The impacts of policies controlling the spatial availability of take‐away alcohol on consumption and harms: A systematic narrative review
Bibliographic record
Abstract
AIM: To systematically review the evidence on the impacts of policies regulating the spatial availability of off-premises alcohol on consumption and harms. METHODS: Narrative review that examined peer-reviewed studies published from 2016 to 2024 on policy changes affecting the spatial availability of off-premises alcohol. Outcomes of interest were alcohol consumption, alcohol-related harms and mortality. RESULTS: The review identified 20 observational studies, primarily natural experiments, examining four policy types: malt liquor restrictions, sales expansion to retail outlets, privatization and changes to allowable alcohol content. Across studies, there was a suggestion that allowing alcohol sales in gas station convenience stores was associated with increased consumption and harms, whereas expanding to grocery stores was not. There was no clear evidence that restricting malt liquor reduces crime. Similarly, privatization was not associated with crime or health outcomes, though it was accompanied by price increases. Increases in allowable alcohol content were not associated with higher consumption, but decreases were associated with fewer alcohol-related emergency visits and hospitalizations. CONCLUSIONS: The impact of policy changes in spatial alcohol availability depends on the policy details and retail outlet types. To mitigate public health impacts, policymakers should consider comprehensive alcohol control measures, such as regulating convenience store sales and accompanying grocery store expansions with minimum unit pricing, taxation and marketing restrictions. High-quality natural experiments with pre-post designs, control groups and confounder adjustments are needed to better understand how these policies impact both the general population and high-risk subgroups.
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 | 0.012 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".