Joint modelling of space and time variation of the risk of disease is an important topic in descriptive epidemiology. Most of the proposals in this field deal with at most two time scales (age–period or age–cohort). We propose a hierarchical Bayesian model that can be used as a general framework to jointly study the evolution in time and the spatial pattern of the risk of disease. The rates are modelled as a function of purely spatial terms (local effects of risk factors that do not vary in time), time effects (on the three time axes: age, calendar period and birth cohort) and space–time interactions that describe area specific time patterns.
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Lagazio et al. (2003) studied this question.
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