From the Department of Epidemiology, Brown University School of Public Health, Providence, RI.
Department of Biostatistics, Brown University School of Public Health, Providence, RI.
Epidemiology. 2023 Jan 1;34(1):131-139. doi: 10.1097/EDE.0000000000001553. Epub 2022 Nov 30.
Summarizing the impact of community-based mitigation strategies and mobility on COVID-19 infections throughout the pandemic is critical for informing responses and future infectious disease outbreaks. Here, we employed time-series analyses to empirically investigate the relationships between mitigation strategies and mobility on COVID-19 incident cases across US states during the first three waves of infections.
We linked data on daily COVID-19 incidence by US state from March to December 2020 with the stringency index, a well-known index capturing the strictness of mitigation strategies, and the trip ratio, which measures the ratio of the number of trips taken per day compared with the same day in 2019. We utilized multilevel models to determine the relative impacts of policy stringency and the trip ratio on COVID-19 cumulative incidence and the effective reproduction number. We stratified analyses by three waves of infections.
Every five-point increase in the stringency index was associated with 2.89% (95% confidence interval = 1.52, 4.26%) and 5.01% (3.02, 6.95%) reductions in COVID-19 incidence for the first and third waves, respectively. Reducing the number of trips taken by 50% compared with the same time in 2019 was associated with a 16.2% (-0.07, 35.2%) decline in COVID-19 incidence at the state level during the second wave and 19.3% (2.30, 39.0%) during the third wave.
Mitigation strategies and reductions in mobility are associated with marked health gains through the reduction of COVID-19 infections, but we estimate variable impacts depending on policy stringency and levels of adherence.
总结大流行期间基于社区的缓解策略和流动性对 COVID-19 感染的影响对于指导应对措施和未来传染病暴发至关重要。在这里,我们采用时间序列分析方法,从经验上研究了美国各州在感染的前三波中,缓解策略和流动性与 COVID-19 病例之间的关系。
我们将 2020 年 3 月至 12 月美国各州每日 COVID-19 发病率数据与严格性指数(一种著名的捕捉缓解策略严格程度的指数)和旅行比率(一种衡量每日旅行次数与 2019 日相同日相比的比率)相关联。我们利用多层次模型确定政策严格性和旅行比率对 COVID-19 累积发病率和有效繁殖数的相对影响。我们对三波感染进行了分层分析。
严格性指数每增加 5 个点,与第一波和第三波 COVID-19 发病率分别降低 2.89%(95%置信区间=1.52,4.26%)和 5.01%(3.02,6.95%)相关。与 2019 年同期相比,旅行次数减少 50%,与第二波和第三波州级 COVID-19 发病率分别降低 16.2%(-0.07,35.2%)和 19.3%(2.30,39.0%)相关。
缓解策略和流动性的减少通过减少 COVID-19 感染,与显著的健康收益相关,但我们估计根据政策严格性和遵守程度的不同,影响也不同。