Tobacco cessation and household spending on non-tobacco goods: results from the US Consumer Expenditure Surveys
Bibliographic record
Abstract
OBJECTIVES: To estimate the impact of tobacco cessation on household spending on non-tobacco goods in the USA. METHODS: 9130 tobacco-consuming households were followed for four quarters. Households were categorised during the fourth quarter as having: (1) recent tobacco cessation, (2) long-term cessation, (3) relapsed cessation or (4) no cessation. Generalised linear models were used to compare fourth quarter expenditures on alcohol, food at home, food away from home, housing, healthcare, transportation, entertainment and other goods between the no-cessation households and those with recent, long-term or relapsed cessation. The full sample was analysed, and then analysed by income quartile. RESULTS: In the full sample, households with long-term and recent cessation had lower spending on alcohol, food, entertainment and transportation (p<0.001). Recent cessation was further associated with reduced spending on food at home (p<0.001), whereas relapsed cessation was associated with higher spending on healthcare and food away from home (p<0.001). In the highest income quartile, long-term and recent cessations were associated with reduced alcohol spending only (p<0.001), whereas in the lowest income quartile, long-term and recent cessations were associated with lower spending on alcohol, food at home, transportation and entertainment (p<0.001). CONCLUSIONS: Households that quit tobacco spend less in areas that enable or complement their tobacco cessation, most of which may be motivated by financial strain. The most robust association between tobacco cessation and spending was the significantly lower spending on alcohol.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".