Trends in Individualized Affordability of Factory-Made Cigarettes: Findings of the 2008–2020 International Tobacco Control Netherlands Surveys
Bibliographic record
Abstract
INTRODUCTION: Cigarette affordability, the price of tobacco relative to consumer income, is a key determinant of tobacco consumption. AIMS AND METHODS: This study examined trends over 12 years in individualized factory-made cigarette affordability in the Netherlands, and whether these trends differed by sex, age, and education. Data from 10 waves (2008-2020) of the International Tobacco Control Netherlands Surveys were used to estimate individualized affordability, measured as the percentage of income required to buy 100 cigarette packs (Relative Income Price [RIP]), using self-reported prices and income. The higher the RIP, the less affordable cigarettes are. Generalized estimating equation regression models assessed trends in individualized affordability over time and by sex, age, and education. RESULTS: Affordability decreased significantly between 2008 and 2020, with RIP increasing from 1.89% (2008) to 2.64% (2020) (p ≤ .001), except for 2008-2010, no significant year-on-year changes in affordability were found. Lower affordability was found among subgroups who have a lower income level: Females (vs. males), 18-24 and 25-39-year-olds (vs. 55 years and over) and low or moderate-educated individuals (vs. highly educated). Interactions between wave and education (p = .007) were found, but not with sex (p = .653) or age (p = .295). A decreasing linear trend in affordability was found for moderately (p = .041) and high-educated (p = .025), but not for low-educated individuals (p = .149). CONCLUSIONS: Cigarettes in the Netherlands have become less affordable between 2008 and 2020, yet this was mostly because of the decrease in affordability between 2008 and 2010. There is a need for more significant increases in tax to further decrease affordability. IMPLICATIONS: Our findings suggest that cigarettes have become less affordable in the Netherlands between 2008 and 2020. But, this appears to be the result of a steep decrease in affordability between 2008 and 2010. Affordability was lower among groups who have on average lower incomes (females, young adults, and low- and moderate-educated individuals), and differences in trends across education levels could be explained by per capita income changes. Our individualized measure indicated lower affordability than published aggregate affordability estimations. Future tax increases should be large enough to result in a lower affordability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".