# Leveraging Extensive Social Determinants Data and Spatial Data Science to Reduce HIV Incidence across the United States Ending the HIV Epidemic Counties > **NIH NIH R01** · YALE UNIVERSITY · 2024 · $825,988 ## Abstract PROJECT SUMMARY/ABSTRACT Geographic and racial/ethnic disparities in rates of HIV diagnosis and pre-exposure prophylaxis (PrEP) uptake are wide and persist. The Ending the HIV Epidemic (EHE) initiative prioritizes targeting 57 jurisdictions including 7 states and 50 counties with the highest HIV rates in the United States (U.S.). To reduce disparities, precise detection and forecast of new HIV diagnosis hotspots are required to accurately identify PrEP shortage areas to inform optimal allocations of PrEP providers who can serve the population efficiently to reduce new infections. This task relies highly on rigorous studies to examine contextual and structural factors such as community mental health prevalence and other socio-structural environmental determinants that are likely critical to preventing new HIV infections. Four inter-related contextual factors that address these gaps are: transportation-based measures of PrEP accessibility, community mental health prevalence, social capital, and religious institution environment in an area. We use spatial data science, cyberinfrastructure methodology, and geospatial statistical analyses to develop novel indicators of these measures by mining data from several sources including AIDS Vu, The American Community Survey, and other proprietary data sources to accomplish the following: AIM 1: Create transportation-based measures of PrEP accessibility using Gaussian two-step floating catchment area (G2SFCA) analysis, at the county and zip code levels, for both urban and rural transport systems. AIM 2: Use Bayesian spatial analyses to quantify how the distribution of religious institutions environment, social capital, community mental health prevalence, and transportation-based PrEP accessibility are associated with: new and late HIV diagnoses rates, and with PrEP uptake at the county, and zip code levels. AIM 3: Develop an interactive HIV data visualization Web tool to identify HIV hotspots and where to allocate additional PrEP providers. The Web tool will also display which (and to what extent) socio-structural variables drive HIV hotspots. We will evaluate the acceptability and feasibility of the tool through semi-structured interviews with n = 20 stakeholders (e.g., HIV surveillance epidemiologists, community leaders, and people living with HIV). Impact: Despite efficacious HIV prevention and care technologies for individuals, HIV-related disparities persist by race/ethnicity across geography. Successful completion of this research can contribute to ongoing EHE efforts to reduce 90% of new HIV infections by 2030. Moreover, the rigorous methods used in this project will contribute to addressing the need for novel approaches for valid and reliable assessments, measures, and estimation of structural factors that contribute to HIV in high-incidence populations. Our HIV data visualization Web tool is novel because it facilitates identifying which determinants influence HIV the most and which areas are changin... ## Key facts - **NIH application ID:** 10923338 - **Project number:** 1R01MH135807-01A1 - **Recipient organization:** YALE UNIVERSITY - **Principal Investigator:** Hui Luan - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $825,988 - **Award type:** 1 - **Project period:** 2024-08-26 → 2028-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10923338 ## Citation > US National Institutes of Health, RePORTER application 10923338, Leveraging Extensive Social Determinants Data and Spatial Data Science to Reduce HIV Incidence across the United States Ending the HIV Epidemic Counties (1R01MH135807-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10923338. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*