OBJECTIVES: The aims of this paper were to develop the composite deprivation index (CDI) for the sub-district (Eup-Myen-Dong) levels based on the theory of social exclusion and to explore the relationship between the CDI and the standardized mortality ratio (SMR). METHODS: The paper calculated the age adjusted SMR and we included five dimensions of social exclusion for CDI; unemployment, poverty, housing, labor and social network. The proxy variables of the five dimensions were the proportion of unemployed males, the percent of recipients receiving National Basic Livelihood Security Act benefits, the proportion of households under the minimum housing standard, the proportion of people with a low social class and the proportion of single-parent household. All the variables were standardized using geometric transformation and then we summed up them for a single index. The paper utilized the 2004-2006 National Death Registry data, the 2003-2006 national residents' registration data, the 2005 Population Census data and the 2005-2006 means-tested benefit recipients' data. RESULTS: The figures were 115.6, 105.8 and 105.1 for the CDI of metropolitan areas (big cities), middle size cities and rural areas, respectively. The distributional variation of the CDI was the highest in metropolitan areas (8.9 - 353.7) and the lowest was in the rural areas (26.8 - 209.7). The extent and relative differences of deprivation increased with urbanization. Compared to the Townsend and Carstairs index, the CDI better represented the characteristics of rural deprivation. The correlation with the SMR was statistically significant and the direction of the CDI effects on the SMR was in accordance with that of the previous studies. CONCLUSIONS: The study findings indicated mortality inequalities due to the difference in the CDI. Despite the attempt to improve deprivation measures, further research is warranted for the consensus development of a deprivation index.
Humans ; Logistic Models ; Male ; *Mortality ; *Prejudice ; *Psychosocial Deprivation ; Republic of Korea ; Social Class ; Social Justice ; Socioeconomic Factors ; Statistics as Topic

OBJECTIVES: This study estimated the annual socioeconomic costs of food-borne disease in 2008 from a societal perspective and using a cost-of-illness method. METHODS: Our model employed a comprehensive set of diagnostic disease codes to define food-borne diseases with using the Korea National Health Insurance (KNHI) reimbursement data. This study classified the food borne illness as three types of symptoms according to the severity of the illness: mild, moderate, severe. In addition to the traditional method of assessing the cost-of-illness, the study included measures to account for the lost quality of life. We estimated the cost of the lost quality of life using quality-adjusted life years and a visual analog scale. The direct cost included medical and medication costs, and the non-medical costs included transportation costs, caregiver's cost and administration costs. The lost productivity costs included lost workdays due to illness and lost earnings due to premature death. RESULTS: The study found the estimated annual socioeconomic costs of food-borne disease in 2008 were 954.9 billion won (735.3 billion won-996.9 billion won). The medical cost was 73.4 - 76.8% of the cost, the lost productivity cost was 22.6% and the cost of the lost quality of life was 26.0%. CONCLUSIONS: Most of the cost-of-illness studies are known to have underestimated the actual socioeconomic costs of the subjects, and these studies excluded many important social costs, such as the value of pain, suffering and functional disability. The study addressed the uncertainty related to estimating the socioeconomic costs of food-borne disease as well as the updated cost estimates. Our estimates could contribute to develop and evaluate policies for food-borne disease.
*Cost of Illness ; Costs and Cost Analysis ; Efficiency ; Foodborne Diseases/*economics ; Health Services/economics/utilization ; Humans ; *Models, Economic ; *Quality-Adjusted Life Years ; Republic of Korea ; Severity of Illness Index ; Socioeconomic Factors

BACKGROUND: Antibiotic resistance has been becoming serious challenge to human beings. Overuse of antibiotics, especially, for infants is concerned, but studies are very few for the prescribing pattern of antibiotic use for infants. This study analyzes prescribing patterns of antibiotics in outpatients of preschool children with acute respiratory tract infections in South Korea. METHODS: Data are used from 2011 Health Insurance Review & Assessment Services-pediatric patients sample. Inclusion criteria is outpatient children (0 to 5 years) with top five frequent diseases. Prescription rates are analyzed by types of disease, provider, specialty, region, and ages. Binary or multinomial logit models are used to analyze determinants of providers' prescription pattern. RESULTS: The main findings are as follows. First, distributions of prescription rates are shown as L-shape or M-shape depending on the types of disease. Second, the prescription variation is so large among providers, where providers are polarized as a group with low prescription rates and the other group with high prescription rates, though the shapes are shown diversified across types of disease. Third, prescription rates appear to be lower in pediatrics and higher in ENT (ear-nose-throat). Fourth, broad spectrum antibiotics are widely used among children. Finally, the logit analysis shows similar results with descriptive statistics, but partly different results across types of disease. CONCLUSION: Antibiotics for respiratory tract infections of infants are used excessively with a large variation among providers, and especially broad spectrum antibiotics are used. The prescription guideline for antibiotics should be provided for each specific disease to reduce antibiotic resistance in the future.
Anti-Bacterial Agents ; Child ; Child, Preschool ; Drug Resistance, Microbial ; Humans ; Infant* ; Insurance, Health ; Korea ; Logistic Models ; Outpatients* ; Pediatrics ; Prescriptions* ; Respiratory System* ; Respiratory Tract Infections*