Incidence of Major Cardiovascular Events in Immigrants to Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Immigrants from ethnic minority groups represent an increasing proportion of the population in many high-income countries but little is known about the causes and amount of variation between various immigrant groups in the incidence of major cardiovascular events. METHODS AND RESULTS: We conducted the Cardiovascular Health in Ambulatory Care Research Team (CANHEART) Immigrant study, a big data initiative, linking information from Citizenship and Immigration Canada's Permanent Resident database to nine population-based health databases. A cohort of 824 662 first-generation immigrants aged 30 to 74 as of January 2002 from eight major ethnic groups and 201 countries of birth who immigrated to Ontario, Canada between 1985 and 2000 were compared to a reference group of 5.2 million long-term residents. The overall 10-year age-standardized incidence of major cardiovascular events was 30% lower among immigrants compared with long-term residents. East Asian immigrants (predominantly ethnic Chinese) had the lowest incidence overall (2.4 in males, 1.1 in females per 1000 person-years) but this increased with greater duration of stay in Canada. South Asian immigrants, including those born in Guyana had the highest event rates (8.9 in males, 3.6 in females per 1000 person-years), along with immigrants born in Iraq and Afghanistan. Adjustment for traditional risk factors reduced but did not eliminate differences in cardiovascular risk between various ethnic groups and long-term residents. CONCLUSIONS: Striking differences in the incidence of cardiovascular events exist among immigrants to Canada from different ethnic backgrounds. Traditional risk factors explain part but not all of these differences.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| 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".