Age-period-cohort effects in pre-existing and pregnancy-associated diseases amongst primiparous women
Bibliographic record
Abstract
BACKGROUND: The average age at first birth is steadily increasing in developed countries; however, demographic shifts in maternal age at childbearing have not occurred in isolation. While temporal increases in adverse pregnancy outcomes are typically attributed to increases in maternal age, little is known about how maternal health status has changed across maternal age, period of delivery, and birth cohort. METHODS: Natality files were used to identify primiparous women delivering liveborn, singleton infants in the USA in 1989, 1994, 1999, 2004, 2009, and 2014 (n = 6,857,185). Age-period-cohort models using the intrinsic estimator adjusted for temporal trends in smoking and gestational weight gain were used to quantify temporal changes in the rates of pre-existing (chronic hypertension, pre-existing diabetes) and pregnancy-associated (pregnancy-associated hypertension, gestational diabetes, eclampsia) diseases. Log-linear models were used to model the impact of temporal changes on preterm birth, small, and large for gestational age (SGA/LGA) births. RESULTS: Significant period effects resulted in temporal increases in the rate of chronic hypertension, pregnancy-associated hypertension, and gestational diabetes, and a significant decrease in the rate of eclampsia. These observed period effects were associated with a 10.6% increase in the rate of SGA and a 7.1% decrease in LGA. Had the rate of pre-existing and pregnancy-associated diseases remained static over this time period, the rate of preterm birth would have increased by 5.9%, but instead only increased by 4.4%. CONCLUSIONS: Independent of changes in the incidence of pre-existing and pregnancy-associated diseases as women age, the obstetric population is becoming less healthy over time. This is important, as these changes have a direct negative impact on short-term obstetric outcomes and women's long-term 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".