Seung-ji Lim

OBJECTIVES: To estimate the annual socioeconomic costs of stroke in Korea in 2005 from a societal perspective. METHODS: We identified those 20 years or older who had at least one national health insurance (NHI) claims record with a primary or a secondary diagnosis of stroke (ICD-10 codes: I60-I69, G45) in 2005. Direct medical costs of the stroke were measured from the NHI claims records. Direct non-medical costs were estimated as transportation costs incurred when visiting the hospitals. Indirect costs were defined as patients' and caregivers' productivity loss associated with office visits or hospitalization. Also, the costs of productivity loss due to premature death from stroke were calculated. RESULTS: A total of 882,143 stroke patients were identified with prevalence for treatment of stroke at 2.44%. The total cost for the treatment of stroke in the nation was estimated to be 3,737 billion Korean won (KRW) which included direct costs at 1,130 billion KRW and indirect costs at 2,606 billion KRW. The per-capita cost of stroke was 3 million KRW for men and 2 million KRW for women. The total national spending for hemorrhagic and ischemic stroke was 1,323 billion KRW and 1,553 billion KRW, respectively, which together consisted of 77.0% of the total cost for stroke. Costs per patient for hemorrhagic and ischemic stroke were estimated at 6 million KRW and 2 million KRW, respectively. CONCLUSIONS: Stroke is a leading public health problem in Korea in terms of the economic burden. The indirect costs were identified as the largest component of the overall cost.

BACKGROUND: It is important to identify and quantify the factors that affect gender differences in high-risk drinking (HRD), from both an academic and a policy perspective. However, little is currently known about them. This study examines these factors and estimates the percentage contribution each makes to gender differences in HRD. METHODS: This study analyzed information on 23,587 adults obtained from the Korea National Health and Nutrition Surveys of 1998, 2001, and 2005. It found that the prevalence of HRD was about 5 times higher among men (0.37) than women (0.08). Using a decomposition approach extended from the Oaxaca-Blinder method, we decomposed the gender difference in HRD to an "overall composition effect" (contributions due to gender differences in the distribution of observed socio-economic characteristics), and an "overall HRD-tendency effect" (contributions due to gender differences in tendencies in HRD for individuals who share socio-economic characteristics). RESULTS: The HRD-tendency effect accounted for 96% of the gender difference in HRD in South Korea, whereas gender differences in observed socio-economic characteristics explained just 4% of the difference. Notably, the gender-specific HRD-tendency effect accounts for 90% of the gender difference in HRD. CONCLUSION: We came to a finding that gender-specific HRD tendency is the greatest contributor to gender differences in HRD. Therefore, to effective reduce HRD, it will be necessary to understand gender differences in socioeconomic characteristics between men and women but also take notice of such differences in sociocultural settings as they experience. And it will be also required to prepare any gender-differentiated intervention strategy for men and women.

BACKGROUND: The recently-observed trend towards younger stroke patients in Korea raises economic concerns, including erosion of the workforce. We compared per-person lifetime costs of stroke according to the age of stroke onset from the Korean societal perspective. METHODS: A state-transition Markov model consisted of three health states ('post primary stroke event', 'alive post stroke', and 'dead') was developed to simulate the natural history of stroke. The transition probabilities for fatal and non-fatal recurrent stroke by age and gender and for non-stroke causes of death were derived from the national epidemiologic data of the Korean Health Insurance Review and Assessment Services and data from the Danish Monitoring Trends in Cardiovascular Disease study. We used an incidence-based approach to estimate the long-term costs of stroke. The model captured stroke-related costs including costs within the health sector, patients' out-of-pocket costs outside the health sector, and costs resulting from loss of productivity due to morbidity and premature death using a human capital approach. Average insurance-covered costs occurring within the health sector were estimated from the National Health Insurance claims database. Other costs were estimated based on the national epidemiologic data and literature. All costs are presented in 2008 Korean currency values (Korean won = KRW). RESULTS: The lifetime costs of stroke were estimated to be: 200.7, 81.9, and 16.4 million Korean won (1,200 KRW is approximately equal to one US dollar) for men who suffered a first stroke at age 45, 55 and 65 years, respectively, and 75.7, 39.2, and 19.3 million KRW for women at the same age. While stroke occurring among Koreans aged 45 to 64 years accounted for only 30% of the total disease incidence, this age group incurred 75% of the total national lifetime costs of stroke. CONCLUSIONS: A higher lifetime burden and increasing incidence of stroke among younger Koreans highlight the need for more effective strategies for the prevention and management of stroke especially for people between 40 and 60 years of ages.

UNLABELLED: BACKGROUND STUDIES: on the cost of injury are useful in setting research and policy priorities and it is valuable to observe differences in the economic burden of injuries across countries. OBJECTIVE: To estimate the treated prevalence rate, economic burden and gender- and age-specific costs of injuries in Korea in 2006. DESIGN: Annual direct healthcare costs associated with injuries were estimated from the National Health Insurance, Medical Aid and Automobile Insurance databases. Annual direct non-health costs were estimated for transport and caregiver's costs. Indirect costs were estimated for premature death, absence from work and disability. Costs were adjusted to 2006 levels using the healthcare component of the Consumer Price Index. MAIN OUTCOME MEASURES: Prevalence-based direct costs, incidence-based indirect costs and total costs for injuries, stratified by gender, age group and type of injury. RESULTS: The treated prevalence rate of injury in 2006 of the Korean population was 26.5 per 100, resulting in an annual economic burden of $39837 million ($4703 million in direct and $35134 million in indirect costs). The cost of medical treatment associated with injuries accounted for 9.5% of the total health expenditure in Korea. The cost of premature death was the largest contributor to the total and automobile-related injuries accounted for 30.3% of total costs. CONCLUSIONS: The estimates were considerably understated because they did not include losses in household production and quality of life. Nevertheless, the size and main components of the injury burden were identified; this information should aid decision-making about research priorities and improve monitoring of the effects of policy initiatives.

BACKGROUND: No previous study has explored the interactions between education and lifestyle in relation to obesity. This study hypothesized that education may be obesogenic through its interplay with lifestyle behaviors. METHODS: Data for a nationally representative sample (6937 men and 9333 women) from the Korea National Health and Nutrition Examination Survey (2010-2012) were analyzed. Multivariate logistic regressions were performed for three education levels and six lifestyle behaviors, each of which comprised two groups. RESULTS: Interactions between education and lifestyle behaviors in relation to obesity were observed for all lifestyle behaviors in women (p for interaction <0.001) and for three lifestyle behaviors in men. Education appeared obesogenic for three groups of lifestyle behaviors in men (p for trend <0.05), but was protective against obesity for 11 groups in women. Each one-unit increase in education level in men increased the odds of obesity by 1.29-fold among under-reported energy intake groups (95 % confidence interval: 1.16, 1.44). CONCLUSIONS: Education may be a risk factor for obesity through its interplay with lifestyle behaviors. Further research is required to examine these findings in different socio-cultural settings.