A One Health/System Dynamics approach to reducing Clostridioides difficile infection (NHRMC Ideas Grant administered by University of Western Australia)

Clostridioides (Clostridium) difficile is an antimicrobial-resistant (AMR) gastrointestinal pathogen that causes life-threatening diarrhoea. The incidence of C. difficile infection (CDI) continues to increase in Australia despite a concerted hospital-based infection prevention and control program and antimicrobial stewardship. This is important because the US Centers for Disease Control and Prevention rates C. difficile in its top 5 most “urgent” AMR threats, causing over 460,000 infections and over 20,000 deaths in the US annually.We have compelling evidence that many human cases of CDI arise from direct or indirect exposure to food, soil or compost, contaminated with animal manure containing C. difficile. We believe that     the continued rise in CDI  is due largely to infection in the community and that environmental sources are a critical preventable source of infection.This project will identify and cost novel interventions to reduce the incidence of human CDI in Australia, by testing the hypotheses that (i) contaminated food, soil and other environments are important sources of human CDI, and (ii) SD modelling can provide insights into the development of community-acquired CDI (CA-CDI) and identify new points of intervention.

Project Objectives

1. Characterise the patterns and predictors of CDI in humans; key factors associated with C. difficile amplification and transmission in animal husbandry; and contamination in animal and human environments.
2. Using the information acquired in Objective 1, develop a causal loop diagram (CLD) to map the interactions and feedback loops among human and animal system components to visualise and understand how C. difficile is transmitted and amplified.
3. Develop an SD model based on the CLD to simulate the amplification and transmission of C. difficile within and between humans, agriculture and the environment (part A), and identify novel intervention points to reduce the incidence of human CDI (part B).
4. Estimate the cost-effectiveness of potential interventions simulated using the SD model.

Collaborators

• UQ (Public health)
• UWA
• Edith Cowan University
• QUT