Abstract
. Clinical data on the location of residence at the time of diagnosis of new lupus cases in Toronto, Canada, for the 40 years to 2007 are modelled with the aim of finding areas of abnormally high risk. Inference is complicated by numerous irregular changes in the census regions on which population is reported. A model is introduced consisting of a continuous random spatial surface and fixed effects for time and ages of individuals. The process is modelled on a fine grid and Bayesian inference performed by using integrated nested Laplace approximations. Predicted risk surfaces and posterior probabilities of exceedance are produced for lupus and, for comparison, psoriatic arthritis data from the same clinic. Simulations studies are also carried out to understand better the performance of the model proposed as well as to compare with existing methods.