OBJECTIVE: To estimate breast cancer incidence in the general population using a method that corrects for lack of sensitivity and specificity in the identification of incident breast cancer in inpatient claims data. STUDY DESIGN AND SETTINGS: Two-phase study: phase 1 to identify incident cases in claims data, and phase 2 to estimate sensitivity and specificity in a subset of the population. Two algorithms (1: principal diagnosis; 2: principal diagnosis+specific surgery procedures) were used to identify incident cases in claims of women aged 20 years or older, living in a French district covered by a cancer registry. Sensitivity and specificity were estimated in one district and used to correct incident cases identified. RESULTS: The sensitivity and specificity for algorithms 1 and 2 were 69.0% and 99.89%, and 64.4% and 99.93%, respectively. In contrast to specificity, the sensitivity for both algorithms was lower for women younger than 40 years and older than 65 years. Cases reported by cancer registries were closer to cases identified with algorithm 2 (-3.2% to +20.1%) and to corrected numbers with algorithm 1 (-1% to +15%). CONCLUSION: To obtain reliable estimates of breast cancer incidence in the general population, sensitivity and specificity, which reflect medical and coding practice variations, are necessary.
OBJECTIVE: To estimate breast cancer incidence in the general population using a method that corrects for lack of sensitivity and specificity in the identification of incident breast cancer in inpatient claims data. STUDY DESIGN AND SETTINGS: Two-phase study: phase 1 to identify incident cases in claims data, and phase 2 to estimate sensitivity and specificity in a subset of the population. Two algorithms (1: principal diagnosis; 2: principal diagnosis+specific surgery procedures) were used to identify incident cases in claims of women aged 20 years or older, living in a French district covered by a cancer registry. Sensitivity and specificity were estimated in one district and used to correct incident cases identified. RESULTS: The sensitivity and specificity for algorithms 1 and 2 were 69.0% and 99.89%, and 64.4% and 99.93%, respectively. In contrast to specificity, the sensitivity for both algorithms was lower for women younger than 40 years and older than 65 years. Cases reported by cancer registries were closer to cases identified with algorithm 2 (-3.2% to +20.1%) and to corrected numbers with algorithm 1 (-1% to +15%). CONCLUSION: To obtain reliable estimates of breast cancer incidence in the general population, sensitivity and specificity, which reflect medical and coding practice variations, are necessary.
Authors: Mackenzie R Bronson; Nirav S Kapadia; Andrea M Austin; Qianfei Wang; Diane Feskanich; Julie P W Bynum; Francine Grodstein; Anna N A Tosteson Journal: Med Care Date: 2018-12 Impact factor: 2.983
Authors: J M Escribà; M Banqué; F Macià; J Gálvez; L Esteban; L Pareja; R Clèries; X Sanz; X Castells; J M Borrás; J Ribes Journal: Clin Transl Oncol Date: 2019-10-04 Impact factor: 3.405
Authors: L Remontet; N Mitton; C M Couris; J Iwaz; F Gomez; F Olive; S Polazzi; A M Schott; B Trombert; N Bossard; M Colonna Journal: Eur J Epidemiol Date: 2008-08-21 Impact factor: 8.082
Authors: C Quantin; E Benzenine; M Hägi; B Auverlot; M Abrahamowicz; J Cottenet; E Fournier; C Binquet; D Compain; E Monnet; A M Bouvier; A Danzon Journal: J Cancer Epidemiol Date: 2012-06-26