title: The added value of claims for cancer surveillance: results of varying case definitions.
abstract: OBJECTIVE
  As cancer diagnosis and treatment has moved to the outpatient healthcare setting, traditional cancer surveillance tools are less effective for complete and unbiased capture of incident cases. This study evaluates the potential for Medicare data to supplement cancer surveillance in a unique manner by using a standard that is independent of a central cancer registry.
  
  
  METHODS
  State cancer registry records were matched with Medicare data. Case validation included inpatient record abstraction combined with a mail/telephone survey of treating physicians. The positive predictive value (PPV), sensitivity (capture rate), and potential additional cases were calculated for 6 Medicare claims-based case definitions.
  
  
  RESULTS
  The PPV varied according to cancer site and definition, ranging from 70%-97% (prostate) to 87%-98% (breast). Sensitivity varied inversely with PPV, ranging from 51%-94% (breast) to 10%-88% (lung). The most important factors that predicted being missed by the registry were having no admission to an ACOS-certified hospital and no surgical treatment.
  
  
  CONCLUSIONS
  Medicare data represent a valid resource for supplementing state cancer registries in surveillance efforts. This potential is especially applicable to cancers predominantly diagnosed and treated outside the hospital setting.
authors.affiliation: Department of Internal Medicine, Division of Quality Health Care, and Massey Cancer Center, Medical College of Virginia of Virginia Commonwealth University, Richmond, Virginia 22398-0306, USA. lpenbert@mail2.vcu.edu
authors.forename: Lynne
authors.initials: L
authors.lastname: Penberthy
authors.forename: Donna
authors.initials: D
authors.lastname: McClish
authors.forename: Claudine
authors.initials: C
authors.lastname: Manning
authors.forename: Sheldon
authors.initials: S
authors.lastname: Retchin
authors.forename: Tom
authors.initials: T
authors.lastname: Smith
mesh_terms: D000368:Aged; D005260:Female; D006801:Humans; D015994:Incidence; D007344:Insurance Claim Reporting; D016015:Logistic Models; D008297:Male; D006278:Medicare; D009369:Neoplasms / Q000453:epidemiology*; D011159:Population Surveillance / Q000379:methods*; D011237:Predictive Value of Tests; D012042:Registries; D012680:Sensitivity and Specificity; D014768:Virginia / Q000453:epidemiology