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

Year 2022, Volume: 9 Issue: 1, 140 - 146, 06.03.2022

Ceren Eda Can Leyla Bakacak Karabenli Serpil Aktaş

https://doi.org/10.30897/ijegeo.936152

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.

Keywords

spatial autocorrelation, CAR models, MCMC, respiratory disease, mortality risk

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