Micronutrient Deficiencies, Nutritional Status and the Determinants of Anemia in Children 0–59 Months of Age and Non-Pregnant Women of Reproductive Age in The Gambia
Bibliographic record
Abstract
Data on micronutrient deficiency prevalence, nutrition status, and risk factors of anemia in The Gambia is scanty. To fill this data gap, a nationally representative cross-sectional survey was conducted on 1354 children (0-59 months), 1703 non-pregnant women (NPW; 15-49 years), and 158 pregnant women (PW). The survey assessed the prevalence of under and overnutrition, anemia, iron deficiency (ID), iron deficiency anemia (IDA), vitamin A deficiency (VAD), and urinary iodine concentration (UIC). Multivariate analysis was used to assess risk factors of anemia. Among children, prevalence of anemia, ID, IDA, and VAD was 50.4%, 59.0%, 38.2%, and 18.3%, respectively. Nearly 40% of anemia was attributable to ID. Prevalence of stunting, underweight, wasting, and small head circumference was 15.7%, 10.6%, 5.8%, and 7.4%, respectively. Among NPW, prevalence of anemia, ID, IDA and VAD was 50.9%, 41.4%, 28.0% and 1.8%, respectively. Anemia was significantly associated with ID and vitamin A insufficiency. Median UIC in NPW and PW was 143.1 µg/L and 113.5 ug/L, respectively. Overall, 18.3% of NPW were overweight, 11.1% obese, and 15.4% underweight. Anemia is mainly caused by ID and poses a severe public health problem. To tackle both anemia and ID, programs such as fortification or supplementation should be intensified.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".