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

项目总结/摘要 城市在全球艾滋病毒感染者中占很大比例。因此，城市有 成为联合国艾滋病规划署通过有针对性的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， 新感染艾滋病毒的人口中，女性和少数民族人口占 不成比例的感染率。这些新出现的艾滋病毒发病率方面的健康差距表明， 成功的“实现零”公共卫生举措（例如，迅速推广接触前预防[PrEP]， 针头交换和安全注射部位等）并没有触及到最脆弱的人群。 利用常规收集的监测数据与原始数据收集相结合， 一项研究是以旧金山弗朗西斯科为例，确定快速通道城市中艾滋病毒感染的残余驱动因素 案子这项建议旨在提供多学科的方法和理论培训，以调查 在“达到零”的时代，艾滋病毒传播的科学知识差距。拟议的培训 领域是：（1）半参数统计建模和机器学习，以提高准确性和 人口规模估计方法的精确性;（2）分子流行病学和系统发育技术， 评估个体之间HIV病毒序列的相关性，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，并将这些特征 在艾滋病毒感染方面存在结构性差异。结合培训内容， 这项研究是：（a）估计有多少人是艾滋病毒感染者在旧金山弗朗西斯科和量化 感染率和获得保健服务方面的差距程度;（B）查明社会人口 成员资格与传播集群的相关性;以及（c）确定最近艾滋病毒的社会结构促进因素 感染，特别是在少数民族人群中，使用病例对照研究设计。产生的证据 从这项工作可以有一个直接的影响，旧金山弗朗西斯科的零运动，并告知小说 其他快速通道城市的干预目标。此外，特殊的方法和实用 从这个项目中获得的经验将定位候选人作为一个独立的有影响力的职业生涯 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.