Socioeconomic disadvantage in childhood and across the life course and all-cause mortality and physical function in adulthood: evidence from the Alameda County Study
Bibliographic record
Abstract
OBJECTIVE: To measure the childhood and life course socioeconomic exposures of people born between 1871 and 1949, and then to estimate the probability of death between 1965 and 1994, the probability of functional limitation in 1994, and the combined probability of dying or experiencing functional limitation during this period. SETTING, PARTICIPANTS AND DESIGN: Data were from the Alameda County Study (California) and pertained to people aged 17-94 years (n = 6,627) in 1965 (baseline). Socioeconomic position (SEP) in childhood was based on respondent's reports of their father's occupation, and life course disadvantage was measured by cross-classifying childhood SEP and the respondent's education and household income in 1965. The health outcomes were all-cause mortality (n = 2,420) and functional limitation measured using the Nagi index (n = 453, 17.4% of those alive in 1994). Relationships were examined before and after adjustment for changed socioeconomic circumstances after 1965. RESULTS: Those from a low SEP in childhood, and those exposed to a greater number of episodes of disadvantage over the life course before 1965, were subsequently more likely to die, to report functional limitation and to experience the greatest health-related burden. CONCLUSIONS: All-cause mortality, functional limitation and overall health-related burden in middle and late adulthood are shaped by socioeconomic conditions experienced during childhood and cumulative disadvantage over the life course. The contributions made to adult health by childhood SEP and accumulated disadvantage suggest that each constitutes a distinct socioeconomic influence that may require different policy responses and intervention options.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".