CutL: an alternative to Kulldorff's scan statistics for cluster detection with a specified cut-off level

Submitted: 2 February 2017
Accepted: 12 June 2017
Published: 6 November 2017
Abstract Views: 2059
PDF: 510
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Authors

When searching for epidemiological clusters, an important tool can be to carry out one's own research with the incidence rate from the literature as the reference level. Values exceeding this level may indicate the presence of a cluster in that location. This paper presents a method of searching for clusters that have significantly higher incidence rates than those specified by the investigator. The proposed method uses the classic binomial exact test for one proportion and an algorithm that joins areas with potential clusters while reducing the number of multiple comparisons needed. The sensitivity and specificity are preserved by this new method, while avoiding the Monte Carlo approach and still delivering results comparable to the commonly used Kulldorff's scan statistics and other similar methods of localising clusters. A strong contributing factor afforded by the statistical software that makes this possible is that it allows analysis and presentation of the results cartographically.

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Supporting Agencies

omasz Wieckowski, PQStat Software Company, Poznan, Poland
Barbara Więckowska, Department of Computer Science and Statistics, Karol Marcinkowski University of Medical Sciences, Poznan

How to Cite

WiÄ™ckowska, B., & Marcinkowska, J. (2017). CutL: an alternative to Kulldorff’s scan statistics for cluster detection with a specified cut-off level. Geospatial Health, 12(2). https://doi.org/10.4081/gh.2017.556