Are Countries of the Eastern Mediterranean Region on Track towards Meeting the World Health Assembly Target for Anemia? A Review of Evidence
Bibliographic record
Abstract
Anemia is a multifactorial condition, with a complex etiology that involves nutritional and non-nutritional factors. The misconception that iron deficiency is equivalent to anemia may mask the need to address other potential causative factors. This review paper aims to (1) assess the burden of anemia vs. iron deficiency anemia (IDA) amongst women of reproductive age (WRA), pregnant women (PW), and children under five years old (underfive children, U5C) in the Eastern Mediterranean region (EMR); (2) evaluate trends in anemia prevalence and whether countries are on track towards meeting the World Health Assembly (WHA) target for 2025; and (3) characterize anemia reduction efforts and provide a road map for future programs. A search of pertinent literature and databases was conducted. Anemia prevalence in the EMR ranged between 22.6% and 63% amongst PW, 27% and 69.6% amongst WRA, and 23.8% and 83.5% amongst U5C. Data showed that the EMR is not on course towards meeting the WHA target. The contribution of IDA to anemia was found to be less than half. Other potential contributors to anemia in the region were identified, including micronutrient deficiencies, parasitic infestations, and poor sanitation. A framework of action was proposed as a roadmap to meet the targets set by the WHA.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".