• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1329-1346
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• 2016

Korea Health Panel (KHP) data and Korea National Health and Nutrition Examination Survey (KNHANES) data are collected by self-assess and self-report for individual's health status and medical use. Previous studies have claimed that the reliability for prevalence rates and health life expectancies obtained from these data should be validated. National Health Insurance Services in Korea recently released a sample cohort DB that contain all data related to the use of medical facilities for all entire Korea citizens. It has been shown that disease-specific prevalence rates calculated from these data are representative and reliable for the entire population. In this paper, we evaluate the reliability of prevalence rates derived from self-reported data such as KHP and KNHANES by comparing to the prevalence rates from the sample cohort DB. We found that both KHP and KNHANES underestimate prevalence rates and in turn overestimate health life expectancies. Moreover, the general trends of health life expectancies might be distorted (except for the sample cohort DB) because of sampling and non-sampling errors.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.13-24
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• 2017

The Sample Cohort DB supplied by the National Health Insurance Service is a valuable resource for statistical studies as well as for health and medical studies. It takes significant time and effort to extract data from this Cohort DB having a large size. As such, we introduce a database system, conveniently called the National Health Insurance Service Cohort DB Extract Tool (NICE Tool), which supports several useful operations for effectively and efficiently managing the Cohort DB. For example, researchers can extract variables and cases related with study by simply clicking a computer mouse without any prior knowledge regarding SAS DATA step or SQL. We expect that NICE Tool will facilitate the faster extraction of data and eventually lead to the active use of the Cohort DB for research purposes.

• The Korean Journal of Applied Statistics
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• v.28 no.4
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• pp.663-683
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• 2015

The change of the population pyramid due to low fertility and rapid aging threatens the financial sustainability of National Health Insurance. We construct statistical models for prevalence rates and medical expenses using National Health Insurance Service (NHIS) sample cohort data from 2002-2013. We then project yearly expenditures and income of national health insurance until 2060 that considers various assumptions in regards to future population structure and economic conditions. We adopt a VECM-LC model for prevalence rates and the double exponentially smoothing method for the per capita co-payment of healthcare expense (in which the two models are institution-disease-sex-age specific) to project of national health insurance expenditures. We accommodate various assumptions of economic situations provided by the national assembly and government to produce a financial projection for national health insurance. Two assumptions of dependents ratios are used for the projection of national health insurance income to conduct two future population structures by the two assumptions of aging progresses and various assumptions on economic circumstances as in the expenditure projection. The health care deficit is projected to be 20-30 trillion won by 2030 and 40-70 trillion won by 2060 in 2015 constant price.

• Journal of the Economic Geographical Society of Korea
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• v.21 no.1
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• pp.1-19
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• 2018

This study aims to analyze the regional patterns of user-provider in medical services for spatial improvement of national health care delivery system in south Korea. By using the National Health Insurance Service National Sample Cohort DB, the characteristics and changes in the medical service cases served outside of residing regions are found. In 2013 the rate of the cases served outside of residing regions was declined slightly and users of medical institutions outside of their residing area tended to have high income level relatively and to visit hospitals than clinics. And departments visited have changed from 2002.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.239-256
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• 2020

Health is an important issue due to increased life expectancy. As a result, the demand for industry and services associated with individual health, health-related programs and services will be facilitated by a method to evaluate and classify the health level of an individual based on various factors. This study suggests a methodology to measure and score an individual health level. A credit scoring model was introduced to implement the categorization of variables, construct a prediction model, and to score individual health level. Cohort DB provided by National Health Insurance Service was used to illustrate overall procedures. It is expected that the suggested model can be utilized in designing and managing health care services as well as other health-related programs.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1027-1041
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• 2017

Hyperlipidemia, the status of blood with high level of low-density lipoprotein cholesterol (LDL-C), is known as a main cause of coronary artery diseases such as myocardiac infarction or brain infarct. Statin is the representative prescription to hyperlipidemia and the effects of it depend on the patient's individual conditions such as health-caring habits or adherence to medication. The main effect of statin is reducing LDL-C, which should reach the target range based on National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) guideline. In this research, the reduction of LDL-C and attainment to patient's target range are considered effects of statin. The association between factors - individual conditions and adherence to medication of patients - and the effects of statin is analyzed with National Health Insurance Service-National Sample Cohort (NHIS-NSC).

• Economic Analysis
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• v.26 no.3
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• pp.48-70
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• 2020

Using data from the Household Income and Expenditure Survey over the period 1994-2016, we estimate the coefficient of relative prudence in order to capture precautionary saving motive. To do this, we adopt a cohort approach, where we transform such microdata into sample cohort means. Together with initial income involving liquidity constraint, we estimate the relative prudence derived from the Euler equation. The two-stage least-squares (2SLS) between estimate of it obtained from the cohort panel data analysis is too small for the existence of precautionary saving motive, as in previous studies, while the 2SLS random effects estimate is so reasonable. Moreover, the liquidity-constrained cohorts tend to be more sensitive to uncertainty, relative to the unconstrained ones.

• Journal of Korean society of Dental Hygiene
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• v.19 no.3
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• pp.409-419
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• 2019

Objectives: The purpose of this study was to analyze the factors affecting antibiotic prescription in dental outpatients. Methods: The present study was conducted using data from the National Health Insurance Service - National Sample Cohort. We analyzed prescriptions issued in the dental outpatient department in 2015, for adults over 19 years of age. Antibiotic prescription rates and mean prescription days were analyzed by sex, age, insurance type, presence of diabetes mellitus and hypertension, season in treatment, type of dental institution, and location of dental institution. Multivariate logistic regression was also performed to analyze the factors affecting antibiotic prescription in dental outpatients. Results: A total of 257,038 prescriptions were analyzed. The mean prescription days of antibiotics in dental outpatients were $3.04{\pm}1.08days$, and the prescription rate was 93.0%. Two variables (presence of diabetes mellitus and insurance type) were excluded from the multivariate logistic regression analysis model because they did not significantly affect antibiotic prescription. The possibility of antibiotic prescription was higher in men ${\geq}61years$ of age and those with hypertension. Furthermore, antibiotics were most frequently prescribed in dental clinics rather than dental hospitals, and more frequently in Busan compared to other areas (p<0.001). Conclusions: Several factors were determined to affect antibiotic prescription, and detailed guidelines for consistent antibiotic prescription are needed.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.475-486
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• 2017

This paper estimates the health life expectancies for Korean people based on a sample cohort database collected through objective measurements by the National Health Insurance Service. Health life expectancy is estimated using the single-state approach of Sullivan (1971). The 9-order correction factor method of Greville (1945) and Brass-logit model of Brass (1971) are also adopted for unobserved or incompletely observed age-specific morbidity and mortality. Based on the mortality and morbidity estimated from sample cohort DB, men and women in Korea are expected to live a 'healthy life' for 61 and 60 years in 2013, respectively, whereas life expectancies of men and women are 80 and 87, respectively. We also estimate certain disease-free life expectancies for each of genders, income levels, and types of insurance from 2003 to 2013 in Korea. We found that there exists an inequality of healthy life expectancy in Korea for different genders, income levels, and types of insurance.

• Journal of Korean Dental Hygiene Science
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• v.7 no.1
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• pp.53-68
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• 2024

Background: Research on the association between renal disease and periodontal conditions has yet to yield definitive results. In this study, we analyzed whether periodontal disease increases the risk of developing renal disease using Korean national cohort data over a period of 11 years. Methods: From 2002 to 2015, a retrospective follow-up investigation was conducted on the 203,538 Korean population using the National Health Insurance Service-National Sample Cohort. Periodontal disease and renal disease were identified through diagnoses using the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) codes. The assessment of periodontal status involved considering the number of dental visits related to periodontal disease during the baseline 3-year period. Results: During the 11-year follow-up period, renal disease occurred in 19,868 out of the total 203,538 individuals. After adjusting for age, gender, income, smoking, drinking, physical activity, diabetes, hypertension, obesity, hypercholesterolemia, ischemic heart disease, and advanced periodontal treatment, periodontal disease increased the risk of renal disease occurrence by 1.04 times (adjusted hazard ratio [aHR] = 1.04, 95% CI = 1.01 to 1.08). Additionally, a higher frequency of dental visits attributed to periodontal disease was associated with an increased risk of renal disease,exhibiting a dose-response trend (aHR = 1.02, 95% CI = 1.00 to 1.06 for once; aHR = 1.08, 95% CI = 1.04 to 1.13 for two times; aHR = 1.11, 95% CI = 1.03 to 1.21 for three times). Conclusions: Our data confirmed that periodontal disease is associated witha higher incidence of renal disease.