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

Date Published: February 23, 2010

Publisher: Public Library of Science

Author(s): Susan S. Huang, Deborah S. Yokoe, John Stelling, Hilary Placzek, Martin Kulldorff, Ken Kleinman, Thomas F. O’Brien, Michael S. Calderwood, Johanna Vostok, Julie Dunn, Richard Platt, Jean-Louis Vincent

Abstract: Susan Huang and colleagues describe an automated statistical software, WHONET-SaTScan, its application in a hospital, and the potential it has to identify hospital infection clusters that had escaped routine detection.

Partial Text: Although hospital-associated outbreaks of infection account for a small proportion of health care–associated infections [1]–[4], the fact that they typically result from transmission within health care facilities means that timely identification is essential for investigation and effective response. Current detection methods rely heavily on temporal or spatial clustering of specific pathogens. Such monitoring usually involves case counting and subjective judgment to adjudicate whether a cluster is occurring. For multidrug-resistant organisms (MDROs), such as methicillin-resistant Staphylococcus aureus (MRSA), rule-based criteria (e.g., three cases within 2 wk in the same ward) are often used to define a cluster. For example, Mellmann et al. used a definition of two cases in 2 wk with identical spa types to define a MRSA outbreak [5].

The All Organism Nosocomial Dataset from 2002 to 2006 included 298 organism codes and 32,482 isolates. The Priority Pathogen Nosocomial Dataset included 41% of those isolates. All but one cluster involved priority pathogens (Table 1).

The automated WHONET-SaTScan cluster detection tool rapidly detected epidemiologically confirmed hospital outbreaks in a large academic medical center and demonstrated that the common use of rule-based criteria (i.e., ≥3 new nosocomial cases on a single ward within 2 wk) for identifying clusters of MDROs often led to the identification of events likely to occur because of normal random fluctuations. Using a statistical method for cluster detection can focus hospital epidemiology efforts and conserve resources for events likely to represent actual outbreaks.



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