Project title: Quantitative Microbial Risk Assessment of Methicillin-resistant Staphylococcus aureus Present in Swine Confined Animal Feeding Operations
Completed in: 2010
Background: The use of antibiotics in swine confined animal feeding operations (CAFOs) is believed to be contributing to an increasing prevalence of antibiotic resistant bacteria in the swine population, and in the facilities. Antibiotic resistant bacteria in these CAFOs pose both an occupational threat and community threat. Among the resistant bacteria found when sampling swine CAFOs, methicillin-resistant Staphylococcus aureus (MRSA) is of highest concern. MRSA has been cultured from both swine and swine workers in the Netherlands, Canada and the United States. Additionally, antibiotic resistant bacteria have been reported as far as 300m downwind of CAFOs.
Methods: In this study, we assessed the risks posed to worker health by applying quantitative microbial risk assessment (QMRA) to MRSA in swine CAFOs. Probabilistic 2D modeling was used to characterize routes of exposure to swine workers during a typical work-day and a Beta-Poisson model was used to predict the dose-response of MRSA colonization. We parameterized our exposure model using data from peer-reviewed literature on CAFO bioaerosol concentrations, inhalation rates for laborers, fomite transfer rates, die-off of MRSA, etc. We parameterized the dose-response model based on data from S. aureus inoculation trials in humans.
Results: A model has been developed to measure swine worker exposure and assess the risk of colonization. Workers are potentially exposed, through inhalation and fomite-to-hand transfer with autoinoculation, to as much as 108 CFU of MRSA within an average workday in the swine confinement building. The predicted risk of colonization is much higher than seen in available epidemiology data. QMRA was also effective in identifying data gaps in the current peer-reviewed literature.
Conclusions: Swine workers are at high risk for colonization with MRSA and further attention is needed in evaluating possible interventions. Inevitable uncertainty present in this QMRA highlights the need to conduct further research to fill necessary data gaps.