New control chart methods for monitoring MROs in Hospitals

Anthony Morton MSc(Appl), MD, Michelle Gatton BSc(Hons), PhD, Edward Tong BA, BSc (Hons), Archie Clements MVM, PhD
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引用次数: 8

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.

监测医院核磁共振成像的新控制图方法
对多种抗生素耐药生物(mro)菌落的常规监测现已广泛开展,这些数据越来越多地汇总在控制图中。他们以这种方式进行分析的目的是提供疾病爆发的早期预警,或判断对旨在降低定植率的系统变化的反应。传统的统计过程控制(SPC)图假定观测值的独立性。此外,需要一组稳定的、非趋势的(平稳的)数据值来获得准确的控制限。MRO的定植不是一个独立的事件,因为它必须涉及来自载体的传播,这可能导致过度变异。此外,MRO患病率数据往往呈现非线性趋势,并表现出时间相关性。后者发生在特定时间的数据更像相关时间(通常是连续时间)的数据,而不是来自较远时间的数据;因此它们不是暂时独立的。这些特点使得用MRO数据实现常规SPC图变得困难。为了克服这些问题,我们建议在没有时间相关性的情况下使用广义加性模型(GAMs),如新的殖民地,以及在存在时间相关性的情况下使用广义加性混合模型(GAMMs);这在流行数据中很常见。我们用多重耐甲氧西林金黄色葡萄球菌(mMRSA)流行率和新的定植数据来说明它们的用途。这些方法能够处理过度变异性、趋势和时间相关性。它们很容易在免费的R软件包中实现。我们的分析表明2004年1月至2006年10月间mMRSA患病率呈非线性上升趋势。mMRSA新定殖数据也显示在2005年9月至2006年5月间呈上升趋势。2005年4月,每月新殖民化率超过了控制上限,2006年5月与控制上限持平。在2006年下半年,新殖民率有一个温和的下降趋势。
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