Abstract 11988: Understanding Ethnic Differences in the Risk of Cardiovascular Events and Mortality Among Immigrants in Canada
Bibliographic record
Abstract
Introduction: Immigrants make up the largest share of the population in Canada, and one in four Canadians has come to the country as an immigrant. The high level of immigration has resulted in significant ethnic diversity in Canada, and each presents with a cardiovascular disease (CVD) risk profile unique to their ethnicity and country of birth. Methods: We performed a literature search of 6 electronic databases, including the grey literature sources of conferences, reports, theses, and dissertations from January 2000 until May 25, 2023. We included pertinent English language literature that summarized the evidence on ethnic differences in CVD risk and mortality among immigrants of the following ethnic groups in Canada: South Asian (East Indian, Pakistani, Sri Lankan), Chinese, Black, Latin American, Southeast Asian (Vietnamese, Cambodian, Malaysian), West Asian (Iran, Afghan), Arab, Korean, Japanese, and Filipinos. Results: Of the 9968 studies identified, 47 studies formed the basis of the review. Four overarching themes of CVD risk were found, comprising individual characteristics, ethnic differences, gender-related risks, and duration of residence in Canada. Among the different ethnic groups, South Asians had the greater risk of cardiovascular events, in which males had a striking difference in mortality of (42%) compared to females (29%), whereas East Asians had the least risk. No significant difference in the incidence of CVD was reported with the duration of residence between varied ethnic groups. However, East Asians showed a notable exception, with an increase in the incidence of CVD after 10 years of stay in Canada by 40% and 60% among males and females, respectively. Conclusions: Ethnic inequalities in CVD attributes to a combination of modifiable and non-modifiable risk factors, and this disparity in CVD incidence can be tackled by targeting interventions according to ethnic differences in risk profiles.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.023 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".