Evidence map›Paper›PMID 34819057›Full record

ArticleBMC public health2021

Spatial determinants of excess all-cause mortality during the first wave of the COVID-19 epidemic in France.

Hugo Pilkington, Thierry Feuillet, Stéphane Rican, Jeanne Goupil de Bouillé, Olivier Bouchaud, Johann Cailhol, Hélène Bihan, Pierre Lombrail, Chantal Julia

Abstract read
In one paragraph

Article in BMC public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

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

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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.

Hugo PilkingtonDépartement de Géographie, Université Paris 8 Vincennes-Saint-Denis, UMR7533 Ladyss, 2 rue de la Liberté, F-93526, Saint-Denis, France. hugo.pilkington@univ-paris8.fr.ORCID http://orcid.org/0000-0002-8245-2806
Thierry FeuilletDépartement de Géographie, Université Paris 8 Vincennes-Saint-Denis, UMR7533 Ladyss, 2 rue de la Liberté, F-93526, Saint-Denis, France.
Stéphane RicanDépartement de Géographie-Aménagement, Université Paris Nanterre, UMR7533 Ladyss, 200 avenue de la République, F-9200, Nanterre, France.
Jeanne Goupil de BouilléInfectious and Tropical Diseases Department, Avicenne Hospital (AP-HP), Bobigny, France.
Olivier BouchaudInfectious and Tropical Diseases Department, Avicenne Hospital (AP-HP), Bobigny, France.
Johann CailholInfectious and Tropical Diseases Department, Avicenne Hospital (AP-HP), Bobigny, France.
Hélène BihanLEPS, Laboratoire Educations Pratiques Santé, UR3412, Université Sorbonne Paris Nord Bobigny, Villetaneuse, France.
Pierre LombrailLEPS, Laboratoire Educations Pratiques Santé, UR3412, Université Sorbonne Paris Nord Bobigny, Villetaneuse, France.
Chantal JuliaSorbonne Paris Cité Epidemiology and Statistics Research Center (CRESS), Inserm U1153, Inra U1125, Cnam, Paris 13 University, Nutritional Epidemiology Research Team (EREN), Bobigny, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe first wave of the COVID-19 pandemic in France was associated with high excess mortality, and anecdotal evidence pointed to differing excess mortality patterns depending on social and environmental determinants. In this study we aimed to investigate the spatial distribution of excess mortality during the first wave of the COVID-19 pandemic in France and relate it at the subnational level to contextual determinants from various dimensions (socioeconomic, population density, overall health status, healthcare access etc.). We also explored whether the determinants identified at the national level varied depending on geographical location.

methodsWe used available national data on deaths in France to calculate excess mortality by department for three age groups: 0-49, 50-74 and > 74 yrs. between March 1st and April 27th, 2020. We selected 15 variables at the department level that represent four dimensions that may be related to overall mortality at the ecological level, two representing population-level vulnerabilities (morbidity, social deprivation) and two representing environmental-level vulnerabilities (primary healthcare supply, urbanization). We modelled excess mortality by age group for our contextual variables at the department level. We conducted both a global (i.e., country-wide) analysis and a multiscale geographically weighted regression (MGWR) model to account for the spatial variations in excess mortality.

resultsIn both age groups, excess all-cause mortality was significantly higher in departments where urbanization was higher (50-74 yrs.: β = 15.33, p < 0.001; > 74 yrs.: β = 18.24, p < 0.001) and the supply of primary healthcare providers lower (50-74 yrs.: β = - 8.10, p < 0.001; > 74 yrs.: β = - 8.27, p < 0.001). In the 50-74 yrs. age group, excess mortality was negatively associated with the supply of pharmacists (β = - 3.70, p < 0.02) and positively associated with work-related mobility (β = 4.62, p < 0.003); in the > 74 yrs. age group our measures of deprivation (β = 15.46, p < 0.05) and morbidity (β = 0.79, p < 0.008) were associated with excess mortality. Associations between excess mortality and contextual variables varied significantly across departments for both age groups.

conclusionsPublic health strategies aiming at mitigating the effects of future epidemics should consider all dimensions involved to develop efficient and locally tailored policies within the context of an evolving, socially and spatially complex situation.

Indexed as

COVID-19AgedFranceHumansInfant, NewbornMiddle AgedMortalityPandemicsSARS-CoV-2COVID-19Delivery of health careDeprivationInequalityMortalitySocioeconomic factors

Identifiers

PMID34819057
PMCPMC8611998

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Registered trials

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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.