Temporal Changes in Alcohol-Related Morbidity and Mortality in Germany
Bibliographic record
Abstract
AIMS: Trends in morbidity and mortality, fully or partially attributable to alcohol, for adults aged 18-64 were assessed for Germany. METHODS: The underestimation of population exposure was corrected by triangulating survey data with per capita consumption. Alcohol-attributable fractions by sex and two age groups were estimated for major disease categories causally linked to alcohol. Absolute numbers, population rates and proportions relative to all hospitalizations and deaths were calculated. RESULTS: Trends of 100% alcohol-attributable morbidity and mortality over thirteen and eighteen years, respectively, show an increase in rates of hospitalizations and a decrease in mortality rates. Comparisons of alcohol-attributable morbidity including diseases partially caused by alcohol revealed an increase in hospitalization rates between 2006 and 2012. The proportion of alcohol-attributable hospitalizations remained constant. Rates of alcohol-attributable mortality and the proportion among all deaths decreased. CONCLUSIONS: The increasing trend in mortality due to alcohol until the mid-1990s has reversed. The constant proportion of all hospitalizations that were attributable to alcohol indicates that factors such as improved treatment and easier health care access may have influenced the general increase in all-cause morbidity. To further reduce alcohol-related mortality, efforts in reducing consumption and increasing treatment utilization are needed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".