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

摘要 在美国，跨性别和非二元（TNB）人群感染艾滋病毒的风险很高。艾滋病毒感染率为14% 在所有TNB成年人中，这一比例高达44%。由于结构性障碍，耻辱， 由于担心受到虐待，TNB患者不太可能参与医疗保健，包括艾滋病毒预防和护理。 因此，在TNB群体中，PrEP摄取、ART覆盖率和病毒抑制率较低， 也增加了艾滋病毒感染和传播的风险。我们迫切需要解决这一被忽视的高风险 通过提高我们对TNB人群中艾滋病毒流行病学的认识， 有针对性的艾滋病毒预防和治疗干预措施。本提案涉及以下问题： 是TNB成年人的性伴侣，他们的性网络如何与其他人群重叠？如何 TNB成年人的艾滋病毒感染率在美国各地和种族/性别亚组之间是否存在差异？有何影响 增加PrEP覆盖率？我们将使用三种方法来回答这些问题： 来自华盛顿州西雅图的关于艾滋病毒风险行为、小区域估计和艾滋病毒数学建模的数据集 传输西雅图已被确定为超过50%的新艾滋病毒诊断发生的48个县之一。 因此，本研究将解决西雅图和全国有关TNB人群的关键知识差距， 地方和国家艾滋病毒预防和公共卫生活动。目的1：描述TNB的HIV危险行为 个人和性伴侣的TNB的人在西雅图，华盛顿州使用横截面数据，从三个 独立的本地数据来源：（1）西雅图的STD诊所，（2）公共卫生-西雅图和金郡的骄傲 调查，和（3）国家艾滋病毒行为监测（NHBS）调查。目的2：估计以下疾病的患病率 居住在大都市统计区的所有美国县的TNB个人中的艾滋病毒使用小 面积估算方法小面积估算描述了一组统计方法，用于计算 小地理区域的流行率，很少观察，直接估计无法提供 由于样本量小，统计数据可靠。我们将应用贝叶斯分层小区域估计模型 根据2015年美国跨性别调查的全国数据，这是迄今为止最大的TNB成年人全国样本， 超过27，000名受访者。目的3：建立一个艾滋病毒在生活在非洲的TNB成年人中传播的流行模型。 在华盛顿州西雅图，预测PrEP使用的增加如何影响变性妇女10年的艾滋病毒发病率， 跨性别男性和非二元成年人。这项研究将为临床护理和有针对性的公共卫生提供信息 针对性别少数群体及其性伴侣的干预措施。制定国家一级艾滋病毒估计数 美国各地的流行率将支持对TNB人群中微流行病的理解。 数学模型是预测有针对性的干预措施对艾滋病毒发病率的影响的有力工具， 在缺乏关于对TNB人口采取干预措施的纵向数据的情况下，少数流行病模型 目标3将是第一个包括变性男性和非二元个体的目标。

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.