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<jats:p>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 <jats:italic>Clostridioides difficile</jats:italic> 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 <jats:italic>C</jats:italic>. <jats:italic>difficile</jats:italic> 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 <jats:italic>C</jats:italic>. <jats:italic>difficile</jats:italic>.</jats:p>

Original publication

DOI

10.1371/journal.pcbi.1008417

Type

Journal

PLOS Computational Biology

Publisher

Public Library of Science (PLoS)

Publication Date

14/01/2021

Volume

17

Pages

e1008417 - e1008417