title: Evaluation of an algorithm to identify incident breast cancer cases using DRGs data.
abstract: Hospital databases have the potential to be inexpensive, timely and nationally representative sources of information about cancer. This study examines the utility of the French hospital database adapted from the Diagnosis Related Group (DRG) classification and named 'Programme de médicalisation des systèmes d'information (PMSI)', as an independent source to identify incident cancer cases. From the 19 679 women hospitalized and treated in 1998 in the public hospitals of the Somme area in France, we identified those diagnosed with breast cancer in the PMSI database. These women were matched with women in the cancer registry of the Somme area who had been diagnosed with breast cancer in 1998. An algorithm was used to identify cancer-related diagnoses and procedures reported to PMSI. The sensitivity, specificity and positive predictive value (PPV) of the PMSI database were calculated using the cancer registry as a gold standard. The PMSI database had 85% sensitivity, 99.9% specificity and 97% PPV for women hospitalized with breast cancer as a principal diagnosis. The sensitivity was higher by 9% for hospitalization with breast cancer as a secondary diagnosis but had a lower PPV (78%). In conclusion, the PMSI database seems to offer an interesting potential to assess breast cancer incidence, because of its high sensitivity, in particular when secondary diagnosis was considered, and its very high specificity and PPV. However, these preliminary results need to be confirmed by other studies in France before such databases are used, particularly in areas without cancer registries.
authors.affiliation: Medical Information, Epidemiology and Biostatistics, Hôpital Nord, Place Pauchet, Amiens University Hospital, 80 054 Amiens Cedex 1, France. ganry.olivier@chu-amiens.fr
authors.forename: O
authors.initials: O
authors.lastname: Ganry
authors.forename: A
authors.initials: A
authors.lastname: Taleb
authors.forename: J
authors.initials: J
authors.lastname: Peng
authors.forename: N
authors.initials: N
authors.lastname: Raverdy
authors.forename: A
authors.initials: A
authors.lastname: Dubreuil
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000453:epidemiology; D003953:Diagnosis-Related Groups; D005260:Female; D005602:France; D006801:Humans; D015994:Incidence; D012042:Registries; D012680:Sensitivity and Specificity; D013997:Time Factors