title: Universal methicillin-resistant Staphylococcus aureus (MRSA) surveillance for adults at hospital admission: an economic model and analysis.
abstract: BACKGROUND
  Methicillin-resistant Staphylococcus aureus (MRSA) transmission and infections are a continuing problem in hospitals. Although some have recommended universal surveillance for MRSA at hospital admission to identify and to isolate MRSA-colonized patients, there is a need for formal economic studies to determine the cost-effectiveness of such a strategy.
  
  
  METHODS
  We developed a stochastic computer simulation model to determine the potential economic impact of performing MRSA surveillance (ie, single culture of an anterior nares specimen) for all hospital admissions at different MRSA prevalences and basic reproductive rate thresholds from the societal and third party-payor perspectives. Patients with positive surveillance culture results were placed under isolation precautions to prevent transmission by way of respiratory droplets. MRSA-colonized patients who were not isolated could transmit MRSA to other hospital patients.
  
  
  RESULTS
  The performance of universal MRSA surveillance was cost-effective (defined as an incremental cost-effectiveness ratio of less than $50,000 per quality-adjusted life-year) when the basic reproductive rate was 0.25 or greater and the prevalence was 1% or greater. In fact, surveillance was the dominant strategy when the basic reproductive rate was 1.5 or greater and the prevalence was 15% or greater, the basic reproductive rate was 2.0 or greater and the prevalence was 10% or greater, and the basic reproductive rate was 2.5 or greater and the prevalence was 5% or greater.
  
  
  CONCLUSIONS
  Universal MRSA surveillance of adults at hospital admission appears to be cost-effective at a wide range of prevalence and basic reproductive rate values. Individual hospitals and healthcare systems could compare their prevailing conditions (eg, the prevalence of MRSA colonization and MRSA transmission dynamics) with the benchmarks in our model to help determine their optimal local strategies.
authors.affiliation: Section of Decision Sciences and Clinical Systems Modeling, Veterans Affairs Pittsburgh Health Care System, Pittsburgh, Pennsylvania, USA. BYL1@pitt.edu
authors.forename: Bruce Y
authors.initials: BY
authors.lastname: Lee
authors.forename: Rachel R
authors.initials: RR
authors.lastname: Bailey
authors.forename: Kenneth J
authors.initials: KJ
authors.lastname: Smith
authors.forename: Robert R
authors.initials: RR
authors.lastname: Muder
authors.forename: Elsa S
authors.initials: ES
authors.lastname: Strotmeyer
authors.forename: G Jonathan
authors.initials: GJ
authors.lastname: Lewis
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Ufberg
authors.forename: Yeohan
authors.initials: Y
authors.lastname: Song
authors.forename: Lee H
authors.initials: LH
authors.lastname: Harrison
mesh_terms: D000328:Adult; D003198:Computer Simulation; D003362:Cost-Benefit Analysis / Q000191:economics; D006801:Humans; D008403:Mass Screening; D055624:Methicillin-Resistant Staphylococcus aureus / Q000302:isolation & purification*; D017059:Models, Econometric; D010343:Patient Admission / Q000191:economics*; D011159:Population Surveillance; D013203:Staphylococcal Infections / Q000175:diagnosis* / Q000191:economics
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