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Cluster: a software system for epidemiologic cluster analysis

H I Hall1, C V Lee, W E Kaye

  • 1Epidemiology and Statistics Branch, Division of Cancer Prevention and Control, Atlanta, GA 30341, USA.

Statistics in Medicine
|April 15, 1996
PubMed
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This study developed cluster analysis software for disease investigation. Analyses of cancer case data found no statistically significant geographic or temporal disease clusters.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Cluster investigations are crucial for public health surveillance.
  • Analyzing spatial and temporal patterns of disease is complex.
  • Existing software may lack comprehensive cluster analysis methods.

Purpose of the Study:

  • To develop and evaluate a software package for cluster analysis.
  • To apply the software to a real-world cancer case investigation.
  • To determine if statistically significant disease clusters exist.

Main Methods:

  • Developed a software package with 12 cluster analysis methods.
  • Utilized geographic information systems (GIS) for spatial data.
  • Applied Knox, Barton, Chen, and Ohno methods for spatial-temporal analysis.

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Main Results:

  • Barton and Knox methods showed no significant spatial clustering (p > 0.05).
  • Chen method found no significant difference between observed and expected county rates (p = 0.61).
  • Ohno method indicated no significant temporal patterns (p > 0.05).

Conclusions:

  • The developed software package is a valuable tool for disease cluster analysis.
  • No statistically significant disease cluster was identified in the investigated cancer cases.
  • The findings support the utility of integrated spatial and temporal analysis methods.