How many alcohol-attributable deaths and hospital admissions could be prevented by alternative pricing and taxation policies? Modelling impacts on alcohol consumption, revenues and related harms in Canada
Bibliographic record
Abstract
INTRODUCTION: In 2017, Canada increased alcohol excise taxes for the first time in over three decades. In this article, we describe a model to estimate various effects of additional tax and price policies that are predicted to improve health outcomes. METHODS: We obtained alcohol sales and taxation data for 2016/17 for all Canadian jurisdictions from Statistics Canada and product-level sales data for British Columbia. We modelled effects of alternative price and tax policies - revenue-neutral taxes, inflation-adjusted taxes and minimum unit prices (MUPs) - on consumption, revenues and harms. We used published price elasticities to estimate impacts on consumption and revenue and the International Model for Alcohol Harms and Policies (InterMAHP) to estimate impacts on alcohol-attributable mortality and morbidity. RESULTS: Other things being equal, revenue-neutral alcohol volumetric taxes (AVT) would have minimal influence on overall alcohol consumption and related harms. Inflation-adjusted AVT would result in 3.83% less consumption, 329 fewer deaths and 3762 fewer hospital admissions. A MUP of $1.75 per standard drink (equal to 17.05mL ethanol) would have reduced consumption by 8.68% in 2016, which in turn would have reduced the number of deaths by 732 and the number of hospitalizations by 8329 that year. Indexing alcohol excise taxes between 1991/92 and 2016/17 would have resulted in the federal government gaining approximately $10.97 billion. We estimated this could have prevented 4000-5400 deaths and 43 000-56 000 hospitalizations. CONCLUSION: Improved public health outcomes would be made possible by (1) increasing alcohol excise tax rates across all beverages to compensate for past failures to index rates, and (2) setting a MUP of at least $1.75 per standard drink. While reducing alcohol-caused harms, these tax policies would have the added benefit of increasing federal government revenues.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".