Trends in Major Adverse Cardiac Events and Healthcare Resource Utilization During the COVID-19 Pandemic in Alberta, Canada
Bibliographic record
Abstract
Background: Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of morbidity and mortality in Canada. The COVID-19 pandemic altered the usual care of ambulatory and acute cardiac patients. This study aimed to describe ASCVD-related clinical outcomes and healthcare resource utilization (HCRU) patterns during the coronavirus disease 2019 (COVID-19) pandemic in Alberta, Canada, relative to the three preceding years. Methods: A repeated cross-sectional study design was conducted over three-month periods using administrative health data between March 15, 2017, and March 14, 2021. ASCVD-related clinical outcomes included major adverse cardiovascular events (MACE) endpoints. HCRU was assessed through general practitioner and other healthcare professional visits (including telehealth claims) for ASCVD events, emergency department visits, ASCVD diagnostic imaging tests, laboratory tests, and hospital length of stay. Results: Relative to the control year period (March to June 2019) ASCVD-related events (i.e., hospitalizations, emergency department (ED) visits and physician office visits) declined by 23% during the three-months COVID-19 period (March to June 2020). Acute declines were not sustained following June 2020. In contrast, in-patient mortality rates involving a primary MACE endpoint increased from March to June 2020 COVID-19 period. Conclusions: This study demonstrates the COVID-19 pandemic and corresponding public health restrictions impacted ASCVD-related care. While many clinical outcomes returned to pre-pandemic levels at the end of the observation period, our results suggest that patients' HCRU declined, which could lead to further CV events and mortality. Understanding the impact of COVID-19 restrictions on ASCVD-related care may help improve healthcare resiliency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".