Chinese Evidence-Based Medicine Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario L8S 4L8, Canada.
Chin Med J (Engl). 2021 Oct 7;134(20):2438-2446. doi: 10.1097/CM9.0000000000001763.
Since the outbreak of coronavirus disease 2019 (COVID-19), human mobility restriction measures have raised controversies, partly because of the inconsistent findings. An empirical study is promptly needed to reliably assess the causal effects of the mobility restriction. The purpose of this study was to quantify the causal effects of human mobility restriction on the spread of COVID-19.
Our study applied the difference-in-difference (DID) model to assess the declines of population mobility at the city level, and used the log-log regression model to examine the effects of population mobility declines on the disease spread measured by cumulative or new cases of COVID-19 over time after adjusting for confounders.
The DID model showed that a continual expansion of the relative declines over time in 2020. After 4 weeks, population mobility declined by -54.81% (interquartile range, -65.50% to -43.56%). The accrued population mobility declines were associated with the significant reduction of cumulative COVID-19 cases throughout 6 weeks (ie, 1% decline of population mobility was associated with 0.72% [95% CI: 0.50%-0.93%] reduction of cumulative cases for 1 week, 1.42% 2 weeks, 1.69% 3 weeks, 1.72% 4 weeks, 1.64% 5 weeks, and 1.52% 6 weeks). The impact on the weekly new cases seemed greater in the first 4 weeks but faded thereafter. The effects on cumulative cases differed by cities of different population sizes, with greater effects seen in larger cities.
Persistent population mobility restrictions are well deserved. Implementation of mobility restrictions in major cities with large population sizes may be even more important.
自 2019 年冠状病毒病（COVID-19）爆发以来，人类流动性限制措施引起了争议，部分原因是结果不一致。需要及时进行实证研究，以可靠地评估流动性限制的因果效应。本研究旨在量化人类流动性限制对 COVID-19 传播的因果影响。
本研究应用差异中的差异（DID）模型评估城市层面的人口流动性下降，并使用对数-对数回归模型，在调整混杂因素后，考察人口流动性下降对 COVID-19 累计或新发病例随时间推移的疾病传播的影响。
DID 模型显示，2020 年相对下降持续扩大。4 周后，人口流动性下降了-54.81%（四分位距，-65.50%至-43.56%）。累计人口流动性下降与 COVID-19 累计病例的显著减少有关，持续 6 周（即，人口流动性下降 1%与 1 周内累计病例减少 0.72%[95%CI：0.50%-0.93%]、2 周减少 1.42%、3 周减少 1.69%、4 周减少 1.72%、5 周减少 1.64%和 6 周减少 1.52%）。对每周新发病例的影响在前 4 周似乎更大，但此后逐渐减弱。对累计病例的影响因城市人口规模不同而不同，较大城市的影响更大。
持续的人口流动性限制是值得的。在人口规模较大的主要城市实施流动性限制可能更为重要。
