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Methods for computational disease surveillance in infection prevention and control: Statistical process control versus Twitter's anomaly and breakout detection algorithms - 02/02/18

Doi : 10.1016/j.ajic.2017.08.005 
Timothy L. Wiemken, PhD, MPH, FAPIC, CIC a, * , Stephen P. Furmanek, MPH, MS b, William A. Mattingly, PhD b, Marc-Oliver Wright, MT(ASCP), MS, CIC, FAPIC c, Annuradha K. Persaud, MPH b, Brian E. Guinn, PhD, MPH b, Ruth M. Carrico, PhD, RN, FNP-C, FSHEA, CIC b, Forest W. Arnold, DO, MSc b, Julio A. Ramirez, MD b
a Department of Epidemiology and Population Health, School of Public Health and Information Sciences, University of Louisville, Louisville, KY 
b Healthcare Epidemiology and Patient Safety Program, Division of Infectious Diseases, University of Louisville, Louisville, KY 
c Department of Infection Prevention and Control, University of Wisconsin Hospitals and Clinics, Madison, WI 

*Address correspondence to Timothy L. Wiemken, PhD, MPH, FAPIC, CIC, University of Louisville, Department of Epidemiology and Population Health, School of Public Health and Information Sciences, 485 E Gray St #230, Louisville, KY 40202. (T.L Wiemken).University of LouisvilleDepartment of Epidemiology and Population HealthSchool of Public Health and Information Sciences485 E Gray St #230LouisvilleKY40202

Highlights

We compared traditional Statistical Process Control (SPC) charts with novel Anomaly/Breakout Detection (ABD) charts using Twitter's Anomaly and Breakout detection algorithms for detecting out of control or anomalous HAI data.
ABD charts appeared to work better than SPC charts in the context of seasonality and autocorrelation, two well-known statistical issues with HAI data.
These new charts may be useful for trending of HAI data and for other quality improvement data monitoring.
An open-access web application is provided for users to apply their own datasets to generate ABD and SPC charts.

El texto completo de este artículo está disponible en PDF.

Abstract

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.

El texto completo de este artículo está disponible en PDF.

Key Words : Health care epidemiology, Patient safety, Outbreak, Multidrug resistant organisms, Device-associated infections


Esquema


 Conflicts of interest: None to report.


© 2018  Association for Professionals in Infection Control and Epidemiology, Inc.. Publicado por Elsevier Masson SAS. Todos los derechos reservados.
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