Chinese and South Asian ethnicity, immigration status, and clinical cancer outcomes in the Ontario Cancer System
Bibliographic record
Abstract
BACKGROUND: In the United States, certain minority groups have been shown to have inferior cancer outcomes compared with the white majority population. However, to the authors' knowledge, the majority of research has not separated ethnicity from immigration status. The objective of the current study was to determine the impact of ethnicity, independent of immigration status, on cancer outcomes in Chinese and South Asian populations in Ontario, Canada. METHODS: The authors conducted a population-based retrospective cohort study using administrative databases in Ontario, Canada. Incident cancer cases were captured in Canadian-born Chinese and South Asian individuals, Chinese and South Asian immigrants, and the general Ontario reference population (non-Chinese/non-South Asian and non-immigrant) between 2000 and 2012. Subjects were followed until death (all-cause and cancer-specific), and Cox proportional hazard models were used to estimate the impact of Chinese and South Asian ethnicity on cancer outcomes after adjusting for explanatory variables. RESULTS: A total of 423,678 cancer cases were identified; at total of 6631 cases were identified in Canadian-born Chinese individuals and 2752 cases in Canadian-born South Asian individuals. After adjustment, the rate of all-cause mortality was lower for Canadian-born Chinese (hazard ratio [HR], 0.829; 95% confidence interval [95% CI], 0.795-0.865), Canadian-born South Asian (HR, 0.856; 95% CI, 0.797-0.919), and Chinese immigrant (recent immigrant: HR, 0.661 [95% CI, 0.610-0.716] and non-recent immigrant: HR, 0.853 [95% CI, 0.803-0.906]) populations compared with the general Ontario population. A similar effect was found for cancer-specific mortality. CONCLUSIONS: Chinese and South Asian ethnic groups appear to have lower cancer mortalities compared with the general Ontario population. After removing the well-documented protective effect of immigration, Chinese and South Asian ethnicities were found to be associated with a cancer survival advantage in Ontario, Canada. Cancer 2018;124:1473-82. © 2018 American Cancer Society.
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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 | 0.000 |
| 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 | 0.000 |
| 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".