Chronic Medical Conditions and Peripartum Mental Illness: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The objective of this systematic review and meta-analysis was to examine the association between maternal chronic medical conditions (CMCs) and peripartum mental illness. MEDLINE, Embase, CINAHL, and PsycINFO were searched to September 2017. Data were extracted and quality was assessed using standardized instruments. We generated unadjusted and adjusted pooled odds ratios and 95% confidence intervals using DerSimonian and Laird random effects models. The review included 16 papers representing 12 studies and 1,626,260 women. CMCs overall were associated with peripartum mental illness overall (adjusted pooled odds ratios (aPOR) = 1.43, 95% confidence interval (CI): 1.25, 1.63). CMCs overall were associated with antepartum (aPOR = 1.41, 95% CI: 1.10, 1.81) and postpartum mental illness separately (aPOR = 1.44, 95% CI: 1.13, 1.85) and with peripartum depression (aPOR = 1.45, 95% CI: 1.25, 1.67) and anxiety separately (aPOR = 1.63, 95% CI: 1.35, 1.95). No studies examined bipolar or psychotic disorders. Diabetes (aPOR = 1.34, 95% CI: 1.07, 1.69), hypertension/heart disease (aPOR = 1.60, 95% CI: 1.05, 2.45), migraine (aPOR = 1.75, 95% CI: 1.20, 2.54), and other neurological disorders (aPOR = 1.45, 95% CI: 1.19, 1.77), but not asthma, were each associated with peripartum mental illness. Findings suggest that mental health resources should be integrated in medical settings where pregnant and postpartum women with CMCs are treated.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".