Use of Binary Cumulative Sums and Moving Averages in Nosocomial Infection Cluster Detection1

Abstract

Clusters of nosocomial infection often occur undetected, at substantial cost to the medical system and individual patients. We evaluated binary cumulative sum (CUSUM) and moving average (MA) control charts for automated detection of nosocomial clusters. We selected two outbreaks with genotyped strains and used resistance as inputs to the control charts. We identified design parameters for the CUSUM and MA (window size, k, α, β, p0, p1) that detected both outbreaks, then calculated an associated positive predictive value (PPV) and time until detection (TUD) for sensitive charts. For CUSUM, optimal performance (high PPV, low TUD, fully sensitive) was for 0.1 <α ≤0.25 and 0.2 <β <0.25, with p0 = 0.05, with a mean TUD of 20 (range 8–43) isolates. Mean PPV was 96.5% (relaxed criteria) to 82.6% (strict criteria). MAs had a mean PPV of 88.5% (relaxed criteria) to 46.1% (strict criteria). CUSUM and MA may be useful techniques for automated surveillance of resistant infections.

Description of a New all Cause Mortality Surveillance System in Sweden as a Warning System Using Threshold Detection Algorithms

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Illustration of a Statistical Process Control Approach to Regional Prescription Opioid Abuse Surveillance

“…Examples of SPC performance analysis and comparisons can be found in the study by Benneyan (1998aBenneyan ( , b, 2001Benneyan ( , 2003, Brown et al (2002), and Montgomery (2004) and is the focus of an ongoing study specific to this type of application. Examples of SPC performance analysis and comparisons can be found in the study by Benneyan (1998aBenneyan ( , b, 2001Benneyan ( , 2003, Brown et al (2002), and Montgomery (2004) and is the focus of an ongoing study specific to this type of application.…”

Cumulative risk adjusted monitoring of 30-day mortality after cardiothoracic transplantation: UK experience

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