title: Distinguishing screening from diagnostic mammograms using Medicare claims data.
abstract: BACKGROUND
  Medicare claims data may be a fruitful data source for research or quality measurement in mammography. However, it is uncertain whether claims data can accurately distinguish screening from diagnostic mammograms, particularly when claims are not linked with cancer registry data.
  
  
  OBJECTIVE
  To validate claims-based algorithms that can identify screening mammograms with high positive predictive value (PPV) in claims data with and without cancer registry linkage.
  
  
  METHODS
  Development of claims-derived algorithms using classification and regression tree analyses within a random half-sample of bilateral mammogram claims with validation in the remaining half-sample.
  
  
  METHODS
  Female fee-for-service Medicare enrollees aged 66 years and older, who underwent bilateral mammography from 1999 to 2005 within Breast Cancer Surveillance Consortium (BCSC) registries in 4 states (CA, NC, NH, and VT), enabling linkage of claims and BCSC mammography data (N=383,730 mammograms obtained from 146,346 women).
  
  
  METHODS
  Sensitivity, specificity, and PPV of algorithmic designation of a "screening" purpose of the mammogram using a BCSC-derived reference standard.
  
  
  RESULTS
  In claims data without cancer registry linkage, a 3-step claims-derived algorithm identified screening mammograms with 97.1% sensitivity, 69.4% specificity, and a PPV of 94.9%. In claims that are linked to cancer registry data, a similar 3-step algorithm had higher sensitivity (99.7%), similar specificity (62.7%), and higher PPV (97.4%).
  
  
  CONCLUSIONS
  Simple algorithms can identify Medicare claims for screening mammography with high predictive values in Medicare claims alone and in claims linked with cancer registry data.
authors.affiliation: *Departments of Family and Community Medicine, Radiology, and the Center for Healthcare Research and Policy, University of California-Davis, Sacramento, CA †Group Health Research Institute, Seattle, WA ‡Departments of Radiology, Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, CA §Department of Biostatistics, University of Washington, Seattle, WA.
authors.forename: Joshua J
authors.initials: JJ
authors.lastname: Fenton
authors.forename: Weiwei
authors.initials: W
authors.lastname: Zhu
authors.forename: Steven
authors.initials: S
authors.lastname: Balch
authors.forename: Rebecca
authors.initials: R
authors.lastname: Smith-Bindman
authors.forename: Paul
authors.initials: P
authors.lastname: Fishman
authors.forename: Rebecca A
authors.initials: RA
authors.lastname: Hubbard
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000178:diet therapy*; D055088:Early Detection of Cancer / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D007345:Insurance Claim Review; D008327:Mammography / Q000706:statistics & numerical data*; D006278:Medicare / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D014481:United States