The potential economic value of screening hospital admissions for Clostridium difficile

  • S. M. Bartsch
  • S. R. Curry
  • L. H. Harrison
  • B. Y. Lee
Article

DOI: 10.1007/s10096-012-1681-z

Cite this article as:
Bartsch, S.M., Curry, S.R., Harrison, L.H. et al. Eur J Clin Microbiol Infect Dis (2012) 31: 3163. doi:10.1007/s10096-012-1681-z

Abstract

Asymptomatic Clostridium difficile carriage has a prevalence reported as high as 51–85 %; with up to 84 % of incident hospital-acquired infections linked to carriers. Accurately identifying carriers may limit the spread of Clostridium difficile. Since new technology adoption depends heavily on its economic value, we developed an analytic simulation model to determine the cost-effectiveness screening hospital admissions for Clostridium difficile from the hospital and third party payer perspectives. Isolation precautions were applied to patients testing positive, preventing transmission. Sensitivity analyses varied Clostridium difficile colonization rate, infection probability among secondary cases, contact isolation compliance, and screening cost. Screening was cost-effective (i.e., incremental cost-effectiveness ratio [ICER] ≤ $50,000/QALY) for every scenario tested; all ICER values were ≤ $256/QALY. Screening was economically dominant (i.e., saved costs and provided health benefits) with a ≥10.3 % colonization rate and ≥5.88 % infection probability when contact isolation compliance was ≥25 % (hospital perspective). Under some conditions screening led to cost savings per case averted (range, $53–272). Clostridium difficile screening, coupled with isolation precautions, may be a cost-effective intervention to hospitals and third party payers, based on prevalence. Limiting Clostridium difficile transmission can reduce the number of infections, thereby reducing its economic burden to the healthcare system.

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • S. M. Bartsch
    • 1
    • 2
  • S. R. Curry
    • 3
  • L. H. Harrison
    • 4
  • B. Y. Lee
    • 1
    • 2
  1. 1.Public Health Computational and Operations Research (PHICOR)University of PittsburghPittsburghUSA
  2. 2.Department of Epidemiology, Graduate School of Public HealthUniversity of PittsburghPittsburghUSA
  3. 3.Division of Infectious DiseasesUniversity of Pittsburgh Medical CenterPittsburghUSA
  4. 4.Infectious Disease Epidemiology Research UnitUniversity of PittsburghPittsburghUSA

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