Global Increasing Incidence of Young-Onset Colorectal Cancer Across 5 Continents: A Joinpoint Regression Analysis of 1,922,167 Cases
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer incidence among young adults in the United States is on the rise, but whether this phenomenon is present in other parts of the world is not well documented. This study aims to explore the temporal change of incidence rates for colorectal cancer in various countries across the globe. METHODS: We extracted colorectal cancer incidence and population data from 1988 to 2007 based on data from the International Agency for Research on Cancer and compared incidence between age groups. Twelve representative jurisdictions from five continents were selected. Young-onset colorectal cancer cases were defined as those ages <50 years. Joinpoint regression was used to measure the trends of colorectal cancer incidence and to estimate the annual percent change (APC). RESULTS: The APC for those ages <50 years was noted to be increasing at a faster rate as compared with those ages ≥50 years in many regions, including Australia (+1.10% vs. -0.35%), Brazil (+9.20% vs. +5.72%), Canada (+2.60% vs. -0.91%), China-Hong Kong (+1.82% vs. -0.10%), China-Shanghai (+1.13% vs. -2.68%), Japan (+2.63% vs. +0.90%), the United Kingdom (+3.33% vs. +0.77%), and the United States (+1.98% vs. -2.88%). These trends were largely driven by rectal cancer, except in Brazil and the United Kingdom. CONCLUSIONS: Increasing incidence of young-onset colorectal cancer was noted in many regions across the globe. IMPACT: Further studies focusing on young-onset colorectal cancer, particularly with regard to risk factors and establishing the optimal age of screening, are 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".