Nutritional Anemia Reductions Due to Food Fortification Among Women of Childbearing Age: A Literature Review and Bayesian Meta‐Analysis
Bibliographic record
Abstract
Food fortification can deliver essential micronutrients to populations at a large scale, thereby reducing nutritional anemia. This study aimed to review and meta-analyze the literature on the impact of wheat flour, maize flour, rice, and oil (singly or combined) fortification on women's (10-49 years) hemoglobin and anemia. A search of 17 databases yielded 2284 results. Longitudinal, pre-post cross-sectional, efficacy, and effectiveness studies were included. Primary outcomes were changes in hemoglobin concentration and anemia prevalence. Studies were synthesized using arm-based network meta-analysis. In women who consumed fortified rice, hemoglobin mean change was 3.24 g/L (95% credibility interval (CrI) 0.9, 5.98), higher than for women in the control, with a 99.1% probability that the true mean difference was > 0. Hemoglobin was 2.08 g/L (95% CrI -0.76, 4.35) higher in women who consumed wheat flour versus control, with a 93.5% probability that the true mean difference was > 0. After rice fortification, anemia prevalence in women was 1.38 percentage points (95% CrI -106.6, 99.2) lower than for control women, with a 51.2% probability that the true mean difference was < 0. Wheat flour fortification decreased anemia prevalence by 1.84 percentage points (95% CrI -93.4, 92.4) with a 52.72% probability that the true mean difference was < 0. The treatment effects of fortified maize flour and fortified oil could not be calculated due to the absence of control arms compared to the intervention arms. Fortified rice and wheat flour appear likely to modestly increase hemoglobin and may also reduce anemia in women of childbearing age.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".