Anemia prevalence and associated factors among school-age children in Accra and Kumasi metropolis in Ghana
Notice bibliographique
Résumé
Anemia remains a serious public health concern, globally, affecting learning ability and physical development of children. Anemic children are at a higher risk of diminished economic productivity and low earning capacity in adulthood due to impaired school performance and reduced work capacity. Anemia contributes to about a quarter of Africa’s nutrition-related Disability Adjusted Life Years. The objective of this study was to determine the prevalence of anemia and its associated risk factors among school-aged children (SAC) between ages 9 and 15 years in urban Ghana. The analysis included a randomly selected subsample of 1,634 children from a larger study on nutrition of SAC enrolled between 2009 and 2012 in private and public basic schools in the Accra and Kumasi Metropolis in Ghana. Socio-demographic and household characteristics were collected with questionnaires. Weight and height were taken to the nearest 0.1kg and 0.1cm, respectively. Dietary information was collected using a food frequency questionnaire. Data were analyzed using IBM SPSS Statistic version 23. The relationship between hemoglobin levels and socio-demographic variables, and predictors of hemoglobin levels were determined using Chi-square and binary logistic regression. The mean hemoglobin concentration of the study participants was 12.9±1.3 g/dL. In Kumasi, SAC had higher mean hemoglobin concentration (13.1±1.2 g/dL) compared to those from Accra (12.6±1.3 g/dL; p=0.001). Mean hemoglobin concentration was significantly higher among males than females (13.0±1.4 g/dL vs 12.8±1.2 g/dL; p=0.002). Prevalence of anemia was 20.4%; mild anemia was most common (13.6% of total sample). Anemia cases were higher in public schools (24.6%) compared to private (18.2%). Two-thirds of anemia cases (64.0%) were from schools in Accra. Males had significantly higher prevalence of anemia (26.5%) than females (15.9%; p <0.05). In the adjusted logistic regression model, only city of residence (OR+1.65, 95% CI: 1.44–1.83), thinness (OR=2.60, 95% CI: 1.11-5.75), stunting (OR=1.85, 95% CI: 1.99-3.10) and overweight (OR=0.60, 95% CI: 0.36-0.94) were significantly associated with anemia. In this study, anemia was significantly associated with location and nutritional status. Key words: anemia, Ghana, hemoglobin, schoolchildren, urban settings, metropolis, Accra, Kumasi
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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 ».