Division of Neurology, Geneva University Hospitals, 1211, Geneva 14, Switzerland; Faculty of Medicine, University of Geneva, CMU, 1211, Geneva 4, Switzerland.
Unit of Population Epidemiology, Division of Primary Care Medicine, Department of Primary Care Medicine, Geneva University Hospitals, 1211, Geneva 14, Switzerland; Geographic Information Research and Analysis in Population Health (GIRAPH) Group, Geneva University Hospitals, Geneva and Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland; Laboratory of Geographical Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland.
Parkinsonism Relat Disord. 2021 Feb;83:41-48. doi: 10.1016/j.parkreldis.2020.12.013. Epub 2021 Jan 12.
The etiology of Parkinson's disease (PD) remains unknown. To approach the issue of PD's risk factors from a new perspective, we hypothesized that coupling the geographic distribution of PD with spatial statistics may provide new insights into environmental epidemiology research. The aim of this case-control study was to examine the spatial dependence of PD prevalence in the Canton of Geneva, Switzerland (population = 474,211).
PD cases were identified through Geneva University Hospitals, private neurologists and nursing homes medical records (n = 1115). Controls derived from a population-based study (n = 12,614) and a comprehensive population census dataset (n = 237,771). All individuals were geographically localized based on their place of residence. Spatial Getis-Ord Gi* statistics were used to identify clusters of high versus low disease prevalence. Confounder-adjustment was performed for age, sex, nationality and income. Tukey's honestly significant difference was used to determine whether nitrogen dioxide and particulate matters PM concentrations were different within PD hotspots, coldspots or neutral areas.
Confounder-adjustment greatly reduced greatly the spatial association. Characteristics of the geographic space influenced PD prevalence in 6% of patients. PD hotspots were concentrated in the urban centre. There was a significant difference in mean annual nitrogen dioxide and PM levels (+3.6 μg/m3 [p < 0.001] and +0.63 μg/m3 [p < 0.001] respectively) between PD hotspots and coldspots.
PD prevalence exhibited a spatial dependence for a small but significant proportion of patients. A positive association was detected between PD clusters and air pollution. Our data emphasize the multifactorial nature of PD and support a link between PD and air pollution.
帕金森病（PD）的病因仍然未知。为了从新的角度探讨 PD 的危险因素，我们假设将 PD 的地理分布与空间统计学相结合，可能为环境流行病学研究提供新的见解。本病例对照研究的目的是检验瑞士日内瓦州（人口为 474211 人）PD 患病率的空间依赖性。
通过日内瓦大学医院、私人神经科医生和疗养院的医疗记录确定 PD 病例（n=1115）。对照组来自基于人群的研究（n=12614）和综合人口普查数据集（n=237771）。所有个体都根据其居住地进行了地理定位。使用空间 Getis-Ord Gi*统计量识别高患病率与低患病率的集群。进行了年龄、性别、国籍和收入的混杂因素调整。使用 Tukey 的诚实显着性差异来确定在 PD 热点、冷点或中性区域内二氧化氮和颗粒物 PM 浓度是否存在差异。
混杂因素调整大大降低了空间关联。地理空间特征影响了 6%患者的 PD 患病率。PD 热点集中在市中心。PD 热点和冷点之间的年平均二氧化氮和 PM 水平存在显著差异（分别为+3.6μg/m3[p<0.001]和+0.63μg/m3[p<0.001]）。
PD 患病率对一小部分患者表现出空间依赖性。PD 集群与空气污染之间存在正相关。我们的数据强调了 PD 的多因素性质，并支持 PD 与空气污染之间的联系。
