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    Automated Detection of Infectious Disease Outbreaks in Hospitals: A Retrospective Cohort Study

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    Authors
    Huang, Susan S.
    Yokoe, Deborah S.
    Stelling, John
    Placzek, Hilary
    Kulldorff, Martin
    Kleinman, Ken
    O'Brien, Thomas F.
    Calderwood, Michael S.
    Vostok, Johanna
    Dunn, Julie
    Platt, Richard
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    Student Authors
    Hilary Placzek
    UMass Chan Affiliations
    Department of Medicine, Division of Infectious Diseases and Immunology
    Document Type
    Journal Article
    Publication Date
    2010-02-23
    Keywords
    Disease Outbreaks; Hospitals; Cohort Studies; Infection Control; Software
    Immunology and Infectious Disease
    Infectious Disease
    Life Sciences
    Medicine and Health Sciences
    
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    Abstract
    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. 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.
    Source
    Huang 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.
    DOI
    10.1371/journal.pmed.1000238
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/33124
    PubMed ID
    20186274
    Related Resources
    Link to article in PubMed
    Rights
    Copyright: 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.
    ae974a485f413a2113503eed53cd6c53
    10.1371/journal.pmed.1000238
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