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Probabilistic transmission models incorporating sequencing data for healthcare-associated Clostridioides difficile outperform heuristic rules and identify strain-specific differences in transmission

Fitting stochastic transmission models to electronic patient data can offer detailed insights into the transmission of healthcare-associated infections and improve infection control. Pathogen whole-genome sequencing may improve the precision of model inferences, but computational constraints have li...

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Autores principales: Eyre, David W., Laager, Mirjam, Walker, A. Sarah, Cooper, Ben S., Wilson, Daniel J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7840057/
https://www.ncbi.nlm.nih.gov/pubmed/33444378
http://dx.doi.org/10.1371/journal.pcbi.1008417
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author Eyre, David W.
Laager, Mirjam
Walker, A. Sarah
Cooper, Ben S.
Wilson, Daniel J.
author_facet Eyre, David W.
Laager, Mirjam
Walker, A. Sarah
Cooper, Ben S.
Wilson, Daniel J.
author_sort Eyre, David W.
collection PubMed
description Fitting stochastic transmission models to electronic patient data can offer detailed insights into the transmission of healthcare-associated infections and improve infection control. Pathogen whole-genome sequencing may improve the precision of model inferences, but computational constraints have limited modelling applications predominantly to small datasets and specific outbreaks, whereas large-scale sequencing studies have mostly relied on simple rules for identifying/excluding plausible transmission. We present a novel approach for integrating detailed epidemiological data on patient contact networks in hospitals with large-scale pathogen sequencing data. We apply our approach to study Clostridioides difficile transmission using a dataset of 1223 infections in Oxfordshire, UK, 2007–2011. 262 (21% [95% credibility interval 20–22%]) infections were estimated to have been acquired from another known case. There was heterogeneity by sequence type (ST) in the proportion of cases acquired from another case with the highest rates in ST1 (ribotype-027), ST42 (ribotype-106) and ST3 (ribotype-001). These same STs also had higher rates of transmission mediated via environmental contamination/spores persisting after patient discharge/recovery; for ST1 these persisted longer than for most other STs except ST3 and ST42. We also identified variation in transmission between hospitals, medical specialties and over time; by 2011 nearly all transmission from known cases had ceased in our hospitals. Our findings support previous work suggesting only a minority of C. difficile infections are acquired from known cases but highlight a greater role for environmental contamination than previously thought. Our approach is applicable to other healthcare-associated infections. Our findings have important implications for effective control of C. difficile.
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spelling pubmed-78400572021-02-02 Probabilistic transmission models incorporating sequencing data for healthcare-associated Clostridioides difficile outperform heuristic rules and identify strain-specific differences in transmission Eyre, David W. Laager, Mirjam Walker, A. Sarah Cooper, Ben S. Wilson, Daniel J. PLoS Comput Biol Research Article Fitting stochastic transmission models to electronic patient data can offer detailed insights into the transmission of healthcare-associated infections and improve infection control. Pathogen whole-genome sequencing may improve the precision of model inferences, but computational constraints have limited modelling applications predominantly to small datasets and specific outbreaks, whereas large-scale sequencing studies have mostly relied on simple rules for identifying/excluding plausible transmission. We present a novel approach for integrating detailed epidemiological data on patient contact networks in hospitals with large-scale pathogen sequencing data. We apply our approach to study Clostridioides difficile transmission using a dataset of 1223 infections in Oxfordshire, UK, 2007–2011. 262 (21% [95% credibility interval 20–22%]) infections were estimated to have been acquired from another known case. There was heterogeneity by sequence type (ST) in the proportion of cases acquired from another case with the highest rates in ST1 (ribotype-027), ST42 (ribotype-106) and ST3 (ribotype-001). These same STs also had higher rates of transmission mediated via environmental contamination/spores persisting after patient discharge/recovery; for ST1 these persisted longer than for most other STs except ST3 and ST42. We also identified variation in transmission between hospitals, medical specialties and over time; by 2011 nearly all transmission from known cases had ceased in our hospitals. Our findings support previous work suggesting only a minority of C. difficile infections are acquired from known cases but highlight a greater role for environmental contamination than previously thought. Our approach is applicable to other healthcare-associated infections. Our findings have important implications for effective control of C. difficile. Public Library of Science 2021-01-14 /pmc/articles/PMC7840057/ /pubmed/33444378 http://dx.doi.org/10.1371/journal.pcbi.1008417 Text en © 2021 Eyre et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Eyre, David W.
Laager, Mirjam
Walker, A. Sarah
Cooper, Ben S.
Wilson, Daniel J.
Probabilistic transmission models incorporating sequencing data for healthcare-associated Clostridioides difficile outperform heuristic rules and identify strain-specific differences in transmission
title Probabilistic transmission models incorporating sequencing data for healthcare-associated Clostridioides difficile outperform heuristic rules and identify strain-specific differences in transmission
title_full Probabilistic transmission models incorporating sequencing data for healthcare-associated Clostridioides difficile outperform heuristic rules and identify strain-specific differences in transmission
title_fullStr Probabilistic transmission models incorporating sequencing data for healthcare-associated Clostridioides difficile outperform heuristic rules and identify strain-specific differences in transmission
title_full_unstemmed Probabilistic transmission models incorporating sequencing data for healthcare-associated Clostridioides difficile outperform heuristic rules and identify strain-specific differences in transmission
title_short Probabilistic transmission models incorporating sequencing data for healthcare-associated Clostridioides difficile outperform heuristic rules and identify strain-specific differences in transmission
title_sort probabilistic transmission models incorporating sequencing data for healthcare-associated clostridioides difficile outperform heuristic rules and identify strain-specific differences in transmission
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7840057/
https://www.ncbi.nlm.nih.gov/pubmed/33444378
http://dx.doi.org/10.1371/journal.pcbi.1008417
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