The impact of smoking and quitting on household expenditure patterns and medical care costs in China
Bibliographic record
Abstract
BACKGROUND: Smoking remains very common in Chinese men, and the economic burden caused by cigarette consumption on smokers and their families may be substantial. Using a large nationally representative household survey, the third National Health Services Survey (NHSS, 2003), we estimated the economic impact of smoking on households. METHODS: Smoking status of all household members (over 15 years) was collected by interview for the NHSS, and households classified into one of seven categories based on their smoking status. Information on household income and expenditure, and use of health services was also obtained. We assessed both the "direct" costs (reducing funds available for spending on other commodities such as food, education, medical care, etc, using a fractional logit model), and "indirect costs" (increasing medical expenditures, using a log-linear model). RESULTS: Every five packets of cigarettes consumed per capita per month reduces household spending on other commodities, most notably on education (by about 17 yuan per capita per annum) and medical care (11 yuan). The effects are greatest among low-income rural households. Households with quitters spend substantially more on medical care than never-smoking households (64 yuan for households with two or more quitters). CONCLUSIONS: If a household member smokes, there is less money available for commodities such as education and medical care. Medical care expenditure is substantially higher among households with quitters, as ill-health is the main reason for quitting smoking in China. Smoking impoverishes a substantial number of poorer rural households.
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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".