Estimated changes in hospital admissions for alcohol intoxication after partial bans on off‐premises sales of alcoholic beverages in the canton of Vaud, Switzerland: an interrupted time–series analysis
Bibliographic record
Abstract
AIMS: To estimate age-specific changes in hospital admissions for alcohol intoxication following two consecutive restrictions on off-premises alcohol sales introduced in the canton of Vaud, Switzerland. DESIGN: Primary analyses used interrupted autoregressive integrated moving average (ARIMA) time-series analyses (repeated cross-sectional), with Lausanne and Vaud as experimental sites and the rest of Switzerland as the control. Secondary analyses used, for example, a different control site (other French-speaking cantons only) or a different statistical model. SETTING: Switzerland between 2010 and 2016. PARTICIPANTS: In-patients (i.e. patients assigned a bed overnight) hospitalized between 8 p.m. and 6 a.m. (n = 1 261 564), as documented in the Swiss Hospital Statistics. INTERVENTIONS: Ban 1, only effective in the canton's capital, Lausanne, prohibited off-premises sales of all alcoholic beverages after 8 p.m. on Fridays and Saturdays from September 2013 to June 2015. In July 2015, Ban 2 replaced this, covered the whole canton and affected off-premises sales of beer and spirits (but not wine) after 9 p.m. (8 p.m. in Lausanne) every night of the week. MEASUREMENTS: Proportions of monthly hospital admissions for alcohol intoxication (ICD-10 diagnoses F10.0/F10.1, T51.0) per 1000 monthly overall admissions. FINDINGS: = -0.008, 95% CI = -0.014, -0.002). Estimated changes in % were largest among 16-19-year-olds. However, as admission rates for alcohol intoxication were more frequent in adulthood than adolescence, the estimated change in number of cases was also relevant to public health among 20-69-year-olds. Secondary analyses supported the findings of the primary analyses. CONCLUSION: Even partial restrictions of off-premises sales of alcohol in Switzerland (only 2 days per week or only for beer and spirits) appeared to reduce hospital admissions for alcohol intoxication across a wide age range (ages 16-69 years).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".