Comparing tuberculosis diagnostic yield in smear/culture and xpert MTB/RIF-based algorithms using a non-randomised stepped-wedge design

Naidoo, Pren ; Dunbar, Rory ; Lombard, Carl ; Du Toit, Elizabeth ; Caldwell, Judy ; Detjen, Anne ; Squire, S. Bertel ; Enarson, Donald A. ; Beyers, Nulda (2016-03)

CITATION: Naidoo, P., et al. 2016. Comparing tuberculosis diagnostic yield in smear/culture and Xpert MTB/RIF-based algorithms using a non-randomised stepped-wedge design. PLoS ONE, 11(3): 1-13, doi: 10.1371/journal.pone.0150487.

Publication of this article was funded by the Stellenbosch University Open Access Fund.

The original publication is available at http://journals.plos.org

Article

Setting Primary health services in Cape Town, South Africa. Study Aim To compare tuberculosis (TB) diagnostic yield in an existing smear/culture-based and a newly introduced Xpert MTB/RIF-based algorithm. Methods TB diagnostic yield (the proportion of presumptive TB cases with a laboratory diagnosis of TB) was assessed using a non-randomised stepped-wedge design as sites transitioned to the Xpert based algorithm. We identified the full sequence of sputum tests recorded in the electronic laboratory database for presumptive TB cases from 60 primary health sites during seven one-month time-points, six months apart. Differences in TB yield and temporal trends were estimated using a binomial regression model. Results TB yield was 20.9% (95% CI 19.9% to 22.0%) in the smear/culture-based algorithm compared to 17.9% (95%CI 16.4% to 19.5%) in the Xpert based algorithm. There was a decline in TB yield over time with a mean risk difference of -0.9% (95% CI -1.2% to -0.6%) (p<0.001) per time-point. When estimates were adjusted for the temporal trend, TB yield was 19.1% (95% CI 17.6% to 20.5%) in the smear/culture-based algorithm compared to 19.3% (95% CI 17.7% to 20.9%) in the Xpert based algorithm with a risk difference of 0.3% (95% CI -1.8% to 2.3%) (p = 0.796). Culture tests were undertaken for 35.5% of smear-negative compared to 17.9% of Xpert negative low MDR-TB risk cases and for 82.6% of smear-negative compared to 40.5% of Xpert negative high MDR-TB risk cases in respective algorithms. Conclusion Introduction of an Xpert based algorithm did not produce the expected increase in TB diagnostic yield. Studies are required to assess whether improving adherence to the Xpert negative algorithm for HIV-infected individuals will increase yield. In light of the high cost of Xpert, a review of its role as a screening test for all presumptive TB cases may be warranted.

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