Age-standardized cancer-incidence trends in Canada, 1971–2015
Bibliographic record
Abstract
BACKGROUND: Although cancer incidence over time is well documented in Canada, trends by birth cohort and age group are less well known. We analyzed age- and sex-standardized incidence trends in Canada for 16 major cancer sites and all cancers combined. METHODS: We obtained nationally representative population-based cancer incidence data in Canada between 1971 and 2015 from the National Cancer Incidence Reporting System (1969-1992) and the Canadian Cancer Registry (1992-2015). We analyzed cancer-incidence trends, reported as annual percent change (APC) for each 10-year group from age 20 to 89 years. We also estimated age-adjusted incidence rate ratios from fitted birth cohort models. RESULTS: Across most age categories, the most recent trends show significant decreases in the incidence of cervical (APC -8.8% to -0.33%), lung (men: -7.42% to -0.36%; women: -6.27% to 1.07%), bladder (women: -4.12% to -0.07%; men: -5.13% to -0.38%) and prostate cancer (-11.11% to -1.11%). Significant increasing trends were observed for kidney, thyroid and uterine cancers. Overall incidence has increased among both sexes younger than 50 years of age, with recent increases in pancreatic cancer among men, breast cancer among women and colorectal cancer among both sexes. From the birth cohort analysis, we observed increasing trends in colorectal, liver and prostate cancers among men; kidney cancer and melanoma among women; and thyroid cancer among both sexes. We observed decreasing trends in cervical and ovarian cancers, and in bladder and lung cancers among men. INTERPRETATION: Cancer incidence is decreasing at many sites targeted by primary-prevention efforts, such as smoking cessation and screening programs. Substantial increases in incidence among younger populations are driven by cancers possibly associated with obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".