Results from early programmatic implementation of Xpert MTB/RIF testing in nine countries
1 Stop TB Partnership, Secretariat, Geneva, Switzerland
2 Interactive Research and Development, Karachi, Pakistan
3 Pôle de Microbiologie, Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain (UCL), Brussels, Belgium
4 Health Alliance International, University of Washington, Seattle, WA, USA
5 University of Wisconsin, Madison, WI, USA
6 Project HOPE, Mulanje, Malawi
7 Warren Alpert School of Medicine at Brown University, Providence, RI, USA
8 Stop TB Unit, WHO, Phnom Penh, Cambodia
9 Center for Health Policies and Studies, Chisinau, Republic of Moldova
10 International Organization for Migration, Kathmandu, Nepal
11 Tuberculosis & Leprosy Research Group; Centre for Communicable Diseases; International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
12 PHTB Consult, Tilburg, Netherlands
13 KIT Development Policy & Practice, Royal Tropical Institute, Amsterdam, Netherlands
BMC Infectious Diseases 2014, 14:2 doi:10.1186/1471-2334-14-2Published: 2 January 2014
The Xpert MTB/RIF assay has garnered significant interest as a sensitive and rapid diagnostic tool to improve detection of sensitive and drug resistant tuberculosis. However, most existing literature has described the performance of MTB/RIF testing only in study conditions; little information is available on its use in routine case finding. TB REACH is a multi-country initiative focusing on innovative ways to improve case notification.
We selected a convenience sample of nine TB REACH projects for inclusion to cover a range of implementers, regions and approaches. Standard quarterly reports and machine data from the first 12 months of MTB/RIF implementation in each project were utilized to analyze patient yields, rifampicin resistance, and failed tests. Data was collected from September 2011 to March 2013. A questionnaire was implemented and semi-structured interviews with project staff were conducted to gather information on user experiences and challenges.
All projects used MTB/RIF testing for people with suspected TB, as opposed to testing for drug resistance among already diagnosed patients. The projects placed 65 machines (196 modules) in a variety of facilities and employed numerous case-finding strategies and testing algorithms. The projects consumed 47,973 MTB/RIF tests. Of valid tests, 7,195 (16.8%) were positive for MTB. A total of 982 rifampicin resistant results were found (13.6% of positive tests). Of all tests conducted, 10.6% failed. The need for continuous power supply was noted by all projects and most used locally procured solutions. There was considerable heterogeneity in how results were reported and recorded, reflecting the lack of standardized guidance in some countries.
The findings of this study begin to fill the gaps among guidelines, research findings, and real-world implementation of MTB/RIF testing. Testing with Xpert MTB/RIF detected a large number of people with TB that routine services failed to detect. The study demonstrates the versatility and impact of the technology, but also outlines various surmountable barriers to implementation. The study is not representative of all early implementer experiences with MTB/RIF testing but rather provides an overview of the shared issues as well as the many different approaches to programmatic MTB/RIF implementation.