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Related Experiment Videos

Exploratory disease mapping: kriging the spatial risk function from regional count data.

Olaf Berke1

  • 1Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, CANADA, N1G 2W1. oberke@uoguelph.ca

International Journal of Health Geographics
|August 31, 2004
PubMed
Summary

This study introduces a disease mapping method combining empirical Bayes smoothing with kriging. This approach stabilizes estimates and overcomes areal bias, producing clearer isopleth maps for disease risk assessment.

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Area of Science:

  • Geostatistics
  • Epidemiology
  • Spatial statistics

Background:

  • Disease mapping interpolates regional data onto continuous surfaces.
  • Kriging is a geostatistical interpolation method with known criticisms.
  • Areal bias is a challenge in disease mapping.

Purpose of the Study:

  • To present a novel disease mapping method.
  • To address criticisms of standard kriging.
  • To improve the interpretability of disease risk maps.

Main Methods:

  • Combining empirical Bayes (shrinkage) estimates with kriging.
  • Utilizing ordinary kriging for data without spatial trend (e.g., SIDS data).
  • Employing universal kriging for data with spatial trend (e.g., veterinary epidemiology data).

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Main Results:

  • Empirical Bayes smoothing stabilizes unstable estimates and variance.
  • The method prevents negative interpolates.
  • Applied successfully to SIDS and veterinary epidemiology datasets.

Conclusions:

  • Interpolation of smoothed regional estimates overcomes areal bias.
  • Resulting isopleth maps are more interpretable than choropleth maps.
  • The empirical Bayesian approach is communicable to epidemiologists.