Social distancing is widely acknowledged as an effective public health policy combating the novel coronavirus. But extreme social distancing has costs and it is not clear how much social distancing is needed to achieve public health effects. In this article, we develop a design-based framework to make inference about the dose-response relationship between social distancing and COVID-19 related death toll and case numbers. We first discuss how to embed observational data with a time-independent, continuous treatment dose into an approximate randomized experiment, and develop a randomization-based procedure that tests if a structured dose-response relationship fits the data. We then generalize the design and testing procedure to accommodate a time-dependent, treatment dose trajectory, and generalize a dose-response relationship to a longitudinal setting. Finally, we apply the proposed design and testing procedures to investigate the effect of social distancing during the phased reopening in the United States on public health outcomes using data compiled from sources including Unacast, the United States Census Bureau, and the County Health Rankings and Roadmaps Program. We test a primary analysis hypothesis that states the social distancing from April 27th to June 28th had no effect on the COVID-19-related death toll from June 29th to August 2nd (p-value < 0.001) and conducted extensive secondary analyses that investigate the dose-response relationship between social distancing and COVID-19 case numbers.
翻译:社会失常被公认为是防治新冠状病毒的有效公共卫生政策。但极端社会失常需要花费成本,也不清楚实现公共健康效果需要多少社会失常才能产生公共健康效果。在本篇文章中,我们开发了一个基于设计的框架,以推断社会失常与COVID-19(与死亡死亡和病例数有关的疾病反应关系)之间的剂量-反应关系。我们首先讨论如何将观察数据与时间独立的连续治疗剂量嵌入一个近似随机的实验中,并开发一种随机化程序,以测试结构化的剂量反应关系是否适合数据。然后,我们推广设计和测试程序,以适应时间依赖的治疗剂量轨迹,并将剂量反应关系概括到纵向环境。最后,我们运用拟议的设计和测试程序,调查在美国分阶段重新讨论公共健康结果时社会失常的影响,利用从Unacast、美国人口普查局、州卫生分级和路线图方案等来源收集的数据。我们测试了一个初步分析假设,即4月27日至8月28日的社会-19日的二级死亡率(与10月27日之间社会失常状况)分析,从4月27日至10月21日的社会-19日的社会-10日的二级调查。