Bayesian Bivariate Disease Mapping

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.

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