Attributable is preventable: Corrected and revised estimates of population attributable fraction of TB related to undernutrition in 30 high TB burden countries
Bibliographic record
Abstract
Introduction: The Global TB Report 2020 estimated the population attributable fractions (PAF) for the major risk factors of TB. Undernourishment emerged as the leading risk factor accounting for 19% of the cases. The WHO however used the terms undernourishment and undernutrition interchangeably in its computation of PAF. Undernourishment is an indirect model derived estimate of decreased per capita energy availability, while undernutrition is defined by direct anthropometric measurements of nutritional status. An estimate of PAF for a risk factor should use the prevalence and the risk ratio of the same risk factor, which is not the case with the current methodology. Methods: in adults for both sexes), and the relative risk (RR) of 3.2. Further, we revised PAF estimates of undernutrition with an RR of 4.49 (95% CI: 2.28, 8.86), in light of recent evidence. Findings: In 30 high TB burden countries, 24.1% (95% CI: 17.6,30.0) of incident TB is attributable to undernutrition. The PAF of undernutrition was highest in Asian countries, unlike the PAF of undernourishment that was highest in Africa. The corrected estimate led up to 65% increase in number of cases attributable to undernutrition in Asian countries. If a revised relative risk was used, 33.0% (95% CI: 10.1, 60.1) of incident TB cases in the selected countries could be attributable to undernutrition. More than one-third to nearly half of incident TB cases in India could be attributable to undernutrition. Interpretation: Estimation of the PAF of TB related to undernutrition is methodologically valid and operationally relevant, rather than PAF related to undernourishment, and should be used for future Global TB reports by WHO. Addressing undernutrition, the leading driver of TB in high TB burden countries (especially Asia) could enable achievement of END TB milestones of TB incidence for 2025.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".