Purpose: Recent trends of HIV in the United States have indicated that the epidemic is no longer an urban issue; however, HIV data in rural settings are incomplete. Our objective was to estimate HIV prevalence in U.S. counties using small area estimation techniques (SAE) to better assess the burden of HIV nationally.
Methods: We performed SAE modeling to predict the reported number of HIV cases across the continental United States, including unreported counties using source data from the CDC National HIV Surveillance System. Our model borrowed strength from auxiliary HIV risk-indicator data, including geospatial information. Cross-validation was conducted to identify and assess the precision of the estimates.
Results: Our findings showed that most of the 677 unreported counties had low HIV prevalence levels (quintiles 1-2). Estimates in the South had high levels of HIV (quintile 4-5). Cross-validation techniques indicated good precision of the estimates, as 42% of the residuals were within ±10 HIV cases.
Conclusions: HIV was highest along the coastlines and in the U.S. South. Cross-validation techniques provided sufficient support of our model. Our study provides a more complete picture of the burden of HIV across the United States and identifies communities in need of future targeted interventions.
Keywords: County-level estimates; HIV; HIV prevalence; Small area estimation.
Published by Elsevier Inc.