Effect of the COVID-19 Pandemic on Patient Volumes, Acuity, and Outcomes in Pediatric Emergency Departments
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to quantify the effect of the COVID-19 pandemic on pediatric emergency department (ED) utilization and outcomes. METHODS: This study is an interrupted-time-series observational study of children presenting to 11 Canadian tertiary-care pediatric EDs. Data were grouped into weeks in 3 study periods: prepandemic (January 1, 2018-January 27, 2020), peripandemic (January 28, 2020-March 10, 2020), and early pandemic (March 11, 2020-April 30, 2020). These periods were compared with the same time intervals in the 2 preceding calendar years. Primary outcomes were number of ED visits per week. The secondary outcomes were triage acuity, hospitalization, intensive care unit (ICU) admission, mortality, length of hospital stay, ED revisits, and visits for trauma and mental health concerns. RESULTS: There were 577,807 ED visits (median age, 4.5 years; 52.9% male). Relative to the prepandemic period, there was a reduction [-58%; 95% confidence interval (CI), -63% to -51%] in the number of ED visits during the early-pandemic period, with concomitant higher acuity. There was a concurrent increase in the proportion of ward [odds ratio (OR), 1.39; 95% CI, 1.32-1.45] and intensive care unit (OR, 1.20; 95% CI, 1.01-1.42) admissions, and trauma-related ED visits among children less than 10 years (OR, 1.51; 95% CI, 1.45-1.56). Mental health-related visits in children declined in the early-pandemic period (in <10 years, -60%; 95% CI, -67% to -51%; in children ≥10 years: -56%; 95% CI, -63% to -47%) relative to the pre-COVID-19 period. There were no differences in mortality or length of stay; however, ED revisits within 72 hours were reduced during the early-pandemic period (percent change: -55%; 95% CI, -61% to -49%; P < 0.001). CONCLUSIONS: After the declaration of the COVID-19 pandemic, dramatic reductions in pediatric ED visits occurred across Canada. Children seeking ED care were sicker, and there was an increase in trauma-related visits among children more than 10 years of age, whereas mental health visits declined during the early-pandemic period. When faced with a future pandemic, public health officials must consider the impact of the illness and the measures implemented on children's health and acute care needs.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".