Impact of COVID-19 on Pediatric Emergency Department Visits: A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract Background & Objective COVID-19 has caused significant shifts in healthcare utilization, including pediatric emergency departments (EDs). We describe variations in visits made to two large pediatric EDs during the first three months of the COVID-19 pandemic, compared to a historical control period. Methods We performed a retrospective cohort study of children presenting to two academic pediatric EDs in Quebec, Canada. We compared the number of ED visits during the first wave of COVID-19 pandemic (March-May 2020) to historical controls (March-May 2015-2019), using Poisson regression, adjusting for site and the underlying baseline trend. Secondary analyses examined variations in ED visits by acuity, disposition, and disease categories. Results From 2015 to 2019, the two EDs had a median of 1,632 visits per week [interquartile range (IQR) 1,548; 1,703]; in 2020, this number decreased to 536 visits per week [IQR 446; 744]. In multivariable analyses, this represent a 53.3% (95%CI: 52.1, 54.4) reduction in the number of ED visits. The reduction was larger among visits triage categories 4 and 5 (lower acuity) than categories 1, 2 and 3 (higher acuity): -54.2% vs. -42.0% (p<0.001). A greater proportion of children presenting to these sites were hospitalized during the COVID period than in pre-COVID period: 11.8% vs. 5.5% (p<0.001). Conclusions During the early stages of the COVID-19 pandemic, there was a large decrease in visits to pediatric EDs. Patients presented with higher acuity at triage and the proportion of patients requiring hospitalization increased.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".