The Economic Burden of Cancer in Canada from a Societal Perspective
Bibliographic record
Abstract
Cancer patients and their families experience considerable financial hardship; however, the current published literature on the economic burden of cancer at the population level has typically focused on the costs from the health system's perspective. This study aims to estimate the economic burden of cancer in Canada from a societal perspective. The analysis was conducted using the OncoSim-All Cancers model, a Canadian cancer microsimulation model. OncoSim simulates cancer incidence and deaths using incidence and mortality data from the Canadian Cancer Registry and demography projections from Statistics Canada. Using a phase-based costing framework, we estimated the economic burden of cancer in Canada in 2021 by incorporating published direct health system costs and patients' and families' costs (out-of-pocket costs, time costs, indirect costs). From a societal perspective, cancer-related costs were CAD 26.2 billion in Canada in 2021; 30% of costs were borne by patients and their families. The economic burden was the highest in the first year after cancer was diagnosed (i.e., initial care). During this time, patients and families' costs amounted to almost CAD 4.8 billion in 2021. This study provides a comprehensive estimate of the economic burden of cancer, which could inform cost-benefit analyses of proposed cancer prevention interventions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".