Evidence map›Paper›PMID 34104903›Full record

ArticleThe Lancet regional health. Europe2021

Evolution of outcomes for patients hospitalised during the first 9 months of the SARS-CoV-2 pandemic in France: A retrospective national surveillance data analysis.

Noémie Lefrancq, Juliette Paireau, Nathanaël Hozé, Noémie Courtejoie, Yazdan Yazdanpanah, Lila Bouadma, Pierre-Yves Boëlle, Fanny Chereau, Henrik Salje, Simon Cauchemez

Abstract read
In one paragraph

Article in The Lancet regional health. Europe, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
–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

26 citing papers in PubMed.

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  5. COVID-19 deaths on weekends.BMC public health · 2023
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  11. Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data.International journal of environmental research and public health · 2022
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  20. Impact of COVID - 19 in patients awaiting liver transplantation.Liver international : official journal of the International Association for the Study of the Liver · 2022
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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

10 authors.

Noémie LefrancqMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
Juliette PaireauMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
Nathanaël HozéMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
Noémie CourtejoieDREES, Ministère des Solidarités et de la Santé, Paris, France.
Yazdan YazdanpanahUniversité of Paris, INSERM UMR 1137 IAME, Paris, France.
Lila BouadmaUniversité of Paris, INSERM UMR 1137 IAME, Paris, France.
Pierre-Yves BoëlleInstitut Pierre Louis d'Epidémiologie et de Santé Publique, Sorbonne Université, INSERM, Paris, France.
Fanny ChereauSanté Publique France, French National Public Health Agency, Saint-Maurice, France.
Henrik SaljeMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
Simon CauchemezMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs SARS-CoV-2 continues to spread, a thorough characterisation of healthcare needs and patient outcomes, and how they have changed over time, is essential to inform planning.

methodsWe developed a probabilistic framework to analyse detailed patient trajectories from 198,846 hospitalisations in France during the first nine months of the pandemic. Our model accounts for the varying age- and sex- distribution of patients, and explore changes in outcome probabilities as well as length of stay.

findingsWe found that there were marked changes in the age and sex of hospitalisations over the study period. In particular, the proportion of hospitalised individuals that were >80y varied between 27% and 48% over the course of the epidemic, and was lowest during the inter-peak period. The probability of hospitalised patients entering ICU dropped from 0·25 (0·24-0·26) to 0·13 (0·12-0·14) over the four first months as case numbers fell, before rising to 0·19 (0·19-0·20) during the second wave. The probability of death followed a similar trajectory, falling from 0·25 (0·24-0·26) to 0·10 (0·09-0·11) after the first wave before increasing again during the second wave to 0·19 (0·18-0·19). Overall, we find both the probability of death and the probability of entering ICU were significantly correlated with COVID-19 ICU occupancy.

interpretationThere are large scale trends in patients outcomes by age, sex and over time. These need to be considered in ongoing healthcare planning efforts.

fundingINCEPTION.

Identifiers

PMID34104903
PMCPMC7981225

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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.