A Bayesian spatiotemporal model for prevalence estimation of a VRE outbreak in a tertiary care hospital

Background: There was a nosocomial outbreak of vancomycin-resistant enterococci (VRE) at the hospital between 1^st January 2018 and 31^st July 2020. The goals of this study were to describe weekly prevalence, and to identify possible effects of the introduction of selected infection control measures. Methods: A room-centric analysis of 12 floors (243 rooms) of the main hospital building was undertaken, including data on 37,558 patients over 22,072 person-weeks for the first 2 years of the outbreak (2018–2019). Poisson Bayesian hierarchical models were fitted to estimate prevalence per room and per 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, 95% confidence interval (CI) 2.6–8.9; P<0.001; compared with general medicine], as did both the cardiac surgery (aPR 3.8, 95% CI 2.0–7.3; P<0.001) and abdominal surgery (aPR 3.7, 95% CI 1.8–7.6; P<0.001) departments. 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 ultraviolet light upon patient discharge) were introduced that resulted in 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 ultraviolet light provided tangible benefits in getting the outbreak under control. Despite additional complexity, Bayesian hierarchical models provide a more flexible platform to study transmission dynamics.