Review and empirical comparison of joint mapping of multiple diseases

Abstract

There has been a substantial amount of recent modelling work in multivariate disease-mapping models in epidemiology. These models provide information on similarities, as well as differences, on the effect of risk factors among diseases. Additionally, they can be used to identify disease-specifc risk factors, which would otherwise have been masked by established factors. The purpose of this article is to provide a review of the biostatistics literature, by comparing four joint disease-mapping models. In particular, multivariate intrinsic conditional autoregressive (ICAR) and multivariate multiple membership multiple classifcation (MMMC) models, as well as, shared-component and proportional mortality models are compared, with regard to similarities and differences between the assumptions and inferences. As an illustration, the four different models are ftted to population-based oesophagus and stomach cancer data. These two cancers share common risk factors associated with smoking, and diet or alcohol consum...