Methods for computational disease surveillance in infection prevention and control: Statistical process control versus Twitter's anomaly and breakout detection algorithms

Publication date: Available online 12 September 2017
Source:American Journal of Infection Control
Author(s): Timothy L. Wiemken, Stephen P. Furmanek, William A. Mattingly, Marc-Oliver Wright, Annuradha K. Persaud, Brian E. Guinn, Ruth M. Carrico, Forest W. Arnold, Julio A. Ramirez
BackgroundAlthough not all health care-associated infections (HAIs) are preventable, reducing HAIs through targeted intervention is key to a successful infection prevention program. To identify areas in need of targeted intervention, robust statistical methods must be used when analyzing surveillance data. The objective of this study was to compare and contrast statistical process control (SPC) charts with Twitter's anomaly and breakout detection algorithms.MethodsSPC and anomaly/breakout detection (ABD) charts were created for vancomycin-resistant Enterococcus, Acinetobacter baumannii, catheter-associated urinary tract infection, and central line-associated bloodstream infection data.ResultsBoth SPC and ABD charts detected similar data points as anomalous/out of control on most charts. The vancomycin-resistant Enterococcus ABD chart detected an extra anomalous point that appeared to be higher than the same time period in prior years. Using a small subset of the central line-associated bloodstream infection data, the ABD chart was able to detect anomalies where the SPC chart was not.DiscussionSPC charts and ABD charts both performed well, although ABD charts appeared to work better in the context of seasonal variation and autocorrelation.ConclusionsBecause they account for common statistical issues in HAI data, ABD charts may be useful for practitioners for analysis of HAI surveillance data.



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