An Interrupted Time-Series Analysis of the Impact of COVID-19 on Hospitalizations for Vascular Events in 3 Canadian Provinces
Bibliographic record
Abstract
Background COVID-19 infection is associated with a pro-coagulable state, thrombosis, and cardiovascular events. However, its impact on population-based rates of vascular events is less understood. We studied temporal trends in hospitalizations for stroke and myocardial infarction in three Canadian provinces (Alberta, Ontario, Nova Scotia) between 2014-2022. Methods Linked administrative data from each province were used to identify admissions for ischemic stroke, intracerebral hemorrhage, cerebral venous thrombosis, or myocardial infarction. Event rates/100,000/quarter, standardized to the 2016 Canadian population, were calculated. We assessed changes from quarterly rates pre-pandemic (2014-2020) compared to the pandemic period (2020-2022) using interrupted time series analysis with a jump discontinuity at pandemic onset. Age group and sex-stratified analyses were also performed. Results We identified 162,497 strokes and 243,182 myocardial infarctions. At pandemic onset there was no significant step change in strokes/100,000/quarter observed in any of the three provinces. During the pandemic, stroke rates were stable in Alberta and Ontario but increased in Nova Scotia (0.44/100,000/quarter, p-value 0.004). At pandemic onset, there was a significant step decrease in myocardial infarctions/100,000/quarter in Alberta (4.72, p-value <0.001) and Ontario (4.84, p-value <0.001), but not in Nova Scotia. During the pandemic, myocardial infarctions/100,000/quarter decreased in Alberta (-0.34, p-value 0.01) but remained stable in Ontario and Nova Scotia. No consistent patterns by age group or sex were noted. Conclusions Hospitalization rates for stroke or myocardial infarction across three Canadian provinces did not substantially increase during the first two years of the pandemic. Continued surveillance is warranted as the virus becomes endemic.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".