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.