Cigarette Affordability in China, 2006–2015: Findings from International Tobacco Control China Surveys
Bibliographic record
Abstract
China is world's largest market of machine-made cigarettes. In 2015, more than 315 million or around 26.9% of the adult population in China were smokers-50.6% among men and 2.2% among women. Growing affordability of cigarettes led to increased cigarette consumption in China to the detriment of public health. This study investigated whether the level and growth in cigarette affordability in China was equally shared by smokers from all demographic and socio-economic statuses (SES) and across all price tiers of cigarette brands. The data came from the urban smoker sample (≥18 years) of the International Tobacco Control China Surveys conducted in five waves over 2006-2015. Cigarette affordability was measured by Relative Income Price-percentage of per capita household income needed to purchase 100 cigarette packs of the last purchased brand. Overall and group-specific trends in affordability by age, gender, SES (e.g., income, education, and employment status), and price tiers were analyzed using generalized estimating equations method. Cigarette affordability was higher among older, female, and higher-SES smokers, and for cheaper brands. It increased overall and across all groups over time. The increase was significantly larger among younger and lower-SES smokers, a trend that poses an added challenge to tobacco control and health equity. To reduce cigarette affordability and consumption among these vulnerable groups, a uniform specific excise system should be introduced in place of the existing tiered ad valorem excise. The specific excise should be periodically adjusted to inflation and per capita income growth observed among younger and lower-SES people, who can potentially experience faster income growth than the national average. The excise tax policy can also be complimented with minimum price regulations and restrictions on price promotions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".