Bayesian Bivariate Disease Mapping

Chapter

Abstract

There has been substantial progress in the development of Bayesian spatial modelling and estimation in recent years to overcome the problem of the sparseness of data across small geographical areas for rare diseases. Attention has also focused on developing spatial models to accommodate multivariate disease mapping, for example in situations where one wishes to test common epidemiological or aetiological features among different conditions. This chapter expands on this work by comparing a classical frequentist approach to full Bayesian estimation for fitting a bivariate spatial disease model. As an illustration, we apply the models to population-based childhood leukaemia and childhood diabetes data from Yorkshire, United Kingdom to determine the similarity in their spatial distribution. The spatial association between the two diseases is modelled using a multivariate normal distribution on the spatial and heterogeneity components within a hierarchical Bayesian random effects model. The effect on the degree of spatial correlation after adjusting for socio-demographic factors previously associated with disease incidence is also assessed.

Keywords

Markov Chain Monte Carlo Acute Lymphoblastic Leukaemia Deviance Information Criterion Spatial Random Effect Markov Chain Monte Carlo Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

We are grateful to Oxford University Press for permission to reproduce tables which originally appeared in the following article: American Journal of Epidemiology, Vol 161, Issue 11 , pp 1168–1180, 2005, “Detecting small-area similarities in the epidemiology of childhood acute lymphoblastic leukemia and diabetes mellitus, Type 1: a Bayesian approach.”

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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Division of Epidemiology, Centre for Epidemiology and Biostatistics, Leeds Institute of Genetics, Health & TherapeuticsUniversity of LeedsLeedsUK
  2. 2.Biostatistics UnitSouth Africa Medical Research CouncilPretoriaSouth Africa

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