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

Autor: Wiemken TL; Department of Epidemiology and Population Health, School of Public Health and Information Sciences, University of Louisville, Louisville, KY. Electronic address: tim.wiemken@louisville.edu., Furmanek SP; Healthcare Epidemiology and Patient Safety Program, Division of Infectious Diseases, University of Louisville, Louisville, KY., Mattingly WA; Healthcare Epidemiology and Patient Safety Program, Division of Infectious Diseases, University of Louisville, Louisville, KY., Wright MO; Department of Infection Prevention and Control, University of Wisconsin Hospitals and Clinics, Madison, WI., Persaud AK; Healthcare Epidemiology and Patient Safety Program, Division of Infectious Diseases, University of Louisville, Louisville, KY., Guinn BE; Healthcare Epidemiology and Patient Safety Program, Division of Infectious Diseases, University of Louisville, Louisville, KY., Carrico RM; Healthcare Epidemiology and Patient Safety Program, Division of Infectious Diseases, University of Louisville, Louisville, KY., Arnold FW; Healthcare Epidemiology and Patient Safety Program, Division of Infectious Diseases, University of Louisville, Louisville, KY., Ramirez JA; Healthcare Epidemiology and Patient Safety Program, Division of Infectious Diseases, University of Louisville, Louisville, KY.
Jazyk: angličtina
Zdroj: American journal of infection control [Am J Infect Control] 2018 Feb; Vol. 46 (2), pp. 124-132. Date of Electronic Publication: 2017 Sep 12.
DOI: 10.1016/j.ajic.2017.08.005
Abstrakt: Background: Although 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.
Methods: SPC 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.
Results: Both 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.
Discussion: SPC charts and ABD charts both performed well, although ABD charts appeared to work better in the context of seasonal variation and autocorrelation.
Conclusions: Because they account for common statistical issues in HAI data, ABD charts may be useful for practitioners for analysis of HAI surveillance data.
(Copyright © 2018 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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