Components of Successful Staple Food Fortification Programs: Lessons From Latin America
Bibliographic record
Abstract
BACKGROUND: There are few effectiveness evaluations of food fortification programs, and little is known about what makes programs successful. OBJECTIVE: We examined 3 food fortification programs in Latin America to identify common features that might explain their success and to draw lessons for program design and implementation everywhere: The vitamin A fortification of sugar in Guatemala with impact on vitamin A status of the population, the fortification of a basket of foods with iron and other micronutrients in Costa Rica with impact on iron status and anemia in women and children, and the fortification of wheat flour with folic acid in Chile, which reduced the incidence of neural tube defects. METHODS: We identified pertinent literature about these preselected programs and asked regional experts for any additional information. We also conducted structured interviews of key informants to provide historical and contextual information. RESULTS: Institutional research capacity and champions of fortification are features of successful programs in Latin America. We also found that private/public partnerships (industry, government, academia, and civil society) might be key for sustainability. To achieve impact, program managers need to use fortification vehicles that are consumed by the nutritionally vulnerable and to add bioavailable fortificants at adequate content levels in order to fill dietary gaps and reduce micronutrient deficiencies. Adequate monitoring and quality control are essential. CONCLUSIONS: For future programs, we recommend that the evaluation be specified up-front, including a baseline/end line and data collection along the program impact pathway to inform needed improvements and to strengthen causal inferences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".