A population-based comparison of cancer and non-cancer related healthcare costs.
Bibliographic record
Abstract
e18900 Background: Costs associated with cancer care are increasing. Evaluating costs in the context of common comorbidities has not been extensively studied in a population-based setting. Knowledge from such analyses can better inform healthcare resource allocation and highlight strategies to reduce overall costs. Methods: Using data from a population-based administrative database comprised of health insurance claims, physician billing, and hospital discharge abstracts, we calculated healthcare costs (in CAD) for common comorbidities among the pediatric and adult population of a Canadian province for the 2014/15 fiscal year. We calculated incidence-adjusted healthcare costs for common cancers and comorbidities such as cardiovascular disease. Subgroup analysis was also performed for provincial administrative regions. Results: Total costs related to cancer care amounted to $522M for the province, of which $74M (14%) were attributed to radiation and chemotherapy. Among different cancer subtypes, hematologic malignancies were most costly at $78M, accounting for 15% of the total cancer budget, followed by colon cancer at $51M (10%) and lung cancer at $45M (9%). Cancer costs both with and without accounting for radiation and chemotherapy surpassed those of cardiovascular diseases, diabetes mellitus, mental health, and trauma, but were exceeded by the costs of liver disease (Table 1). Cancer costs varied by provincial administrative region. Conclusions: Cancer costs were greater than those of other common comorbidities, both with and without the costs of radiation and chemotherapy. Using provincial datasets to establish cost trends can help inform healthcare allocation and budget decision-making. Table 1. Incidence-adjusted costs (in CAD) per person per year for cancer and comorbidities. R&C = radiation and chemotherapy. Total Costs Cancer, with R&C 1951 Cancer, without R&C 1917 Cardiovascular disease 806 Diabetes mellitus 663 Liver disease 5208 Musculoskeletal disease 656 Mental health issues 487 Trauma 613
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| 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) | 0.002 | 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".