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dc.contributor.authorHuang, Susan S.
dc.contributor.authorYokoe, Deborah S.
dc.contributor.authorStelling, John
dc.contributor.authorPlaczek, Hilary
dc.contributor.authorKulldorff, Martin
dc.contributor.authorKleinman, Ken
dc.contributor.authorO'Brien, Thomas F.
dc.contributor.authorCalderwood, Michael S.
dc.contributor.authorVostok, Johanna
dc.contributor.authorDunn, Julie
dc.contributor.authorPlatt, Richard
dc.date2022-08-11T08:08:54.000
dc.date.accessioned2022-08-23T16:11:16Z
dc.date.available2022-08-23T16:11:16Z
dc.date.issued2010-02-23
dc.date.submitted2010-12-16
dc.identifier.citationHuang SS, Yokoe DS, Stelling J, Placzek H, Kulldorff M, et al. (2010) Automated Detection of Infectious Disease Outbreaks in Hospitals: A Retrospective Cohort Study. PLoS Med 7(2): e1000238. doi:10.1371/journal.pmed.1000238.
dc.identifier.doi10.1371/journal.pmed.1000238
dc.identifier.pmid20186274
dc.identifier.urihttp://hdl.handle.net/20.500.14038/33124
dc.description.abstractBACKGROUND: Detection of outbreaks of hospital-acquired infections is often based on simple rules, such as the occurrence of three new cases of a single pathogen in two weeks on the same ward. These rules typically focus on only a few pathogens, and they do not account for the pathogens' underlying prevalence, the normal random variation in rates, and clusters that may occur beyond a single ward, such as those associated with specialty services. Ideally, outbreak detection programs should evaluate many pathogens, using a wide array of data sources. METHODS AND FINDINGS: We applied a space-time permutation scan statistic to microbiology data from patients admitted to a 750-bed academic medical center in 2002-2006, using WHONET-SaTScan laboratory information software from the World Health Organization (WHO) Collaborating Centre for Surveillance of Antimicrobial Resistance. We evaluated patients' first isolates for each potential pathogenic species. In order to evaluate hospital-associated infections, only pathogens first isolated >2 d after admission were included. Clusters were sought daily across the entire hospital, as well as in hospital wards, specialty services, and using similar antimicrobial susceptibility profiles. We assessed clusters that had a likelihood of occurring by chance less than once per year. For methicillin-resistant Staphylococcus aureus (MRSA) or vancomycin-resistant enterococci (VRE), WHONET-SaTScan-generated clusters were compared to those previously identified by the Infection Control program, which were based on a rule-based criterion of three occurrences in two weeks in the same ward. Two hospital epidemiologists independently classified each cluster's importance. From 2002 to 2006, WHONET-SaTScan found 59 clusters involving 2-27 patients (median 4). Clusters were identified by antimicrobial resistance profile (41%), wards (29%), service (13%), and hospital-wide assessments (17%). WHONET-SaTScan rapidly detected the two previously known gram-negative pathogen clusters. Compared to rule-based thresholds, WHONET-SaTScan considered only one of 73 previously designated MRSA clusters and 0 of 87 VRE clusters as episodes statistically unlikely to have occurred by chance. WHONET-SaTScan identified six MRSA and four VRE clusters that were previously unknown. Epidemiologists considered more than 95% of the 59 detected clusters to merit consideration, with 27% warranting active investigation or intervention. CONCLUSIONS: Automated statistical software identified hospital clusters that had escaped routine detection. It also classified many previously identified clusters as events likely to occur because of normal random fluctuations. This automated method has the potential to provide valuable real-time guidance both by identifying otherwise unrecognized outbreaks and by preventing the unnecessary implementation of resource-intensive infection control measures that interfere with regular patient care. Please see later in the article for the Editors' Summary.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=20186274&dopt=Abstract">Link to article in PubMed</a>
dc.rightsCopyright: c2010 Huang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.subjectDisease Outbreaks; Hospitals; Cohort Studies; Infection Control; Software
dc.subjectImmunology and Infectious Disease
dc.subjectInfectious Disease
dc.subjectLife Sciences
dc.subjectMedicine and Health Sciences
dc.titleAutomated Detection of Infectious Disease Outbreaks in Hospitals: A Retrospective Cohort Study
dc.typeJournal Article
dc.source.journaltitlePLoS Medicine
dc.source.volume7
dc.source.issue2
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=2671&amp;context=gsbs_sp&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/gsbs_sp/1666
dc.identifier.contextkey1699829
refterms.dateFOA2022-08-23T16:11:16Z
html.description.abstract<p>BACKGROUND: Detection of outbreaks of hospital-acquired infections is often based on simple rules, such as the occurrence of three new cases of a single pathogen in two weeks on the same ward. These rules typically focus on only a few pathogens, and they do not account for the pathogens' underlying prevalence, the normal random variation in rates, and clusters that may occur beyond a single ward, such as those associated with specialty services. Ideally, outbreak detection programs should evaluate many pathogens, using a wide array of data sources.</p> <p>METHODS AND FINDINGS: We applied a space-time permutation scan statistic to microbiology data from patients admitted to a 750-bed academic medical center in 2002-2006, using WHONET-SaTScan laboratory information software from the World Health Organization (WHO) Collaborating Centre for Surveillance of Antimicrobial Resistance. We evaluated patients' first isolates for each potential pathogenic species. In order to evaluate hospital-associated infections, only pathogens first isolated >2 d after admission were included. Clusters were sought daily across the entire hospital, as well as in hospital wards, specialty services, and using similar antimicrobial susceptibility profiles. We assessed clusters that had a likelihood of occurring by chance less than once per year. For methicillin-resistant Staphylococcus aureus (MRSA) or vancomycin-resistant enterococci (VRE), WHONET-SaTScan-generated clusters were compared to those previously identified by the Infection Control program, which were based on a rule-based criterion of three occurrences in two weeks in the same ward. Two hospital epidemiologists independently classified each cluster's importance. From 2002 to 2006, WHONET-SaTScan found 59 clusters involving 2-27 patients (median 4). Clusters were identified by antimicrobial resistance profile (41%), wards (29%), service (13%), and hospital-wide assessments (17%). WHONET-SaTScan rapidly detected the two previously known gram-negative pathogen clusters. Compared to rule-based thresholds, WHONET-SaTScan considered only one of 73 previously designated MRSA clusters and 0 of 87 VRE clusters as episodes statistically unlikely to have occurred by chance. WHONET-SaTScan identified six MRSA and four VRE clusters that were previously unknown. Epidemiologists considered more than 95% of the 59 detected clusters to merit consideration, with 27% warranting active investigation or intervention.</p> <p>CONCLUSIONS: Automated statistical software identified hospital clusters that had escaped routine detection. It also classified many previously identified clusters as events likely to occur because of normal random fluctuations. This automated method has the potential to provide valuable real-time guidance both by identifying otherwise unrecognized outbreaks and by preventing the unnecessary implementation of resource-intensive infection control measures that interfere with regular patient care. Please see later in the article for the Editors' Summary.</p>
dc.identifier.submissionpathgsbs_sp/1666
dc.contributor.departmentDepartment of Medicine, Division of Infectious Diseases and Immunology
dc.source.pagese1000238
dc.contributor.studentHilary Placzek


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