Mathematical Modeling of the Transmission Dynamics of Clostridium difficile Infection and Colonization in Healthcare Settings: A Systematic Review

Abstract

BackgroundWe conducted a systematic review of mathematical models of transmission dynamic of Clostridium difficile infection (CDI) in healthcare settings, to provide an overview of existing models and their assessment of different CDI control strategies.MethodsWe searched MEDLINE, EMBASE and Web of Science up to February 3, 2016 for transmission-dynamic models of Clostridium difficile in healthcare settings. The models were compared based on their natural history representation of Clostridium difficile, which could include health states (S-E-A-I-R-D: Susceptible-Exposed-Asymptomatic-Infectious-Resistant-Deceased) and the possibility to include healthcare workers and visitors (vectors of transmission). Effectiveness of interventions was compared using the relative reduction (compared to no intervention or current practice) in outcomes such as incidence of colonization, CDI, CDI recurrence, CDI mortality, and length of stay.ResultsNine studies describing six different models met the inclusion criteria. Over time, the models have generally increased in complexity in terms of natural history and transmission dynamics and number/complexity of interventions/bundles of interventions examined. The models were categorized into four groups with respect to their natural history representation: S-A-I-R, S-E-A-I, S-A-I, and S-E-A-I-R-D. Seven studies examined the impact of CDI control strategies. Interventions aimed at controlling the transmission, lowering CDI vulnerability and reducing the risk of recurrence/mortality were predicted to reduce CDI incidence by 3–49%, 5–43% and 5–29%, respectively. Bundles of interventions were predicted to reduce CDI incidence by 14–84%.ConclusionsAlthough CDI is a major public health problem, there are very few published transmission-dynamic models of Clostridium difficile. Published models vary substantially in the interventions examined, the outcome measures used and the representation of the natural history of Clostridium difficile, which make it difficult to synthesize results and provide a clear picture of optimal intervention strategies. Future modeling efforts should pay specific attention to calibration, structural uncertainties, and transparent reporting practices.

Comparing Intervention Strategies for Reducing Clostridioides difficile Transmission in Acute Healthcare Settings: An Agent-Based Modeling Study

“…Similarly, our model ranked restriction of high-risk antibiotics as more effective compared with reduction on the overall antibiotic use. Overall, antibiotic stewardship has been rarely evaluated in mathematical models for C. difficile; when evaluated, antibiotic stewardship was found to have low effectiveness in decreasing CDI [44]. Yacob et al [45] reported that antibiotic stewardship was ineffective in reducing clinical diseases.…”

Disinfectant Wipes with Nonsporicidal Claims Transfer Clostridioides difficile Spores from Contaminated Surfaces to Uncontaminated Surfaces during the Disinfection Process

“…In a similar study, Becker et al did not find significant differences in the titer of viruses detected from three 25 cm 2 uncontaminated surfaces after wiping them with a QAC disinfectant wipe previously used on a contaminated surface [26]. The reported risk of cross-contamination by C. difficile spores is particularly relevant in healthcare settings as C. difficile infections have been associated with contaminated environmental surfaces [30,31,32].…”

Assessing the effect of patient screening and isolation on curtailing Clostridium difficile infection in hospital settings

“…We implemented the model stochastically, and used the Gillespie direct algorithm to simulate the stochastic model within a (non-ICU) hospital ward with 50 beds (as the population size) within the range of previous studies for C. difficile [18]. To estimate the transition time between the two consecutive events in the stochastic process, we let dt  =  − log( n 1 )/ Δ , where n 1 is a random number drawn from the uniform distribution on the unit interval [0 , 1], and Δ is equal to the sum of the rates for all possible events.…”