Urban-Rural Disparities in HIV Testing Practices across the United States, 2012-2016

Authors: Lam Tran*, School of Public Health, University of Michigan, Phoebe Tran, Yale School of Public Health, Liem Tran , University of Tennessee at Knoxville
Topics: Medical and Health Geography, Urban Geography, Rural Geography
Keywords: Urban-rural disparities, BRFSS, logistic regression, Average Adjusted Predictions, Average Marginal Effects, marginal probability
Session Type: Paper
Day: 4/7/2019
Start / End Time: 3:55 PM / 5:35 PM
Room: Tyler, Marriott, Mezzanine Level
Presentation File: No File Uploaded


Purpose: HIV testing has helped to reduce transmission and new HIV cases in the US. While HIV testing patterns among Men who have sex with Men and different racial groups have been extensively studied, there is little work on the influence of living in an urban area (urbanity) on HIV testing. Existing work on urbanity/rurality and HIV testing is either outdated, covers a limited geographic area, does not consider the association at multiple spatial scales, or does not allow for direct comparisons of HIV testing estimates between different US regions and states. Methods: Using data from the 2012, 2014, and 2016 Behavioral Risk Factor Surveillance Systems surveys, we explored the independent role of urbanity/rurality in influencing HIV testing estimates at the national, regional, and state level after controlling for key sociodemographic and clinical factors of HIV risk and health seeking behaviors. Findings: We found significant disparities in testing between urban and rural residents in all states. The highest urban-rural testing disparities were observed in states with high HIV testing estimates. Additionally, states where estimates of people who had undergone a recent HIV test were near the national mean had the highest testing disparities between urban and rural areas. Regionally, the South had the highest estimates of HIV and recent HIV testing while the Midwest had the lowest. Conclusions: This study provides a detailed look at the isolated influence of urbanity/rurality on HIV testing and identifies specific areas with low estimates of people who have taken a HIV test recently.

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