The tale of two pandemics: Understanding racial and ethnic disparities from the collision of HIV and COVID-19 in the U.S.

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PROJECT SUMMARY The collision of the COVID-19 pandemic with the existing HIV epidemic in the U.S. has exacerbated the decades old racial/ethnic disparities in HIV. For example, Blacks account for 42-44% of HIV diagnoses and deaths among people living with HIV (PLWH) while accounting for only 12% of the population. These racialized disparities in the U.S. HIV epidemic are further compounded by the same disparities emerging in COVID-19. We have shown that PLWH appear to be at higher risk of poorer COVID-19 outcomes than persons not living with HIV (PNLWH), and that the odds of incident COVID-19 infection among PLWH are 60% and 118% higher among Black and Latinx persons, respectively, than whites. These racialized disparities are likely largely driven by social determinants of health (SDoH) underlying our health systems—an understanding of the SDoH pathways that elucidate these disparities is urgently needed to develop the next generation of HIV interventions operating at the structural and social levels, and ever more now in the context of COVID-19. The National COVID Cohort Collaborative (N3C) leverages real-world, national data and presents an unprecedented opportunity to inform the NIH priority aims to understand the social and biologic factors that may affect both HIV and COVID-19 outcomes. N3C is the largest electronic health record (EHR) repository in U.S. history (>10M patients), contains both unparalleled individual-level granular clinical and historical data, and represents the largest U.S. cohort of PLWH with their HIV and COVID-19 outcomes data (>77K), allowing us to evaluate the bi-directional impact of existing HIV infection and COVID-19 outcomes. Furthermore, individual-level data in the N3C are uniquely positioned to merge publicly available datasets that measure area- level SDoH. Our central hypothesis is that the observed racial/ethnic disparities in HIV and COVID-19 occur in a larger context of individuals embedded in social, political, and economic contexts, i.e., SDoH. Understanding these forces, centered on SDoH, allows us to determine the next generation of HIV interventions. Our three aims respond to the NIH call using data science, rigorous machine and statistical learning, and multi-level mediation and epidemic modeling. The goal of Aim 1 (HIV outcomes) is to identify multilevel, social determinants of racial/ethnic disparities in HIV outcomes (e.g., viral suppression [VS] and hospitalization) during the COVID-19 pandemic. The goal of Aim 2 (COVID outcomes) is to understand the independent and aggregated impact of SDoH and clinical characteristics on HIV immune dysfunction for COVID-19 outcomes and vaccine effectiveness (2a) and quantify the differential impact of HIV on COVID-19 outcomes at the U.S. population level by race/ethnicity (2b). The goal of Aim 3 (HIV epidemic modeling) is to quantify the impact of the COVID-19 pandemic on HIV treatment (VS and hospitalization) and prevention (pre-exposure prophylaxis [PrEP] use and HIV/sexually transmitted infections [STI] testing frequency) outcomes by race/ethnicity at the population-level for the national Ending the HIV Epidemic (EHE) initiative’s priority jurisdictions.