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

TL;DR

This study used the Causal Roadmap framework to estimate the association between early mask mandates and reductions in COVID-19 cases and deaths across US states, highlighting the potential benefits of timely public health policies.

Contribution

It demonstrates the application of the Causal Roadmap and advanced statistical methods to estimate associations of mask mandates with COVID-19 outcomes, despite challenges in causal identification.

Findings

9% reduction in COVID-19 cases with early mandates

16% reduction in COVID-19 deaths with early mandates

Application of TMLE and Super Learner for robust estimation

Abstract

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%CI: 0.88-0.95) and a 16% reduction in new COVID-19 deaths (aRR: 0.84; 95%CI: 0.76-0.93). Although lack 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.