Dpt. d'Estadística i I. O., Universitat de Valéncia, Spain.
Stat Med. 2011 May 10;30(10):1057-71. doi: 10.1002/sim.4160. Epub 2011 Jan 12.
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.
疾病聚集的检测是流行病学中的一个重要工具，它可以帮助识别与疾病相关的风险因素，并了解其病因。在本文中，我们提出了一种在病例和对照的位置已知的情况下检测空间聚集的方法。该方法基于空间关联函数（LISA 函数）的局部指标，特别是对空间点过程的二阶特征的乘积密度的局部版本的开发。通过模拟研究评估了该方法的性能，并与 Kulldorff 的空间扫描统计进行了比较。结果表明，当用于检测不同大小和形状的模拟聚集时，LISA 方法具有较高的灵敏度和特异性。当用于检测不规则形状的聚集时，它也比空间扫描统计表现更好；然而，当病例数量较高时，它的第一类错误率相对较高。这两种方法都被用于检测 2008 年西班牙巴伦西亚市的肾脏疾病的空间聚集。
