The future burden of cancer in Canada: Long-term cancer incidence projections 2013–2042
Bibliographic record
Abstract
BACKGROUND: Cancer is the leading cause of death in Canada and the estimated annual spending associated with cancer is approximately $7.5 billion. Projecting the future burden of cancer in Canada is essential for health planning and evaluation. We aimed to estimate the future incidence of cancer in Canada to 2042. METHODS: Age-sex-region-specific cancer incidence data were obtained for the years 1983-2012 and cancer incidence was projected from 2013 to 2042 for the top five cancer sites. The modelling algorithm combined a mixture of cancer projection methods to select the best-fitted model. When the chosen model produced by the modelling algorithm resulted in estimates that were not consistent with expert opinion, an alternate model was selected that took into consideration historical changes in policy, screening and lifestyle behaviours. Incidence projections were made for Canada and its provinces. RESULTS: Lung cancer incidence is estimated to rise to 14,866 cases in men and 19,162 in women in 2042. Colorectal cancer incidence is estimated to rise to 28,146 in men and 21,102 in women. Cases of bladder cancer are projected to rise to 10,708 and 3,364 in men and women, respectively. Breast cancer incidence is predicted to rise to 40,712 and prostate cancer incidence is projected to rise to 92,949. CONCLUSION: These cancer incidence projections up to 2042 can be used for planning cancer control strategies and prevention programs. Given the ongoing changes in the prevalence of risk factors and in cancer prevention policies, these estimates should be interpreted with caution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".