RESUMO
Background: Health inequities among transgender and gender diverse (TGD) populations are well-documented and may be partially explained by the complex social power dynamics that lead to stigmatization. Healthcare Stereotype Threat (HCST) refers to the fear and threat of being perceived negatively based on identity-related stereotypes and may influence health and healthcare experiences. Few studies have investigated associations of HCST with healthcare access and health outcomes for TGD individuals. Methods: We analyzed the U.S. Transgender Population Health Survey, a cross-sectional national probability sample of 274 TGD adults recruited April 2016-December 2018. Participants self-reported HCST through a 4-item scale. We estimated prevalence ratios (PR) for the association between HCST and binary healthcare access indicators and health outcomes using Poisson models with robust variance. Prevalence ratios (PR) were estimated using negative binomial models for the association between HCST and number of past-month poor physical and mental health days. Models adjusted for sociodemographics and medical gender affirmation. Results: The mean age was 34.2 years; 30.9 % identified as transgender men, 37.8 % transgender women, and 31.3 % genderqueer/nonbinary. HCST threat was associated with increased prevalence of not having a personal doctor/healthcare provider (PR = 1.25; 95 %CI = 1.00-1.56) and reporting fair/poor general health vs good/very good/excellent health (PR = 1.92; 95 %CI = 1.37-2.70). Higher HCST was also associated with more frequent past-month poor physical (PR = 1.34; 95 %CI = 1.12-1.59) and mental (PR = 1.49; 95 %CI = 1.33-1.66) health days. Conclusion: HCST may contribute to adverse healthcare access and health outcomes in TGD populations, though prospective studies are needed. Multilevel interventions are recommended to create safe, gender-affirming healthcare environments that mitigate HCST.
RESUMO
OBJECTIVES: The focus of this study was to calculate and contextualize response rates for a community-based study conducted during the COVID-19 pandemic, a topic on which scant data exist, and to share lessons learned from recruiting and enrolling for implementation of future studies. DESIGN: The Life+Health Study, a cross-sectional population-based study designed to advance novel methods to measure and analyze multiple forms of discrimination for population health research. SETTING: The study recruited participants from 3 community health centers in Boston, Massachusetts, between May 2020 and July 2022. PARTICIPANTS: A total of 699 adult participants between the ages of 25 and 64 years who were born in the United States and had visited one of the health centers within the last 2 years. MAIN OUTCOME MEASURES: The response rate was calculated as follows: (number of completions + number of dropouts)/(dropouts + enrollments). To contextualize this response rate, we synthesized evidence pertaining to local COVID-19 case counts, sociopolitical events, pandemic-related restrictions and project protocol adjustments, and examples of interactions with patients. RESULTS: Our study had a lower-than-expected response rate (48.4%), with the lowest rates from the community health centers serving primarily low-income patients of color. Completion rates were lower during periods of higher COVID-19 case counts. We describe contextual factors that led to challenges and lessons learned from recruiting during the pandemic, including the impact of US sociopolitical events. CONCLUSIONS: The Life+Health Study concluded recruitment during the pandemic with a lower-than-expected response rate, as also reported in 4 other US publications focused on the impact of COVID-19 on response rates in community-based studies. Our results provide an example of the impact of the pandemic and related US sociopolitical events on response rates that can serve as a framework for contextualizing other research conducted during the pandemic and highlight the importance of best practices in research recruitment with underserved populations.
