French claims data as a source of information to describe cancer incidence: predictive values of two identification methods of incident prostate cancers

Claims data from the "Programme de Médicalisation du Système d'Information" (PMSI) have been commonly used for several years to complement cancer registries and describe cancer incidence in France. It is less clear whether or not it is possible to use these data as an independent sour...

Full description

Saved in:
Bibliographic Details
Published in:Journal of medical systems 2006-12, Vol.30 (6), p.459-463
Main Authors: Couris, Chantal Marie, Seigneurin, Arnaud, Bouzbid, Sabiha, Rabilloud, Muriel, Perrin, Paul, Martin, Xavier, Colin, Cyrille, Schott, Anne-Marie
Format: Article
Language:English
Subjects:
Cancer therapies
France - epidemiology
Humans
Insurance Claim Review
Life Sciences
Male
Medical Records
Other
Predictive Value of Tests
Prostate cancer
Prostatic Neoplasms - epidemiology
Registries
Statistical methods
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Claims data from the "Programme de Médicalisation du Système d'Information" (PMSI) have been commonly used for several years to complement cancer registries and describe cancer incidence in France. It is less clear whether or not it is possible to use these data as an independent source of information to assess cancer incidence, in the absence of a regional cancer registry. Following a similar study on breast cancer, we present a study which aimed to evaluate two methods of identifying incident prostate cancer using claims data. These methods were developed using claims data from the Hospices Civils de Lyon (HCL) and their validity was tested against medical records. The first method (M1) identified incident patients as those who had at least one stay with a principal diagnosis of prostate cancer. The second method (M2) had a prostate cancer treatment code in addition to the criteria for the first method. Both methods of identification had similar results, indicating a low rate of false negatives (negative predictive values: M1 = 100 [CI95: 93.8-100], M2 = 98.6 [CI95: 90.1-99.6]) and a high rate of false positives (positive predictive values: M1 = 33.3 [CI95: 23.2-42.1], M2 = 33.7 [CI95: 24.2-43.2]). The sample size did not allow us to produce consistent estimates of sensitivity and specificity. Our results showed that an estimation of the number of incident cases of prostate cancer using both methods of identification would be biased because of the high rate of false positives. Statistical methods that correct identification errors should be used.
ISSN:0148-5598
1573-689X
DOI:10.1007/s10916-006-9028-x