JMIR Publications

ABSTRACT

Background:

Mask-wearing has likely played a role in the trajectory of the COVID-19 pandemic in the United States. While numerous studies have investigated demographic predictors of masking behavior nationally, most suffer from survey biases and none have characterized mask-wearing at fine spatial scales through different phases of the pandemic.

Objective:

Urgently needed is a debiased spatiotemporal characterization of mask-wearing behavior, information critical for assessing the effectiveness of masking, forecasting disease surges, and guiding public health decision-making.

Methods:

We analyze spatiotemporal masking patterns in 8+ million behavioral survey responses from across the United States from September 2020 to May 2021. We adjust for sample size and representation using binomial regression models and survey raking 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. 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. Self-reported behavior estimates are especially prone to social desirability and non-response biases; 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 need to characterize public health behaviors at fine spatiotemporal scales to capture heterogeneities that drive outbreak trajectories, as well as the role of behavioral big data to inform public health efforts. We advocate for a social sensing approach to behavioral surveillance to enable more accurate estimates of public health behaviors.