Literature suggests that federal funding allocation for HIV-related research in the US may not align with HIV disease burden but is influenced by structural disparities. This study sought to examine how federal funding allocation is associated with HIV disease burden and research capacity of states by applying Big Data integration, text mining, and statistics. Using text mining, we identified 20,678 HIV-related federal projects from 2008 to 2018 in NIH ExPORTER, which were then integrated with data from AtlasPlus and US Census Bureau. We developed Gini coefficients to assess the inequality of funding and the Generalized Estimating Equations model to examine the associations between funding allocation and (1) state HIV disease burden, (2) state research capacity, and (3) geographic regions, respectively. The Gini coefficients (0.60 to 0.80) suggest a highly skewed funding distribution. Funding allocation was not associated with state HIV disease burden (p = 0.269) but HIV research capacity (p = 0.000). The South (with the heaviest HIV disease burden) did not receive significantly more federal funding. Our findings for the first time identified disparities of federal funding allocation, suggesting that federal agencies favor states of high research capacity over heavy disease burden, which may reinforce the HIV-related health disparities.
Keywords: Big Data; HIV; funding allocation; healthcare disparities.