Risk factors modifying the double burden of malnutrition of young children in Thailand
Bibliographic record
Abstract
Thailand is now faced with a double burden of malnutrition. Using nationally representative data from the 2015-2016 Multiple Indicator Cluster Survey, we utilized multinomial logistic regression models to examine factors associated with stunting only, wasting only, overweight only, concurrent stunting and overweight, and concurrent stunting and wasting among children 0-59 months of age (n = 11,068). The prevalences of <5 stunting only (height-for-age Z score < -2 SD) and wasting only (WHZ < -2 SD) were 8.5% and 4.7%, respectively. The prevalence of <5 overweight only (WHZ > +2 SD) was 7.8%. Children 12-23 months (risk ratio [RR], 95% confidence interval [CI]: 1.47 [1.18, 1.83]; p < .01) and 24-35 months (RR, 95% CI: 1.56 [1.26, 1.94]; p < .001) were at increased risk for stunting only, compared with children 48-59 months. The strongest risk factor for stunting only was low birth weight (RR, 95% CI: 3.42 [2.86, 4.10]; p < .001). Children 0-5 months were at highest risk for wasting only, compared with children 48-59 months (RR, 95% CI: 2.91 [2.16, 3.92]; p < .001). Children 48-59 months and male children were more likely to be overweight only. Higher household wealth and smaller household size were also significant predictors of overweight only. A small proportion of children were concurrently stunted and overweight (1.3%) and concurrently stunted and wasted (0.6%). A multipronged approach focused on adequate prenatal care, improving breastfeeding and complementary feeding practices, and mitigating the growing burden of overweight is needed to address the double burden of malnutrition in Thailand.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".