County-Level Spatiotemporal Patterns of New HIV Diagnoses and Pre-exposure Prophylaxis (PrEP) Use in Mississippi, 2014–2018: A Bayesian Analysis of Publicly Accessible Censored Data

In the South region of the United States, HIV is disproportionately high and levels of pre-exposure prophylaxis (PrEP) use, which is highly effective in reducing the risk of acquiring HIV, are among the lowest across the country. Simultaneously examining the geographical distributions of both new HIV diagnoses and PrEP use as well as how they evolve over time at the county level is valuable for developing locally tailored intervention programs to target areas most in need of help. There is scant research on this topic using publicly accessible data sets, however, partly because of statistical challenges in modeling censored spatiotemporal data. This study fills this gap by applying a Bayesian spatiotemporal model to analyze interval-censored new HIV diagnoses and left-censored PrEP user data sets in Mississippi at the county level between 2014 and 2018. Suppressed values were modeled with Poisson distributions restricted to ranges where the possible values lie within. A simulation study indicates that the proposed model performs well in estimating censored values and regression coefficients as well as detecting hot spots. At the state level, new HIV diagnoses had a stable trend and PrEP use sharply increased during the study period. DeSoto and Hinds counties warrant special attention because their trends in new HIV diagnoses departed from the state-level trend. We demonstrate that publicly accessible, censored new HIV diagnosis and PrEP user data could be analyzed in ways that yield robust results, which can help health departments and other stakeholders more confidently identify areas that should be prioritized for aggressive HIV prevention.