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Delaunay M, Godard V, Le Barbier M, et al. Geographic dimensions of a health network dedicated to occupational and work related diseases. Int J Health Geogr. 2016;15(1):34doi: 10.1186/s12942-016-0063-7.
Delaunay, M., Godard, V., Le Barbier, M., Gilg Soit Ilg, A., Aubert, C., Maître, A., Barbeau, D., & Bonneterre, V. (2016). Geographic dimensions of a health network dedicated to occupational and work related diseases. International journal of health geographics, 15(1), 34. https://doi.org/10.1186/s12942-016-0063-7
Delaunay, Marie, et al. "Geographic dimensions of a health network dedicated to occupational and work related diseases." International journal of health geographics vol. 15,1 (2016): 34. doi: https://doi.org/10.1186/s12942-016-0063-7
Delaunay M, Godard V, Le Barbier M, Gilg Soit Ilg A, Aubert C, Maître A, Barbeau D, Bonneterre V. Geographic dimensions of a health network dedicated to occupational and work related diseases. Int J Health Geogr. 2016 Sep 27;15(1):34. doi: 10.1186/s12942-016-0063-7. PMID: 27678070; PMCID: PMC5039888.
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Int J Health Geogr. 2016 Sep 27;15(1):34. doi: 10.1186/s12942-016-0063-7.

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

International journal of health geographics

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

Affiliations

  1. TIMC Research Laboratory (UMR CNRS 5525), EPSP Team (Environnement et Prédiction de la Santé des Populations), Université Grenoble Alpes, 38041, Grenoble, France.
  2. LADYSS Research Laboratory (UMR CNRS 7533) (Laboratoire Dynamiques sociales et recomposition des espaces), Université Paris 8, 93526, Saint-Denis, France.
  3. MSH Paris Nord (Maison des Sciences de l'Homme), Universités Paris 8 et Paris 13, 93210, Saint-Denis, France.
  4. 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.
  5. Occupational Health Department, Santé Publique France (French National Public Health Agency), 94415, Saint-Maurice Cedex, France.
  6. Occupational Health Department, CHU Grenoble-Alpes (Grenoble Teaching Hospital), 38043, Grenoble, France.
  7. Occupational and Environmental Toxicology Laboratory, CHU Grenoble-Alpes (Grenoble Teaching Hospital), 38043, Grenoble, France.
  8. TIMC Research Laboratory (UMR CNRS 5525), EPSP Team (Environnement et Prédiction de la Santé des Populations), Université Grenoble Alpes, 38041, Grenoble, France. [email protected].
  9. Occupational Health Department, CHU Grenoble-Alpes (Grenoble Teaching Hospital), 38043, Grenoble, France. [email protected].

PMID: 27678070 PMCID: PMC5039888 DOI: 10.1186/s12942-016-0063-7

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.

Keywords: France; Geographic Information System; Occupational diseases; Occupational health; Spatial analysis; Stakeholders; Surveillance network

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