title: Assessment of the patient, health system, and population effects of Xpert MTB/RIF and alternative diagnostics for tuberculosis in Tanzania: an integrated modelling approach.
abstract: BACKGROUND
  Several promising new diagnostic methods and algorithms for tuberculosis have been endorsed by WHO. National tuberculosis programmes now face the decision on which methods to implement and where to place them in the diagnostic algorithm.
  
  
  METHODS
  We used an integrated model to assess the effects of different algorithms of Xpert MTB/RIF and light-emitting diode (LED) fluorescence microscopy in Tanzania. To understand the effects of new diagnostics from the patient, health system, and population perspective, the model incorporated and linked a detailed operational component and a transmission component. The model was designed to represent the operational and epidemiological context of Tanzania and was used to compare the effects and cost-effectiveness of different diagnostic options.
  
  
  RESULTS
  Among the diagnostic options considered, we identified three strategies as cost effective in Tanzania. Full scale-up of Xpert would have the greatest population-level effect with the highest incremental cost: 346 000 disability-adjusted life-years (DALYs) averted with an additional cost of US$36·9 million over 10 years. The incremental cost-effectiveness ratio (ICER) of Xpert scale-up ($169 per DALY averted, 95% credible interval [CrI] 104-265) is below the willingness-to-pay threshold ($599) for Tanzania. Same-day LED fluorescence microscopy is the next most effective strategy with an ICER of $45 (95% CrI 25-74), followed by LED fluorescence microscopy with an ICER of $29 (6-59). Compared with same-day LED fluorescence microscopy and Xpert full rollout, targeted use of Xpert in presumptive tuberculosis cases with HIV infection, either as an initial diagnostic test or as a follow-on test to microscopy, would produce DALY gains at a higher incremental cost and therefore is dominated in the context of Tanzania.
  
  
  CONCLUSIONS
  For Tanzania, this integrated modelling approach predicts that full rollout of Xpert is a cost-effective option for tuberculosis diagnosis and has the potential to substantially reduce the national tuberculosis burden. It also estimates the substantial level of funding that will need to be mobilised to translate this into clinical practice. This approach could be adapted and replicated in other developing countries to inform rational health policy formulation.
authors.affiliation: Liverpool School of Tropical Medicine, Liverpool, UK.
authors.forename: Ivor
authors.initials: I
authors.lastname: Langley
authors.affiliation: Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taiwan. Electronic address: hsienho@ntu.edu.tw.
authors.forename: Hsien-Ho
authors.initials: HH
authors.lastname: Lin
authors.affiliation: National Tuberculosis and Leprosy Programme, Dar es Salaam, Tanzania.
authors.forename: Saidi
authors.initials: S
authors.lastname: Egwaga
authors.affiliation: National Tuberculosis and Leprosy Programme, Dar es Salaam, Tanzania.
authors.forename: Basra
authors.initials: B
authors.lastname: Doulla
authors.affiliation: Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taiwan.
authors.forename: Chu-Chang
authors.initials: CC
authors.lastname: Ku
authors.affiliation: Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
authors.forename: Megan
authors.initials: M
authors.lastname: Murray
authors.affiliation: Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Division of Global Health Equity, Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: Ted
authors.initials: T
authors.lastname: Cohen
authors.affiliation: Liverpool School of Tropical Medicine, Liverpool, UK.
authors.forename: S Bertel
authors.initials: SB
authors.lastname: Squire
mesh_terms: D000465:Algorithms; D000995:Antitubercular Agents / Q000494:pharmacology; D003362:Cost-Benefit Analysis; D024881:Drug Resistance, Bacterial; D006078:Government Programs / Q000191:economics; D006801:Humans; D008856:Microscopy, Fluorescence / Q000191:economics* / Q000295:instrumentation*; D017059:Models, Econometric; D012293:Rifampin / Q000494:pharmacology; D013183:Sputum / Q000382:microbiology*; D013636:Tanzania; D013997:Time Factors; D014376:Tuberculosis / Q000175:diagnosis*
chemical_list: D000995:Antitubercular Agents; D012293:Rifampin