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.
Cities account for a large proportion of HIV prevalence and incidence, and therefore are the focus of UNAIDS' ?Fast Track? approach to ending the AIDS epidemic. Several ?Fast Track Cities?, including San Francisco, CA, have achieved significant reductions in new HIV infections, and yet despite this success racial/ethnic and gender disparities in new infections are emerging. The proposed study will use semi-parametric statistical modeling and HIV phylogenetics applied to routinely collected public health surveillance data, paired with primary data collection informed by a minority stress theoretical framework, to identify the residual drivers of HIV infection and offer novel targets for intervention to achieve zero new infections.