Alcohol attributable burden of incidence of cancer in eight European countries based on results from prospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To compute the burden of cancer attributable to current and former alcohol consumption in eight European countries based on direct relative risk estimates from a cohort study. DESIGN: Combination of prospective cohort study with representative population based data on alcohol exposure. Setting Eight countries (France, Italy, Spain, United Kingdom, the Netherlands, Greece, Germany, Denmark) participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. PARTICIPANTS: 109,118 men and 254,870 women, mainly aged 37-70. MAIN OUTCOME MEASURES: Hazard rate ratios expressing the relative risk of cancer incidence for former and current alcohol consumption among EPIC participants. Hazard rate ratios combined with representative information on alcohol consumption to calculate alcohol attributable fractions of causally related cancers by country and sex. Partial alcohol attributable fractions for consumption higher than the recommended upper limit (two drinks a day for men with about 24 g alcohol, one for women with about 12 g alcohol) and the estimated total annual number of cases of alcohol attributable cancer. RESULTS: If we assume causality, among men and women, 10% (95% confidence interval 7 to 13%) and 3% (1 to 5%) of the incidence of total cancer was attributable to former and current alcohol consumption in the selected European countries. For selected cancers the figures were 44% (31 to 56%) and 25% (5 to 46%) for upper aerodigestive tract, 33% (11 to 54%) and 18% (-3 to 38%) for liver, 17% (10 to 25%) and 4% (-1 to 10%) for colorectal cancer for men and women, respectively, and 5.0% (2 to 8%) for female breast cancer. A substantial part of the alcohol attributable fraction in 2008 was associated with alcohol consumption higher than the recommended upper limit: 33,037 of 178,578 alcohol related cancer cases in men and 17,470 of 397,043 alcohol related cases in women. CONCLUSIONS: In western Europe, an important proportion of cases of cancer can be attributable to alcohol consumption, especially consumption higher than the recommended upper limits. These data support current political efforts to reduce or to abstain from alcohol consumption to reduce the incidence of cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".