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.

Keywords

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

References

1. Badur, S., Öztürk, S., Ozakay, A., Khalaf, M., Saha, D., Van Damme, P. (2021). A review of the experience of childhood hepatitis A vaccination in Saudi Arabia and Turkey: implications for hepatitis A control and prevention in the Middle East and North African region, Human Vaccines and Immunotherapeutics, 17:10, 3710-3728, doi.10.1080/21645515.2021. 1920871
2. Banerjee, S., Carlin, B.P., Gelfand, A.E. (2014). Hierarchical modelling and analysis for spatial data. Second edition, Chapman & Hall/CRC Monographs on Statistics & Applied Probability.
3. Besag, J., York, J., Mollie, A. (1991). Bayesian image restoration with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43(1), 1-20.
4. Bivand, S.R., Pebesma, E., Gomez-Rubio, V. (2013). Applied spatial data analysis with R. Second Edition, New York, Springer.
5. Cressie, N. (1993). Statistics for spatial data. Second Edition, Wiley Classics Library.
6. Doğru, AO., David, RM, Uluğtekin, N., Göksel, Ç., Şeker, DZ., Sozen, S (2017). GIS based spatial pattern analysis: children with Hepatitis A in Turkey. Environ Res. 156:349–57. doi. 10.1016/ j.envres.2017.04.001.
7. Lawson, A. (2008). Bayesian disease mapping: hierarchical modelling in spatial epidemiology. Chapman & Hall/CRC interdisciplinary statistics series.
8. Lee, D. (2011). A comparison of conditional autoregressive models used in Bayesian disease mapping. Spatial and Spatio-temporal Epidemiology, 2, 79-89.

9. Lee, D. (2013). CARBayes: An R package for Bayesian spatial modelling with conditional autoregressive priors. Journal of Statistical Software, 55(13).
10. Lee, D., Mitchell, R. (2012). Boundary detection in disease mapping studies. Biostatistics, 13(3), 415-426.
11. Lee, D., Sarran, C. (2015). Controlling for unmeasured confounding and spatial misalignment in long-term air pollution and health studies. Environmetrics, 26, 477-487.
12. Leroux, B., Lei, X., Breslow, N. (2000). Estimation of disease rates in small areas: a new mixed model for spatial dependence. In: Halloran, M., Berry, D. (Eds.), Statistical models in epidemiology, the environment and clinical trials (pp. 179-191), New York, Springer-Verlag.
13. Republic of Turkey General Directorate of Forestry (GDF), Forestry statistics (2014), Retrieved October 2017 from https://www.ogm.gov.tr
14. Republic of Turkey Ministry of Environment and Urbanization, Air Quality Monitoring Stations (AQMS) Website, Retrieved October 2017 from http://www.havaizleme.gov.tr
15. The Union of Chambers and Commodity Exchanges of Turkey (TOBB), Economic Report (2014), Retrieved October 2017 from https://www.tobb.org.tr
16. Turkish Statistical Institute (TURKSTAT), Regional Statistics, Retrieved October 2017 from http://www.tuik.gov.tr