Temporal trends in cardiovascular disease risk factors among white, South Asian, Chinese and black groups in Ontario, Canada, 2001 to 2012: a population-based study
Bibliographic record
Abstract
OBJECTIVES: To determine ethnic-specific temporal trends in cardiovascular risk factors in Ontario between 2001 and 2012. DESIGN: A population-based repeated cross-sectional study. SETTING: Ontario, Canada. PARTICIPANTS: 219,276 participants of the Canadian Community Health Survey (205,326 white, 5620 South Asian, 4368 Chinese and 3962 black) during the period 2001 to 2012. MAIN OUTCOME MEASURES: Age-standardised ethnic-sex-specific prevalence of cardiovascular risk factors for three time periods: 2001-2004, 2005-2008 and 2009-2012 among Canada's four major ethnic groups: white, South Asian, Chinese and black. RESULTS: During the study period, the prevalence of diabetes increased 2.3-fold (p = 0.0001) among South Asian males and 1.9-fold (p = 0.02) among black females. The prevalence of obesity (body mass index ≥ 30 kg/m(2)) increased over time across all ethnic groups, with the largest relative increases observed among males of Chinese (2.1-fold increase, p = 0.04) and black (1.7-fold increase, p = 0.06) descent. The prevalence of hypertension increased the most among black females. Smoking prevalence decreased by more than 20% among South Asian, Chinese and white females. Overall, South Asian males and black males and females showed the greatest declines in cardiovascular health over the study period. CONCLUSIONS: We observed important ethnic differences in the temporal trends in cardiovascular risk factor profiles in Ontario. Awareness of the direction and magnitude of these risk factor trends may be useful in informing targeted strategies for preventing cardiovascular diseases in multiethnic populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".