Population-level mortality burden from novel coronavirus (COVID-19) in Europe and North America
Bibliographic record
Abstract
As of 31 January 2021, 63.9 million cases and 1.4 million deaths had been reported in Europe and North America, which accounted for 62.5% and 62.4% of the global total, respectively. Comparing the level of mortality across countries has proven difficult because of inherent limitations in the most commonly cited measures (e.g., case-fatality rates). We collected the cumulative number of confirmed deaths from COVID-19 by age in 2020 from the L'Institut National d'études Démographiques (INED) database and Statistics Canada for 15 European and North American countries. We calculated age-specific death rates and age-standardized death rates (ASDR) for each country over a 1-year period from 6 February 2020 (date of first COVID-19 death in Europe and North America) to 5 February 2021 using established demographic methods. We estimated that COVID-19 was the second leading cause of death behind cancer in England and Wales and France and the third leading cause of death behind cancer and heart disease in nine countries including the US. Countries with higher all-cause mortality prior to the COVID-19 experienced higher COVID-19 mortality than countries with lower all-cause mortality prior to the pandemic. The COVID-19 ASDR varied substantially within country (e.g., a 5-fold difference among the highest and lowest mortality states in Germany). Consistently strong public health measures may have lessened the level of mortality for some European and North American countries. In contrast, many of the largest countries and economies in these regions may continue to experience a high mortality level because of poor implementation and adherence to such measures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41118-021-00115-9.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".