Lung cancer incidence trends by histology and individual- and county-level sociodemographic characteristics in the United States from 2000 to 2019
Bibliographic record
Abstract
BACKGROUND: Lung cancer incidence has been decreasing in the United States, largely due to smoking reductions. However, adenocarcinoma incidence has been relatively stable compared with other histological subtypes. Histology-specific lung cancer incidence varies by key sociodemographic characteristics, but trends are not well characterized. METHODS: SEER 17 registry data was used to calculate annual age-adjusted lung cancer incidence over 2000-2019 by histology stratified by individual-level sex and race/ethnicity and county-level education, poverty, or urbanicity. Histology was categorized into 4 groups: adenocarcinoma, small cell, squamous cell, and other histologies. Age-adjusted incidence rates were computed using the 2000 US Standard Population. Incidence trends were characterized using Joinpoint regression. RESULTS: For most histological subtypes, lung cancer incidence has been decreasing since 2000 in both sexes and all racial/ethnic groups, with some variations. However, lung adenocarcinoma incidence was relatively constant. Lung cancer incidence decreases with increasing education and income. It is lower in urban vs rural areas across histological subtypes, except for adenocarcinoma among females. Counties with higher education levels or lower poverty rates experienced faster declines in small cell and squamous cell lung cancer incidence in recent years. The incidence of small cell and squamous cell lung cancer has been decreasing more rapidly in urban than rural areas. CONCLUSION: Disadvantaged groups have higher lung cancer rates and slower decreases in incidence over time for most histological subtypes, resulting in widening disparities. This highlights the need for targeted tobacco and lung cancer prevention strategies to accelerate decreases in vulnerable populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".