Mapping Respiratory Disease Mortality in Turkey by Using Bayesian Conditional Autoregressive Model

Abstract

Spatial analysis plays a prominent role in revealing and characterizing the spatial patterns over a geographical region by considering both the attributes of objects in a data set and their locations. The response variable can display spatial autocorrelation. The objects close together tend to produce more similar observations than objects further apart. Despite covariates in the model, we cannot capture spatial autocorrelation explicitly. It remains in the model residuals. Then, the independence assumption is violated by the residuals. We apply conditional autoregressive (CAR) model to prevent the residual spatial autocorrelation. In this study, we consider the problem of identifying the provinces at high risk to respiratory diseases mortality in Turkey. The number of deaths from respiratory diseases in 81 provinces of Turkey are modelled by using Leroux Model. We assume that the observed number of deaths have a Poisson distribution. Disease mapping is performed over calculated risk values. The results show that an increase in the household consumption of alcoholic beverages, cigarettes and tobacco and, also in the rate of people aged over 65 years in a province trigger a significant increase in respiratory disease mortality. Furthermore, Kastamonu has the highest mortality risk from respiratory diseases.

Spatial Estimation for Tuberculosis Relative Risk in Aceh Province, Indonesia: A Bayesian Conditional Autoregressive Approach with the Besag-York-Mollie (BYM) Model

“…Therefore, the CAR method is an optimal alternative for estimating relative risk by incorporating spatial information into the model. This approach was used successfully in a study to identify provinces in Turkey with a high risk of death from respiratory diseases [18]. In addition, a study has also successfully estimated the disease relative risk of dengue fever for districts in Aceh Province [19].…”