Educational attainment and cigarette smoking: a causal association?
Bibliographic record
Abstract
BACKGROUND: Despite abundant evidence that lower education is associated with a higher risk of smoking, whether the association is causal has not been convincingly established. METHODS: We investigated the association between education and lifetime smoking patterns in a birth cohort established in 1959 and followed through adulthood (n = 1311). We controlled for a wide range of potential confounders that were measured prior to school entry, and also estimated sibling fixed effects models to control for unmeasured familial vulnerability to smoking. RESULTS: In the full sample of participants, regression analyses adjusting for multiple childhood factors (including socioeconomic status, IQ, behavioural problems, and medical conditions) indicated that the number of pack-years smoked was higher among individuals with less than high school education [rate ratio (RR) = 1.58, confidence interval (CI) = 1.31, 1.91]. However, in the sibling fixed effects analysis the RR was 1.23 (CI = 0.80, 1.93). Similarly, adjusted models estimated in the full sample showed that individuals with less than high school education had fewer short-term (RR = 0.40; CI = 0.23, 0.69) and long-term (RR = 0.59; CI = 0.42, 0.83) quit attempts, and were less likely to quit smoking (odds ratio = 0.34; CI = 0.19, 0.62). The effects of education on quitting smoking were attenuated in the sibling fixed effects models that controlled for familial vulnerability to smoking. CONCLUSIONS: A substantial portion of the education differential in smoking that has been repeatedly observed is attributable to factors shared by siblings that contribute to shortened educational careers and to lifetime smoking trajectories. Reducing disparities in cigarette smoking, including educational disparities, may therefore require approaches that focus on factors early in life that influence smoking risk over the adult life span.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".