Intergenerational education and premature mortality: a registry population-based study
Bibliographic record
Abstract
Abstract Inequalities in premature mortality due to individual's attained education are well documented. Intergenerational educational trajectories, whereby both parental and individual education may affect mortality, have received less attention. Our aim was to assess the effect of intergenerational educational trajectories on sex- and cause-specific risks of chronic disease-related premature mortality in Switzerland. Data were from 695,972 individuals born between 1971 and 1980, who were followed from adolescence (10-19y) to mid-life (38-47y) in the Swiss National Cohort, a registry population-based study. Educational trajectories were categorized into four levels: High-High, High-Low, Low-High, Low-Low, which corresponded to the sequence of parental-individual attained education (exposure). Cause of death categories were cardiovascular disease (CVD), cancer, substance use and all other chronic diseases (outcome). We implemented a counterfactual-based framework to quantify inequalities and ran negative outcome controls to triangulate findings. Overall, inequalities were negligible for women and substantial for men, particularly in CVD and substance use deaths. Specifically, inequalities in CVD were negligible by age 30, while by age 45 inequalities due to a High-Low or Low-Low trajectory corresponded to 229 (95% confidence interval (CI): 99, 381) additional CVD deaths per 100,000 persons compared to a High-High trajectory. Inequalities in substance use deaths due to a High-Low trajectory corresponded to 106 (95% CI: 47, 208) additional deaths per 100,000 persons compared to a High-High trajectory by age 30, and increased afterwards. We identified sex- and cause-specific groups at high-risk of premature mortality, and life periods when inequalities due to both parental and individual education arise. Prioritization of prevention strategies in those life periods and groups may help reduce educational inequalities in chronic disease-related premature mortality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".