Lessons learned and lessons missed: impact of the coronavirus disease 2019 (COVID-19) pandemic on all-cause mortality in 40 industrialised countries prior to mass vaccination
Bibliographic record
Abstract
Background: Industrialised countries had varied responses to the coronavirus disease 2019 (COVID-19) pandemic, and how they adapted to new situations and knowledge since it began. These differences in preparedness and policy may lead to different death tolls from COVID-19 as well as other diseases. Methods: We applied an ensemble of 16 Bayesian probabilistic models to vital statistics data to estimate the impacts of the pandemic on weekly all-cause mortality for 40 industrialised countries from mid-February 2020 through mid-February 2021, before a large segment of the population was vaccinated in these countries. Results: Over the entire year, an estimated 1,410,300 (95% credible interval 1,267,600-1,579,200) more people died in these countries than would have been expected had the pandemic not happened. This is equivalent to 141 (127-158) additional deaths per 100,000 people and a 15% (14-17) increase in deaths in all these countries combined. In Iceland, Australia and New Zealand, mortality was lower than would be expected if the pandemic had not occurred, while South Korea and Norway experienced no detectable change in mortality. In contrast, the USA, Czechia, Slovakia and Poland experienced at least 20% higher mortality. There was substantial heterogeneity across countries in the dynamics of excess mortality. The first wave of the pandemic, from mid-February to the end of May 2020, accounted for over half of excess deaths in Scotland, Spain, England and Wales, Canada, Sweden, Belgium, the Netherlands and Cyprus. At the other extreme, the period between mid-September 2020 and mid-February 2021 accounted for over 90% of excess deaths in Bulgaria, Croatia, Czechia, Hungary, Latvia, Montenegro, Poland, Slovakia and Slovenia. Conclusions: Until the great majority of national and global populations have vaccine-acquired immunity, minimising the death toll of the pandemic from COVID-19 and other diseases will require actions to delay and contain infections and continue routine health care.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".