The Unfinished Agenda for Food Fortification in Low- and Middle-Income Countries: Quantifying Progress, Gaps and Potential Opportunities
Bibliographic record
Abstract
Large-scale food fortification (LSFF) is a cost-effective intervention that is widely implemented, but there is scope to further increase its potential. To identify gaps and opportunities, we first accessed the Global Fortification Data Exchange (GFDx) to identify countries that could benefit from new fortification programs. Second, we aggregated Fortification Assessment Coverage Toolkit (FACT) survey data from 16 countries to ascertain LSFF coverage and gaps therein. Third, we extended our narrative review to assess current innovations. We identified 84 countries as good candidates for new LSFF programs. FACT data revealed that the potential of oil/ghee and salt fortification is not being met due mainly to low coverage of adequately fortified foods (quality). Wheat, rice and maize flour fortification have similar quality issues combined with lower coverage of the fortifiable food at population-level (< 50%). A four-pronged strategy is needed to meet the unfinished agenda: first, establish new LSFF programs where warranted; second, systems innovations informed by implementation research to address coverage and quality gaps; third, advocacy to form new partnerships and resources, particularly with the private sector; and finally, exploration of new fortificants and vehicles (e.g. bouillon cubes; salt fortified with multiple nutrients) and other innovations that can address existing challenges.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".