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Prevalence of Prenatal Depression Symptoms Among 2 Generations of Pregnant Mothers

2018· article· en· W2884375873 on OpenAlexfundno aff
Rebecca M. Pearson, Rebecca Carnegie, Callum Cree, Claire Rollings, Louise Rena-Jones, Jonathan Evans, Alan Stein, Kate Tilling, Melanie Lewcock, Debbie A. Lawlor

Bibliographic record

VenueJAMA Network Open · 2018
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
FundersNational Institute of Child Health and Human DevelopmentMedical Research CouncilGrand Challenges CanadaUniversity of BristolNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchNational Institutes of HealthBritish AcademyBritish Heart FoundationWellcome TrustNational Institute for Health and Care ResearchEconomic and Social Research CouncilEuropean CommissionNational Institute of Diabetes and Digestive and Kidney DiseasesBill and Melinda Gates Foundation
KeywordsEdinburgh Postnatal Depression ScaleOffspringPregnancyDepression (economics)MedicineMoodLongitudinal studyCohortDemographyCohort studyPediatricsObstetricsPsychiatryDepressive symptomsAnxietyInternal medicine

Abstract

fetched live from OpenAlex

Importance: Depression during pregnancy (prenatal depression) is common and has important consequences for mother and child. Evidence suggests an increasing prevalence of depression, especially in young women. It is unknown whether this is reflected in an increasing prevalence of prenatal depression. Objective: To compare the prevalence of depression during pregnancy in today's young mothers with their mothers' generation. Design, Setting, and Participants: In a longitudinal cohort study, we compared prenatal depressive symptoms in 2 generations of women who participated in the Avon Longitudinal Study of Parents and Children. Participants were the original mothers (recruited when they were pregnant) and their female offspring, or female partners of male offspring, who became pregnant. Both groups were limited to the same age range (19-24 years). The first generation of pregnancies occurred in 1990 to 1992 (n = 2390) and the second in 2012 to 2016 (n = 180). In both generations, women were born in the same geographical area (southwest England). Main Outcomes and Measures: Depressed mood measured prenatally using the Edinburgh Postnatal Depression Scale in self-reported surveys in both generations. A score of 13 or greater on a scale of 0 to 30 indicated depressed mood. Results: Of 2390 pregnant women in the first generation who were included in analysis (mean [SD] age, 22.1 [2.5] years), 408 (17%) had high depressive symptom scores (≥13). Of 180 pregnant women in the second generation who were included in the analysis (mean [SD] age, 22.8 [1.3] years), 45 (25%) had high depressive symptom scores. Having high depressive symptom scores was more common in the second generation of young pregnant women than in their mothers' generation (relative risk, 1.51; 95% CI, 1.15-1.97), with imputation for missing confounding variable data and adjustment for age, parity, education, smoking, and body mass index not substantially changing this difference. Results were essentially the same when analyses were restricted to the 66 mother-offspring pairs. Maternal prenatal depression was associated with daughters' prenatal depression (relative risk, 3.33; 95% CI, 1.65-6.67). Conclusions and Relevance: In this unique study of 2 generations of women who answered identical questionnaires in pregnancy, evidence was found that depressed mood may be higher in young pregnant women today than in their mothers' generation. Because of the multiple and diverse consequences of prenatal depression, an increase in prevalence has important implications for families, health care professionals, and society.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.305
Teacher spread0.286 · 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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Citations103
Published2018
Admission routes1
Has abstractyes

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