title: An Evaluation of Algorithms for Identifying Metastatic Breast, Lung, or Colorectal Cancer in Administrative Claims Data.
abstract: BACKGROUND
  Administrative health care claims data are used for epidemiologic, health services, and outcomes cancer research and thus play a significant role in policy. Cancer stage, which is often a major driver of cost and clinical outcomes, is not typically included in claims data.
  
  
  OBJECTIVE
  Evaluate algorithms used in a dataset of cancer patients to identify patients with metastatic breast (BC), lung (LC), or colorectal (CRC) cancer using claims data.
  
  
  METHODS
  Clinical data on BC, LC, or CRC patients (between January 1, 2007 and March 31, 2010) were linked to a health care claims database. Inclusion required health plan enrollment ≥3 months before initial cancer diagnosis date. Algorithms were used in the claims database to identify patients' disease status, which was compared with physician-reported metastases. Generic and tumor-specific algorithms were evaluated using ICD-9 codes, varying diagnosis time frames, and including/excluding other tumors. Positive and negative predictive values, sensitivity, and specificity were assessed.
  
  
  RESULTS
  The linked databases included 14,480 patients; of whom, 32%, 17%, and 14.2% had metastatic BC, LC, and CRC, respectively, at diagnosis and met inclusion criteria. Nontumor-specific algorithms had lower specificity than tumor-specific algorithms. Tumor-specific algorithms' sensitivity and specificity were 53% and 99% for BC, 55% and 85% for LC, and 59% and 98% for CRC, respectively.
  
  
  CONCLUSIONS
  Algorithms to distinguish metastatic BC, LC, and CRC from locally advanced disease should use tumor-specific primary cancer codes with 2 claims for the specific primary cancer >30-42 days apart to reduce misclassification. These performed best overall in specificity, positive predictive values, and overall accuracy to identify metastatic cancer in a health care claims database.
authors.affiliation: *CSL Behring, King of Prussia, PA †OptumInsight, Health Economics and Outcomes Research, Eden Prairie, MN ‡Heme Onc Associates and AHT BioPharma Advisory Services, Carlsbad, CA §Center for Observational Research, Amgen Inc., Thousand Oaks, CA.
authors.forename: Joanna L
authors.initials: JL
authors.lastname: Whyte
authors.forename: Nicole M
authors.initials: NM
authors.lastname: Engel-Nitz
authors.forename: April
authors.initials: A
authors.lastname: Teitelbaum
authors.forename: Gabriel
authors.initials: G
authors.lastname: Gomez Rey
authors.forename: Joel D
authors.initials: JD
authors.lastname: Kallich
mesh_terms: D000368:Aged; D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology*; D015179:Colorectal Neoplasms / Q000473:pathology*; D005260:Female; D006801:Humans; D007345:Insurance Claim Review; D038801:International Classification of Diseases; D008175:Lung Neoplasms / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D009362:Neoplasm Metastasis; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014481:United States