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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. firstname.lastname@example.org.
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.
A method of differentiating individuals based on the analysis of qualitative or quantitative biological traits or patterns. This process which has applications in forensics and identity theft prevention includes DNA profiles or DNA fingerprints, hand fingerprints, automated facial recognition, iris scan, hand geometry, retinal scan, vascular patterns, automated voice pattern recognition, and ultrasound of fingers.
A center in the PUBLIC HEALTH SERVICE which is primarily concerned with the collection, analysis, and dissemination of health statistics on vital events and health activities to reflect the health status of people, health needs, and health resources.
An illusion of vision usually affecting spatial relations.
Works consisting of presentations of numerical data on particular subjects.