Changes in emergency department use in British Columbia, Canada, during the first 3 years of the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown reductions in the volume of emergency department visits early in the COVID-19 pandemic, but few have evaluated the pandemic's impact over time or stratified analyses by reason for visits. We aimed to quantify such changes in British Columbia, Canada, cumulatively and during prominent nadirs, and by reason for visit, age and acuity. METHODS: We included data from the National Ambulatory Care Reporting System for 30 emergency departments across BC from January 2016 to December 2022. We fitted generalized additive models, accounting for seasonal and annual trends, to the monthly number of visits to estimate changes throughout the pandemic, compared with the expected number of visits in the absence of the pandemic. We determined absolute and relative differences at various times during the study period, and cumulatively since the start of the pandemic until the overall volume of emergency department visits returned to expected levels. RESULTS: Over the first 16 months of the pandemic, the volume of emergency department visits was reduced by about 322 300 visits, or 15% (95% confidence interval 12%-18%), compared with the expected volume. A sharp drop in pediatric visits accounted for nearly one-third of the reduction. The timing of the return to baseline volume of visits differed by subgroup. The largest and most sustained decreases were in respiratory-related emergency department visits, visits among children, visits among oldest adults and non-urgent visits. Later in the pandemic, we observed increased volumes of highest-urgency visits, visits among children and visits related to ear, nose and throat. INTERPRETATION: We have extended evidence that the impact of the COVID-19 pandemic and associated mitigation strategies on emergency department visits in Canada was substantial. Both our findings and methods are relevant in public health surveillance and capacity planning for emergency departments in pandemic and nonpandemic times.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".