Enhanced COhort methods for HIV Research and Epidemiology (ENCORE) among transgender women in the United States

在美国，变性人妇女(TW)受到艾滋病毒的影响不成比例，并在 美国结束疫情的国家战略。多个个人、人际和结构上的漏洞助长了 艾滋病毒在TW中的流行，以及助长合并症(共生和强化疾病或其他 健康状况，如艾滋病毒、药物使用和精神健康障碍，这些通常是 社会不平等)。国家队列对于监测疫情趋势和重大事件(例如， 流行病、政策变化、新的生物医学干预)影响艾滋病毒和其他健康状况；这是 对于经常被排除在国家监测工作之外的TW来说，这一点尤其重要。这样做的目的是 建议使用一种新的、混合的“社区中心”支持的数字队列(“中心支持的数字队列”) 在美国TW(N=3,000)人群中评估HIV发病率、危险因素和合并症的模型。在这 模范、实地社区“枢纽”将支持招收和保留种族/族裔和 文化多元化，全国性的数字群体。我们将在研究最优数字队列方法的同时 结构和心理社会综合体验对HIV发病率的影响和数理参数 为未来的多层次组合艾滋病毒预防干预确定目标的模式。具体目标是： 1)确定使用新型集线器支持的数字队列模型进行登记和 保留种族/民族多样性的TW样本，用于艾滋病毒研究。2)评估患病率并确定其特征 TW之间的共性经验模式。3)估计TW的艾滋病毒发病率，每6个月跟踪一次 至少24个月的时间来确定组合的、多学科的艾滋病毒预防干预措施的量身定制的方法。 3.1)审查共同经历和背景结构因素对TW中艾滋病毒发病率的影响 在美国。3.2)描述TW之间的PrEP连续体以及随时间推移与艾滋病毒发病率的关系； 包括对新出现的配方的吸收，艾滋病毒风险和依从性的纵向模式，以及 PrEP连续统一体中的共性类别和上下文因素PrEP的吸收、坚持和保留。4.) 建立多层次组合的艾滋病预防干预措施动态模型，并在TW到 模拟评估的干预措施对2030年前艾滋病毒发病率的影响，对应于国家 艾滋病毒战略。有必要改进和评估混合数字队列模型，同时注意缓解 弱势群体的选择偏向和自然减员--这是一个对流行病特别重要的研究领域 突出表现在社会和健康方面的差距。我们开发混合队列模型的明确努力主要集中在 公平支持不同人群的研究参与，并提供代表性和可推广性 数据。研究结果将为未来的艾滋病毒预防研究提供关键的流行病学参数，提供 探索其他研究问题的平台，并通报循证和 可接受的艾滋病毒干预措施，以减少美国变性人妇女感染艾滋病毒的机会。

In the United States, transgender women (TW) are disproportionately impacted by HIV and are prioritized in the US national strategy to end the epidemic. Multiple individual, interpersonal, and structural vulnerabilities fuel the HIV epidemic among TW, as well as fuel syndemic conditions (co-occurring and reinforcing diseases or other health conditions, such as HIV, substance use, and mental health disorder, that are often a consequence of social inequity). National cohorts are critical to monitoring epidemic trends and how major events (e.g., epidemics, policy change, new biomedical interventions) impact HIV and other health conditions; this is particularly important for TW who are often excluded from national surveillance efforts. The objective of this proposal is to use a novel, hybrid “community hub”-supported digital cohort (“hub-supported digital cohort”) model to evaluate HIV incidence, risk factors, and syndemic conditions among TW in the US (N=3,000). In this model, on-the-ground community “hubs” will support enrollment and retention of a racially/ethnically and culturally diverse, nationwide digital cohort. We will refine optimal digital cohort methods while examining the impact of structural and psychosocial syndemic experiences on HIV incidence and parameterizing mathematical models to identify targets for future multi-level combination HIV prevention interventions. The specific aims are: 1) Determine the efficiency and acceptability of using a novel, hub-supported digital cohort model to enroll and retain a sample of racially/ethnically diverse TW for HIV research. 2) Estimate the prevalence and characterize patterns of syndemic experiences among TW. 3) Estimate HIV incidence in TW, followed every 6 months for at least 24 months to identify tailored approaches for combination, multi-disciplinary HIV prevention interventions. 3.1) Examine the effect of syndemic experiences and contextual structural factors on HIV incidence among TW in the US. 3.2) Characterize the PrEP continuum among TW and associations with HIV incidence over time, including uptake of newly emerging formulations, longitudinal patterns of HIV risk and adherence, and the role of syndemic classes and contextual factors in the PrEP continuum PrEP uptake, adherence, and retention. 4.) Develop dynamic models of multi-level combination HIV prevention interventions and scale-up among TW to simulate the impact of evaluated interventions on HIV incidence through 2030, corresponding to the National HIV strategy. There is a need to refine and evaluate hybrid digital cohort models with attention to mitigating selection bias and attrition of vulnerable populations – an area of research particularly important for an epidemic underscored by social and health disparities. Our explicit efforts to develop a hybrid cohort model center on equity to support research participation across diverse populations and provide representative and generalizable data. Study findings will provide critical epidemiologic parameters for future HIV prevention research, provide a platform for exploration of other research questions, and inform the development of evidence-based and acceptable HIV interventions to reduce HIV acquisition among transgender women in the US.