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

Bayes Theorem Cross Infection / epidemiology Disease Outbreaks Gram-Positive Bacterial Infections / epidemiology Humans Prevalence Tertiary Care Centers Vancomycin-Resistant Enterococci
Bayesian modelling Outbreak Prevalence Screening VRE

Journal

The Journal of hospital infection
ISSN: 1532-2939
Titre abrégé: J Hosp Infect
Pays: England
ID NLM: 8007166

Informations de publication

Date de publication:
Apr 2022
Historique:
received: 02 11 2021
revised: 01 12 2021
accepted: 02 12 2021
pubmed: 30 1 2022
medline: 1 4 2022
entrez: 29 1 2022
Statut: ppublish

Résumé

There was a nosocomial outbreak of vancomycin-resistant enterococci (VRE) at the hospital between 1 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. 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). 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.

Sections du résumé

BACKGROUND BACKGROUND
There was a nosocomial outbreak of vancomycin-resistant enterococci (VRE) at the hospital between 1
METHODS 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 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 CONCLUSIONS
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.

Identifiants

DOI: 10.1016/j.jhin.2021.12.024 PMID: 35090955
pubmed: 35090955
pii: S0195-6701(22)00012-3
doi: 10.1016/j.jhin.2021.12.024
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

108-114

Informations de copyright

Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Auteurs

A Atkinson (A)

Department of Infectious Diseases, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland. Electronic address: andrew.atkinson@insel.ch.

B Ellenberger (B)

Insel Data Science Centre, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.

V Piezzi (V)

Department of Infectious Diseases, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.

T Kaspar (T)

Department of Infectious Diseases, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.

O Endrich (O)

Medical Directorate, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.

A B Leichtle (AB)

Insel Data Science Centre, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland; University Institute of Clinical Chemistry, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.

M Zwahlen (M)

Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.

J Marschall (J)

Department of Infectious Diseases, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland; Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA.

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Classifications MeSH

questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Probabilité › Théorème de Bayes › A Bayesian spatiotemporal model for prevalence...
questionsmedicales.fr › États, signes et symptômes pathologiques › Processus pathologiques › Caractéristiques de la maladie › Maladie iatrogène › Infection croisée › A Bayesian spatiotemporal model for prevalence...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Épidémies de maladies › A Bayesian spatiotemporal model for prevalence...
questionsmedicales.fr › Infection › Infections bactériennes et mycoses › Infections bactériennes › Infections bactériennes à Gram positif › A Bayesian spatiotemporal model for prevalence...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › A Bayesian spatiotemporal model for prevalence...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Collecte de données › Registre civil › Morbidité › Prévalence › A Bayesian spatiotemporal model for prevalence...
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questionsmedicales.fr › Bactéries › Bactéries à Gram positif › Lactobacillales › Enterococcaceae › Enterococcus › Entérocoques résistants à la vancomycine › A Bayesian spatiotemporal model for prevalence...