Background
Methods
Results
Placement and utilization
Country | WHO Country classifications | Placement | Number of GeneXpert machines | Case finding approach | |
---|---|---|---|---|---|
2-Module | 4-Module | ||||
Bangladesh | HBC, HDR | Two urban private diagnostic laboratories | 2 | Systematic screening of all attendees | |
Cambodia | HBC | Both machines used at one-day mobile camps set up at primary care facilities | 2 | Active with promotion of testing days in community | |
DR Congo | HBC, HDR, HTH | One primary care center, one sub-district hospital, five district hospitals and one provincial laboratory. | 7 | 1 | Primarily passive but with employed community groups to refer people with suspected TB |
Kenya | HBC, HTH | Two district hospitals and one health center. | 3 | Mixed, employed community cough workers to augment numbers of people tested | |
All had ART delivery | |||||
Malawi | HTH | One central hospital, four district hospitals, one health center in a district with no district hospital, one mission hospital and one community hospital. | 8 | Passive | |
Moldova | HDR | 17 in public facilities at lowest level for TB diagnosis, two in regional AIDS centers, two in the national reference laboratory, two in the regional reference laboratory, and two in the penitentiary sector | 16 | 9 | Passive |
Mozambique | HBC, HTH | Four district hospitals | 4 | Passive | |
Nepal | HBC | Four regional hospitals (two public and two private), one district hospital, two primary health centers, one private referral center, and one reference laboratory | 3 | 6 | Primarily passive with some awareness raising/and educational activities to improve numbers of people tested |
Pakistan | HBC, HDR | Four urban private diagnostic laboratories | 4 | Systematic screening of all attendees |
Implementation area | Challenges | Recommendations |
---|---|---|
Machine utilization
| Overestimation of testing numbers and underutilization of machine capacity. | Conduct a needs assessment that includes the current number of people with suspected TB tested at the facility, the need for a referral system and the impact of the proposed testing algorithm on testing numbers. |
Two-module machines may be a less expensive alternative in many settings. | ||
Testing algorithms
| Lack of national guidance on testing algorithms and use of questionable testing strategies. | To improve yield and reduce cartridge use, consider screening with CXR. |
Consider elimination of smear microscopy as a first test to reduce delay, loss to follow-up, and avoid repeat testing of most individuals due to large proportion of smear negative results. | ||
Time to diagnosis
| Sputum transport systems and testing algorithms can prolong delay between sputum submission, results and treatment initiation. | Xpert MTB/RIF testing sites should be located as close to the patient as possible to allow for rapid treatment initiation keeping in mind throughput and electrical power limitations. |
Referral networks can help utilization rates but can also be costly to maintain. | ||
Procurement
| Short shelf life of cartridges, use of machine beyond date of needed calibration, unanticipated extra costs of shipping and customs clearance, local procurement agent not always helpful. | Stagger cartridge shipments to avoid stock outs and expiry. |
Plan for module calibration by ordering test kits early. | ||
Plan for extra costs associated with shipment and customs clearance. | ||
Training
| Staff rotation and new practices around request forms, specimen transport and clinical decisions for rifampicin resistant results. | Testing can be easily done by well-trained lay workers to support laboratory staff. |
The manufacturer has conducted web trainings via videoconference and webinars and has released a web-based training video which some projects used as an adjunct tool for facilitation of trainings. | ||
Infrastructure and power supply
| UPS (15 minutes) Cepheid offers is inadequate in many settings. Most laboratories need some infrastructure improvements to allow proper testing. | A standard 800-2000VA inverter and a 12 V/120-200 AH battery provided power for over 6 hours to one four-module GeneXpert machine and a laptop (200 wattage required). As a general rule, a 10AH battery will be able to power a requirement of 100 watts per hour, but more AH are needed when the discharge is over a short period of time (less than 20 hours). These can be procured locally. |
Failed tests
| Differences between types of failed tests are unclear and available data not always used. |
Error results are coded by the machine and should be documented. The .gxx files can provide this information and the types, reason, locations, and technicians associated with the error should be tracked. |
High failed test rates in certain projects. |
No result test results are often caused by a power failure. | |
Invalid results are not caused by testing a specimen of insufficient volume or one which contains saliva the way poor sputum quality is defined in a NTP. Rather, they seem to be caused by other problems with sputum. Emphasizing correct sputum collection techniques, including mouth rinsing to remove food or particles which could inhibit the assay, may help reduce Invalid results, as well as improve yield. | ||
Drug resistance
| Some confusion about clinical decisions after receiving rifampicin resistant and indeterminate results. | Patients with indeterminate results have TB, but rifampicin resistance cannot be confirmed due to a very low burden of TB bacilli in the specimen. Unless there is documented DR-TB risk, first-line treatment can be started. Follow-up of these patients is warranted. |
Trepidation over Xpert MTB/RIF use overburdening DR-TB programs. | The majority of drug-resistant cases in almost all countries will be found among new cases, requiring testing of people with suspected TB rather than patients already in TB treatment. | |
Patients with rifampicin resistance will be detected in greater numbers and with greater speed than under current conditions, and significant resources and coordination will be required. | ||
Recording and reporting
| Supplied GeneXpert Dx software is not appropriate for analyzing patient cohorts, many times failed tests are recorded on paper with a generic error ‘result’, underreporting of errors, lack of clear guidance from national programs on recording and reporting of cases identified by Xpert MTB/RIF. | Dissemination and uptake of WHO guidelines on recording and reporting should be adopted by countries and their NTPs. |
Automated reporting mechanisms can improve both the timeliness and accuracy of reporting as well as assist in supply chain management. |
Algorithms and case finding approaches
Country | Xpert MTB/RIF testing strategy | Point-of-care (POC) or referral for treatment | Laboratory process for requesting Xpert test | Time from initial screen to Xpert + result | Time from submission of sputum for Xpert MTB/RIF test to result |
---|---|---|---|---|---|
Bangladesh | A - Direct to Xpert for all with history of previous TB treatment | POC | Automatic in laboratory | 0-1 day | Same day |
B - Xpert following SS- results and TB Suggestive CXR | |||||
Cambodia | Xpert following positive verbal and/or CXR screens | POC | Automatic in laboratory | 0-1 day | 0-1 day |
DR Congo | Xpert following SS- results | Both, using a hub and spoke transport system to augment testing numbers | After follow up tests and review | 7-10 days | 1 day |
Kenya | Xpert following SS- results | Both, using a hub and spoke transport system to augment testing numbers | Automatic in laboratory for those with negative smear results | 1-4 days | 1 day |
Malawi | A - Direct to Xpert for all hospitalized patients with suspected TB | Both, using a hub and spoke transport system to augment testing numbers | Automatic in laboratory for those with negative smear results | 1 day for walk in patients and up to 10 days for referral patients | 0-3 days |
B Xpert following SS- results | |||||
Moldova | Direct to Xpert; parallel smear microscopy | POC at most facilities; referral at AIDS Centers | Automatic in laboratory | 0-1 days | 0-1 days |
Mozambique | Xpert following SS- results | Referral | Automatic in laboratory for those with negative smear results | Up to 7 days for referral patients | 2 days |
Nepal | Xpert following SS- results and a suggestive CXR | Both, using a hub and spoke transport system to augment testing numbers | After follow up tests and review | 1 day for walk in patients and up to 10 days for referral patients | 1-2 days |
Pakistan | A - Direct to Xpert for all with history of previous TB treatment | POC | Automatic in laboratory | 0-1 day | Same day |
B - Xpert following SS- results and TB Suggestive CXR |
Time to diagnosis
Preparation and installation requirements
Procurement and service
Training/human resource issues
Xpert site | Tests | MTB Positive | Rifampicin | Failed Tests | Type of failed test | MTB + w/o failed tests | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
N | % | Sensitive | Indeterminate | Resistant | n | % | Error | Invalid | No result | |||||||||
n | % | N | % | n | % | N | % | n | % | n | % | |||||||
Bangladesh | 1428 | 286 | 20.0% | 249 | 87.1% | 11 | 3.8% | 26 | 9.1% | 99 | 6.9% | 85 | 85.9% | 10 | 10.1% | 4 | 4.0% | 21.5% |
Cambodia | 3697 | 768 | 20.8% | 741 | 96.5% | 22 | 2.9% | 5 | 0.7% | 439 | 11.9% | 169 | 38.5% | 264 | 60.1% | 6 | 1.4% | 23.6% |
DR Congo | 6348 | 567 | 8.9% | 498 | 87.8% | 17 | 3.0% | 30 | 5.3% | 1032 | 16.3% | 868 | 84.1% | 107 | 10.4% | 57 | 5.5% | 10.7% |
Kenya | 2803 | 258 | 9.2% | 238 | 92.2% | 11 | 4.3% | 9 | 3.5% | 266 | 9.5% | 240 | 90.2% | 23 | 8.6% | 3 | 1.1% | 10.2% |
Malawi | 6258 | 632 | 10.1% | 602 | 93.5% | 9 | 1.4% | 21 | 3.3% | 853 | 13.6% | 546 | 64.0% | 265 | 31.1% | 42 | 4.9% | 11.7% |
Moldova | 11472 | 1965 | 17.1% | 1227 | 62.4% | 48 | 2.4% | 690 | 35.1% | 673 | 5.9% | 387 | 61.7% | 105 | 15.6% | 181 | 26.9% | 18.2% |
Mozambique | 6823 | 910 | 13.3% | 826 | 90.8% | 18 | 2.0% | 66 | 7.3% | 871 | 12.8% | 521 | 59.8% | 216 | 24.8% | 134 | 15.4% | 15.3% |
Nepal | 6943 | 1376 | 19.8% | 1257 | 91.4% | 14 | 1.0% | 105 | 7.6% | 744 | 10.7% | 409 | 55.0% | 148 | 19.9% | 187 | 25.1% | 22.2% |
Pakistan | 2201 | 433 | 19.7% | 390 | 90.1% | 13 | 3.0% | 30 | 6.9% | 130 | 5.9% | 77 | 59.2% | 37 | 28.5% | 16 | 12.3% | 20.9% |
All Countries
|
47973
|
7195
|
15.0%
|
6028
|
83.8%
|
163
|
2.3%
|
982
|
13.6%
|
5107
|
10.6%
|
3302
|
64.7%
|
1175
|
23.0%
|
630
|
12.3%
|
16.8%
|
South Africa
a
|
1180669
|
171792
|
14.6%
|
155811
|
90.7%
|
2443
|
1.4%
|
12266
|
7.1%
|
32561
|
2.8%
|
15.0%
|