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Spatial EpidemiologyMethods and Applications$
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Paul Elliott, Jon Wakefield, Nicola Best, and David Briggs

Print publication date: 2001

Print ISBN-13: 9780198515326

Published to Oxford Scholarship Online: September 2009

DOI: 10.1093/acprof:oso/9780198515326.001.0001

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Investigating the genetic association between diabetes and malaria: an application of Bayesian ecological regression models with errors in covariates

Investigating the genetic association between diabetes and malaria: an application of Bayesian ecological regression models with errors in covariates

Chapter:
(p.286) 16. Investigating the genetic association between diabetes and malaria: an application of Bayesian ecological regression models with errors in covariates
Source:
Spatial Epidemiology
Author(s):

L. Bernardinelli

C. Pascutto

C. Montomoli

W. Gilks

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198515326.003.0016

This chapter describes a Bayesian hierarchical model and applies it to a new dataset on insulin-dependent diabetes mellitus (IDDM) prevalence among 18-year-old males born in Sardinia between 1936 and 1973, using malaria prevalence in 1938–40 as the ecological covariate. It shows how to deal with the potential bias associated with using such a proxy by extending the Bayesian model to allow for covariate measurement error. It provides a method for choosing the hyperprior distributions for the spatial variation parameters of the model, and discusses the sensitivity of the results to different choices.

Keywords:   Bayesian model, hierarchical model, diabetes mellitus, malaria, bias, spatial epidemiology

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