Objective: To develop and disseminate a spatially explicit model of contact transmission of pathogens in the intensive care unit. Design: A model simulating the spread of a pathogen transmitted by direct contact (such as methicillin-resistant Staphylococcus aureus or vancomycin-resistant Enterococcus) was constructed. The modulation of pathogen dissemination attending changes in clinically relevant pathogen- and institution-specific factors was then systematically examined. Setting and Patients: The model was configured as a hypothetical 24-bed intensive care unit. The model can be parameterized with different pathogen transmissibilities, durations of caregiver and/or patient contamination, and caregiver allocation and flow patterns. Interventions: Pathogen- and institution-specific factors examined included pathogen transmissibility, duration of caregiver contamination, regional cohorting of contaminated or infected patients, delayed detection and isolation of newly contaminated patients, reduction of the number of caregiver visits, and alteration of caregiver allocation among patients. Measurements and Main Results: The model predicts the probability that a given fraction of the population will become contaminated or infected with the pathogen of interest under specified spatial, initial prevalence, and dynamic conditions. Perencounter pathogen acquisition risk and the duration of caregiver pathogen carriage most strongly affect dissemination. Regional cohorting and rapid detection and isolation of contaminated patients each markedly diminish the likelihood of dissemination even absent other interventions. Strategies reducing "crossover" between caregiver domains diminish the likelihood of more wide-spread dissemination. Conclusions: Spatially explicit discrete element models, such as the model presented, may prove useful for analyzing the transmission of pathogens within the intensive care unit.
|Original language||English (US)|
|Number of pages||9|
|Journal||Critical care medicine|
|State||Published - Jan 2005|
- Infection control
- Mathematical model