Reintegrating contexts into quantitative intersectional analyses of health inequalities

Quantitative intersectional analyses often overlook the roles of contexts in shaping intersectional experiences and outcomes. This study advances a novel approach for integrating quantitative intersectional methods with models of contextual-level determinants of health inequalities. Building on recent methodological advancements, I propose an adaptation of intersectional MAIHDA (multilevel analysis of individual heterogeneity and discriminatory accuracy) where respondents are nested hierarchically in social strata defined by gender, race/ethnicity and socioeconomic classifications interacted with contextual classifications. To demonstrate this approach I examine past-month adolescent cigarette use intersectionally by school- and neighborhood-poverty status in Wave 1 of the National Longitudinal Study of Adolescent to Adult Health (N = 17,234). I conclude by discussing the adaptability of this approach to a variety of research questions, including intersectional effects that vary by contextual exposures over time, positions in social networks, and exposures to social policies.