Perinatal Complications as a Mediator of the Association Between Chronic Disease and Postpartum Mental Illness
Bibliographic record
Abstract
Background: Chronic disease is associated with increased risk of postpartum mental illness, but the mechanisms underlying this association are unclear. Our aim was to explore the mediating role of perinatal complications in the association between chronic disease and postpartum mental illness. Materials and Methods: This was a population-based retrospective cohort study of all women in Ontario, Canada, from 2005 to 2015 with a singleton live birth and no recent history of mental illness during or 2 years before pregnancy. The outcome was mental illness diagnosis between delivery and 365 days postpartum, with perinatal complications, including pregnancy, delivery, and neonatal complications. Modified Poisson regression models were used to examine the association between chronic disease and perinatal mental illness, with generalized estimating equations for the calculation of total, direct, and indirect effects. All models were adjusted for sociodemographic characteristics and remote history of mental health care. Results: Of the 792,972 women, 21.1% had a chronic disease. Chronic disease was associated with an increased risk of postpartum mental illness (adjusted relative risk [aRR] 1.15 [95% confidence interval, CI 1.14–1.16]). There was no evidence of an indirect effect of chronic disease on postpartum mental illness via perinatal complications (aRR 1.003, 95% CI 1.002–1.003). Perinatal complications explained only 1.5% of the association between chronic disease and postpartum mental illness. Results were consistent by type of perinatal complication and chronic disease diagnosis. Conclusion: We observed no clinically meaningful mediating effect of perinatal complications in the association between chronic disease and postpartum mental illness. Future research should investigate alternative mechanisms explaining this association.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".