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 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.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.