Micronutrient adequacy is poor, but not associated with stunting between 12-24 months of age: A cohort study findings from a slum area of Bangladesh
Notice bibliographique
Résumé
The prevalence of stunting among children below 5 years of age is higher in the slum-dwelling population of Bangladesh compared to that in both urban and rural areas. Studies have reported that several factors such as inadequate nutrition, low socio-economic status, poor hygiene and sanitation and lack of maternal education are the substantial predictors of childhood stunting. Almost all these factors are universally present in the slum-dwelling population of Bangladesh. However, few studies have prospectively examined such determinants of stunting among slum populations. In this paper, we reveal the findings of a cohort study with an aim to explore the status of micronutrient adequacy among such vulnerable children and establish its association with stunting along with other determinants. Two-hundred-sixty-five children were enrolled and followed since birth until 24 months of age. We collected anthropometric, morbidity and dietary intake data monthly. We used the 24-hour multiple-pass recall approach to collect dietary intake data from the age of 9 months onward. Micronutrient adequacy of the diet was determined by the mean adequacy ratio (MAR) which was constructed from the average intake of 9 vitamins and 4 minerals considered for the analysis. We used generalized estimating equation (GEE) regression models to establish the determinants of stunting between 12-24 months of age in our study population. The prevalence of low-birth-weight (LBW) was about 28.7% and approximately half of the children were stunted by the age of 24 months. The average micronutrient intake was considerably lower than the recommended dietary allowance and the MAR was only 0.48 at 24 months of age compared to the optimum value of 1. However, the MAR was not associated with stunting between 12-24 months of age. Rather, LBW was the significant determinant (AOR = 3.03, 95% CI: 1.69-5.44) after adjusting for other factors such as age (AOR = 2.12, 95% CI: 1.45-3.11 at 24 months and AOR = 1.97, 95% CI: 1.49-2.59 at 18 months, ref: 12 months) and sex (AOR = 1.98, 95% CI: 1.17-3.33, ref: female). Improving the nutritional quality of complementary food in terms of adequacy of micronutrients is imperative for optimum growth but may not be adequate to mitigate under-nutrition in this setting. Further research should focus on identifying multiple strategies that can work synergistically to diminish the burden of stunting in resource-poor settings.
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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) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».