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dc.contributor.authorWei, Yinghui
dc.contributor.authorKypraios, T
dc.contributor.authorO’Neill, PD
dc.contributor.authorHuang, SS
dc.contributor.authorRifas-Shiman, SL
dc.contributor.authorCooper, BS
dc.date.accessioned2016-11-19T11:27:26Z
dc.date.available2016-11-19T11:27:26Z
dc.date.issued2018-01
dc.identifier.issn0962-2802
dc.identifier.issn1477-0334
dc.identifier.urihttp://hdl.handle.net/10026.1/6764
dc.description.abstract

<jats:p> Nosocomial pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE) are the cause of significant morbidity and mortality among hospital patients. It is important to be able to assess the efficacy of control measures using data on patient outcomes. In this paper, we describe methods for analysing such data using patient-level stochastic models which seek to describe the underlying unobserved process of transmission. The methods are applied to detailed longitudinal patient-level data on vancomycin-resistant Enterococci from a study in a US hospital with eight intensive care units (ICUs). The data comprise admission and discharge dates, dates and results of screening tests, and dates during which precautionary measures were in place for each patient during the study period. Results include estimates of the efficacy of the control measures, the proportion of unobserved patients colonized with vancomycin-resistant Enterococci, and the proportion of patients colonized on admission. </jats:p>

dc.format.extent269-285
dc.format.mediumPrint-Electronic
dc.languageen
dc.language.isoeng
dc.publisherSAGE Publications
dc.subjectAntimicrobial resistance
dc.subjectBayesian methods
dc.subjecthealthcare-associated infections
dc.subjectMRSA
dc.subjectvancomycin-resistant Enterococci
dc.titleEvaluating hospital infection control measures for antimicrobial-resistant pathogens using stochastic transmission models: Application to vancomycin-resistant enterococci in intensive care units
dc.typejournal-article
dc.typeJournal Article
dc.typeResearch Support, N.I.H., Extramural
dc.typeResearch Support, Non-U.S. Gov't
dc.typeResearch Support, U.S. Gov't, P.H.S.
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000419874400019&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue1
plymouth.volume27
plymouth.publication-statusPublished
plymouth.journalStatistical Methods in Medical Research
dc.identifier.doi10.1177/0962280215627299
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
plymouth.organisational-group/Plymouth/Users by role/Researchers in ResearchFish submission
dc.publisher.placeEngland
dc.identifier.eissn1477-0334
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1177/0962280215627299
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
plymouth.oa-locationhttps://arxiv.org/pdf/1602.04721v1.pdf


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