Authors

Document Type

Article

Publication Date

2-14-2021

Publication Title

Annals of Epidemiology

Volume

57

First page number:

46

Last page number:

53

Abstract

Background and Objective: Community mitigation strategies could help reduce COVID-19 incidence, but there are few studies that explore associations nationally and by urbanicity. In a national county-level analysis, we examined the probability of being identified as a county with rapidly increasing COVID-19 incidence (rapid riser identification) during the summer of 2020 by implementation of mitigation policies prior to the summer, overall and by urbanicity. Methods: We analyzed county-level data on rapid riser identification during June 1–September 30, 2020 and statewide closures and statewide mask mandates starting March 19 (obtained from state government websites). Poisson regression models with robust standard error estimation were used to examine differences in the probability of rapid riser identification by implementation of mitigation policies (P-value< .05); associations were adjusted for county population size. Results: Counties in states that closed for 0–59 days were more likely to become a rapid riser county than those that closed for >59 days, particularly in nonmetropolitan areas. The probability of becoming a rapid riser county was 43% lower among counties that had statewide mask mandates at reopening (adjusted prevalence ratio = 0.57; 95% confidence intervals = 0.51–0.63); when stratified by urbanicity, associations were more pronounced in nonmetropolitan areas. Conclusions: These results underscore the potential value of community mitigation strategies in limiting the COVID-19 spread, especially in nonmetropolitan areas.

Keywords

Closures; COVID-19; Mask mandates; Mitigation strategies

Disciplines

Epidemiology | Public Health

File Format

PDF

File Size

1064 KB

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

Creative Commons License

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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