Modelling the impact of alcohol consumption on cardiovascular disease mortality for comparative risk assessments: an overview
Bibliographic record
Abstract
BACKGROUND: Although alcohol consumption has long been considered as a risk factor for chronic disease, the relationship to cardiovascular disease (CVD) is complex and involves at least two dimensions: average volume of alcohol consumption and patterns of drinking. The objective of this contribution was to estimate the burden of CVD mortality caused by alcohol consumption. METHODS: Risk assessment modelling with alcohol-attributable CVD mortality as primary outcome. The mortality burden of ischaemic heart disease (IHD) and ischaemic stroke (IS) attributable to alcohol consumption was estimated using attributable-fraction methodology. Relative Risk (RR) data for IHD and IS were obtained from the most comprehensive meta-analyses (except for Russia and surrounding countries where alcohol RR data were obtained from a large cohort study). Age-group specific RRs were calculated, based on large studies. Data on mortality were obtained from the World Health Organization's Global Health Estimates and alcohol consumption data were obtained from the Global Information System on Alcohol and Health. Risk of former drinkers was modelled taking into account global differences in the prevalence of sick quitters among former drinkers. Alcohol-attributable mortality estimates for all other CVD causes except IHD and IS were obtained from the 2014 Global Status Report on Alcohol and Health. RESULTS: An estimated 780,381 CVD deaths (441,893 and 338,490 CVD deaths among men and women respectively) were attributable to alcohol consumption globally in 2012, accounting for 1.4 % of all deaths and 26.6 % of all alcohol-attributable deaths. This is in contrast to the previously estimated 1,128,273 CVD deaths attributable to alcohol consumption globally, and represents a decrease of 30.8 % in alcohol-attributable CVD mortality and of 10.6 % in the global burden of all alcohol-attributable deaths. CONCLUSIONS: When the most comprehensive and recent systematic reviews and meta-analyses are taken as bases, the net impact of alcohol consumption on CVD is lower than previously estimated.
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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 | 0.061 | 0.106 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".