Mask-wearing and control of SARS-CoV-2 transmission in the USA: a cross-sectional study

Abstract

Background Face masks have become commonplace across the USA because of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic. Although evidence suggests that masks help to curb the spread of the disease, there is little empirical research at the population level. We investigate the association between self-reported maskwearing, physical distancing, and SARS-CoV-2 transmission in the USA, along with the effect of statewide mandates on mask uptake.Methods Serial cross-sectional surveys were administered via a web platform to randomly surveyed US individuals aged 13 years and older, to query self-reports of face mask-wearing. Survey responses were combined with instantaneous reproductive number (R t ) estimates from two publicly available sources, the outcome of interest. Measures of physical distancing, community demographics, and other potential sources of confounding (from publicly available sources) were also assessed. We fitted multivariate logistic regression models to estimate the association between mask-wearing and community transmission control (R t <1). Additionally, mask-wearing in 12 states was evaluated 2 weeks before and after statewide mandates.Findings 378 207 individuals responded to the survey between June 3 and July 27, 2020, of which 4186 were excluded for missing data. We observed an increasing trend in reported mask usage across the USA, although uptake varied by geography. A logistic model controlling for physical distancing, population demographics, and other variables found that a 10% increase in self-reported mask-wearing was associated with an increased odds of transmission control (odds ratio 3•53, 95% CI 2•03-6•43). We found that communities with high reported mask-wearing and physical distancing had the highest predicted probability of transmission control. Segmented regression analysis of reported mask-wearing showed no statistically significant change in the slope after mandates were introduced; however, the upward trend in reported mask-wearing was preserved.Interpretation The widespread reported use of face masks combined with physical distancing increases the odds of SARS-CoV-2 transmission control. Self-reported mask-wearing increased separately from government mask mandates, suggesting that supplemental public health interventions are needed to maximise adoption and help to curb the ongoing epidemic.

Decline in mitigation readiness facilitated second waves ofSARS-CoV-2

“…S6). This discrepancy may indicate a lower conformity with mitigation measures within young cohorts than assumed by the optimal transmission regulation of the model, which is corroborated by studies revealing stronger non-conform attitudes among adolescents and young adults during the pandemics [35,36,37,38]. There, increasing dominance of more defended organisms reflects a correlation between infection and fitness.…”

Social media and public perception as core aspect of public health: The cautionary case of @realdonaldtrump and COVID-19

“…While the data are only correlational, we argue that the central presence of @realdonaldtrump in appearances of the term “Mask” on Twitter is likely to have influenced public perception of their use, the risk of not using them, and of actions by the public. This assertion is supported via the recent finding that “self-reported mask-wearing increased separately from government mask mandates, suggesting that supplemental public health interventions are needed to maximize adoption and help to curb the ongoing epidemic” [ 24 ]. One final bit of support for the pattern we propose is that nearly all states, and all federal health agencies, recommended if not required masks by September 2020, yet mask use in the USA dropped slightly from ~70% to ~65% between the end of July 2020 and early October 2020, when it began to rise to the level of ~75% (by later November 2020 [ 25 ]).…”

Homophily in risk and behavior complicate understanding the COVID-19 epidemic curve

“…While heterogeneous mixing among such distinct etiological and behavioral cohorts is certainly not the only factor influencing currently observed trends, it would be a mistake to ignore the potential contribution of this effect going forwards. If these heterogeneities are sufficiently distinct, as they well may be ( 39, 40 ), the current rate of decline in new cases may easily slow, or even reverse, leading to additional future waves as an inevitable feature of social dynamics and disease behavior in the United States. Without including heterogeneous social mixing patterns into our considered factors, such dynamics may be attributed directly and completely to the gradual failures of previously successful mitigations, or to the emergence of new variants that are capable of overcoming available behavioral protections and/or vaccines.…”