title: A high positive predictive value algorithm using hospital administrative data identified incident cancer cases.
abstract: OBJECTIVE
  We have developed and validated an algorithm based on Piedmont hospital discharge abstracts for ascertainment of incident cases of breast, colorectal, and lung cancer.
  
  
  METHODS
  The algorithm training and validation sets were based on data from 2000 and 2001, respectively. The validation was carried out at an individual level by linkage of cases identified by the algorithm with cases in the Piedmont Cancer Registry diagnosed in 2001.
  
  
  RESULTS
  The sensitivity of the algorithm was higher for lung cancer (80.8%) than for breast (76.7%) and colorectal (72.4%) cancers. The positive predictive values were 78.7%, 87.9%, and 92.6% for lung, colorectal, and breast cancer, respectively. The high values for colorectal and breast cancers were due to the model's ability to distinguish prevalent from incident cases and to the accuracy of surgery claims for case identification.
  
  
  CONCLUSIONS
  Given its moderate sensitivity, this algorithm is not intended to replace cancer registration, but it is a valuable tool to investigate other aspects of cancer surveillance. This method provides a valid study base for timely monitoring cancer practice and related outcomes, geographic and temporal variations, and costs.
authors.affiliation: Cancer Epidemiology Unit, University of Turin and C.P.O. Piemonte, San Giovanni Battista Hospital, Via Santena 7, 10126 Turin, Italy. ileana.baldi@cpo.it
authors.forename: Ileana
authors.initials: I
authors.lastname: Baldi
authors.forename: Piera
authors.initials: P
authors.lastname: Vicari
authors.forename: Daniela
authors.initials: D
authors.lastname: Di Cuonzo
authors.forename: Roberto
authors.initials: R
authors.lastname: Zanetti
authors.forename: Eva
authors.initials: E
authors.lastname: Pagano
authors.forename: Rosalba
authors.initials: R
authors.lastname: Rosato
authors.forename: Carlotta
authors.initials: C
authors.lastname: Sacerdote
authors.forename: Nereo
authors.initials: N
authors.lastname: Segnan
authors.forename: Franco
authors.initials: F
authors.lastname: Merletti
authors.forename: Giovannino
authors.initials: G
authors.lastname: Ciccone
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000453:epidemiology; D015179:Colorectal Neoplasms / Q000453:epidemiology; D016208:Databases, Factual; D005260:Female; D006801:Humans; D015994:Incidence; D007350:Insurance, Hospitalization / Q000706:statistics & numerical data*; D007558:Italy / Q000453:epidemiology; D008175:Lung Neoplasms / Q000453:epidemiology; D008297:Male; D008498:Medical Record Linkage / Q000379:methods*; D009369:Neoplasms / Q000453:epidemiology*; D010351:Patient Discharge / Q000706:statistics & numerical data*; D012372:ROC Curve; D012042:Registries; D012680:Sensitivity and Specificity