Ethnic Inequalities in Cardiovascular Disease Risk
Bibliographic record
Abstract
Background: Cardiovascular diseases are a leading cause of death globally, and a major risk factor is obesity in early age groups. Obesity in children and youth is a growing public health concern, and inequalities exist across social groups. Evidence on ethnic disparities in obesity risk is mixed, and little is known about ethnicity and obesity in late adolescence. Moreover, broad ethnic identity categories may be less informative for understanding disparities in obesity risk than the psychosocial process of ethnic identity development during this unique developmental period. Differences in the salience of ethnic identity seem particularly relevant to examining obesity inequalities in multicultural, multigenerational settings. Aims and Objectives: To examine the gender-specific associations of strength of ethnic identity with the obesity prevalence in ethnically diverse urban youth from British Columbia (BC), Canada. Materials and Methods: Cross-sectional study of an adolescent cohort with self-reported data on ethnic identity, sociodemographics, height and weight using regression modeling with interaction terms. Results: Above-average ethnic identity was associated with the higher obesity prevalence in young men only. Multivariable-adjusted models showed that young men reporting the strongest ethnic identity had 57% higher odds of being obese (odds ratio 1.57 [95% confidence interval: 1.05–2.37]). Conclusion: Associations varied by gender and ethnic group: stronger ethnic identity was significantly associated with the higher obesity prevalence in young men from Asian and Indigenous cultural heritage, whereas young women from Indigenous backgrounds with stronger ethnic identity showed a nonsignificant lower obesity prevalence. Future research directions and public health program implications are discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".