Diagnosis

This section provides brief descriptions of WHO-recommended technologies for the detection of TB and DR-TB, summarizes WHO recommendations for such technologies and refers to the WHO consolidated guidelines on tuberculosis Module 3: Diagnosis – rapid diagnostics for tuberculosis detection (7) for a thorough discussion of the technologies and recommendations.

Globally, tuberculosis (TB) continues to be a significant public health problem, with an estimated 10.6 million people developing TB in 2022 and 7.5 million reported as being newly diagnosed (1). The gap between the numbers estimated and reported is large, and it worsened during the coronavirus disease (COVID-19) pandemic (2). However, there has been a major recovery after the 2 years of disruptions related to COVID-19.

1. Global tuberculosis report 2023. Geneva: World Health Organization; 2023 (https://www.who.int/publications/i/item/9789240083851).
2. Impact of the COVID-19 pandemic on TB detection and mortality in 2020. .

• WHO guidelines stress the importance of DST before treatment, especially for medicines for which mWRDs are available.
• People with TB that is RIF susceptible, INH susceptible or unknown should be started on a first-line regimen for drug-susceptible TB (55).
• Globally, Hr-TB prevalence is 7.4% (95% CI: 6.5–8.4%) in new cases and 11.4% (95% CI: 9.4–13.4%) in people who were treated previously (56). The prevalence in some settings can exceed 25%. Contacts of a person known to have Hr-TB are also at increased risk.

Based on reviews of available data, WHO has recommended against using tests that do not provide reliable information for diagnosing TB.

The moderate complexity automated NAATs class of tests includes rapid and accurate tests for the detection of pulmonary TB from respiratory samples. Overall pooled sensitivity for TB detection was 93.0% (95% confidence interval [CI]: 90.9–94.7%) and specificity 97.7% (95% CI: 95.6–98.8%) (Tables 3.1–3.4 in Section 3). Moderate complexity automated NAATs are also able to simultaneously detect resistance to both RIF and INH, and are less complex to perform than phenotypic DST and LPAs. After the sample preparation step, the tests are largely automated.

Culture-based DST methods for certain anti-tuberculosis (anti-TB) medicines are reliable and reproducible, but these methods are time consuming, and require specific laboratory infrastructure, skilled staff and adherence to quality control.

Algorithm 4 is a follow-on algorithm, the purpose of which is to detect resistance in individuals with RIF-susceptible TB at risk of having DR-TB and individuals with Hr-TB. People at high risk for having DR-TB include those who have prior drug exposure; reside in settings where the probability of resistance to RIF, INH or FQs is high (≥5%) or belong to subgroups where the probability of such resistance is high; or have a history of contact with a known person with DR-TB.

Modifications to diagnostic algorithms must be put in place only after a formal evaluation, review and approval by officials within the MoH, NTP and NTRL. Often, nationally appointed thematic working groups are used to evaluate new technologies and develop implementation plans, which typically include revising current algorithms. These groups comprise local ministry officials, implementing partners, civil society and professionals (laboratory and medical), who will decide the optimal use and placement of the new technology within the current network structure.