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

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.

Malaria intensity in Colombia by regions and populations

“…[ 66 ] have also implemented the scan statistic method in China to identify clusters and suggest public health resource optimization. Faires [ 67 ] used this method to identify clusters of Clostridium difficile over time in Ontario, Canada. Duczma [ 68 ] implemented the scan statistic to study Chagas’ disease in Brazil, while [ 69 ] do the same for end-stage renal disease (ESRD) in northen France.…”

Spatial, temporal, and spatiotemporal analysis of under-five diarrhea in Southern Ethiopia

“…Analysis of disease trends in space and time provides context which can be linked to possible risk factors in a research environment [ 26 ]. Scan statistics has been used widely in the field of epidemiology for investigation of spatial, temporal, and space-time clusters of infectious disease such as hemorrhagic fever [ 27 ], Clostridium difficile infection clusters [ 28 ], healthcare-associated infections or colonizations with Pseudomonas aeruginosa [ 29 ], visceral leishmaniasis [ 30 ], typhoid fever [ 31 ], cholera [ 32 ], malaria [ 33 ], and diarrhea [ 14 , 15 ].…”

Automated detection of hospital outbreaks: A systematic review of methods

“…Conversely, in three studies, outbreaks were monitored at the level of a single intensive care unit [ 12 , 16 , 22 ]. Additional data allowed in six studies to stratify outbreak detection at different spatial levels, from the whole hospital to services and units [ 25 – 27 , 33 – 35 ].…”

“…Elaborating on these models, scan statistics represented another popular category of algorithms (n = 6) [ 18 , 25 – 27 , 34 , 35 ]. It even served as a reference standard in an additional study by Mellmann et al [ 38 ].…”

“…More recently, two studies by Faires et al provided interesting insights about how a scan statistic-based algorithm compared to traditional infection control surveillance: it retrospectively identified most of the outbreaks already investigated by the infection control team but also flagged other potentially undetected epidemic events [ 25 , 26 ].…”