Determinants of smoking-induced deprivation in China
Bibliographic record
Abstract
OBJECTIVE: Spending on cigarettes may deprive households of other items like food. The goal of this study was to examine the prevalence of and factors associated with this smoking-induced deprivation among adult smokers in China. METHODS: The data came from Waves 1-3 of the International Tobacco Control (ITC) China Survey, conducted from 2006 to 2009 among urban adults aged 18 years or older in China. We focus on the samples of current smokers from six cities (N=7981). Smoking-induced deprivation was measured with the survey question, "In the last six months, have you spent money on cigarettes that you knew would be better spent on household essentials like food?" We examined whether sociodemographic factors, smoking intensity and price paid per pack of cigarettes were associated with smoking-induced deprivation using generalised estimating equations modelling. FINDINGS: 7.3% of smokers reported smoking-induced deprivation due to purchasing cigarettes. Low-income and middle-income smokers were more likely to have smoking-induced deprivation compared with high-income smokers (adjusted OR (AOR)=2.06, 95% CI 1.32 to 2.31; AOR=1.44, 95% CI 1.10 to 1.69); smokers living in Shenyang (AOR=1.68, 95% CI 1.25 to 2.24) and Yinchuan (AOR=2.50, 95% CI 1.89 to 3.32) were more likely to have smoking-induced deprivation compared with smokers living in Beijing. Retired smokers were less likely to have smoking-induced deprivation compared with employed smokers (AOR=0.67, 95% CI 0.52 to 0.87). There was no statistically significant relationship between smoking intensity, price paid per pack of cigarettes and smoking-induced deprivation. CONCLUSIONS: Our findings indicate that certain groups of smokers in China acknowledge spending money on cigarettes that could be better spent on household essentials. Tobacco control policies that reduce smoking in China may improve household living standards by reducing smoking-induced deprivation.
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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 | 0.000 | 0.000 |
| 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".