Intergenerational educational trajectories and premature mortality from chronic diseases: A registry population-based study
Bibliographic record
Abstract
The tracking of educational gradients in mortality across generations could create a long shadow of social inequality, but it remains understudied. We aimed to assess whether intergenerational educational trajectories shape inequalities in early premature mortality from chronic diseases. The study included 544 743 participants of the Swiss National Cohort, a registry population-based study. Individuals were born 1971-1980 and aged 10-19 at the start of the study (1990). Mortality follow-up was until 2018. Educational trajectories were High-High (reference), High-Low, Low-High, Low-Low, corresponding to the sequence of parental-individual attained education. Examined deaths were related to cardiovascular diseases (CVD), cancers, and substance use. Sex-specific inequalities in mortality were quantified via standardized cumulative risk differences/ratios between age 20 and 45. We triangulated findings with a negative outcome control. For women, inequalities were negligible. For men, while inequalities in cancers deaths were negligible, inequalities in CVD mortality were associated to low individual education regardless of parental education. Excess CVD deaths for Low-High were negligible while High-Low provided 234 (95% confidence intervals: 100 to 391) and Low-Low 185 (115 to 251) additional CVD deaths per 100 000 men compared to High-High. That corresponded to risk ratios of 2.7 (1.6 to 4.5) and 2.3 (1.6 to 3.4), respectively. Gradients in substance use mortality were observed only when education changed across parent-offspring. Excess substance use deaths for Low-Low were negligible while High-Low provided 225 (88 to 341) additional and Low-High 80 (23 to 151) fewer substance use deaths per 100 000 men compared to High-High. That corresponded to risk ratios of 1.8 (1.3 to 2.5) and 0.7 (0.5 to 0.9), respectively. Inequalities in premature mortality were driven by individual education and by parental education for some chronic diseases. This could justify the development of intergenerational prevention strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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".