The impact of COVID-19 on hospital admissions and emergency department visits: A population-based study
Bibliographic record
Abstract
BACKGROUND: As a result of the novel coronavirus disease 2019 (COVID-19), there have been widespread changes in healthcare access. We conducted a retrospective population-based study in Alberta, Canada (population 4.4 million), where there have been approximately 1550 hospital admissions for COVID-19, to determine the impact of COVID-19 on hospital admissions and emergency department (ED visits), following initiation of a public health emergency act on March 15, 2020. METHODS: We used multivariable negative binomial regression models to compare daily numbers of medical/surgical hospital admissions via the ED between March 16-September 23, 2019 (pre COVID-19) and March 16-September 23, 2020 (post COVID-19 public health measures). We compared the most frequent diagnoses for hospital admissions pre/post COVID-19 public health measures. A similar analysis was completed for numbers of daily ED visits for any reason with a particular focus on ambulatory care sensitive conditions (ACSC). FINDINGS: There was a significant reduction in both daily medical (incident rate ratio (IRR) 0.86, p<0.001) and surgical (IRR 0.82, p<0.001) admissions through the ED in Alberta post COVID-19 public health measures. There was a significant decline in daily ED visits (IRR 0.65, p<0.001) including ACSC (IRR 0.75, p<0.001). The most common medical/surgical diagnoses for hospital admissions did not vary substantially pre and post COVID-19 public health measures, though there was a significant reduction in admissions for chronic obstructive pulmonary disease and a significant increase in admissions for mental and behavioral disorders due to use of alcohol. CONCLUSIONS: Despite a relatively low volume of COVID-19 hospital admissions in Alberta, there was an extensive impact on our healthcare system with fewer admissions to hospital and ED visits. This work generates hypotheses around causes for reduced hospital admissions and ED visits which warrant further investigation. As most publicly funded health systems struggle with health-system capacity routinely, understanding how these reductions can be safely sustained will be critical.
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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 | 0.003 |
| 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) | 0.001 | 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".