Lessons from COVID-19 mortality data across countries
Bibliographic record
Abstract
OBJECTIVE: Several online sources provide up-to-date open-access data on numbers, rates and proportions of COVID-19 deaths. Our article aims of comparing and interpreting between-country trends of mortality rate, case-fatality and all-cause excess mortality. METHODS: We used data from open databases (Our World in Data mostly) for comparing mortality of eleven western countries (Austria, Belgium, Canada, France, Germany, Italy, Netherlands, Spain, Sweden, UK, USA). Between-country trends in mortality rate and case-fatality (both including deaths for COVID-19 as numerator and therefore labelled as COVID-19 mortality metrics) and all-cause excess mortality (i.e. observed deaths during the epidemic compared with those expected based on mortality in the same periods of previous years) were compared. RESULTS: Although Belgium ranks first in mortality from COVID-19 (possibly due to the broadest criterion for attributing a death to COVID-19), it does not rank first for all-cause excess mortality. Conversely, compared with Belgium, the UK, Italy and Spain have reported lower COVID-19 mortality (possibly due to the narrower definitions for a COVID-19 death) but higher all-cause excess mortality. Germany and Austria are the unique countries for which COVID-19 mortality, case-fatality and all-cause excess mortality consistently exhibited the lowest rates. CONCLUSION: Between-country heterogeneity of COVID-19 mortality metrics could be largely explained by differences of criteria for attributing a death to COVID-19; in age/comorbidity structures; in policies for identifying asymptomatic people affected from SARS-CoV-2 infection. All-cause excess mortality is recommended as a more reliable metric for comparing countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".