Trends and projected burden of early-onset gastrointestinal malignancies in the United States: a population-based analysis (2001-2021)
Bibliographic record
Abstract
BACKGROUND: Early-onset colorectal cancer (CRC) has become a serious public health concern in recent years. This study aimed to contribute to the growing body of evidence on the rise in early-onset gastrointestinal (GI) cancers and anatomical subsites of early-onset CRC, and to explore racial and sex disparities in these trends. METHODS: We analyzed data from the National Program of Cancer Registries-Surveillance, Epidemiology, and End Results database (2001-2021) for people aged 20 to 49 years with GI cancers. The dataset covers cancer incidence rates for approximately 98% of the US population. Joinpoint regression was used to calculate average annual percent change, and polynomial regression was applied to forecast rates from 2021 to 2031. RESULTS: A total of 527 411 cases were analyzed. Colorectal cancer made up the highest number of cases (n = 313 513), followed by pancreatic cancer (n = 50 448). Intrahepatic bile duct cancer saw the highest average annual percent change (+6.24%, 95% CI = 5.20 to 7.45), followed by small intestine cancer (+3.19%, 95% CI = 2.69 to 3.72), early-onset CRC (+1.65%, 95% CI = 1.45 to 1.92), pancreatic cancer (+1.52, 95% CI = 1.37 to 1.66), and stomach cancer (+1.20, 95% CI = 0.89 to 1.53). Among CRC cases, rectal cancer had the highest average annual percent change (+2.09%). Women (+1.81%) experienced a disproportionate rise to men (+0.83%). Our projection suggests a demographic shift, with women surpassing men in the overall age-adjusted rate of early-onset GI cancers. CONCLUSION: The study highlights that early-onset CRC is not an isolated phenomenon but part of a broader epidemiologic shift across GI malignancies. The parallel rise in cancers of other sites suggests shared upstream risk factors or exposures and supports investigations into potential environmental, dietary, microbiome, and hormone risk factors.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".