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

Identifieur interne : 000039 ( Pmc/Curation ); précédent : 000038; suivant : 000040

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

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PMC:5039888

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<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>
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<pmc article-type="research-article">
<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Int J Health Geogr</journal-id>
<journal-id journal-id-type="iso-abbrev">Int J Health Geogr</journal-id>
<journal-title-group>
<journal-title>International Journal of Health Geographics</journal-title>
</journal-title-group>
<issn pub-type="epub">1476-072X</issn>
<publisher>
<publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">27678070</article-id>
<article-id pub-id-type="pmc">5039888</article-id>
<article-id pub-id-type="publisher-id">63</article-id>
<article-id pub-id-type="doi">10.1186/s12942-016-0063-7</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Geographic dimensions of a health network dedicated to occupational and work related diseases</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Delaunay</surname>
<given-names>Marie</given-names>
</name>
<address>
<email>delaunay_marie@hotmail.fr</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
<xref ref-type="aff" rid="Aff2">2</xref>
<xref ref-type="aff" rid="Aff3">3</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Godard</surname>
<given-names>Vincent</given-names>
</name>
<address>
<email>vgodard@univ-paris8.fr</email>
</address>
<xref ref-type="aff" rid="Aff2">2</xref>
<xref ref-type="aff" rid="Aff3">3</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Le Barbier</surname>
<given-names>Mélina</given-names>
</name>
<address>
<email>melina.lebarbier@anses.fr</email>
</address>
<xref ref-type="aff" rid="Aff4">4</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Gilg Soit Ilg</surname>
<given-names>Annabelle</given-names>
</name>
<address>
<email>a.gilg@invs.sante.fr</email>
</address>
<xref ref-type="aff" rid="Aff5">5</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Aubert</surname>
<given-names>Cédric</given-names>
</name>
<address>
<email>CAubert@chu-grenoble.fr</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
<xref ref-type="aff" rid="Aff6">6</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Maître</surname>
<given-names>Anne</given-names>
</name>
<address>
<email>anne.maitre@ujf-grenoble.fr</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
<xref ref-type="aff" rid="Aff7">7</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Barbeau</surname>
<given-names>Damien</given-names>
</name>
<address>
<email>dbarbeau@chu-grenoble.fr</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
<xref ref-type="aff" rid="Aff7">7</xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0003-2353-7102</contrib-id>
<name>
<surname>Bonneterre</surname>
<given-names>Vincent</given-names>
</name>
<address>
<phone>00 33 4 76 76 58 51</phone>
<email>VBonneterre@chu-grenoble.fr</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
<xref ref-type="aff" rid="Aff6">6</xref>
</contrib>
<aff id="Aff1">
<label>1</label>
TIMC Research Laboratory (UMR CNRS 5525), EPSP Team (Environnement et Prédiction de la Santé des Populations), Université Grenoble Alpes, 38041 Grenoble, France</aff>
<aff id="Aff2">
<label>2</label>
LADYSS Research Laboratory (UMR CNRS 7533) (Laboratoire Dynamiques sociales et recomposition des espaces), Université Paris 8, 93526 Saint-Denis, France</aff>
<aff id="Aff3">
<label>3</label>
MSH Paris Nord (Maison des Sciences de l’Homme), Universités Paris 8 et Paris 13, 93210 Saint-Denis, France</aff>
<aff id="Aff4">
<label>4</label>
Mission 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</aff>
<aff id="Aff5">
<label>5</label>
Occupational Health Department, Santé Publique France (French National Public Health Agency), 94415 Saint-Maurice Cedex, France</aff>
<aff id="Aff6">
<label>6</label>
Occupational Health Department, CHU Grenoble-Alpes (Grenoble Teaching Hospital), 38043 Grenoble, France</aff>
<aff id="Aff7">
<label>7</label>
Occupational and Environmental Toxicology Laboratory, CHU Grenoble-Alpes (Grenoble Teaching Hospital), 38043 Grenoble, France</aff>
</contrib-group>
<pub-date pub-type="epub">
<day>27</day>
<month>9</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="pmc-release">
<day>27</day>
<month>9</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="collection">
<year>2016</year>
</pub-date>
<volume>15</volume>
<elocation-id>34</elocation-id>
<history>
<date date-type="received">
<day>29</day>
<month>5</month>
<year>2016</year>
</date>
<date date-type="accepted">
<day>14</day>
<month>9</month>
<year>2016</year>
</date>
</history>
<permissions>
<copyright-statement>© The Author(s) 2016</copyright-statement>
<license license-type="OpenAccess">
<license-p>
<bold>Open Access</bold>
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated.</license-p>
</license>
</permissions>
<abstract id="Abs1">
<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>
</abstract>
<kwd-group xml:lang="en">
<title>Keywords</title>
<kwd>Occupational health</kwd>
<kwd>Occupational diseases</kwd>
<kwd>Surveillance network</kwd>
<kwd>Stakeholders</kwd>
<kwd>Geographic Information System</kwd>
<kwd>Spatial analysis</kwd>
<kwd>France</kwd>
</kwd-group>
<funding-group>
<award-group>
<funding-source>
<institution>ANSES</institution>
</funding-source>
<award-id>2012-CRD-06 (n°596A)</award-id>
<principal-award-recipient>
<name>
<surname>Delaunay</surname>
<given-names>Marie</given-names>
</name>
</principal-award-recipient>
</award-group>
</funding-group>
<custom-meta-group>
<custom-meta>
<meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2016</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
</record>

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