Saib Mahdi-Salim, Caudeville Julien, Carre Florence, Ganry Olivier, Trugeon Alain, Cicolella Andre
French National Institute for Industrial Environment and Risks, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France.
University Hospital of Amiens, Place Victor Pauchet Amiens 80054, France.
Int J Environ Res Public Health. 2014 Apr 3;11(4):3765-86. doi: 10.3390/ijerph110403765.
Spatial health inequalities have often been analyzed in terms of socioeconomic and environmental factors. The present study aimed to evaluate spatial relationships between spatial data collected at different spatial scales. The approach was illustrated using health outcomes (mortality attributable to cancer) initially aggregated to the county level, district socioeconomic covariates, and exposure data modeled on a regular grid. Geographically weighted regression (GWR) was used to quantify spatial relationships. The strongest associations were found when low deprivation was associated with lower lip, oral cavity and pharynx cancer mortality and when low environmental pollution was associated with low pleural cancer mortality. However, applying this approach to other areas or to other causes of death or with other indicators requires continuous exploratory analysis to assess the role of the modifiable areal unit problem (MAUP) and downscaling the health data on the study of the relationship, which will allow decision-makers to develop interventions where they are most needed.
空间健康不平等现象常常依据社会经济和环境因素进行分析。本研究旨在评估在不同空间尺度上收集的空间数据之间的空间关系。该方法通过最初汇总到县级的健康结果（归因于癌症的死亡率）、地区社会经济协变量以及基于规则网格建模的暴露数据进行说明。地理加权回归（GWR）用于量化空间关系。当低贫困与较低的唇、口腔和咽癌死亡率相关，以及低环境污染与低胸膜癌死亡率相关时，发现了最强的关联。然而，将此方法应用于其他地区、其他死因或其他指标时，需要持续的探索性分析，以评估可修改面积单元问题（MAUP）的作用，并在研究关系时对健康数据进行降尺度处理，这将使决策者能够在最需要的地方制定干预措施。
