Population-based differences in cancer incidence between immigrants and non-immigrants in Canada between 1992 and 2015
Bibliographic record
Abstract
OBJECTIVES: With increasing immigration in Canada and strained cancer treatment infrastructure, there's a pressing need for long-term data on immigrant health and cancer incidence. This information is crucial for planning future cancer services and to alleviate the burden on both the population and healthcare system. METHODS: Statistics Canada data were linked from the 1991 Canadian Census, Canadian Cancer Registry, and Canadian Vital Statistics Database to follow a cohort from 1992 to 2015 and compare cancer incidence between immigrants and the Canadian-born for any cancer and specific types of cancers. Immigrants were further classified based on time spent in Canada. RESULTS: Immigrants had lower odds of developing any cancer (OR = 0.92, 95% CI [0.92-0.93], p < 0.001) compared to non-immigrants. However, for stomach cancer and non-cervical gynecological cancers, the odds of cancer incidence were greater for immigrants than for the Canadian-born. Cox regression showed that recent immigrants (0-4 years in Canada) had a lower hazard ratio (HR = 0.77, 95% CI [0.71-0.84], p < 0.001) compared to non-immigrants. Those who lived 5-9 years and 10-19 years in Canada had a higher hazard ratio (HR = 0.82, 95% CI [0.75-0.89], p < 0.001; HR = 0.90, 95% CI [0.82-0.98], p = 0.011), respectively. Immigrants who had been in Canada for 20 years or longer had the highest hazard ratio (HR = 0.98, 95% CI [0.90-1.07], p = 0.632), indicating that the so-called "healthy immigrant effect" lessens over time. CONCLUSION: Results demonstrated the healthy immigrant effect lessens over time spent in Canada. However, this effect was not uniform across countries of origin and cancer types. Therefore, this research, provides a deeper understanding of immigrant cancer outcomes and will be useful for cancer planning services and cancer control strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".