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ADL-dependent older adults were identified in medico-administrative databases

dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHUCTEAU, Emilie
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorNOIZE, Pernelle
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPARIENTE, Antoine
IDREF: 13395711X
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHELMER, Catherine
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPERES, Karine
ORCID: 0000-0002-0720-0684
IDREF: 080634001
dc.date.accessioned2021-08-19T13:43:19Z
dc.date.available2021-08-19T13:43:19Z
dc.date.issued2021-06-21
dc.identifier.issn0895-4356en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/110168
dc.description.abstractEnOBJECTIVE: . We aimed to develop an algorithm for the identification of basic Activities of Daily Living (ADL)-dependency in health insurance databases. STUDY DESIGN AND SETTING: . We used the AMI (Aging Multidisciplinary Investigation) population-based cohort including both individual face-to-face assessment of ADL-dependency and merged health insurance data. The health insurance factors associated with ADL-dependency were identified using a LASSO logistic regression model in 1000 bootstrap samples. An external validation on a 1/97(th) representative sample of the French Health Insurance general population of Affiliates has been performed. RESULTS: . Among 995 participants of the AMI cohort aged ≥ 65y, 114 (11.5%) were ADL-dependent according to neuropsychologists individual assessments. The final algorithm developed included: age, sex, four drug classes (dopaminergic antiparkinson drugs, antidepressants, antidiabetic agents, lipid modifying agents), three type of medical devices (medical bed, patient lifter, incontinence equipment), four medical acts (GP's consultations at home, daily and non-daily nursing at home, transport by ambulance) and four long-term diseases (stroke, heart failure, coronary heart disease, Alzheimer and other dementia). Applying this algorithm, the estimated prevalence of ADL-dependency was 12.3% in AMI and 9.5% in the validation sample. CONCLUSION: . This study proposes a useful algorithm to identify ADL-dependency in the health insurance data.
dc.language.isoENen_US
dc.subject.enDependency
dc.subject.enActivities of daily living
dc.subject.enAged
dc.subject.enPharmacoepidemiology
dc.subject.enHealth insurance data
dc.subject.enCohort study
dc.title.enADL-dependent older adults were identified in medico-administrative databases
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/j.jclinepi.2021.06.014en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed34166754en_US
bordeaux.journalJournal of Clinical Epidemiologyen_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamPharmacoEpi-Drugsen_US
bordeaux.teamSEPIAen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.identifier.funderIDAgence Nationale de Sécurité du Médicament et des Produits de Santéen_US
hal.identifierhal-03322689
hal.version1
hal.date.transferred2021-08-19T13:43:22Z
hal.exporttrue
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