Household income and maternal education in early childhood and activity-limiting chronic health conditions in late childhood: findings from birth cohort studies from six countries
Bibliographic record
Abstract
BACKGROUND: We examined absolute and relative relationships between household income and maternal education during early childhood (<5 years) with activity-limiting chronic health conditions (ALCHC) during later childhood in six longitudinal, prospective cohorts from high-income countries (UK, Australia, Canada, Sweden, Netherlands, USA). METHODS: Relative inequality (risk ratios, RR) and absolute inequality (Slope Index of Inequality) were estimated for ALCHC during later childhood by maternal education categories and household income quintiles in early childhood. Estimates were adjusted for mother ethnicity, maternal age at birth, child sex and multiple births, and were pooled using meta-regression. RESULTS: Pooled estimates, with over 42 000 children, demonstrated social gradients in ALCHC for high maternal education versus low (RR 1.54, 95% CI 1.28 to 1.85) and middle education (RR 1.24, 95% CI 1.11 to 1.38); as well as for high household income versus lowest (RR 1.90, 95% CI 1.66 to 2.18) and middle quintiles (RR 1.34, 95% CI 1.17 to 1.54). Absolute inequality showed decreasing ALCHC in all cohorts from low to high education (range: -2.85% Sweden, -13.36% Canada) and income (range: -1.8% Sweden, -19.35% Netherlands). CONCLUSION: We found graded relative risk of ALCHC during later childhood by maternal education and household income during early childhood in all cohorts. Absolute differences in ALCHC were consistently observed between the highest and lowest maternal education and household income levels across cohort populations. Our results support a potential role for generous, universal financial and childcare policies for families during early childhood in reducing the prevalence of activity limiting chronic conditions in later childhood.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".