Alcohol sales in Canadian liquor outlets as a predictor of subsequent COVID‐19 infection rates: a time‐series analysis
Bibliographic record
Abstract
AIMS: Government alcohol sales data were used to examine whether age 15+ per-capita alcohol consumption (PCAC) (i) changed during COVID-19 and (ii) predicted COVID-19 infections 2-5 weeks later. DESIGN: Interrupted time-series analyses were applied to panels of data before and after COVID-19 restrictions were introduced in Canada. SETTING AND PARTICIPANTS: The populations, aged 15+, of the provinces of Ontario (ON), British Columbia (BC) and Nova Scotia (NS), Canada. INTERVENTION: Expansion of home delivery options and hours of trading for liquor stores while restrictions on travel, social and economic activities were imposed by governments during COVID-19 from 17 March 2020 until 29 March 2021. MEASUREMENTS: Weekly estimates of (i) age 15+ PCAC using sales data supplied by provincial government alcohol distributors for liquor stores, bars and restaurants, (ii) stringency of public health measures assessed by the Public Health Agency of Canada (PHAC) and (iii) new COVID-19 infections reported by PHAC. FINDINGS: PCAC increased by 7.10% (P = 0.013) during the pandemic versus previous years, with increased private liquor store sales partly offset by reduced bar/restaurant sales. Consumption was positively associated with stringency of public health measures. Weekly PCAC was positively associated with new COVID-19 infections 2 weeks later (+6.34% for a one drink/week increase, P < 0.001). Lagged associations with COVID-19 infections 2 or 3 weeks later were observed for PCAC from all sales channels, with larger effect sizes per standard drink/person/week increase for on-premise outlets (+77.27% week 2, P = 0.009) than government liquor stores (+6.49%, week 2, P < 0.001) or private liquor stores (+7.13%, week 4, P < 0.001). CONCLUSIONS: Alcohol consumption increased in three Canadian provinces during COVID-19 to degrees corresponding to the extent of the strictness of measures imposed to prevent viral spread. Increased consumption of alcohol was associated with increased COVID-19 infection rates 2 weeks later.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".