116 Association between family income, risk for food insecurity and iron deficiency in healthy young Canadian children
Notice bibliographique
Résumé
Abstract Background Iron deficiency peaks in prevalence (12% or higher) in early childhood and has been associated with poor developmental outcomes. Previous research examining associations between income and food insecurity (FI) with iron deficiency has been inconsistent and most did not measure iron status directly using serum ferritin or control for potential confounding variables. Objectives To examine the independent effects of family income and family risk for FI on iron status in healthy young children attending primary care. Design/Methods Healthy children ages 12–29 months were included in a cross-sectional analysis. Family income and risk for FI were collected from parents through self-reported questionnaires. Children with an affirmative response to the 1-item FI screen on the NutriSTEP (a validated screening tool of nutritional risk) or to at least one of the 2 items on the 2-item FI screen based on the 18-item Household Food Security Survey were categorized as a family at risk for FI. Iron status was assessed by serum ferritin. Children with C-reactive protein (CRP) >5 mg/L were excluded. Multivariable logistic regression analyses were used to examine the associations between both family income and family risk for FI with iron deficiency (serum ferritin <12µg/L) and IDA (serum ferritin <12 µg/L and hemoglobin <110 g/L), adjusting for age, sex, birthweight, zBMI, CRP, breastfeeding duration, bottle use, cow’s milk intake, formula feeding in the first year. Results Of 1245 children included, 131 (10.5%) of children were from households with a family income of <$40,000, 77 (6.2%) children were from families at risk for FI, 15% had iron deficiency, and 5% had IDA. The odds of children with a family income of <$40,000 having iron deficiency was 3 times (95% CI: 1.75, 5.26; P<0.0001) and having IDA was 4 times (95% CI: 1.71, 9.25; P=0.001) that for children in the highest family income group. Fully adjusted logistic regression showed weak evidence of a decreased odds of iron deficiency among children in families at risk for FI (OR 0.44, 95% CI: 0.19, 1.04; P=0.06) than all other children, and no association with IDA (OR 0.18, 95% CI: 0.02, 1.38; P=0.10). Conclusion A low family income of <$40,000 was associated with an increased risk for iron deficiency and IDA in young children. Risk for FI was not a risk factor for iron deficiency or IDA. Targeting income security may be more effective than targeting access to food to reduce health inequities in iron deficiency.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».