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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. Copyright © 2011 John Wiley & Sons, Ltd.
Department of Statistics, Purdue University, 250 North UniversityStreet, West Lafayette, IN 47907-2066, USA. email@example.com.
This article was published in the following journal.
Name: Statistics in medicine
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Used for general articles concerning statistics of births, deaths, marriages, etc.
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