Evaluating the Impact of State-Level Public Masking Mandates on New COVID-19 Cases and Deaths in the United States: A Demonstration of the Causal Roadmap

10/12/2021 ∙ by Angus K. Wong, et al. ∙ 0

At a national-level, we sought to investigate the effect of public masking mandates on COVID-19 in Fall 2020. Specifically, we aimed to evaluate how the relative growth of COVID-19 cases and deaths would have differed if all states had issued a mandate to mask in public by September 1, 2020 versus if all states had delayed issuing such a mandate. To do so, we applied the Causal Roadmap, a formal framework for causal and statistical inference. The outcome was defined as the state-specific relative increase in cumulative cases and in cumulative deaths 21, 30, 45, 60-days after September 1. Despite the natural experiment in state-level masking policies, the causal effect of interest was not identifiable. Nonetheless, we specified the target statistical parameter as the adjusted rate ratio (aRR): the expected outcome with early implementation divided by the expected outcome with delayed implementation, after adjusting for state-level confounders. To minimize strong estimation assumptions, primary analyses used targeted maximum likelihood estimation (TMLE) with Super Learner. After 60-days and at a national-level, early implementation was associated 9 reduction in new COVID-19 cases (aRR: 0.91; 95 reduction in new COVID-19 deaths (aRR: 0.84; 95 of identifiability prohibited causal interpretations, application of the Causal Roadmap facilitated estimation and inference of statistical associations, providing timely answers to pressing questions in the COVID-19 response.

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