Suppr超能文献

增强队列方法在 HIV 研究和流行病学中的应用(ENCORE):一项全美范围内针对美国跨性别女性的混合队列研究方案。

Enhanced Cohort Methods for HIV Research and Epidemiology (ENCORE): Protocol for a Nationwide Hybrid Cohort for Transgender Women in the United States.

机构信息

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.

Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.

出版信息

JMIR Res Protoc. 2024 Aug 27;13:e59846. doi: 10.2196/59846.

Abstract

BACKGROUND

In the United States, transgender women are disproportionately impacted by HIV and prioritized in the national strategy to end the epidemic. Individual, interpersonal, and structural vulnerabilities underlie HIV acquisition among transgender women and fuel syndemic conditions, yet no nationwide cohort monitors their HIV and other health outcomes.

OBJECTIVE

Our objective is to develop a nationwide cohort to estimate HIV incidence, identify risk factors, and investigate syndemic conditions co-occurring with HIV vulnerability or acquisition among US transgender women. The study is informed by the Syndemics Framework and the Social Ecological Model, positing that stigma-related conditions are synergistically driven by shared multilevel vulnerabilities.

METHODS

To address logistical and cost challenges while minimizing technology barriers and research distrust, we aim to establish a novel, hybrid community hub-supported digital cohort (N=3000). The digital cohort is the backbone of the study and is enhanced by hubs strategically located across the United States for increased engagement and in-person support. Study participants are English or Spanish speakers, are aged ≥18 years, identify as transgender women or along the transfeminine spectrum, reside in 1 of the 50 states or Puerto Rico, and do not have HIV (laboratory confirmed). Participants are followed for 24 months, with semiannual assessments. These include a questionnaire and laboratory-based HIV testing using self-collected specimens. Using residential zip codes, person-level data will be merged with contextual geolocated data, including population health measures and economic, housing, and other social and structural factors. Analyses will (1) evaluate the contribution of hub support to the digital cohort using descriptive statistics; (2) estimate and characterize syndemic patterns among transgender women using latent class analysis; (3) examine the role of contextual factors in driving syndemics and HIV prevention over time using multilevel regression models; (4) estimate HIV incidence in transgender women and examine the effect of syndemics and contextual factors on HIV incidence using Poisson regression models; and (5) develop dynamic, compartmental models of multilevel combination HIV prevention interventions among transgender women to simulate their impact on HIV incidence through 2030.

RESULTS

Enrollment launched on March 15, 2023, with data collection phases occurring in spring and fall. As of February 24, 2024, a total of 3084 individuals were screened, and 996 (32.3%) met the inclusion criteria and enrolled into the cohort: 2.3% (23/996) enrolled at a hub, and 53.6% (534/996) enrolled through a community hub-supported strategy. Recruitment through purely digital methods contributed 61.5% (1895/3084) of those screened and 42.7% (425/996) of those enrolled in the cohort.

CONCLUSIONS

Study findings will inform the development of evidence-based interventions to reduce HIV acquisition and syndemic conditions among US transgender women and advance efforts to end the US HIV epidemic. Methodological findings will also have critical implications for the design of future innovative approaches to HIV research.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59846.

摘要

背景

在美国,跨性别女性受到 HIV 的不成比例影响,并在国家终结艾滋病流行的战略中被优先考虑。跨性别女性感染 HIV 的个体、人际和结构脆弱性以及导致多种疾病同时发生的条件,然而,没有全国性的队列监测他们的 HIV 和其他健康结果。

目的

我们的目标是建立一个全国性的队列,以估计 HIV 发病率,确定风险因素,并研究与 HIV 脆弱性或获得相关的同时发生的多种疾病状况在美国跨性别女性中。该研究以综合征理论框架和社会生态模型为指导,假设与耻辱感相关的状况是由共同的多层次脆弱性协同驱动的。

方法

为了解决后勤和成本方面的挑战,同时最大限度地减少技术障碍和研究不信任,我们旨在建立一个新的、混合的社区中心支持的数字队列(N=3000)。数字队列是该研究的骨干,通过战略性地分布在美国各地的中心来增强,以提高参与度和现场支持。研究参与者是英语或西班牙语使用者,年龄≥18 岁,自认为是跨性别女性或跨女性光谱中的一员,居住在美国 50 个州或波多黎各中的一个,并且没有 HIV(实验室确认)。参与者将被随访 24 个月,每半年评估一次。这包括问卷调查和使用自我采集样本进行基于实验室的 HIV 检测。使用居住邮政编码,将个人层面的数据与包括人口健康指标以及经济、住房和其他社会和结构因素在内的地理位置相关数据合并。分析将(1)使用描述性统计评估中心支持对数字队列的贡献;(2)使用潜在类别分析估计和描述跨性别女性中的综合征模式;(3)使用多层回归模型研究上下文因素在随时间推移驱动综合征和 HIV 预防中的作用;(4)估计跨性别女性中的 HIV 发病率,并使用泊松回归模型研究综合征和上下文因素对 HIV 发病率的影响;(5)为跨性别女性制定多层次组合 HIV 预防干预的动态、隔室模型,以模拟它们对 2030 年之前 HIV 发病率的影响。

结果

2023 年 3 月 15 日启动了入组,春季和秋季进行数据收集阶段。截至 2024 年 2 月 24 日,共有 3084 人接受了筛查,其中 996 人(32.3%)符合纳入标准并入组队列:2.3%(23/996)在中心入组,53.6%(534/996)通过社区中心支持策略入组。通过纯粹的数字方法招募的人数占筛查人数的 61.5%(1895/3084),占入组人数的 42.7%(425/996)。

结论

研究结果将为减少美国跨性别女性中 HIV 获得和综合征状况提供循证干预措施,并为终结美国 HIV 流行的努力提供信息。方法学发现也将对未来 HIV 研究的创新方法的设计具有至关重要的意义。

国际注册报告标识符(IRRID):DERR1-10.2196/59846。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74b6/11387927/b80d80229918/resprot_v13i1e59846_fig1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验