Modeling the Predictors of Stunting in Ethiopia: Analysis of 2016 Ethiopian Demographic Health Survey Data (EDHS)
Bibliographic record
Abstract
Abstract Background Despite the continued effort to address malnutrition, there is a minimal reduction in the rate of stunting in developing countries including Ethiopia. Nutritional and socioeconomic factors have not been assed using a rigorous analysis so far. Therefore, this study aims to model the effect of nutritional and socioeconomic predictors using 2016 Ethiopian Demographic health survey. Methods This study is a secondary of data analysis of the 2016 EDHS survey. 7909 children of age 6-59 months were included. Descriptive statistics using frequency and percentage for categorical data and mean and standard deviation for metric data were conducted. Linearity, confounding and multicolinearity were checked. Bivariable and multivariable logistic regression were carried out. Odds ratio and 95% confidence interval were calculated. Receiver operative curve was built to estimate the sensitivity and specificity of the model. Result The study identified as 39.2% of children included to this analysis were stunted. 76.47%, 84.27%, 92.62% of the children did not consume fruits and vegetables, legumes and lentils, and meat and its products respectively. Children aged 24 month to 59 months were at 9.71 times higher risk of being stunted compared to younger children of age 6-24 months old (AOR: 9.71; CI: 8.07, 11.6 children) with weights below 9.1 kilogram were at 27.86 odds of being stunted compared to those weight of 23.3 kg and above. Moreover, mothers below 150 cm tall (AOR: 2.01; CI: 1.76, 2.5), living in a rural area (AOR: 1.3, CI: 1.09, 1.54) and being male (AOR: 1.4; CI: 1.26, 1.56) were associated with stunting. Predictive ability of the model was 77% correctly such pair of observation. Conclusion The model indicates that being born male, being from a mother of short stature, living in the rural area, small child size, mother with mild anemia, being from a husband with no education or primary education only, having low weight, and reaching age above 24 months increases the likelihood of stunting. On the other hand, being born of an overweight or obese mother decreases the likelihood of stunting.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".