Direct Medical Spending on Young and Average-Age Onset Colorectal Cancer before and after Diagnosis: a Population-Based Costing Study
Bibliographic record
Abstract
BACKGROUND: Despite a better understanding of the increasing incidence of young-onset colorectal cancer (yCRC; age at diagnosis <50 years), little is known about its economic burden. Therefore, we estimated direct medical spending on yCRC before and after diagnosis. METHODS: We used linked administrative health databases in British Columbia, Canada, to create a study population of yCRC and average-age onset colorectal cancer (aCRC; age at diagnosis ≥50 years) cases, along with cancer-free controls. Over the 1-year period preceding a colorectal cancer diagnosis, we estimated direct medical spending on hospital visits, healthcare practitioners, and prescription medications. After diagnosis, we calculated cost attributable to yCRC and aCRC, which additionally included the cost of cancer treatments (e.g., chemotherapy and radiotherapy) across phases of care. RESULTS: We included 1,058 yCRC (45.4% females; age at diagnosis 42.4 ± 6.2 years) and 12,619 aCRC (44.8% females; age at diagnosis of 68.1 ± 9.2 years) cases. Direct medical spending on the average yCRC and aCRC case during the year before diagnosis was $6,711 and $8,056, respectively. After diagnosis, the overall average annualized cost attributable to yCRC significantly differed in comparison with aCRC for the initial ($50,216 vs. $37,842; P < 0.001), continuing ($8,361 vs. $5,014; P < 0.001), and end-of-life cancer phase ($86,125 vs. $61,512; P < 0.001) but not end-of-life non-cancer phase ($77,273 vs. $23,316; P = 0.372). CONCLUSIONS: Reported cost estimates may be used as inputs for future economic evaluations pertaining to yCRC. IMPACT: We provided comprehensive cost estimates for healthcare spending on young-onset colorectal cancer.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".