Trends in use and cost of initial cancer treatment in Ontario: a population-based descriptive study
Bibliographic record
Abstract
BACKGROUND: Cancer incidence and treatment-related costs are rising in Canada. We estimated health care use and costs in the first year after diagnosis for patients with 7 common types of cancer in Ontario to examine temporal trends in patterns of care and costs. METHODS: We selected patients aged 19-44 years who had received a diagnosis of melanoma, breast cancer (female only), testicular cancer or thyroid cancer, in addition to patients aged 45 years and older who had received a diagnosis of breast (female only), prostate, lung or colorectal cancer, between 1997 and 2007. Patients were identified from the Ontario Cancer Registry. Using linked administrative databases, we determined use and costs of chemotherapy, radiotherapy, cancer-related surgery, other admissions to hospital and home care. We adjusted all costs to 2009 Canadian dollars. RESULTS: We identified 20 821 patients aged 19-44 years and 178 797 patients aged 45 years and older. The greatest increases in costs during the study period were for melanoma, breast cancer, colorectal cancer, lung cancer and prostate cancer (p < 0.05). For prostate and lung cancers, mean costs increased 50% (from $11 490 and $22 037 to $15 170 and $34 473, respectively). Mean costs doubled for breast (from $15 460 and $12 909 to $35 977 and $29 362 for younger and older patients, respectively) and colorectal cancers (from $24 769 to $43 964), and nearly tripled for melanoma (from $3581 to $8934). Costs related to hospital admissions accounted for the largest portion of total costs. The use of chemotherapy, radiotherapy and home care generally increased for all cancers. INTERPRETATION: The significant increase in mean costs of initial cancer treatment among the patients included in this study was primarily due to more patients receiving adjuvant therapy and home care, and to the increasing expenditures for these services and cancer-related surgeries. Understanding trends in health care use and costs can help policy-makers to take the necessary measures to achieve a more accountable, high-performing health care system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".