Prevalence of self-reported mental disorders in pregnancy and associations with adverse neonatal outcomes: a population-based cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Mental disorders in pregnancy are common causes of morbidity and mortality with associated risks of adverse neonatal outcomes. Our aims were to evaluate the prevalence of self-reported mental disorders in women presenting to maternity services and to determine the association between history of self-reported maternal mental disorder and adverse neonatal outcomes. METHODS: Data on all singleton pregnancies known to maternity services in Northern Ireland over the period 2010 to 2015 were extracted from the Northern Ireland Maternity System (NIMATS), including frequency data for number of pregnancies where the mother reported a history of mental disorder. Odds ratios were derived from logistic regression analyses to determine the associations between self-reported maternal mental disorder and preterm birth, low infant birth weight and APGAR scores. RESULTS: In total, 140,569 singleton pregnancies were registered using NIMATS over this period. In 18.9% of these pregnancies, the mother reported a history of at least one mental disorder. After adjustment for potential confounding factors, significant associations were demonstrated between self-reported maternal mental disorder and preterm birth (odds ratio [OR] 1.31, 95% confidence interval [CI] 1.25-1.37), low infant birth weight (OR 1.29, 95% CI 1.21-1.38) and APGAR score < 7 at 1 min (OR 1.14, 95% CI 1.10-1.19) and 5 min (OR 1.23, 95% CI 1.12 to 1.34). CONCLUSIONS: These findings emphasise the critical importance of routine enquiry regarding psychiatric history when women present to maternity services and the impact of maternal mental illnesses upon outcomes for their infants.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".