Risk Factors for Anemia in Preschool Children in Sub-Saharan Africa
Bibliographic record
Abstract
Iron is a mineral that is found in nature and foods. It is involved in many physiological functions in the body, and poor iron intake can lead to iron deficiency and later to anemia. Iron deficiency anemia (IDA) is the most prevalent nutritional disorder in the world despite iron being the fourth most common element on earth. Anemia is amongst the most important contributing factors to the global burden of disease. According to a recent WHO report on the global prevalence of anemia, one in four people is affected by anemia worldwide Two thirds of preschool-age children are affected in developing regions of Africa and South East-Asia, and about 40% of the world's anaemic preschool-age children reside in South-East Asia Of the 293.1 million children who suffer from anemia worldwide, 83 million (28%) are in sub-Saharan Africa, representing 67% of the total population of children of this age group in the continent. Adverse health consequences of anemia in preschool children include altered cognitive function, impaired motor development and growth, poor school performance, poor immune function and susceptibility to infections, decreased in responsiveness and activity, increased in body tension and fatigue. Even before clinical symptoms are visible, iron deficiency that leads to anemia is detrimental to children and may condemn one third of the world population to live permanently below their full mental and physical potential. Indeed, the impact of iron deficiency anemia on psychomotor development and cognitive function in children under the age of two years may be irreversible despite adequate therapy The authors estimated an additional US$14.46 per capita lost in cognitive function, for a total annual loss (cognitive & productive) of about $50 billion in GDP worldwide from iron deficiency anemia. Due to its detrimental effects among children, effective interventions
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".