Determinants of Health in Korea: A Comparative Analysis among Organization for Economic Cooperation and Development Countries / 보건행정학회지

BACKGROUND: This study aims to utilize Organization for Economic Cooperation and Development (OECD) data to identify macroscopic determinants of health at national level and to utilize it in health policy development through comparison and analysis with Korea. METHODS: The potential years of life lost (PYLL) were used as dependent variables and 19 indicators were selected as health determinants to be independent variables based on the results of previous studies. Data analysis was done using SAS ver. 9.4 package (SAS Institute Inc., Cary, NC, USA) and model used in technical statistics concerning PYLL by countries, multi-linearity test between independent variables and OECD economic studies were modified and used. RESULTS: From 1994 to 2012, the average PYLL for OECD countries was 4,262.9 years, the highest in Estonia and the lowest in Iceland. As a result of the analysis using the fixed effect model, the significant variables affecting PYLL were four variables: gross domestic product, nitric oxide, tobacco consumption, and number of doctors. The health determinants that had more influence on the PYLL of Korean people compared to other OECD countries were tobacco consumption, calorie consumption, fat intake and total health expenditure. CONCLUSION: In order to effectively reduce unnecessary deaths, we must continue to strengthen our smoking policy and nutrition policies such as calorie and fat intake. It is necessary to prevent the increase of total health expenditure due to the increase in the prevalence of chronic diseases and to strengthen the public health aspect.

Effectiveness of Repeated Examination to Diagnose Enterobiasis in Nursery School Groups

The aim of this study was to estimate the benefit from repeated examinations in the diagnosis of enterobiasis in nursery school groups, and to test the effectiveness of individual-based risk predictions using different methods. A total of 604 children were examined using double, and 96 using triple, anal swab examinations. The questionnaires for parents, structured observations, and interviews with supervisors were used to identify factors of possible infection risk. In order to model the risk of enterobiasis at individual level, a similarity-based machine learning and prediction software Constud was compared with data mining methods in the Statistica 8 Data Miner software package. Prevalence according to a single examination was 22.5%; the increase as a result of double examinations was 8.2%. Single swabs resulted in an estimated prevalence of 20.1% among children examined 3 times; double swabs increased this by 10.1%, and triple swabs by 7.3%. Random forest classification, boosting classification trees, and Constud correctly predicted about 2/3 of the results of the second examination. Constud estimated a mean prevalence of 31.5% in groups. Constud was able to yield the highest overall fit of individual-based predictions while boosting classification tree and random forest models were more effective in recognizing Enterobius positive persons. As a rule, the actual prevalence of enterobiasis is higher than indicated by a single examination. We suggest using either the values of the mean increase in prevalence after double examinations compared to single examinations or group estimations deduced from individual-level modelled risk predictions.