The impact of tobacco tax increases on cost-minimising behaviours and subsequent smoking cessation in Australia: an analysis of the International Tobacco Control Policy Evaluation Project
Bibliographic record
Abstract
Objective We examined the relationship in Australia from 2007 to 2020 between tobacco tax increases and use of cost-minimising behaviours (CMBs) when purchasing tobacco and: (1) tobacco expenditure and (2) smoking cessation attempts and quit success. Methods We used data collected from adults who smoked factory-made and/or roll-your-own (RYO) cigarettes in nine waves (2007–2020) of the International Tobacco Control Policy Evaluation Project Australia Survey (N sample =4975, N observations =10 474). CMBs included buying RYO tobacco, cartons, large-sized packs, economy packs, or tax avoidance/evasion, smoking reduction and e-cigarette use. Logistic regression, fit using generalised estimating equations, estimated the CMB-outcome association for quit attempts and quit success at the next wave follow-up (N subsample =2984, N observations =6843). Results Over half of respondents used a CMB for tobacco purchase (P-CMB) at baseline (57.1% in 2007–2008), increasing to 76.8% (2018) post-tax increases. Participating in any P-CMB was associated with having higher weekly tobacco expenditure. Engaging in any P-CMB was negatively associated with attempting to quit (aOR=0.82, 95% CI 0.69–0.98). Purchasing RYO tobacco or cartons was associated with making no quit attempts (aOR=0.66, 95% CI 0.52–0.83; aOR=0.72, 95% CI 0.59–0.89, respectively). Among respondents smoking cigarettes who made quit attempts, there were no significant associations between all P-CMBs and quit success. Neither smoking reduction nor vaping were significantly associated with quit attempts. Conclusion P - CMBs are associated with reduced smoking cessation. Reducing opportunities for industry to promote purchasing-related CMB options, such as by standardising pack sizes and reducing the price differential between RYO and manufactured cigarettes could increase the effectiveness of 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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".