Bayesian bivariate spatial shared component model: mapping breast and cervical cancer mortality in Southern Brazil

Spatial analysis techniques are used in the data of ecological studies, which consider geographical areas as observation units. In this article, we propose a Bayesian bivariate spatial shared component model to mapping breast and cervical cancer mortality Southern Brazil, based on models introduced by Knorr-Held Best (2001) Held et al. (2005). Markov Chain Monte Carlo (MCMC) methods were spatially smooth standardized ratios (SMR) for both diseases. Local Indicator Association (LISA) was verify existence clusters specific areas. This study carried out using secondary obtained from publicly available health information systems.