Effects of Xpert MTB/RIF and alternative diagnostics for tuberculosis in Tanzania

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·  Background: WHO has endorsed several promising new diagnostic methods and algorithms for tuberculosis. National tuberculosis programs must decide which methods to implement and where to place them in the diagnostic process.

·  Methods: An integrated model was used to assess the effects of different diagnostic algorithms involving Xpert MTB/RIF and LED fluorescence microscopy in Tanzania. The model incorporated both operational and transmission components, representing the epidemiological context of Tanzania. The analysis compared the effects and cost-effectiveness of various diagnostic options.

·  Findings:

  • Three strategies were identified as cost-effective in Tanzania.
  • Full scale-up of Xpert would have the greatest population-level impact, averting 346,000 DALYs at an additional cost of $36.9 million over 10 years.
  • The incremental cost-effectiveness ratio (ICER) of Xpert scale-up is $169 per DALY averted (95% CrI 104–265), below Tanzania's willingness-to-pay threshold of $599.
  • Same-day LED fluorescence microscopy has an ICER of $45 (95% CrI 25–74), while standard LED fluorescence microscopy has an ICER of $29 (95% CrI 6–59).
  • Targeted use of Xpert for presumptive tuberculosis cases with HIV infection, whether as an initial test or follow-up to microscopy, is less cost-effective compared to full rollout or same-day LED microscopy.

·  Interpretation: Full rollout of Xpert is a cost-effective strategy for tuberculosis diagnosis in Tanzania and could significantly reduce the national tuberculosis burden. However, substantial funding will be required to implement it in practice. This approach can be adapted for other developing countries to support rational health policy decisions.

Langley, I., Lin, H.H., Egwaga, S., Doulla, B., Ku, C.C., Murray, M., Cohen, T. and Squire, S.B., 2014. Assessment of the patient, health system, and population effects of Xpert MTB/RIF and alternative diagnostics for tuberculosis in Tanzania: an integrated modelling approach. The Lancet Global Health, 2(10), pp.e581-e591.

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