The impact of the COVID-19 on childhood growth amongst children under 6 years old A longitudinal cohort study in Ontario, Canada
Notice bibliographique
Résumé
ObjectiveThere is evidence that the COVID-19 pandemic has had an impact on childhood growth and obesity prevalence; however, the extent to which it has affected young children (<6 years old) living in Canada is unclear. Examining the impact of the COVID-19 pandemic on childhood growth among young children is important to understanding the potential enduring health and developmental repercussions. In this study, we sought to determine: 1) the association between the COVID-19 pandemic and the rate of change in the standardized body mass index (zBMI); 2) the association between the COVID-19 pandemic and the mean zBMI; and 3) whether the association between the COVID-19 pandemic and the rate of change in zBMI differed by zBMI quantiles.MethodsThis was a longitudinal cohort study that used Ontario electronic medical records data from the practiced-based research network UTOPIAN. The population was children <6 years old (N=22,307) who had had at least one primary care visit between March 10th, 2018 – March 11th, 2022. The main exposure was the COVID-19 era (March 11, 2020-March 11, 2022). The outcome of interest was zBMI. Piecewise and linear mixed effects models with knots at the onset of COVID and 1 year into-COVID and quantile regression models were used to test the association between exposures and outcomes adjusted for rurality, racialized and newcomer index, income quintile and Material Resources Index.ResultsThe cohort comprised of 83,269 visits from 22,307 unique children. The majority of visits (67.9%) occurred before COVID. Among the children, there was an even sex distribution, a high prevalence of urban residency, and a higher diversity in terms of racialized and newcomer populations. Overall, 17.6% of the children were classified as overweight or affected by obesity. The mean zBMI pre- and during COVID were -0.122 (SD: 1.300) and -0.394 (SD: 1.330), respectively. The piecewise linear mixed effects model revealed a pre-COVID annual increase inzBMI (0.009 SD units per year on average (95% CI: 0.001, 0.017), which did not change during COVID (-0.004; 95% CI:-0.019, 0.011). The linear mixed effects model found evidence of a relationship between the COVID-19 era and an increase in mean zBMI (0.158 SD units, 95% CI: 0.256, 0.291). In all models, rural, more diverse, and more material resourced populations were associated with a decrease in the rate of change in zBMI. In the quantile regression analysis, zBMI quantiles did not show evidence of an association with the COVID-19 era.ConclusionOur analysis revealed that while the pre-COVID rate of change in zBMI was increasing, there was no evidence of an association between the COVID-19 era and the rate of change in zBMI. However, the COVID era was associated with an overall increase in mean zBMI. Further, there was no evidence of an association between the COVID-19 era and rate of change in zBMI differing by zBMI quantile. Our findings highlight an important ongoing public health emergency, overweight and obesity, that has persisted through the pandemic. This epidemic is far from seeing an end like the COVID pandemic. Effective primary care and public health interventions are required that would ideally address the multiple environmental and socio- cultural dimensions of childhood growth
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) | 0,000 | 0,000 |
| 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 | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».