英国全国封锁:对所有人都有同样的限制,但对COVID-19死亡风险的影响是否因地而异?

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Spatial and Spatio-Temporal Epidemiology Pub Date : 2023-02-01 DOI:10.1016/j.sste.2022.100559
Robin Muegge, Nema Dean, Eilidh Jack, Duncan Lee
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引用次数: 2

摘要

量化封锁对新冠肺炎死亡风险的影响是公共卫生抗击该病毒的重要优先事项,但几乎所有现有研究都只进行了宏观全国评估或有限的多国比较。相比之下,全国封锁影响的国内差异程度尚待彻底调查,这是本文填补的知识库空白。我们的研究重点是英格兰,该国在2020年3月至2021年3月期间实施了3次全国封锁。我们对英格兰大陆312个地方当局地区的每周新冠肺炎死亡率进行了建模,我们的目标是了解封锁对国家和地区层面的影响。具体而言,我们的目标是量化实施封锁后,国家层面的死亡率风险降低了多久,这些影响在多大程度上因地区而异,以及英格兰哪些地区表现出类似的影响。由于空间聚合的每周新冠肺炎死亡率计数规模较小,我们使用泊松对数线性平滑模型来估计死亡率风险的时空趋势,该模型借用了相邻数据点之间估计的强度。推理是基于贝叶斯范式,使用马尔可夫链蒙特卡罗模拟。我们的主要发现是,死亡风险通常在封锁后3至4周内开始降低,封锁影响似乎存在城乡差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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National lockdowns in England: The same restrictions for all, but do the impacts on COVID-19 mortality risks vary geographically?

Quantifying the impact of lockdowns on COVID-19 mortality risks is an important priority in the public health fight against the virus, but almost all of the existing research has only conducted macro country-wide assessments or limited multi-country comparisons. In contrast, the extent of within-country variation in the impacts of a nation-wide lockdown is yet to be thoroughly investigated, which is the gap in the knowledge base that this paper fills. Our study focuses on England, which was subject to 3 national lockdowns between March 2020 and March 2021. We model weekly COVID-19 mortality counts for the 312 Local Authority Districts in mainland England, and our aim is to understand the impact that lockdowns had at both a national and a regional level. Specifically, we aim to quantify how long after the implementation of a lockdown do mortality risks reduce at a national level, the extent to which these impacts vary regionally within a country, and which parts of England exhibit similar impacts. As the spatially aggregated weekly COVID-19 mortality counts are small in size we estimate the spatio-temporal trends in mortality risks with a Poisson log-linear smoothing model that borrows strength in the estimation between neighbouring data points. Inference is based in a Bayesian paradigm, using Markov chain Monte Carlo simulation. Our main findings are that mortality risks typically begin to reduce between 3 and 4 weeks after lockdown, and that there appears to be an urban–rural divide in lockdown impacts.

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来源期刊
Spatial and Spatio-Temporal Epidemiology
Spatial and Spatio-Temporal Epidemiology PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
5.10
自引率
8.80%
发文量
63
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