Factors Influencing Iron Levels Among Women of Reproductive Age and Children 6–48 Months in Livestock‐Keeping Communities in Narok County, Kenya
Notice bibliographique
Résumé
ABSTRACT Proper nutrition is vital for maintaining good health for all people across their lifespan, especially children and mothers, who are especially vulnerable due to their specific nutrient needs. Despite the necessity of improved nutrition for these groups, some members do not fully meet their recommended daily micronutrient needs, a challenge exacerbated by different socioeconomic, cultural, and communal constraints resulting in malnutrition. Iron deficiency anaemia is a major concern among children and mothers, especially in pastoralist communities, due to poor nutrition and other related factors. Using a community‐based cross sectional study, this study investigated factors associated with hemoglobin levels among children and women in Narok County, Kenya. Anthropometrics were estimated using body mass index measurements for mothers, and the nutritional status of children was calculated using Z ‐score measurements. Haemoglobin was measured using a rapid test (Hemocue 301). Multiple logistic regression models were fitted to assess the association between child and maternal risk factors and anaemia. Anaemia in children was associated with age (OR = 1.99, p = 0.047), pastoralism (OR = 2.25, p = 0.002), educational of the mother (OR = 0.74, p = 0.008), severe and moderate undernourishment (OR = 1.14, p = 0.049 and OR = 1.10, p = 0.023), respectively, not meeting children dietary diversity (OR = 1.18, p = 0.027), number of people in a household (OR = 1.84, p = 0.003), and maternal age (OR = 0.30, p = 0.010). On the other hand, the occurrence of anaemia in women was associated with pastoralism (OR = 2.22, p = 0.001), having a primary school level of education (OR = 0.51, p = 0.028), pregnancy status (OR = 5.36, p = 0.002), not meeting maternal dietary diversity (OR = 1.39, p = 0.026), number of household members (OR = 1.93, p = 0.023), age of the mother (OR = 0.53, p = 0.018), and having animals infected with East Coast Fever ( Theileria parva ) within the household (OR = 1.10, p = 0.023). The results highlight the multifaceted nature of malnutrition, specifically anaemia in pastoral households, with interventions aimed at reducing disease infections in cattle, improved household dietary diversity, and community health education geared towards maternal and child nutrition being best placed to improve the overall household health outcomes relating to anaemia.
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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) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| 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 ».