The fields of geographical epidemiology and public health surveillance have benefited from combined advances in hierarchical model building and in geographical information systems. Exploring and characterising a variety of spatial patterns of diseases at a fine geographical resolution has become possible (Banerjee, Carlin and Gelfand 2004). Insight into the sensitivity of the resulting inference to the choice of the structure of the different components of the hierarchical model has been gained through the use of simulation studies (Best, Richardson and Thomson, 2005) and numerous case studies. Baseline results on how to use the posterior distribution of relative risk estimates to detect areas of increased risks have been discussed (Richardson, Thomson, Best and Elliott, 2004) Extending hierarchical disease mapping models to models that simultaneously consider space and time and/or several diseases leads to a number of benefits in terms of interpretation and potential for detection of localised excesses. Such extension is accompanied by an increase of the complexity of the model structures that might be specified. The presentation will first outline classes of hierarchical space time models that can be used to characterise the patterns of chronic diseases. Space-time analysis of related diseases that that tease out common and specific space and time structures will be discussed next and illustrated on an example related to male and female lung cancer (Richardson, Abellan and Best, 2006). Finally, the use of space-time models to better characterise the stability of spatial patterns and to highlight atypical areas with unusual variability in their risk time pattern will be discussed and illustrated in a number of realistic scenarios. In particular, we will show how to model the space-time interactions and exploit their posterior distributions in order to classify the areas' risk pattern over time as 'predictable/ repeatable' or 'atypical/ highly variable'. References Banerjee S, Carlin B and Gelfand A (2004.) Hierarchical modelling and analysis of spatial data. Chapman and Hall, New York. Best N, Richardson S and Thompson A. (2005) A comparison of Bayesian spatial models for disease mapping. Statistical Methods in Medical Research. 14:35-59. Richardson S, Thomson A, Best N & Elliott P. Interpreting posterior relative risk estimates in disease mapping studies. Environmental Health Perspective , 2004, 112: 1016-25 S. Richardson, J-J. Abellan and N. Best. Bayesian spatio-temporal analysis of joint patterns of male and female lung cancer risks in Yorkshire (UK). Statistical Methods in Medical Research, 15: 385-407, (2006). Sylvia Richardson - Imperial College, London Bande son disponible au format mp3 Durée : 57 mn