Evidence-based Interventions - Individual, Community, and Structural Factors That Predict Lower Rates of COVID-19 Screening Testing in Underserved and Vulnerable Populations

The purpose of this proposal was to identify individual, community (population) and structural factors associated with lower rates of COVID-19 testing in Northern New England (NNE), with a focus on underserved and vulnerable populations. This study included several COVID-19 medically and/or socially vulnerable populations: communities with high levels of social vulnerability; community-dwelling older adults; individuals with medical comorbidities known to increase risk of severe COVID-19 and, particularly, rural and remote communities. Analytically, the study qualitatively estimated individual, population and structural factors associated with higher or lower probability of having been tested for COVID-19 by combining comprehensive all-payer claims data across two states with state-level COVID-19 testing data and the CDC vulnerable community index. This study also assessed the geospatial distribution of disparities in COVID-19 testing in NNE using geographic information system methods to examine factors like testing center density and distance on testing rates. The study exploited differences in structure between Vermont and Maine to identify system level factors, including provider accessibility, testing availability and provider payment rules. The key outcomes were COVID-19 testing, hospitalizations and excess mortality among underserved and vulnerable populations in NNE. This study augmented the quantitative analysis with focus groups to identify additional barriers to testing. This study conducted multiple focus groups with individuals from vulnerable populations to identify barriers to COVID-19 testing. Once the individual, community (population) and structural factors that create barriers to COVID-19 testing and excess mortality were identified, potential interventions were tested in two different ways, First, the study developed and deployed a Discrete Choice Experiment (DCE) both in vulnerable communities in NNE and in a nationally representative sample of rural adults to test optimal strategies to increase testing using hypothetical scenarios. Second, the study tested the effect of targeted communication using a rural communication network using optimal communication strategies to facilitate increased testing and test if the communications change individual behavior and reduce health disparities. This study was conducted in partnership with the Department of Health in both Vermont and Maine and numerous community partners. This mixed methods project combined data from qualitative and quantitative analyses to identify barriers to COVID-19 testing with a focus on individuals and communities with high social vulnerability. Approaches to reduce testing disparities using experimental and interventional data were then tested.