Towards joint disease mapping

Leonhard Held*, Isabel Natário, Sarah Elaine Fenton, Haavard Rue, Nikolaus Becker

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

108 Scopus citations

Abstract

This article discusses and extends statistical models to jointly analyse the spatial variation of rates of several diseases with common risk factors. We start with a review of methods for separate analyses of diseases, then move to ecological regression approaches, where the rates from one of the diseases enter as surrogate covariates for exposure. Finally, we propose a general framework for jointly modelling the variation of two or more diseases, some of which share latent spatial fields, but with possibly different risk gradients. In our application, we consider mortality data on oral, oesophagus, larynx and lung cancers for males in Germany, which all share smoking as a common risk factor. Furthermore, the first three cancers are also known to be related to excessive alcohol consumption. An empirical comparison of the different models based on a formal model criterion as well as on the posterior precision of the relative risk estimates strongly suggests that the joint modelling approach is a useful and valuable extension over individual analyses.

Original languageEnglish (US)
Pages (from-to)61-82
Number of pages22
JournalStatistical Methods in Medical Research
Volume14
Issue number1
DOIs
StatePublished - Feb 1 2005

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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