Evidence map›Paper›PMID 38970000›Full record

ArticleBMC public health2024

The impact of hospital saturation on non-COVID-19 hospital mortality during the pandemic in France: a national population-based cohort study.

Laurent Boyer, Vanessa Pauly, Yann Brousse, Veronica Orleans, Bach Tran, Dong Keon Yon, Pascal Auquier, Guillaume Fond, Antoine Duclos

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

9 authors.

Laurent BoyerCEReSS - Health Service Research and Quality of Life Center, UR3279, Aix-Marseille University, APHM, Marseille, 13005, France. laurent.boyer@ap-hm.fr.
Vanessa PaulyCEReSS - Health Service Research and Quality of Life Center, UR3279, Aix-Marseille University, APHM, Marseille, 13005, France.
Yann BrousseCEReSS - Health Service Research and Quality of Life Center, UR3279, Aix-Marseille University, APHM, Marseille, 13005, France.
Veronica OrleansCEReSS - Health Service Research and Quality of Life Center, UR3279, Aix-Marseille University, APHM, Marseille, 13005, France.
Bach TranCEReSS - Health Service Research and Quality of Life Center, UR3279, Aix-Marseille University, APHM, Marseille, 13005, France.
Dong Keon YonCenter for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea.
Pascal AuquierCEReSS - Health Service Research and Quality of Life Center, UR3279, Aix-Marseille University, APHM, Marseille, 13005, France.
Guillaume FondCEReSS - Health Service Research and Quality of Life Center, UR3279, Aix-Marseille University, APHM, Marseille, 13005, France.
Antoine DuclosRESHAPE - Research on Healthcare Performance Lab, Inserm U1290, Claude Bernard Lyon 1 University, Lyon, 69424, France.

Funding

French Ministry of Health (PHRC National/ FTcovid19_vague2, Direction générale de l'offre de soins) covid-19-20-0017
6 · The paper itself

Abstract

backgroundA previous study reported significant excess mortality among non-COVID-19 patients due to disrupted surgical care caused by resource prioritization for COVID-19 cases in France. The primary objective was to investigate if a similar impact occurred for medical conditions and determine the effect of hospital saturation on non-COVID-19 hospital mortality during the first year of the pandemic in France.

methodsWe conducted a nationwide population-based cohort study including all adult patients hospitalized for non-COVID-19 acute medical conditions in France between March 1, 2020 and 31 May, 2020 (1st wave) and September 1, 2020 and December 31, 2020 (2nd wave). Hospital saturation was categorized into four levels based on weekly bed occupancy for COVID-19: no saturation (< 5%), low saturation (> 5% and ≤ 15%), moderate saturation (> 15% and ≤ 30%), and high saturation (> 30%). Multivariate generalized linear model analyzed the association between hospital saturation and mortality with adjustment for age, sex, COVID-19 wave, Charlson Comorbidity Index, case-mix, source of hospital admission, ICU admission, category of hospital and region of residence.

resultsA total of 2,264,871 adult patients were hospitalized for acute medical conditions. In the multivariate analysis, the hospital mortality was significantly higher in low saturated hospitals (adjusted Odds Ratio/aOR = 1.05, 95% CI [1.34-1.07], P < .001), moderate saturated hospitals (aOR = 1.12, 95% CI [1.09-1.14], P < .001), and highly saturated hospitals (aOR = 1.25, 95% CI [1.21-1.30], P < .001) compared to non-saturated hospitals. The proportion of deaths outside ICU was higher in highly saturated hospitals (87%) compared to non-, low- or moderate saturated hospitals (81-84%). The negative impact of hospital saturation on mortality was more pronounced in patients older than 65 years, those with fewer comorbidities (Charlson 1-2 and 3 vs. 0), patients with cancer, nervous and mental diseases, those admitted from home or through the emergency room (compared to transfers from other hospital wards), and those not admitted to the intensive care unit.

conclusionsOur study reveals a noteworthy "dose-effect" relationship: as hospital saturation intensifies, the non-COVID-19 hospital mortality risk also increases. These results raise concerns regarding hospitals' resilience and patient safety, underscoring the importance of identifying targeted strategies to enhance resilience for the future, particularly for high-risk patients.

Indexed as

COVID-19Hospital MortalityPandemicsAdultAgedAged, 80 and overBed OccupancyCohort StudiesFemaleFranceHospitalizationHospitalsHumansMaleMiddle AgedSARS-CoV-2COVID-19Health services researchPublic health

Identifiers

PMID38970000
PMCPMC11227237

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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.