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dc.rights.licenseopenen_US
dc.contributor.authorFOULON, S.
dc.contributor.authorCONY-MAKHOUL, P.
dc.contributor.authorGUERCI-BRESLER, A.
dc.contributor.authorDELORD, M.
dc.contributor.authorSOLARY, E.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorMONNEREAU, Alain
dc.contributor.authorBONASTRE, J.
dc.contributor.authorTUBERT-BITTER, P.
dc.date.accessioned2020-06-11T10:08:46Z
dc.date.available2020-06-11T10:08:46Z
dc.date.issued2019-06
dc.identifier.issn2045-7634 (Electronic) 2045-7634 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/7882
dc.description.abstractEnBACKGROUND: Data on Chronic Myeloid Leukemia (CML) prevalence are scarce. Here we provide an estimation of the prevalence of CML in France for the year 2014 using French national health insurance data. METHODS: We selected patients claiming reimbursement for tyrosine kinase inhibitors (TKI) or with hospital discharge diagnoses for CML, BCR/ABL-positive or with full reimbursement of health care expenses for myeloid leukemia. We built an algorithm which we validated on a random sample of 100 potential CML patients by comparing the results obtained using the algorithm and the opinion of two hematologists who reviewed the patient demographics and sequence of care abstracted from claims data (internal validity). For external validity, we compared the number of incident CML patients identified using the algorithm with those recorded in French population-based cancer registries in departments covered by such a registry. RESULTS: We identified 10 789 prevalent CML patients in 2014, corresponding to a crude prevalence rate of 16.3 per 100 000 inhabitants [95% confidence interval (CI) 16.0-16.6]: 18.5 in men [18.0-19.0] and 14.2 in women [13.8-14.6]. The crude CML prevalence was less than 1.6 per 100 000 [1.2-2.0] under age 20, increasing to a maximum of 48.2 [45.4-51.2) at ages 75-79. It varied from 10.2 to 23.8 per 100 000 across French departments. The algorithm showed high internal and external validity. Concordance rate between the algorithm and the hematologists was 96%, and the numbers of incident CML patients identified using the algorithm and the registries were 162 and 150, respectively. CONCLUSION: We built and validated an algorithm to identify CML patients in administrative healthcare databases. In addition to prevalence estimation, the algorithm could be used for future economic evaluations or pharmaco-epidemiological studies in this population.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subject.enEPICENE
dc.title.enUsing healthcare claims data to analyze the prevalence of BCR-ABL-positive chronic myeloid leukemia in France: A nationwide population-based study
dc.typeArticle de revueen_US
dc.identifier.doi10.1002/cam4.2200en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed31038849en_US
bordeaux.journalCancer Medicineen_US
bordeaux.page3296-3304en_US
bordeaux.volume8en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.issue6en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamEPICENE_BPH
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03212260
hal.version1
hal.date.transferred2021-04-29T12:30:36Z
hal.exporttrue
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Cancer%20Medicine&rft.date=2019-06&rft.volume=8&rft.issue=6&rft.spage=3296-3304&rft.epage=3296-3304&rft.eissn=2045-7634%20(Electronic)%202045-7634%20(Linking)&rft.issn=2045-7634%20(Electronic)%202045-7634%20(Linking)&rft.au=FOULON,%20S.&CONY-MAKHOUL,%20P.&GUERCI-BRESLER,%20A.&DELORD,%20M.&SOLARY,%20E.&rft.genre=article


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