Abstract
BackgroundThe cluster detection of health care–associated infections (HAIs) is crucial for identifying HAI outbreaks in the early stages.ObjectiveWe aimed to verify whether multisource surveillance based on the process data in an area network can be effective in detecting HAI clusters.MethodsWe retrospectively analyzed the incidence of HAIs and 3 indicators of process data relative to infection, namely, antibiotic utilization rate in combination, inspection rate of bacterial specimens, and positive rate of bacterial specimens, from 4 independent high-risk units in a tertiary hospital in China. We utilized the Shewhart warning model to detect the peaks of the time-series data. Subsequently, we designed 5 surveillance strategies based on the process data for the HAI cluster detection: (1) antibiotic utilization rate in combination only, (2) inspection rate of bacterial specimens only, (3) positive rate of bacterial specimens only, (4) antibiotic utilization rate in combination + inspection rate of bacterial specimens + positive rate of bacterial specimens in parallel, and (5) antibiotic utilization rate in combination + inspection rate of bacterial specimens + positive rate of bacterial specimens in series. We used the receiver operating characteristic (ROC) curve and Youden index to evaluate the warning performance of these surveillance strategies for the detection of HAI clusters.ResultsThe ROC curves of the 5 surveillance strategies were located above the standard line, and the area under the curve of the ROC was larger in the parallel strategy than in the series strategy and the single-indicator strategies. The optimal Youden indexes were 0.48 (95% CI 0.29-0.67) at a threshold of 1.5 in the antibiotic utilization rate in combination–only strategy, 0.49 (95% CI 0.45-0.53) at a threshold of 0.5 in the inspection rate of bacterial specimens–only strategy, 0.50 (95% CI 0.28-0.71) at a threshold of 1.1 in the positive rate of bacterial specimens–only strategy, 0.63 (95% CI 0.49-0.77) at a threshold of 2.6 in the parallel strategy, and 0.32 (95% CI 0.00-0.65) at a threshold of 0.0 in the series strategy. The warning performance of the parallel strategy was greater than that of the single-indicator strategies when the threshold exceeded 1.5.ConclusionsThe multisource surveillance of process data in the area network is an effective method for the early detection of HAI clusters. The combination of multisource data and the threshold of the warning model are 2 important factors that influence the performance of the model.
Highlights
Health care–associated infections (HAIs) are a socially sensitive and important public health issue that threatens patient safety, prolongs hospital stays, and increases economic burden
The multisource surveillance of process data in the area network is an effective method for the early detection of Health care–associated infection receiver operating characteristic (ROC) (HAI) clusters
A total of 23,119 patients were admitted to the 4 HAI high-risk units in Wuhan Union Hospital (WHUH) during the study period
Summary
Health care–associated infections (HAIs) are a socially sensitive and important public health issue that threatens patient safety, prolongs hospital stays, and increases economic burden. In China, the extra medical expenses per HAI patient varied from 9725 to 18,909 RMB (US $1427 to 2775) [2], and the total medical costs due to HAI have increased by nearly 70% [3]. In the past 40 years, there have been 465 major HAI outbreak events in China, with an average of 11.6 outbreak events annually reported by the media [5,6]. Because a significant number of HAI outbreaks have not been detected or reported in a timely manner, the severity of HAI outbreaks in China is likely to be seriously underestimated. The cluster detection of health care–associated infections (HAIs) is crucial for identifying HAI outbreaks in the early stages
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
