Spatial Analysis of Chronic Obstructive Pulmonary Disease (COPD) on the Basis of Provinces in Turkey with regards to Actuarial Risk Assessment

The goal of this thesis is to analyze and understand the risks of Chronic Obstructive Pulmonary Disease (COPD) 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 may affect the life expectancy as much as the premiums. COPD prevalence may exhibit spatial autocorrelation due to spatial similarity of provinces. Hence understanding of spatial pattern of COPD prevalence may provide better actuarial decisions. In this research, common risk factors of COPD 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 prevalence are explored in this study. The raw data of the morbidity is collected from the Social Security 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 (TUIK) and Tobacco and Alcohol Market Regulatory Authority (TAPDK). First of all, COPD prevalence ratios are grouped by 81 provinces of Turkey and it is separated by gender. Then, it needs to be decided the variables which define prevalence of COPD. Age, gender, socio-economic status, urbanization, schooling rate, tobacco sales and air quality may be some of the random variables which are categorized by provinces for COPD. 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 the morbidity 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 the morbidity 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. After comparison, models for risk assessments are fitted with the same explanatory variables in order to see how effective these variables are on the risks of COPD prevalence ratios.

As a result, the parameters show spatial autocorrelation which means that; risk models of COPD prevalence ratios 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 analysis, 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 COPD is not given clearly, it is not possible to calculate an individual health insurance premium, but it is possible to consider these risk results in the calculations of some health insurance products.