Prevalence and Determinants of Double and Triple Burden of Malnutrition Among School Going Children and Adolescents in Zanzibar, 2022
Notice bibliographique
Résumé
ABSTRACT Background Malnutrition stands as a profound global health concern, and its dimensions are evolving. In Zanzibar, the burden of malnutrition especially among school going children is not well unknown, as such, this study was conducted to assess the prevalence and factors associated with double and triple burden of malnutrition among school going children and adolescents in Zanzibar. Methods This was a School-based cross-sectional study involving mainly quantitative data collection method. Data was collected as part of the National School Health and Nutrition Survey of 2022, which included primary and secondary school children and adolescents aged 5-19 years, from Zanzibar. Anthropometric measurements and hemoglobin levels of selected students were collected. A multinomial regression model was used to assess factors associated with double burden of malnutrition (DBM) and the triple burden of malnutrition (TBM). Z-scores for weight, height and body mass index for the scholars aged 5-19 years were generated using WHO AnthroPlus and data analysis was done using Stata Version 17. Results A total of 2556 primary and secondary school children were enrolled, 51.7% (n= 1,322) were girls. Almost 2 in 5 were individuals with 10-14 years, and most of them were from primary schools. Slightly over 5 in 10 were residing in urban areas. Overall, the prevalence of malnutrition defined as malnutrition of any kind (stunting or underweight/thinness or overweight or anemia) in Zanzibar was 58.4 per cent. The overall prevalence of DBM defined by the coexistence of both undernutrition and over-malnutrition in Zanzibar was 12.0 per cent. Similarly, the overall prevalence of TBM defined as the coexistence of undernutrition, anemia, and overnutrition in Zanzibar was 1.8%. In an adjusted multinomial regression model, the prevalence of DBM was 30% lower if the child was 5 to 9 years 0.7 (95% CI, 0.5–0.9), p = 0.02), and 1.5-fold greater if the student was living in a lowest wealth quantile family (1.5(95%CI, 1.01-2.3), p=0.04). In the contrary, the prevalence of single malnutrition was 1.4-fold greater if the student was a girl (1.4(95%CI,1.2-1.6); p=0.001). Proportion of students with TBM was 2.0-fold greater if there were no school deworming education (2.0(95%CI, 1.0-3.9); p=0.05 Conclusion Over half of the students in Zanzibar are malnourished, with a significant burden of DBM, indicating the need for stringent actions to reduce the prevalence of malnutrition in the country.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».