Quantifying harms to others due to alcohol consumption in Germany: a register-based study
Bibliographic record
Abstract
BACKGROUND: The consumption of alcohol increases the risk of drinkers harming others. The extent of alcohol's morbidity and mortality harms to others in Germany in 2014 was estimated for (1) fetal alcohol syndrome (FAS) or fetal alcohol spectrum disorders (FASD) among newborns, (2) road traffic fatalities, and (3) interpersonal violence-related deaths. METHODS: The incidences of FAS and FASD were estimated by means of a meta-analytical approach, combining data on alcohol use during pregnancy and the risk relationship between alcohol consumption during pregnancy and FAS/FASD. In order to estimate alcohol-attributable road traffic fatalities and interpersonal violence due to the drinking of others, an attributable fraction methodology was applied to cause-of-death statistics for road traffic and interpersonal violence-related deaths. RESULTS: For 2014, the incidences of FAS and FASD were estimated at 41 children per 10,000 live births (95% CI 24; 63) and 177 children per 10,000 live births (95% CI 135; 320), or 2930 (95% CI 1720; 4500) and 12,650 (95% CI 9650; 23,310) children, respectively. Furthermore, alcohol was estimated to be responsible for 1214 (95% CI 1141; 1287) third-party road traffic fatalities and 55 (95% CI 46; 64) deaths from interpersonal violence, representing 45.1% of all third-party road traffic fatalities and 14.9% of all interpersonal violence deaths. CONCLUSION: These study's estimates indicate there is a substantial degree of health harm to third parties caused by alcohol in Germany. While more research on harms to others caused by alcohol is needed to provide comprehensive estimates, the results indicate a need for effective prevention.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".