Early-life socioeconomic circumstances explain health differences in old age, but not their evolution over time
Bibliographic record
Abstract
BACKGROUND: Early-life socioeconomic circumstances (SEC) are associated with health in old age. However, epidemiological evidences on the influence of these early-life risk factors on trajectories of healthy ageing are inconsistent, preventing drawing solid conclusion about their potential influence. Here, to fill this knowledge gap, we used a statistical approach adapted to estimating change over time and an outcome-wide epidemiology approach to investigate whether early-life SEC were associated with the level of and rate of decline of physical, cognitive and emotional functioning over time. METHODS: We used data on more than 23 000 adults in older age from the Survey of Health, Ageing and Retirement in Europe, a 12-year large-scale longitudinal study with repeated measurements of multiple health indicators of the same participants over time (2004 -2015, assessments every 2 years). Confounder-adjusted linear growth curve models were used to examine the associations of early-life SEC with the evolution of muscle strength, lung function, cognitive function, depressive symptoms and well-being over time. RESULTS: We consistently found an association between early-life SEC and the mean levels of all health indicators at age 63.5, with a critical role played by the cultural aspect of disadvantage. These associations were only partly explained by adult-life SEC factors. By contrast, evidences supporting an association between early-life SEC and the rate of change in health indicators were weak and inconsistent. CONCLUSIONS: Early-life SEC are associated with health in old age, but not with trajectories of healthy ageing. Conceptual models in life course research should consider the possibility of a limited influence of early-life SEC on healthy ageing trajectories.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".