Bayesian spatial and temporal epidemiology of non-communicable diseases and mortality

Abstract

Spatial epidemiology combines spatial statistical modelling and disease epidemiology for studying geographic variation in mortality and morbidity. The effects of putative risk factors may be examined using ecological regression models. On the other hand, age-period-cohort models can be used to study the variation of mortality and morbidity through time. Bayesian hierarchical statistical models offer a flexible framework for these studies and enable the estimation of uncertainties in the results. The models are usually estimated using computer-intensive Markov chain Monte Carlo simulations. In this dissertation the first four publications present practical epidemiological studies on geographic variation in non-communicable diseases in Finland. In the last publication we study the long-time variation in all-cause mortality in several European countries. New statistical models are developed for these studies. This work provides new epidemiological information on the geographic variation of acute myocardial infarctions (AMI), ischaemic stroke and parkinsonism in Finland. An extended model for studying shared and disease specific geographic variation is presented using data on AMI and ischaemic stroke incidence. Existing results on the inverse association of water hardness and AMI are refined. New models for interpolation of geochemical data with non-detected values are presented with case studies using real data. Finally, the Bayesian age-period-cohort model is extended with versatile interactions and better prediction ability. The model is then used to study long-term variation in mortality in Europe.