Automation and Evaluation of Real-Time Transmission Network-Based HIV Prevention Services in New York City

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ABSTRACT Introduction. The HIV epidemic in the United States persists. Public health departments across the country are exploring how to better use viral genetic sequence data to reconstruct transmission networks and guide prevention efforts. In 2017, the Centers for Disease Control and Prevention (CDC) and University of California, San Diego (UCSD) unveiled a nationwide initiative, Secure HIV-TRACE, to allow public health departments to construct genetic transmission networks in near-real time. Concurrently, the New York City (NYC) Public Health Labs (PHL) began sequencing viral genotypes themselves (i.e., point-of-diagnosis genotyping) for people diagnosed by select providers, in order to reduce the time between diagnosis and sequence analysis. The goal of this these programs are to direct standard pubic health resources in near real-time to cases, clusters, and locations of greatest concern (i.e., greatest potential for future cases). The underlying assumption of this strategy is that prioritization based on cluster growth dynamics inferred from genetic sequence data will disproportionately reduce future disease burden. Therefore, the important question is not whether real-time time targeting can prevent incident infections; rather, the important question is: Can real-time targeting prevent more incident infections than current, network-agnostic public health strategies? Methods. Here, we propose an observational study designed to evaluate the impact of these real-time strategies, which are currently implemented in NYC. We will automate Secure HIV-TRACE to construct genetic transmission clusters in real- time to guide standard public health services in NYC. Using these clusters, we will identify Index Cases (i.e., HIV-infected people diagnosed by select provides who receive point-of-diagnosis genotyping by PHL) and then to identify Priority Partners (i.e., in-care viremic or out-of-care HIV-infected partners, genetically linked to the Index Case). Standard public health services will then be offered to these Priority Partners [e.g., return to care, antiretroviral therapy (ART) adherence support, partner elicitation services]. Using data routinely reported to NYC Department of Health and Mental Hygiene, we will assess whether these services provided to Priority Partners lead to (i) increased rates of viral suppression in the Priority Partners, (ii) less than predicted incident HIV cases in their transmission cluster, (iii) an interaction with past cluster growth to result in even less than predicted incident HIV cases in their transmission cluster, and (iv) an interaction with past cluster growth to identify more than predicted prevalent, undiagnosed HIV cases in their transmission cluster. Conclusions. If we observe an interaction between past cluster growth and public health outcomes resulting from standard prevention services, this will suggest that real-time network based prevention should become standard of care in NYC and beyond. Through Secure HIV-TRACE, we can expand this scope approach nationwide.