The impacts of minimum alcohol pricing on alcohol attributable morbidity in regions of British Colombia, Canada with low, medium and high mean family income
Bibliographic record
Abstract
BACKGROUND AND AIMS: Previous research indicates that minimum alcohol pricing (MAP) is associated negatively with alcohol-attributable (AA) hospitalizations. Modeling studies predict that this association will be stronger for people on lower incomes. The objective of this study was to test whether the association between MAP and AA hospitalizations is greater in low-income regions. DESIGN: Cross-sectional versus time-series analysis using multivariate multi-level effect models. SETTING: All 89 Local Health Areas in British Columbia (BC), Canada, 2002-13 (48 quarters). PARTICIPANTS: BC population. MEASUREMENTS: Quarterly rates of AA hospital admissions, mean consumer price index-adjusted minimum dollars per standard alcoholic drink and socio-demographic covariates. FINDINGS: Family income was related inversely to the effect of minimum prices on rates of some types of AA morbidity. A 1% price increase was associated with reductions of 3.547% [95% confidence interval (CI) = -5.719, -1.377; P < 0.01] in low family-income regions and 1.64% (95% CI = -2.765, -0.519; P < 0.01) across all income regions for 100% acute AA hospital admissions. Delayed (lagged) effects on chronic AA morbidity were found 2-3 years after minimum price increases for low income regions and all regions combined; a 1% increase in minimum price was associated with reductions of 2.242% (95% CI = -4.097, -0.388; P < 0.05) for 100% chronic AA and 2.474% (95% CI = -3.937, -1.011; P < 0.01) for partially chronic AA admissions for low-income regions. CONCLUSION: In Canada, minimum price increases for alcohol are associated with reductions in alcohol attributable hospitalizations, especially for populations with lower income, both for immediate effects on acute hospitalizations and delayed effects on chronic hospitalizations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".