Identification of smoking using Medicare data — a validation study of claims‐based algorithms. Issue 4 (13th January 2016)

• Record Type: Journal Article
• Authors: Desai, Rishi J; Solomon, Daniel H; Shadick, Nancy; Iannaccone, Christine; Kim, Seoyoung C
• Is Part Of: Pharmacoepidemiology and drug safety. Volume 25:Issue 4(2016)
• Page Start: 472
• Page End: 475
• Publication Date: 2016-01-13
• Subjects: claims‐based algorithm -- smoking -- validation -- pharmacoepidemiology; Pharmacoepidemiology -- Periodicals; Chemotherapy -- Periodicals; Epidemiology -- Periodicals; 615.705
• DOI: 10.1002/pds.3953
• Languages: English
• ISSNs: 1053-8569

Abstract

Abstract: Purpose: This study examined the accuracy of claims‐based algorithms to identify smoking against self‐reported smoking data. Methods: Medicare patients enrolled in the Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study were identified. For each patient, self‐reported smoking status was extracted from Women's Hospital Rheumatoid Arthritis Sequential Study and the date of this measurement was defined as the index‐date. Two algorithms identified smoking in Medicare claims: (i) only using diagnoses and procedure codes and (ii) using anti‐smoking prescriptions in addition to diagnoses and procedure codes. Both algorithms were implemented: first, only using 365‐days pre‐index claims and then using all available pre‐index claims. Considering self‐reported smoking status as the gold standard, we calculated specificity, sensitivity, positive predictive value, negative predictive value (NPV), and area under the curve (AUC). Results: A total of 128 patients were included in this study, of which 48% reported smoking. The algorithm only using diagnosis and procedure codes had the lowest sensitivity (9.8%, 95%CI 2.4%–17.3%), NPV (54.9%, 95%CI 46.1%–63.9%), and AUC (0.55, 95%CI 0.51–0.59) when applied in the period of 365 days pre‐index. Incorporating pharmacy claims and using all available pre‐index information improved the sensitivity (27.9%, 95%CI 16.6%–39.1%), NPV (60.4%, 95%CI 51.3%–69.5%), and AUC (0.64, 95%CI 0.58–0.70). The specificity and positive predictive value was 100% for all the algorithms tested. Conclusion: Claims‐based algorithms can identify smokers with limited sensitivity but very high specificity. In the absence of other reliable means, use of a claims‐based algorithm to identify smoking could be cautiously considered in observational studies. Copyright © 2016 John Wiley & Sons, Ltd.