Ending TB in South-East Asia: flagship priority and response transformation
Bibliographic record
Abstract
Over the decades, the global tuberculosis (TB) response has evolved from sanatoria-based treatment to DOTS (Directly Observed Therapy Shortcourse) strategy and the more recent End TB Strategy. The WHO South-East Asia Region, which accounted for 45% of new TB patients and 50% of deaths globally in 2021, is pivotal to the global fight against TB. "Accelerate Efforts to End TB" by 2030 was adopted as a South-East Asia Regional Flagship Priority (RFP) in 2017. This article illustrates intensified and transformed approaches to address the disease burden following the adoption of RFP and new challenges that emerged during the COVID-19 pandemic. TB case notifications improved by 25% and treatment success rates improved by 6% between 2016 and 2019 due to interventions ranging from galvanising political commitments to empowering and engaging communities. Cumulative TB programme budget allocations in 2022 reached US$ 1.4 billion, about two and a half times the budget in 2016. An ambitious Regional Strategic Plan towards ending TB, 2021-2025, identifies priority interventions that will need investments of up to US$ 3 billion a year to fully implement them. Moving forward, countries in the Region need to leverage RFP and take up intensified, people-centred, holistic interventions for prevention, diagnosis, treatment and care of TB with commensurate investments and cross-ministerial and multi-sectoral coordination.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".