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印度封锁政策的中断时间序列分析:COVID-19 发病率的国家级分析。

An interrupted time series analysis of the lockdown policies in India: a national-level analysis of COVID-19 incidence.

机构信息

Johns Hopkins University School of Nursing, 525 N. Wolfe Street, Baltimore, MD 21205, USA.

Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

出版信息

Health Policy Plan. 2021 Jun 3;36(5):620-629. doi: 10.1093/heapol/czab027.

Abstract

India implemented a national mandatory lockdown policy (Lockdown 1.0) on 24 March 2020 in response to Coronavirus Disease 2019 (COVID-19). The policy was revised in three subsequent stages (Lockdown 2.0-4.0 between 15 April to 18 May 2020), and restrictions were lifted (Unlockdown 1.0) on 1 June 2020. This study evaluated the effect of lockdown policy on the COVID-19 incidence rate at the national level to inform policy response for this and future pandemics. We conducted an interrupted time series analysis with a segmented regression model using publicly available data on daily reported new COVID-19 cases between 2 March 2020 and 1 September 2020. National-level data from Google Community Mobility Reports during this timeframe were also used in model development and robustness checks. Results showed an 8% [95% confidence interval (CI) = 6-9%] reduction in the change in incidence rate per day after Lockdown 1.0 compared to prior to the Lockdown order, with an additional reduction of 3% (95% CI = 2-3%) after Lockdown 4.0, suggesting an 11% (95% CI = 9-12%) reduction in the change in COVID-19 incidence after Lockdown 4.0 compared to the period before Lockdown 1.0. Uptake of the lockdown policy is indicated by decreased mobility and attenuation of the increasing incidence of COVID-19. The increasing rate of incident case reports in India was attenuated after the lockdown policy was implemented compared to before, and this reduction was maintained after the restrictions were eased, suggesting that the policy helped to 'flatten the curve' and buy additional time for pandemic preparedness, response and recovery.

摘要

印度于 2020 年 3 月 24 日针对 2019 年冠状病毒病(COVID-19)实施了全国强制性封锁政策(封锁 1.0)。该政策随后分三个阶段修订(2020 年 4 月 15 日至 5 月 18 日的封锁 2.0-4.0),并于 2020 年 6 月 1 日解除限制(解锁 1.0)。本研究评估了封锁政策对全国 COVID-19 发病率的影响,以为本次和未来的大流行提供政策应对依据。我们使用 2020 年 3 月 2 日至 9 月 1 日期间公共报告的每日新 COVID-19 病例的公开数据,进行了具有分段回归模型的中断时间序列分析。在此期间,谷歌社区流动性报告的国家数据也用于模型开发和稳健性检查。结果表明,与封锁令实施前相比,封锁 1.0 后,发病率的日变化率降低了 8%(95%置信区间[CI] = 6-9%),封锁 4.0 后,发病率的日变化率进一步降低了 3%(95% CI = 2-3%),这表明封锁 4.0 后 COVID-19 发病率的日变化减少了 11%(95% CI = 9-12%)与封锁 1.0 前相比。封锁政策的实施表明流动性下降,COVID-19 发病率的上升趋势减弱。与封锁前相比,印度的确诊病例报告增长率在封锁政策实施后有所降低,而且在限制放宽后,这种降低仍在持续,这表明该政策有助于“拉平曲线”,为大流行的准备、应对和恢复争取更多时间。

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