Availability of Alcohol on an Online Third-Party Delivery Platform Across London Boroughs, England: Exploratory Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Higher availability of alcohol is associated with higher levels of alcohol consumption and harm. Alcohol is increasingly accessible online, with rapid delivery often offered by a third-party driver. Remote delivery and online availability are important from a public health perspective, but to date, relatively little research has explored the availability of alcohol offered by online platforms. OBJECTIVE: This cross-sectional exploratory study describes the availability of alcohol on the third-party platform Deliveroo within London, England. METHODS: We extracted the number of outlets offering alcohol on Deliveroo for each London borough and converted these into crude rates per 1000 population (18-64 years). Outlets were grouped as outlets exclusively selling alcohol, off-licenses, and premium. We calculated Pearson correlation coefficients to explore the association between borough's crude rate of outlets per 1000 population and average Indices of Multiple Deprivation (IMD) 2019 scores. We extracted the number of outlets also selling tobacco or e-cigarettes and used non-Deliveroo drivers. We searched addresses of the top 20 outlets delivering to the most boroughs by outlet type (60 total) to determine their associated premise. RESULTS: We identified 4277 total Deliveroo-based outlets offering alcohol across London, including outlets delivering in multiple boroughs. The crude rate of outlets per 1000 population aged 18-64 years was 0.73 and ranged from 0.22 to 2.29 per borough. Most outlets exclusively sold alcohol (3086/4277, 72.2%), followed by off-licenses (770/4277, 18.0%) and premium (421/4277, 9.8%). The majority of outlets exclusively selling alcohol sold tobacco or e-cigarettes (2951/3086, 95.6%) as did off-licenses to a lesser extent (588/770, 76.4%). Most outlets exclusively offering alcohol used drivers not employed by Deliveroo (2887/3086, 93.6%), and the inverse was true for premium outlets (50/421, 11.9%) and off-licenses (73/770, 9.5%). There were 1049 unique outlets, of which 396 (37.8%) were exclusively offering alcohol-these outlets tended to deliver across multiple boroughs unlike off-licenses and premium outlets. Of outlets with confirmed addresses, self-storage units were listed as the associated premise for 85% (17/20) of outlets exclusively offering alcohol, 11% (2/19) of off-licenses, and 12% (2/17) of premium outlets. We found no significant relationship between borough IMD scores and crude rate of outlets per 1000 population overall (P=.87) or by any outlet type: exclusively alcohol (P=.41), off-license (P=.58), and premium (P=.18). CONCLUSIONS: London-based Deliveroo outlets offering alcohol are common and are sometimes operating from self-storage units that have policies prohibiting alcohol storage. This and the potential for increased alcohol accessibility online have implications for public health given the relationship between alcohol's availability and consumption or harm. There is a need to ensure that regulations for delivery are adequate for protecting children and vulnerable adults. The Licensing Act 2003 may require modernization in the digital age. Future research must explore a relationship between online alcohol availability and deprivation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".