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Record W2971150732 · doi:10.1371/journal.pmed.1002864

Chronic physical conditions and risk for perinatal mental illness: A population-based retrospective cohort study

2019· article· en· W2971150732 on OpenAlexafffundabout
Hilary K. Brown, Andrew S. Wilton, Joel G. Ray, Cindy‐Lee Dennis, Astrid Guttmann, Simone N. Vigod

Bibliographic record

VenuePLoS Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsSt. Michael's HospitalInstitute for Clinical Evaluative SciencesHospital for Sick ChildrenPublic Health OntarioWomen's College HospitalUniversity of Toronto
FundersCanadian Institutes of Health ResearchGovernment of Ontario
KeywordsMedicinePopulationMental illnessPregnancyMental healthPsychiatryCohort studyRetrospective cohort studyCohortEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: One in 5 women experience mental illness in pregnancy or post partum. Universal preventive interventions have not lowered the incidence of perinatal mental illness, perhaps because those at highest risk were not targeted. Outside of pregnancy, chronic physical conditions are known to confer increased risk for mental illness. Our objective was to examine the association between chronic physical conditions and risk of perinatal mental illness. METHODS AND FINDINGS: We conducted a population-based retrospective cohort study using linked health administrative data sets in Ontario, Canada, in 2005 to 2015. We compared 77,385 women with chronic physical conditions to 780,619 women without such conditions, all of whom had a singleton live birth. Excluded were women with a mental illness diagnosis within 2 years before pregnancy. Chronic physical conditions were captured using the Agency for Healthcare Research and Quality Chronic Condition Indicator, applied to acute healthcare encounters in the 2 years before pregnancy. The outcome was perinatal mental illness, defined by a mental illness or addiction diagnosis arising between conception and 365 days post partum. The outcome was further defined by timing (prenatal or post partum) and specific diagnosis (psychotic disorder, mood or anxiety disorder, substance use disorder, self-harm, or other). Modified Poisson regression generated relative risks and 95% confidence intervals (CIs), adjusted for age, parity, rural residence, income quintile, and remote history of mental health care. Women in the cohort had an average age of 29.6 years (standard deviation 5.4), 44.2% were primiparous, 11.0% lived in a rural area, 40.1% were in the lowest 2 income quintiles, and 47.9% had a remote history of mental health care. More women with (20.4%) than without (15.6%) a chronic physical condition experienced perinatal mental illness-an adjusted relative risk (aRR) of 1.20 (95% CI 1.18-1.22, p < 0.0001). The aRRs were statistically significant for mental illness in pregnancy (1.12, 95% CI 1.10-1.15, p < 0.0001) and post partum (1.25, 95% CI 1.23-1.28, p < 0.0001). Psychotic disorders (aRR 1.50, 95% CI 1.36-1.65, p < 0.0001), mood or anxiety disorders (aRR 1.19, 95% CI 1.17-1.21, p < 0.0001), substance use disorders (aRR 1.47, 95% CI 1.34-1.62, p < 0.0001), and other mental illness (aRR 1.68, 95% CI 1.50-1.87, p < 0.0001) were more likely in women with than without chronic physical conditions, but not self-harm (aRR 1.14, 95% CI 0.87-1.48, p = 0.34). The study was limited by reliance on acute health care encounters to measure chronic physical conditions and the inability to capture undiagnosed mental health problems. CONCLUSIONS: Findings from this study suggest that women with a chronic physical condition predating pregnancy may be at heightened risk of developing mental illness in the perinatal period. These women may require targeted efforts to lower the severity of their condition and improve their coping strategies and supports in pregnancy and thereafter.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.013
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.310
Teacher spread0.298 · 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 teacher head, 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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Citations52
Published2019
Admission routes3
Has abstractyes

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