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LPAs are a family of DNA strip-based tests that detect mutations associated with drug resistance. They do this either directly, through binding DNA amplification products (amplicons) to probes targeting the most commonly occurring mutations (MUT probes), or indirectly, inferred by the lack of binding the amplicons to the corresponding wild-type probes.
Successful implementation of the plan will require financial and human resource commitments from the ministry of health (MoH) or national tuberculosis (TB) programme (NTP), with possible support from implementing partners. Consider integrating TB testing on existing multidisease platforms in locations where integrated testing is feasible, to share costs across disease programmes. A budget should be developed to address activities in collaboration with key partners, using the considerations outlined below. Technical assistance may be needed.
The predictive values of a test vary depending on the prevalence of TB in the population being tested. Table 3.1 provides examples of population-level projections of the results of testing with the various mWRDs in settings with different levels of TB prevalence, based on pooled sensitivity and specificity estimates that were extracted from the WHO consolidated guidelines on tuberculosis. Module 3: Diagnosis – rapid diagnostics for tuberculosis detection, 2021 update (13) for each test.
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.
General considerations for Algorithm 2a and Algorithm 2b
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.
