The relation between different dimensions of alcohol consumption and burden of disease: an overview
Bibliographic record
Abstract
AIMS: As part of a larger study to estimate the global burden of disease and injury attributable to alcohol: to evaluate the evidence for a causal impact of average volume of alcohol consumption and pattern of drinking on diseases and injuries; to quantify relationships identified as causal based on published meta-analyses; to separate the impact on mortality versus morbidity where possible; and to assess the impact of the quality of alcohol on burden of disease. METHODS: Systematic literature reviews were used to identify alcohol-related diseases, birth complications and injuries using standard epidemiological criteria to determine causality. The extent of the risk relations was taken from meta-analyses. RESULTS: Evidence of a causal impact of average volume of alcohol consumption was found for the following major diseases: tuberculosis, mouth, nasopharynx, other pharynx and oropharynx cancer, oesophageal cancer, colon and rectum cancer, liver cancer, female breast cancer, diabetes mellitus, alcohol use disorders, unipolar depressive disorders, epilepsy, hypertensive heart disease, ischaemic heart disease (IHD), ischaemic and haemorrhagic stroke, conduction disorders and other dysrhythmias, lower respiratory infections (pneumonia), cirrhosis of the liver, preterm birth complications and fetal alcohol syndrome. Dose-response relationships could be quantified for all disease categories except for depressive disorders, with the relative risk increasing with increased level of alcohol consumption for most diseases. Both average volume and drinking pattern were linked causally to IHD, fetal alcohol syndrome and unintentional and intentional injuries. For IHD, ischaemic stroke and diabetes mellitus beneficial effects were observed for patterns of light to moderate drinking without heavy drinking occasions (as defined by 60+ g pure alcohol per day). For several disease and injury categories, the effects were stronger on mortality compared to morbidity. There was insufficient evidence to establish whether quality of alcohol had a major impact on disease burden. CONCLUSIONS: Overall, these findings indicate that alcohol impacts many disease outcomes causally, both chronic and acute, and injuries. In addition, a pattern of heavy episodic drinking increases risk for some disease and all injury outcomes. Future studies need to address a number of methodological issues, especially the differential role of average volume versus drinking pattern, in order to obtain more accurate risk estimates and to understand more clearly the nature of alcohol-disease relationships.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".