Emergency Department Visits, Care, and Outcome After Stroke and Myocardial Infarction During the COVID-19 Pandemic Phases
Bibliographic record
Abstract
BackgroundIt is not known if initial reductions in hospitalization for stroke and myocardial infarction early during the coronavirus disease–2019 pandemic were followed by subsequent increases. We describe the rates of emergency department visits for stroke and myocardial infarction through the pandemic phases.MethodsWe used linked administrative data to compare the weekly age- and sex-standardized rates of visits for stroke and myocardial infarction in Ontario, Canada in the first 9 months of 2020 to the mean baseline rates (2015-2019) using rate ratios (RRs) and 95% confidence intervals (CIs). We compared care and outcomes by pandemic phases (pre-pandemic was January-March, lockdown was March-May, early reopening was May-July, and late reopening was July-September).ResultsWe identified 15,682 visits in 2020 for ischemic stroke (59.2%; n = 9279), intracerebral hemorrhage (12.2%; n = 1912), or myocardial infarction (28.6%; n = 4491). The weekly rates for stroke visits in 2020 were lower during the lockdown and early reopening than at baseline (RR 0.76, 95% CI [0.66, 0.87] for the largest weekly decrease). The weekly rates for myocardial infarction visits were lower during the lockdown only (RR 0.61, 95% CI [0.46, 0.77] for the largest weekly decrease), and there was a compensatory increase in visits following reopening. Ischemic stroke 30-day mortality was increased during the lockdown phase (11.5% pre-coronavirus disease; 12.2% during lockdown; 9.2% during early reopening; and 10.6% during late reopening, P = 0.015).ConclusionAfter an initial reduction in visits for stroke and myocardial infarction, there was a compensatory increase in visits for myocardial infarction. The death rate after ischemic stroke was higher during the lockdown than in other phases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".