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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
- Source :
- PLoS Computational Biology, Vol 17, Iss 1, p e1008417 (2021), PLoS Computational Biology
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- 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.<br />Author summary Preventing infections spreading in hospitals is a major priority for healthcare systems globally. Mathematical models can be used with electronic hospital records to reconstruct how infections spread, which in turn can help guide infection control interventions. Sequencing the genetic code (DNA) of the bacteria that cause infections can help to follow when transmission is occurring, as bacterial DNA from two patients infected from the same source will likely be very similar. Our paper describes a new statistical method for combining hospital records and sequencing data to track infections. We use our approach to study over 1200 infections of Clostridioides difficile (C. diff.), which is a common cause of diarrhoea in hospitals. We show that only a minority of infections are acquired from other unwell patients, but the amount of spread varies by the subtype of C. diff involved. We also show that different C. diff subtypes survive in the hospital environment for longer than others and may need enhanced control strategies. We also can detect differences in spread at different hospitals and show that by the end of the study we had largely eliminated transmission of C. diff from unwell patients in our hospitals.
- Subjects :
- 0301 basic medicine
Nosocomial Infections
Epidemiology
law.invention
Disease Outbreaks
0302 clinical medicine
Medical Conditions
law
Microbial Physiology
Credible interval
Environmental Microbiology
Medicine and Health Sciences
Infection control
Heuristics
030212 general & internal medicine
Bacterial Physiology
Biology (General)
Cross Infection
Ecology
Simulation and Modeling
Genomics
Hospitals
Transmission (mechanics)
Infectious Diseases
Computational Theory and Mathematics
Modeling and Simulation
Research Article
medicine.medical_specialty
Heuristic (computer science)
Clostridium Difficile
QH301-705.5
Sequencing data
Computational biology
Biology
Research and Analysis Methods
Microbiology
03 medical and health sciences
Cellular and Molecular Neuroscience
medicine
Genetics
Humans
Bacterial Spores
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Models, Statistical
Bacteria
Clostridioides difficile
Gut Bacteria
Probabilistic logic
Organisms
Computational Biology
Biology and Life Sciences
Bacteriology
Human Genetics
United Kingdom
Health Care
030104 developmental biology
Health Care Facilities
Clostridium Infections
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 17
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....30c95bacb53e41e70d222596a8ce988e