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

Author(s)
Diego Gafuri Silva State University of Maring´a, Master Program in Biostatistics, Maring´a, Brazil
Larissa Intrebartoli Resende State University of Maring´a, Master Program in Biostatistics, Maring´a, Brazil
Elisaˆngela Aparecida da Silva Lizzi Federal University of Technology – Paran´a, Corn´elio Proc´opio, Brazil
Jorge Alberto Achcar Ribeir˜ao Preto Medical School, Universidade de S˜ao Paulo, Brazil
Edson Zangiacomi Martinez Ribeir˜ao Preto Medical School, Universidade de S˜ao Paulo, Brazil
Abstract
Spatial analysis techniques are used in the data analysis of ecological studies, which consider geographical areas as observation units. In this article, we propose a Bayesian bivariate spatial shared component model to map breast and cervical cancer mortality in Southern Brazil, based on the models introduced by Knorr-Held and Best (2001) and Held et al. (2005). Markov Chain Monte Carlo (MCMC) methods were used to spatially smooth the standardized mortality ratios (SMR) for both diseases. The local Indicator of Spatial Association (LISA) was used to verify the existence of spatial clusters in specific geographical areas. This study was carried out using secondary data obtained from publicly available health information systems.
Publication Details
Page(s) 775-788
DOI 10.18187/pjsor.v18i4.4095
Published Journal: Pakistan Journal of Statistics and Operation Research, Volume: 18, Issue: 4, Year: 2022

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Keywords
Epidemiology, Spatial analysis, Gaussian Markov Random Field, Ecological Studies., Bayesian Methods
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