Cigarette Smoking and Its Financial Burden among Iranian Households: Evidence from Household Income and Expenditures Survey
Bibliographic record
Abstract
Background: The financial burden of cigarette smoking on households’ budget is not well documented in Iran. We aimed to identify the determinants of cigarette consumption and its financial burden among households in Iran. Study design: A cross-sectional study. Methods: A total of 39,864 Iranian’s households from 31 provinces were included in the analysis. Data on sociodemographic and socioeconomic characteristics (age, sex, household size, education level, employment status, income and wealth index), living area, number of cigarettes smoked and cigarette expenditures for households were extracted from the 2016 Household Income and Expenditures Survey (HIES). Tobit model was used to identify the determinants of cigarette smoking frequency and expenditures among Iranian households. Results: The average number of cigarettes smoked and cigarettes expenditures by all household members was 85.25 cigarettes and US$ 2.64 per month. Living in urban areas, wealth index of households, household income, household size and low educational attainment of household members were positively associated with frequency and expenditures of cigarette smoking. Results also indicated increasing patterns in the number of cigarettes smoked and cigarettes expenditures from east to west of the country. East Azerbaijan, Hamadan, Markazi and Chaharmahal va Bakhtiari provinces had higher cigarette smoking frequency and expenditures in Iran. Conclusions: Tobacco control interventions in Iran should focus more on households living in urban areas and low-educated households. As the frequency of cigarette smoking was higher in the western region of Iran, comprehensive tobacco control policies should be adopted in western provinces.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".