Improved micronutrient status and health outcomes in low- and middle-income countries following large-scale fortification: evidence from a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Micronutrient malnutrition is highly prevalent in low- and middle-income countries (LMICs) and disproportionately affects women and children. Although the effectiveness of large-scale food fortification (LSFF) of staple foods to prevent micronutrient deficiencies in high-income settings has been demonstrated, its effectiveness in LMICs is less well characterized. This is important as food consumption patterns, potential food vehicles, and therefore potential for impact may vary substantially in these contexts. OBJECTIVES: The aim of this study was to determine the real-world impact of LSFF with key micronutrients (vitamin A, iodine, iron, folic acid) on improving micronutrient status and functional health outcomes in LMICs. METHODS: All applicable published/unpublished evidence was systematically retrieved and analyzed. Studies were not restricted by age or sex. Meta-analyses were performed for quantitative outcomes and results were presented as summary RRs, ORs, or standardized mean differences (SMDs) with 95% CIs. RESULTS: LSFF increased serum micronutrient concentrations in several populations and demonstrated a positive impact on functional outcomes, including a 34% reduction in anemia (RR: 0.66; 95% CI: 0.59, 0.74), a 74% reduction in the odds of goiter (OR: 0.26; 95% CI: 0.16, 0.43), and a 41% reduction in the odds of neural tube defects (OR: 0.59; 95% CI: 0.49, 0.70). Additionally, we found that LSFF with vitamin A could protect nearly 3 million children per year from vitamin A deficiency. We noted an age-specific effect of fortification, with women (aged >18 y) attaining greater benefit than children, who may consume smaller quantities of fortified staple foods. Several programmatic/implementation factors were also reviewed that may facilitate or limit program potential. CONCLUSIONS: Measurable improvements in the micronutrient and health status of women and children are possible with LSFF. However, context and implementation factors are important when assessing programmatic sustainability and impact, and data on these are quite limited in LMIC studies.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".