title: Spatial scan statistics with overdispersion.
abstract: The spatial scan statistic has been widely used in spatial disease surveillance and spatial cluster detection for more than a decade. However, overdispersion often presents in real-world data, causing not only violation of the Poisson assumption but also excessive type I errors or false alarms. In order to account for overdispersion, we extend the Poisson-based spatial scan test to a quasi-Poisson-based test. The simulation shows that the proposed method can substantially reduce type I error probabilities in the presence of overdispersion. In a case study of infant mortality in Jiangxi, China, both tests detect a cluster; however, a secondary cluster is identified by only the Poisson-based test. It is recommended that a cluster detected by the Poisson-based scan test should be interpreted with caution when it is not confirmed by the quasi-Poisson-based test.
authors: Department of Statistics, Purdue University, 250 North University Street, West Lafayette, IN 47907-2066, USA. tlzhang@purdue.edu
authors: Tonglin
authors: T
authors: Zhang
authors: Zuoyi
authors: Z
authors: Zhang
authors: Ge
authors: G
authors: Lin
mesh_terms: D002681:China / Q000453:epidemiology; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007223:Infant; D007226:Infant Mortality; D016012:Poisson Distribution; D011159:Population Surveillance / Q000379:methods*