In-kind Nutritional Supplementation for Persons With Drug-susceptible Tuberculosis and Their Household Contacts Would be Cost-effective for Reducing Tuberculosis Incidence and Mortality in Jharkhand, India: A Modeling Study
Bibliographic record
Abstract
BACKGROUND: Undernutrition is the leading cause of tuberculosis (TB) globally, but nutritional interventions are often considered cost-prohibitive. The RATIONS study demonstrated that nutritional supplementation to household contacts of persons with TB can reduce TB incidence, yet economic evaluations of such strategies remain limited. METHODS: Using a Markov model, we assessed the cost-effectiveness of a RATIONS-style intervention (monthly food basket providing 750 kcal, 23 g of protein, and a multi-micronutrient tablet daily) for household contacts of persons with TB, as compared to no nutritional support. We calculated health outcomes (TB episodes, TB deaths, and disability-adjusted life years [DALYs]) over the lifetime of intervention recipients and assessed costs from healthcare and societal perspectives. We tested the robustness of results to parameter changes via deterministic and probabilistic sensitivity analysis. FINDINGS: Over 2 years, household contacts receiving the RATIONS intervention experienced 38% (95% uncertainty interval [UI]: 23-52) fewer TB episodes and 58% (95% UI: 44-70) fewer TB deaths. Over the lifetime of a cohort of 100 000 household contacts, the intervention was projected to avert 11 524 DALYs (95% UI: 7446-17 393) and was cost-effective from both the healthcare (incremental cost-effectiveness ratio [ICER]: $289 per DALY averted [95% UI: 156-537]) and societal perspectives ($229 per DALY averted [95% UI: 102-468]). Cost-effectiveness was most sensitive to the cost of the nutritional supplement. CONCLUSIONS: Prompt nutritional support for household contacts of persons with TB disease would be cost-effective in reducing TB incidence and mortality in India.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".