title: Automated detection of infectious disease outbreaks in hospitals: a retrospective cohort study.
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.
  
  
  RESULTS
  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.
authors.affiliation: Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California, United States of America. susan.huang@uci.edu
authors.forename: Susan S
authors.initials: SS
authors.lastname: Huang
authors.forename: Deborah S
authors.initials: DS
authors.lastname: Yokoe
authors.forename: John
authors.initials: J
authors.lastname: Stelling
authors.forename: Hilary
authors.initials: H
authors.lastname: Placzek
authors.forename: Martin
authors.initials: M
authors.lastname: Kulldorff
authors.forename: Ken
authors.initials: K
authors.lastname: Kleinman
authors.forename: Thomas F
authors.initials: TF
authors.lastname: O'Brien
authors.forename: Michael S
authors.initials: MS
authors.lastname: Calderwood
authors.forename: Johanna
authors.initials: J
authors.lastname: Vostok
authors.forename: Julie
authors.initials: J
authors.lastname: Dunn
authors.forename: Richard
authors.initials: R
authors.lastname: Platt
mesh_terms: D015331:Cohort Studies; D004196:Disease Outbreaks / Q000517:prevention & control*; D006761:Hospitals / Q000706:statistics & numerical data*; D006801:Humans; D017053:Infection Control; D015233:Models, Statistical; D012189:Retrospective Studies; D012984:Software