Relative Pandemic Severity in Canada and Four Peer Nations During the SARS-CoV-2 Pandemic
Bibliographic record
Abstract
Abstract Introduction National responses to the SARS-CoV-2 pandemic have been highly variable, which may explain some of the heterogeneity in the pandemic’s health and economic impacts across the world. We sought to explore the effectiveness of the Canadian pandemic response relative to responses in four peer countries with similar political, economic and health systems, and with close historical and cultural ties to Canada (the United States, United Kingdom, France, and Australia) from March 2020 to May 2022. Methods We used reported age-specific mortality data to generate estimates of pandemic mortality standardized to the Canadian population. Age-specific case fatality, hospitalization, and intensive care admission probabilities for the Canadian province of Ontario were applied to estimated deaths in order to calculate hospitalizations and intensive care admissions averted by the Canadian response. The monetary value of averted hospitalizations was estimated using cost estimates from the Canadian Institute for Health Information. Age-specific quality-adjusted life-years (QALY) lost due to fatality were estimated using published estimates. QALY were monetized using a net expected benefit approach. Results Relative to the United States, United Kingdom, and France, the Canadian pandemic response was estimated to have averted 94,492, 64,306 and 13,641 deaths respectively, with more than 480,000 hospitalizations averted, and 1 million QALY saved, relative to the United States. A United States pandemic response applied to Canada would have resulted in more than $40 billion in economic losses due to healthcare expenditures and lost QALY; losses relative to the United Kingdom and France would have been $21 billion and $5 billion respectively. By contrast, an Australian pandemic response would have averted over 28,000 additional deaths and averted nearly $9 billion in costs in Canada. Conclusions Canada outperformed peer countries that aimed for mitigation, rather than elimination, of SARS-CoV-2 in the first two years of the pandemic, likely because of a more stringent public health response to disease transmission. This resulted in substantial numbers of lives saved and economic costs averted. However, comparison with Australia demonstrates that an elimination focus would have allowed Canada to save tens of thousands of lives, and would have saved substantial economic costs.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".