Evidence map›Paper›PMID 33556327›Full record

ArticleThe Lancet. Public health2021

Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study.

Jean Gaudart, Jordi Landier, Laetitia Huiart, Eva Legendre, Laurent Lehot, Marc Karim Bendiane, Laurent Chiche, Aliette Petitjean, Emilie Mosnier, Fati Kirakoya-Samadoulougou and 3 more

Open access · goldAbstract read
In one paragraph

Article in The Lancet. Public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 1 pooled it
10.6field-weighted citation impact, top 1% of its field
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

52 citing papers in PubMed, 1 synthesis or guideline pooled it, 123 citations in OpenAlex.

  1. Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022
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  20. Structural and Managerial Risk Factors for COVID-19 Occurrence in French Nursing Homes.International journal of health policy and management · 2022
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 9 institutions in 3 countries.

Jean GaudartAix Marseille University, National Institute of Health and Medical Research (INSERM), Institute of Research for Development (IRD), Economic and Social Sciences for Health and Medical Information Processing (SESSTIM), UMR1252, Marseille, France; Public Assistance Marseille Hospitals (APHM), Biostatistics and Information and Communication Technologies Service (BioSTIC), Marseille, France. Electronic address: jean.gaudart@univ-amu.fr.
Jordi LandierAix Marseille University, National Institute of Health and Medical Research (INSERM), Institute of Research for Development (IRD), Economic and Social Sciences for Health and Medical Information Processing (SESSTIM), UMR1252, Marseille, France.
Laetitia HuiartLuxembourg Institute of Health, Luxembourg.
Eva LegendreAix Marseille University, National Institute of Health and Medical Research (INSERM), Institute of Research for Development (IRD), Economic and Social Sciences for Health and Medical Information Processing (SESSTIM), UMR1252, Marseille, France.
Laurent LehotAix Marseille University, National Institute of Health and Medical Research (INSERM), Institute of Research for Development (IRD), Economic and Social Sciences for Health and Medical Information Processing (SESSTIM), UMR1252, Marseille, France.
Marc Karim BendianeAix Marseille University, National Institute of Health and Medical Research (INSERM), Institute of Research for Development (IRD), Economic and Social Sciences for Health and Medical Information Processing (SESSTIM), UMR1252, Marseille, France.
Laurent ChicheInternal Medicine and Clinical Research Unit, European Hospital Marseille, Marseille, France.
Aliette PetitjeanAix Marseille University, National Institute of Health and Medical Research (INSERM), Institute of Research for Development (IRD), Economic and Social Sciences for Health and Medical Information Processing (SESSTIM), UMR1252, Marseille, France.
Emilie MosnierAix Marseille University, National Institute of Health and Medical Research (INSERM), Institute of Research for Development (IRD), Economic and Social Sciences for Health and Medical Information Processing (SESSTIM), UMR1252, Marseille, France.
Fati Kirakoya-SamadoulougouResearch Centre for Epidemiology, Biostatistics, and Research Clinic, School of Public Health, Free University of Brussels, Brussels, Belgium.
Jacques DemongeotUniversity Grenoble Alpes, AGEIS EA 7404, La Tronche, France.
Renaud PiarrouxSorbonne University, INSERM, Pierre-Louis Institute of Epidemiology and Public Health (IPLESP), AP-HP, Laboratory of Parasitology and Mycologie, Pitié-Salpêtrière Hospital, Paris, France.
Stanislas RebaudetEuropean Hospital Marseille, Aix-Marseille University, INSERM, IRD, SESSTIM, IPLESP, Marseille, France.
Economic & Social Sciences, Health Systems & Medical Informatics · FRInstitut de Recherche pour le Développement · FRAix-Marseille Université · FRHôpital Européen · FRInserm · FRInstitut Pierre Louis d‘Épidémiologie et de Santé Publique · FRLuxembourg Institute of Health · LUUniversité Grenoble Alpes · FRUniversité Libre de Bruxelles · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe objective of this study was to better understand the factors associated with the heterogeneity of in-hospital COVID-19 morbidity and mortality across France, one of the countries most affected by COVID-19 in the early months of the pandemic.

methodsThis geo-epidemiological analysis was based on data publicly available on government and administration websites for the 96 administrative departments of metropolitan France between March 19 and May 11, 2020, including Public Health France, the Regional Health Agencies, the French national statistics institute, and the Ministry of Health. Using hierarchical ascendant classification on principal component analysis of multidimensional variables, and multivariate analyses with generalised additive models, we assessed the associations between several factors (spatiotemporal spread of the epidemic between Feb 7 and March 17, 2020, the national lockdown, demographic population structure, baseline intensive care capacities, baseline population health and health-care services, new chloroquine and hydroxychloroquine dispensations, economic indicators, degree of urbanisation, and climate profile) and in-hospital COVID-19 incidence, mortality, and case fatality rates. Incidence rate was defined as the cumulative number of in-hospital COVID-19 cases per 100 000 inhabitants, mortality rate as the cumulative number of in-hospital COVID-19 deaths per 100 000, and case fatality rate as the cumulative number of in-hospital COVID-19 deaths per cumulative number of in-hospital COVID-19 cases.

findingsFrom March 19 to May 11, 2020, hospitals in metropolitan France notified a total of 100 988 COVID-19 cases, including 16 597 people who were admitted to intensive care and 17 062 deaths. There was an overall cumulative in-hospital incidence rate of 155·6 cases per 100 000 inhabitants (range 19·4-489·5), in-hospital mortality rate of 26·3 deaths per 100 000 (1·1-119·2), and in-hospital case fatality rate of 16·9% (4·8-26·2). We found clear spatial heterogeneity of in-hospital COVID-19 incidence and mortality rates, following the spread of the epidemic. After multivariate adjustment, the delay between the first COVID-19-associated death and the onset of the national lockdown was positively associated with in-hospital incidence (adjusted standardised incidence ratio 1·02, 95% CI 1·01-1·04), mortality (adjusted standardised mortality ratio 1·04, 1·02-1·06), and case fatality rates (adjusted standardised fatality ratio 1·01, 1·01-1·02). Mortality and case fatality rates were higher in departments with older populations (adjusted standardised ratio for populations with a high proportion older than aged >85 years 2·17 [95% CI 1·20-3·90] for mortality and 1·43 [1·08-1·88] for case fatality rate). Mortality rate was also associated with incidence rate (1·0004, 1·0002-1·001), but mortality and case fatality rates did not appear to be associated with baseline intensive care capacities. We found no association between climate and in-hospital COVID-19 incidence, or between economic indicators and in-hospital COVID-19 incidence or mortality rates.

interpretationThis ecological study highlights the impact of the epidemic spread, national lockdown, and reactive adaptation of intensive care capacities on the spatial distribution of COVID-19 morbidity and mortality. It provides information for future geo-epidemiological analyses and has implications for preparedness and response policies to current and future epidemic waves in France and elsewhere.

fundingNone.

Indexed as

AdultAgedAged, 80 and overCOVID-19Epidemiologic StudiesFemaleFranceGeography, MedicalHospital MortalityHumansIncidenceMaleMiddle AgedRisk FactorsSpatial Analysis

Identifiers

PMID33556327
PMCPMC7864788
OpenAlexW3128602408

What OpenQuestion holds

Textmetadata
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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 OpenQuestion graph.