Trends in alcohol‐attributable morbidity and mortality in Germany from 2000 to 2021: A modelling study
Bibliographic record
Abstract
INTRODUCTION: We aimed to assess: (i) trends in alcohol-specific - that is, fully attributable - morbidity and mortality in the German adult population aged 15-69 between 2000 and 2021; and (ii) changes in alcohol-attributable disease burden - that is, fully and partially alcohol-attributable categories - for 2006, 2012, 2018 and 2021. METHODS: Morbidity data was pulled from hospitalisation and rehabilitation statistics and mortality data was pulled from the causes of death registry. Alcohol use, adjusted for unrecorded consumption, was estimated using the Epidemiological Survey of Substance Abuse and triangulated with per capita consumption from annual sales data. For major disease categories, alcohol-attributable fractions were estimated for males and females by age groups (15-29, 30-49, 50-69 years) using the comparative risk assessment methodology. RESULTS: For males and females, the age-standardised rate of alcohol-specific morbidity peaked in 2012 and decreased thereafter showing a steep decline from 2019 to 2021. The rates of alcohol-specific mortality decreased constantly from 2000 to 2019 but increased from 2019 to 2021. Compared to 2006 the age-standardised alcohol-attributable morbidity and mortality rates in males and females were lower in 2021. For both sexes, the age-standardised alcohol-attributable morbidity and mortality rates and the proportions of morbidity/mortality rates relative to all-cause morbidity/mortality decreased between 2006 and 2021. DISCUSSION: The declines in alcohol-attributable morbidity and mortality are in line with decreases in consumption and signal that the importance of alcohol in health service utilisation and mortality has weakened. Sex ratios in morbidity and mortality do not indicate a strong converging trend.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".