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On Bayesian shared component disease mapping and ecological regression with errors in covariates.

Ying C MacNab1

  • 1Division of Epidemiology and Biostatistics, School of Population and Public Health, University of British Columbia, British Columbia, Canada. ymacnab@interchange.ubc.ca

Statistics in Medicine
|March 6, 2010
PubMed
Summary
This summary is machine-generated.

This study explores Bayesian shared component models (SCMs) for joint spatial disease mapping, considering covariate errors. The research clarifies SCMs and multivariate disease mapping models (MultiVMs) using road traffic accident injury data.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Spatial Analysis

Background:

  • Bayesian disease mapping literature includes shared component models (SCMs) for joint spatial modeling of multiple diseases with shared risk factors.
  • Ecological regression models are essential for analyzing disease rates in relation to ecological risk factors within defined populations.
  • Multivariate disease mapping models (MultiVMs), including multivariate conditional autoregressive models, are also part of recent Bayesian disease mapping advancements.

Purpose of the Study:

  • To explore and develop Bayesian hierarchical formulations of shared component disease mapping and ecological models, incorporating covariate errors.
  • To review and compare shared component models (SCMs) with multivariate disease mapping models (MultiVMs), highlighting their connections and distinctions.
  • To discuss critical issues in SCM formulation, including shared/disease-specific components, prior choices for random effects, and parameter identification for small area health outcome analysis.

Main Methods:

  • Development of Bayesian hierarchical formulations for shared component disease mapping and ecological models.
  • Review and comparison of shared component models (SCMs) and multivariate disease mapping models (MultiVMs).
  • Application of methods to a four-variate analysis of road traffic accident injury (RTAI) data using Markov chain Monte Carlo (MCMC) simulations for fully Bayesian inference.

Main Results:

  • The study provides insights into the relationships and differences between SCM and MultiVM approaches in Bayesian disease mapping.
  • Key considerations for formulating SCMs, such as defining shared and disease-specific components and selecting appropriate spatial or non-spatial random effects priors, are discussed.
  • The analysis demonstrates the application of these models to real-world data, specifically gender-specific fatal and non-fatal RTAI rates in British Columbia.

Conclusions:

  • Bayesian shared component models offer a flexible framework for joint spatial modeling of multiple diseases, accounting for shared risk factors and covariate errors.
  • Understanding the nuances between SCMs and MultiVMs is crucial for appropriate model selection in multivariate disease mapping.
  • The presented methods and illustrated analysis provide valuable tools for spatial and ecological analysis of small area health outcomes and associated risk factors.