Childhood stunting and wasting in Myanmar: Key drivers and implications for policies and programmes
Bibliographic record
Abstract
Child undernutrition is a public health and development problem in Myanmar that is jeopardizing children's physical and cognitive development and the country's social and economic progress. We identified key drivers of child stunting (low height-for-age) and wasting (low weight-for-height) in a nationally representative sample (n = 3,981) of children 0-59 months of age. The national prevalence of child stunting and wasting was 28% and 7%, respectively. Boys were more likely to be stunted or wasted than girls. Older children 24-35 months were at the highest risk of stunting compared with children under 6 months (risk ratios [RR] 10.34; 95% CI [6.42, 16.65]) whereas the youngest, under 6 months, were at the highest risk of wasting compared with children 36-59 months (RR 2.04; 95% CI [1.16, 3.57]). Maternal height <145 cm (RR 5.10; 95% CI [3.15, 8.23]), perceived small child size at birth (RR 2.08; 95% CI [1.62, 2.69]), and not benefiting from institutional delivery (RR 1.52; 95% CI [1.24, 1.87]) were associated with an increased risk of child stunting, as were maternal occupation, unimproved household drinking water, living in delta, coastal or upland areas, and poorer household wealth index quintile. Increased risk of child wasting was associated with maternal underweight (RR 1.64; 95% CI [1.11, 2.42]) and open defecation (RR 1.91; 95% CI [1.25, 2.92]) as well as maternal occupation and residence in a coastal area. Our findings indicate that the key drivers of child undernutrition in Myanmar are multifaceted and start in utero. Investing in scaling-up multisectoral approaches that include nutrition-specific and nutrition-sensitive interventions with a focus on improving maternal nutrition is essential for reducing child undernutrition and contributing to further gains in the country's human and economic development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".