COVID-19 prevention is shaped by polysocial risk: A cross-sectional study of vaccination and testing disparities in underserved populations - David R. Brown; Derek D. Cyr; Lisa Wruck; Troy A. Stefano; Nader Mehri; Zoran Bursac; Richard Munoz; Marianna K. Baum; Eileen Fluney; Prasad Bhoite; Nana Aisha Garba; Frederick W. Anderson; et al. (PLoS ONE, 2025)
Understanding disparities in COVID-19 preventive efforts among underserved populations requires a holistic approach that considers multiple social determinants of health (SDOH). While disparities in individual COVID-19 risk factors are well-documented, the cumulative impact of these factors on vaccine uptake and testing remains insufficiently quantified. This study applies a polysocial risk framework to assess the combined influence of geo-demographic, economic, and health-related factors on COVID-19 vaccination and testing. Using cross-sectional data from 9,758 participants enrolled in the NIH Rapid Acceleration of Diagnostics – Underserved Populations (RADx-UP) program (February 2020–April 2023), we analyzed associations between polysocial risk and preventive behaviors using multivariable generalized estimating equations (GEE). Overall, 72.5% of participants reported COVID-19 vaccination, and 82.1% reported testing. However, disparities were evident across polysocial risk profiles. Individuals experiencing intersecting geo-demographic (Non-Hispanic Black, age 45, Southern residence), economic (low education, unemployment, financial hardship), and health-related risk factors (substance use, low CVD risk, no flu vaccination) were 43−48 percentage points less likely to be vaccinated compared to groups with higher adoption (p < 0.001). Testing disparities were narrower but remained significant, with differences ranging from 2 to 27 percentage points depending on the specific polysocial risk profiles. The findings underscore the utility of polysocial risk modeling as a predictive tool for identifying populations at highest risk of disengagement from preventive care, informing targeted precision public health interventions. Beyond COVID-19, this approach has broader applicability for understanding disparities in chronic disease prevention, cancer screening, maternal and child health, and health-related social needs (HRSN) interventions. Integrating polysocial risk assessments into clinical and public health settings can enhance data-driven strategies to improve population health outcomes.