Rheumatology International

, Volume 33, Issue 12, pp 2985–2992 | Cite as

Development of an algorithm for identifying rheumatoid arthritis in the Korean National Health Insurance claims database

  • Soo-Kyung Cho
  • Yoon-Kyoung Sung
  • Chan-Bum Choi
  • Jeong-Mi Kwon
  • Eui-Kyung Lee
  • Sang-Cheol Bae
Original Article


This study aimed to develop an identification algorithm for validating the International Classification of Diseases-Tenth diagnostic codes for rheumatoid arthritis (RA) in the Korean National Health Insurance (NHI) claims database. An individual copayment beneficiaries program for rare and intractable diseases, including seropositive RA (M05), began in South Korea in July 2009. Patients registered in this system pay only 10 % of their total medical costs, but registration requires an official report from a doctor documenting that the patient fulfills the 1987 ACR criteria. We regarded patients registered in this system as gold standard RA and examined the validity of several algorithms to define RA diagnosis using diagnostic codes and prescription data. We constructed nine algorithms using two highly specific prescriptions (positive predictive value >90 % and specificity >90 %) and one prescription with high sensitivity (>80 %) and accuracy (>75 %). A total of 59,823 RA patients were included in this validation study. Among them, 50,082 (83.7 %) were registered in the individual copayment beneficiaries program and considered true RA. We tested nine algorithms that incorporated two specific regimens [biologics and leflunomide alone, methotrexate plus leflunomide, or more than 3 disease-modifying anti-rheumatic drugs (DMARDs)] and one sensitive drug (any non-steroidal anti-inflammatory drug (NSAID), any DMARD, or any NSAID plus any DMARD). The algorithm that included biologics, more than 3 DMARDs, and any DMARD yielded the highest accuracy (91.4 %). Patients with RA diagnostic codes with prescription of biologics or any DMARD can be considered as accurate cases of RA in Korean NHI claims database.


Rheumatoid arthritis Algorithm Validation study Claims data 



This study was supported by the Korea Healthcare Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (A102065).

Conflict of interest



  1. 1.
    Doran MF, Pond GR, Crowson CS, O’Fallon WM, Gabriel SE (2002) Trends in incidence and mortality in rheumatoid arthritis in Rochester, Minnesota, over a forty-year period. Arthritis Rheum 46:625–631PubMedCrossRefGoogle Scholar
  2. 2.
    Cooper NJ (2000) Economic burden of rheumatoid arthritis: a systematic review. Rheumatology 39:28–33PubMedCrossRefGoogle Scholar
  3. 3.
    Yoon SJ, Bae SC, Lee SI, Chang H, Jo HS, Sung JH, Park JH, Lee JY, Shin Y (2007) Measuring the burden of the disease in Korea. J Korean Med Sci 22:518–523PubMedCrossRefGoogle Scholar
  4. 4.
    National Health Insurance Corporation: National Health Insurance Statistics.
  5. 5.
    Kim JY, Kim HY, Im JH (2007) Development of risk adjustment and prediction methods for care episodes using National Health Insurance Database. Health Insurance Review & Assessment Service, SeoulGoogle Scholar
  6. 6.
    Ko MJ, Han JT, Lee AK, Kim MK, Park SH (2008) Health checkup examinees cohort establishment: the development of assessment. National Health Insurance Corporation, Seoul.
  7. 7.
    Park BJ, Sung JH, Park KD, Seo SW, Kim SW (2003) Improving the validity of health insurance Disease code and the utilization of health insurance claim data. Seoul National University College of Medicine, Seoul, pp 19–30Google Scholar
  8. 8.
    Losina E, Barrett J, Baron JA, Katz JN (2003) Accuracy of Medicare claims data for rheumatologic diagnoses in total hip replacement recipients. J Clin Epidemiol 56:515–519PubMedCrossRefGoogle Scholar
  9. 9.
    Katz J, Barrett J, Liang M, Bacon AM, Kaplan H, Kieval RI, Lindsey SM, Roberts WN, Sheff DM, Spencer RT, Weaver AL, Baron JA (1997) Sensitivity and positive predictive value of Medicare Part B physician claims for rheumatologic diagnoses and procedures. Arthritis Rheum 40:1594–1600PubMedCrossRefGoogle Scholar
  10. 10.
    Gabriel S (1994) The sensitivity and specificity of computerized databases for the diagnosis of rheumatoid arthritis. Arthritis Rheum 37:821–823PubMedCrossRefGoogle Scholar
  11. 11.
    Singh J, Holmgren A, Noorbaloochi S (2004) Accuracy of Veterans Administration databases for a diagnosis of rheumatoid arthritis. Arthritis Rheum 51:952–957PubMedCrossRefGoogle Scholar
  12. 12.
    Kim YJ, Choi CB, Sung YK, Lee H, Bae SC (2009) Characteristics of Korean Patients with RA: A Single Center Cohort Study. J Korean Rheum Assoc 16:204–212CrossRefGoogle Scholar
  13. 13.
    Sung YK, Cho SK, Choi CB et al (2012) Korean Observational Study Network for Arthritis (KORONA): establishment of a prospective multicenter cohort for rheumatoid arthritis in South Korea. Semin Arthritis Rheum 41:745–751PubMedCrossRefGoogle Scholar
  14. 14.
    Orlewska E, Ancuta I, Anic B et al (2011) Access to biologic treatment for rheumatoid arthritis in Central and Eastern European (CEE) countries. Med Sci Monit 17:SR1–SR13PubMedCrossRefGoogle Scholar
  15. 15.
    Kim SY, Servi A, Polinski JM, Mogun H, Weinblatt ME, Katz JN, Solomon DH (2011) Validation of rheumatoid arthritis diagnoses in health care utilization data. Arthr Res Ther 13(1):R32CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Soo-Kyung Cho
    • 1
  • Yoon-Kyoung Sung
    • 1
  • Chan-Bum Choi
    • 1
  • Jeong-Mi Kwon
    • 2
  • Eui-Kyung Lee
    • 3
  • Sang-Cheol Bae
    • 1
  1. 1.Department of RheumatologyHanyang University Hospital for Rheumatic DiseasesSeoulSouth Korea
  2. 2.Graduate School of Clinical PharmacySookmyung Women’s UniversitySeoulSouth Korea
  3. 3.School of Pharmacy, Sungkyunkwan UniversitySuwonSouth Korea

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