Dataset Information

County-level Predictors of Coronavirus Disease 2019 (COVID-19) Cases and Deaths in the United States: What Happened, and Where Do We Go from Here?

ABSTRACT:

Background

The United States has been heavily impacted by the coronavirus disease 2019 (COVID-19) pandemic. Understanding microlevel patterns in US rates of COVID-19 can inform specific prevention strategies.

Methods

Using a negative binomial mixed-effects regression model, we evaluated the associations 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 22 January 2020 and 31 August 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, persons aged 20-49 years, racial/ethnic minorities, residential housing segregation, income inequality, uninsured persons, diabetics, or mobility outside the home during the pandemic.

Conclusions

To our knowledge, this study provides results from 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.

SUBMITTER: McLaughlin JM

PROVIDER: S-EPMC7717189 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:People of different racial/ethnic backgrounds, demographics, health, and socioeconomic characteristics have experienced disproportionate rates of infection and death due to COVID-19. This study tests if and how county-level rates of infection and death have changed in relation to societal county characteristics through time as the pandemic progressed. This longitudinal study sampled monthly county-level COVID-19 case and death data per 100,000 residents from April 2020 to March 2022, and studied the relationships of these variables with racial/ethnic, demographic, health, and socioeconomic characteristics for 3125 or 97.0% of U.S. counties, accounting for 96.4% of the U.S. population. The association of all county-level characteristics with COVID-19 case and death rates changed significantly through time, and showed different patterns. For example, counties with higher population proportions of Black, Native American, foreign-born non-citizen, elderly residents, households in poverty, or higher income inequality suffered disproportionately higher COVID-19 case and death rates at the beginning of the pandemic, followed by reversed, attenuated or fluctuating patterns, depending on the variable. Patterns for counties with higher White versus Black population proportions showed somewhat inverse patterns. Counties with higher female population proportions initially had lower case rates but higher death rates, and case and death rates become more coupled and fluctuated later in the pandemic. Counties with higher population densities had fluctuating case and death rates, with peaks coinciding with new variants of COVID-19. Counties with a greater proportion of university-educated residents had lower case and death rates throughout the pandemic, although the strength of this relationship fluctuated through time. This research clearly shows that how different segments of society are affected by a pandemic changes through time. Therefore, targeted policies and interventions that change as a pandemic unfolds are necessary to mitigate its disproportionate effects on vulnerable populations, particularly during the first six months of a pandemic.