{"title":"National lockdowns in England: The same restrictions for all, but do the impacts on COVID-19 mortality risks vary geographically?","authors":"Robin Muegge, Nema Dean, Eilidh Jack, Duncan Lee","doi":"10.1016/j.sste.2022.100559","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"44 ","pages":"Article 100559"},"PeriodicalIF":2.1000,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719849/pdf/","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spatial and Spatio-Temporal Epidemiology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S187758452200082X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 2
Abstract
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.