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Risk analysis based on spatial analysis of chronic obstructive pulmonary disease (COPD) and lung cancer with respect to provinces in Turkey

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2012
Çiftçi, Sezgin
The goal of this thesis is to analyze and understand the risks of Chronic Obstructive Pulmonary Disease (COPD) and lung cancer with respect to the provinces of Turkey according to the results of spatial analysis. The insurance sector of the country needs that kind of analysis to make more precise pricing in insurance products. Especially in health and life insurance products, morbidities like COPD and lung cancer may a ect the life expectancy as much as the premiums. COPD and lung cancer prevalence may exhibit spatial autocorrelation due to spatial similarity of provinces. Hence understanding of spatial pattern of COPD and lung cancer prevalence may provide better actuarial decisions. In this research, common risk factors of COPD and lung cancer are considered to be tobacco sales, air pollution, urbanization, gross schooling rate, life expectancy, median age and GDP per capita of the provinces. The spatial patterns of these factors in Turkey as well as their correlations to COPD and lung cancer prevalence are explored in this study. The raw data of the morbidities (COPD and lung cancer) are collected from the Social Seiv curity Institution (SGK) and the useful data are selected in these raw data. The data of the independent variables are collected and derived from the Turkish Statistical Institute (TUİK) and Tobacco and Alcohol Market Regulatory Authority (TAPDK). First of all, COPD prevalence ratios and lung cancer prevalence ratios are grouped by 81 provinces of Turkey and every morbidity is separated by gender. Then, it needs to be decided the variables which define prevalence of COPD and that of lung cancer. Age, gender, socio-economic status, urbanization, schooling rate, life expectancy, tobacco sales and air quality may be some of the random variables which are categorized by provinces for both morbidities. After data collection spatial analysis is applied with visualization, explanatory analysis and modeling by using Geographic Information Systems (GIS). In visualization, general spatial patterns are identified for morbidities and variables. In explanatory analysis part, proximity matrices are used to evaluate Moran’s I values for understanding the spatial autocorrelation. Then, these Moran’s I values are used for plotting correlograms in order to follow the spatial dependence better. After identifying spatial dependence of the variables, Ordinary Linear Regression and Spatial Regression models are established and compared. Finally, as a result of those findings in the analysis, actuarial risk assessments are found for both two morbidities with respect to provinces and gender. The risk assessments are mapped and compared with the explanatory variables in the models which are found in the previous part and the relations between risks and variables are observed. As a result, the parameters show spatial autocorrelation which means that; financial risk assessments of COPD and lung cancer should be taken into account when deciding the pricing of some actuarial products such as health insurance. Generally, spatial correlation is ignored in this kind of calculations, but due to the high autocorrelation the results may indicate serious change. From the actuarial perspective, the results of the analysis are suggested to be used in health insurance premium pricing. Since the analysis could not have been made on the basis of individuals, and financial burden of morbidities for insurance companies are not given clearly, it is not possible to calculate any health insurance product premium, but it is more appropriate to consider the importance of these risk results in the calculations of health insurance products.