Socioeconomic status, lung cancer and smoking in a pooled analysis of case-control studies
Bibliographic record
Abstract
Background The association between socioeconomic status (SES) and lung cancer has been demonstrated in various studies. However, many studies did not adequately control for smoking habits and the findings to which extent SES was explained by smoking are not consistent. We studied the association between lung cancer and occupationally derived SES using data from the international pooled SYNERGY study. Methods Twelve case-control studies from Europe and Canada were included in the analysis. We measured SES using the International Socio-Economic Index of Occupational Status (ISEI) and the European Socio-economic Classification (ESeC) based on three classes. We divided the ISEI-range into quarters and, alternatively, created four ISEI-categories based on quartiles of the distribution among controls. We calculated odds ratios (OR) and 95% confidence intervals (CI) by unconditional logistic regression models, adjusting for age, sex, study, and smoking behaviour. We stratified analyses by sex, study region, histological subtype of lung cancer, birth cohort, and education and conducted several sensitivity analyses. Results The final analysis included 17,021 cases and 20,885 controls. We observed an association of lung cancer and SES that was reduced by up to 50% by adjustment for smoking. Analyses revealed a social gradient (by descending SES): OR 1.19 (95% CI 1.07–1.33), OR 1.71 (95% CI 1.54–1.90), OR 1.80 (95% CI 1.61–2.02) for ISEI and OR 1.11 (95% CI 1.03–1.20) and OR 1.50 (95% CI 1.42–1.59) for ESeC. Adjustment for education further reduced the risk estimates. SES-differences on lung cancer risk were more pronounced among men. Conclusion SES remained a risk factor for lung cancer after adjustment for smoking habits. Still, the elimination of smoking continues to be the major target for the prevention of lung cancer. Key message Smoking does not explain all SES-differences of lung cancer, but it remains the most important risk factor and prevention target
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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 | 0.044 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.019 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".