Difference in impact on emergency department visits following four major peaks of COVID-19 cases
Bibliographic record
Abstract
Objective: Several variants of SARS-CoV-2 have emerged since its first appearance in 2019, greatly impacting healthcare systems across the globe. Previous literature indicated a substantial decline in emergency department (ED) visits in hospitals since the start of the COVID-19 pandemic. However, little research has been done to compare different variants’ (Ancestral, Alpha, Delta, Omicron, etc.) impact on patients presenting to the ED. Thus, the purpose of this retrospective observational study is to compare the changes in total ED volume following four major peaks of SARS-CoV-2 infection within a multi-hospital health system.Methods: Utilizing electronic healthcare record (EHR) data, total ED visits (484,268) and COVID-19 case counts (24,358) were collected and analyzed to compare ED census and COVID-19 trends across four years and four variant peak periods, from January 2019 to June 2022.Results: Results showed that ED visits declined after the first two major peaks (Ancestral and Alpha) in COVID-19 cases, which was consistent with national trends and prevailing literature. In contrast, ED visits increased following the fourth major peak (Omicron) in COVID-19 cases.Conclusions: The increase in ED visits following the fourth major peak was inconsistent with previous literature and trends. This may be attributed to the severity differences between variants, increased vaccination uptake, newly adopted public countermeasures, and evolving perceptions of safety and fear regarding COVID-19. These results underscore the critical importance for health administrators and policy planners to be cognizant of new strategies that alleviate barriers to receiving emergency care, especially during times of crisis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".