Maternal Micronutrient Supplementation and Long Term Health Impact in Children in Rural Bangladesh
Bibliographic record
Abstract
BACKGROUND: Limited data is available on the role of prenatal nutritional status on the health of school-age children. We aimed to determine the impact of maternal micronutrient supplementation on the health status of Bangladeshi children. METHODS: Children (8.6-9.6 years; n = 540) were enrolled from a longitudinal mother-child cohort, where mothers were supplemented daily with either 30mg iron and 400μg folic acid (Fe30F), or 60mg iron and 400μg folic acid (Fe60F), or Fe30F including 15 micronutrients (MM), in rural Matlab. Blood was collected from children to determine the concentration of hemoglobin (Hb) and several micronutrients. Anthropometric and Hb data from these children were also available at 4.5 years of age and mothers at gestational week (GW) 14 and 30. RESULTS: MM supplementation significantly improved (p≤0.05) body mass index-for-age z-score (BAZ), but not Hb levels, in 9 years old children compared to the Fe30F group. MM supplementation also reduced markers of inflammation (p≤0.05). About 28%, 35% and 23% of the women were found to be anemic at GW14, GW30 and both time points, respectively. The prevalence of anemia was 5% and 15% in 4.5 and 9 years old children, respectively. The adjusted odds of having anemia in 9 year old children was 3-fold higher if their mothers were anemic at both GW14 and GW30 [Odds Ratio (OR) = 3.05; 95% Confidence Interval (CI) 1.42, 6.14, P = 0.002] or even higher if they were also anemic at 4.5 years of age [OR = 5.92; 95% CI 2.64, 13.25; P<0.001]. CONCLUSION: Maternal micronutrient supplementation imparted beneficial effects on child health. Anemia during pregnancy and early childhood are important risk factors for the occurrence of anemia in school-age children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".