Global Trends and Predictors of Face Mask Usage During the COVID-19 Pandemic
Elena Badillo-Goicoechea, Ting-Hsuan Chang, Esther Kim, Sarah LaRocca, Katherine Morris, Xiaoyi Deng, Samantha Chiu, Adrianne Bradford, Andres Garcia, Christoph… 展开作者
Elena Badillo-Goicoechea, Ting-Hsuan Chang, Esther Kim, Sarah LaRocca, Katherine Morris, Xiaoyi Deng, Samantha Chiu, Adrianne Bradford, Andres Garcia, Christoph Kern, Curtiss Cobb, Frauke Kreuter, Elizabeth A. Stuart
Background: Guidelines and recommendations from public health authorities related to face masks have been essential in containing the COVID-19 pandemic. We assessed the prevalence and correlates of mask usage during the pandemic.
Methods: We examined a total of 13,723,810 responses to a daily cross-sectional representative online survey in 38 countries who completed from April 23, 2020 to October 31, 2020 and reported having been in public at least once during the last seven days. The outcome was individual face mask usage in public settings, and the predictors were country fixed effects, country-level mask policy stringency, calendar time, individual sociodemographic factors, and health prevention behaviors. Associations were modelled using survey-weighted multivariable logistic regression.
Findings: Mask-wearing varied over time and across the 38 countries. While some countries consistently showed high prevalence throughout, in other countries mask usage increased gradually, and a few other countries remained at low prevalence. Controlling for time and country fixed effects, sociodemographic factors (older age, female gender, education, urbanicity) and stricter mask-related policies were significantly associated with higher mask usage in public settings, while social behaviors considered risky in the context of the pandemic (going out to large events, restaurants, shopping centers, and socializing outside of the household) were associated with lower mask use.
Interpretation: The decision to wear a face mask in public settings is significantly associated with sociodemographic factors, risky social behaviors, and mask policies. This has important implications for health prevention policies and messaging, including the potential need for more targeted policy and messaging design.
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College of Computing, Georgia Institute of Technology（佐治亚理工学院计算学院）；Department of Computer Science, Virginia Tech（弗吉尼亚理工大学计算机系）；Department of Computer Science, University of Iowa（爱荷华大学计算机系）
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