Objectives: This study aimed to estimate the risk of disease- and death-causing obesity and to calculate direct and indirect costs using national health insurance big data. Methods: We constructed cohort data for 10,091,251 participants who underwent a national health screening between 2003 and 2004. Cox’s proportional hazard models were adopted to analyze the relative risk (RR) of related diseases and causes of death, adjusted for age, smoking, alcohol consumption, and exercise. Population attributable risk (PAR) of obesity was calculated considering prevalence of obesity and RR of incidence and death of obesity related diseases. Socioeconomic costs were estimated including direct and indirect costs. Results: The risk of diabetes increased with increasing body mass index (BMI). In severely obese males, the risk of diabetes was 4.83 times higher than in normal weight males. In severely obese females, the risk of diabetes was 4.01 times higher than in normal weight females. In case of the socioeconomic cost of obesity, the direct and indirect costs differed by sex: the direct cost of male and female obesity was 50.8 % and 85.9 % in 2015, respectively. Conclusions: This study identified that obesity is not only a significant risk factor of diseases and death but also the cause of economic loss for the health insurance sector. Therefore, it is considered that it is necessary to develop an intervention program to manage examinees who are determined to be obese.

Objectives: The aim of this study was to investigate cognitive function decline by socio-demographic and health-related characteristics (health behaviors and health status) using 5th Korean Longitudinal Study of Aging panel data. Methods: The subjects were 4,440 community-dwelling people aged over 57 years. The data were analyzed with descriptive statistics, frequency analysis, χ²-test, and binary logistic regression analysis using SPSS ver. 25.0. Results: The findings revealed that socio-demographic characteristics (gender, age, area of residence, educational level, marital status, number of children, number of grand-children) and health-related characteristics (smoking, drinking, regular exercise, weight category by body mass index, hypertension and diabetes mellitus) were factors that influenced cognitive function decline (p<.05). Conclusions: Cognitive function decline was closely related to health behaviors and disease types. Future studies must examine related constructs to accurately determine these relationships among various populations. The present study could be used as a tool for the development and implementation of health promotion and prevention strategies.