A comparison of the geographic patterns of HIV prevalence and hurricane events in the United States. (June 2019)

• Record Type: Journal Article
• Authors: Danielle Sharpe, J.
• Is Part Of: Public health. Volume 171(2019)
• Page Start: 131
• Page End: 134
• Publication Date: 2019-06
• Subjects: Hurricane -- HIV -- Severe weather -- Spatial epidemiology -- Geography -- Spatial -- Geographic mapping; Public health -- Periodicals; Public health -- Periodicals; Electronic journals; 362.1
• DOI: 10.1016/j.puhe.2019.04.001
• Languages: English
• ISSNs: 0033-3506

Abstract

Abstract: Objectives: Previous research has documented the adverse association between hurricanes and HIV health outcomes; however, no research has been conducted to examine whether there is spatial overlap between hurricane events and the epidemiology of HIV in the United States. The objectives of this study were to assess the geographic distributions of and spatial autocorrelation between HIV prevalence and the occurrences of hurricanes in the US. Study design: This was a cross-sectional study. Methods: Data on HIV prevalence rates were obtained from the Centers for Disease Control and Prevention, and data on US hurricane events were obtained from the National Oceanic and Atmospheric Administration. ArcGIS, version 10.6, was used for mapping HIV prevalence rates and occurrences of hurricane events, and GeoDa, version 1.12, was used to conduct all univariate and bivariate spatial autocorrelation analyses. Results: HIV and hurricanes primarily affected states located in the south and along the Gulf Coast. Major hurricanes were reported among these regions also. States recording 20 or more hurricanes between 1851 and 2017 had an average HIV prevalence rate of 453.2 cases per 100, 000 in 2016. States recording five or more major hurricanes between 1851 and 2017 had an average HIV prevalence rate of 421.8 cases per 100, 000 in 2016. Regarding univariate spatial autocorrelation, HIV prevalence was clustered (Moran's I : 0.1913; pseudo P -value: 0.003). Hurricane events were also clustered (Moran's I : 0.2826; pseudo P -value: 0.004), as were major hurricanes (Moran's I : 0.1982; pseudo P -value: 0.009). There was statistically significant bivariate spatial autocorrelation between neither HIV and hurricanes nor HIV and major hurricanes. Conclusion: The epidemiology of HIV prevalence and hurricane events has overlapping geographic patterns. This may have implications for hurricane readiness and recovery planning with respect to people living with HIV.