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

美国跨性别女性的艾滋病毒研究和流行病学增强队列方法 (ENCORE)

• 批准号: 10681497
• 负责人: Sari Reisner
• 金额: $ 177.56万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-09 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10681497
• 关键词: AIDS prevention; Address; Adherence; Age; Alcohol abuse; Area; Attention; Automobile Driving; Black race; Cessation of life; Cities; Clinical Trials; Communities; Coupled; Data; Development; Disease; Disparity; Drug usage; Economics; Enhancement Technology; Enrollment; Ensure; Epidemic; Epidemiology; Equity; Ethnic Origin; Event; Exclusion; Formulation; Future; HIV; HIV Seronegativity; HIV Seropositivity; HIV risk; Health; Healthcare; High Risk Woman; Homicide; Hybrids; Imprisonment; Incidence; Individual; Inequity; Intervention; Latinx; Measures; Mental Health; Mental disorders; Methods; Modeling; Monitor; Overdose; Participant; Patient Self-Report; Pattern; Persons; Policies; Population; Population Heterogeneity; Populations at Risk; Prevalence; Prevention; Prevention Research; Prevention strategy; Race; Reporting; Research; Research Support; Risk; Risk Factors; Role; Sampling; Selection Bias; Sister; Site; Social isolation; Suicide; Technology; Testing; Time; Unemployment; United States; Vulnerable Populations; Work; cohort; contextual factors; demographics; digital; distrust; effectiveness testing; ethnic diversity; evidence base; experience; follow-up; gender affirming care; health disparity; literacy; mathematical model; men who have sex with men; mortality; multidisciplinary; multilevel analysis; national surveillance; neglect; novel; personalized approach; pre-exposure prophylaxis; preventive intervention; psychosocial; racial diversity; recruit; scale up; sex; social; social disparities; structural determinants; substance use; syndemic; transgender women; trend; uptake; violence victimization; virtual

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.

在美国，跨性别女性 (TW) 受到艾滋病毒的影响尤为严重，并且在艾滋病毒感染中处于优先地位。 美国结束疫情的国家战略。多重个人、人际关系和结构上的脆弱性助长了 台湾地区的艾滋病毒流行，以及加剧综合症（同时发生和加强的疾病或其他疾病） 健康状况，例如艾滋病毒、药物滥用和精神健康障碍，通常是由 社会不平等）。国家队列对于监测流行趋势以及重大事件（例如， 流行病、政策变化、新的生物医学干预措施）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视工作之外的台湾人来说尤其重要。此举的目的 建议是使用一种新颖的、混合的“社区中心”支持的数字队列（“中心支持的数字队列”） 模型来评估美国 TW 中的 HIV 发病率、危险因素和综合症（N=3,000）。在这个 模型中，实地社区“中心”将支持种族/族裔和 文化多元化、全国性的数字群体。我们将在检查的同时完善最佳的数字队列方法 结构性和心理社会性流行病经历对艾滋病毒发病率的影响以及参数化数学 模型以确定未来多层次组合艾滋病毒预防干预措施的目标。具体目标是： 1) 确定使用新颖的、中心支持的数字队列模型来注册和接受的效率和可接受性 保留种族/民族多样化的台湾样本用于艾滋病毒研究。 2) 估计患病率并描述特征 台湾地区流行病经历的模式。 3) 估计台湾的艾滋病毒发病率，每 6 个月随访一次，持续时间为 至少 24 个月的时间来确定针对组合、多学科艾滋病毒预防干预措施的定制方法。 3.1) 检查台湾地区流行病经历和背景结构因素对艾滋病毒发病率的影响 在美国。 3.2) 描述 TW 中 PrEP 连续性的特征以及随时间推移与 HIV 发病率的关系， 包括新出现的制剂的采用、艾滋病毒风险和依从性的纵向模式以及作用 PrEP 连续体 PrEP 的摄取、依从性和保留中的流行病类别和背景因素。 4.) 开发多层次组合艾滋病毒预防干预措施的动态模型，并在台湾地区扩大规模 模拟到 2030 年所评估的干预措施对艾滋病毒发病率的影响，对应于国家 艾滋病毒战略。有必要完善和评估混合数字队列模型，并注意缓解 弱势群体的选择偏差和消耗——一个对于流行病特别重要的研究领域 社会和健康差异凸显了这一点。我们开发混合队列模型的明确努力集中于 公平支持不同人群的研究参与并提供代表性和普遍性 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，并为基于证据和证据的发展提供信息 可接受的艾滋病毒干预措施，以减少美国跨性别女性中艾滋病毒的感染。