Projected estimates of cancer in Canada in 2024
Bibliographic record
Abstract
BACKGROUND: Cancer surveillance data are essential to help understand where gaps exist and progress is being made in cancer control. We sought to summarize the expected impact of cancer in Canada in 2024, with projections of new cancer cases and deaths from cancer by sex and province or territory for all ages combined. METHODS: We obtained data on new cancer cases (i.e., incidence, 1984-2019) and deaths from cancer (i.e., mortality, 1984-2020) from the Canadian Cancer Registry and Canadian Vital Statistics Death Database, respectively. We projected cancer incidence and mortality counts and rates to 2024 for 23 types of cancer, overall, by sex, and by province or territory. We calculated age-standardized rates using data from the 2011 Canadian standard population. RESULTS: In 2024, the number of new cancer cases and deaths from cancer are expected to reach 247 100 and 88 100, respectively. The age-standardized incidence rate (ASIR) and mortality rate (ASMR) are projected to decrease slightly from previous years for both males and females, with higher rates among males (ASIR 562.2 per 100 000 and ASMR 209.6 per 100 000 among males; ASIR 495.9 per 100 000 and ASMR 152.8 per 100 000 among females). The ASIRs and ASMRs of several common cancers are projected to continue to decrease (i.e., lung, colorectal, and prostate cancer), while those of several others are projected to increase (i.e., liver and intrahepatic bile duct cancer, kidney cancer, melanoma, and non-Hodgkin lymphoma). INTERPRETATION: Although the overall incidence of cancer and associated mortality are declining, new cases and deaths in Canada are expected to increase in 2024, largely because of the growing and aging population. Efforts in prevention, screening, and treatment have reduced the impact of some cancers, but these short-term projections highlight the potential effect of cancer on people and health care systems in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".