The impact of prenatal maternal mental health during the COVID-19 pandemic on birth outcomes: two nested case-control studies within the CONCEPTION cohort
Bibliographic record
Abstract
OBJECTIVE: Assess the association between prenatal mental health during the COVID-19 pandemic and preterm birth (PTB; delivery < 37 weeks gestation) and low birth weight (LBW; < 2500 g). METHODS: Pregnant individuals, > 18 years, were recruited in Canada and provided data through a web-based questionnaire. We analyzed data on persons recruited between 06/2020 and 08/2021 who completed questionnaires while pregnant and 2 months post-partum. Data on maternal sociodemographics, comorbidities, medication use, mental health (Edinburgh Postnatal Depression Scale, General Anxiety Disorder-7, stress), pandemic hardship (CONCEPTION-Assessment of Stress from COVID-19), and on gestational age at delivery and birth weight were self-reported. Crude and adjusted odds ratios (aOR) with 95% confidence interval (95%CI) were calculated to quantify the association between PTB/LBW and maternal mental health. RESULTS: A total of 1265 and 1233 participants were included in the analyses of PTB and LBW, respectively. No associations were observed between PTB and prenatal mental health (depression [aOR 1.01, 95%CI 0.91-1.11], anxiety [aOR 1.04, 95%CI 0.93-1.17], stress [aOR 0.88, 95%CI 0.71-1.10], or hardship [aOR 1.00, 95%CI 0.96-1.04]) after adjusting for potential confounders. The risk of PTB was increased with non-white ethnicity/race (aOR 3.85, 95%CI 1.35-11.00), consistent with the literature. Similar findings were observed for LBW (depression [aOR 1.03, 95%CI 0.96-1.13], anxiety [aOR 1.05, 95%CI 0.95-1.17], COVID stress [aOR 0.92, 95%CI 0.77-1.09], or overall hardship [aOR 0.97, 95%CI 0.94-1.01]). CONCLUSION: No association was found between prenatal mental health nor hardship during the COVID-19 pandemic and the risk of PTB or LBW. However, it is imperative to continue the follow-up of mothers and their offspring to detect long-term health problems early.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".