Quantitative summarization of high-touch surfaces and epidemiological parameters of Clostridioides difficile...

The study aimed to summarize estimates of key epidemiological parameters to improve the effectiveness of Clostridioides difficile infection (CDI) mathematical models and quantitatively characterize high-touch surfaces (HTSs) and mutual-touch surfaces in healthcare settings.

We systematically searched four databases and applied predefined eligibility criteria to screen, select, and include peer-reviewed studies in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The study is registered in the International Prospective Register of Systematic Reviews (CRD42023408483).

Among the 21 C. difficile infection modeling studies, 76.2% used compartmental model approaches that group patients into infection disease categories such as susceptible, infected, or recovered, while 23.8% applied agent-based model approaches that simulate individual patients, staff, or surfaces. Key epidemiological parameters varied widely: estimates of how many new cases one patient could cause-the basic reproduction number (R₀)-ranged from 0.28, suggesting limited hospital spread, to as high as 2.6, which implies sustained in-hospital transmission. Incubation periods were reported between 4 and 18 days. Recovery and recurrence rates also differed across studies. Quantitative HTSs ranking revealed that bed rails, bedside tables, and supply carts were the top three most frequently touched surfaces.

Our findings highlight that modeling studies used different assumptions and estimates, creating variations in results. Clinicians should interpret modeling outputs, such as predicted spread or effectiveness of an intervention carefully, as differences may reflect real-world variation between hospitals or methodological variation. Developing infection models that reflect real-world conditions will enable healthcare teams better simulate and prioritize interventions, optimize cleaning protocols, and improve CDI transmission models for more targeted prevention.