Clostridium difficile exposure as an insidious source of infection in healthcare settings: an epidemiological model

Abstract

Background: Clostridium difficile is the leading cause of infectious diarrhea in hospitalized patients. Its epidemiology has shifted in recent years from almost exclusively infecting elderly patients in whom the gut microbiota has been disturbed by antimicrobials, to now also infecting individuals of all age groups with no recent antimicrobial use.

Methods: A stochastic mathematical model was constructed to simulate the modern epidemiology of C. difficile in a healthcare setting, and, to compare the efficacies of interventions.

Results: Both the rate of colonization and the incidence of symptomatic disease in hospital inpatients were insensitive to antimicrobial stewardship and to the prescription of probiotics to expedite healthy gut microbiota recovery, suggesting these to be ineffective interventions to limit transmission. Comparatively, improving hygiene and sanitation and reducing average length of stay more effectively reduced infection rates. Although the majority of new colonization events are a result of within-hospital ward exposure, simulations demonstrate the importance of imported cases with new admissions.

Conclusions: By analyzing a wide range of screening sensitivities, we identify a previously ignored source of pathogen importation: although capturing all asymptomatic as well as symptomatic introductions, individuals who are exposed but not yet colonized will be missed by even a perfectly sensitive screen on admission. Empirical studies to measure the duration of this latent period of infection will be critical to assessing C. difficile control strategies. Moreover, identifying the extent to which the exposed category of individual contributes to pathogen importation should be explicitly considered for all infections relevant to healthcare settings.

Figure 1

Compartmental design of epidemiological model for Clostridium difficile. Individuals are either ‘U’nexposed, ‘E’xposed, ‘C’olonized or ‘D’iseased and are increasingly ‘vul’nerable when they have taken antimicrobials (‘Ab’). Patients of all epidemiological states can be admitted but only discharged if not symptomatic.

Figure 2

A typical simulation using baseline model parameterization. Subplots show the percentage of Clostridium difficile colonized patients who are admitted and discharged (top), the incidence of symptomatic disease (middle) and the proportional distribution of inpatient infection status (bottom). Stochastic (Direct Gillespie algorithm) simulations are run for a hospital with 1000 beds whereby newly admitted patients perfectly balance discharged individuals.

Figure 3

The effects of different control measures on Clostridium difficile in the simulated hospital. The ratio of patients colonized with C. difficile when discharged compared to new admissions (left Y axis, points) are shown along with associated incidence of symptomatic disease per 1000 hospital bed days (right Y axis, crosses) across a wide range of the four tested control measures: reducing antimicrobial prescription (top-left), improved sanitation and hygiene (top-right), administering pro-biotics (bottom-left) and reducing the average length of stay (bottom-right). Each point is the average value of a 1-year simulation.

Figure 4

The effects of screening on Clostridium difficile in the simulated hospital. The ratio of patients that are colonized with C. difficile when discharged relative to on admission (left) are shown along with the associated incidence in symptomatic CDI per 1000 hospital bed days (right). Even highly sensitive screening (Y axis) can do little to impact either of these epidemiological outcomes. However, time to colonization (X axis), a component of the pathogen’s life course for which data is absent, is highly influential in its epidemiology.