title: Identification of smoking using Medicare data--a validation study of claims-based algorithms.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, USA.
authors.forename: Rishi J
authors.initials: RJ
authors.lastname: Desai
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, USA.|Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, USA.
authors.forename: Daniel H
authors.initials: DH
authors.lastname: Solomon
authors.affiliation: Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, USA.
authors.forename: Nancy
authors.initials: N
authors.lastname: Shadick
authors.affiliation: Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, USA.
authors.forename: Christine
authors.initials: C
authors.lastname: Iannaccone
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, USA.|Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, USA.
authors.forename: Seoyoung C
authors.initials: SC
authors.lastname: Kim
mesh_terms: D000368:Aged; D000465:Algorithms; D016208:Databases, Factual / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data*; D008875:Middle Aged; D011237:Predictive Value of Tests; D011446:Prospective Studies; D057566:Self Report; D012680:Sensitivity and Specificity; D012907:Smoking / Q000453:epidemiology*; D014481:United States
keywords: claims-based algorithm; pharmacoepidemiology; smoking; validation