RetourThe structure of the network and the testing packages available at each level should be tailored to meet the needs of the community and the local epidemiology of TB. When considering placement of a diagnostic test, targets to be considered should be demand based rather than population based and should include:
- the volume of testing at a laboratory, which is likely to vary between dense urban settings and sparse rural communities;
- a strategy for providing optimal access to quality testing, either by increasing the number of sites providing a test or by transporting specimens to high-volume testing centres through an efficient specimen referral system – the strategy of choice will be determined by geography, infrastructure for transporting of specimens and result reporting, and epidemiologic situation; and
- interlinking of the different levels; for example, the results of an initial test (e.g. RIF resistance detected) may trigger a follow-on test (e.g. testing for FQ resistance), which may not be available at the same level of the health system. Although the levels described are useful conceptually, in practice they may overlap considerably. Careful logistical planning by mapping the current network of health facilities, population densities, the testing burden across the different facilities, transport infrastructure and the available laboratory network will aid placement. As an example, primary TB testing of people presumed to have TB will be relevant across all health facilities where individuals are screened for TB. In contrast, people with RR-TB may be managed at selected sites, and placement of testing for FQ and BDQ resistance may only be needed in selected laboratories that serve those sites.
The Framework of indicators and targets for laboratory strengthening under the End TB Strategy can serve as a guide for implementing and monitoring improvements to TB testing and TB diagnostic networks (8).
Several considerations should guide the placement of a new diagnostic test within the existing laboratory network structure, including:
- resources available for implementation;
- infrastructural requirements;
- biosafety requirements;
- specimen types and collection procedures;
- projected testing volumes;
- requirement for rapid diagnosis in people who are severely ill;
- minimum number of tests needed to maintain expertise and optimal use of instruments;
- current and planned testing algorithms;
- trained human resources (HR) capacity;
- links to other laboratories for further testing;
- specimen referral and result reporting systems; and
- possibility of integration with testing, specimen referral and reporting systems for other diseases.
Well-designed specimen referral systems underpin a strong diagnostics network and can help to:
- optimize access to services, and improve promptness of testing, use of instruments, biosafety and biosecurity, maintenance of proficiency and QA;
- facilitate linkages to care;
- provide solutions adapted to the local geography and epidemiology; and
- make it possible to integrate sample transportation with testing for other diseases, thus providing broader testing services in underserved settings.
The GLI guide to TB specimen referral systems and integrated networks (31) and the GLI specimen referral toolkit (32) are useful sources of information for designing, implementing and monitoring systems for referring specimens and reporting results.