Analyzing multiple types of discrimination using implicit and explicit measures, comparing target vs. Dominant groups, in a study of smoking/vaping among community health center members in Boston, Massachusetts (2020-2022).

Abstract

Background: In the United States (U.S.), the physical and mental health sequelae of diverse types of discrimination are far-reaching, severe, and contribute to population health inequities, with this work informing research on discrimination and health in both the Global North and Global South. To date, limited population health research has examined the joint impacts of discrimination measures that are explicit (i.e., self-report) and implicit (i.e., automatic mental representations), both singly and for multiple types of discrimination.

Methods: Between May 28, 2020-August 4, 2022, we conducted Life + Health, a cross-sectional population-based study regarding six types of discrimination-racism, sexism, heterosexism, cissexism, ageism, and sizeism-with 699 participants (US-born, ages 25-64) from three community health centers in Boston, Massachusetts. Participants completed a Brief Implicit Association Test (B-IAT) and self-reported survey. Spearman's correlation coefficient was estimated to assess the strength and direction of discrimination types across target/dominant groups; logistic regression models were fit to assess the association of each type of discrimination with smoking/vaping following by random-effects meta-regression modeling to pool effects across discrimination types.

Results: Mean age was 37.9 years (SD = 11.2 years). Overall, 31.6% were people of color; 31.8% identified as transgender or nonbinary/genderqueer; 68.6% were sexual minority. For education, 20.5% had some college/vocational school or no college. Current cigarette/vaping was reported by 15.4% of the study population. Implicit and explicit measures were generally correlated with one another, but associations varied across discrimination types and for target/dominant groups. In random-effects meta-regression modeling, explicit compared to implicit discrimination measures were associated with a 1.18 (95% CI = 1.00-1.39) greater odds of smoking/vaping among dominant group members, but no such difference was observed among target group members.

Conclusion: Implicit and explicit discrimination measures yielded distinct yet complementary insights, highlighting the importance of both. Meta-regression provided evidence of health impacts across discrimination types. Future research on discrimination and health, in diverse country contexts, should consider using both implicit and explicit measures to analyze health impacts across multiple types of discrimination.