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  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  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 1. EFFECTIVENESSAre the desired outcomes achieved? 2. EQUITYWho needs the intervention most? Who benefits? Socio-economic; gender; age, patient groups eg HIV+ etc 3. HEALTH SYSTEMHuman resource, infrastructure Operating procedures, procurement M&E implications 4. SCALE-UPCosts and benefits of scale-up 5. HORIZON SCANNINGWhat other options are available or likely to become available? How do these compare? From: Beyond accuracy: creating a comprehensive evidence base for TB diagnostic tools Mann G et al; Int J Tuberc Lung Dis 2010;14:

TB Testing Algorithm Universal Algorithm: Xpert MTB/RIF™ replaced smear All presumptive TB cases 2 sputa submitted Specimen 1Specimen 2 XpertNegative Culture if HIV+ Discard if HIV-/unknown MTB+, Rif sensitiveSmear MTB+, Rif resistantSmear, culture, LPA and 2nd line DST Smear if only 1 sputum sample submitted Targeted Algorithm: Smear/Culture/DST (LPA) Low MDR-risk2 sputa for smears (3 rd for culture if Sm-, HIV+) High MDR-risk2 sputa for smears, Culture, LPA DST

Are More TB Cases Diagnosed? Targeted Universal

Targeted Universal Are More TB Cases Diagnosed?

Targeted Universal Total 188 CasesTotal 196 Cases Are More MDR-TB Cases Diagnosed?

Do Patients Commence TB Treatment Earlier? Q Group 1 and Group 2 – Smear/Culture Q Group 1 - Xpert Group 2 – Smear/Culture Q2 ‘11Q4 ‘11 Group 1Group 2Group 1Group 2 Median DS-TCT (95% CI) (days)6 (6-7) 4 (4-5)5 (4-5)

Targeted (n=360)Universal (n=120) Median MDR-TB TCT (95% CI) [IQR] (days) 43 (40-46) [IQR: 30-64] 17 (13-22) [IQR: 7-36] Matched analysisMean diff: 25 days (95% CI days) p<0.001 Median Lab TAT (95% CI) [IQR] (days) 25 (24 -27) [IQR:19-35] <1 (<1-2) [IQR<1-17] Matched analysisMean diff: 20 days (95% CI days) p<0.001 MDR-TB Treatment Commencement Time (TCT)Laboratory Turn Around Time (TAT) Do Patients Commence MDR-TB Rx Earlier?

Which MDR-TB Patients Benefit? p=0.056 HIV+: HR 3.3 (95% CI )  No benefit by age, gender or HIV status p = Low risk: HR 3.3 (95% CI: ) High risk: HR 2.0 (95% CI: ) MDR-TB TCT by MDR-Risk ProfileMDR-TB TCT by HIV Status

TB Laboratory Costs per Algorithm (ZAR) For presumptive TB cases only Total: R1,724,735Total: R3,745,218

Comparison of Median Patient Costs in the Targeted and Universal Algorithms

Comparison of Median Patient Visits* MedianIQRMin-maxP value Targeted (n=89) p<0.001 Universal (n=64) *Calculated from first health care visit to any provider for current illness to MDR-TB treatment commencement

Patient’s Perspectives  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 Perspectives  “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  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 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 ). The contents are the responsibility of the authors and do not necessarily reflect the views of USAID.