Trajectories of Socioeconomic Status Across Children's Lifetime Predict Health
Bibliographic record
Abstract
OBJECTIVE: Socioeconomic status is one of the most robust social factors associated with health, but the dynamics of how socioeconomic status over time affects children's health remains unclear. This study tested how various models of childhood socioeconomic status (accumulation, change, variability, and critical periods of family income) would predict health outcomes at a final time point in childhood. METHODS: This was a prospective, longitudinal study of 6306 children who were aged 10 to 11 years and whose families were interviewed every other year from birth onward. The sample came from the US National Longitudinal Survey of Youth-Children. In the same data set, a replication sample of 4305 14- to 15-year-old children was also examined. Primary outcomes included parent report of asthma and conditions that limited activity and school and required physician treatment. RESULTS: Lower cumulative family income was associated with higher odds for having a condition that limited childhood activities, as well as a condition that required treatment by a physician at ages 10 to 11. Cumulative family income was a stronger predictor than change in income or variability in income. Lower family income early in life (ages 0-5 years) was associated with higher odds for having a condition that limited activities and a condition that required treatment by a physician at ages 10 to 11, independent of current socioeconomic status. Findings were replicated in the 14- to 15-year-old sample. CONCLUSIONS: These findings suggest that the accumulation of socioeconomic status in terms of family income across childhood is more important than social mobility or variability in socioeconomic status, although there may be certain periods of time (early life) that have stronger effects on health. These findings suggest the importance of childhood interventions for reducing health disparities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".