Cancer Incidence by Race and Immigration Status in Canada: Value of Enhanced Sociodemographic Data for Disease Surveillance
Bibliographic record
Abstract
Metrics of cancer burden stratified by race can inform tailored prevention strategies. Examining how these metrics, such as incidence, vary by immigration status can provide insight into the drivers of differential cancer risk by race. The conduct of such analyses in Canada has historically been hindered by a lack of sociodemographic data in routine health data sources, including cancer registries. In their recent study, Malagón and colleagues overcome this challenge by using National Cancer Registry data linked to self-reported race and place of birth from the Canadian census. The study provides estimates of cancer incidence for 19 cancer sites across more than 10 racial groups. Compared with the total population, they found that cancer risk tended to be lower among persons belonging to non-White, non-Indigenous racial groups. Exceptions were stomach, liver, and thyroid cancers where incidence rates were higher in minority groups than in the White population. For some cancers and racial groups, incidence was lower irrespective of immigration status, suggesting the healthy immigrant effect may be sustained across generations or that other factors are also at play. The results highlight potential areas for deeper inquiry and underscore the value of sociodemographic data for disease surveillance. See related article by Malagón et al., p. 906.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".