Education and Coronary Heart Disease Risk
Bibliographic record
Abstract
OBJECTIVE: Education is inversely associated with coronary heart disease (CHD) risk; however the mechanisms are poorly understood. The study objectives were to evaluate the extent to which rarely measured factors (literacy, time preference, sense of control) and more commonly measured factors (income, depressive symptomatology, body mass index) in the education-CHD literature explain the associations between education and CHD risk. METHOD: The study sample included 346 participants, aged 38 to 47 years (59.5% women), of the New England Family Study birth cohort. Ten-year CHD risk was calculated using the validated Framingham risk algorithm that utilizes diabetes, smoking, blood pressure, total cholesterol, high-density lipoprotein cholesterol, age, and gender. Multivariable regression and mediation analyses were performed. RESULTS: Regression analyses adjusting for age, race/ethnicity, and childhood confounders (e.g., parental socioeconomic status, intelligence) demonstrated that relative to those with greater than or equal to college education, men and women with less than high school had 73.7% (95% confidence interval [CI; 29.5, 133.0]) and 48.2% (95% CI [17.5, 86.8]) higher 10-year CHD risk, respectively. Mediation analyses demonstrated significant indirect effects for reading comprehension in women (7.2%; 95% CI [0.7, 19.4]) and men (7.2%; 95% CI [0.8, 19.1]), and depressive symptoms (11.8%; 95% CI [2.5, 26.6]) and perceived constraint (6.7%, 95% CI [0.7, 19.1]) in women. CONCLUSIONS: Evidence suggested that reading comprehension in women and men, and depressive symptoms and perceived constraint in women, may mediate some of the association between education and CHD risk. If these mediated effects are interpreted causally, interventions targeting reading, depressive symptoms, and perceived constraint could reduce educational inequalities in CHD.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".