title: A spatial scan statistic for multinomial data.
abstract: As a geographical cluster detection analysis tool, the spatial scan statistic has been developed for different types of data such as Bernoulli, Poisson, ordinal, exponential and normal. Another interesting data type is multinomial. For example, one may want to find clusters where the disease-type distribution is statistically significantly different from the rest of the study region when there are different types of disease. In this paper, we propose a spatial scan statistic for such data, which is useful for geographical cluster detection analysis for categorical data without any intrinsic order information. The proposed method is applied to meningitis data consisting of five different disease categories to identify areas with distinct disease-type patterns in two counties in the U.K. The performance of the method is evaluated through a simulation study.
authors.affiliation: Department of Biostatistics, Yonsei University College of Medicine, Seoul, Korea. ijung@yuhs.ac
authors.forename: Inkyung
authors.initials: I
authors.lastname: Jung
authors.forename: Martin
authors.initials: M
authors.lastname: Kulldorff
authors.forename: Otukei John
authors.initials: OJ
authors.lastname: Richard
mesh_terms: D000293:Adolescent; D000465:Algorithms; D016000:Cluster Analysis; D006801:Humans; D008581:Meningitis / Q000453:epidemiology; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D006113:United Kingdom / Q000453:epidemiology