Changes in the dollar value of per capita alcohol, essential, and non-essential retail sales in Canada during COVID-19
Bibliographic record
Abstract
BACKGROUND: Multiple survey reports suggest that alcohol use has increased in Canada during the COVID-19 pandemic. However, less is known about how per capita alcohol sales, which predict population-level alcohol use, have changed and whether changes in alcohol sales differ from changes in sales of other products due to pandemic factors. METHODS: We obtained monthly retail sales data by industry from Statistics Canada, for the six largest provinces in Canada (containing 93% of the national population), between January 2010 and November 2020, representing time before and 9 months after the start of the pandemic in Canada. We used an interrupted time series analysis to estimate pandemic impacts on the dollar value of monthly per capita (per individuals 15+ years) alcohol, essential and non-essential retail sales. We adjusted our analyses for pre-pandemic sales trends, inflation, seasonality and changing population demographics over time. RESULTS: During the first 9 months of the pandemic, the values of per capita alcohol, essential and non-essential sales were, respectively, 13.2% higher, 3.6% higher and 13.1% lower than the average values during the same period in the prior 3 years. Interrupted time series models showed significant level change for the value of monthly per capita alcohol sales (+$4.86, 95% CIs: 2.88, 6.83), essential sales (-$59.80, 95% CIs: - 78.47, - 41.03) and non-essential sales (-$308.70, 95% CIs: - $326.60, - 290.79) during the pandemic. Alcohol sales were consistently elevated during the pandemic, and the pre- and post-pandemic slopes were comparable. In contrast, essential and non-essential retail sales declined in the early months of the pandemic before returning to regular spending levels. CONCLUSION: During the first 9 months of the pandemic, per capita alcohol sales were moderately elevated in Canada. In contrast, non-essential sales were lower than prior years, driven by large decreases during the initial months of the pandemic. These findings suggest that the pandemic was associated with increased population-level alcohol consumption, which may lead to increased alcohol-related harms. Ongoing research is needed to examine how factors, including pandemic-related stressors and specific alcohol sales-related policies, may have influenced changes in alcohol use and harms.
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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 | 0.003 |
| 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) | 0.002 | 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".