Multilevel Modeling of Health Inequalities at the Intersection of Multiple Social Identities in Canada

ABSTRACT Health inequities in Canada are pervasive. Intersectional theory and novel quantitative methods can be used to understand health inequities. Drawing on a sample of adults from the 2015 and 2016 Canadian Community Health Survey, this study uses multilevel analysis individual heterogeneity and discriminatory accuracy (MAIHDA) to examine the intersectional effect of race, sex, income and immigration status on perceived health and perceived mental health. Small variance partition coefficients of the final models suggest that most of the variance across social strata is explained by the main effects for the four variables. Intersectional interaction effects for each social strata are reported.