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Detection of spatial disease clusters with LISA functions.

Paula Moraga1, Francisco Montes

  • 1Dpt. d'Estadística i I. O., Universitat de Valéncia, Spain.

Statistics in Medicine
|April 13, 2011
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This study introduces a new Local Indicators of Spatial Association (LISA) method for detecting disease clusters using case and control locations. The LISA method shows high accuracy, especially for irregular shapes, outperforming the spatial scan statistic in simulations.

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Area of Science:

  • Epidemiology
  • Spatial statistics
  • Geographic information systems

Background:

  • Disease cluster detection is crucial for identifying risk factors and understanding disease etiology.
  • Existing methods for spatial cluster detection have limitations, particularly with irregular cluster shapes.

Purpose of the Study:

  • To propose and evaluate a novel method for detecting spatial disease clusters using Local Indicators of Spatial Association (LISA) functions.
  • To compare the performance of the proposed LISA method against Kulldorff's spatial scan statistic.

Main Methods:

  • Development of a local version of the product density, a second-order characteristic of spatial point processes, based on LISA functions.
  • Simulation studies were conducted to evaluate the sensitivity, specificity, and type I error of the LISA method.
  • Comparison with Kulldorff's spatial scan statistic using simulated and real-world data.

Main Results:

  • The LISA method demonstrated high sensitivity and specificity in detecting simulated clusters of various sizes and shapes.
  • The LISA method outperformed the spatial scan statistic in identifying clusters with irregular shapes.
  • A relatively high type I error rate was observed for the LISA method when the number of cases was high.

Conclusions:

  • The proposed LISA-based method is a valuable tool for spatial disease cluster detection, offering improved performance for irregular cluster shapes.
  • Further research may be needed to address the type I error rate in high-case-number scenarios.
  • The method was successfully applied to identify spatial clusters of kidney disease in Valencia, Spain.