Diabetes Metab J.  2022 Jul;46(4):552-563. 10.4093/dmj.2022.0193.

Current Trends of Big Data Research Using the Korean National Health Information Database

Affiliations
  • 1Division of Endocrinology and Metabolism, Department of Internal Medicine, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
  • 2Department of Statistics and Actuarial Science, Soongsil University, Seoul, Korea
  • 3Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
  • 4Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea

Abstract

Recently, medical research using big data has become very popular, and its value has become increasingly recognized. The Korean National Health Information Database (NHID) is representative of big data that combines information obtained from the National Health Insurance Service collected for claims and reimbursement of health care services and results obtained from general health examinations provided to all Korean adults. This database has several strengths and limitations. Given the large size, various laboratory data, and questionnaires obtained from medical check-ups, their longitudinal nature, and long-term accumulation of data since 2002, carefully designed studies may provide valuable information that is difficult to obtain from other forms of research. However, consideration of possible bias and careful interpretation when defining causal relationships is also important because the data were not collected for research purposes. After the NHID became publicly available, research and publications based on this database have increased explosively, especially in the field of diabetes and metabolism. This article reviews the history, structure, and characteristics of the Korean NHID. Recent trends in big data research using this database, commonly used operational diagnosis, and representative studies have been introduced. We expect further progress and expansion of big data research using the Korean NHID.

Keyword

Database; Diabetes mellitus; Korea; Metabolism; National health programs

Figure

  • Fig. 1 Operational structure of National Health Insurance System (NHIS). Reproduced from Kim et al. [4]. HIRA, Health Insurance Review & Assessment Service.

  • Fig. 2 The number of publications using National Health Information database from 2008 to 2021.


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Reference

1. National Health Insurance Service. 2020. National Health Insurance statistical yearbook. Available from: https://www.nhis.or.kr/nhis/together/wbhaec06300m01.do?mode=view&articleNo=10812384&article.offset=0&articleLimit=10(updated 2021 Nov 5).
2. National Health Insurance Service. 2020. National Health Screening statistical yearbook. Available from: https://www.nhis.or.kr/nhis/together/wbhaec07000m01.do?mode=view&articleNo=10813922&article.offset=0&articleLimit=10(updated 2021 Dec 30).
3. National Health Insurance Sharing Service. Introduction. Available from: https://nhiss.nhis.or.kr/bd/ab/bdaba012eng.do(cited 2022 Jul 5).
4. Kim HK, Song SO, Noh J, Jeong IK, Lee BW. Data configuration and publication trends for the Korean National Health Insurance and Health Insurance Review & Assessment Database. Diabetes Metab J. 2020; 44:671–8.
Article
5. Choi EK. Cardiovascular research using the Korean National Health Information Database. Korean Circ J. 2020; 50:754–72.
Article
6. Korean Statistical Information Service. Health examination statistics. Available from: https://kosis.kr/statisticsList/statisticsListIndex.do?menuId=M_01_01&vwcd=MT_ZTITLE&parmTabId=M_01_01&outLink=Y&entrType=#content-group(cited 2022 Jul 5).
7. National Health Insurance Sharing Service. Sample cohort 2.2 database user manual. Available from: https://nhiss.nhis.or.kr/bd/ab/bdaba002cv.do(cited 2022 Jul 5).
8. Lee J, Lee JS, Park SH, Shin SA, Kim K. Cohort profile: The National Health Insurance Service-National Sample Cohort (NHIS-NSC), South Korea. Int J Epidemiol. 2017; 46:e15.
Article
9. Bahk J, Kim YY, Kang HY, Lee J, Kim I, Lee J, et al. Using the National Health Information Database of the National Health Insurance Service in Korea for monitoring mortality and life expectancy at national and local levels. J Korean Med Sci. 2017; 32:1764–70.
Article
10. Kim YI, Kim YY, Yoon JL, Won CW, Ha S, Cho KD, et al. Cohort profile: National Health Insurance Service-Senior (NHIS-Senior) cohort in Korea. BMJ Open. 2019; 9:e024344.
Article
11. Hong S, Kim KS, Han K, Park CY. Acromegaly and cardiovascular outcomes: a cohort study. Eur Heart J. 2022; 43:1491–9.
Article
12. Kyoung DS, Kim HS. Understanding and utilizing claim data from the Korean National Health Insurance Service (NHIS) and Health Insurance Review & Assessment (HIRA) Database for research. J Lipid Atheroscler. 2022; 11:103–10.
Article
13. Lee YH, Han K, Ko SH, Ko KS, Lee KU; Taskforce Team of Diabetes Fact Sheet of the Korean Diabetes Association. Data analytic process of a nationwide population-based study using National Health Information Database established by National Health Insurance Service. Diabetes Metab J. 2016; 40:79–82.
Article
14. Lee EY, Han K, Kim DH, Park YM, Kwon HS, Yoon KH, et al. Exposure-weighted scoring for metabolic syndrome and the risk of myocardial infarction and stroke: a nationwide population-based study. Cardiovasc Diabetol. 2020; 19:153.
Article
15. Kim MK, Han K, Kim HS, Park YM, Kwon HS, Yoon KH, et al. Cholesterol variability and the risk of mortality, myocardial infarction, and stroke: a nationwide population-based study. Eur Heart J. 2017; 38:3560–6.
Article
16. Lee HJ, Choi EK, Han KD, Lee E, Moon I, Lee SR, et al. Bodyweight fluctuation is associated with increased risk of incident atrial fibrillation. Heart Rhythm. 2020; 17:365–71.
Article
17. Cho SM, Lee H, Lee HH, Baek J, Heo JE, Joo HJ, et al. Dyslipidemia fact sheets in Korea 2020: an analysis of nationwide population-based data. J Lipid Atheroscler. 2021; 10:202–9.
Article
18. Kim MK, Han K, Koh ES, Kim ES, Lee MK, Nam GE, et al. Blood pressure and development of cardiovascular disease in Koreans with type 2 diabetes mellitus. Hypertension. 2019; 73:319–26.
Article
19. Cho Y, Han K, Kim DH, Park YM, Yoon KH, Kim MK, et al. Cumulative exposure to metabolic syndrome components and the risk of dementia: a nationwide population-based study. Endocrinol Metab (Seoul). 2021; 36:424–35.
Article
20. Lee SR, Choi EK, Kwon S, Jung JH, Han KD, Cha MJ, et al. Oral anticoagulation in Asian patients with atrial fibrillation and a history of intracranial hemorrhage. Stroke. 2020; 51:416–23.
Article
21. Lee E, Choi EK, Han KD, Lee H, Choe WS, Lee SR, et al. Mortality and causes of death in patients with atrial fibrillation: a nationwide population-based study. PLoS One. 2018; 13:e0209687.
Article
22. Kim HC, Lee H, Lee HH, Seo E, Kim E, Han J, et al. Korea hypertension fact sheet 2021: analysis of nationwide population-based data with special focus on hypertension in women. Clin Hypertens. 2022; 28:1.
Article
23. Lee SH, Han K, Kwon HS, Kim MK. Frequency of exposure to impaired fasting glucose and risk of mortality and cardiovascular outcomes. Endocrinol Metab (Seoul). 2021; 36:1007–15.
Article
24. Lee SH, Han K, Kim HS, Cho JH, Yoon KH, Kim MK. Predicting the development of myocardial infarction in middle-aged adults with type 2 diabetes: a risk model generated from a nationwide population-based cohort study in Korea. Endocrinol Metab (Seoul). 2020; 35:636–46.
Article
25. Kim JY, Kang K, Kang J, Koo J, Kim DH, Kim BJ, et al. Executive summary of stroke statistics in Korea 2018: a report from the Epidemiology Research Council of the Korean Stroke Society. J Stroke. 2019; 21:42–59.
Article
26. Kim MK, Han K, Park YM, Kwon HS, Kang G, Yoon KH, et al. Associations of variability in blood pressure, glucose and cholesterol concentrations, and body mass index with mortality and cardiovascular outcomes in the general population. Circulation. 2018; 138:2627–37.
Article
27. Kim MK, Han K, Cho JH, Kwon HS, Yoon KH, Lee SH. A model to predict risk of stroke in middle-aged adults with type 2 diabetes generated from a nationwide population-based cohort study in Korea. Diabetes Res Clin Pract. 2020; 163:108157.
Article
28. Park J, Kwon S, Choi EK, Choi YJ, Lee E, Choe W, et al. Validation of diagnostic codes of major clinical outcomes in a National Health Insurance database. Int J Arrhythm. 2019; 20:5.
Article
29. Lee HJ, Kim HK, Han KD, Lee KN, Park JB, Lee H, et al. Age-dependent associations of body mass index with myocardial infarction, heart failure, and mortality in over 9 million Koreans. Eur J Prev Cardiol. 2022. May. 17. [Epub]. https://doi.org/10.1093/eurjpc/zwac094 .
Article
30. Ahn HJ, Lee SR, Choi EK, Han KD, Jung JH, Lim JH, et al. Association between exercise habits and stroke, heart failure, and mortality in Korean patients with incident atrial fibrillation: a nationwide population-based cohort study. PLoS Med. 2021; 18:e1003659.
Article
31. Han SJ, Ha KH, Lee N, Kim DJ. Effectiveness and safety of sodium-glucose co-transporter-2 inhibitors compared with dipeptidyl peptidase-4 inhibitors in older adults with type 2 diabetes: a nationwide population-based study. Diabetes Obes Metab. 2021; 23:682–91.
Article
32. Bae EH, Lim SY, Jung JH, Oh TR, Choi HS, Kim CS, et al. Chronic kidney disease risk of isolated systolic or diastolic hypertension in young adults: a nationwide sample based-cohort study. J Am Heart Assoc. 2021; 10:e019764.
Article
33. Koh ES, Han KD, Kim MK, Kim ES, Lee MK, Nam GE, et al. Changes in metabolic syndrome status affect the incidence of end-stage renal disease in the general population: a nationwide cohort study. Sci Rep. 2021; 11:1957.
Article
34. Bae JH, Han KD, Ko SH, Yang YS, Choi JH, Choi KM, et al. Diabetes fact sheet in Korea 2021. Diabetes Metab J. 2022; 46:417–26.
Article
35. Kim KS, Hong S, Han K, Park CY. The clinical characteristics of gestational diabetes mellitus in Korea: a National Health Information Database Study. Endocrinol Metab (Seoul). 2021; 36:628–36.
Article
36. Kim MK, Han K, Joung HN, Baek KH, Song KH, Kwon HS. Cholesterol levels and development of cardiovascular disease in Koreans with type 2 diabetes mellitus and without pre-existing cardiovascular disease. Cardiovasc Diabetol. 2019; 18:139.
Article
37. Son JS, Choi S, Kim K, Kim SM, Choi D, Lee G, et al. Association of blood pressure classification in Korean young adults according to the 2017 American College of Cardiology/American Heart Association guidelines with subsequent cardiovascular disease events. JAMA. 2018; 320:1783–92.
Article
38. Kohsaka S, Lam CS, Kim DJ, Cavender MA, Norhammar A, Jorgensen ME, et al. Risk of cardiovascular events and death associated with initiation of SGLT2 inhibitors compared with DPP-4 inhibitors: an analysis from the CVD-REAL 2 multinational cohort study. Lancet Diabetes Endocrinol. 2020; 8:606–15.
39. Gerstein HC. Patient data from routinely collected medical records complement evidence from SGLT2 inhibitor outcome trials. Lancet Diabetes Endocrinol. 2020; 8:557–8.
Article
40. Kim NH, Kim SG. Fibrates revisited: potential role in cardiovascular risk reduction. Diabetes Metab J. 2020; 44:213–21.
Article
41. Kim NH, Han KH, Choi J, Lee J, Kim SG. Use of fenofibrate on cardiovascular outcomes in statin users with metabolic syndrome: propensity matched cohort study. BMJ. 2019; 366:l5125.
Article
42. Park S, Lee S, Kim Y, Lee Y, Kang MW, Han K, et al. Altered risk for cardiovascular events with changes in the metabolic syndrome status: a nationwide population-based study of approximately 10 million persons. Ann Intern Med. 2019; 171:875–84.
Article
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