Executive summary

The political declaration at the first United Nations (UN) high-level meeting on tuberculosis (TB) held on 26 September 2018 included commitments by Member States to four new global targets.⁴ One of these targets is diagnosing and treating 40 million people with TB in the 5-year period 2018-2022. The approximate breakdown of the target is about 7 million in 2018 and about 8 million in subsequent years. The traditional method for diagnosing TB using a light microscope, developed more than 100 years ago, has in recent years been surpassed by several new methods and tools. These methods are based on either the detection of mycobacterial antigens or DNA.

The novel tools to detect the presence of Mycobacterium tuberculosis and resistance to anti-TB drugs call for evidence-based policy recommendations. The World Health Organization (WHO) has published many guidelines developed by WHO-convened Guideline Development Groups (GDGs), using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to summarize the evidence and to formulate policy recommendations and accompanying remarks. However, the growing number of published guidelines makes navigating and being up to date with the latest recommendations complex for the intended audience which include health care personnel, national TB programmes and policy-makers. WHO recognized the emerging need and consolidated the recommendations into one document. This document presents recommendations from five guidelines previously published by WHO between 2016 and 2020, as summarized in the box below. Earlier guidelines on diagnostics that were not developed according to the GRADE approach have not been included in this consolidated document.

Finally, recommendations for three new classes of technologies evaluated in 2020-21 are included in the current document and constitute the 2021 update. The three classes are:

  • moderate complexity automated NAATs for the detection of TB and resistance to rifampicin and isoniazid;
  • low complexity automated NAATs for the detection of resistance to isoniazid and secondline anti-TB agents, and;
  • high complexity reverse hybridization-based NAATs for the detection of pyrazinamideresistance.

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