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New molecular diagnostic tests for TB: Do patients benefit?. HSRC Roundtable 02 June 2014 Pren Naidoo Rory Dunbar, Elizabeth Du Toit, Margaret van Niekerk, Carl Lombard, Judy Caldwell, Nulda Beyers. Background. Key TB Challenges MDR-TB Estimated global cases (2012): 450,000

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New molecular diagnostic tests for tb do patients benefit

New molecular diagnostic tests for TB: Do patients benefit?

HSRC Roundtable

02 June 2014

Pren Naidoo

Rory Dunbar, Elizabeth Du Toit, Margaret van Niekerk,

Carl Lombard, Judy Caldwell, Nulda Beyers


  • Key TB Challenges

    • MDR-TB

      • Estimated global cases (2012): 450,000

      • Global cases reported: 94,000

      • Poor availability of DST contributes to the low number of cases diagnosed

    • Diagnosis of smear negative TB

      • Smear microscopy has ~ 60% sensitivity (~40% in HIV prevalent areas)

  • Early adoption of molecular diagnostics by DOH

    • Xpert MTB/RIF introduced from 2011

Efficacy of molecular diagnostic tests
Efficacy of Molecular Diagnostic Tests

  • Cochrane Review of studies where Xpert was used as an initial test replacing smear microscopy:

    • MTB (15 studies)

      • Pooled sensitivity: 88% (95%CrI 83% - 92%)

      • Pooled specificity: 98% (95%CrI 97% - 99%)

    • Rif R (11 studies)

      • Pooled sensitivity: 94% (95% CrI 87% - 97%)

      • Pooled specificity: 98% (95% CrI 97% - 99%)

Impact assessment framework liverpool school of tropical medicine
Impact Assessment FrameworkLiverpool School of Tropical Medicine

From:Beyond accuracy: creating acomprehensive evidence base for TB diagnostic toolsMann G et al; Int J Tuberc Lung Dis 2010;14:1518-24.

Tb testing algorithm
TB Testing Algorithm

Universal Algorithm: Xpert MTB/RIF™ replaced smear

All presumptive TB cases 2 sputa submitted

Specimen 1Specimen 2

Xpert Negative Culture if HIV+

Discard if HIV-/unknown

MTB+, Rif sensitive Smear

MTB+, Rif resistant Smear, culture, LPA and 2nd line DST

Smear if only 1 sputum sample submitted

Targeted Algorithm: Smear/Culture/DST (LPA)

Low MDR-risk 2 sputa for smears (3rd for culture if Sm-, HIV+)

High MDR-risk 2 sputa for smears, Culture, LPA DST

Are more tb cases diagnosed
Are More TB Cases Diagnosed?





Are More TB Cases Diagnosed?



Are More MDR-TB Cases Diagnosed?

Total 188 Cases

Total 196 Cases

Do patients commence tb treatment earlier
Do Patients Commence TB Treatment Earlier?

Q2 2011

Q4 2011

Group 1 and Group 2 – Smear/Culture

Group 1 - Xpert Group 2 – Smear/Culture

Do Patients Commence MDR-TB Rx Earlier?

MDR-TB Treatment Commencement Time (TCT)

Laboratory Turn Around Time (TAT)

Which mdr tb patients benefit
Which MDR-TB Patients Benefit?

  • No benefit by age, gender or HIV status

MDR-TB TCT by HIV Status

MDR-TB TCT by MDR-Risk Profile


HIV+: HR 3.3 (95% CI 0.4 - 1.0)

p = 0.037

Low risk: HR 3.3 (95% CI:2.4-4.5)

High risk: HR 2.0 (95% CI:1.4-2.8)

Tb laboratory costs per algorithm zar for presumptive tb cases only
TB Laboratory Costs per Algorithm (ZAR)For presumptive TB cases only

Total: R1,724,735

Total: R3,745,218

Comparison of median patient visits
Comparison of Median Patient Visits* Universal Algorithms

*Calculated from first health care visit to any provider for current illness to MDR-TB treatment commencement

Patient s perspectives
Patient’s Perspectives Universal Algorithms

  • Many patients with previous TB identified their symptoms as attributable to TB and went directly to the clinic for tests

  • “My mother said I must go to the clinic for a TB test. She was worried that I may have TB because my sister also had TB. I did not want to go, too scared that if I go for a TB tests they will also test me for HIV” (T2).

  • “But at all these times I was not sick it was just a cough, sweat at night and I felt that I was also losing weight nothing else, not a day I ever felt like I was sick” (T8).

  • “ I was having a terrible cough and I was sweating at night, but this did not ring an alarm for me, because I still thought this was just a fever and the change of season and that everything was going to be fine” (T3).

  • “…at the time when I started to feel sick I feel that I had to act a little bit strong not to let the family know how weak I really feel. I must not let them down. Although I could feel some pain I felt I must be a man to face this disease” (U7).

  • “I did not think it was serious, just thought it was a cough…Got cough meds at pharmacy… helped but coughed again…I went back again and again, got a different medication every time. I must have gone there 5 times...” (T12).

Patient s perspectives1
Patient’s Perspectives Universal Algorithms

  • “I was at the day hospital for 24 hours in December and I waited for the doctor but the doctor was busy and so they told me that I had infection in my lungs and they then gave me the drip and antibiotics…In the same month I didn’t feel so well so I went back to the same day hospital… and they gave me the same drip and antibiotics”.

  • “They don’t care about the patient. Once I was there and the nurses will just go on tea even if the very sick people are waiting on them. I told the nurse about a sick, old man and she said he must just wait.” T10

  • “After returning for my results and waiting for a long time, I was told that I needed to come back again after two days. After another two days I was told my results were not received due to a broken clinic fax machine. After this day I decided not to come back because I was waiting too long in the queue for my results and I was feeling better at this stage.”

  • “I was expecting long queues and sitting for ages before getting help. I am not sure what is the situations at the other clinics, but .. there was no queue and I got helped within 10 minutes…Staff in the TB room is very helpful and treats the patients with respect.” U9

Comments Universal Algorithms

Summary Findings

No increase in yield for TB / MDR-TB cases

25-day reduction in MDR-TB TCT

Substantial increase in laboratory costs

Cost saving for MDR-TB patients

Both patient and health system factors contribute to delay

New technology on its own does not suffice

Need to strengthen health systems and address patient factors to optimise test benefits


Acknowledgements Universal Algorithms

Cape Town Health Directorate Western Cape Provincial Department of Health National Health Laboratory Services

This research was supported by a United States Agency for International Development (USAID) Cooperative Agreement (TREAT TB – Agreement No. GHN-A-00-08-00004-00). The contents are the responsibility of the authors and do not necessarily reflect the views of USAID.