Department of Biostatistics & Epidemiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA.
Epidemiology. 2022 Mar 1;33(2):228-236. doi: 10.1097/EDE.0000000000001453.
We sought to investigate the effect of public masking mandates in US states on COVID-19 at the national level 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 1 September 2020 versus if all states had delayed issuing such a mandate.
We applied the Causal Roadmap, a formal framework for causal and statistical inference. We defined the outcome as the state-specific relative increase in cumulative cases and in cumulative deaths 21, 30, 45, and 60 days after 1 September. Despite the natural experiment occurring at the state-level, the causal effect of masking policies on COVID-19 outcomes 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 with Super Learner.
After 60 days and at a national level, early implementation was associated with a 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.
我们旨在 2020 年秋季调查美国各州实施公众戴口罩规定对全国层面 COVID-19 的影响。具体而言，我们旨在评估如果所有州都在 2020 年 9 月 1 日之前发布公共场所戴口罩的规定，而不是所有州都推迟发布此类规定，那么 COVID-19 病例和死亡人数的相对增长率会有何不同。
我们应用了因果路径图，这是一种用于因果推断和统计推断的正式框架。我们将结果定义为 9 月 1 日后 21、30、45 和 60 天，各州累计病例数和累计死亡数的相对增长率。尽管自然实验发生在州一级，但口罩政策对 COVID-19 结果的因果效应无法确定。尽管如此，我们还是将目标统计参数指定为调整后的比率比（aRR）：早期实施的预期结果除以延迟实施的预期结果，在调整了州级混杂因素后。为了最小化强估计假设，主要分析使用了针对最大似然估计的 Super Learner。
在 60 天后和全国范围内，早期实施与新的 COVID-19 病例减少 9%（aRR=0.91[95%CI=0.88,0.95]）和新的 COVID-19 死亡人数减少 16%（aRR=0.84[95%CI=0.76,0.93]）相关。
尽管缺乏可识别性禁止进行因果解释，但因果路径图的应用促进了统计关联的估计和推断，为 COVID-19 应对中的紧迫问题提供了及时的答案。
