The impact of immigration status on cancer outcomes in Ontario, Canada.
Bibliographic record
Abstract
285 Background: In the delivery of cancer care, barriers to access could potentially result in inferior outcomes and survival. Although a relationship has been demonstrated between disadvantaged socio-economic status and mortality, the impact of immigration on outcomes is less clear. Methods: Administrative databases were linked to create a cohort of all incident cases of colorectal, lung, prostate, head/neck, breast and hematologic malignancies from Jan 2000 to Dec 2012 in Ontario, Canada. Cases were defined according to immigration status and followed from diagnosis until death (or cancer-specific death). Cox proportional hazards models were constructed to study the impact of immigration status on survival after adjusting for relevant variables. Additional adjusted models studied the relationship of time since immigration on mortality. Results: During the study period, 11,485 cancer cases were diagnosed in recent immigrants (0-10 years in Canada), 17,844 cases in non-recent immigrants (11-25 years), and 416,118 cases in non-immigrants. Following adjustment for relevant predictors by Cox regression, survival was improved for recent immigrants (HR 0.843; 95% CI 0.814-0.873) and non-recent immigrants (HR 0.902; 95% CI 0.876-0.928) compared to non-immigrants. Cancer-specific survival was also better for recent immigrants (HR 0.857; 95% CI 0.823-0.893) and non-recent immigrants (HR 0.907; 95% CI 0.875-0.94) compared to non-immigrants. Amongst immigrants, each year from the original landing in Canada was associated with increased mortality (HR 1.004; 1.000-1.009) and a trend to increased cancer-specific mortality (HR 1.005; 0.999-1.010) that was not statistically significant. Immigrants from all WHO world regions were found to have similar reductions in mortality and cancer-specific mortality. Conclusions: Immigrants to Canada demonstrate a “healthy immigrant” effect, with lower mortality compared to Canadian-born individuals. This benefit appears to diminish over time, as the health of immigrants potentially converges with the Canadian norm. Potential contributors to the benefit include self-selection for immigration, health requirements for entrance, and differences in disease distribution related to ethnicity.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".