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

Identifieur interne : 000058 ( France/Analysis ); précédent : 000057; suivant : 000059

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

Auteurs : Marie Delaunay [France] ; Vincent Godard [France] ; Mélina Le Barbier [France] ; Annabelle Gilg Soit Ilg [France] ; Cédric Aubert [France] ; Anne Maître [France] ; Damien Barbeau [France] ; Vincent Bonneterre [France]

Source :

RBID : PMC:5039888

Abstract

Background

Although 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.

Methods

We 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).

Results

While 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.

Conclusion

The 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.

Electronic supplementary material

The online version of this article (doi:10.1186/s12942-016-0063-7) contains supplementary material, which is available to authorized users.


Url:
DOI: 10.1186/s12942-016-0063-7
PubMed: 27678070
PubMed Central: 5039888


Affiliations:


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PMC:5039888

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<placeName>
<region type="region" nuts="2">Auvergne-Rhône-Alpes</region>
<region type="old region" nuts="2">Rhône-Alpes</region>
<settlement type="city">Grenoble</settlement>
</placeName>
</affiliation>
</author>
<author>
<name sortKey="Bonneterre, Vincent" sort="Bonneterre, Vincent" uniqKey="Bonneterre V" first="Vincent" last="Bonneterre">Vincent Bonneterre</name>
<affiliation wicri:level="4">
<nlm:aff id="Aff1">TIMC Research Laboratory (UMR CNRS 5525), EPSP Team (Environnement et Prédiction de la Santé des Populations), Université Grenoble Alpes, 38041 Grenoble, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>TIMC Research Laboratory (UMR CNRS 5525), EPSP Team (Environnement et Prédiction de la Santé des Populations), Université Grenoble Alpes, 38041 Grenoble</wicri:regionArea>
<placeName>
<region type="region" nuts="2">Auvergne-Rhône-Alpes</region>
<region type="old region" nuts="2">Rhône-Alpes</region>
<settlement type="city">Grenoble</settlement>
</placeName>
<orgName type="university">Université Grenoble-Alpes</orgName>
</affiliation>
<affiliation wicri:level="3">
<nlm:aff id="Aff6">Occupational Health Department, CHU Grenoble-Alpes (Grenoble Teaching Hospital), 38043 Grenoble, France</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Occupational Health Department, CHU Grenoble-Alpes (Grenoble Teaching Hospital), 38043 Grenoble</wicri:regionArea>
<placeName>
<region type="region" nuts="2">Auvergne-Rhône-Alpes</region>
<region type="old region" nuts="2">Rhône-Alpes</region>
<settlement type="city">Grenoble</settlement>
</placeName>
</affiliation>
</author>
</analytic>
<series>
<title level="j">International Journal of Health Geographics</title>
<idno type="eISSN">1476-072X</idno>
<imprint>
<date when="2016">2016</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc>
<textClass></textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">
<sec>
<title>Background</title>
<p>Although 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.</p>
</sec>
<sec>
<title>Methods</title>
<p>We 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).</p>
</sec>
<sec>
<title>Results</title>
<p>While 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.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>The 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.</p>
</sec>
<sec>
<title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/s12942-016-0063-7) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
</front>
<back>
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</div1>
</back>
</TEI>
<affiliations>
<list>
<country>
<li>France</li>
</country>
<region>
<li>Auvergne-Rhône-Alpes</li>
<li>Rhône-Alpes</li>
<li>Île-de-France</li>
</region>
<settlement>
<li>Grenoble</li>
<li>Maisons-Alfort</li>
<li>Saint-Denis</li>
<li>Saint-Maurice</li>
</settlement>
<orgName>
<li>Université Grenoble-Alpes</li>
<li>Université Paris 8</li>
</orgName>
</list>
<tree>
<country name="France">
<region name="Auvergne-Rhône-Alpes">
<name sortKey="Delaunay, Marie" sort="Delaunay, Marie" uniqKey="Delaunay M" first="Marie" last="Delaunay">Marie Delaunay</name>
</region>
<name sortKey="Aubert, Cedric" sort="Aubert, Cedric" uniqKey="Aubert C" first="Cédric" last="Aubert">Cédric Aubert</name>
<name sortKey="Aubert, Cedric" sort="Aubert, Cedric" uniqKey="Aubert C" first="Cédric" last="Aubert">Cédric Aubert</name>
<name sortKey="Barbeau, Damien" sort="Barbeau, Damien" uniqKey="Barbeau D" first="Damien" last="Barbeau">Damien Barbeau</name>
<name sortKey="Barbeau, Damien" sort="Barbeau, Damien" uniqKey="Barbeau D" first="Damien" last="Barbeau">Damien Barbeau</name>
<name sortKey="Bonneterre, Vincent" sort="Bonneterre, Vincent" uniqKey="Bonneterre V" first="Vincent" last="Bonneterre">Vincent Bonneterre</name>
<name sortKey="Bonneterre, Vincent" sort="Bonneterre, Vincent" uniqKey="Bonneterre V" first="Vincent" last="Bonneterre">Vincent Bonneterre</name>
<name sortKey="Delaunay, Marie" sort="Delaunay, Marie" uniqKey="Delaunay M" first="Marie" last="Delaunay">Marie Delaunay</name>
<name sortKey="Delaunay, Marie" sort="Delaunay, Marie" uniqKey="Delaunay M" first="Marie" last="Delaunay">Marie Delaunay</name>
<name sortKey="Gilg Soit Ilg, Annabelle" sort="Gilg Soit Ilg, Annabelle" uniqKey="Gilg Soit Ilg A" first="Annabelle" last="Gilg Soit Ilg">Annabelle Gilg Soit Ilg</name>
<name sortKey="Godard, Vincent" sort="Godard, Vincent" uniqKey="Godard V" first="Vincent" last="Godard">Vincent Godard</name>
<name sortKey="Godard, Vincent" sort="Godard, Vincent" uniqKey="Godard V" first="Vincent" last="Godard">Vincent Godard</name>
<name sortKey="Le Barbier, Melina" sort="Le Barbier, Melina" uniqKey="Le Barbier M" first="Mélina" last="Le Barbier">Mélina Le Barbier</name>
<name sortKey="Maitre, Anne" sort="Maitre, Anne" uniqKey="Maitre A" first="Anne" last="Maître">Anne Maître</name>
<name sortKey="Maitre, Anne" sort="Maitre, Anne" uniqKey="Maitre A" first="Anne" last="Maître">Anne Maître</name>
</country>
</tree>
</affiliations>
</record>

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