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?:abstract
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BACKGROUND: The United States has been heavily impacted by the COVID-19 pandemic. Understanding micro-level patterns in US rates of COVID-19 can inform specific prevention strategies. METHODS: Using a negative binomial mixed-effects regression model we evaluated the association between a broad set of US county-level sociodemographic, economic, and health-status-related characteristics and cumulative rates of laboratory-confirmed COVID-19 cases and deaths between January 22, 2020 and August 31, 2020. RESULTS: Rates of COVID-19 cases and deaths were higher in US counties that were more urban or densely-populated or that had more crowded housing, air pollution, women, 20–49-year-olds, racial/ethnic minorities, residential segregation, income inequality, uninsured, diabetics, or mobility outside the home during the pandemic. CONCLUSIONS: To our knowledge, this study provides the most comprehensive multivariable analysis of county-level predictors of rates of COVID-19 cases and deaths conducted to date. Our findings make clear that ensuring that COVID-19 preventive measures, including vaccines when available, reach vulnerable and minority communities and are distributed in a manner that meaningfully disrupts transmission (in addition to protecting those at highest risk of severe disease) will likely be critical to stem the pandemic.
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