Social disadvantage and cardiovascular disease: development of an index and analysis of age, sex, and ethnicity effects
Bibliographic record
Abstract
BACKGROUND: Social disadvantage is defined by adverse socio-economic characteristics and is distributed unequally by age, sex, and ethnicity. We studied the relationship between social disadvantage, cardiovascular risk factors, and cardiovascular disease (CVD) among men and women from diverse ethno-racial backgrounds. METHODS: A total of 1227 men and women of South Asian, Chinese, Aboriginal, and European ancestry were randomly selected from four communities in Canada to undergo a health assessment. Socio-economic factors, conventional and novel CV risk factors, atherosclerosis, and CVD were measured. A social disadvantage index was generated and included employment status, income, and marital status. Social disadvantage was examined in relation to risk factors for CVD, atherosclerosis, and prevalent CVD. RESULTS: Social disadvantage was higher among older people, women, and non-white ethnic groups. Cigarette smoking, glucose, overweight, abdominal obesity, and CRP were higher among individuals with higher social disadvantage, whereas systolic blood pressure, lipids, norepinephrine, and atherosclerosis were not. Social disadvantage is an independent predictor of CVD after adjustment for conventional and novel risk markers for CVD (OR for 1 point increase = 1.25; 95% CI 1.06-1.47). CONCLUSION: The social disadvantage index combines social and economic exposures into a single continuous measure. Significant variation in social disadvantage by age, sex, and ethnic group exists. Increased social disadvantage is associated with an increased burden of some CV risk factors, and is an independently associated with CVD.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".