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Using Gini coefficient to determining optimal cluster reporting sizes for spatial scan statistics.

Junhee Han1, Li Zhu2, Martin Kulldorff3

  • 1Division of Biostatistics, Research Institute of Convergence for Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan, Korea.

International Journal of Health Geographics
|August 5, 2016
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Summary
This summary is machine-generated.

The Gini coefficient helps select significant disease clusters from spatial scan statistics. This method refines reporting by identifying the most relevant non-overlapping clusters for disease surveillance.

Keywords:
Cancer mortalityCluster detectionCluster reporting sizeDisease surveillanceGini coefficientLog likelihood ratioSaTScanScan statisticSpatial statistics

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Spatial and space-time scan statistics are crucial for disease surveillance, identifying areas with elevated risk and detecting outbreaks.
  • Traditional scan statistics often yield numerous overlapping clusters, making it challenging to report meaningful findings.
  • Selecting the most relevant clusters from a multitude of similar ones is a significant challenge in disease surveillance.

Purpose of the Study:

  • To introduce and evaluate the Gini coefficient as a novel method for selecting significant, non-overlapping clusters from spatial scan statistics.
  • To provide a more intuitive and refined approach to reporting cluster findings in disease surveillance.
  • To compare the Gini coefficient method with traditional cluster reporting approaches.

Main Methods:

  • The Gini coefficient was employed to assess the heterogeneity of collections of clusters identified by spatial scan statistics.
  • Simulation studies and analysis of real cancer mortality data were used for evaluation.
  • The Gini coefficient's performance was compared against the standard method of reporting non-overlapping clusters.

Main Results:

  • The Gini coefficient effectively identifies a more refined set of non-overlapping clusters for reporting.
  • It can distinguish between reporting a single large cluster versus multiple smaller ones.
  • The method demonstrates desirable theoretical properties, including invariance to uniform population scaling.

Conclusions:

  • The Gini coefficient offers a valuable tool for determining which set of non-overlapping clusters to report in disease surveillance.
  • This approach enhances the clarity and utility of spatial scan statistic outputs.
  • The Gini coefficient has been integrated into SaTScan™ software (version 9.3).