Is it possible to estimate the incidence of breast cancer from medico-administrative databases?
One approach to estimate cancer incidence in the French Départements is to quantify the relationship between data in cancer registries and data obtained from the PMSI (Programme de Médicalisation des Systèmes d'Information Médicale). This relationship may then be used in Départements without registries to infer the incidence from local PMSI data. We present here some methodological solutions to apply this approach. Data on invasive breast cancer for 2002 were obtained from 12 Départemental registries. The number of hospital stays was obtained from the National PMSI using two different algorithms based on the main diagnosis only (Algorithm 1) or on that diagnosis associated to a mention of "resection" (Algorithm 2). Considering registry data as gold standard, a calibration approach was used to model the ratio of the number of hospital stays to the number of incident cases. In Départements with registries, validation of the predictions was done through cross-validation. In Départements without registries, validation was done through a study of homogeneity of the mean number of hospital stays per patient. Cross-validation showed that the estimates predicted by the model were true with data extracted by Algorithm 1 but not by Algorithm 2. However, with Algorithm 1, there was an important heterogeneity between French Départements as to the mean number of hospital stays per patient, which had an important impact on the estimations. In the near future, the method will allow using medico-administrative data (after calibration with registry data) to estimate Départemental incidence of selected cancers.
Remontet
L
L
Service de Biostatistique, Batiment 4D, Centre Hospitalier Lyon Sud, Pierre-Benite, 69495, France. laurent.remontet@chu-lyon.fr
Mitton
N
N
Couris
C M
CM
Iwaz
J
J
Gomez
F
F
Olive
F
F
Polazzi
S
S
Schott
A M
AM
Trombert
B
B
Bossard
N
N
Colonna
M
M
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000453:epidemiology*; D016208:Databases, Factual; D016021:Epidemiologic Studies; D005260:Female; D005602:France; D006801:Humans; D008499:Medical Records / Q000706:statistics & numerical data*; D008875:Middle Aged; D015233:Models, Statistical; D012042:Registries; D055815:Young Adult