Patterns in the Risk Factors of Undernutrition Among Children 0 to 24 Months and 25 to 59 Months Old in Ethiopia: Evidence From the 2016 National Survey
Bibliographic record
Abstract
The objective of this study is to examine the contribution of child, maternal and household factors on undernutrition of children under five years in Ethiopia. We used the 2016 Ethiopian Demographic and Health Survey data. We have included 4199 young children (0–24 months) and 5497 older age group (25–59 months), giving a total of 9696 children. Among the younger age group 29% were stunted, 14% were wasted and 19% were underweight, and among the older age group prevalence of stunting, wasting and underweight were 47%, 8% and 28% respectively. Being female, intake of multiple micronutrients, households having piped source of drinking water, high maternal BMI, higher household wealth, 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 anemic, had small birth weight, drank from bottle, and children of stunted or wasted or working mother were more likely to be stunted, wasted or underweight in both groups (P < 0.05). While most predictors and/or risk factors followed similar pattern across the two groups, child factors had higher leverage in the younger than the older groups across the three forms of undernutrition. 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 the intra household food distribution. N/A.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".