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New control charts for monitoring MRO’s in hospitals

New control charts for monitoring MRO’s in hospitals
New control charts for monitoring MRO’s in hospitals
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
1329-9360
14-18
Morton, Anthony
ee78b16b-ca31-4dbb-ba6b-8667e03a94de
Gatton, Michelle
d13ea4db-2e1d-43d0-9dbe-7b1c35983d2c
Tong, Edward
57229491-4fbf-411d-a187-a7ceb2196f8b
Clements, Archie
f11de8e2-9a7a-4196-90f7-591d6d902dc4
Morton, Anthony
ee78b16b-ca31-4dbb-ba6b-8667e03a94de
Gatton, Michelle
d13ea4db-2e1d-43d0-9dbe-7b1c35983d2c
Tong, Edward
57229491-4fbf-411d-a187-a7ceb2196f8b
Clements, Archie
f11de8e2-9a7a-4196-90f7-591d6d902dc4

Morton, Anthony, Gatton, Michelle, Tong, Edward and Clements, Archie (2007) New control charts for monitoring MRO’s in hospitals. Healthcare Infection, 12 (1), 14-18. (doi:10.1071/HI07014).

Record type: Article

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

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More information

Published date: 2007
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 199575
URI: http://eprints.soton.ac.uk/id/eprint/199575
ISSN: 1329-9360
PURE UUID: 3ef7d525-94e6-435e-8cf4-a5fc6822e930

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Date deposited: 19 Oct 2011 10:10
Last modified: 14 Mar 2024 04:16

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Contributors

Author: Anthony Morton
Author: Michelle Gatton
Author: Edward Tong
Author: Archie Clements

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