[论文解读] The effect of stay-at-home orders on COVID-19 cases and fatalities in the United States

James H. Fowler, Seth J. Hill+2 more|arXiv (Cornell University)|Apr 13, 2020

COVID-19 epidemiological studies被引用 4

一句话总结

本研究采用双重差分设计，分析美国居家令对县一级新冠肺炎病例和死亡人数的影响。研究发现，居家令实施三周后，每周病例数减少48.6%，死亡人数减少59.8%，在第一个月内避免了约39万例病例和4.1万例死亡。

ABSTRACT

Governments issue "stay at home" orders to reduce the spread of contagious diseases, but the magnitude of such orders' effectiveness is uncertain. In the United States these orders were not coordinated at the national level during the coronavirus disease 2019 (COVID-19) pandemic, which creates an opportunity to use spatial and temporal variation to measure the policies' effect with greater accuracy. Here, we combine data on the timing of stay-at-home orders with daily confirmed COVID-19 cases and fatalities at the county level in the United States. We estimate the effect of stay-at-home orders using a difference-in-differences design that accounts for unmeasured local variation in factors like health systems and demographics and for unmeasured temporal variation in factors like national mitigation actions and access to tests. Compared to counties that did not implement stay-at-home orders, the results show that the orders are associated with a 30.2 percent (11.0 to 45.2) reduction in weekly cases after one week, a 40.0 percent (23.4 to 53.0) reduction after two weeks, and a 48.6 percent (31.1 to 61.7) reduction after three weeks. Stay-at-home orders are also associated with a 59.8 percent (18.3 to 80.2) reduction in weekly fatalities after three weeks. These results suggest that stay-at-home orders reduced confirmed cases by 390,000 (170,000 to 680,000) and fatalities by 41,000 (27,000 to 59,000) within the first three weeks in localities where they were implemented.

研究动机与目标

• 评估居家令在减少美国各县新冠肺炎传播和死亡人数方面的有效性。
• 通过控制未测量的地方和时间混杂因素，分离居家令的因果效应。
• 估算在疫情早期阶段，这些居家令所避免的病例和死亡人数。
• 为非药物干预措施在快速传播的传染病中的公共卫生价值提供证据。

提出的方法

• 将双重差分（DiD）模型应用于居家令实施、确诊病例和死亡人数的县一级数据。
• 通过固定效应控制未测量的县特定因素，如人口统计特征和医疗基础设施。
• 同时考虑未测量的全国性时间趋势，包括检测可及性和更广泛的缓解措施。
• 模型估算实施居家令的县与未实施的县在每周病例和死亡率变化上的差异。
• DiD估计量通过比较政策实施前后的趋势，分离出政策的因果效应。
• 稳健性检验包括替代模型设定和敏感性分析，以验证结果的可靠性。

实验结果

研究问题

• RQ1 居家令对确诊病例每周增长率的因果影响是什么？
• RQ2 居家令如何影响美国每周死亡人数的变化轨迹？
• RQ3 这些居家令在实施初期三周内，对病例和死亡总数的减少程度如何？
• RQ4 未测量的地方和全国性因素如何影响居家令估计影响的评估？

主要发现

• 居家令实施一周后，确诊病例的周增长率降低了30.2%，95%置信区间为11.0%至45.2%。
• 两周后，病例周增长率的降幅增至40.0%，95%置信区间为23.4%至53.0%。
• 到第三周，病例周增长率的降幅达到48.6%，95%置信区间为31.1%至61.7%。
• 居家令实施三周后，每周死亡人数减少了59.8%，95%置信区间为18.3%至80.2%。
• 估计在实施居家令的县中，前三周内避免了约39万例病例（17万至68万例）和4.1万例死亡（2.7万至5.9万例）。
• 结果表明，居家令对减缓新冠肺炎传播和降低死亡率具有显著且统计上显著的影响。