title: Optimal triage test characteristics to improve the cost-effectiveness of the Xpert MTB/RIF assay for TB diagnosis: a decision analysis.
abstract: BACKGROUND
  High costs are a limitation to scaling up the Xpert MTB/RIF assay (Xpert) for the diagnosis of tuberculosis in resource-constrained settings. A triaging strategy in which a sensitive but not necessarily highly specific rapid test is used to select patients for Xpert may result in a more affordable diagnostic algorithm. To inform the selection and development of particular diagnostics as a triage test we explored combinations of sensitivity, specificity and cost at which a hypothetical triage test will improve affordability of the Xpert assay.
  
  
  METHODS
  In a decision analytical model parameterized for Uganda, India and South Africa, we compared a diagnostic algorithm in which a cohort of patients with presumptive TB received Xpert to a triage algorithm whereby only those with a positive triage test were tested by Xpert.
  
  
  RESULTS
  A triage test with sensitivity equal to Xpert, 75% specificity, and costs of US$5 per patient tested reduced total diagnostic costs by 42% in the Uganda setting, and by 34% and 39% respectively in the India and South Africa settings. When exploring triage algorithms with lower sensitivity, the use of an example triage test with 95% sensitivity relative to Xpert, 75% specificity and test costs $5 resulted in similar cost reduction, and was cost-effective by the WHO willingness-to-pay threshold compared to Xpert for all in Uganda, but not in India and South Africa. The gain in affordability of the examined triage algorithms increased with decreasing prevalence of tuberculosis among the cohort.
  
  
  CONCLUSIONS
  A triage test strategy could potentially improve the affordability of Xpert for TB diagnosis, particularly in low-income countries and with enhanced case-finding. Tests and markers with lower accuracy than desired of a diagnostic test may fall within the ranges of sensitivity, specificity and cost required for triage tests and be developed as such.
authors.affiliation: Amsterdam Institute for Global Health and Development (AIGHD), Amsterdam, The Netherlands ; Department of Global Health, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: Anna H
authors.initials: AH
authors.lastname: van't Hoog
authors.affiliation: Amsterdam Institute for Global Health and Development (AIGHD), Amsterdam, The Netherlands ; Department of Global Health, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: Frank
authors.initials: F
authors.lastname: Cobelens
authors.affiliation: Social and Mathematical Epidemiology Group (SAME), Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, United Kingdom.
authors.forename: Anna
authors.initials: A
authors.lastname: Vassall
authors.affiliation: KNCV Tuberculosis Foundation, The Hague, The Netherlands.
authors.forename: Sanne
authors.initials: S
authors.lastname: van Kampen
authors.affiliation: Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
authors.forename: Susan E
authors.initials: SE
authors.lastname: Dorman
authors.affiliation: New Jersey Medical School-UMDNJ, Department of Medicine, Newark, New Jersey, United States of America.
authors.forename: David
authors.initials: D
authors.lastname: Alland
authors.affiliation: Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, United States of America.
authors.forename: Jerrold
authors.initials: J
authors.lastname: Ellner
mesh_terms: D000465:Algorithms; D003362:Cost-Benefit Analysis; D003661:Decision Support Techniques; D006801:Humans; D007194:India; D013019:South Africa; D014218:Triage / Q000191:economics*; D014376:Tuberculosis / Q000175:diagnosis*; D014454:Uganda