Multiple micronutrient supplementation cost–benefit tool for informing maternal nutrition policy and investment decisions
Bibliographic record
Abstract
Antenatal multiple micronutrient supplementation (MMS) is an intervention that can help reach three of the six global nutrition targets, either directly or indirectly: a reduction in low birth weight, stunting, and anaemia in women of reproductive age. To support global guideline development and national decision-making on investments into maternal nutrition, Nutrition International developed a modelling tool called the MMS cost-benefit tool to help users understand whether antenatal MMS is better value for money than iron and folic acid supplementation (IFAS) during pregnancy. The MMS cost-benefit tool can generate estimates on the potential health impact, budget impact, economic value, cost-effectiveness and benefit-cost ratio of investing in MMS compared to IFAS in LMICs. In the 33 countries with data included in the tool, the MMS cost-benefit tool shows that transitioning is expected to generate substantial health benefits in terms of morbidity and mortality averted and can be very cost-effective in multiple scenarios for these countries. The cost per DALY averted averages at US$ 23.61 and benefit-cost ratio ranges from US$ 41-US$ 1304: $1.0, which suggest MMS is good value for money compared with IFAS. With its user-friendly design, open access availability, and online data-driven analytics, the MMS cost-benefit tool can be a powerful resource for governments and nutrition partners seeking timely and evidence-based analyses to inform policy-decision and investments towards the scale-up of MMS for pregnant women globally.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 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".