The science for the last mile: Enhanced epidemiologic surveillance to accelerate HIV elimination

项目摘要/摘要 城市占全球艾滋病毒携带者人口的很大比例。因此，城市已经 成为联合国艾滋病规划署通过有针对性地扩大规模来结束艾滋病流行的“快车道”方法的重点 预防和检测服务。在美国，艾滋病毒监测数据表明构成发生了变化 在新感染艾滋病毒的人口中，女性和少数族裔人口占 不成比例的感染率。艾滋病毒发病率方面出现的健康差异表明，在很大程度上 成功的“达到零”公共卫生倡议(例如，迅速扩大暴露前预防措施[PrEP]， 更换针头和安全注射地点等。)没有接触到最脆弱的人群。 利用常规收集的监控数据与主要数据收集相结合，主要目标是 这项研究以旧金山为测试对象，旨在确定快速通道城市中艾滋病毒感染的残余驱动因素 凯斯。这项提议旨在提供多学科的方法和理论培训，以调查 在“零传播”的时代，人们对艾滋病病毒传播的科学认识存在差距。拟议中的培训 主要有：(1)半参数统计建模和机器学习，以提高预测的精度和精度 种群规模估计方法的精确度；(2)分子流行病学和系统发育技术 评估个人之间艾滋病毒病毒序列的关联性，推断共同的感染源；以及 (3)少数民族压力理论，以衡量环境的(社会结构)特征并将其联系起来 结构性暴露于艾滋病毒感染的差异。与培训组成部分相一致，研究目标是 这项研究旨在：(A)估计旧金山有多少艾滋病毒携带者，并量化 感染率和获得保健服务的机会差距之大；(B)确定社会人口 成员资格与传播群组的相关性；以及(C)确定最近艾滋病毒的社会结构促进者 采用病例对照研究设计，研究感染情况，特别是在少数族裔人群中。所产生的证据 这项工作可能会对旧金山的归零运动和通知小说产生直接影响 其他快车道城市的干预目标。此外，特别的方法论和实用性 从这个项目中获得的经验将使候选人成为一个有影响力的职业生涯的独立人士 研究员。

PROJECT SUMMARY/ABSTRACT Cities account for a large proportion of the global population of people living with HIV. As a result, Cities have become the focus of UNAIDS's “Fast Track” approach to ending the AIDS epidemic through targeted scale-up of prevention and testing services. In the United States, HIV surveillance data indicates a shifting composition of the population of people newly infected with HIV, with females and minority populations accounting for disproportionate rates of infection. These emerging health disparities in HIV incidence suggest that the largely successful “Getting to Zero” public health initiatives (e.g. rapid expansion of pre-exposure prophylaxis [PrEP], needle exchange and safe injecting sites, etc.) are not reaching the most vulnerable populations. Leveraging routinely collected surveillance data paired with primary data collection, the major goal of this research is to identify the residual drivers of HIV infection in Fast Track cities, using San Francisco as a test case. This proposal seeks to provide multidisciplinary methodological and theoretical training to investigate the scientific knowledge gap of ongoing HIV transmission in the era of “Getting to Zero.” The proposed training areas are: (1) semi-parametric statistical modeling and machine learning in order to improve the accuracy and precision of population size estimation methods; (2) molecular epidemiology and phylogenetic techniques to assess the relatedness of HIV viral sequences between individuals, inferring a shared source of infection; and (3) minority stress theory to measure the (socio-structural) characteristics of the environment and relate these structural exposures to disparities in HIV infection. Aligned with the training components, the research goals of this study are to: (a) estimate how many people are living with HIV in San Francisco and quantify the magnitude of disparities in infection rates and access to health care services; (b) identify the sociodemographic correlates of membership to a transmission cluster; and (c) identify the socio-structural facilitators of recent HIV infections, particularly among minority populations, using a case-control study design. The evidence generated from this work could have a direct impact on San Francisco's Getting to Zero campaign and inform novel intervention targets for other Fast Track cities. Additionally, the exceptional methodological and practical experience gained from this project will position the candidate for an impactful career as an independent researcher.