COVID-19 excess deaths in Eastern European countries associated with weaker regulation implementation and lower vaccination coverage
Bibliographic record
Abstract
Background: Since winter 2020, excess deaths due to COVID-19 have been higher in Eastern Europe than most of Western Europe, partly because regulatory enforcement was poor. Methods: This paper analysed data from 50 countries in the WHO European Region, in addition to data from USA and Canada. Excess mMortality and vaccination data were retrieved from "Our World In Data" and regulation implementation was assessed using standard methods. Multiple linear regression was used to assess the association between mortality and each covariate. Results: Excess mortality increased by 4.1 per 100 000 (P = 0.038) for every percentage decrease in vaccination rate and with 6/100 000 (p=0.011) for every decreased unit in the regulatory implementation score a country achieved in the Rule of Law Index. Conclusion: Degree of regulation enforcement, likely including public health measure enforcement, may be an important factor in controlling COVID-19's deleterious health impacts.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".