Delaunay Marie, Godard Vincent, Le Barbier Mélina, Gilg Soit Ilg Annabelle, Aubert Cédric, Maître Anne, Barbeau Damien, Bonneterre Vincent
TIMC Research Laboratory (UMR CNRS 5525), EPSP Team (Environnement et Prédiction de la Santé des Populations), Université Grenoble Alpes, 38041, Grenoble, France.
LADYSS Research Laboratory (UMR CNRS 7533) (Laboratoire Dynamiques sociales et recomposition des espaces), Université Paris 8, 93526, Saint-Denis, France.
Int J Health Geogr. 2016 Sep 27;15(1):34. doi: 10.1186/s12942-016-0063-7.
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.
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).
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.
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.
尽管地理信息系统（GIS）在近40年前就已引入，但从未被用于研究不同类型、规模或数据来源的职业健康信息。职业病的地理分布及其潜在工作活动一直是独立分析的。我们的目的是将法国职业病（OD）诊所网络，即“法国国家职业病监测与预防网络”（rnv3p）视为一个空间对象，以描述其服务范围。
我们在工作场所层面绘制了rnv3p的观测数据。我们首先参照其自身数据分析rnv3p的覆盖情况，然后参照潜在劳动力（法国国家统计局“就业区域”）进行分析，最后将其对一种典型职业病（间皮瘤）的覆盖情况与一个被认为详尽的监测系统（PNSM）提供的外部数据集进行比较。
虽然该网络覆盖了整个国家，但观测密度随着与31家OD诊所（位于法国主要城市）距离的增加而降低。考虑到潜在劳动力，我们发现OD诊所之间在捕获和调查OD的概率（以每万名工人中的OD发病率评估）方面也存在很大差异。这种捕获率可能因疾病而异，间皮瘤就是一个例子。
借助GIS工具提供的可能性对该网络进行地理分析，有助于更好地理解该网络在国家层面的覆盖范围，以及所有OD诊所捕获率的可视化。突出地理和主题阴影区域为职业健康数据的分析带来了新的视角，并应能提高职业健康警惕性和监测水平。
