Comparison of cardiovascular risk profiles among ethnic groups using population health surveys between 1996 and 2007
Bibliographic record
Abstract
BACKGROUND: Although people of South Asian, Chinese and black ethnic backgrounds represent about 60% of the world's population, most knowledge of cardiovascular risk is derived from studies conducted in white populations. We conducted a large, population-based comparison of cardiovascular risk among people of white, South Asian, Chinese and black ethnicity living in Ontario, Canada. METHODS: We examined the age- and sex-standardized prevalence of eight cardiovascular risk factors, heart disease and stroke among 154,653 white people, 3364 South Asian people, 3038 Chinese people and 2742 black people. For this study, we pooled respondent data from five cross-sectional health surveys conducted between 1996 and 2007: the National Population Health Survey of 1996 and the Canadian Community Health Survey, versions 1.1, 2.1, 3.1 and 4.1. RESULTS: The four ethnic groups varied considerably in the prevalence of the four major cardiovascular risk factors that we examined: for smoking, South Asian 8.6%, Chinese 8.7%, black 11.4% and white 24.8%; for obesity, Chinese 2.5%, South Asian 8.1%, black 14.1% and white 14.8%; for diabetes mellitus, white 4.2%, Chinese 4.3%, South Asian 8.1% and black 8.5%; and for hypertension, white 13.7%, Chinese 15.1%, South Asian 17.0% and black 19.8%. The prevalence of heart disease ranged from a low of 3.2% in the Chinese population to a high of 5.2% in the South Asian population, and the prevalence of stroke ranged from a low of 0.6% in the Chinese population to a high of 1.7% in the South Asian population. Although the black population had the least favourable cardiovascular risk factor profile overall, this group had a relatively low prevalence of heart disease (3.4%). INTERPRETATION: Ethnic groups living in Ontario had striking differences in cardiovascular risk profiles. Awareness of these differences may help in identifying priorities for the development of cardiovascular disease prevention programs for specific ethnic groups.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".