• Journal of the Korea Society for Simulation
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• v.25 no.1
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• pp.29-34
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• 2016

Many researchers have used cohort DB (database) to predict the occurrence of disease or to keep track of disease spread. Cohort DB is Big Data which has simply stored disease and health information as separated DB table sets. To measure the relations between health information, It is necessary to reconstruct cohort DB which follows research purpose. In this paper, XML descriptor, editor has been used to construct ontology-based Big Data cohort DB. Also, we have developed ontology based cohort DB search system to check results of relations between health information. XML editor has used 7 layered Ontology development 101 and OWL API to change cohort DB into ontology-based. Ontology-based cohort DB system can measure the relation of disease and health information and can be used effectively when semantic relations are found. We have developed ontology-based cohort DB search system which can measure the relations between disease and health information. And it is very effective when searched results are semantic relations.

• 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.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.

• 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.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.

• 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.

• 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.

• Therapeutic Science for Rehabilitation
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• v.12 no.1
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• pp.51-63
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• 2023

Objective : This study aimed to analyze the association between lifestyle and medical expenses of older adults with mental illness using claims data. Methods : We conducted secondary data analysis using the older adult cohort database provided by the Korea National Health Insurance Service. The lifestyle and medical expense variables were extracted from the cohort database. We used a generalized linear model to examine the association between lifestyle and medical expenses. Results : In total, 32,853 records were extracted. The results showed that smokers had medical expenses (estimate = -218,255, p = .037). As the number of days of walking increased, medical expenses significantly decreased (estimate = -58,843, p < .0001). Furthermore, as the number of days of drinking decreased, medical expenses increased (estimate = 692,289, p < .0001). Conclusion : This study analyzed the estimates of medical expenses according to lifestyle among older adults with mental illness. Smoking and exercise were negatively associated with medical expenses. These results suggest the importance of a healthy lifestyle for older adults with mental illness. In addition, this study can be used as clinical evidence for lifestyle management programs to improve physical and mental health.

• The Journal of the Korean dental association
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• v.54 no.8
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• pp.604-612
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• 2016

The aim of this study was to evaluate the effect of national health insurance coverage of periodontal scaling using the National Health Insurance Service-National Sample Cohort for 2009-2013. After the enforcement of periodontal scaling covered by national health insurance, the number of patients diagnosed with periodontal disease and received treatment, has increased from 35,234 to 99,576 people in the last 4 years. Further, the number of patients who received the treatment of periodontal disease more than once, have also increased to around 69% in 2013 when compared to 2012. Moreover, the number of patients receiving periodontal scaling has been steadily increasing every year. Among the patients who visited hospital for periodontal disease, there has been an increase of 280%. As a result, continuous public relations and long-term research on the effect of periodontal scaling as a prophylactic treatment is necessary.

• Health Policy and Management
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• v.32 no.2
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• pp.205-215
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• 2022

Background: The purpose of this study is to identify the factors infecting the medical care utilization from a new perspective by newly classifying the categories of administrative districts using the urban decline index and medical vulnerability index as indicators. Methods: This study targeted 150,940 people who used medical services using the 2015 cohort database (DB), 2010-2015 urban regeneration analysis index DB, and 2014-2015 public health and medical statistics DB. The decline of the region was classified using the urban decline index typed using k-means clustering and the medical vulnerability index typed using the quantile score calculation. Regression analysis was performed 3 times with medical expenditure, length of stay, and the number of outpatient visits as dependent variables. Results: There were 37 stable region (47.4%), 29 health vulnerable region (37.2%), and 12 decline region (15.4%). The health vulnerable region had lower medical expenditure, fewer outpatient visits, and a higher length of stay than the stable region. The decline region was all higher than the stable region but had no significant effect. Conclusion: The factors that cause the health disparity between regions are not only factors related to individual health behavior but also environmental factors of the local community. Therefore, there is a need for a systematic alternative that properly considers the resources within the community and reflects the characteristics of the population.