Emerging cancer incidence trends in Canada: The growing burden of young adult cancers
Bibliographic record
Abstract
Background Recent studies have identified increases in cancer incidence among younger adults for some cancers. This study examined incidence trends for 28 cancers in Canada by age and birth cohort from 1983 to 2012. Methods Canadian incidence data for 20 to 84 year‐olds were obtained from the Cancer Incidence in Five Continents Plus database. Age‐period‐cohort modeling was used to estimate the average annual percentage changes (AAPCs) and incidence rate ratios (IRRs) for 10‐year birth cohorts (reference cohort, 1943) for 28 cancer types. Results Incidence increased for 13 cancer sites among adults younger than 50 years (1983‐2012), with the largest increase occurring for rectal cancer (AAPC20‐24, 5.62; 95% confidence interval [CI], 3.77‐7.51) and colon cancer (AAPC20‐24, 4.08; 95% CI, 2.89‐5.29). Compared with the 1943 birth cohort, persons born circa 1988 had approximately 5‐ and 2‐fold greater risks of rectal cancer (IRR, 4.98; 95% CI, 2.87‐8.63) and colon cancer (IRR, 2.31; 95% CI, 1.62‐3.30), respectively. Incidence decreased among younger adults for 9 sites (1983‐2012), with the largest decreases observed for lung cancer (AAPC25‐29,−2.29; 95% CI, −3.57 to −0.98), cervical cancer (AAPC25‐29, −1.29; 95% CI, −1.67 to −0.90), and melanoma (AAPC25‐29, −0.61; 95% CI, −0.97 to −0.24). Decreased risks in recent birth cohorts were observed for all sites with decreasing trends in younger adults. For example, the risk of lung cancer was 60% lower in the 1988 birth cohort than the 1943 birth cohort (IRR, 0.42; 95% CI, 0.23‐0.78). Conclusions Incidence among young adults is increasing for some cancers associated with obesity but decreasing for many cancers associated with infections or smoking. Although further studies are needed to replicate these findings and understand the etiology of early‐onset cancers, measures to promote healthy behaviors in young adults warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".