Child‐level double burden of malnutrition in the MENA and LAC regions: Prevalence and social determinants
Bibliographic record
Abstract
Although the prevalence of obesity has rapidly increased in the low- and middle-income countries of the Middle East and North Africa (MENA) and Latin America and the Caribbean (LAC) regions, child undernutrition remains a public-health challenge. We examined region-specific sociodemographic determinants of this double burden of malnutrition, specifically, the co-occurrence of child stunting and overweight, using Demographic and Health Survey and Multiple Indicator Cluster Survey data (2003-2016) from 11 countries in the MENA (n = 118,585) and 13 countries in the LAC (n = 77,824) regions. We used multiple logistic regressions to model region-specific associations of maternal education and household wealth with child nutritional outcomes (6-59 months). The prevalence of stunting, overweight, and their co-occurrence was 24%, 10%, and 4.3% in children in the MENA region, respectively, and 19%, 5%, and 0.5% in children in the LAC region, respectively. In both regions, higher maternal education and household wealth were significantly associated with lower odds of stunting and higher odds of overweight. As compared with the poorest wealth quintiles, decreased odds of co-occurring stunting and overweight were observed among children from the second, third, and fourth wealth quintiles in the LAC region. In the MENA region, this association was only statistically significant for the second wealth quintile. In both regions, double burden was not statistically significantly associated with maternal education. The social patterning of co-occurring stunting and overweight in children varied across the two regions, indicating potential differences in the underlying aetiology of the double burden across regions and stages of the nutrition transition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".