Introduction: Face masks are widely recommended as a COVID-19 prevention strategy. State mask mandates have generally reduced the spread of the disease, but decisions to wear a mask depend on many factors. Recent increases in case rates in rural areas following initial outbreaks in more densely populated areas highlight the need to focus on prevention and education. Messaging about disease risk has faced challenges in rural areas in the past. While surges in cases within some communities are likely an impetus for behavior change, rising case rates likely explain only part of mask-wearing decisions. The current study examined the relationship between county-level indicators of rurality and mask wearing in the USA. Methods: National data from the New York Times’ COVID-19 cross-sectional mask survey was used to identify the percentage of a county’s residents who reported always/frequently wearing a mask (2–14 July 2020). The New York Times’ COVID-19 data repository was used to calculate county-level daily case rates for the 2 weeks preceding the mask survey (15 June – 1 July 2020), and defined county rurality using the Index of Relative Rurality (n=3103 counties). Multivariate linear regression was used to predict mask wearing across levels of rurality. The model was adjusted for daily case rates and other relevant county-level confounders, including county-level indicators of age, race/ethnicity, gender, political partisanship, income inequality, and whether each county was subject to a statewide mask mandate. Results: Large clusters of counties with high rurality and low mask wearing were observed in the Midwest, upper Midwest, and mountainous West. Holding daily case rates and other county characteristics constant, the predicted probability of wearing a mask decreased significantly as counties became more rural (β=–0.560; p<0.0001). Conclusion: Upticks in COVID-19 cases and deaths in rural areas are expected to continue, and localized outbreaks will likely occur indefinitely. The present findings highlight the need to better understand the mechanisms underlying perceptions of COVID-19 risk in rural areas.
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