title: Using administrative data to identify and stage breast cancer cases: implications for assessing quality of care.
abstract: OBJECTIVE
  The study evaluated the use of Italian hospital discharge data (SDO, scheda di dimissione ospedaliera) for identifying women with incident breast cancer, determining stage at diagnosis and assessing quality of care.
  
  
  METHODS
  Women aged 20+ years residing in the Regione Emilia-Romagna, Italy, between 2002 and 2005 were studied. Case identification using algorithms based on ICD-9-CM codes on hospital discharge data were compared with AIRTUM-accredited cancer registry data. Sensitivity, specificity and positive predictive value were computed overall, by age and cancer stage. Compliance with guidelines for radiation therapy using registry and hospital data were compared.
  
  
  RESULTS
  A total of 11,615 women was identified by AIRTUM-accredited cancer registries as incident cases, whereas 10,876 women were identified by the SDO algorithm. Sensitivity was 84.8%, specificity was 99.9%, and the positive predictive value was 90.6%. Of the 1,022 who were false positives, 363 (35.5%) were women identified in registry data as having an incident case prior to 2002 and therefore were not included in the analysis. There were 1,761 false negatives; nearly 50% were over 70 years of age or did not undergo a surgical procedure and therefore were not included in our SDO-based case finding. Sensitivity declined as the patient population became older. However, we observed relatively good positive predictive value for all age groups. Algorithms using the SDO data did not clearly identify specific cancer stages. However, the algorithm may have utility where stages are grouped together for use in quality measures.
  
  
  CONCLUSIONS
  Cases were identified with good sensitivity, specificity and positive predictive value with SDO data, with better rates than similar previously published algorithms based on Italian data. These hospital claims-based algorithms facilitate quality of care analyses for large populations when registry data are not available by identifying individual women and their subsequent use of health care services.
authors: Center for Research in Medical Education and Healthcare, Thomas Jefferson University, Philadelphia, PA, USA. elaine.yuen@jefferson.edu
authors: Elaine
authors: E
authors: Yuen
authors: Daniel
authors: D
authors: Louis
authors: Luca
authors: L
authors: Cisbani
authors: Carol
authors: C
authors: Rabinowitz
authors: Rossana
authors: R
authors: De Palma
authors: Vittorio
authors: V
authors: Maio
authors: Maurizio
authors: M
authors: Leoni
authors: Roberto
authors: R
authors: Grilli
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000453:epidemiology* / Q000401:mortality / Q000473:pathology; D015986:Confounding Factors, Epidemiologic; D005260:Female; D006801:Humans; D015994:Incidence; D038801:International Classification of Diseases; D007558:Italy / Q000453:epidemiology; D008875:Middle Aged; D009367:Neoplasm Staging; D010351:Patient Discharge / Q000592:standards / Q000706:statistics & numerical data; D019984:Quality Indicators, Health Care; D011787:Quality of Health Care; D012042:Registries; D012680:Sensitivity and Specificity