Evidence map›Paper›PMID 36707192›Full record

ArticleSpatial and spatio-temporal epidemiology2023

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

Robin Muegge, Nema Dean, Eilidh Jack, Duncan Lee

Abstract read
In one paragraph

Article in Spatial and spatio-temporal epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Robin MueggeSchool of Mathematics and Statistics, University of Glasgow, United Kingdom. Electronic address: Robin.Muegge@glasgow.ac.uk.
Nema DeanSchool of Mathematics and Statistics, University of Glasgow, United Kingdom. Electronic address: Nema.Dean@glasgow.ac.uk.
Eilidh JackSchool of Mathematics and Statistics, University of Glasgow, United Kingdom. Electronic address: Eilidh.Jack@glasgow.ac.uk.
Duncan LeeSchool of Mathematics and Statistics, University of Glasgow, United Kingdom. Electronic address: Duncan.Lee@glasgow.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19Bayes TheoremCommunicable Disease ControlComputer SimulationEnglandHumansBayesian inferenceCOVID-19 mortalityLockdownsSpatio-temporal modelling

Identifiers

PMID36707192
PMCPMC9719849

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.