Relationship Between Iron Deficiency Anemia and Stunting in Pediatric Populations in Developing Countries: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Iron deficiency anemia (IDA) and stunting are prevalent global health issues, particularly in developing countries, where previous studies have suggested a potential relationship between them. This systematic review aims to analyze the relationship between iron deficiency anemia and stunting in pediatric populations in developing countries. METHODS: Literature searches were conducted on PubMed, EMBASE, Cochrane Library, and EBSCO Host. The primary outcome was the association between IDA and stunting. Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS) for cohort studies and the Effective Public Health Practice Project (EPHPP) for other observational studies. Meta-analysis was performed with a random-effects model and heterogeneity assessment. A Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) assessment was performed to determine the certainty and importance of the study. RESULTS: Out of 19,095 articles, 15 studies were included in the systematic review, and 4 studies were included in the meta-analysis, encompassing 21,936 subjects aged 0 to 12 years. IDA prevalence ranged from 3.6 to 58.8%, while stunting prevalence varied from 6.6 to 44.5%. Nine articles supported a significant relationship between IDA and stunting, revealing that stunted children had a 1.31-6.785 times higher risk of developing IDA. The odds ratio of children with IDA to be stunted was 2.27 (95% CI = 1.30-3.95). All studies exhibited a moderate risk of bias. GRADE assessment suggested that the evidence's certainty is low but important. CONCLUSIONS: The high IDA prevalence in developing countries, including Indonesia, is associated with stunting in children, suggesting a synergistic relationship.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".