70 Mental Health in Children during the COVID-19 Pandemic: The Experience of a North American Tertiary Care Pediatric Emergency Department
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".