A systematic analysis and future projections of the nutritional status and interpretation of its drivers among school-aged children in South-East Asian countries
Bibliographic record
Abstract
Background: Nutrition inadequacy during childhood and adolescence can cause suboptimal growth, intergenerational effects on offspring and an increased risk of chronic diseases in adulthood. There is little information on the prevalence and drivers of malnutrition in children aged 5-19 years, in the South-East Asian setting, since most existing interventions have to date targeted undernutrition. We assessed the national prevalence of nutritional indicators, their trends, and associated risk factors among children aged 5-19 years from 11 countries of WHO South-East Asia Region (SEA Region) in order to provide evidence to guide future policy direction. Methods: We included 5,210,646 children for analysis from 345 studies and 25 survey datasets. A Newcastle-Ottawa Scale was used to assess the quality of the study. Bayesian regression models were used to estimate the prevalence of malnutrition between 2000 and 2030, and a series of subgroup analyses were performed to assess variation in pooled estimates by different socio-demographic and lifestyle factors. The protocol was registered with PROSPERO database (CRD42023400104). Findings: Overall, pooled analysis demonstrated that indicators of undernutrition in SEA is predicted to decrease between 2000 and 2030 including stunting (36.6%-27.2%), thinness (29.5%-6.2%), and underweight (29.2%-15.9%). However, a substantial increase in prevalence of overweight (6.0% in 2000-16.9% in 2030), and obesity (2.6%-9.5%) are predicted. The prevalence of micronutrient deficiencies between 2000 and 2030 is predicted to decrease-vitamin A by 84% and vitamin D by 53%. Parents' education levels and household wealth were inversely associated with malnutrition. Children's health-related behaviours, such as unhealthy dietary habits and spending more time watching TV, playing games, or using the computer, were associated with increased chance of overweight and obesity. There were no clear signs of publication bias in our study. Interpretation: Our analysis highlights the pattern of a double burden of malnutrition, with clear differences between different socio-demographic groups. Despite a substantial reduction in the prevalence of stunting, underweight, and anaemia since 2000, an emerging increase in overweight/obesity and micronutrient deficiencies warrants urgent attention. Funding: World Health Organization Regional Office for South-East Asia New Delhi, India.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".