Magnitude of COVID-19 deaths relative to other leading causes of death: a global analysis
Bibliographic record
Abstract
OBJECTIVES: To quantify the burden of death that COVID-19 contributes relative to the top three causes of death for all countries. DESIGN: We performed uncertainty analyses and created contour plots for COVID-19 mortality to place the number of COVID-19 deaths in context relative to the top three causes of death in each country, across a plausible range of values for two key parameters: case fatality rate and magnitude of under-reporting. SETTING: All countries that have reported COVID-19 cases to the WHO and are included in the Global Burden of Disease Study by the Institute of Health Metrics and Evaluation. MAIN OUTCOMES AND MEASURES: Monthly number of deaths caused by COVID-19 and monthly number of deaths caused by the top three causes of death for every country. RESULTS: For countries that were particularly hard hit during the outbreak in 2020, most combinations of model parameters resulted in COVID-19 ranking within the top three causes of death. For countries not as hard hit on a per-capita basis, such as China and India, COVID-19 did not rank higher than the third leading cause of death at any combination of the model parameters within the given ranges. Up-to-date ranking of COVID-19 deaths relative to the top three causes of death for all countries globally is provided in an interactive online application. CONCLUSIONS: Estimating the country-level burden of death that COVID-19 contributes relative to the top three causes of death is feasible through contour graphs, even when the actual number of deaths or cases is unknown. This method can help convey importance by placing the magnitude of COVID-related deaths in context relative to more familiar causes of death by communicating when COVID-related deaths rank among the top three causes of death.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".