Smokers’ strategies to reduce tobacco spending: self-reported use and differences across subgroups. Findings from the International Tobacco Control (ITC) Netherlands Survey
Bibliographic record
Abstract
BACKGROUND: The cost of tobacco is one of the most reported reasons for smoking cessation. Rather than quitting, smokers can use also strategies to reduce tobacco expenditure while continuing smoking, such as smoking less or using price-minimising strategies. The Netherlands announced to increase the price of a pack cigarettes from seven (2018) to ten euros (2023), to reduce tobacco prevalence and consumption. This study explores the self-reported strategies to reduce tobacco spending among Dutch smokers, and whether this differed per age, income, and education. Additionally, we analysed among quitters in these subgroups whether price played a role in their decision to quit. METHODS: Cross-sectional survey data from the International Tobacco Control (ITC) Netherlands Wave 2 (September-November 2020, N = 1915) was used. Strategies to reduce spending among smokers (N = 1790) were: reducing consumption, bulk buying, switching to cheaper products or buying from low-taxed sources. These were collapsed into: reducing consumption (solely or in combination with other behaviours), solely price-minimising behaviours (such as buying cheaper brands), or no strategies to reduce spending. Associations between strategies and characteristics were analysed through multinomial and binary logistic regression models. Second, we explored which subgroups were more likely to report that price played a role in their decision to quit among quitters (N = 125). RESULTS: The majority of smokers used strategies to reduce tobacco spending: 35.6% reduced consumption and 19.3% used solely price-minimising strategies. 82.1% of quitters reported that price played a role in their decision to quit. Low-income individuals were more likely to report price as a reason for quitting and reduce consumption, but also to buy cheaper products. Highly nicotine dependent smokers were more likely to use price-minimising behaviours, and less likely to reduce consumption. CONCLUSIONS: The majority reported using strategies to reduce spending or that price played a role in their decision to quit. Reducing consumption was the most reported strategy. Low-income smokers were more likely to reportedly reduce consumption, buy cheaper products, or quit. Price policies have the potential to reduce socioeconomic inequalities in smoking. To discourage price-minimising behaviours, such as switching to cheaper products, reducing price differences between products should be prioritized.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".