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Identifying Drugs Inducing Prematurity by Mining Claims Data with High-Dimensional Confounder Score Strategies

dc.rights.licenseopenen_US
dc.contributor.authorDEMAILLY, R.
dc.contributor.authorESCOLANO, S.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHARAMBURU, Françoise
dc.contributor.authorTUBERT-BITTER, P.
dc.contributor.authorAHMED, I.
dc.date.accessioned2021-01-21T10:37:26Z
dc.date.available2021-01-21T10:37:26Z
dc.date.issued2020
dc.identifier.issn1179-1942 (Electronic) 0114-5916 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/25940
dc.description.abstractEnBackground Pregnant women are largely exposed to medications. However, knowledge is lacking about their effects on pregnancy and the fetus. Objective This study sought to evaluate the potential of high-dimensional propensity scores and high-dimensional disease risk scores for automated signal detection in pregnant women from medico-administrative databases in the context of drug-induced prematurity. Methods We used healthcare claims and hospitalization discharges of a 1/97th representative sample of the French population. We tested the association between prematurity and drug exposure during the trimester before delivery, for all drugs prescribed to at least five pregnancies. We compared different strategies (1) for building the two scores, including two machine-learning methods and (2) to account for these scores in the final logistic regression models: adjustment, weighting, and matching. We also proposed a new signal detection criterion derived from these scores: the p value relative decrease. Evaluation was performed by assessing the relevance of the signals using a literature review and clinical expertise. Results Screening 400 drugs from a cohort of 57,407 pregnancies, we observed that choosing between the two machine-learning methods had little impact on the generated signals. Score adjustment performed better than weighting and matching. Using the p value relative decrease efficiently filtered out spurious signals while maintaining a number of relevant signals similar to score adjustment. Most of the relevant signals belonged to the psychotropic class with benzodiazepines, antidepressants, and antipsychotics. Conclusions Mining complex healthcare databases with statistical methods from the high-dimensional inference field may improve signal detection in pregnant women.
dc.language.isoENen_US
dc.subjectPharmacoEpi-Drugs
dc.title.enIdentifying Drugs Inducing Prematurity by Mining Claims Data with High-Dimensional Confounder Score Strategies
dc.title.alternativeDrug Safen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1007/s40264-020-00916-5en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed32124266en_US
bordeaux.journalDrug Safetyen_US
bordeaux.page549-559en_US
bordeaux.volume43en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.issue6en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamPharmacoEpi-Drugsen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03117501
hal.version1
hal.date.transferred2021-01-21T10:37:29Z
hal.exporttrue
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