Variation in relationships between maternal age at first birth and pregnancy outcomes by maternal race: a population-based cohort study in the United States
Bibliographic record
Abstract
OBJECTIVE: To estimate absolute risks of obstetric outcomes in the USA according to maternal age at first birth from age 15 to 45 separately by maternal race. DESIGN AND SETTING: Population-based cohort study. SETTING: Vital statistics Birth Cohort-Linked Birth- Infant Death Data Files and Fetal Death Data Files in the USA. PARTICIPANTS: 16 514 849 births to nulliparous women from 2004 to 2013. OUTCOME MEASURES: We estimated absolute risks of obstetric outcomes (multiple gestations, caesarean delivery, early and late preterm birth, small for gestational age birth, stillbirth, neonatal mortality, postneonatal infant mortality) at each year of maternal age from 15 to 45 years using logistic regression in the overall population and stratified by maternal race. We modelled maternal age flexibly to allow curvilinear shapes and plotted risk curves for each outcome. RESULTS: In the overall population, multiple gestations, caesarean delivery and stillbirth risks were lowest at young maternal ages with linear or quadratic increases with age. Curves for preterm birth, small for gestational age, neonatal mortality and postneonatal mortality were u or j shaped, with nadirs between 20 and 29 years, and elevated risks at both younger and older maternal ages. In race-stratified analyses, the shapes of the curves were generally similar across races. Risks increased for all women for all outcomes after age 30. However, increased risks at young maternal ages were most pronounced for white and Asian/Pacific Islander women, for whom young childbearing was least common. Conversely, risks at older ages were more pronounced for Black and American Indian/Alaska Native women, for whom delayed childbearing was least common. CONCLUSION: Our findings confirm risks associated with first births to women younger than 20 and older than 30 years, provide easily interpretable risk curves and illuminate variability in these relationships across categories of maternal race in the USA.
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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 | 0.001 | 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".