Understanding the costs of cancer care before and after diagnosis for the 21 most common cancers in Ontario: a population-based descriptive study
Bibliographic record
Abstract
BACKGROUND: The first year after cancer diagnosis is a period of intensive treatment and high cost. We sought to estimate costs for the 21 most common cancers in Ontario in the 3-month period before and the first year after diagnosis. METHODS: We used the Ontario Cancer Registry to select patients who received diagnoses between 1997 and 2007 at 19 years of age or older, with valid International Classification of Diseases for Oncology (ICD-O) and histology codes, who survived 30 days or longer after diagnosis and had no second cancer within 90 days of the initial cancer (n = 402 399). We used linked administrative data to calculate mean costs for each cancer during the pre- and postdiagnosis periods for patients who died within 1 year after diagnosis and patients who survived beyond 1 year after diagnosis. RESULTS: Mean prediagnosis costs were $2060 (95% confidence interval [CI] $2023-$2098) for all patients with cancer. Costs ranged from $890 (95% CI $795-$985) for melanoma to $4128 (95% CI $3591-$4664) for liver cancer among patients who survived beyond 1 year after diagnosis, and ranged from $2188 (95% CI $2040-$2336) for esophageal cancer to $5142 (95% CI $4664-$5620) for multiple myeloma among patients who died within 1 year. The mean postdiagnosis cost for our cohort was $25 914 (95% CI $25 782-$26 046). Mean costs were lowest for melanoma ($8611 [95% CI $8221-$9001]) and highest for esophageal cancer ($50 620 [95% CI $47 677-$53 562] among patients who survived beyond 1 year after diagnosis, and ranged from $27 560 (95% CI $25 747-$29 373) for liver cancer to $81 655 (95% CI $58 361-$104 949) for testicular cancer among patients who died within 1 year. INTERPRETATION: Our research provides cancer-related cost estimates for the pre- and postdiagnosis phases and offers insight into the economic burden incurred by the Ontario health care system. These estimates can help inform policy-makers' decisions regarding resource allocation for cancer prevention and control, and can serve as important input for economic evaluations.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".