Understanding HIV Prevalence and Modeling the Impact of PrEP on HIV Incidence among Transgender and Non-binary Adults in Seattle, Washington

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ABSTRACT Transgender and non-binary (TNB) people in the U.S. are at high risk for HIV acquisition. HIV prevalence is 14% among all TNB adults, and as high as 44% among black transgender women. Due to structural barriers, stigma, and fear of mistreatment, TNB people are less likely to engage in health care, including HIV prevention and care. Consequently, PrEP uptake, ART coverage, and viral suppression are low in the TNB community, factors which also increase HIV acquisition and transmission risk. We urgently need to address this neglected high risk population by improving our understanding of the epidemiology of HIV in TNB populations and by developing of targeted interventions for HIV prevention and treatment. This proposal addresses the following questions: Who are the sex partners of TNB adults, and how does their sexual network overlap with other populations? How does HIV prevalence among TNB adults vary across the US and by racial/gender subgroups? What is the impact of increased PrEP coverage? We will use three approaches to answer these questions: triangulation of three datasets from Seattle, WA on HIV risk behaviors, small area estimation, and mathematical modeling of HIV transmission. Seattle has been identified as one of 48 counties in which over 50% of new HIV diagnoses occur. Thus, this research will address critical knowledge gaps about TNB people in Seattle and nationally to inform local and national HIV prevention and public health activities. AIM 1: Characterize HIV risk behaviors of TNB individuals and sexual partners of TNB persons in Seattle, WA using cross-sectional data from three independent local data sources: (1) Seattle's STD Clinic, (2) Public Health-Seattle & King County's Pride Surveys, and (3) the National HIV Behavioral Surveillance (NHBS) survey. AIM 2: Estimate the prevalence of HIV among TNB individuals for all U.S. counties residing in metropolitan statistical areas using small area estimation methods. Small area estimation describes a collection of statistical methods used to calculate prevalence for small geographic areas with few observations, for which direct estimates would not provide reliable statistics due to small sample sizes. We will apply a Bayesian hierarchical small area estimation model to national data from the 2015 U.S. Transgender Survey, the largest national sample of TNB adults to date, with over 27,000 respondents. AIM 3: Develop an epidemic model of HIV transmission among TNB adults living in Seattle, WA to predict how increased PrEP use impacts 10-year HIV incidence for transgender women, transgender men, and non-binary adults. This research will inform clinical care and targeted public health interventions for gender minorities and their sex partners. The development of county-level estimates of HIV prevalence across the U.S. will support the understanding of microepidemics among TNB populations. Mathematical models are powerful tools for predicting the impact of targeted interventions on HIV incidence, and are important in the absence of longitudinal data on interventions for TNB populations. Few epidemic models include TNB individuals, and Aim 3 will be the first to include transgender men and non-binary individuals.