title: Modeling the impact of interventions against Acinetobacter baumannii transmission in intensive care units.
abstract: The efficacy of infection control interventions against Acinetobacter baumannii remains unclear, despite such information being critical for effective prevention of the transmission of this pathogen. Mathematical modeling offers an alternative to clinical trials, which may be prohibitively expensive, unfeasible or unethical, in predicting the impact of interventions. Furthermore, it allows the ability to ask key "what if" questions to evaluate which interventions have the most impact. We constructed a transmission dynamic model to quantify the effects of interventions on reducing A. baumannii prevalence and the basic reproduction ratio (R0) in intensive care units (ICUs). We distinguished between colonization and infection, and incorporated antibiotic exposure and transmission from free-living bacteria in the environment. Under the assumptions and parameterization in our model, 25% and 18% of patients are colonized and infected with A. baumannii, respectively; and R0 is 1.4. Improved compliance with hand hygiene (≥87%), enhanced environmental cleaning, reduced length of ICU stay of colonized patients (≤ 10 days), shorter durations of antibiotic treatment of A. baumannii (≤6 days), and isolation of infected patients combined with cleaning of isolation rooms are effective, reducing R0 to below unity. In contrast, expediting the recovery of the intestinal microbiota (e.g. use of probiotics) is not effective. This study represents a biologically realistic model of the transmission dynamics of A. baumannii, and the most comprehensive analysis of the effectiveness of interventions against this pathogen. Our study provides important data for designing effective infection control interventions.
authors.affiliation: a Centre for Medicine Use and Safety; Faculty of Pharmacy and Pharmaceutical Sciences; Monash University ; Melbourne , VIC Australia.|b Victorian Infectious Diseases Service; Royal Melbourne Hospital ; Melbourne , VIC Australia.|c The Peter Doherty Institute for Infection and Immunity ; Melbourne , VIC Australia.
authors.forename: Tan N
authors.initials: TN
authors.lastname: Doan
authors.affiliation: a Centre for Medicine Use and Safety; Faculty of Pharmacy and Pharmaceutical Sciences; Monash University ; Melbourne , VIC Australia.
authors.forename: David C M
authors.initials: DC
authors.lastname: Kong
authors.affiliation: b Victorian Infectious Diseases Service; Royal Melbourne Hospital ; Melbourne , VIC Australia.|c The Peter Doherty Institute for Infection and Immunity ; Melbourne , VIC Australia.|d Department of Medicine ; University of Melbourne ; Melbourne , VIC Australia.
authors.forename: Caroline
authors.initials: C
authors.lastname: Marshall
authors.affiliation: a Centre for Medicine Use and Safety; Faculty of Pharmacy and Pharmaceutical Sciences; Monash University ; Melbourne , VIC Australia.
authors.forename: Carl M J
authors.initials: CM
authors.lastname: Kirkpatrick
authors.affiliation: b Victorian Infectious Diseases Service; Royal Melbourne Hospital ; Melbourne , VIC Australia.|c The Peter Doherty Institute for Infection and Immunity ; Melbourne , VIC Australia.|d Department of Medicine ; University of Melbourne ; Melbourne , VIC Australia.
authors.forename: Emma S
authors.initials: ES
authors.lastname: McBryde
mesh_terms: D000151:Acinetobacter Infections / Q000382:microbiology / Q000517:prevention & control / Q000635:transmission*; D003428:Cross Infection / Q000517:prevention & control; D004196:Disease Outbreaks / Q000517:prevention & control; D024901:Drug Resistance, Multiple, Bacterial; D006801:Humans; D017053:Infection Control / Q000379:methods*; D007362:Intensive Care Units; D008962:Models, Theoretical
keywords: Acinetobacter baumannii; infection control; intensive care units; mathematical modeling; transmission dynamics