Do cancer incidence and mortality rates differ among ethnicities in Canada?
Bibliographic record
Abstract
BACKGROUND: Cancer incidence rates have been shown to vary by ethnicity, and the increasing awareness of and interest in reporting ethnic health inequalities have been growing internationally. The objective of this study was to assess cancer incidence and mortality rates by ethnicity in Canada. DATA AND METHODS: The study used the 2006 Canadian Census Health and Environment Cohort, linked to the Canadian Cancer Registry and the Canadian Vital Statistics-Death Database, to determine cancer cases and mortality from 2006 to 2016. Ethnicity was categorized as non-Indigenous North American (NINA); European; Caribbean; Latin, Central and South American (LCSA); African; East Asian; South Asian; and West Central Asian and Middle Eastern. RESULTS: Europeans had the highest standardized incidence rates, while NINA had the highest mortality rates. Rates varied substantially by ethnicity and immigrant status. The top three cancers accounted for 46.5% to 61.9% of all new cancers, while the top three cancer deaths accounted for 36.1% to 61.9% of all deaths. The distribution of cancers within the top 10 cancers and the top 10 cancer deaths also differed; e.g., stomach cancer was found to be more prevalent in the East Asian, LCSA, African and Caribbean groups. Non-immigrant African males had the highest cancer incidence rates, and non-immigrant South Asian females had the highest mortality rates. DISCUSSION: There is considerable variability in cancer incidence and cancer mortality rates by ethnicity, and this study addresses the knowledge gap in Canada in this area. Establishing baseline indicators, such as cancer rates by ethnicity, is essential to understanding the differences within the diverse Canadian population and to informing targeted interventions that may help reduce health inequalities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| 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".