[The analysis of the spatial aggregation of health events using risk surface density estimation methods]

Methods for the analysis of the spatial aggregation of health events has received growing attention under the pressure of public opinion concern and as tools for the identification of potential risk sources, for monitoring relevant geographical areas and, finally, for public health decisions. The development of statistical methods for the detection and localization of spatial clusters has mainly concerned individual data. This paper is aimed at describing one of the methods proposed for the identification of clusters in the case of information at individual level and at presenting its extension to grouped data. This method, the surface density estimation method using the Kernel approach, offers remarkable advantages in terms of simplicity of implementation and flexibility, this latter being an extremely important characteristic in the case of exploratory analyses. For exemplification purposes, the density estimation method has been applied to individual data concerning the spatial distribution of cerebral tumors in Campi Bisenzio (FI) and to the distribution of gastric cancer mortality in the municipalities around Arezzo and Pesaro.