Site-Specific Cancer Incidence by Race and Immigration Status in Canada 2006–2015: A Population-Based Data Linkage Study
Bibliographic record
Abstract
BACKGROUND: The Canadian Cancer Registry (CCR) does not collect demographic data beyond age and sex, making it difficult to monitor health inequalities. Using data linkage, we compared site-specific cancer incidence rates by race. METHODS: The 2006 and 2011 Canadian Census Health and Environment Cohorts are population-based probabilistically linked datasets of 5.9 million respondents of the 2006 long-form census and 6.5 million respondents of the 2011 National Household Survey. Race was self-reported. Respondent data were linked with the CCR up to 2015. We calculated age-standardized incidence rate ratios (ASIRR), comparing group-specific rates to the overall population rate with bootstrapped 95% confidence intervals (CI). We used negative binomial regressions to adjust for socioeconomic variables and assess interactions with immigration status. RESULTS: The age-standardized overall cancer incidence rate was lower in almost all non-White racial groups than in the overall population, except for White and Indigenous peoples who had higher incidence rates than the overall population (ASIRRs, 1.03-1.04). Immigrants had substantially lower age-standardized overall cancer incidence rates than nonimmigrants (ASIRR, 0.83; 95% CI, 0.82-0.84). Stomach, liver, and thyroid cancers and multiple myelomas were the sites where non-White racial groups had consistently higher site-specific cancer incidence rates than the overall population. Immigration status was an important modifier of cancer risk in the interaction model. CONCLUSIONS: Differences in cancer incidence between racial groups are likely influenced by differences in lifestyles, early life exposures, and selection factors for immigration. IMPACT: Data linkage can help monitor health inequalities and assess progress in preventive interventions against cancer. See related commentary by Withrow and Gomez, p. 876.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.021 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 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".