Impact of Immigration Status on Cancer Outcomes in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Prior studies have documented inferior health outcomes in vulnerable populations, including racial minorities and those with disadvantaged socioeconomic status. The impact of immigration on cancer-related outcomes is less clear. METHODS: Administrative databases were linked to create a cohort of incident cancer cases (colorectal, lung, prostate, head and neck, breast, and hematologic malignancies) from 2000 to 2012 in Ontario, Canada. Cancer patients who immigrated to Canada (from 1985 onward) were compared with those who were Canadian born (or immigrated before 1985). Patients were followed from diagnosis until death (cancer-specific or all-cause). Cox proportional hazards models were estimated to determine the impact of immigration on mortality after adjusting for explanatory variables. Additional adjusted models studied the relationship of time since immigration and cancer-specific and overall mortality. RESULTS: From 2000 to 2012, 11,485 cancer cases were diagnosed in recent immigrants (0 to 10 years in Canada), 17,844 cases in nonrecent immigrants (11 to 25 years), and 416,118 cases in nonimmigrants. After adjustment, the hazard of mortality was lower for recent immigrants (hazard ratio [HR], 0.843; 95% CI, 0.814 to 0.873) and nonrecent immigrants (HR, 0.902; 95% CI, 0.876 to 0.928) compared with nonimmigrants. Cancer-specific mortality was also lower for recent immigrants (HR, 0.857; 95% CI, 0.823 to 0.893) and nonrecent immigrants (HR, 0.907; 95% CI, 0.875 to 0.94). Among immigrants, each year from the original landing was associated with increased mortality (HR, 1.004; 95% CI, 1.000 to 1.009) and a trend to increased cancer-specific mortality (HR, 1.005; 95% CI, 0.999 to 1.010). CONCLUSION: Immigrants demonstrate a healthy immigrant effect, with lower cancer-specific mortality compared with Canadian-born individuals. This benefit seems to diminish over time, as the survival of immigrants from common cancers potentially converges with the Canadian norm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".