Large-Scale Food Fortification and Biofortification in Low- and Middle-Income Countries: A Review of Programs, Trends, Challenges, and Evidence Gaps
Bibliographic record
Abstract
BACKGROUND: Food fortification and biofortification are well-established strategies to address micronutrient deficiencies in vulnerable populations. However, the effectiveness of fortification programs is not only determined by the biological efficacy of the fortified foods but also by effective and sustainable implementation, which requires continual monitoring, quality assurance and control, and corrective measures to ensure high compliance. OBJECTIVE: To provide an overview of efficacy, effectiveness, economics of food fortification and biofortification, and status of and challenges faced by large-scale food fortification programs in low- and middle-income countries (LMIC). METHODS: A literature review of PubMed publications in English from 2000 to 2017, as well as gray literature, targeting nongovernmental organizations whose work focuses on this topic, complemented by national reports and a "snowball" process of citation searching. The article describes remaining technical challenges, barriers, and evidence gap and prioritizes recommendations and next steps to further accelerate progress and potential of impact. RESULTS: The review identifies and highlights essential components of successful programs. It also points out issues that determine poor program performance, including lack of adequate monitoring and enforcement and poor compliance with standards by industry. CONCLUSIONS: In the last 17 years, large-scale food fortification initiatives have been reaching increasingly larger segments of populations in LMIC. Large-scale food fortification and biofortification should be part of other nutrition-specific and nutrition-sensitive efforts to prevent and control micronutrient deficiencies. There are remaining technical and food system challenges, especially in relation to improving coverage and quality of delivery and measuring progress of national programs.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".