Association between tobacco prices and smoking onset: evidence from the TCP India Survey
Bibliographic record
Abstract
BACKGROUND: Tobacco use is prevalent among youth and adults in India. However, direct evidence on how increasing taxes or prices affect tobacco use onset is scarce. OBJECTIVE: To analyse the associations between cigarette and bidi prices and smoking onset in India, and how these associations differ by socioeconomic status. METHODOLOGY: The Wave 1 of the Tobacco Control Policy Evaluation India Survey by the International Tobacco Control Project contains information on the age at smoking onset for cigarettes and bidis. Using this information, data were expanded to a yearly pseudo-panel dataset that tracked respondents at risk of smoking onset from 1998 to 2011. The associations between bidi prices and bidi smoking onset, between cigarette prices and cigarette smoking onset, and between bidi and cigarette prices and any smoking onset were examined using a discrete-time hazard model with a logit link function. Stratified analyses were conducted to examine the difference in these associations by rural versus urban division. RESULTS: We found that higher bidi prices were significantly associated with a lowered hazard of bidi smoking onset (OR 0.42, 95% CI 0.35 to 0.51). Higher cigarette prices were significantly (OR 0.87, 95% CI 0.83 to 0.92) associated with a lowered hazard of cigarette smoking onset among urban residents, but this association was non-significant when SEs were clustered at the state level. In addition, the association between increasing bidis prices and lowered hazards of bidi smoking onset was greater for urban residents than for rural ones (p<0.01). CONCLUSIONS: Under the new regime of a central goods and service system, policymakers may need to raise the prices of tobacco products sufficiently to curb smoking onset.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".