estimation and variable selection. Sensitivity analyses examining the bias-variance tradeoff suggest MAIHDA as the preferred unbiased method for accurate estimation of high-dimensional intersections at smaller sample sizes. Larger sample sizes are more imperative for other methods. Results support the adoption of an intersectional approach to descriptive epidemiology.

Original languageEnglish (US)
Article number101032
JournalSSM - Population Health
Volume17
DOIs
StatePublished - Mar 2022
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by an Ontario Graduate Student Scholarship to MM and by a Canadian Institutes of Health Research Sex and Gender Science Chair to GB [ GSB-171372 ].

Publisher Copyright:
© 2022 The Authors

Keywords

  • Biostatistics
  • Epidemiological studies
  • Health equity
  • Intersectionality
  • Research design

PubMed: MeSH publication types

  • Journal Article

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