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A comparison of Bayesian spatial models for disease mapping.

Nicky Best1, Sylvia Richardson, Andrew Thomson

  • 1Small Area Health Statistics Unit, Department of Epidemiology and Public Health, Imperial College Faculty of Medicine, Norfolk Place, London W2 1PG, UK. n.best@ic.ac.uk

Statistical Methods in Medical Research
|February 5, 2005
PubMed
Summary

This study reviews Bayesian hierarchical models for small area disease mapping, addressing data sparseness and spatial dependence. It compares model performance to guide the selection of appropriate spatial priors for health data analysis.

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Area of Science:

  • Geographical epidemiology
  • Spatial statistics
  • Bayesian hierarchical modeling

Background:

  • Routine health data at fine geographical resolutions enable small area disease mapping.
  • Data sparseness and local spatial dependence are key challenges in this field.
  • Hierarchical models are well-suited for analyzing latent spatial processes corrupted by observational noise.

Purpose of the Study:

  • To provide a comprehensive review of Bayesian spatial models for disease mapping.
  • To compare the performance of different spatial models using simulated data.
  • To guide the selection of structural priors for hierarchical models and assess sensitivity.

Main Methods:

  • Review of main classes of spatial models within a Bayesian estimation framework.

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  • Performance comparison of representative models using simulated datasets.
  • Consideration of extensions for joint spatial distribution of multiple health indicators.

    • Main Results:

    • Identified and reviewed key Bayesian spatial models for disease mapping.
    • Demonstrated model properties and performance differences through simulation studies.
    • Highlighted the importance of structural prior choice and its sensitivity.

    Conclusions:

    • Bayesian hierarchical models are effective for small area disease mapping.
    • Model selection and prior specification are critical for accurate spatial analysis.
    • Further research into joint modeling of multiple health indicators is warranted.