Socioeconomic Position and Lung Cancer Risk
Bibliographic record
Abstract
BACKGROUND: Although it has been reported that low socioeconomic position (SEP) is associated with lung cancer, the extent to which this reflects SEP differences in cigarette smoking is unclear. We investigated how various modeling approaches for smoking might influence this observed association. METHODS: We used data from a case-control study conducted in Montreal, Canada (1996-2002), comprising 1203 subjects with incident lung cancer and 1513 population controls. SEP was measured by census-based and self-reported income, residential value, education level, and occupational class. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression, and Akaike's Information Criterion (AIC) was used to compare model fit. RESULTS: Associations were observed between SEP indicators and lung cancer, but gradually disappeared with more comprehensive adjustment for smoking. For comparisons of the highest to lowest categories of census-based income, the OR for lung cancer was 0.58 (95% CI = 0.32-1.05) when adjusting only for smoking status (never, former, current), but 0.97 (0.51-1.86) when adjusting for smoking status, cigarette-years, and time since cessation. For comparisons of highest to lowest levels of education, the ORs for lung cancer were 0.50 (0.38-0.65) and 0.76 (0.57-1.02), when making the least and most comprehensive adjustments for smoking, respectively. Similarly, comparing highly skilled with unskilled manual workers, the ORs were 0.78 (0.54-1.12) and 1.00 (0.68-1.47), respectively. With thorough smoking adjustment, associations between SEP indicators and lung cancer virtually disappeared, and SEP did not improve model fit. CONCLUSIONS: Previously reported associations of SEP with lung cancer may be attributable to incomplete adjustment for smoking. Our findings underline the importance of adjusting for several dimensions of smoking behavior to make correct inferences.
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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 | 0.001 | 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 | 0.000 |
| 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".