Impact of Infant and Young Child Feeding (IYCF) Nutrition Interventions on Breastfeeding Practices, Growth and Mortality in Low- and Middle-Income Countries: Systematic Review
Bibliographic record
Abstract
Undernutrition is associated with 45% of total infant deaths, totalling 2.7 million globally per year. The vast majority of the burden is felt in low- and middle-income countries (LMICs). This review aims to assess the effectiveness of infant and young child feeding (IYCF) interventions. We searched multiple databases including Cochrane Controlled Trials Register (CENTRAL), MEDLINE, EMBASE. Title/abstract screening and full-text screening and data extraction filtered 77 studies for inclusion. Breastfeeding education interventions (n = 38) showed 20% increase in rates of early initiation of breastfeeding, 102% increase in exclusive breastfeeding (EBF) at 3 months and 53% increase in EBF at 6 months and 24% decreases in diarrheal diseases. Complementary feeding education intervention (n=12) showed a 0.41 standard deviation (SD) increase in WAZ, and 0.25 SD in HAZ in food secure setting. Complementary food provision with or without education (n=17) showed a 0.14 SD increase in HAZ and 36% decrease in stunting. Supplementary food interventions (n=12) showed a significant 0.15 SD increase in WHZ. Subgroup analyses showed healthcare professional led interventions were largely more effective, especially on breastfeeding outcomes. We believe this is a comprehensive review of the existing literature on IYCF studies in LMICs. Though breastfeeding education is well supported in its effectiveness on breastfeeding practices, limited evidence exists for growth outcomes. Supplementation interventions seem to have better effects at improving growth. However, more research is required to reach more substantial conclusions.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".