Ethiopia’s high childhood undernutrition explained: analysis of the prevalence and key correlates based on recent nationally representative data
Bibliographic record
Abstract
OBJECTIVE: To examine the contribution of child, maternal and household factors in stunting, wasting and underweight among children under 5 years in Ethiopia. DESIGN: Quantitative cross-sectional design based on nationally representative data. SETTING: Urban and rural areas of Ethiopia.ParticipantsYounger (0-24 months; n 4199) and older age groups (25-59 months; n 5497), giving a total of 9696 children. RESULTS: Among the younger age group, 29 % were stunted, 14 % were wasted and 19 % were underweight; and among the older age group, the prevalence of stunting, wasting and underweight was 47, 8 and 28 %, respectively. Being female, intake of multiple micronutrients, household having a piped source of drinking-water, high maternal BMI, higher household wealth and higher maternal education were associated with decreased odds of at least one form of undernutrition in both groups. On the other hand, children who were anaemic, had low birth weight, drank from a bottle, and children of stunted or wasted or working mothers were more likely to be stunted, wasted or underweight in both groups (P<0·05). While most predictors and/or risk factors followed a similar pattern across the two age groups, child factors had higher leverage in the younger than the older group across the three forms of undernutrition. CONCLUSIONS: Multiple set of factors predicted childhood undernutrition in Ethiopia. The study underscores the importance of intervening in the first 1000 days through promoting maternal education, maternal-child health services, mother's nutrition and improving intrahousehold food distribution.
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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 | 0.001 | 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".