Excess deaths associated with COVID-19 by rurality and demographic factors in the United States.

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Muskie School, MRHRC, population health, COVID-19, rural, statistical analysis, demography, epidemiology, social determinants of health

Publication Title

The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association


[JOURNAL ABSTRACT] PURPOSE: To estimate percent excess deaths during the COVID-19 pandemic by rural-urban residence in the United States and to describe rural-urban disparities by age, sex, and race/ethnicity.

METHODS: Using US mortality data, we used overdispersed Poisson regression models to estimate monthly expected death counts by rurality of residence, age group, sex, and race/ethnicity, and compared expected death counts with observed deaths. We then summarized excess deaths over 6 6-month time periods.

FINDINGS: There were 16.9% (95% confidence interval [CI]: 16.8, 17.0) more deaths than expected between March 2020 and February 2023. The percent excess varied by rurality (large central metro: 18.2% [18.1, 18.4], large fringe metro: 15.6% [15.5, 15.8], medium metro: 18.1% [18.0, 18.3], small metro: 15.5% [15.3, 15.7], micropolitan rural: 16.3% [16.1, 16.5], and noncore rural: 15.8% [15.6, 16.1]). The percent excess deaths were 20.2% (20.1, 20.3) for males and 13.6% (13.5, 13.7) for females, and highest for Hispanic persons (49% [49.0, 49.6]), followed by non-Hispanic Black persons (28% [27.5, 27.9]) and non-Hispanic White persons (12% [11.6, 11.8]). The 6-month time periods with the highest percent excess deaths for large central metro areas were March 2020-August 2020 and September 2020-February 2021; for all other areas, these time periods were September 2020-February 2021 and September 2021-February 2022.

CONCLUSION: Percent excess deaths varied by rurality, age group, sex, race/ethnicity, and time period. Monitoring excess deaths by rurality may be useful in assessing the impact of the pandemic over time, as rural-urban patterns appear to differ.


The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US Federal Office of Rural Health Policy or that of the National Center for Health Statistics of the Centers for Disease Control and Prevention. These data are available from the National Center for Health Statistics. The results reported in this paper correspond to the specific aims of a project supported by cooperative agreement #U1CRH03716

Funding Organization

HRSA-Federal Office of Rural Health Policy

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