Rapid diagnosis of tuberculosis with the Xpert MTB/RIF assay in high burden countries: a cost-effectiveness analysis.

BACKGROUND: Xpert MTB/RIF (Xpert) is a promising new rapid diagnostic technology for tuberculosis (TB) that has characteristics that suggest large-scale roll-out. However, because the test is expensive, there are concerns among TB program managers and policy makers regarding its affordability for low- and middle-income settings. METHODS AND FINDINGS: We estimate the impact of the introduction of Xpert on the costs and cost-effectiveness of TB care using decision analytic modelling, comparing the introduction of Xpert to a base case of smear microscopy and clinical diagnosis in India, South Africa, and Uganda. The introduction of Xpert increases TB case finding in all three settings; from 72%-85% to 95%-99% of the cohort of individuals with suspected TB, compared to the base case. Diagnostic costs (including the costs of testing all individuals with suspected TB) also increase: from US$28-US$49 to US$133-US$146 and US$137-US$151 per TB case detected when Xpert is used "in addition to" and "as a replacement of" smear microscopy, respectively. The incremental cost effectiveness ratios (ICERs) for using Xpert "in addition to" smear microscopy, compared to the base case, range from US$41-$110 per disability adjusted life year (DALY) averted. Likewise the ICERS for using Xpert "as a replacement of" smear microscopy range from US$52-$138 per DALY averted. These ICERs are below the World Health Organization (WHO) willingness to pay threshold. CONCLUSIONS: Our results suggest that Xpert is a cost-effective method of TB diagnosis, compared to a base case of smear microscopy and clinical diagnosis of smear-negative TB in low- and middle-income settings where, with its ability to substantially increase case finding, it has important potential for improving TB diagnosis and control. The extent of cost-effectiveness gain to TB programmes from deploying Xpert is primarily dependent on current TB diagnostic practices. Further work is required during scale-up to validate these findings.