Disparities in the prevalence and risk factors of anaemia among children aged 6–24 months and 25–59 months in Ethiopia
Bibliographic record
Abstract
Despite global efforts made to address anaemia, the prevalence remains high in most Sub-Saharan African countries. In Ethiopia, anaemia poses a very strong public health concern. The purpose of the present study was to examine the key risk factors related to anaemia among children aged 6-24 months (younger age group) and 25-59 months (older age group). We used the 2016 Ethiopian Demographic and Health Survey data, collected from 11 023 mothers with under five children. Ordered logistic regression modelling was used for assessing risk factors of childhood anaemia. The results suggest that the prevalence of anaemia was 72 % in the younger and 49 % in the older age groups. The risk factors for anaemia in the younger age group were morbidity (odds ratio (OR) 1⋅77; CI 1⋅21, 2⋅60), having no piped water source (OR 1⋅76; CI 1⋅07, 3⋅01) and no toilet facility (OR 1⋅60; CI 1⋅07, 2⋅38). The key risk factors for anaemia in the older age group were no micronutrient intake (OR 1⋅69; CI 1⋅23, 2⋅31), having a young mother (15-24 years old) (OR 1⋅35; CI 0⋅84, 1⋅91) and a non-working mother (OR 1⋅50; CI 1⋅15, 1⋅96). Anaemia also varied by region, place of residence and economic factors. Multiple factors contributed to the high prevalence of anaemia. Given the structural problem that the country has intervention strategies should consider the unique characteristics of regions and rural residences where the prevalence of anaemia is above the national average.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".