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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| 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 | 0.001 |
| 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".