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Power Evaluation of Focused Cluster Tests.

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  • 1South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, USA ; Departments of Epidemiology and Biostatistics and Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, USA.

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Statistical tests for focused disease clusters are often designed for radial patterns. This study evaluated tests for various spatial patterns, finding directional effects crucial for accurate environmental health investigations.

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Area of Science:

  • Environmental epidemiology
  • Spatial statistics
  • Public health

Background:

  • Focused disease clusters around environmental contaminants are significant public health concerns.
  • Existing statistical tests primarily detect radial clustering patterns.
  • Environmental contaminant dispersion is influenced by factors like meteorology and topography, leading to diverse spatial patterns.

Purpose of the Study:

  • To evaluate the power of different statistical tests in detecting various spatial patterns of focused disease clusters.
  • To assess the impact of environmental contaminant dispersion patterns on the selection of appropriate cluster detection methods.
  • To investigate the influence of extra variation in risk on test power.

Main Methods:

  • Simulated disease cluster data reflecting five distinct spatial patterns (radial, radial with peak, angular, angular with distance decay, angular with peak and distance decay).
  • Evaluated the detection power of Stone's Maximum Likelihood Ratio Test, Tango's Focused Test, Bithell's Linear Risk Score Test, and Lawson-Waller Score Test variations.
  • Assessed the influence of extra variation in risk on the power of these tests.

Main Results:

  • The power of focused-cluster tests varied significantly depending on the spatial pattern of the disease cluster.
  • Tests showed differential performance in detecting radial versus angular dispersion patterns of environmental contaminants.
  • Directional effects in contaminant dispersion were found to be particularly important for accurate cluster detection.

Conclusions:

  • The selection of statistical tests for focused disease cluster investigations should account for diverse environmental contaminant dispersion patterns.
  • Considering directional effects is crucial for improving the accuracy and reliability of environmental health cluster analyses.
  • While extra variation in risk was considered, it did not substantially impact test power in this study.