Statistical Modelling 1 (2001), 1729
A hierarchical Bayesian model for space-time variation of disease risk
Corrado Lagazio
Department of Statistical Science
University of Udine
Udine, Italy
Emanuela Dreassi
Department of Statistics 'G . Parenti'
University of Florence
Florence, Italy
Annibale Biggeri
Department of Statistics 'G . Parenti'
University of Florence
Florence, Italy
Abstract:
In this paper we propose a hierarchical Bayesian model to study the
variation in space and time of disease risk. We represent spatial
effects following the usual Bayesian specification
of a Gaussian convolution of unstructured and structured components,
while we adopt the birth cohort (instead of the commonly used period
of death) as the main time scale. The model also includes space-time
interaction terms to take into account structured inseparable space-time
variability. The model is applied to lung cancer death certificate data
in Tuscany, for males during the period 1971-94. While a calendar period
analysis points out a general increase of mortality levelling off in the
last period (1990-94), the cohort model shows a general and substantial
decrease of the relative risk for the youngest cohorts born after 1930.
Moreover, the pattern of the epidemic by birth cohort presents a
maximum which varies by municipalities, with a strong north-west/south-east
gradient.
Keywords:
cohort effects; hierarchical Bayesian model; space-time analysis.
Downloads:
Data in
zipped file
back