COVID Community-Engaged Testing in Alabama: Reaching Underserved Rural Populations Through Collaboration.

Rural communities are often underserved by public health testing initiatives in Alabama. As part of the National Institutes of Health's Rapid Acceleration of Diagnostics‒Underserved Populations initiative, the University of Alabama at Birmingham, along with community partners, sought to address this inequity in COVID-19 testing. We describe the participatory assessment, selection, and implementation phases of this project, which administered more than 23 000 COVID-19 tests throughout the state, including nearly 4000 tests among incarcerated populations. (Am J Public Health. 2022;112(10):1399-1403. https://doi.org/10.2105/AJPH.2022.306985).

Using publicly available data to identify priority communities for a SARS-CoV-2 testing intervention in a southern U.S. state.

Background: The U.S. Southeast has a high burden of SARS-CoV-2 infections and COVID-19 disease. We used public data sources and community engagement to prioritize county selections for a precision population health intervention to promote a SARS-CoV-2 testing intervention in rural Alabama during October 2020 and March 2021. Methods: We modeled factors associated with county-level SARS-CoV-2 percent positivity using covariates thought to associate with SARS-CoV-2 acquisition risk, disease severity, and risk mitigation practices. Descriptive epidemiologic data were presented to scientific and community advisory boards to prioritize counties for a testing intervention. Results: In October 2020, SARS-CoV-2 percent positivity was not associated with any modeled factors. In March 2021, premature death rate (aRR 1.16, 95% CI 1.07, 1.25), percent Black residents (aRR 1.00, 95% CI 1.00, 1.01), preventable hospitalizations (aRR 1.03, 95% CI 1.00, 1.06), and proportion of smokers (aRR 0.231, 95% CI 0.10, 0.55) were associated with average SARS-CoV-2 percent positivity. We then ranked counties based on percent positivity, case fatality, case rates, and number of testing sites using individual variables and factor scores. Top ranking counties identified through factor analysis and univariate associations were provided to community partners who considered ongoing efforts and strength of community partnerships to promote testing to inform intervention. Conclusions: The dynamic nature of SARS-CoV-2 proved challenging for a modelling approach to inform a precision population health intervention at the county level. Epidemiological data allowed for engagement of community stakeholders implementing testing. As data sources and analytic capacities expand, engaging communities in data interpretation is vital to address diseases locally.