Socioeconomic disparities in smoking are partially explained by chronic financial stress: marginal structural model of older US adults
Bibliographic record
Abstract
BACKGROUND: A persistent socioeconomic gradient in smoking has been observed in a variety of populations. While stress is hypothesised to play a mediating role, the extent of this mediation is unclear. We used marginal structural models (MSMs) to estimate the proportion of the effect of socioeconomic status (SES) on smoking, which can be explained by an indicator of stress related to SES, experiences of chronic financial stress. METHODS: Using the Health and Retirement Study (waves 7-12, 2004-2014), a survey of older adults in the USA, we analysed a total sample of 15 260 people. A latent variable corresponding to adult SES was created using several indicators of socioeconomic position (wealth, income, education, occupation and labour force status). The main analysis was adjusted for other factors that influence the pathway from adult SES to stress and smoking, including personal coping resources, health-related factors, early-life SES indicators and other demographic variables to estimate the proportion of the effect explained by these pathways. RESULTS: Compared with those in the top SES quartile, those in the bottom quartile were more than four times as likely to be current smokers (rate ratio 4.37, 95% CI 3.35 to 5.68). The estimate for the MSM attenuated the effect size to 3.34 (95% CI 2.47 to 4.52). Chronic financial stress explained 30.4% of the association between adult SES and current smoking (95% CI 13 to 48). CONCLUSION: While chronic financial stress accounts for part of the socioeconomic gradient in smoking, much remains unexplained.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".