Use of health insurance claim patterns to identify patients using nonsteroidal anti-inflammatory drugs for rheumatoid arthritis

ABSTRACT Purpose To determine healthcare claim patterns associated using nonsteroidal anti‐inflammatory drugs (NSAIDs) for rheumatoid arthritis (RA). Methods The CADEUS study randomly identified NSAID users within the French health insurance database. One‐year claims data were extracted, and NSAID i...

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Bibliographic Details
Published in:Pharmacoepidemiology and drug safety 2012-06, Vol.21 (6), p.573-583
Main Authors: Bernard, Marie-Agnès, Bénichou, Jacques, Blin, Patrick, Weill, Alain, Bégaud, Bernard, Abouelfath, Abdelilah, Moore, Nicholas, Fourrier-Réglat, Annie
Format: Article
Language:English
Subjects:
Adult
Aged
Anti-Inflammatory Agents, Non-Steroidal - administration & dosage
Anti-Inflammatory Agents, Non-Steroidal - therapeutic use
Arthritis, Rheumatoid - drug therapy
Arthritis, Rheumatoid - epidemiology
Drug Utilization Review - methods
Drug Utilization Review - statistics & numerical data
Female
France
Health insurance
Humans
Insurance Claim Review - statistics & numerical data
Insurance claims
Logistic Models
Male
Middle Aged
Nonsteroidal anti-inflammatory drugs
Pharmacoepidemiology
predictive model
prescription databases
Rheumatoid arthritis
Young Adult
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Summary:ABSTRACT Purpose To determine healthcare claim patterns associated using nonsteroidal anti‐inflammatory drugs (NSAIDs) for rheumatoid arthritis (RA). Methods The CADEUS study randomly identified NSAID users within the French health insurance database. One‐year claims data were extracted, and NSAID indication was obtained from prescribers. Logistic regression was used in a development sample to identify claim patterns predictive of RA and models applied to a validation sample. Analyses were stratified on the dispensation of immunosuppressive agents or specific antirheumatism treatment, and the area under the receiver operating characteristic curve was used to estimate discriminant power. Results NSAID indication was provided for 26 259 of the 45 217 patients included in the CADEUS cohort; it was RA for 956 patients. Two models were constructed using the development sample (n = 13 143), stratifying on the dispensation of an immunosuppressive agent or specific antirheumatism treatment. Discriminant power was high for both models (AUC > 0.80) and was not statistically different from that found when applied to the validation sample (n = 13 116). Conclusions The models derived from this study may help to identify patients prescribed NSAIDs who are likely to have RA in claims databases without medical data such as treatment indication. Copyright © 2012 John Wiley & Sons, Ltd.
ISSN:1053-8569
1099-1557
DOI:10.1002/pds.3221