Impact of COVID-19 Confinement on Alcohol Purchases in Great Britain: Controlled Interrupted Time-Series Analysis During the First Half of 2020 Compared With 2015–2018
Bibliographic record
Abstract
AIMS: To investigate if COVID-19 confinement led to excess alcohol purchases by British households. METHODS: We undertake controlled interrupted time series analysis of the impact of COVID-19 confinement introduced on 26 March 2020, using purchase data from Kantar Worldpanel's of 23,833 British households during January to early July 2020, compared with 53,428 British households for the same time period during 2015-2018. RESULTS: Excess purchases due to confinement during 2020 were 178 g of alcohol per 100 households per day (adjusted for numbers of adults in each household) above an expected base of 438 g based on averaged 2015-2018 data, representing a 40.6% increase. However, when adjusting for expected normal purchases from on-licenced premises (i.e. bars, restaurants, etc.), there was evidence for no excess purchases of grams of alcohol (a 0.7% increase). With these adjustments, beer purchases dropped by 40%, wine purchases increased by 15% and spirits purchases by 22%. Excess purchases increased the richer the household and the lower the age of the main shopper. Confinement was associated with a shift in purchases from lower to higher strength beers. CONCLUSION: During the COVID-19 confinement, the evidence suggests that households did not buy more alcohol for the expected time of the year, when adjusting for what they normally would have purchased from on-licenced premises.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".