Mortality and potential years of life lost attributable to alcohol consumption in Canada in 2005
Bibliographic record
Abstract
BACKGROUND: Alcohol is a substantial risk factor for mortality according to the recent 2010 World Health Assembly strategy to reduce the harmful use of alcohol which outlined the need to characterize and monitor this burden. Accordingly, using new methodology we estimated 1) the number of deaths caused and prevented by alcohol consumption, and 2) the potential years of life lost (PYLLs) attributable to alcohol consumption in Canada in 2005. METHODS: Mortality attributable to alcohol consumption was estimated by calculating Alcohol-Attributable Fractions (AAFs) (defined as the proportion of mortality that would be eliminated if the exposure was eliminated) using data from various sources. Indicators for alcohol consumption were obtained from the Canadian Alcohol and Drug Use Monitoring Survey 2008 and corrected for adult per capita recorded and unrecorded alcohol consumption. Risk relations were taken from the Comparative Risk Assessment within the current Global Burden of Disease (GBD) study. Due to concerns about the reliability of information specifying causes of death for people aged 65 or older, our analysis was limited to individuals aged 0 to 64 years. Calculation of the 95% confidence intervals (CIs) for the AAFs was performed using Monte Carlo random sampling. Information on mortality was obtained from Statistics Canada. A sensitivity analysis was performed comparing the mortality results obtained using our study methods to results obtained using previous methodologies. RESULTS: In 2005, 3,970 (95% CI: 810 to 7,170) deaths (4,390 caused and 420 prevented) and 134,555 (95% CI: 36,690 to 236,376) PYLLs were attributable to alcohol consumption for individuals aged 0 to 64 years. These figures represent 7.7% (95% CI: 1.6% to 13.9%) of all deaths and 8.0% (95% CI: 2.2% to 14.1%) of all PYLLs for individuals aged 0 to 64 years. The sensitivity analysis showed that the number of deaths as measured by this new methodology is greater than that if mortality was estimated using previous methodologies. CONCLUSIONS: The mortality burden attributable to alcohol consumption for Canada is large, unnecessary, and could be substantially reduced in a short period of time if effective public health policies were implemented. A monitoring system on alcohol consumption is imperative and would greatly assist in planning and evaluating future Canadian public health policies related to alcohol consumption.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".