Cancer incidence attributable to alcohol consumption in Alberta in 2012
Bibliographic record
Abstract
BACKGROUND: Alcohol consumption has been associated with risk of oral cavity/pharyngeal, laryngeal, esophageal, liver, colorectal and breast cancers. The purpose of this study was to estimate the proportion and total number of these cancers in Alberta in 2012 attributable to alcohol consumption. METHODS: We estimated cancers attributable to alcohol consumption in adults in Alberta using population attributable risk calculations. Relative risks were obtained from recent meta-analyses, and alcohol consumption in Alberta was quantified with the use of data from the Canadian Community Health Survey. We obtained age-, site- and sex-specific cancer incidence data for 2012 from the Alberta Cancer Registry. The impact of potential underestimation of alcohol consumption in Canadian Community Health Survey data was evaluated with the use of per-capita alcohol sales data from Statistics Canada. RESULTS: Proportions of cancers attributable to alcohol consumption at individual cancer sites were estimated to be as low as 5.1% (liver) and as high as 19.9% (oral cavity/pharynx) among men and as low as 2.1% (liver) and as high as 7.6% (oral cavity/pharynx) among women in Alberta. The total number of alcohol-attributable cancer cases was highest for common cancers (colorectal, female breast), whereas at individual cancer sites, population attributable risks were highest for upper aerodigestive tract cancers. A total of 4.8% of alcohol-associated cancers (1.6% of all cancers) in Alberta could be attributed to alcohol consumption. After adjustment for recorded alcohol consumption, our estimates of population attributable risk increased to 10.7% of alcohol-associated cancers and 3.5% of all cancers. INTERPRETATION: Alcohol consumption is estimated to account for 1.6%-3.5% of total cancer cases in Alberta. Given that no level of alcohol consumption is considered safe with respect to cancer risk, strategies to reduce alcohol consumption have the potential to reduce Alberta's cancer burden.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".