The effect of tobacco tax increase on price‐minimizing tobacco purchasing behaviours: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Tobacco product excise taxes are a cost-effective method for reducing tobacco consumption, but industry pricing and marketing strategies encourage consumers to engage in price-minimizing behaviours (PMBs). We investigated the relationship between tobacco tax increases and PMBs, measuring whether PMBs intensify following tax increases, whether low-income consumers with higher nicotine dependence are more likely to engage in PMBs and whether PMBs are negatively related to smoking cessation. METHOD: This was a systematic review with meta-analysis of cross-sectional and longitudinal studies from seven databases up to March 2023, using studies that reported any product- and purchasing-related smoking behaviours post-tobacco tax increase in a general representative population. Sixty-eight studies were quality-assessed using the Newcastle-Ottawa scale. All studies were narratively synthesized, with five studies involving 13 068-26 575 participants providing data for pooled analyses on PMBs [purchasing lower-priced brands, roll-your-own (RYO) tobacco and cartons] pre- and post-tax increases using a random effects meta-analytical model. RESULTS: = 96%). Lower income and higher nicotine dependence were associated with purchasing lower-priced brands and RYO, whereas higher income and nicotine dependence were associated with purchasing cartons, large-sized packs and cross-border sales. Less evidence associated illicit tobacco purchases with tax increases or PMBs with smoking cessation. CONCLUSIONS: Tobacco purchasers' PMBs vary widely by state, country and time-period within countries. Both legal and illegal PMBs, potentially influenced by industry pricing tactics, may exacerbate health inequalities and dilute the public health benefits of tobacco tax increases.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".