• Journal of Society of Preventive Korean Medicine
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• v.28 no.1
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• pp.69-87
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• 2024

Objectives : This study is to investigate the current National Health Insurance Database cohort studies related to complications of Parkinson's Disease (PD) and suggest the design of Korean medical epidemiological studies of PD. Methods : Nationwide longitudinal studies of PD patients in South Korea were collected through Pubmed and the Korea Citation Index (KCI). We selected cohort studies that used the National Health Insurance Database in Korea and targeted Parkinson's disease patients. Studies published before February 2024 were categorized according to study designs. We examined variables and covariates, enroll dates and matching methods. Results : Of a total of 536 studies, 18 studies met the inclusion criteria. All studies used the National Health Insurance (NHI) Research Database and among them, 5 used sample data and one senior database. Studies can be classified into two types. 11 cohort studies were comparing PD patients and non-PD patients. Another type was 4 PD patients cohort studies. Most studies used two diagnostic codes (G20 and V124) for inclusion criteria. Enroll periods were from 2002 to 2017, and follow-up periods were from 7 to 14 years. 16 studies considered age and sex as covariates. 15 studies used the propensity score matching method to increase the level of causality. There was only one study related to the Korean medical treatment. Conclusion : In future cohort studies on Korean medical treatment, more attempts should be made to reveal the effect of the treatments on PD patients by defining inclusion criteria for patient groups, covariates, exposure variables, and assessment indicators more operatively.

• Journal of Preventive Medicine and Public Health
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• v.55 no.3
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• pp.253-262
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• 2022

We aimed to review the current data composition of the Korean Tuberculosis and Post-Tuberculosis Cohort, which was constructed by linking the Korean Tuberculosis Surveillance System (KNTSS; established and operated by the Korean Disease Control and Prevention Agency since 2000) and the National Health Information Database (NHID; established by the National Health Insurance Service in 2012). The following data were linked: KNTSS data pertaining to patients diagnosed with tuberculosis between 2011 and 2018, NHID data of patients with a history of tuberculosis and related diseases between 2006 and 2018, and data (obtained from the Statistics Korea database) on causes of death. Data from 300 117 tuberculosis patients (177 206 men and 122 911 women) were linked. The rate of treatment success for new cases was highest in 2015 (86.7%), with a gradual decrease thereafter. The treatment success rate for previously treated cases showed an increasing trend until 2014 (79.0%) and decreased thereafter. In total, 53 906 deaths were confirmed among tuberculosis patients included in the cohort. The Korean Tuberculosis and Post-Tuberculosis Cohort can be used to analyze different measurement variables in an integrated manner depending on the data source. Therefore, these cohort data can be used in future epidemiological studies and research on policy-effect analysis, treatment outcome analysis, and health-related behaviors such as treatment discontinuation.

• Journal of Preventive Medicine and Public Health
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• v.40 no.2
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• pp.95-101
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• 2007

Focusing on complex diseases of public health significance, strategic issues regarding the on-going Korean Genome Cohort were reviewed: target size and diseases, measurements, study design issues, and follow-up strategy of the cohort. Considering the epidemiologic characteristics of Korean population as well as strengths and drawbacks of current research environment, we tried to tailor the experience of other existing cohorts into proposals for this Korean study. Currently 100,000 individuals have been participating the new Genome Cohort in Korea. Target size of de novo collection is recommended to be set as between 300,000 to 500,000. This target size would allow acceptable power to detect genetic and environmental factors of moderate effect size and possible interactions between them. Family units and/or special subgroups are recommended to parallel main body of adult individuals to increase the overall efficiency of the study. Given that response rate to the conventional re-contact method may not be satisfactory, successful follow-up is the main key to the achievement of the Korean Genome Cohort. Access to the central database such as National Health Insurance data can provide enormous potential for near-complete case detection. Efforts to build consensus amongst scientists from broad fields and stakeholders are crucial to unleash the centralized database as well as to refine the commitment of this national project.

• 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.

• Journal of Cancer Prevention
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• v.23 no.4
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• pp.183-190
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• 2018

Background: As the number of big-cohort studies increases, validation becomes increasingly more important. We aimed to validate administrative database categorized as colorectal cancer (CRC) by the International Classification of Disease (ICD) 10th code. Methods: Big-cohort was collected from Clinical Data Warehouse using ICD 10th codes from May 1, 2003 to November 30, 2016 at Seoul National University Bundang Hospital. The patients in the study group had been diagnosed with cancer and were recorded in the ICD 10th code of CRC by the National Health Insurance Service. Subjects with codes of inflammatory bowel disease or tuberculosis colitis were selected for the control group. For the accuracy of registered CRC codes (C18-21), the chart, imaging results, and pathologic findings were examined by two reviewers. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for CRC were calculated. Results: A total of 6,780 subjects with CRC and 1,899 control subjects were enrolled. Of these patients, 22 subjects did not have evidence of CRC by colonoscopy, computed tomography, magnetic resonance imaging, or positron emission tomography. The sensitivity and specificity of hospitalization data for identifying CRC were 100.00% and 98.86%, respectively. PPV and NPV were 99.68% and 100.00%, respectively. Conclusions: The big-cohort database using the ICD 10th code for CRC appears to be accurate.

• Nutritional Sciences
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• v.7 no.1
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• pp.53-58
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• 2004

The Multiethnic Cohort Study was designed to study prospectively the relationship of diet and other lifestyle factors to the risk of cancer. The cohort was established in 1993-1996 in Hawaii and California (primarily Los Angeles) and included a representative sample of more than 215,000 men and women primarily from five different ethnic groups: Japanese, whites, Native Hawaiians, Latinos, and African- Americans. Because of the emphasis on diet, great attention was paid to developing and pre-testing a self-administered quantitative food frequency questionnaire that would adequately assess food and nutrient intakes in these groups. An extensive food composition database was also created for the study. In addition, during data collection, a calibration study was conducted that makes possible adjustment for measurement error in nutrient intakes and valid comparison of intakes across the several ethnic groups. At the present time, blood and urine specimens are being collected from cohort participants and should yield a biorepository of more than 80,000 subjects. Baseline data indicate that the cohort is well representative of the general population of Hawaii and California, so that results can be generalized. These data also show a wide range in dietary intakes and in other lifestyle variables that should facilitate the testing of etiologic hypotheses.

• Journal of Preventive Medicine and Public Health
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• v.53 no.5
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• pp.313-322
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• 2020

Objectives: This study was conducted to determine the incidence and risk factors of myocardial infarction (MI) and stroke in farmers compared to the general population and to establish 5-year prediction models. Methods: The farmer cohort and the control cohort were generated using the customized database of the National Health Insurance Service of Korea database and the National Sample Cohort, respectively. The participants were followed from the day of the index general health examination until the events of MI, stroke, or death (up to 5 years). Results: In total, 734 744 participants from the farmer cohort and 238 311 from the control cohort aged between 40 and 70 were included. The age-adjusted incidence of MI was 0.766 and 0.585 per 1000 person-years in the farmer and control cohorts, respectively. That of stroke was 0.559 and 0.321 per 1000 person-years in both cohorts, respectively. In farmers, the risk factors for MI included male sex, age, personal history of hypertension, diabetes, current smoking, creatinine, metabolic syndrome components (blood pressure, triglycerides, and high-density lipoprotein cholesterol). Those for stroke included male sex, age, personal history of hypertension, diabetes, current smoking, high γ-glutamyl transferase, and metabolic syndrome components (blood pressure, triglycerides, and high-density lipoprotein cholesterol). The prediction model showed an area under the receiver operating characteristic curve of 0.735 and 0.760 for MI and stroke, respectively, in the farmer cohort. Conclusions: Farmers had a higher age-adjusted incidence of MI and stroke. They also showed distinct patterns in cardiovascular risk factors compared to the general population.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.10
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• pp.6185-6187
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• 2013

Cervical cancer has emerged as a major public health problem in Lucknow and New York in the $21^{st}$ century. Cancer genetic studies are essential to identify/stratify disease-susceptible individuals in a population-based cohort. Sample size homogeneity and North Indian caste in female urology genetic-studies are significant issues in meaningful interpretation of data. A review of scientific literature using Pubmed database was conducted, including an assessment of cervical cancer genetic studies conducted as part of the author's doctoral dissertation at a premier Lucknow-based medical research Institute. Sample size numbers and caste criteria in the North Indian cohort ($N{\leq}400$ subjects) were evaluated with homogeneity in the sample cohort data set(s). Subgroup caste-stratification of North Indian cohort is equally essential, for instance, Brahmin (e.g. Pandey), Vaishya (e.g. Mittal), Rajput (e.g. Singh) and Kshudra (e.g. Yadav) during the conception and design of genetics-based studies. Sample size homogeneity in histopathologically confirmed case and control numbers and caste-based stratification in a North Indian cohort is essential in single nucleotide polymorphism (SNP) studies in cervical cancer susceptible populations to draw more definitive conclusions.

• Korean Medical Education Review
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• v.25 no.2
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• pp.126-131
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• 2023

The aim of this study was to systematically collect data for evaluating short- and long-term outcomes using Kirkpatrick's four-level evaluation model, Chonnam National Medical School has established plans for developing and managing a database of student and graduate cohorts. The Education Evaluation Committee, with assistance from the Medical Education Office, manages the development and maintenance of cohort data. Data collection began in the 2022 academic year with first- through fourth-year medical students and graduates of the year 2022. The collected data include sociodemographic characteristics, admission information, psychological test results, academic performance data, extracurricular activity data, scholarship records, national medical licensing exam results, and post-graduation career paths. The Education Evaluation Committee and the Medical Education Office analyze the annually updated student and graduate cohort data and report the results to the dean and relevant committees. These results are used for admissions processes, curriculum improvement, and the development of educational programs. Applicants interested in using the student and graduate cohort data to evaluate the curriculum or conduct academic research must undergo review by the Educational Evaluation Committee before being granted access to the data. It is expected that the collected data from student and graduate cohorts will provide a sound and scientific basis for evaluating short- and long-term achievements based on student, school, and other characteristics, thereby supporting medical education policies, innovation, and implementation.