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Optimizing the maximum reported cluster size for the multinomial-based spatial scan statistic.

Jisu Moon1, Minseok Kim1, Inkyung Jung2

  • 1Division of Biostatistics, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.

International Journal of Health Geographics
|November 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to improve spatial disease cluster detection for multinomial data. The spatial cluster information criterion (SCIC) optimizes maximum reported cluster size, leading to more accurate and meaningful results in public health surveillance.

Keywords:
Gini coefficientInformation criterionMaximum scanning window sizeSaTScanSpatial cluster detection

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Area of Science:

  • Epidemiology
  • Public Health
  • Biostatistics

Background:

  • Accurate spatial disease cluster identification is crucial for public health and epidemiology.
  • The spatial scan statistic is a common tool, but its default maximum reported cluster size (MRCS) can lead to inaccurate cluster reporting.
  • Existing methods for optimizing MRCS are not available for the multinomial model.

Purpose of the Study:

  • To develop and evaluate a method for optimizing the maximum reported cluster size (MRCS) in spatial scan statistics for multinomial data.
  • To improve the accuracy and meaningfulness of spatial disease cluster detection.

Main Methods:

  • Proposed two versions of a spatial cluster information criterion (SCIC) for selecting the optimal MRCS.
  • Applied SCIC to the multinomial-based spatial scan statistic.
  • Conducted simulation studies and analyzed Korea Community Health Survey (KCHS) data.

Main Results:

  • The proposed SCIC method improves the accuracy of reporting true spatial disease clusters.
  • SCIC identifies more meaningful smaller clusters compared to the default MRCS setting.
  • Simulation studies support the effectiveness of SCIC in optimizing MRCS for multinomial data.

Conclusions:

  • The developed SCIC method enhances the performance of the spatial scan statistic for multinomial models.
  • This approach offers more accurate and meaningful spatial cluster detection for public health and disease surveillance, particularly for data like disease subtypes.