Association of maternal age with child health: A Japanese longitudinal study
Bibliographic record
Abstract
Average maternal age at birth has been rising steadily in Western and some Asian countries. Older maternal age has been associated with adverse pregnancy and birth outcomes; however, studies on the relationship between maternal age and young children's health remain scarce. Therefore, we sought to investigate the association of maternal age with child health outcomes in the Japanese population. We analyzed data from two birth cohorts of the nationwide Japanese Longitudinal Survey of Babies in 21st Century (n2001 = 47,715 and n2010 = 38,554). We estimated risks of unintentional injuries and hospital admissions at 18 and 66 months according to maternal age, controlling for the following potential confounders: parental education; maternal parity, smoking status, and employment status; household income; paternal age, and sex of the child. We also included the following as potential mediators: preterm births and birthweight. We observed a decreasing trend in the risks of children's unintentional injuries and hospital admissions at 18 months according to maternal age in both cohorts. In the 2001 cohort, compared to mothers <25 years, odds ratios of hospital admission at 18 months were 0.97 [95% CI: 0.86, 1.09], 0.92 [0.81, 1.05], 0.76 [0.65, 0.90], and 0.71 [0.51, 0.98] for mothers aged 25.0-29.9, 30.0-34.9, 35.0-39.9, and >40.0 years, respectively, controlling for confounders. Our findings were in line with previous findings from population-based studies conducted in the United Kingdom and Canada suggesting that older maternal age may be beneficial for early child health.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".