Inequalities in relative cancer survival by race, immigration status, income, and education for 22 cancer sites in Canada, a cohort study
Bibliographic record
Abstract
There is a paucity of disaggregated data to monitor cancer health inequalities in Canada. We used data linkage to estimate site-specific cancer relative survival by race, immigration status, household income, and education level in Canada. We pooled the Canadian Census Health and Environment Cohorts, which are linked datasets of 5.9 million respondents of the 2006 long-form census and 6.5 million respondents of the 2011 National Household Survey. Individual-level respondent data from these surveys were probabilistically linked with the Canadian Cancer Registry up to 2015 and with the Canadian Vital Statistics Death database up to 2019. We used propensity score matching and Poisson models to calculate age-standardized relative survival by equity stratifiers for all cancers combined and for 22 individual cancer sites for the period 2006-2019. There were 560,905 primary cancer cases diagnosed over follow-up included in survival analyses; the age-standardized period relative survival was 72.9% at 5 years post-diagnosis. 5-year relative survival was higher in immigrants (74.1%, 95%CI 73.8-74.4) than in Canadian-born persons (69.6%, 95%CI 69.4-69.8), and higher in racial groups with high proportions of immigrants. There was a marked socioeconomic gradient, with 11%-12% lower relative survival in cancer patients in the lowest household income and education levels than in the highest levels. Socioeconomic gradients were observed for most cancer sites, though the magnitude varied by site. The observed differences in relative survival suggest there remain important inequities in cancer control and care delivery and quality even in a universal healthcare system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".