Socioeconomic inequality in tobacco expenditure in Iran: a cross-sectional analysis at national and subnational levels
Bibliographic record
Abstract
Abstract Background Tobacco expenditure has adverse impacts on expenditure on basic needs and resource allocation of the households. Using data from a nationally representative survey, we measured socioeconomic inequality in tobacco expenditure as the share of household budget (TEHB) and explained its main determinants among Iranian households at the national and sub-national levels. Methods This cross-sectional study used data from the Iranian Household Income and Expenditure Survey (IHIES), 2018. We included a total of 7649 households with tobacco expenditure more than zero in the analysis. Province-level data on the Human Development Index (HDI) was obtained from the Institute for Management Research at Radbound University. The concentration curve (CC) and the concentration index (C) were used to measure socioeconomic inequality in TEHB at national and sub-national levels. The C was decomposed to identify the factors explaining the observed socioeconomic inequality in TEHB. Results At the national level, households with at least one smoker spent more than 5% of their budget for tobacco consumption in the last month. Households from the urban areas allocated less of their budgets on tobacco products compared to rural households (4.6% vs. 5.8%). Overall, TEHB was more concentrated among the poorer households (C = 0.1423, 95% CI: − 0.1552 to − 0.1301). In other words, the distribution of TEHB was pro-poor in Iran. Pro-poor inequality in TEHB was also found in urban (C = − 0.1707, 95% CI: − 0.1998 to − 0.1516) and rural (C = − 0.1314, 95% CI: − 0.1474 to − 0.1152) areas. We also found that pro-poor inequalities were higher in Iranian provinces with low HDI. The decomposition results indicate that wealth and education were the main factors contributing to the concentration of TEHB among the poorer households. Conclusion This study found that TEHB was disproportionality concentrated among poorer households in Iran. The extent of inequality in TEHB was higher in urban areas and less developed provinces. Designing and implementing tobacco control interventions to decrease the smoking prevalence and increase smoking cessation could protect worse-off households against the financial burden of tobacco spending.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".