The contribution of modifiable risk factors to socioeconomic inequities in cardiovascular disease morbidity and mortality: A nationally representative population-based cohort study
Bibliographic record
Abstract
This study examined the individual and joint effects of modifiable risk factors mediating the associations between socioeconomic position (SEP) and morbidity and mortality from cardiovascular diseases (CVD) in a nationally representative sample of adults in Canada. Participants in the Canadian Community Health Survey (n = 289,800) were followed longitudinally for CVD morbidity and mortality using administrative health and mortality data. SEP was measured as a latent variable consisting of household income and individual educational attainment. Mediators included smoking, physical inactivity, obesity, diabetes and hypertension. The primary outcome was CVD morbidity and mortality, defined as the first fatal/nonfatal CVD event during follow-up (median 6.2 years). Generalized structural equation modeling tested the mediating effects of modifiable risk factors in associations between SEP and CVD in the total population and stratified by sex. Lower SEP was associated with 2.5 times increased odds of CVD morbidity and mortality (OR: 2.52, 95% CI: 2.28, 2.76). Modifiable risk factors mediated 74% of associations between SEP and CVD morbidity and mortality in the total population and were more important mediators of associations in females (83%) than males (62%). Smoking mediated these associations independently and jointly with other mediators. The mediating effects of physical inactivity were through joint mediating effects with obesity, diabetes or hypertension. There were additional joint mediating effects of obesity through diabetes or hypertension in females. Findings point to modifiable risk factors as important targets for interventions along with interventions that target structural determinants of health to reduce socioeconomic inequities in 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".