Effects of nutrition interventions during pregnancy on low birth weight: an overview of systematic reviews
Bibliographic record
Abstract
Introduction Low birth weight (LBW, birth weight less than 2500 g) is associated with infant mortality and childhood morbidity. Poor maternal nutritional status is one of several contributing factors to LBW. We systematically reviewed the evidence for nutrition-specific (addressing the immediate determinants of nutrition) and nutrition-sensitive (addressing the underlying cause of undernutrition) interventions to reduce the risk of LBW and/or its components: preterm birth (PTB) and small-for-gestational age (SGA). Methods We conducted a comprehensive literature search in MEDLINE, EMBASE, CINAHL and the Cochrane Database of Systematic Reviews (September 2015). Systematic reviews of randomised controlled trials focusing on nutritional interventions before and during pregnancy to reduce LBW and its components were eligible for inclusion into the overview review. We assessed the methodological quality of the included reviews using A Measurement Tool to Assess Reviews (AMSTAR), PROSPERO: CRD42015024814. Results We included 23 systematic reviews which comprised 34 comparisons. Sixteen reviews were of high methodological quality, six of moderate and only one review of low quality. Six interventions were associated with a decreased risk of LBW: oral supplementation with (1) vitamin A, (2) low-dose calcium, (3) zinc, (4) multiple micronutrients (MMN), nutritional education and provision of preventive antimalarials. MMN and balanced protein/energy supplementation had a positive effect on SGA, while high protein supplementation increased the risk of SGA. High-dose calcium, zinc or long-chain n-3 fatty acid supplementation and nutritional education decreased the risk of PTB. Conclusion Improving women’s nutritional status positively affected LBW, SGA and PTB. Based on current evidence, especially MMN supplementation and preventive antimalarial drugs during pregnancy may be considered for policy and practice. However, for most interventions evidence was derived from a small number of trials and/or participants. There is a need to further explore the evidence of nutrition-specific and nutrition-sensitive interventions in order to reach the WHO’s goal of a 30% reduction in the global rate of LBW by 2025.
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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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".