70 Mental Health in Children during the COVID-19 Pandemic: The Experience of a North American Tertiary Care Pediatric Emergency Department
Notice bibliographique
Résumé
Abstract Background The COVID-19 pandemic is a unique distressing period of experience to the public with strict physical, social and economic restrictions. Moreover, the impact of this experience on children with school closures, online education and social isolation may have a profound psychosocial impact. Objectives The objective was to evaluate and characterize the changes in mental health issues in children presenting to a large tertiary pediatric emergency department (ED) before and during the COVID-19 pandemic. Design/Methods We performed a retrospective chart review of all children who presented with any mental health related diagnosis according to the ICD-10 classification from March 1, 2019 to February 28, 2020, and during pandemic periods (March 1, 2020 to February 28, 2021). Data on presentation, diagnosis and outcome were extracted and compared using Chi square test and z tests as appropriate. Results Our centre experienced a 39% reduction (57,522 visits vs. 35,485 visits) in all ED visits but a significant increase (p=0.002) in mental health issues during the pandemic period of 5.22% compared to 4.88% during the previous year (2811/57522 visits vs. 1959/35485). Among 1,959 children presenting to ED during the pandemic, the majority (53%) were female children compared to a male predominance of 55% before the pandemic. The highest peak of visits was observed in the 14 to 16 years age group, irrespective of gender and pre/pandemic periods. The category that included “eating disorders” showed a distinct rise of 46% (p<0.001) during the pandemic period. Two categories “Intentional Self-Harm” and “External Causes of Morbidity with Undetermined Intention” did not show any difference, while “Disorders of psychological development” had a significantly low 46.5% (P<0.001) presentation during pandemic period. There wasn’t an increase in presentation of depression and anxiety to ED during COVID-19. However, admissions were significantly high (p<0.001) during the pandemic 46.5% compared to the pre-pandemic (41.6%) period. Conclusion During the pandemic, the percentage of visits related to mental health diagnoses in children increased significantly although the overall ED visits for all patients declined. As COVID-19 is expected to stay with us for the foreseeable future, we hope that this research will provide health care workers with the knowledge and understanding to optimize their approach to children at the ED.
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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 | 0,000 | 0,001 |
| 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 | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 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 ».