Association between prenatal exposure to ambient air pollutants and postpartum depressive symptoms: A multi-city cohort study
Bibliographic record
Abstract
Women are vulnerable to depression during postpartum period. While several studies have shown associations between ambient air pollution exposure and depression in general population, there was few studies focused on the effect of various air pollutants on postpartum depression (PPD). This study is designed to explore the association between prenatal exposure to air pollutants and PPD, and to reveal the potential vulnerable exposure time point. The study enrolled 10,209 pregnant women who delivered between October 2019 and February 2021 in 5 participating hospitals from 3 cities in China. Edinburgh Postnatal Depression Scale (EPDS) was administered at 6 weeks postpartum to identify PPD symptoms. Associations between PPD symptoms and exposure levels in PM2.5, PM10, SO2, CO, NO2, and O3 averaged over the whole pregnancy and each trimester were estimated using logistic regression models after adjusting for potential confounding factors. Distributed lag models (DLMs) were used to determine the relevant associations in each gestational week. The risk for developing PPD symptoms was significant following a 10 μg/m3 increase in PM10 (aOR = 1.47, 95%CI:1.36–1.59), NO2 (aOR = 1.63, 95%CI:1.44–1.85), and 0.1 mg/m3 increase in CO (aOR = 2.31, 95%CI: 1.99–2.69) during the whole pregnancy. Similar results were also found in exposure during each trimester of pregnancy. Besides, SO2 exposure during the second trimester was a major risk factor for developing PPD symptoms (aOR = 1.10, 95%CI:1.03–1.18). Consistent effects were also observed in DLMs, except for PM2.5 and O3, which showed no significant sensitive windows throughout pregnancy period. Exposure to PM10, CO, NO2, and SO2 in pregnancy is associated with increased risks of developing depression at 6 weeks postpartum. Our findings reveal the importance of air pollution control for preventing maternal mental health disorders among the public.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".