PhD alumni Ethan Cicero, Janice Humphreys, professor; and Susan Silva, associate professor; published an article entitled "Application of Behavioral Risk Factor Surveillance System Sampling Weights to Transgender Health Measurement" in Nursing Research, and "The health status of transgender and gender nonbinary adults in the United States" in Plos One. Co-authors include Elizabeth Merwin, University of Texas at Arlington, and Sari Reisner, Harvard Medical School.

Abstract for Nursing Research

Background: Obtaining representative data from the transgender population is fundamental to improving their health and well-being and advancing transgender health research. The addition of the Behavioral Risk Factor Surveillance System (BRFSS) gender identity measure is a promising step towards better understanding transgender health. However, methodological concerns have emerged regarding the validity of data collected from transgender participants and its effect on the accuracy of population parameters derived from those data.

Objectives: To provide rationale substantiating concerns with the formulation and application of the 2015 BRFFS sampling weights and address the methodological challenges that arise when using this surveillance data to study transgender population health.

Methods: We examined the 2015 BRFSS methodology and used the BRFSS data to present a comparison of poor health status using two methodological approaches (a matched-subject design and the full BRFSS sample with sampling weights applied) to compare their effects on parameter estimates.

Results: Measurement error engendered by BRFSS data collection procedures introduced sex/gender identity discordance and contributed to problematic sampling weights. The sex-specific “raking” algorithm used by BRFSS to calculate the sampling weights was contingent on the classification accuracy of transgender by participants. Due to the sex/gender identity discordance of 74% of the transgender women and 66% of transgender men, sampling weights may not be able to adequately remove bias. The application of sampling weights has the potential to result in inaccurate parameter estimates when evaluating factors that may influence transgender health.

Discussion: Generalizations made from the weighted analysis may obscure the need for healthcare policy and clinical interventions aimed to promote health and prevent illness for transgender adults. Methods of public health surveillance and population surveys should be reviewed to help reduce systematic bias and increase the validity of data collected from transgender people.

Abstract for Plos One

The goal of this exploratory study was to delineate health differences among transgender subpopulations (transgender women/TW, transgender men/TM, gender nonbinary/GNB adults). 2015 Behavioral Risk Factor Surveillance System data were analyzed to compare the health of three groups (TW:N = 369; TM:N = 239; GNB:N = 156). Logistic regression and adjusted odds ratios were used to determine whether health outcomes (fair/poor health, frequent physical and mental unhealthy days, chronic health conditions, and health problems/impairments) are related to group and its interaction with personal characteristics and socioeconomic position. Group was a significant predictor of fair/poor health and frequent mental unhealthy days, revealing significant health differences between the transgender groups. The odds of poor/fair health were approximately 2.5 times higher in TM and GNB adults relative to TW. The odds of frequent mental unhealthy days for TM were approximately 1.5–2 times greater than TW and GNB adults. Among those with health insurance, the odds of fair/poor health for GNB adults was more than 1.5–2 times higher that of TM and TW. Among those without health insurance, TM had over 7 times greater odds of fair/poor health than TW. This study underscores the importance of classifying and examining the health of the transgender population as unique subpopulations, as notable health differences were discovered. TM and GNB adults have significant health concerns, requiring the attention of clinical interventions aimed at promoting health and preventing illness.