Quantitative intersectionality research: Past, present, and future directions

Intersectionality, rooted in Black feminist scholarship, offers a critical lens for examining how interlocking systems of oppression shape the health of individuals and populations at the intersection of multiple social identities. Despite growing interest in quantitative intersectionality research within epidemiology and public health, researchers still face substantial measurement- and analysis-related challenges. Most research to-date has relied on static, unidimensional, individual-level measures of social identities as proxies for exposure to systems of oppression. Recently, researchers have called for the development of lifecourse-informed, multidimensional, and structurally-focused measures of both social identities and systems of oppression. Analytic methods for quantitative intersectionality research, including Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (I-MAIHDA), have expanded opportunities to examine the role of multiple contexts and structural-level exposures in shaping intersectional health inequities both cross-sectionally and across the lifecourse, yet use of causal inference methods to quantify the potential impacts of specific structural-level interventions remains scarce. Importantly, key epidemiologic considerations, including measurement, selection, and confounding bias, remain under-examined and under-theorized, which is especially concerning given the complexities of quantitative intersectionality research. Addressing these important measurement- and analysis-related challenges is essential for generating valid and actionable evidence to guide efforts to advance health equity.