Distributions of alcohol use and alcohol‐caused death and disability in Canada: Defining alcohol harm density functions and new perspectives on the prevention paradox
Bibliographic record
Abstract
AIMS: The aims of this study were to examine the distribution of alcohol use and to define 'harm density functions' representing distributions of alcohol-caused health harm in Canada, by sex, towards better understanding which groups of drinkers experience the highest aggregate harms. DESIGN: This was an epidemiological modeling study using survey and administrative data on alcohol exposure, death and disability and risk relationships from epidemiological meta-analyses. SETTING: This work took place in Canada, 2019. PARTICIPANTS: Canadians aged 15 years or older participated. MEASUREMENTS: Measures included modeled life-time mean daily alcohol use in grams of pure alcohol (ethanol) per day, alcohol-caused deaths and alcohol-caused disability-adjusted life-years. FINDINGS: As a life-time average, more than half of Canadians aged 15+ (62.8% females, 46.9% males) use fewer than 10 g of pure alcohol per day (g/day). By volume, the top 10% of the population consume 45.9% of the total ethanol among males and 47.1% of the total ethanol among females. The remaining 90% of the population experience a slim majority of alcohol-caused deaths (males 55.3%, females 46.9%). Alcohol harm density functions compose the size of the using population and the risk experienced at each volume level to show that the population-level harm experienced is highest for males at 25 g/day and females at 13 g/day. CONCLUSIONS: Almost 50% of alcohol use in Canada is concentrated among the highest 10% of drinkers, but more than half of the alcohol-caused deaths in Canada in 2019 were experienced by the bottom 90% of the population by average volume, providing evidence for the prevention paradox. New alcohol harm density functions provide insight into the aggregate health harm experienced across the mean alcohol use spectrum and may therefore be used to help determine where alcohol policies should be targeted for highest efficacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".