title: Identifying a cohort of patients with early-stage breast cancer: a comparison of hospital discharge and primary data.
abstract: BACKGROUND
  Hospital discharge data are a potential source of information for quality of care; however, they lack detailed clinical data.
  
  
  OBJECTIVE
  To assess the usefulness of hospital discharge data for describing patterns of care.
  
  
  METHODS
  Cohort study comparing hospital discharge data with data collected from medical records and patients.
  
  
  METHODS
  Women diagnosed with early-stage breast cancer in Massachusetts and Minnesota (1993-1995).
  
  
  METHODS
  The percentage of patients in the primary data set who did not match a record in the discharge data set, and the percentage of patients in the discharge data set who did not match a record in the primary data set. Odds ratios for appearing in one data set, but not the other according to patient and hospital characteristics.
  
  
  RESULTS
  For patients in the primary data set, 26.9% from Massachusetts and 13.2% from Minnesota did not match a record in the discharge data set. In both states, factors associated with failure to match to the discharge data included receipt of breast conserving surgery, shorter length of stay, and treatment hospital. For patients in the discharge data set, 43.4% in Massachusetts and 30.3% in Minnesota did not match a patient in the primary data set. In both states, factors associated with failure to match to the primary data included treatment hospital and the presence of positive lymph nodes.
  
  
  CONCLUSIONS
  Hospital discharge data were fairly sensitive when linked to patients with early-stage breast cancer who were identified through hospital records. The discharge data lacked specificity, however. If discharge data are used to characterize patterns care for inpatients with early stage disease, estimates are likely to be inaccurate due to the inclusion of unsuitable patients in the denominator used to calculate procedure rates.
authors.affiliation: Department of Mathematics and Statistics, Mississippi State University, Mississippi State, MS, USA.
authors.forename: J N
authors.initials: JN
authors.lastname: Jonkman
authors.forename: S L
authors.initials: SL
authors.lastname: Normand
authors.forename: R
authors.initials: R
authors.lastname: Wolf
authors.forename: C
authors.initials: C
authors.lastname: Borbas
authors.forename: E
authors.initials: E
authors.lastname: Guadagnoli
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000473:pathology / Q000601:surgery*; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D016015:Logistic Models; D008404:Massachusetts / Q000453:epidemiology; D015412:Mastectomy, Segmental / Q000706:statistics & numerical data*; D008498:Medical Record Linkage; D008875:Middle Aged; D008910:Minnesota / Q000453:epidemiology; D009367:Neoplasm Staging; D010351:Patient Discharge; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*; D011320:Primary Health Care; D014600:Utilization Review / Q000379:methods / Q000706:statistics & numerical data