In research published this week in PLoS Medicine, Susan Huang and colleagues describe the use of a novel automated cluster detection tool, WHONET-SaTScan, made by integrating two freely available software packages, to identify hospital infection clusters. After applying the software to microbiology data from patients admitted to a 750-bed academic medical center in the US across four years (2002-2006), the authors found that the tool identified a number of hospital clusters that had not been detected by routine methods. It also classified many previously identified clusters as events likely to occur because of normal random fluctuations and hence not requiring further control measures. The authors state "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."
Citation: 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
Funding: This work was funded by US National Institutes of Health (MIDAS, 1U01 GM076672, Platt; (http://www.nigms.nih.gov/Initiatives/MIDAS/) and NIH RR025040-02 (Stelling). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: RP has received research grants from Sanofi-Aventis, GlaxoSmithKline, Pfizer, and TAP Pharmaceuticals in the past two years. DSY has received research support from Sage Products. MK developed the space-time permutation scan statistic method and the free SaTScan software that were used and evaluated in the study. All other authors report no disclosures.
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