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302 Modeling the Spread of Methicillin-Resistant Staphylococcus aureus Throughout Orange County, California

Saturday, April 2, 2011: 4:00 PM
Coronado A (Hilton Anatole)
Sarah M. McGlone, MPH , University of Pittsburgh, Pittsburgh, PA
Susan S. Huang, MD, MPH , Division of Infectious Diseases and Health Policy Research Institute, University of California, Irvine School of Medicine, Irvine, CA
Kim F. Wong, PhD , University of Pittsburgh, Pittsburgh, PA
S. Levent Yilmaz, PhD , University of Pittsburgh, Pittsburgh, PA
Yeohan Song , University of Pittsburgh, Pittsburgh, PA
Taliser R. Avery, BS , Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
Richard Christie, PhD , University of Pittsburgh, Pittsburgh, PA
Shawn T. Brown, PhD , Pittsburgh Supercomputing Center, Pittsburgh, PA
Joshua Epstein, PhD , Johns Hopkins University, Baltimore, MD
Stephen Eubank, PhD , Virginia Bioinformatics Institute, Blacksburg, VA
Donald S. Burke, MD , University of Pittsburgh, Pittsburgh, PA
Richard Platt, MD , Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, MA
Bruce Y. Lee, MD, MBA , University of Pittsburgh, Pittsburgh, PA
Background: Methicillin-resistant Staphylococcus aureus (MRSA) is a substantial public health problem that continues to cause considerable morbidity and mortality.  Since hospitals are prime transmission locations and hospitals in a region often share patients, an outbreak in one hospital could affect other hospitals.  Understanding the spread of MRSA outbreaks can help develop county-wide, rather than single institution, policy and control measures.  

Objective: We endeavored to determine how a MRSA outbreak in each hospital could subsequently spread to other hospitals in Orange County (OC), California.  

 

Methods: Utilizing extensive data from OC health care facilities, we developed an agent-based model to represent patient movement among all the hospitals in OC.  Each agent represented a patient with its own set of characteristics (e.g., age, gender, MRSA colonization status) and each hospital consisted of ICUs and multiple general wards.  Upon admission to the hospital, an agent could enter either a general ward or ICU, where he/she remained for a length of stay drawn from a distribution specific to that hospital ward.  Each day, MRSA positive patients could transmit MRSA to other patients within the same ward based on that ward’s transmission coefficient (β) and the number of susceptible and infectious patients in that ward.  Upon discharge, a patient could be transferred directly to another hospital or return to the community and subsequently could be readmitted to the same or a different hospital.  Experiments simulated MRSA outbreaks in various wards, institutions, and regions.  Sensitivity analysis varied length of stay, MRSA loss rate, probability of transfer or readmission, and time to readmission.

Results: Each outbreak eventually percolated throughout all the hospitals in the network with effects depending on the outbreak size and location.  A MRSA prevalence increase in one hospital (from 5% to 15%) resulted in an average relative increase in prevalence of 2.9% in all other OC hospitals (range: no effect to 46.4%).  Long term acute care facilities showed the greatest relative change.  A MRSA increase from 5% to 50% had an even greater effect on the network.  A regional outbreak (all hospitals in the City of Orange) caused an average relative increase of 7.3% in other hospitals (range: 3.0% to 58.3%).  An outbreak in on hospital’s ICUs caused an average relative change of 1.1% in all other OC hospitals (range: no effect to 12.7%), with long term acute care facilities experiencing the greatest change in 89% of outbreaks.    

Conclusions: MRSA outbreaks may rarely be confined to a single hospital, but instead may affect all the hospitals in a region, suggesting that MRSA control strategies and policies should account for the fact that hospitals are highly interconnected by patient sharing.