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A tree-based scan statistic for database disease surveillance.

Martin Kulldorff1, Zixing Fang, Stephen J Walsh

  • 1Division of Epidemiology and Biostatistics, School of Medicine, and Department of Statistics, University of Connecticut, USA. martink@neuron.uchc.edu

Biometrics
|August 21, 2003
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Summary
This summary is machine-generated.

This study introduces a novel tree-based scan statistic for health surveillance, identifying potential disease risk factors within hierarchical databases like occupations. The method efficiently detects unsuspected relationships, adjusting for multiple testing complexities.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health Surveillance

Background:

  • Health event and risk factor analysis relies on extensive databases.
  • Hierarchical data structures, such as occupations and drugs, are common in health databases.
  • Detecting unexpected disease-risk relationships is crucial for public health surveillance.

Purpose of the Study:

  • To propose a novel tree-based scan statistic for health surveillance.
  • To enable the detection of unsuspected relationships between health events and hierarchical risk factors.
  • To adjust for multiple testing inherent in analyzing numerous potential risk factor combinations.

Main Methods:

  • Development of a tree-based scan statistic.
  • Application to hierarchical data, specifically occupations.
  • Utilizing the National Center for Health Statistics Multiple Cause of Death Database.

Main Results:

  • The proposed method effectively analyzes hierarchical data for disease surveillance.
  • Demonstrated application in identifying occupation-related risks for silicosis.
  • The statistic adjusts for multiple comparisons, enhancing reliability.

Conclusions:

  • The tree-based scan statistic is a valuable tool for public health surveillance.
  • It facilitates the discovery of novel associations between occupations and health outcomes.
  • This approach offers a robust method for analyzing complex, hierarchical health data.