Development and Application of a Novel Dose-Response Model for the Quantification of Clostridioides difficile Infection Risks.

The lack of a dose-response model for Clostridioides difficile, attributed to insufficient human data, challenges effective C. difficile infection (CDI) risk management. This study presents a novel murine-derived dose-response model for C. difficile, developed from experimental data that closely mirrors human CDI, advancing the field of quantitative microbial risk assessment (QMRA) and the strategic management of this pathogen. Animal dose-response datasets for C. difficile were evaluated against established criteria, and a mouse model of C. difficile-associated colitis that mirrors critical aspects of CDI in human beings was selected as the most appropriate dataset for analysis. Using maximum likelihood estimation in R, these data were fitted to the beta-Poisson and exponential models and assessed through Akaike information criterion, Bayesian information criterion, likelihood tests, and a sensitivity analysis. The beta-Poisson model was identified as the best fit (α = 0.56 and N50 = 2871.56) with estimated mean doses for 10% and 50% infection rates of 505 and 3994 CFU, respectively. This model was then applied in a QMRA framework to assess CDI risk associated with commonly encountered clinical surfaces based on published surface contamination data and healthcare worker contact rates. Annual risk estimates from the model's application suggest that, out of every 100,000 healthcare workers exposed in clinical settings, approximately 33 CDI cases could result from contact with bed rails, 12 from computer keyboards, and fewer than 1 from door handles. By providing a formally derived dose-response relationship for C. difficile, this study offers a starting point for future QMRA research on this pathogen and supports the development of more detailed, context-specific risk assessments for C. difficile.