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Geographic dimensions of a health network dedicated to occupational and work related diseases
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 a...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5039888/ https://www.ncbi.nlm.nih.gov/pubmed/27678070 http://dx.doi.org/10.1186/s12942-016-0063-7 |
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author | Delaunay, Marie Godard, Vincent Le Barbier, Mélina Gilg Soit Ilg, Annabelle Aubert, Cédric Maître, Anne Barbeau, Damien Bonneterre, Vincent |
author_facet | Delaunay, Marie Godard, Vincent Le Barbier, Mélina Gilg Soit Ilg, Annabelle Aubert, Cédric Maître, Anne Barbeau, Damien Bonneterre, Vincent |
author_sort | Delaunay, Marie |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5039888 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50398882016-10-05 Geographic dimensions of a health network dedicated to occupational and work related diseases Delaunay, Marie Godard, Vincent Le Barbier, Mélina Gilg Soit Ilg, Annabelle Aubert, Cédric Maître, Anne Barbeau, Damien Bonneterre, Vincent Int J Health Geogr Research 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. BioMed Central 2016-09-27 /pmc/articles/PMC5039888/ /pubmed/27678070 http://dx.doi.org/10.1186/s12942-016-0063-7 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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 (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Delaunay, Marie Godard, Vincent Le Barbier, Mélina Gilg Soit Ilg, Annabelle Aubert, Cédric Maître, Anne Barbeau, Damien Bonneterre, Vincent Geographic dimensions of a health network dedicated to occupational and work related diseases |
title | Geographic dimensions of a health network dedicated to occupational and work related diseases |
title_full | Geographic dimensions of a health network dedicated to occupational and work related diseases |
title_fullStr | Geographic dimensions of a health network dedicated to occupational and work related diseases |
title_full_unstemmed | Geographic dimensions of a health network dedicated to occupational and work related diseases |
title_short | Geographic dimensions of a health network dedicated to occupational and work related diseases |
title_sort | geographic dimensions of a health network dedicated to occupational and work related diseases |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5039888/ https://www.ncbi.nlm.nih.gov/pubmed/27678070 http://dx.doi.org/10.1186/s12942-016-0063-7 |
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