Assessment of the excess mortality in Ukraine caused by COVID-19
Bibliographic record
Abstract
Demographic statistics of Ukraine and other countries of the world indicate that the impact of the SARS-CoV-2 pandemic on mortality is not limited only to COVID-19 fatalities. Analysis of all-cause mortality statistics allows for examining the consequences of the pandemic regardless of the problem of determining the cause of death, which is especially relevant in the case of poor COVID-19 testing. The basic approach of such analysis includes comparing the number of deaths from all causes during the pandemic period with a forecast based on the tendencies of previous years. For the period from March 2020 to January 2022, there are considered three methods, the biggest difference between which is the interpretation of data for months when all-cause mortality is lower than expected. Other methodological issues arose while estimating the phenomenon of excess mortality in September-December 2020, whose ratio to the confirmed COVID-19 deaths was higher than on the average in 2021. It can be partially explained by the low volumes of tests in the fourth quarter of 2020. The reality of the increased excess mortality in September-December 2020 is evidenced by the fact that a similar phenomenon was observed in many neighboring countries. The conducted study shows that the excess mortality associated with the pandemic is at least 1.8–2.1 times higher than the confirmed COVID-19 deaths accord-ing to the Ministry of Health, and at least 1.6–1.9 times exceeds the similar indicator according to the State Statistics Service of Ukraine. The obtained estimates are consistent with similar es-timates from the Institute for Health Metrics and Evaluation (IHME, USA, Washington state) and the World Health Organization (WHO). The found estimates should be considered as lower given that the expected mortality from all causes for 2020-2022 was not adjusted for the phe-nomenon of mortality reduction observed in the first half of 2020.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".