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Detection of Disease Clustering

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Scan Statistics

Part of the book series: Statistics for Industry and Technology ((SIT))

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Abstract

In epidemiological studies, it is often of interest to evaluate whether a disease is randomly distributed over time and/or space after being adjusted for a known heterogeneity, which may provide clues to the etiology of disease. To do this, we can apply tests for spatial randomness, or disease clustering. In this paper, I review the existing tests for disease clustering and discuss the advantages and disadvantages of these test statistics. These tests are illustrated and compared with several real temporal and spatial data sets.

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Author information

Authors and Affiliations

  1. Department of Technology Assessment and Biostatistics, National Institute of Public Health, Wako-shi, Japan

    Toshiro Tango

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  1. Toshiro Tango
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Editor information

Editors and Affiliations

  1. Dept. Statistics, University of Connecticut, Glenbrook Rd. 215, Storrs, 06269-3120, U.S.A.

    Joseph Glaz

  2. Dept. Statistics, University of Connecticut, Glenbrook Road 215, Storrs, 06269-4120, U.S.A.

    Vladimir Pozdnyakov

  3. Center for Biomathematical Sciences, Mount Sinai School of Medicine, Gustave L Levy Place 1, New York, 10029, U.S.A.

    Sylvan Wallenstein

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© 2009 Birkhäuser Boston, a part of Springer Science+Business Media, LLC

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Tango, T. (2009). Detection of Disease Clustering. In: Glaz, J., Pozdnyakov, V., Wallenstein, S. (eds) Scan Statistics. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4749-0_17

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