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National lockdowns in England: The same restrictions for all, but do the impacts on COVID-19 mortality risks vary geographically?
Muegge, Robin; Dean, Nema; Jack, Eilidh; Lee, Duncan.
  • Muegge R; School of Mathematics and Statistics, University of Glasgow, United Kingdom. Electronic address: Robin.Muegge@glasgow.ac.uk.
  • Dean N; School of Mathematics and Statistics, University of Glasgow, United Kingdom. Electronic address: Nema.Dean@glasgow.ac.uk.
  • Jack E; School of Mathematics and Statistics, University of Glasgow, United Kingdom. Electronic address: Eilidh.Jack@glasgow.ac.uk.
  • Lee D; School of Mathematics and Statistics, University of Glasgow, United Kingdom. Electronic address: Duncan.Lee@glasgow.ac.uk.
Spat Spatiotemporal Epidemiol ; 44: 100559, 2023 02.
Article in English | MEDLINE | ID: covidwho-2132433
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: Europa Language: English Journal: Spat Spatiotemporal Epidemiol Year: 2023 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: Europa Language: English Journal: Spat Spatiotemporal Epidemiol Year: 2023 Document Type: Article