Increased alcohol‐specific mortality in Germany during <scp>COVID</scp>‐19: State‐level trends from 2010 to 2020
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic may have led to an increase in the alcohol-specific mortality. Against this backdrop, the aim of this report is to explore alcohol-specific mortality trends in Germany of the years 2010 to 2020. METHOD: Alcohol-specific mortality data aggregated by sex, 5-year age groups and state were collected from the annual cause-of-death statistics and analysed descriptively by visual inspection. RESULTS: The overall alcohol-specific mortality rate (age-standardised) has mainly decreased between 2010 and 2020. However, increased alcohol-specific mortality rates for the year 2020 compared to 2019 were found for both, women (+4.8%) and men (+5.5%), particularly in age groups between 40 and 69 years. Changes in alcohol-specific mortality rates differed between federated states, with steeper increases in East Germany. DISCUSSION AND CONCLUSIONS: Different mechanisms related to the increase in alcohol consumption, particularly among high-risk drinkers, and reduced resources in health care may have led to an increase in alcohol-specific mortality in Germany in 2020. Despite the recent decline in the alcohol-specific mortality in Germany, an increase in the death toll was observed in 2020.
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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 | 0.001 |
| 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 | 0.000 | 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".