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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Detecting multiple spatial disease clusters: information criterion and scan statistic approach.

Kunihiko Takahashi1, Hideyasu Shimadzu2,3

  • 1Department of Biostatistics, M&D Data Science Center, Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan. kunihikot.dsc@tmd.ac.jp.

International Journal of Health Geographics
|September 4, 2020
PubMed
Summary

This study introduces a new statistical framework for detecting multiple spatial disease clusters. The method demonstrates higher sensitivity and detection power than existing approaches for public health surveillance.

Keywords:
Cluster detection testGeneralized linear modelInformation criteriaMultiple clusteringScan statistic

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Early detection of disease geographical tendencies is crucial for preventing severe health outcomes.
  • Information technology advancements necessitate comprehensive frameworks for simultaneous spatial cluster detection.
  • Identifying disease clusters, whether scattered or centered around epicenters, is a key public health challenge.

Purpose of the Study:

  • To develop and evaluate a novel statistical framework for simultaneous detection of multiple spatial disease clusters.
  • To compare the performance of the new methodology against existing cluster detection methods.
  • To introduce an information criterion for selecting the optimal number of disease clusters.

Main Methods:

  • Development of a novel framework integrating scan statistics and generalized linear models.
  • Application of a new information criterion for determining the number of spatial disease clusters.
  • Evaluation using real-world hospital admission data for chronic obstructive pulmonary disease (COPD) in England and simulated data.

Main Results:

  • The proposed method demonstrated superior performance compared to conventional cluster detection procedures.
  • Higher sensitivity in detecting spatial disease clusters was observed with the new framework.
  • Case study and simulation studies confirmed the method's effectiveness in identifying multiple clusters.

Conclusions:

  • A new statistical framework enables simultaneous detection and evaluation of multiple disease clusters across large study areas.
  • The proposed approach offers significantly higher detection power than traditional methods.
  • This framework advances spatial epidemiology and public health surveillance capabilities.