Epidemiologists and other population health researchers have made considerable developments in bridging intersectionality theory with epidemiological methods over the past decade. The following list attempts to reflect the dynamic and non-linear nature of this endeavor and is organized as follows. The first set of articles are conceptual, reviewing the why and how of incorporating an intersectional lens into epidemiological research. The second set of articles are methodological, presenting new or novel applications of existing analytic approaches to studying the distribution and determinants of health and disease. When possible, I mention published commentaries and responses to these articles to highlight the ongoing challenges involved in “quantifying” intersectionality and to emphasize the fact that no single method is inherently intersectional… read more »

Methodological Articles

Veenstra G. Race, gender, class, and sexual orientation: Intersecting axes of inequality and self-rated health in Canada. Int J Equity Health. 2011;10(3):1–11. 

Veenstra’s article is one of the first to explicitly present a quantitative analytic approach for incorporating intersectionality theory into epidemiological research. He uses statistical interaction (i.e., two- and three-way cross-product terms between social identity variables in multivariable models) to assess whether multiply marginalized groups are more likely to report poor self-rated health relative to singly- or non-marginalized groups, drawing on intersectionality core tenets of directionality, simultaneity, multiplicativity, and multiple jeopardy. Although the use of statistical interaction to assess intersectionality is increasingly critiqued by social epidemiologists, this article and those informed by it helped motivate the development of the methods introduced in the following articles. 

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Jackson JW, Williams DR, VanderWeele TJ. Disparities at the intersection of marginalized groups. Soc Psychiatry Psychiatr Epidemiol. 2016;51(10):1349–1359. 

Building off the aforementioned critiques of using statistical interaction to assess intersectionality, this article from Jackson, Williams, and VanderWeele argues in favor of evaluating additive-scale interaction (i.e., a situation in which the combined effect of two factors differ from the sum of each factor’s individual effect) as an alternative quantitative analytic approach. They present a novel additive-scale interaction method that is specific to intersectionality, the joint disparity and its decomposition, and illustrate how it and related measures can be used to quantify excess (i.e., intersectional) risk of disease among groups at the nexus of two marginalized social identities. Technical appendices provide equations for applying the methods to continuous and binary outcomes. 

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Commentaries:

Schwartz S. Commentary: on the application of potential outcomes-based methods to questions in social psychiatry and psychiatric epidemiology. Soc Psychiatry Psychiatr Epidemiol. 2017;52(2):139–42. 

Jackson JW. Explaining intersectionality through description, counterfactual thinking, and mediation analysis. Soc Psychiatry Psychiatr Epidemiol. 2017;52(7):785–93.

Wemrell M, Mulinari S, Merlo J. Intersectionality and risk for ischemic heart disease in Sweden: Categorical and anti-categorical approaches. Soc Sci Med. 2017;177:213–22. 

The majority of intersectionality-informed epidemiological research adopts an intracategorical or intercategorical orientation; much less attention has been directed to developing quantitative methods consistent with anticategorical intersectionality (see McCall, 2005 listed above). Wemrell and colleagues help to fill this gap by introducing an innovative method for conducting quantitative anticategorical intersectionality-informed research based on the epidemiological concept of discriminatory accuracy (DA). Specifically, the authors contend that quantifying the ability of social identity-based categories to distinguish between those with and without a health outcome of interest (as is typically done in epidemiological research) can demonstrate the large degree of heterogeneity within these categories, and thus what anticategorical intersectionality describes as the “simplifying social fictions” (McCall, 2005, p. 1773) of social identities. They outline their analytic approach (using area under the receiver-operating characteristic curves) and provide guidance for intersectionality theory-consistent interpretations.

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Evans CR, Williams DR, Onnela JP, Subramanian SV. A multilevel approach to modeling health inequalities at the intersection of multiple social identities. Soc Sci Med. 2018;203:64–73. 

In this pioneering article, Evans and colleagues introduce a novel modeling approach for intercategorical intersectionality-informed epidemiological research that is referred to in subsequent articles as Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA). Briefly, Intersectional MAIHDA models are multilevel models in which individuals are nested within groups defined by intersecting social identities, which enables investigations of within- and between-group heterogeneity and the ability to identify and quantify interaction (i.e., intersectional) effects for all groups. The authors outline methodological and theoretical advantages of the method over “conventional” intersectional models (i.e., fixed effects models with cross-product terms between social identity variables) and provide an illustrative example. 

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Commentaries: 

Merlo J. Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) within an intersectional framework. Soc Sci Med. 2018; 

Evans CR. Adding interactions to models of intersectional health inequalities: Comparing multilevel and conventional methods. Soc Sci Med. 2019;221:95–105. 

Evans further develops the Intersectional MAIHDA method by investigating whether adding additional dimensions of identity into the models reveals or explains away intersectional effects. She also compares findings from Intersectional MAIHDA models to those obtained from conventional intersectional models and discusses plausible statistically-based reasons for the observed differences. Stata code for all analyses are provided in the supplementary materials. 

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Commentaries:

Bauer GR. Math versus meaning in MAIHDA: A commentary on multilevel statistical models for quantitative intersectionality. Soc Sci Med. 2019; 112500. 

Evans CR, Leckie G, Merlo J. Multilevel versus single-level regression for the analysis of multilevel information: The case of quantitative intersectional analysis. Soc Sci Med. 2019;245:112499. 

Evans CR. Reintegrating contexts into quantitative intersectional analyses of health inequalities. Heal Place. 2019;60:102214. 

Another Intersectional MAIHDA methods article, Evans outlines how to incorporate social contexts (e.g., schools and neighborhoods) into the models. She also suggests alternative conceptualizations of contexts for quantitative intersectionality-informed research, with the goal of motivating epidemiologists to better attend to socio-structural processes in their analyses.

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Scheim AI, Bauer GR. The Intersectional Discrimination Index: Development and validation of measures of self-reported enacted and anticipated discrimination for intercategorical analysis. Soc Sci Med. 2019;226:236–45. 

In the only measurement-specific article on this list, Scheim and Bauer develop and validate the Intersectional Discrimination Index (InDI), a measure of attribution-free anticipated and enacted discrimination to be used in intercategorical intersectionality-informed epidemiological research. The authors include the final version of the index within the paper, provide guidance regarding its scoring, and outline an analytic approach for using the index to assess discrimination as social determinant of intersectional health inequities in a companion article (listed below). 

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Commentaries:

Harnois CE, Bastos JL. The promise and pitfalls of intersectional scale development. Soc Sci Med. 2019;223:73–6. 

Bauer GR, Scheim AI. Methods for analytic intercategorical intersectionality in quantitative research: Discrimination as a mediator of health inequalities. Soc Sci Med. 2019;226:236–45. 

In the companion article to that listed above, Bauer and Scheim illustrate a novel method for analytic (i.e., process-oriented) intersectionality-informed epidemiological research. They start with a helpful overview of extant analytic approaches for incorporating intersectionality theory into epidemiological research and highlight persisting methodological challenges, with an emphasis on research aiming to conduct causal analyses. They then introduce their method, a causal mediation analysis (adapted from VanderWeele’s three-way decomposition method and informed by the potential outcomes framework) that allows for heterogeneity of the mediated effect across groups defined by intersecting social identities, and provide an illustrative demonstration complete with equations and SAS code. 

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Commentaries:

Evans CR. Modeling the intersectionality of processes in the social production of health inequalities. Soc Sci Med. 2019;226:249–53.

Jackson JW, VanderWeele TJ. Intersectional decomposition analysis with differential exposure, effects, and construct. Soc Sci Med. 2019;226:254–9. 

Richman LS, Zucker AN. Quantifying intersectionality: An important advancement for health inequality research. Soc Sci Med. 2019;226:246–8. 

Bauer GR, Scheim AI. Advancing quantitative intersectionality research methods: Intracategorical and intercategorical approaches to shared and differential constructs. Soc Sci Med. 2019;226:260–2.