Treffer: A Geospatial Analysis of Social and Structural Determinants of Health and High HIV Prevalence in Alabama, USA.

Objective: This study utilizes geospatial analytic techniques to examine HIV hotspots in Alabama leveraging Medicaid utilization data. Methods: This cross-sectional study leveraged Medicaid utilization data from Alabama's 67 counties, averaging 9,861 Medicaid recipients aged > 18 years old per county. We used Alabama Medicaid administrative claims data from January 1, 2016, to December 31, 2020, to identify individuals with HIV. Using Microsoft SQL Server, we obtained the average annual count of HIV Medicaid claims in each of the 67 Alabama counties (numerator) and the number of adult Medicaid recipients in each county (denominator), and standardized with a multiplier of 100,000. We also examined several other area-level summary variables (e.g., non-high school completion, income greater than four times the federal poverty level, social associations, urbanicity/rurality) as social and structural determinants of health. County-boundary choropleth maps were created representing the geographic distribution of HIV rates per 100,000 adult Medicaid recipients in Alabama. Leveraging ESRI ArcGIS and local indicators of spatial association (LISA), results were examined using local Moran's I to identify geographic hotspots. Results: Eleven counties had HIV rates higher than 100 per 100,000. Three were hotspots. Being an HIV hotspot was significantly associated with relatively low educational attainment and less severe poverty than other areas in the state. Conclusions: Findings suggesting that the HIV clusters in Alabama were categorized by significantly less severe poverty and lower educational attainment can aid ongoing efforts to strategically target resources and end the HIV epidemic in U.S.' Deep South. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Community Health is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Volltext ist im Gastzugang nicht verfügbar. Login für vollen Zugriff.