Intergenerational educational trajectories and inequalities in longevity: A population-based study of adults born before 1965 in 14 European countries
Bibliographic record
Abstract
Background: While educational gradients in longevity have been observed consistently in adult Europeans, these inequalities have been understudied within the context of family- and country-level influences. We utilized population-based multi-generational multi-country data to assess the role (1) of parental and individual education in shaping intergenerational inequalities in longevity, and (2) of country-level social net expenditure in mitigating these inequalities. Methods: We analyzed data from 52,271 adults born before 1965 who participated in the Survey of Health, Ageing and Retirement in Europe, comprising 14 countries. Mortality from all causes (outcome) was ascertained between 2013 and 2020. Educational trajectories (exposure) were High-High (reference), Low-High, High-Low, and Low-Low, corresponding to the sequence of parental-individual educational attainment. We quantified inequalities as years of life lost (YLL) between the ages of 50 and 90 estimated via differences in the area under standardized survival curves. We assessed the association between country-level social net expenditure and YLL via meta-regression. Results: Inequalities in longevity due to educational trajectories were associated with low individual education regardless of parental education. Compared to High-High, having High-Low and Low-Low led to 2.2 (95% confidence intervals: 1.0 to 3.5) and 2.9 (2.2 to 3.6) YLL, while YLL for Low-High were 0.4 (-0.2 to 0.9). A 1% increase in social net expenditure led to an increase of 0.01 (-0.3 to 0.3) YLL for Low-High, 0.007 (-0.1 to 0.2) YLL for High-Low, and a decrease of 0.02 (-0.1 to 0.2) YLL for Low-Low. Conclusion: In European countries, individual education could be the main driver of inequalities in longevity for adults older than 50 years of age and born before 1965. Further, higher social expenditure is not associated with smaller educational inequalities in longevity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".