Sağlik Düzeyi̇ Göstergeleri̇ Açisindan Oecd Ülkeleri̇ni̇n Siralamasi Ve Geli̇r-Sağlik Harcamalari Etki̇si̇ (original) (raw)

The analysis of health status indicators provides important information to policymakers and healthcare providers about the achievement of the desired objectives. The analyzes conducted to compare the performance of health systems internationally have attracted the attention of different actors playing a role in health policies. In this study, the current health status of the countries included in the reports titled "Health at a Glance: Europe, 2018" and "Health at a Glance: Europe, 2019" published by OECD according to certain health indicators has been revealed. From this point of view, the current health needs of the countries are estimated, and comparisons are made between the countries. MOORA method, which is one of the Multiple Criteria Decision Making (MCDM) methods, has used to compare and rank health status indicators of the countries through two scenarios developed in the study. As a result of the analysis, the MOORA score, which expresses the health status indicators of the countries and enables a ranking among the countries, has been obtained and low MOORA score expresses high health level. Then, we evaluated the relationship between MOORA score and determined socioeconomic indicators. For this purpose, spearman correlation and regression analyses were applied. When the results obtained by the MOORA method are examined, it is seen that the best countries in terms of health status indicators used in the study are Norway, Sweden and Iceland according to scenarios 1 and 2; according to scenario 1 the worst countries are Turkey, Latvia and Estonia; according to scenario 2 are Hungary, Italy and Latvia. As a result of correlation analysis, the MOORA score obtained from scenarios 1 negatively correlated with income and health expenditures; positive correlation with air pollution and unemployment rate. MOORA score obtained from scenarios 2 negatively correlated with income, health expenditures, and access to drinking water services; positive correlation with air pollution and unemployment rate. According to the results of the regression analysis, negative relationships were found between the MOORA score for scenario 1 and 2 and income, and health expenditures. As the income level and health expenditure increases, the MOORA score decreases and the health level increases.