Atkinson, A.; Ellenberger, B; Piezzi, V.; Kaspar, T.; Endrich, O.; Leichtle, A.B.; Zwahlen, M.; Marschall, J. (2022). A Bayesian spatial-temporal model for prevalence estimation of a VRE outbreak in a tertiary care hospital. Journal of hospital infection, 122, pp. 108-114. Elsevier 10.1016/j.jhin.2021.12.024
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BACKGROUND
There was a nosocomial outbreak of vancomycin-resistant enterococci (VRE) in our hospital from 1.1.2018 to 31.7.2020. The goals of the study were to describe weekly prevalence, and to identify possible effects of the introduction of selected infection control measures.
METHODS
We performed a room centric analysis of 12 floors (243 rooms) of the main hospital building, including data on 37,558 patients over 22,072 person weeks for the first two years of the outbreak (2018-19). Poisson Bayesian hierarchical models were fitted to estimate prevalence per room and week, including both spatial and temporal random effects terms.
RESULTS
Exploratory data analysis revealed significant variability in prevalence between departments and floors, along with sporadic spatial and temporal clustering during colonization "flare-ups". The oncology department experienced slightly higher prevalence over the 104 week study period (adjusted prevalence ratio (aPR) 4.8 [2.6, 8.9], p<0.001, compared to general medicine), as did both the cardiac surgery (aPR 3.8 [2.0, 7.3], p<0.001) and abdominal surgery departments (aPR 3.7 [1.8, 7.6], p<0.001). Estimated peak prevalence was reached in July 2018, at which point a number of new infection control measures (including the daily disinfection of rooms and room cleaning with UV light upon patient discharge) were introduced that resulted in a decreasing prevalence (aPR=0.89 per week, 95% CI [0.87, 0.91], p<0.001).
CONCLUSION
Relatively straightforward, but personnel-intensive cleaning with disinfectants and UV light provided tangible benefits in getting the outbreak under control. Despite additional complexity, Bayesian Hierarchical Models provide a more flexible platform for studying transmission dynamics.
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