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The impact of implementing a Xpert MTB/RIF algorithm on drug-sensitive pulmonary tuberculosis: a retrospective analysis

  • K. REES (a1) (a2), N. MUDITAMBI (a1), M. MASWANGANYI (a3), J. RAILTON (a1), J. A. MCINTYRE (a1) (a2), H. E. STRUTHERS (a1) (a4), P. B. FOURIE (a5) and R. P. H. PETERS (a1) (a5) (a6)...

Summary

Xpert MTB/RIF (Xpert) is the preferred first-line test for all persons with tuberculosis (TB) symptoms in South Africa in line with a diagnostic algorithm. This study evaluates pre- and post-implementation trends in diagnostic practices for drug-sensitive, pulmonary TB in adults in an operational setting, following the introduction of the Xpert-based algorithm. We retrospectively analysed data from the national TB database for Greater Tzaneen sub-district, Limpopo Province. Trends in a number of cases, diagnosis and outcome and characteristics associated with death are reported. A total of 8407 cases were treated from 2008 until 2015, with annual cases registered decreasing by 31·7% over that time period (from 1251 to 855 per year). After implementation of Xpert, 69·9% of cases were diagnosed by Xpert, 29·4% clinically, 0·6% by smear microscopy and 0·1% by culture. Cases with a recorded microbiological test increased from 76·2% to 96·4%. Cases started on treatment without confirmation, but with a negative microbiological test increased from 7·1% to 25·7%. Case fatality decreased from 15·0% to 9·8%, remaining consistently higher in empirically treated groups, regardless of HIV status. Implementation of the algorithm coincided with a reduced number of TB cases treated and improved coverage of microbiological testing; however, a substantial proportion of cases continued to start treatment empirically.

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Corresponding author

*Author for correspondence: R. P. H. Peters, Anova Health Institute, 12 Sherborne Rd, Parktown, Johannesburg 2193, South Africa. (Email: rph.peters@gmail.com)

References

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1. World Health Organisation. Automated Real-Time Nucleic Acid Amplification Technology for Rapid and Simultaneous Detection of Tuberculosis and Rifampicin Resistance: Xpert MTB/RIF Assay for the Diagnosis of Pulmonary and Extrapulmonary TB in Adults and Children. Policy update. Geneva: World Health Organisation, 2013.
2. Churchyard, GJ, et al. Tuberculosis control in South Africa: successes, challenges and recommendations. South African Medical Journal 2014; 104(3): 244.
3. Naidoo, P, et al. Comparing tuberculosis diagnostic yield in smear/culture and Xpert(R) MTB/RIF-based algorithms using a non-randomised stepped-wedge design. PLOS ONE 2016; 11(3): e0150487.
4. Durovni, B, et al. Impact of replacing smear microscopy with Xpert MTB/RIF for diagnosing tuberculosis in Brazil: a stepped-wedge cluster-randomized trial. PLOS Medicine 2014; 11(12): e1001766.
5. Theron, G, et al. Do high rates of empirical treatment undermine the potential effect of new diagnostic tests for tuberculosis in high-burden settings? The Lancet Infectious Diseases 2014; 14(6): 527532.
6. McCarthy, K, et al. What happens after a negative test for tuberculosis? Evaluating adherence to TB diagnostic algorithms in South African primary health clinics. Journal of Acquired Immune Deficiency Syndromes 2016; 71(5): e119e126.
7. Massyn, N, et al. District Health Barometer 2015/16. Durban: Health Systems Trust, 2016.
8. Republic of South Africa Department of Health. National Tuberculosis Management Guidelines 2014. Pretoria: Fishwicks PTA, 2014.
9. Nadol, P, et al. Electronic tuberculosis surveillance systems: a tool for managing today's TB programs. International Journal of Tuberculosis and Lung Disease 2008; 12(3): S8S16.
10. Johnson, LF, Dorrington, RE, Moolla, H. Modelling the Impact of HIV in South Africa's Provinces. Centre for Infectious Disease Epidemiology and Research working paper. Cape Town: University of Cape Town, 2016.
11. Churchyard, GJ, et al. Xpert MTB/RIF versus sputum microscopy as the initial diagnostic test for tuberculosis: a cluster-randomised trial embedded in South African roll-out of Xpert MTB/RIF. The Lancet Global Health 2015; 3(8): e450e457.
12. World Health Organisation. Definitions and Reporting Framework for Tuberculosis – 2013 Revision. Geneva: World Health Organisation, 2013.
13. Badri, M, Wilson, D, Wood, R. Effect of highly active antiretroviral therapy on incidence of tuberculosis in South Africa: a cohort study. The Lancet 2002; 359(9323): 20592064.
14. Nicholas, S, et al. Incidence of tuberculosis in HIV-infected patients before and after starting combined antiretroviral therapy in 8 Sub-Saharan African HIV programs. Journal of Acquired Immune Deficiency Syndromes 2011; 57: 311318.
15. Kanyerere, H, et al. Scale-up of ART in Malawi has reduced case notification rates in HIV-positive and HIV-negative tuberculosis. Public Health in Action 2016; 6(4): 247251.
16. Takarinda, KC, et al. Declining tuberculosis case notification rates with the scale-up of antiretroviral therapy in Zimbabwe. Public Health in Action 2016; 6(3): 164168.
17. Nanoo, A, et al. Nationwide and regional incidence of microbiologically confirmed pulmonary tuberculosis in South Africa, 2004–12: a time series analysis. The Lancet Infectious Diseases 2015; 15(9): 10661076.
18. Hermans, S, et al. The impact of the roll-out of rapid molecular diagnostic testing for tuberculosis on empirical treatment in Cape Town, South Africa. Bulletin of the World Health Organization 2017; 95(8): 554563.
19. Theron, G, et al. Xpert MTB/RIF results in patients with previous tuberculosis: can we distinguish true from false positive results? Clinical Infectious Diseases 2016; 62(8): 9951001.
20. Korenromp, E, et al. The measurement and estimation of tuberculosis mortality. International Journal of Tuberculosis and Lung Disease 2009; 13(3): 283303.
21. Nglazi, MD, et al. The impact of HIV status and antiretroviral treatment on TB treatment outcomes of new tuberculosis patients attending co-located TB and ART services in South Africa: a retrospective cohort study. BMC Infectious Diseases 2015; 15: 536.
22. Slaymaker, E, et al. How have ART treatment programmes changed the patterns of excess mortality in people living with HIV? Estimates from four countries in East and Southern Africa. Global Health Action 2014; 7: 22789.
23. Myburgh, H, et al. Implementation of an electronic monitoring and evaluation system for the antiretroviral treatment programme in the Cape Winelands district, South Africa: a qualitative evaluation. PLOS ONE 2015; 10(5): e0127223.
24. Theron, G, et al. Do adjunct tuberculosis tests, when combined with Xpert MTB/RIF, improve accuracy and the cost of diagnosis in a resource-poor setting? European Respiratory Journal 2012; 40(1): 161168.
25. Theron, G, et al. Feasibility, accuracy, and clinical effect of point-of-care Xpert MTB/RIF testing for tuberculosis in primary-care settings in Africa: a multicentre, randomised, controlled trial. Lancet 2014; 383: 424435.
26. Budgell, EP, et al. Outcomes of treatment of drug-susceptible tuberculosis at public sector primary healthcare clinics in Johannesburg, South Africa: a retrospective cohort study. South African Medical Journal 2016; 106(10): 10021009.
27. Hanrahan, CF, et al. Time to treatment and patient outcomes among TB suspects screened by a single point-of-care Xpert MTB/RIF at a primary care clinic in Johannesburg, South Africa. PLOS ONE 2013; 8(6): e65421.
28. Peter, J, Theron, G. The progression of TB diagnosis in the HIV era: from microscopes to molecules and back to the bedside. Continuing Medical Education 2011; 29(10): 404408.
29. Schnippel, K, et al. Diagnosing Xpert MTB/RIF negative TB: impact and cost of alternative algorithms for South Africa. South African Medical Journal 2013; 103(2): 101106.

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The impact of implementing a Xpert MTB/RIF algorithm on drug-sensitive pulmonary tuberculosis: a retrospective analysis

  • K. REES (a1) (a2), N. MUDITAMBI (a1), M. MASWANGANYI (a3), J. RAILTON (a1), J. A. MCINTYRE (a1) (a2), H. E. STRUTHERS (a1) (a4), P. B. FOURIE (a5) and R. P. H. PETERS (a1) (a5) (a6)...

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