Methods for structural sexism and population health research: Introducing a novel analytic framework to capture life-course and intersectional effects.

Accumulating evidence links structural sexism to gendered health inequities, yet methodological challenges have precluded comprehensive examinations into life-course and/or intersectional effects. To help address this gap, we introduce an analytic framework that uses sequential conditional mean models (SCMMs) to jointly account for longitudinal exposure trajectories and moderation by multiple dimensions of social identity/position, which we then apply to study how early life-course exposure to U.S. state-level structural sexism shapes mental health outcomes within and between gender groups. Data came from the Growing Up Today Study, a cohort of 16,875 children aged 9-14 years in 1996 who we followed through 2016. Using a composite index of relevant public policies and societal conditions (e.g., abortion bans, wage gaps), we assigned each U.S. state a year-specific structural sexism score and calculated participants' cumulative exposure by averaging the scores associated with states they had lived in during the study period, weighted according to duration of time spent in each. We then fit a series of SCMMs to estimate overall and group-specific associations between cumulative exposure from baseline through a given study wave and subsequent depressive symptomology; we also fit models using simplified (i.e., non-cumulative) exposure variables for comparison purposes. Analyses revealed that cumulative exposure to structural sexism: (1) was associated with significantly increased odds of experiencing depressive symptoms by the subsequent wave; (2) disproportionately impacted multiply marginalized groups (e.g., sexual minority girls/women); and (3) was more strongly associated with depressive symptomology compared to static or point-in-time exposure operationalizations (e.g., exposure in a single year). Substantively, these findings suggest that long-term exposure to structural sexism may contribute to the inequitable social patterning of mental distress among young people living in the U.S. More broadly, the proposed analytic framework represents a promising approach to examining the complex links between structural sexism and health across the life course and for diverse social groups.

Complicating Narratives of Sexual Minority Mental Health: An Intersectional Analysis of Frequent Mental Distress at the Intersection of Sexual Orientation, Gender Identity, and Race/Ethnicity.

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.

Health Care Satisfaction in Relation to Gender Identity: Behavioral Risk Factor Surveillance Survey, 20 States (2014-2018).

BACKGROUND: Health care satisfaction is a key component of patient-centered care. Prior research on transgender populations has been based on convenience samples, and/or grouped all gender minorities into a single category. OBJECTIVE: The objective of this study was to quantify differences in health care satisfaction among transgender men, transgender women, gender nonconforming, and cisgender adults in a diverse multistate sample. RESEARCH DESIGN: Cross-sectional analysis of 2014-2018 Behavioral Risk Factor Surveillance System data from 20 states, using multivariable logistic models. SUBJECTS: We identified 167,468 transgender men, transgender women, gender-nonconforming people, cisgender women, and cisgender men and compared past year health care satisfaction across these groups. RESULTS: Transgender men and women had the highest prevalence of being "not at all satisfied" with the health care they received (14.6% and 8.6%, respectively), and gender-nonconforming people had the lowest prevalence of being "very satisfied" with their health care (55.7%). After adjustment for sociodemographic characteristics, transgender men were more likely to report being "not at all satisfied" with health care than cisgender men (odds ratio: 4.45, 95% confidence interval: 1.72-11.5) and cisgender women (odds ratio: 3.40, 95% confidence interval: 1.31-8.80). CONCLUSIONS: Findings indicate that transgender and gender-nonconforming adults report considerably less health care satisfaction relative to their cisgender peers. Interventions to address factors driving these differences are needed.

Design, deployment, and usability of a mobile system for cardiovascular health monitoring within the electronic Framingham Heart Study.

Background: The electronic Framingham Heart Study (eFHS) is an ongoing nested study, which includes FHS study participants, examining associations between health data from mobile devices with cardiovascular risk factors and disease. Objective: To describe application (app) design, report user characteristics, and describe usability and survey response rates. Methods: Eligible FHS participants were consented and offered a smartwatch (Apple Watch), a digital blood pressure (BP) cuff, and the eFHS smartphone app for administering surveys remotely. We assessed usability of the new app using 2 domains (functionality, aesthetics) of the Mobile App Rating Scale (MARS) and assessed survey completion rates at baseline and 3 months. Results: A total of 196 participants were recruited using the enhanced eFHS app. Of these, 97 (49.5%) completed the MARS instrument. Average age of participants was 53 ± 9 years, 51.5% were women, and 93.8% were white. Eighty-six percent of participants completed at least 1 measure on the baseline survey, and 50% completed the 3-month assessment. Overall subjective score of the app was 4.2 ± 0.7 on a scale from 1 to 5 stars. Of those who shared their health data with others, 46% shared their BP and 7.7% shared their physical activity with a health care provider. Conclusion: Participants rated the new, enhanced eFHS app positively overall. Mobile app survey completion rates were high, consistent with positive in-app ratings from participants. These mobile data collection modalities offer clinicians new opportunities to engage in conversations about health behaviors.