Evaluation of Colorectal Cancer Incidence in the United States From 2021 to 2024 Using a National Multi-Payer Claims Database
Bibliographic record
Abstract
PURPOSE: Colorectal cancer (CRC) remains the second leading cause of cancer-related death in the U.S. This study aimed to evaluate national CRC incidence from 2021 to 2024 using a large, multi-payer claims database. METHODS: This retrospective, cross-sectional study used a national multi-payer claims database to estimate annual CRC incidence from 2021 to 2024. Adults aged 45 to 75 years were included if they had no history of CRC diagnosis from 2015 through the year prior to the given calendar year (2021, 2022, 2023, or 2024) and had at least 1 medical or pharmacy event in a 3-year window centered on that year. CRC incidence was defined as a new diagnosis claim during each study year. Annual incidence rates per 100,000 individuals were calculated and stratified by sociodemographic characteristics. Associations between CRC incidence status (new diagnosis vs. no diagnosis) and sociodemographic subgroups were assessed using Pearson's chi-square tests. FINDINGS: From 2021 to 2024, CRC incidence declined from 136.9 to 115.9 per 100,000 among 145 to 161 million eligible individuals, with declines in those aged 50 to 64 (120.8-101.7) and 65 to 75 (203.6-162.5). In contrast, incidence among those aged 45 to 49 increased from 59.5 to 63.1 over the same period. Incidence remained higher in males than females (129.4 vs. 104.3 in 2024), and although it decreased, it remained highest among Black individuals (225.1-156.8). Medicare Advantage enrollees had the highest incidence throughout (276.8-214.0), while those with commercial insurance had one of the lowest (112.8-93.4). Regional differences narrowed from 2021 to 2024 across the Northeast, Midwest, South, and West; CRC incidence status remained significantly associated with region (Pearson's chi-square P < 0.001). CONCLUSIONS: Overall, CRC incidence declined from 2021 to 2024, though rising rates among adults aged 45 to 49 highlight a growing early-onset burden. Associations between CRC incidence status and age, sex, race/ethnicity, insurance type, and region were observed, suggesting disparities that may reflect underlying equity gaps in prevention.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".