Individuals Exposed to C. diff Will be Missed by Even a Sensitive Screen on Admission

Article

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. Yakob, et al. (2013) constructed a stochastic mathematical model to simulate the modern epidemiology of C. difficile in a healthcare setting and to compare the efficacies of interventions.
 
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.

By analyzing a wide range of screening sensitivities, the researchers identified 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.

Reference: Yakob L, Riley TV, Paterson DL and Clements AC. Clostridium difficile exposure as an insidious source of infection in healthcare settings: an epidemiological model. BMC Infectious Diseases 2013, 13:376 doi:10.1186/1471-2334-13-376
 

 

 

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