A Bayesian spatial-temporal model for prevalence estimation of a VRE outbreak in a tertiary care hospital.

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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Institute of Clinical Chemistry
04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Infectiology
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)

UniBE Contributor:

Atkinson, Andrew David, Piezzi, Vanja, Kaspar, Tanja, Endrich, Olga, Leichtle, Alexander Benedikt (B), Zwahlen, Marcel, Marschall, Jonas

Subjects:

600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services

ISSN:

0195-6701

Publisher:

Elsevier

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Andrea Flükiger-Flückiger

Date Deposited:

14 Feb 2022 19:53

Last Modified:

02 Mar 2023 23:35

Publisher DOI:

10.1016/j.jhin.2021.12.024

PubMed ID:

35090955

Uncontrolled Keywords:

Bayesian modelling Outbreak VRE prevalence screening

BORIS DOI:

10.48350/165122

URI:

https://boris.unibe.ch/id/eprint/165122

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