Baptista, HelenaCongdon, PeterMendes, Jorge M.Rodrigues, Ana M.Canhão, HelenaDias, Sara Simões2021-04-222021-04-222020-11-11http://hdl.handle.net/10400.8/5691Recent advances in the spatial epidemiology literature have extended traditional approaches by including determinant disease factors that allow for non-local smoothing and/or non-spatial smoothing. In this article, two of those approaches are compared and are further extended to areas of high interest from the public health perspective. These are a conditionally specified Gaussian random field model, using a similaritybased non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping; and a spatially adaptive conditional autoregressive prior model.engBayesian modellingBayesian modellingLimiting health problemsSpatial epidemiologySimilarity-based and adaptive modelsjournal article10.1515/em-2019-0025