Risk of Anxiety and Depression after Diagnosis of Young-Onset Colorectal Cancer: A Population-Based Cohort Study
Bibliographic record
Abstract
Given the increasing incidence of young-onset colorectal cancer (yCRC; <50 years), we aimed to evaluate the risk of depression and anxiety in individuals with yCRC in comparison to average-age-onset CRC (aCRC; ≥50 years) and to cancer-free controls, with stratification by sex. Our cohort study identified individuals (≥18 years) with CRC and cancer-free controls (10:1) matched on age and sex using population-based linked administrative health databases in British Columbia, Canada. We assessed depression and anxiety using validated algorithms. We evaluated the risk of depression and anxiety using multivariable Cox proportional hazard models. The cohort included 54,634 individuals with CRC (46.5% female, mean age 67.9 years) and 546,340 controls (46.5% female, mean age 67.9 years). Those with yCRC as compared to aCRC had an increased risk for depression (adjusted hazard ratio [aHR] 1.41; 95% confidence interval [CI] 1.25 to 1.60), and when stratified by sex, the risk was only significant among males (aHR 1.76; 95% CI 1.48 to 2.10). When comparing individuals with yCRC to cancer-free controls, the overall risk of depression (aHR 1.00; 95% CI 0.92 to 1.10) and anxiety (aHR 1.10; 95% CI 0.95 to 1.27) was non-significant; however, males had a significantly higher risk for mental health disorders, specifically depression (aHR 1.17; 95% CI 1.03 to 1.33). Altogether, our findings that individuals with yCRC experience higher risk of depression compared to those with aCRC as well as cancer-free controls, particularly among males, suggest effects of age and sex on mental health outcomes.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".