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. 2011 Jun;32(6):562-72.
doi: 10.1086/660014.

Modeling the spread of methicillin-resistant Staphylococcus aureus (MRSA) outbreaks throughout the hospitals in Orange County, California

Bruce Y Lee  1 , Sarah M McGlone, Kim F Wong, S Levent Yilmaz, Taliser R Avery, Yeohan Song, Richard Christie, Stephen Eubank, Shawn T Brown, Joshua M Epstein, Jon I Parker, Donald S Burke, Richard Platt, Susan S Huang
Affiliations

Modeling the spread of methicillin-resistant Staphylococcus aureus (MRSA) outbreaks throughout the hospitals in Orange County, California

Bruce Y Lee et al. Infect Control Hosp Epidemiol. 2011 Jun.

Erratum in

  • Infect Control Hosp Epidemiol. 2011 Sep;32(9):938

Abstract

Background: Since hospitals in a region often share patients, an outbreak of methicillin-resistant Staphylococcus aureus (MRSA) infection in one hospital could affect other hospitals.

Methods: Using extensive data collected from Orange County (OC), California, we developed a detailed agent-based model to represent patient movement among all OC hospitals. Experiments simulated MRSA outbreaks in various wards, institutions, and regions. Sensitivity analysis varied lengths of stay, intraward transmission coefficients (β), MRSA loss rate, probability of patient transfer or readmission, and time to readmission.

Results: Each simulated outbreak eventually affected all of the hospitals in the network, with effects depending on the outbreak size and location. Increasing MRSA prevalence at a single hospital (from 5% to 15%) resulted in a 2.9% average increase in relative prevalence at all other hospitals (ranging from no effect to 46.4%). Single-hospital intensive care unit outbreaks (modeled increase from 5% to 15%) caused a 1.4% average relative increase in all other OC hospitals (ranging from no effect to 12.7%).

Conclusion: MRSA outbreaks may rarely be confined to a single hospital but instead may affect all of the hospitals in a region. This suggests that prevention and control strategies and policies should account for the interconnectedness of health care facilities.

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Figures

Figure 1
Figure 1
Schematic flow of patients within the network and transmission.
Figure 2
Figure 2
Effects of outbreak on other hospitals over time.
Figure 3
Figure 3
Bubble maps for hospital-specific lengths of stay, with increased probability of transfer and delayed readmission, for a 15% increase in prevalence of methicillin-resistant Staphylococcus aureus (MRSA). Each column represents a single experiment. The number above each column indicates the hospital experiencing the outbreak. Each row in the column lists the relative change in prevalence from preoutbreak levels for all other Orange County hospitals.
Figure 4
Figure 4
Scatterplots of effects of (A) outbreaks on the network, (B) outbreaks on hospitals, (C) out-degree on the network (the total number of hospitals to which a given hospital sends patients), and (D) in-degree on hospitals (the total number of hospitals from which a given hospital receives patients).
Figure 5
Figure 5
Effects of a 3-month outbreak on other Orange County hospitals.
Figure 6
Figure 6
Bubble maps for hospital-specific lengths of stay, with increased probability of transfer and delayed readmission, for a 50% increase in prevalence of methicillin-resistant Staphylococcus aureus (MRSA). Each column represents a single experiment. The number on the top of each column is the hospital experiencing the outbreak. Each row in the column lists the relative change in prevalence from preoutbreak levels for all other Orange County hospitals.
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