Population and Individual-Level Double Burden of Malnutrition Among Adolescents in Two Emerging Cities in Northern and Southern Nigeria: A Comparative Cross-Sectional Study
Notice bibliographique
Résumé
<strong>Background:</strong> Over the past three decades, double burden of malnutrition (DBM), a situation where high levels of undernutrition (stunting, thinness, or micronutrient deficiency) coexist with overnutrition (overweight and obesity), continues to rise in sub-Saharan Africa. Compared to other countries in the region, the evidence on DBM is limited in Nigeria. <strong>Objective:</strong> This paper aimed to determine the comparative prevalence of population-level and individual-level DBM among adolescents in two emerging cities in northern and southern Nigeria. <strong>Methods:</strong> This was a comparative cross-sectional study among apparently healthy secondary school adolescents aged 10–18 years in Gombe (northern Nigeria) and Uyo (southern Nigeria) between January 2015 and June 2017. A multistage random sampling technique was implemented to recruit adolescents from 24 secondary schools in both cities. Measures of general obesity (body mass index) and stature (height-for-age) were classified and Z-scores generated using the WHO <em>AnthroPlus</em> software, which is based on the WHO 2006 growth reference. Population-level DBM was defined as the occurrence of thinness and overweight/obesity within the population. Individual-level DBM was defined as the proportion of individuals who were concurrently stunted and had truncal obesity or stunted and were overweight/obese. <strong>Findings:</strong> Overall, at the population-level in both settings, 6.8% of adolescents had thinness, while 12.4% were overweight/obese signifying a high burden of population-level DBM. Comparatively, the population-level DBM was higher in Gombe compared to Uyo (thinness: 11.98% vs 5.3% and overweight/obesity: 16.08% vs 11.27% in Gombe vs Uyo respectively). Overall, at the individual level, 6.42% of stunted adolescents had coexisting truncal obesity, while 8.02% were stunted and had coexisting general overweight/obesity. Like the trend with population-level DBM, individual-level DBM was higher in Gombe (northern Nigeria) compared to Uyo (southern Nigeria). <strong>Conclusion:</strong> High levels of population-level and individual-level DBM exist in Gombe and Uyo. However, the level of DBM (under- and over-nutrition) is higher in Gombe located in northern Nigeria compared to Uyo in southern Nigeria.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».