Correlates of Anemia in Children Under Five: A Meta-analysis of 1.39 Million Cases in India
Bibliographic record
Abstract
Context: To help children grow healthily, early detection and prevention of anemia are necessary.Objective: The present meta-analysis focused on estimating the pooled prevalence and key risk factors of anemia among children (under 5 years) in this population. Evidence acquisition:The present study searched PubMed/Google Scholar for studies published (2000-2025), reporting anemia prevalence.A total of 24 studies had 13,90,567 children were included.Data analyses using common/random-effects models were used to estimate pooled prevalence and associated risk factors.Heterogeneity (I²), subgroup analysis, and publication bias (Egger's test) were assessed.The Newcastle-Ottawa Scale (NOS) was used for quality assessment.Results: Among 24 studies, the pooled prevalence of anemia among children under five was 62.2% (95% CI: 53.4-70.2%)under the randomeffects model.Inter-state prevalence varied from 4.7 to 92.9%, with Uttar Pradesh reporting the highest pooled estimate (77.4%).Toddlers (12-35 months) were identified as a high-risk age-group for anemia, stunting, under-nutrition, and severe anemia in mothers, with almost triple the likelihood of an anemic child.Low iron biomarkers are the best diagnostic parameter for anemia.Heterogeneity was present across studies (I² > 75%) with no significant publication bias (p = 0.203).Conclusion: Overall, the study showed anemia continues to affect a large proportion of Indian children, with significant interstate and demographic disparities.Policymakers must target vulnerable groups, significant risk factors, and region-specific strategies to reduce the anemia burden in early childhood.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.048 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".