Evidence map›Paper›PMID 27678070›Full record

ArticleInternational journal of health geographics2016

Geographic dimensions of a health network dedicated to occupational and work related diseases.

Marie Delaunay, Vincent Godard, Mélina Le Barbier, Annabelle Gilg Soit Ilg, Cédric Aubert, Anne Maître, Damien Barbeau, Vincent Bonneterre

Abstract read
In one paragraph

Article in International journal of health geographics, 2016. 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

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

1 citing paper in PubMed.

  1. Article
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

8 authors.

Marie DelaunayTIMC Research Laboratory (UMR CNRS 5525), EPSP Team (Environnement et Prédiction de la Santé des Populations), Université Grenoble Alpes, 38041, Grenoble, France.
Vincent GodardLADYSS Research Laboratory (UMR CNRS 7533) (Laboratoire Dynamiques sociales et recomposition des espaces), Université Paris 8, 93526, Saint-Denis, France.
Mélina Le BarbierMission RNV3P (French Network for Occupational Diseases Prevention and Vigilance Network), ANSES (French Agency for Health Safety in Food, Environment and Work), 94701, Maisons-Alfort Cedex, France.
Annabelle Gilg Soit IlgOccupational Health Department, Santé Publique France (French National Public Health Agency), 94415, Saint-Maurice Cedex, France.
Cédric AubertTIMC Research Laboratory (UMR CNRS 5525), EPSP Team (Environnement et Prédiction de la Santé des Populations), Université Grenoble Alpes, 38041, Grenoble, France.
Anne MaîtreTIMC Research Laboratory (UMR CNRS 5525), EPSP Team (Environnement et Prédiction de la Santé des Populations), Université Grenoble Alpes, 38041, Grenoble, France.
Damien BarbeauTIMC Research Laboratory (UMR CNRS 5525), EPSP Team (Environnement et Prédiction de la Santé des Populations), Université Grenoble Alpes, 38041, Grenoble, France.
Vincent BonneterreTIMC Research Laboratory (UMR CNRS 5525), EPSP Team (Environnement et Prédiction de la Santé des Populations), Université Grenoble Alpes, 38041, Grenoble, France. VBonneterre@chu-grenoble.fr.ORCID http://orcid.org/0000-0003-2353-7102

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough introduced nearly 40 years ago, Geographic Information Systems (GISs) have never been used to study Occupational Health information regarding the different types, scale or sources of data. The geographic distribution of occupational diseases and underlying work activities were always analyzed independently. Our aim was to consider the French Network of Occupational Disease (OD) clinics, namely the "French National OD Surveillance and Prevention Network" (rnv3p) as a spatial object in order to describe its catchment.

methodsWe mapped rnv3p observations at the workplace level. We initially analyzed rnv3p capture with reference to its own data, then to the underlying workforce (INSEE "Employment Areas"), and finally compared its capture of one emblematic occupational disease (mesothelioma) to an external dataset provided by a surveillance system thought to be exhaustive (PNSM).

resultsWhile the whole country is covered by the network, the density of observations decreases with increase in the distance from the 31 OD clinics (located within the main French cities). Taking into account the underlying workforce, we show that the probability to capture and investigation of OD (assessed by rates of OD per 10,000 workers) also presents large discrepancies between OD clinics. This capture rate might also show differences according to the disease, as exemplified by mesothelioma.

conclusionThe geographic approach to this network, enhanced by the possibilities provided by the GIS tool, allow a better understanding of the coverage of this network at a national level, as well as the visualization of capture rates for all OD clinics. Highlighting geographic and thematic shading zones bring new perspectives to the analysis of occupational health data, and should improve occupational health vigilance and surveillance.

Indexed as

FranceGeographic Information SystemOccupational diseasesOccupational healthSpatial analysisStakeholdersSurveillance network

Identifiers

PMID27678070
PMCPMC5039888

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.