A hierarchical Bayesian model for space-time variation of disease risk

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.

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