Abstract

Purpose: Research indicates that sexual minority populations experience mental health inequities. However, few studies have examined mental health outcomes in sexual minority populations while including intersecting dimensions of social identity. This study had two objectives: (1) to quantify the prevalence of frequent mental distress among U.S. adults across intersecting social identity categories and (2) to evaluate the contribution of intersectional interactions to observed inequities. Methods: Using data from the Behavioral Risk Factor Surveillance System 2014-2019 (N = 1,024,261), we performed an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA). Participants were nested in 45 intersectional groups defined by combining 3 sexual orientation (gay/lesbian, bisexual, and heterosexual), 5 gender identity (transgender women, transgender men, gender nonconforming, cisgender women, and cisgender men), and 3 racial/ethnic (non-Hispanic Black, Hispanic/Latinx, and non-Hispanic White) categories. We estimated the predicted probability of frequent mental distress for each stratum. We then calculated the variance partition coefficient (VPC) and proportional change in variance (PCV). Results: We found that multiply marginalized groups tended to have the highest prevalence of frequent mental distress. Groups with racial/ethnic minority individuals were equally represented among low- and high-prevalence groups. The VPC indicated that slightly over 10% of observed variance in prevalence was attributable to group-level differences, while the PCV revealed that a small but meaningful amount of observed heterogeneity in prevalence was due to intersectional interactions between the dimensions of social identity. Conclusion: I-MAIHDA is a promising method for examining the patterning of sexual orientation-based mental health inequities at the population level.

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