Evidence map›Paper›PMID 38714971›Full record

ArticleBMC public health2024

Age at death during the Covid-19 lockdown in French metropolitan regions: a non parametric quantile regression approach.

Jonathan Roux, Marlène Faisant, Diane François, Olivier Retel, Alain Le Tertre

Abstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jonathan RouxSanté Publique France (SpF), Cellule Régionale Bretagne, Direction Des Régions 6 Place Des Colombes, Rennes Cedex, 35042, France.
Marlène FaisantSanté Publique France (SpF), Cellule Régionale Bretagne, Direction Des Régions 6 Place Des Colombes, Rennes Cedex, 35042, France.
Diane FrançoisSanté Publique France (SpF), Cellule Régionale Bourgogne-Franche-Comté, Direction Des Régions, 21035, Dijon, France.
Olivier RetelSanté Publique France (SpF), Cellule Régionale Bourgogne-Franche-Comté, Direction Des Régions, 21035, Dijon, France.
Alain Le TertreSanté Publique France (SpF), Cellule Régionale Bretagne, Direction Des Régions 6 Place Des Colombes, Rennes Cedex, 35042, France. alain.letertre@santepubliquefrance.fr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLockdowns have been implemented to limit the number of hospitalisations and deaths during the first wave of 2019 coronavirus disease. These measures may have affected differently death characteristics, such age and sex. France was one of the hardest hit countries in Europe with a decreasing east-west gradient in excess mortality. This study aimed at describing the evolution of age at death quantiles during the lockdown in spring 2020 (17 March-11 May 2020) in the French metropolitan regions focusing on 3 representatives of the epidemic variations in the country: Bretagne, Ile-de-France (IDF) and Bourgogne-Franche-Comté (BFC).

methodsData were extracted from the French public mortality database from 1 January 2011 to 31 August 2020. The age distribution of mortality observed during the lockdown period (based on each decile, plus quantiles 1, 5, 95 and 99) was compared with the expected one using Bayesian non-parametric quantile regression.

resultsDuring the lockdown, 5457, 5917 and 22 346 deaths were reported in Bretagne, BFC and IDF, respectively. An excess mortality from + 3% in Bretagne to + 102% in IDF was observed during lockdown compared to the 3 previous years. Lockdown led to an important increase in the first quantiles of age at death, irrespective of the region, while the increase was more gradual for older age groups. It corresponded to fewer young people, mainly males, dying during the lockdown, with an increase in the age at death in the first quantile of about 7 years across regions. In females, a less significant shift in the first quantiles and a greater heterogeneity between regions were shown. A greater shift was observed in eastern region and IDF, which may also represent excess mortality among the elderly.

conclusionsThis study focused on the innovative outcome of the age distribution at death. It shows the first quantiles of age at death increased differentially according to sex during the lockdown period, overall shift seems to depend on prior epidemic intensity before lockdown and complements studies on excess mortality during lockdowns.

Indexed as

COVID-19AdolescentAdultAgedAged, 80 and overAge DistributionAge FactorsBayes TheoremChildChild, PreschoolCommunicable Disease ControlFemaleFranceHumansInfantInfant, NewbornAge at deathCovid-19FranceMortalityQuantile regressionSARS-CoV-2

Identifiers

PMID38714971
PMCPMC11075327

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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 OpenQuestion graph.