The impact of prenatal mental health on birth outcomes before and during the COVID-19 pandemic in Anhui, China
Bibliographic record
Abstract
Adverse birth outcomes remain challenging public health problems in China. Increasing evidence indicated that prenatal depression and anxiety are associated with adverse birth outcomes, highlighting the importance and severity of prenatal depression and anxiety in China. The COVID-19 pandemic is likely to further exacerbate prenatal mental health problems and increase the risk of adverse birth outcomes. The aim of this study is to assess and compare the impacts of prenatal mental health issues on birth outcomes before and during the COVID-19 pandemic in Ma'anshan, Anhui, China. Participants in this study were women who visited local maternal and child health hospitals in Ma'anshan, Anhui, China. Two independent sets of individual maternal data (npre-pamdemic = 1148; npandemic = 2249) were collected. Prenatal depression and anxiety were measured online using the Edinburgh Postnatal Depression Scale (EPDS) and the General Anxiety Disorder-7 (GAD-7). Adverse birth outcomes were determined using hospital-recorded infant birth weight and gestational age at delivery. In this study, we found that the pandemic cohort had lower mean EPDS and GAD-7 scores than the pre-pandemic cohort. The prevalence of prenatal depression (14.5%) and anxiety (26.7%) among the pandemic cohort were lower than the pre-pandemic cohort (18.6% and 36.3%). No significant difference was found in the prevalence of adverse birth outcomes comparing the two cohorts. Prenatal depression was associated with small gestational age only in the pandemic cohort (OR = 1.09, 95% CI 1.00-1.19, p = 0.042). Overall, this study highlighted an association between prenatal depression and small for gestational age in Anhui, China. Addressing prenatal depression may thus be key in improving birth outcomes. Future studies could focus on potential causal relationships.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".