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Record W4385659917 · doi:10.1192/j.eurpsy.2023.741

The impact of prenatal maternal mental health during the COVID-19 pandemic on birth outcomes: A cohort study within the CONCEPTION cohort

2023· article· en· W4385659917 on OpenAlexaffabout
Anick Bérard, Vanina Tchuente, Nicole Pagès, Jessica Gorgui, Tasnim Fareh, Susan King, Guillaume Elgbeili

Bibliographic record

VenueEuropean Psychiatry · 2023
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsMcGill UniversityUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineAnxietyDepression (economics)Mental healthCohort studyConfoundingGestational agePregnancyCohortPandemicLow birth weightObstetricsBirth weightRelative riskPsychiatryCoronavirus disease 2019 (COVID-19)Confidence intervalInternal medicineDisease

Abstract

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Introduction External natural events, such as the COVID-19 pandemic, can contribute to increased stress, depression and anxiety in pregnant persons. Thus far, studies on the impact of maternal mental health during the pandemic on perinatal outcomes have been conflicting. Objectives Assess the impact of prenatal mental health during the COVID-19 pandemic on preterm birth (PTB) and low birthweight (LBW). Methods Pregnant individuals, >18 years were recruited in Canada, their data were collected through a web-based questionnaire. Our analysis includes data on individuals recruited between 06/2020 and 08/2021, who completed questionnaires at baseline and 2-month post-partum. Data on maternal sociodemographic, comorbidities, medication, mental health measures (Edinburgh Perinatal Depression Scale, General Anxiety Disorder-7, stress), hardship (CONCEPTION study Assessment of Stress from COVID-19 –150 points), gestational age at delivery and birth weight were self-reported. PTB defined as delivery before 37 weeks of gestation. LBW defined as birth weight less than 2,500 grams. Results A total of 1,265 and 1,233 participants were included in the analyses of PTB and LBW, respectively. After adjusting for potential confounders, we found no differences between prenatal mental health and PTB ([depression [adjusted RR [aRR] 1.01, CI 95% 0.91 to 1.11], anxiety [aRR 1.04, CI 95% 0.93 to 1.17], stress [aRR 0.88, CI 95% 0.71 to 1.10], hardship [aRR 1.00, CI 95% 0.96 to 1.04]). However, we found that the risk of PTB was increased with ethnicity/race (aRR 3.85, CI 95% 1.35 to 11.00), obstetrician/gynecologist follow-up (aRR 2.77, CI 95% 1.12 to 6.83). We didn’t find any significant association between prenatal mental health and LBW. However, annual household income, previous delivery were associated with a decreased risk of LBW (aRR 0.15, CI 95% 0.05 to 0.49; aRR 0.39, CI 95% 0.20 to 0.77, respectively). Conclusions Conclusion: No association was found between prenatal mental health 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 in order to detect early any long-term health problems. Disclosure of Interest None Declared

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.030
GPT teacher head0.362
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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