80 Yearly change in child zBMI and the COVID-19 pandemic in Canada: A cohort study
Notice bibliographique
Résumé
Abstract Background Emerging literature suggests accelerated weight gain in children during the pandemic; however, data from Canadian children are limited and individual factors underlying this trend remain unclear. Objectives To determine whether the COVID-19 pandemic was associated with increased yearly change in age- and sex-standardized body mass index (zBMI) in Canadian children and whether staying at home during the pandemic was associated with increased change in zBMI per year. Design/Methods A repeated-measures longitudinal study was conducted with children from an existing practice-based research network in Canada age 1-18y with at least one zBMI measurement pre- and post-onset of the pandemic. Height and weight were measured through primary care office visits and supplemented with at-home parent measurements post-pandemic. Weekly questionnaires were used to measure days stayed at home per week. Piecewise linear mixed effects models accounting for repeated measures through random effects were used to assess yearly change in zBMI. A knot location was placed at the start of the pandemic (March 14, 2020) to compare slopes pre- and post-pandemic onset. Similar mixed effects models were used to explore the association between days at home per week and change in zBMI post-pandemic onset. Stratification by sex and a sensitivity analysis in children with clinic-measured anthropometrics were explored. All models were adjusted for covariates identified a priori. Results In the overall population (n=849 children, 5070 observations), there was no significant difference in slope for yearly change in zBMI post-pandemic (Mar 2020-Sept 2022) compared to prior (Oct 2004-Mar 2020) in fully adjusted models accounting for child age, sex, maternal ethnicity, maternal BMI, family income, birthweight, breastfeeding duration, anthropometrics measurement method (clinic vs parent) and pre-COVID zBMI (post-COVID β=0.03; 95%CI -0.02,0.08; pre-COVID β=0.009; 95%CI-0.009,0.027; p-interaction=0.46). In children with clinic-measured anthropometrics (n=849, 4318 observations), there was a significant increase in yearly change in zBMI after the onset of the pandemic in the fully adjusted model (β=0.07; 95%CI 0.02,0.12) compared to prior (β=0.004; 95%CI -0.01,0.02; p-interaction=0.01). This was driven by changes in boys (post-COVID β=0.08; 95%CI 0.007,0.15; pre-COVID β=-0.003; 95%CI-0.03,0.02; p-interaction 0.04). In boys with clinic-measured anthropometrics post-pandemic onset (n=77 children, 85 observations), there was marginal evidence for increase in yearly zBMI change for every additional day at home per week in the fully adjusted model (β=0.16; 95%CI -0.006,0.32; p=0.06). Conclusion The COVID-19 pandemic was associated with accelerated increase in zBMI per year in Canadian children with clinic-measured height and weight, particularly among boys. Potential competing interests Funding was obtained by J.J. Joseph Jamnik for consultant work for Nutrigenomix Inc, a University of Toronto start-up which offers evidence-based genetic testing for personalized nutrition. Consulting work terminated July 1, 2023. This relationship is unrelated to the work submitted. Research funding: Understanding and mitigating the impacts of the COVID-19 pandemic on children, youth and families in Canada - Operating Grant. CIHR grant 468613. 2021-2024.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».