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dc.rights.licenseopenen_US
dc.contributor.authorGOULARD, Helene
dc.contributor.authorHOMERE, Julie
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorMAURISSET, Sylvain
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorCOUREAU, Gaelle
dc.contributor.authorDEFOSSEZ, Gautier
dc.contributor.authorD'ALMEIDA, Tania
dc.contributor.authorLAPOTRE-LEDOUX, Benedicte
dc.contributor.authorGUIZARD, Anne-Valerie
dc.contributor.authorBOUVIER, Veronique
dc.contributor.authorBARA, Simona
dc.contributor.authorPLOUVIER, Sandrine
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorMONNEREAU, Alain
dc.date.accessioned2023-12-18T09:39:33Z
dc.date.available2023-12-18T09:39:33Z
dc.date.issued2023-10-26
dc.identifier.issn1099-1557en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/186686
dc.description.abstractEnPURPOSE: Three generic claims-based algorithms based on the Illness Classification of Diseases (10th revision- ICD-10) codes, French Long-Term Illness (LTI) data, and the Diagnosis Related Group program (DRG) were developed to identify retirees with cancer using data from the French national health insurance information system (Système national des données de santé or SNDS) which covers the entire French population. The present study aimed to calculate the algorithms' performances and to describe false positives and negatives in detail. METHODS: Between 2011 and 2016, data from 7544 participants of the French retired self-employed craftsperson cohort (ESPrI) were first matched to the SNDS data, and then toFrench population-based cancer registries data, used as the gold standard. Performance indicators, such as sensitivity and positive predictive values, were estimated for the three algorithms in a subcohort of ESPrI. RESULTS: The third algorithm, which combined the LTI and DRG program data, presented the best sensitivities (90.9%-100%) and positive predictive values (58.1%-95.2%) according to cancer sites. The majority of false positives were in fact nearby organ sites (e.g., stomach for esophagus) and carcinoma in situ. Most false negatives were probably due to under declaration of LTI. CONCLUSION: Validated algorithms using data from the SNDS can be used for passive epidemiological follow-up for some cancer sites in the ESPrI cohort.
dc.language.isoENen_US
dc.subject.enAdministrative health data
dc.subject.enCancer
dc.subject.enCancer registry
dc.subject.enClaims-based algorithm
dc.subject.enIncident case
dc.title.enValidation of an algorithm for identifying incident cancer cases based on long-term illness and diagnosis related group program data from the French National Health Insurance Information System (SNDS)
dc.title.alternativePharmacoepidemiol Drug Safen_US
dc.typeArticle de revue
dc.identifier.doi10.1002/pds.5709en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed37881134en_US
bordeaux.journalPharmacoepidemiology and Drug Safetyen_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamEPICENE_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.identifier.funderIDCaisse nationale de l'Assurance Maladieen_US
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Pharmacoepidemiology%20and%20Drug%20Safety&rft.date=2023-10-26&rft.eissn=1099-1557&rft.issn=1099-1557&rft.au=GOULARD,%20Helene&HOMERE,%20Julie&MAURISSET,%20Sylvain&COUREAU,%20Gaelle&DEFOSSEZ,%20Gautier&rft.genre=article


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