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<dc:title xml:lang="fr">Les entérocoques résistants aux glycopeptides : épidémiologie et modélisation de leur transmission hospitalière</dc:title>
<dcterms:alternative xml:lang="en">Glycopeptide-resistant enterococci : epidemiology and hospital transmission modeling</dcterms:alternative>
<dc:subject xml:lang="fr">Entérocoques résistants aux glycopeptides</dc:subject>
<dc:subject xml:lang="fr">Bayésien</dc:subject>
<dc:subject xml:lang="fr">Modèle basé sur les agents</dc:subject>
<dc:subject xml:lang="fr">Épidémie</dc:subject>
<dc:subject xml:lang="fr">Mesures barrières</dc:subject>
<dc:subject xml:lang="fr">Bactéries hautement résistantes aux antibiotiques</dc:subject>
<dc:subject xml:lang="en">Glycopeptide-resistant enterococci</dc:subject>
<dc:subject xml:lang="en">Bayesian</dc:subject>
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<dcterms:abstract xml:lang="fr">L’objectif de notre travail était d’étudier les facteurs d'acquisition des entérocoques résistants aux glycopeptides (ERG) en situation épidémique, de décrire leur histoire naturelle et de modéliser leur transmission entre 3 services spécialisés. L’analyse multivariée bayésienne de notre première étude a montré que des antécédents d'hospitalisation et la prise d'antibiotiques et d'antiacides pendant l'hospitalisation favorisaient l'acquisition. La description de la cohorte de patients suivis depuis 2007a fait apparaître que la moitié des patients étaient pauci-excréteurs après 3 mois. Enfin l’analyse de la littérature a révélé que le modèle le plus pertinent pour simuler la diffusion hospitalière des ERG était celui basé sur les agents (agent-based model). Les simulations ont confirmé l’importance de l’hygiène des mains pour la prise en charge des patients, au regard d’autres mesures barrières. Avec une compliance à 80%, il n’y avait pas de cas secondaires dans 50% des simulations.</dcterms:abstract>
<dcterms:abstract xml:lang="en">The objective of our work was to study the factors associated with acquisition of glycopeptide-resistant enterococci (GRE) during a single-strain outbreak, to describe their natural history and to model their transmission between 3 specialized wards. The Bayesian multivariable analysis of our first study showed that a history of hospitalization and the use of antibiotics and antacids during hospitalization were associated with an increased risk of GRE acquisition. The description of GRE-carriers followed since 2007 then showed that half of the patients had negative screenings after 3months. Finally, the literature review revealed that the most relevant model for simulating GRE hospital diffusion was an agent-based model. The simulations confirmed the importance of hand hygiene for patient care in comparison to other barrier measures. With 80% compliance, there were no secondary cases in 50% of the simulations.</dcterms:abstract>
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