Prevention Paradox and the Case for Universal Alcohol Policies
Bibliographic record
Abstract
Background: Alcohol is a leading cause of death and disability in Canada. Governments in Ontario have been weakening universal alcohol policies, including controls on price and availability, while increasing investment in addiction treatment for high-risk individuals. According to Rose’s “prevention paradox”,1 more disease cases accrue in populations with low or moderate disease risk (i.e., low and medium volume users) because they are more numerous in the population than those at higher risk (i.e., high volume users). It is important to understand “who” experiences health harm from alcohol and at “what” level of consumption to inform policy that addresses alcohol-attributable health harm in Canada. Objective: To describe the distribution of alcohol-attributable healthcare encounters (emergency department (ED) visits and hospitalizations) across the drinking population in Ontario in 2019. Methods: The International Model of Alcohol Harms and Policies estimated alcohol-attributable healthcare encounters experienced across male and female drinking groups: (1) former (no past-year consumption); (2) low volume (≤5 standard drinks (SD)/week for females and males); (3) medium volume (>5 to 10 SD/week for females and >5 to 15 SD/week for males); and (4) high volume (>10 SD/week for females and >15 SD/week for males) (1 SD=13.45g ethanol). Healthcare encounters in Ontario included all fully and partially alcohol-attributable ED visits (n=231,914) in 2019 and hospitalizations (n=34,158) in 2018. Alcohol use exposure data were obtained from the Canadian Substance Use Exposure Database. Results: High volume drinkers (males: 23%, females: 13%) experienced a large portion of all alcohol-attributable ED visits (males: 65%, females: 49%) and hospitalizations (males: 71%, females: 50%). However, substantial healthcare encounters occurred among low and medium volume drinkers, especially among females. For example, 63% of females (males: 44%) were low volume drinkers who consumed 22% (males: 7%) of alcohol sold and experienced 31% and 20% (males: 14% and 7%) of alcohol-attributable ED visits and hospitalizations, respectively. Discussion: Evidence from Ontario supports the prevention paradox as substantial alcohol-attributable healthcare encounters were experienced among low and medium volume drinkers. Evidence-informed universal policy approaches are necessary to reduce population-level alcohol-attributable harm in Ontario. References McLaren L, McIntyre L, Kirkpatrick S. Rose’s population strategy of prevention need not increase social inequalities in health. Int J Epidemiol. 2010;39(2):372-377. doi:10.1093/ije/dyp315
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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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".