Impact of evolving fertility policies on maternal and neonatal outcomes in southeastern China: a 10-year population-based cohort study
Bibliographic record
Abstract
BACKGROUND: After over three decades of the one-child policy (OCP), China introduced the partial two-child policy (PTCP) in 2013 and the universal two-child policy (UTCP) in 2015. However, their potential impacts on maternal and neonatal health remain unclear. This study aimed to examine temporal changes in maternal characteristics and neonatal outcomes across different policy periods. METHODS: We used data from a population-based pregnancy registry in Ximen, China. Maternal characteristics and neonatal outcomes were compared across the three periods: OCP (2012-2014), PTCP (2014-2016), and UTCP (2016-2021). Joinpoint regression and interrupted time series (ITS) model were applied to evaluate temporal trends and quantify the effect of policy implementation on trends over time. RESULTS: Among 491 895 pregnancies, the proportion of advanced maternal age and multiparity rose significantly after policy shifts. Maternal obesity, hypertensive disorders of pregnancy, and gestational diabetes showed steady increases. Monthly births peaked in 2016, then declined below the pre-PTCP baseline level by 2020. Compared to OCP, the prevalence of birth defects (BDs) increased by 62% during PTCP and 204% during UTCP. Preterm birth and low Apgar scores also rose. ITS indicated a significant post-UTCP acceleration in BDs (β3 = 2.57), largely driven by circulatory system BDs, with maternal age acting as a partial mediator. CONCLUSION: China's TCP implementation was associated with notable shifts in maternal risk profiles and increased adverse neonatal outcomes, underscoring the need for continuous maternal-child health monitoring during fertility policy transitions.
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".