title: Cost-effectiveness of Diagnostic Algorithms for Tuberculosis in Children Less Than 5 Years of Age.
abstract: The objective of this analysis was to assess the cost-effectiveness of TB diagnosis using microscopic observation drug susceptibility (MODS), Xpert MTB/RIF (Xpert) and empiric treatment for all patients, in addition to current clinical diagnostic practices in children less than 5 years of age in a national tuberculosis (TB) referral hospital in Uganda.
  
  
  
  A decision analysis was conducted from the healthcare perspective, with a primary outcome of incremental cost-effectiveness expressed as cost per year of life gained (YLG).
  
  
  
  Cost-effectiveness of the algorithms depended strongly on 3 variables: the prevalence of TB, probability of death if TB was untreated and accuracy of existing diagnostic algorithms. Xpert and MODS had similar cost-effectiveness profiles and were preferred in settings where the prevalence of TB and probability of death from untreated TB were low. As the underlying probability of TB disease and death increased, treating all children with clinically suspected disease became more cost-effective. In settings where the probability that an untreated child will die of TB-whether a result of high prevalence of TB or high mortality from untreated TB-treating all children for TB is likely to be the most cost-effective approach until better diagnostic tests can be developed.
  
  
  
  The cost-effectiveness of diagnostic tools for TB in children depends on the population, natural history of untreated TB and existing diagnostic practices. In settings where the risk of TB death is high, empiric treatment of all children for TB should be considered until a more sensitive, low-cost diagnostic test is available.
authors.affiliation: From the *Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; †Makerere University, Johns Hopkins University Research Collaboration/MU-JHU CARE LTD, Kampala, Uganda; &‡Department of Global Community Health Behavioral Sciences, Tulane University School of Public Health, New Orleans, Louisiana; and §Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
authors.forename: Amanda K
authors.initials: AK
authors.lastname: Debes
authors.forename: Robert H
authors.initials: RH
authors.lastname: Gilman
authors.forename: Carolyne
authors.initials: C
authors.lastname: Onyango-Makumbi
authors.forename: Andrea
authors.initials: A
authors.lastname: Ruff
authors.forename: Richard
authors.initials: R
authors.lastname: Oberhelman
authors.forename: David W
authors.initials: DW
authors.lastname: Dowdy
mesh_terms: D000465:Algorithms; D002675:Child, Preschool; D003362:Cost-Benefit Analysis; D003661:Decision Support Techniques; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D014376:Tuberculosis / Q000175:diagnosis* / Q000191:economics*; D014454:Uganda