New control chart methods for monitoring MROs in hospitals

Morton, Anthony, Gatton, Michelle, Tong, Edward N. C. and Clements, Archie (2007) New control chart methods for monitoring MROs in hospitals. Australian Infection Control, 12 1: 14-18. doi:10.1071/HI07014


Author Morton, Anthony
Gatton, Michelle
Tong, Edward N. C.
Clements, Archie
Title New control chart methods for monitoring MROs in hospitals
Journal name Australian Infection Control   Check publisher's open access policy
ISSN 1329-9360
1835-5625
Publication date 2007-12-01
Sub-type Article (original research)
DOI 10.1071/HI07014
Volume 12
Issue 1
Start page 14
End page 18
Total pages 5
Place of publication Collingwood, VIC, Australia
Publisher CSIRO Publishing
Language eng
Subject 11 Medical and Health Sciences
Abstract Routine surveillance of colonisations with multiple antibiotic resistant organisms (MROs) is now widespread and these data are increasingly summarised in control charts. The purpose of their analysis in this manner is to provide early warning of outbreaks or to judge the response to system changes designed to reduce colonisation rates. Conventional statistical process control (SPC) charts assume independence of observations. In addition, there needs to be a run of stable, non-trended (stationary) data values to obtain accurate control limits. Colonisation with an MRO is not an independent event as it must involve transmission from a carrier and this can lead to excessive variation. In addition, non-linear trends are often present and MRO prevalence data display temporal correlation. The latter occurs when data at particular times are more like data at related, usually contiguous times, than data from more distant times; thus they are not temporally independent. These characteristics make it difficult to implement conventional SPC charts with MRO data. To overcome these problems, we suggest the use of generalised additive models (GAMs) when there is no temporal correlation, as with new colonisations, and generalised additive mixed models (GAMMs) when temporal correlation is present; as occurs commonly with prevalence data. We illustrate their use with multi-resistant methicillin-resistant Staphylococcus aureus (mMRSA) prevalence and new colonisation data. These methods are able to deal with excess variability, trends and temporal correlation. They are easily implemented in the freely available R software package. Our analysis demonstrates an upward non-linear trend in mMRSA prevalence between January 2004 and October 2006. The mMRSA new colonisation data also display an upward trend between September 2005 and May 2006. Monthly new colonisation rates exceeded the upper control limit in April 2005 and equalled it in May 2006. There was a modest downward trend in the new colonisation rate in the latter part of 2006. (author abstract)
Keyword Cross infection - Prevention
Infection - Prevention
Infection
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
ERA 2012 Admin Only
School of Public Health Publications
 
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Created: Sat, 29 Mar 2008, 02:56:09 EST