A systematic analysis and future projections of the nutritional status and interpretation of its drivers among school-aged children in South-East Asian countries
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».