Changes in Black-White Difference in Lung Cancer Incidence among Young Adults
Bibliographic record
Abstract
BACKGROUND: We previously reported that lung cancer incidence between Blacks and Whites younger than 40 years of age converged in women and approached convergence in men. Whether this pattern has continued in contemporary young birth cohorts is unclear. METHODS: We examined 5-year age-specific lung cancer incidence in Blacks and Whites younger than 55 years of age by sex and calculated the Black-to-White incidence rate ratios (IRRs) and smoking prevalence ratios by birth cohort using nationwide incidence data from 1997 to 2016 and smoking data from 1970 to 2016 from the National Health Interview Survey. RESULTS: Five-year age-specific incidence decreased in successive Black and White men born since circa 1947 and women born since circa 1957, with the declines steeper in Blacks than Whites. Consequently, the Black-to-White IRRs became unity in men born 1967-1972 and reversed in women born since circa 1967. For example, the Black-to-White IRRs in ages 40-44 years born between 1957 and 1972 declined from 1.92 (95% confidence interval [CI] = 1.82 to 2.03) to 1.03 (95% CI = 0.93 to 1.13) in men and from 1.32 (95% CI = 1.24 to 1.40) to 0.71 (95% CI = 0.64 to 0.78) in women. Similarly, the historically higher sex-specific smoking prevalence in Blacks than Whites disappeared in men and reversed in women born since circa 1965. The exception to these patterns is that the incidence became higher in Black men than White men born circa 1977-1982. CONCLUSIONS: The historically higher lung cancer incidence in young Blacks than young Whites in the United States has disappeared in men and reversed in women, coinciding with smoking patterns, though incidence again became higher in Black men than White men born circa 1977-1982.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".