[Critical analysis of French DRG based information system (PMSI) databases for the epidemiology of cancer: a longitudinal approach becomes possible].

Background: Use of French Diagnosis Related Groups (DRGs) program databases, apart from financial purposes, has recently been improved since a unique anonymous patient identification number has been created for each inpatient in administrative case mix database. Based on the work of the group for cancer epidemiological observation in the Rhône-Alpes area, (ONC-EPI group), we review the remaining difficulties in the use of DRG data for epidemiological purposes and we consider a longitudinal approach based on analysis of database over several years. We also discuss limitations of this approach.
Difficulties: The main problems are related to a lack of quality of administrative data, especially coding of diagnoses. These errors come from missing or inappropriate codes, or not being in accordance with prioritization rules (causing an over- or under-reporting or inconsistencies in coding over time). One difficulty, partly due to the hierarchy of coding and the type of cancer, is the choice of an extraction algorithm. In two studies designed to estimate the incidence of cancer cared in hospitals (breast, colon-rectum, kidney, ovaries), a first algorithm, including a code of cancer as principal diagnosis with a selection of surgical procedures less performed than the second one including a code of cancer as principal diagnosis only, for which the number of hospitalizations per patient ratio was stable across time and space. The chaining over several years allows, by tracing the trajectory of the patient, to detect and correct inaccuracies, errors and missing values, and for incidence studies, to correct incident cases by removing prevalent cases.
Discussion: However, linkage, complete only since 2007, does not correct data in all cases. Ways of future improvement certainly pass through improved algorithms for case identification and especially by linking DRG data with other databases.
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