Peer Review Report For: Options for modifying UK alcohol and tobacco tax: A rapid scoping review of the evidence over the period 1997–2018 [version 3; peer review: 2 approved]
Bibliographic record
Abstract
Background Increased taxation is recognised worldwide as one of the most effective interventions for decreasing tobacco and harmful alcohol use, with many variations of policy options available. This rapid scoping review was part of a NIHR-funded project (‘SYNTAX’ 16/105/26) and was undertaken during 2018 to inform interviews to be conducted with UK public health stakeholders with expertise in alcohol and tobacco pricing policy. Methods Objectives: To synthesise evidence and debates on current and potential alcohol and tobacco taxation options for the UK, and report on the underlying objectives, evidence of effects and mediating factors. Eligibility criteria: Peer-reviewed and grey literature; published 1997–2018; English language; UK-focused; include taxation interventions for alcohol, tobacco, or both. Sources of evidence: PubMed, Scopus, Cochrane Library, Google, stakeholder and colleague recommendations. Charting methods Excel spreadsheet structured using PICO framework, recording source characteristics and content. Results Ninety-one sources qualified for inclusion: 49 alcohol, 36 tobacco, 6 both. Analysis identified four policy themes: changes to excise duty within existing tax structures, structural reforms, industry measures, and hypothecation of tax revenue for public benefits. For alcohol, policy options focused on raising the price of cheap, high-strength alcohol. For tobacco, policy options focused on raising the price of all tobacco products, especially the cheapest products, which are hand-rolling tobacco. For alcohol and tobacco, there were options such as levies that take money from the industries to help reduce the societal costs of their products. Due to the perceived social and economic importance of alcohol in contrast to tobacco, policy options also discussed supporting pubs and small breweries. Conclusions This review has identified a set of tax policy options for tobacco and alcohol, their objectives, evidence of effects and related mediating factors. The differences between alcohol and tobacco tax policy options and debates suggest an opportunity for cross-substance policy learning.
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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 | 0.064 | 0.324 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.245 | 0.149 |
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".