Background Intersectionality is a theoretical framework rooted in the premise that human experience is jointly shaped by multiple social positions (e.g. race, gender), and cannot be adequately understood by considering social positions independently. Used widely in qualitative studies, its uptake in quantitative research has been more recent. Objectives To characterize quantitative research applications of intersectionality from 1989 to mid-2020, to evaluate basic integration of theoretical frameworks, and to identify innovative methods that could be applied to health research. Methods Adhering to PRISMA guidelines, we conducted a systematic review of peer-reviewed articles indexed within Scopus, Medline, ProQuest Political Science and Public Administration, and PsycINFO. Original English-language quantitative or mixed-methods research or methods papers that explicitly applied intersectionality theoretical frameworks were included. Experimental studies on perception/stereotyping and measures development or validation studies were excluded. We extracted data related to publication, study design, quantitative methods, and application of intersectionality. Results 707 articles (671 applied studies, 25 methods-only papers, 11 methods plus application) met inclusion criteria. Articles were published in journals across a range of disciplines, most commonly psychology, sociology, and medical/life sciences; 40.8% studied a health-related outcome. Results supported concerns among intersectionality scholars that core theoretical tenets are often lost or misinterpreted in quantitative research; about one in four applied articles (26.9%) failed to define intersectionality, while one in six (17.5%) included intersectional position components not reflective of social power. Quantitative methods were simplistic (most often regression with interactions, cross-classified variables, or stratification) and were often misapplied or misinterpreted. Several novel methods were identified. Conclusions Intersectionality is frequently misunderstood when bridging theory into quantitative methodology. Further work is required to (1) ensure researchers understand key features that define quantitative intersectionality analyses, (2) improve reporting practices for intersectional analyses, and (3) develop and adapt quantitative methods.

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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.

Background: Intersectionality theoretical frameworks have been increasingly incorporated into quantitative research. A range of methods have been applied to describing outcomes and disparities across large numbers of intersections of social identities or positions, with limited evaluation. Methods: Using data simulated to reflect plausible epidemiologic data scenarios, we evaluated methods for intercategorical intersectional analysis of continuous outcomes, including cross-classification, regression with interactions, multilevel analysis of individual heterogeneity (MAIHDA), and decision-tree methods (classification and regression trees [CART], conditional inference trees [CTree], random forest). The primary outcome was estimation accuracy of intersection-specific means. We applied each method to an illustrative example using National Health and Nutrition Examination Study (NHANES) systolic blood pressure data. Results: When studying high-dimensional intersections at smaller sample sizes, MAIHDA, CTree, and random forest produced more accurate estimates. In large samples, all methods performed similarly except CART, which produced less accurate estimates. For variable selection, CART performed poorly across sample sizes, although random forest performed best. The NHANES example demonstrated that different methods resulted in meaningful differences in systolic blood pressure estimates, highlighting the importance of selecting appropriate methods. Conclusions:This study evaluates some of a growing toolbox of methods for describing intersectional health outcomes and disparities. We identified more accurate methods for estimating outcomes for highdimensional intersections across different sample sizes. As estimation is rarely the only objective for epidemiologists, we highlight different outputs each method creates, and suggest the sequential pairing of methods as a strategy for overcoming certain technical challenges.

Purpose An intersectionality framework has been increasingly incorporated into quantitative study of health inequity, to incorporate social power in meaningful ways. Researchers have identified “person-centered” methods that cluster within-individual characteristics as appropriate to intersectionality. We aimed to review their use and match with theory. Methods We conducted a multidisciplinary systematic review of English-language quantitative studies wherein authors explicitly stated an intersectional approach, and used clustering methods. We extracted study characteristics and applications of intersectionality. Results 782 studies with quantitative applications of intersectionality were identified, of which 16 were eligible: eight using latent class analysis, two latent profile analysis, and six clustering methods. Papers used cross-sectional data (100.0%) primarily had U.S. lead authors (68.8%) and were published within psychology, social sciences, and health journals. While 87.5% of papers defined intersectionality and 93.8% cited foundational authors, engagement with intersectionality method literature was more limited. Clustering variables were based on social identities/positions (e.g., gender), dimensions of identity (e.g., race centrality), or processes (e.g., stigma). Results most commonly included four classes/clusters (60.0%), which were frequently used in additional analyses. These described sociodemographic differences across classes/clusters, or used classes/clusters as an exposure variable to predict outcomes in regression analysis, structural equation modeling, mediation, or survival analysis. Author rationales for method choice included both theoretical/intersectional and statistical arguments. Conclusion Latent variable and clustering methods were used in varied ways in intersectional approaches, and reflected differing matches between theory and methods. We highlight situations in which these methods may be advantageous, and missed opportunities for additional uses.

Objectives Visible minorities are a group categorized in health research to identify and track inequalities, or to study the impact of racialization. We compared classifications obtained from a commonly used measure (Statistics Canada standard) with those obtained by two direct questions-whether one is a member of a visible minority group and whether one is perceived or treated as a person of colour. Methods A mixed-methods analysis was conducted using data from an English-language online survey (n = 311) and cognitive interviews with a maximum diversity subsample (n = 79). Participants were Canadian residents age 14 and older. Results Agreement between the single visible minority item and the standard was good (Cohen's Κ = 0.725; 95% CI = 0.629, 0.820). However, participants understood "visible minority" in different and often literal ways, sometimes including those living with visible disabilities or who were visibly transgender or poor. Agreement between the single person of colour item and the standard was very good (Κ = 0.830; 95% CI = 0.747, 0.913). "Person of colour" was more clearly understood to reflect ethnoracial background and may better capture the group likely to be targeted for racism than the Statistics Canada standard. When Indigenous participants who reported being persons of colour were reclassified to reflect the government definition of visible minority as non-Indigenous, this measure had strong agreement with the current federal standard measure (K = 0.851; 95% CI = 0.772, 0.930). Conclusion A single question on perception or treatment as a person of colour appears to well identify racialized persons and may alternately be recoded to approximate government classification of visible minorities.

Introduction This study evaluated seven quantitative methods for their predictive accuracy for intersectionally defined subgroups, via a simulation study. The methods were single-level regression with interaction terms, cross-classification, multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA), and four decision tree Methods classification and regression trees (CART), conditional inference trees, chi-square automatic interaction detector, and random forest. Also evaluated was how well methods identified variables relevant to the outcome. An example analysis will be presented using data from the U.S. National Health and Nutritional Examination Survey. Methods The simulated datasets varied by outcome variable type (binary and continuous), input variable types, sample size, and size and direction of the effects. Accuracy was evaluated using mean squared error or mean absolute percentage error. The secondary outcome was evaluated via significance and confidence interval coverage of regression terms and variable selection of the machine learning methods. Results Predictive accuracy improved with increasing sample size for all methods except CART. At small sample sizes random forest and MAIHDA generally created the most precise predictions. Variable selection consistently faced a high type 1 error for CTree and CHAID. While performing well for prediction, variable selection by random forest and confidence interval coverage and power of MAIHDA main effects coefficients were suboptimal. Discussion From this study emerge recommendations for applying methods in quantitative intersectionality. Different methodologies are optimal for different purposes, for example while random forest and MAIHDA performed well for prediction, they were less reliable for variable identification. In our discussion, we will work through how to select, apply, and interpret methodologies to achieve analytic goals that align with intersectionality theory.