Income, income inequality and youth smoking in low‐ and middle‐income countries
Bibliographic record
Abstract
AIMS: To examine the relationships between income, income inequality and current smoking among youth in low- and middle-income countries. DESIGN: Pooled cross-sectional data from the Global Youth Tobacco Surveys, conducted in low- and middle-income countries, were used to conduct multi-level logistic analyses that accounted for the nesting of students in schools and of schools in countries. PARTICIPANTS: A total of 169 283 students aged 13-15 from 63 low- and middle-income countries. MEASUREMENTS: Current smoking was defined as having smoked at least one cigarette in the past 30 days. Gross domestic product (GDP) per capita was our measure of absolute income. Contemporaneous and lagged (10-year) Gini coefficients, as well as the income share ratio of the top decile of incomes to the bottom decile, were our measures of income inequality. FINDINGS: Our analyses reveal a significant positive association between levels of income and youth smoking. We find that a 10% increase in GDP per capita increases the odds of being a current smoker by at least 2.5%, and potentially considerably more. Our analyses also suggest a relationship between the distribution of incomes and youth smoking: youth from countries with more unequal distributions of income tend to have higher odds of currently smoking. CONCLUSIONS: There is a positive association between gross domestic product and the odds of a young person in a low- and middle-income country being a current smoker. Given the causal links between smoking and a wide range of youth morbidities, the association between smoking and income inequality may underlie a substantial portion of the health disparities observed that are currently experiencing rapid economic growth.
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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 | 0.002 | 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 | 0.001 |
| 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".