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AB0474 Identification of Tuberculosis Incidence Through the Use of a Validated Claims-Based Algorithm Among Rheumatoid Arthritis Patients Treated with Disease-Modifying Antirheumatic Drugs
  1. N. Baker1,
  2. S. Suissa2,
  3. H. Kawabata1,
  4. M. Skovron3,
  5. V. Moorthy4,
  6. T.A. Simon1
  1. 1Bristol-Myers Squibb, Hopewell, United States
  2. 2McGill University, Montreal, Canada
  3. 3Epidemiologist, Lawrenceville
  4. 4Mu Sigma, Northbrook, United States


Background Tuberculosis (TB), a reportable disease world-wide, is rare (3 cases per 100,000 persons in the US)1 and is an event of interest in patients receiving biologic DMARDs. Large healthcare claims databases are useful in assessing rare events, but confirmation of TB in the absence of a positive culture can be challenging. Typically, an International Classification of Diseases, Ninth Revision (ICD-9) code is used to identify an event, though these codes may be limited by varying sensitivity and poor positive predictive value.2 In a published study by Calderwood et al., combinations of diagnostic codes, dispensed antituberculous medications and procedure codes (e.g., chest radiographs or sputum staining for acid-fast bacteria) detected TB cases with high sensitivity. Dispensing of two or more antituberculous medications has proven to be the most sensitive criterion, with a sensitivity of 89%.3

Objectives To apply a validated algorithm to estimate the incidence of TB among patients with RA enrolled in a claims database and treated with DMARDs.

Methods Adult patients diagnosed with RA in the MarketScan Commercial and Supplemental Medicare databases who initiated treatment with a DMARD between 1 July 2006 and 30 June 2012 were eligible for inclusion in the analysis. Patients were required to have at least 180 days of continuous health plan enrolment prior to and ≥1 day following initiation of the qualifying RA treatment. Four algorithms with increasingly stringent criteria were applied to the data to identify cases of incident TB. The first three algorithms use ICD-9 codes (010.xx-018.xx), indicating a TB event; the fourth is based on a claims-based algorithm created by Calderwood et al.3

Results Overall, 323 TB events were identified, using ≥1 ICD-9 code, in 187,841 treated RA patients with 153,793 person-years. After applying more strict algorithms including ≥2 ICD-9 codes, ≥1 ICD-9 code and a chest radiograph, and the Calderwood algorithm, the number of TB cases was reduced to 72, 67, and 23, respectively. The table presents the distribution of these cases and incidence rates by algorithm.

Conclusions Although we are unable to determine true positive or negative cases of TB in the claims-based data, applying the Calderwood algorithm provided estimates consistent with published rates of TB among patients with RA.4 These results are also consistent with previous research demonstrating that TB diagnostic codes alone have poor positive predictive value. Validated algorithms are important when using claims-based data to evaluate TB events among patients with RA and in studies assessing the relationship between TB and RA treatment.


  1. Centers for Disease Control and Prevention. Accessed January 2015.

  2. Winthrop KL, et al. Pharmacoepidemiol Drug Saf 2011;20:229–35.

  3. Calderwood MS, et al. Public Health Rep 2010; 125:843–50.

  4. Arkema EV, et al. Ann Rheum Dis 2014; Published Online First: doi:10.1136/annrheumdis-2013-204960.

Disclosure of Interest N. Baker Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, S. Suissa Consultant for: Bristol-Myers Squibb, Genentech, Roche, H. Kawabata Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, M. Skovron Shareholder of: Bristol-Myers Squibb, Consultant for: Bristol-Myers Squibb (member of Abatacept Postmarketing Epdemiology Expert Committee), V. Moorthy: None declared, T. A. Simon Employee of: Bristol-Myers Squibb

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