Site-specific cancer incidence by race and immigration status in Canada 2006-2015: a population-based data linkage study
Bibliographic record
Abstract
Abstract Introduction The Canadian Cancer Registry does not collect demographic data beyond age and sex, making it hard to monitor health inequalities in cancer incidence in Canada, a country with public healthcare and many immigrants. Using data linkage, we compared site-specific cancer incidence rates by race. Methods We used data from the 2006 and 2011 Canadian Census Health and Environment Cohorts (CanCHECs), which are population-based probabilistically linked datasets of 5.9 million respondents of the 2006 Canadian long-form census and 6.5 million respondents of the 2011 National Household Survey. Respondents’ race was self-reported using Indigenous identity and visible minority group identity questions. Respondent data were linked with the Canadian Cancer Registry 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 (95%CI). We used negative binomial regressions to adjust rates for socioeconomic variables and assess interactions with immigration status. Results The age-standardized cancer incidence rate was lower in almost all non-White racial groups than in White individuals, except for Indigenous peoples who had a similar overall age-standardized cancer incidence rate (ASIRR 0.99, 95%CI 0.97-1.01). Immigrants had substantially lower age-standardized overall cancer incidence rates than non-immigrants (ASIRR 0.83, 95%CI 0.82-0.84). Non-White racial groups generally had significantly lower or equivalent site-specific cancer incidence rates than the overall population, except for stomach, liver, and thyroid cancers and for multiple myeloma. Differences in incidence rates by race persisted even after adjusting for household income, education, and rural residence, with immigration status being an important modifier of cancer risk. Conclusions Differences in cancer incidence between racial groups are likely influenced by differences in lifestyles and early life exposures, as well as selection factors for immigration. This suggests a strong role of environment in determining cancer risk and further potential for cancer prevention.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 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".