Frontiers in public health · 2026
A cross-sectional analysis of county-level determinants of pandemic-era policy implementation.
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Williams Natasha J, Molina Melanie F, Yunyu Chiang Amy, Park Soo, Brandner Matthew, Faulkner Modrow Madelaine, Meissner Paul, Kornak John, Kitzman Heather, Isasi Carmen R, Kaul Alan F, Djibo Djeneba Audrey, Sudat Sylvia, Orozco Jaime, Garcia Torres Janna, Nair Vinit P, Ozluk Pelin, Carton Thomas, Pletcher Mark J, Hamad Rita
Аннотация
INTRODUCTION: Local COVID-19 policies are increasingly recognized as determinants of variation in disease transmission and other health outcomes. There is significant geographic variability in policy implementation, but little understanding of factors contributing to policy variation.
DATA AND METHODS: Using data from the US COVID-19 County Policy Database, we analyzed correlates of county-level policy implementation (N = 309 counties in 50 states across 2020-2021). Using multivariable regression, we examined the association of county characteristics with policy comprehensiveness overall and across three policy domains (containment/closure, economic support, public health). We focused on county characteristics capturing sociodemographic, political, and environmental factors that may represent predictors of policy adoption.
RESULTS: Urbanicity was positively associated with increased adoption of more public health policies, Democratic voter percentages were positively associated with increased adoption of all policies, and average temperature was negatively associated with adoption of all policies.
CONCLUSION: These findings provide insight into the possible predictors of the variation in local policy implementation during the COVID-19 pandemic, as well as informing future policy research and public health crisis management.
DATA AND METHODS: Using data from the US COVID-19 County Policy Database, we analyzed correlates of county-level policy implementation (N = 309 counties in 50 states across 2020-2021). Using multivariable regression, we examined the association of county characteristics with policy comprehensiveness overall and across three policy domains (containment/closure, economic support, public health). We focused on county characteristics capturing sociodemographic, political, and environmental factors that may represent predictors of policy adoption.
RESULTS: Urbanicity was positively associated with increased adoption of more public health policies, Democratic voter percentages were positively associated with increased adoption of all policies, and average temperature was negatively associated with adoption of all policies.
CONCLUSION: These findings provide insight into the possible predictors of the variation in local policy implementation during the COVID-19 pandemic, as well as informing future policy research and public health crisis management.