Estimating all-cause excess mortality during COVID-19 pandemic in Serbia, 2020-2022
Bibliographic record
Abstract
Early reports indicated that Serbia was among the best-performing European countries in dealing with COVID-19 health issues based on relatively small COVID-19 mortality. This success can be partly attributed to the government's rapid response and implementation of restrictive measures to curb the spread of the virus. It can also be noted that the high level of solidarity among citizens contributed to the effective containment of the pandemic, with many adhering to experts' prescribed measures and recommendations. Serbia has become an example of good practice in the fight against COVID-19, resulting in positive assessments by international organizations and experts. However, Mortality from the virus alone is insufficient to describe the pandemic's health effects, unlike excess mortality from all causes. This paper aims to estimate excess mortality in Serbia during 2020-2022 and to compare estimated mortality with the reported COVID-19 deaths. Excess was calculated using a negative binomial regression with historical 2015-2019 data. Estimation provides a P-score as a percentage difference between the reported and expected number of deaths. Mortality excess in Serbia was 15,437 in 2020 and 35,836 in 2021, with 224 and 524 per 100,000 population rates and P-scores of 15% and 36%, respectively. Three prominent waves of excess were observed: the winter of 2020 and the spring and last quarter of 2021. The highest monthly excess was noticed in December 2020, with a rate of 113 per 100,000 and a P-score of 84%. The ratios of reported COVID-19 deaths to calculated excess mortality were 20% in 2020 and 27% in 2021. The excess mortality dramatically increases with age. Serbia faced high levels of mortality excess in 2020 and 2021, particularly among older people. During 2022, there was a decrease in mortality trends compared to pandemic years. That year 109,203 died with a rate of 1,639 per 100,000. The excess in 2022 was 7733 (116 per 100,000) with a P-score of 8%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".