Race, Ethnicity and Cardiovascular Risk: A Population-based Study in Ontario, Canada
Bibliographic record
Abstract
Background: Ethnic and immigrant groups represent a large and growing segment of the Canadian population, however, little is known about how these groups differ in their cardiovascular risk factor profiles when compared to the White population. This thesis describes three large, population-based studies examining cardiovascular risk among people of White, South Asian, Chinese and Black ethnicity living in Ontario. It was hypothesized that ethnic groups would differ significantly in their cardiovascular risk factor profiles.\n\nMethods: The study population included 154 653 White, 3364 South Asian, 3038 Chinese, and 2742 Black subjects derived from Statistics Canada’s National Population Health Survey and Canadian Community Health Surveys. In Project 1, the age- and sex-standardized prevalence of cardiovascular risk factors, heart disease, and stroke were compared across the four ethnic groups. In Project 2, the degree to which cardiovascular risk factor profiles differed between recent immigrants and long-term residents was compared across ethnic groups. In Project 3, a subsample of the study population was used to compare the ethnic-specific incidence and age at diagnosis of diabetes. We also derived ethnically appropriate body-mass index (BMI) cutoff values for obesity for assessing diabetes risk. \n\nResults: Ethnic groups living in Ontario differ strikingly in their cardiovascular risk profiles. The Chinese group had the most favourable cardiovascular risk factor profile, with 4.3% of the population reporting ≥2 major cardiovascular risk factors (i.e., smoking, obesity, diabetes, hypertension), followed by the South Asian (7.9%), White (10.1%) and Black (11.1%) groups. For all ethnic groups, cardiovascular risk factor profiles were worse among those with longer duration of residency in Canada. Nonwhite subjects developed diabetes at a higher rate, at an earlier age, and at lower ranges of BMI than White subjects. For the equivalent incidence rate of diabetes at a BMI of 30 in White subjects, the BMI cutoff value was 24, 25, and 26 in South Asian, Chinese, and Black subjects, respectively.\n\nInterpretation: These findings highlight the need for designing ethnically tailored cardiovascular disease prevention strategies and for lowering current targets for ideal body weight for nonwhite populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".