Sex Differences in the Mediating Effects of Modifiable Risk Factors in Associations Between Socioeconomic Position and Cardiovascular Disease Morbidity and Mortality
Bibliographic record
Abstract
Socioeconomic inequities in cardiovascular diseases (CVDs) are greater in females than males. However, the mechanisms underpinning the excess CVD risk conferred by low socioeconomic position (SEP) in females remain unclear. This study aimed to quantify the mediating effects of modifiable risk factors to associations between SEP and CVD morbidity and mortality. We combined eight cycles of the nationally-representative cross-sectional Canadian Community Health Survey (2000–2011) to identify a cohort of adults (≥35 years) without CVD at the time of survey administration (n = 289,800). Survey data were linked to administrative health (Hospital Discharge Abstract Database) and mortality (Canadian Mortality Database) records for 2000–2012. SEP was captured by a latent variable consisting of self-reported educational level and household income. Self-reported modifiable risk factors, including smoking, physical inactivity, obesity, diabetes, and hypertension were considered as potential mediators. The outcome was CVD morbidity and mortality as reported in administrative health and mortality databases. Generalized structural equation modeling was used to estimate the mediating effects of modifiable risk factors in associations between SEP and CVD morbidity and mortality in males and females. The total effects of SEP on CVD morbidity and mortality were higher in females than in males. The modifiable risk factors mediated the higher proportion of the total effects in females than in males (87.80% vs 687.8%). Smoking was the most important mediator of associations between SEP and CVD morbidity and mortality for both males and females. The pathways linking SEP with obesity, diabetes and hypertension and subsequent CVD morbidity and mortality were significant in females but not among males. These findings revealed evidence of some sex-specific patterns in the mediating effects of modifiable risk factors on associations between SEP and CVD morbidity and mortality. The findings suggest that different strategies may be required to reduce inequities in CVD in males and females. This study was funded by a Libin Cardiovascular Research Institute of Alberta Innovation Seed Grant and the Petro-Canada Young Innovator Award in Community Health.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".