Detection of spatial disease clusters with LISA functions

Abstract

Detection of disease clusters is an important tool in epidemiology that can help to identify risk factors associated with the disease and in understanding its etiology. In this article we propose a method for the detection of spatial clusters where the locations of a set of cases and a set of controls are available. The method is based on local indicators of spatial association functions (LISA functions), particularly on the development of a local version of the product density, which is a second-order characteristic of spatial point processes. The behavior of the method is evaluated and compared with Kulldorff's spatial scan statistic by means of a simulation study. It is shown that the LISA method yields high sensitivity and specificity when it is used to detect simulated clusters of different sizes and shapes. It also performs better than the spatial scan statistic when they are used to detect clusters of irregular shape; however, it presents relatively high type I error in situations where the number of cases is high. Both methods are applied for detecting spatial clusters of kidney disease in the city of Valencia, Spain, in the year 2008.