Health system costs for cancer medications and radiation treatment in Ontario for the 4 most common cancers: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Previous costing and resource estimates for cancer have not been complete owing to lack of comprehensive data on cancer-related medication and radiation treatment. Our objective was to calculate the mean overall costs per patient of cancer-related medications and radiation, as well as by disease subtype and stage, in the first year after diagnosis for the 4 most prevalent cancers in Ontario. METHODS: We conducted a retrospective cohort study using provincial health administrative databases to identify population health system resources and costs for all patients diagnosed with breast, colorectal, lung or prostate cancer between Jan. 1, 2010, and Dec. 31, 2015 in Ontario. The primary outcome measure was the overall average cost per patient in the 365 days after diagnosis for cancer-related medications and radiation treatment, calculated with the use of 2 novel costing algorithms. We determined the cost by disease, disease subtype and stage as secondary outcomes. RESULTS: There were 168 316 Ontarians diagnosed with cancer during the study period, 50 141 with breast cancer, 38 108 with colorectal cancer, 34 809 with lung cancer and 45 258 with prostate cancer. The mean per-patient cost for cancer-related medications was $8167 (95% confidence interval [CI] $8023-$8311), $6568 (95% CI $6446-$6691), $2900 (95% CI $2816-$2984) and $1211 (95% CI $1175-$1247) for breast, colorectal, lung and prostate cancer, respectively. The corresponding mean radiation treatment costs were $18 529 (95% CI $18 415-$18 643), $15 177 (95% CI $14 899-$15 456), $10 818 (95% CI $10 669-$10 966) and $16 887 (95% CI $16 648-$17 125). In general, stage III and IV cancers were the most expensive stages for both medications and radiation across all 4 disease sites. INTERPRETATION: Our work updates previous costing estimates to help understand costs and resources critical to health care system planning in a single-payer system. More refined costing estimates are useful as inputs to allow for more robust health economic modelling and health care system planning.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".