Marginalized identities, discrimination burden, and mental health: empirical exploration of an interpersonal-level approach to modeling intersectionality.

Intersectionality is a term used to describe the intersecting effects of race, class, gender, and other marginalizing characteristics that contribute to social identity and affect health. Adverse health effects are thought to occur via social processes including discrimination and structural inequalities (i.e., reduced opportunities for education and income). Although intersectionality has been well-described conceptually, approaches to modeling it in quantitative studies of health outcomes are still emerging. Strategies to date have focused on modeling demographic characteristics as proxies for structural inequality. Our objective was to extend these methodological efforts by modeling intersectionality across three levels: structural, contextual, and interpersonal, consistent with a social-ecological framework. We conducted a secondary analysis of a database that included two components of a widely used survey instrument, the Everyday Discrimination Scale. We operationalized a meso- or interpersonal-level of intersectionality using two variables, the frequency score of discrimination experiences and the sum of characteristics listed as reasons for these (i.e., the person's race, ethnicity, gender, sexual orientation, nationality, religion, disability or pregnancy status, or physical appearance). We controlled for two structural inequality factors (low education, poverty) and three contextual factors (high crime neighborhood, racial minority status, and trauma exposures). The outcome variables we modeled were posttraumatic stress disorder symptoms and a quality of life index score. We used data from 619 women who completed the Everyday Discrimination Scale for a perinatal study in the U.S. state of Michigan. Statistical results indicated that the two interpersonal-level variables (i.e., number of marginalized identities, frequency of discrimination) explained 15% of variance in posttraumatic stress symptoms and 13% of variance in quality of life scores, improving the predictive value of the models over those using structural inequality and contextual factors alone. This study's results point to instrument development ideas to improve the statistical modeling of intersectionality in health and social science research.