Grant: Operating Grant

Clostridium difficile (CD) is the most common cause of infectious diarrhea in hospitalized patients worldwide, and the leading cause of healthcare-associated infections in North America. In Canada, despite investments of $135 million in treatment and control, CD Infection (CDI) remains a major public health problem with 15,000 new cases and 1,500 associated deaths each year. New technologies (diagnostic tests and vaccines) and prevention policies (antibiotic stewardship) offer tremendous promise for the reduction of hospital-associated CDI. However, despite the need for evidence, empirical trials to identify optimal combination of CDI control policies are unfeasible due to their prohibitive costs (e.g., large sample size), challenges in comparing many strategies within one study, and issues of generalization of results (e.g., best strategy may vary according to hospital characteristics and population). Mathematical models have proven useful in these situations, as they provide a formal framework to integrate data from various sources (e.g., clinical trials, epidemiological studies) to make predictions about effectiveness and cost-effectiveness for many different what-if scenarios. Very few transmission-dynamic modeling studies have examined CDI control, and none have examined the optimal use of vaccination, screening and different control strategies. To fill this important gap in knowledge, our multidisciplinary team will use state-of-the-art transmission-dynamic modeling techniques combined with unique high-quality data to answer the following key question: What are the most effective and cost-effective vaccination, screening and infection control strategies to reduce CDI in Canada, depending on hospital characteristics and patient population? Our analysis directly addresses the needs of policy-makers for evidence on optimal CDI control policies. Ultimately, our results will be translated into customized messages for maximum adoption by policymakers.