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A spatial scan statistic for compound Poisson data

Authors

  • Rhonda J. Rosychuk,

    1. Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
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  • Hsing-Ming Chang

    Corresponding author
    1. Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
    • Correspondence to: Hsing-Ming Chang, Department of Pediatrics, University of Alberta, 3-077B Edmonton Clinic Health Academy, 11405 87 Avenue NW, Edmonton, Alberta T6G 1C9, Canada.

      E-mail: hsingmin@ualberta.ca

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Abstract

The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease-related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease-related visits. Copyright © 2013 John Wiley & Sons, Ltd.

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