An Analysis of Life-Year Lost Due to COVID-19 - 34 Countries, December 2019-March 2021.
Bibliographic record
Abstract
What is already known about this topic?: The coronavirus disease 2019 (COVID-19) pandemic has caused severe health consequences. Though most COVID-19 deaths occurred among very old people, their life-year loss might be very large because of their life expectancy at that age. What is added by this report?: This study quantified how many years of life were lost due to COVID-19 in 34 countries. COVID-19 caused 9 to 21 years of life lost (YLL) per deceased patient. East Asia and Oceania had substantially lower per capita YLL than North America and Europe. Among all countries included, the United States had the greatest total YLL, Peru had the largest YLL per 100,000 people, and Mexico had the largest YLL per 100,000 COVID-19 patients. What are the implications for public health practice?: The YLL quantification indicated that the vulnerable population, especially the elderly, should be protected under careful public health measures to reduce their YLL. It also implied that it might be too early to lift anti-epidemic restrictions now, since the extreme disproportionate consequences (total and per-capita YLL) in different countries underscored the scrutinization over the variation in disease control strategies to optimize future disease control and prevention.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".