Spatiotemporal trends in self-reported mask-wearing behavior in the United States: Analysis of a large cross-sectional survey

Abstract

Background: Face mask-wearing has been identified as an effective strategy to prevent transmission of SARS-CoV-2, yet mask mandates were never imposed nationally in the United States. This decision resulted in a patchwork of local policies and varying compliance potentially generating heterogeneities in the local trajectories of COVID-19 in the U.S. While numerous studies have investigated patterns and predictors of masking behavior nationally, most suffer from survey biases and none have been able to characterize mask-wearing at fine spatial scales across the U.S. through different phases of the pandemic.

Objective: Urgently needed is a debiased spatiotemporal characterization of mask-wearing behavior in the U.S. This information is critical to further assess the effectiveness of masking, evaluate drivers of transmission at different time points during the pandemic, and guide future public health decisions through, for example, forecasting disease surges.

Methods: We analyze spatiotemporal masking patterns in over eight million behavioral survey responses from across the United States starting in September 2020 through May 2021. We adjust for sample size and representation using binomial regression models and survey raking, respectively, to produce county-level monthly estimates of masking behavior. We additionally debias self-reported masking estimates using bias measures derived by comparing vaccination data from the same survey to official records at the county-level. Lastly, we evaluate whether individuals' perceptions of their social environment can serve as a less biased form of behavioral surveillance than self-reported data.

Results: We find that county-level masking behavior is spatially heterogeneous along an urban-rural gradient, with mask-wearing peaking in winter 2021 and declining sharply through May 2021. Our results identify regions where targeted public health efforts could have been most effective and suggest that individuals' frequency of mask-wearing may be influenced by national guidance and disease prevalence. We validate our bias-correction approach by comparing debiased self-reported mask-wearing estimates with community-reported estimates, after addressing issues of small sample size and representation. Self-reported behavior estimates are especially prone to social desirability and non-response biases and our findings demonstrate that these biases can be reduced if individuals are asked to report on community rather than self behaviors.

Conclusions: Our work highlights the importance of characterizing public health behaviors at fine spatiotemporal scales to capture heterogeneities that may drive outbreak trajectories. Our findings also emphasize the need for a standardized approach to incorporating behavioral big data into public health response efforts. Even large surveys are prone to bias; thus, we advocate for a social sensing approach to behavioral surveillance to enable more accurate estimates of health behaviors. Finally, we invite the public health and behavioral research communities to use our publicly available estimates to consider how bias-corrected behavioral estimates may improve our understanding of protective behaviors during crises and their impact on disease dynamics.

Figure 1.. Visualization of spatially heterogeneous data processing effects.

(A) Residuals following binomial regression model. (B) Residuals following binomial regression model with raking/sample rebalancing. (C) Residuals following binomial regression model with raking/sample rebalancing, and an offset for bias. Residuals are defined as the difference between the modeled and observed masking estimates at each analysis stage, where negative values indicate model estimates were higher than observed values, and positive residuals indicate model estimates were lower than observed values. All maps shown for February 2021.

Figure 2.. Bias-corrected masking behavior is spatially heterogeneous and higher in urban areas.

(A) Map of bias-corrected masking behavior in October 2020 reveals high spatial heterogeneity. Masking proportions vary substantially even within a single state. Spatial heterogeneity does not notably vary over time (Figure S5). A selection of other months in the study period are shown in Figures S6, S7, and S8. (B) Breakdown of county masking proportions over all survey months by NCHS urban-rural classification. A direct relationship between median masking proportion and population density is observed.

Figure 3.. Bias-corrected masking behavior peaks in the winter of 2020–21 and falls in the spring of 2021, mirroring new cases and increasing vaccinations.

Top curves show time series of z-score of bias-corrected masking proportions for each county colored by average masking proportion across the survey period. Inset plot shows z-scores of 7-day rolling average of new cases (green), proportion of individuals vaccinated nationally (orange), and reported worry about severe illness from COVID-19 in CTIS respondents (purple). Masking z-scores are based on the mean and standard deviation of each county’s masking estimates over the survey period.

Figure 4.. Community-reported masking gives a good estimate of bias-corrected self-reported masking.

Community-reported masking refers to the CTIS question where individuals report how many people in their community are masking, which may decrease non-response and social desirability bias compared to asking individuals to self-report their masking behavior. Point color denotes urban-rural classes. Comparisons of individual and community-reported estimates at different analysis stages are shown in Figures S11, and S12.