Detection of Clostridium difficile infection clusters, using the temporal scan statistic, in a community hospital in southern Ontario, Canada, 2006-2011.

Meredith C Faires,William A Ciccotelli,J Scott Weese,David L Pearl,Olaf Berke,Richard J Reid-Smith

doi:10.1186/1471-2334-14-254

Meredith C Faires, William A Ciccotelli + Show 4 more

Open Access

https://doi.org/10.1186/1471-2334-14-254

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Abstract

BackgroundIn hospitals, Clostridium difficile infection (CDI) surveillance relies on unvalidated guidelines or threshold criteria to identify outbreaks. This can result in false-positive and -negative cluster alarms. The application of statistical methods to identify and understand CDI clusters may be a useful alternative or complement to standard surveillance techniques. The objectives of this study were to investigate the utility of the temporal scan statistic for detecting CDI clusters and determine if there are significant differences in the rate of CDI cases by month, season, and year in a community hospital.MethodsBacteriology reports of patients identified with a CDI from August 2006 to February 2011 were collected. For patients detected with CDI from March 2010 to February 2011, stool specimens were obtained. Clostridium difficile isolates were characterized by ribotyping and investigated for the presence of toxin genes by PCR. CDI clusters were investigated using a retrospective temporal scan test statistic. Statistically significant clusters were compared to known CDI outbreaks within the hospital. A negative binomial regression model was used to identify associations between year, season, month and the rate of CDI cases.ResultsOverall, 86 CDI cases were identified. Eighteen specimens were analyzed and nine ribotypes were classified with ribotype 027 (n = 6) the most prevalent. The temporal scan statistic identified significant CDI clusters at the hospital (n = 5), service (n = 6), and ward (n = 4) levels (P ≤ 0.05). Three clusters were concordant with the one C. difficile outbreak identified by hospital personnel. Two clusters were identified as potential outbreaks. The negative binomial model indicated years 2007–2010 (P ≤ 0.05) had decreased CDI rates compared to 2006 and spring had an increased CDI rate compared to the fall (P = 0.023).ConclusionsApplication of the temporal scan statistic identified several clusters, including potential outbreaks not detected by hospital personnel. The identification of time periods with decreased or increased CDI rates may have been a result of specific hospital events. Understanding the clustering of CDIs can aid in the interpretation of surveillance data and lead to the development of better early detection systems.

Highlights

In hospitals, Clostridium difficile infection (CDI) surveillance relies on unvalidated guidelines or threshold criteria to identify outbreaks
Clostridium difficile represents a significant burden to public health in terms of outbreaks, infection control measures, increased patient morbidity and mortality rates, and patient costs [1]
A commonly used statistical technique to detect disease clusters, the scan statistic has been employed to investigate a wide array of infectious diseases or pathogens including Shigella [6], malaria [7], meningococcal disease [8], Escherichia coli 0157 [5], and listeriosis [9]

Summary

Introduction

Clostridium difficile infection (CDI) surveillance relies on unvalidated guidelines or threshold criteria to identify outbreaks. This can result in false-positive and -negative cluster alarms. The application of statistical methods to identify and understand CDI clusters may be a useful alternative or complement to standard surveillance techniques. The objectives of this study were to investigate the utility of the temporal scan statistic for detecting CDI clusters and determine if there are significant differences in the rate of CDI cases by month, season, and year in a community hospital. Understanding the clustering of infectious diseases, spatially and/or temporally, can be used to identify risk factors [3], facilitate detailed investigations to determine the association between exposures and disease interventions [4], and detect outbreaks [5]. A commonly used statistical technique to detect disease clusters, the scan statistic has been employed to investigate a wide array of infectious diseases or pathogens including Shigella [6], malaria [7], meningococcal disease [8], Escherichia coli 0157 [5], and listeriosis [9]

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