Postpartum psychiatric hospital admissions: A population-based study from British Columbia, Canada
Bibliographic record
Abstract
The risk of psychiatric hospitalization increases in the year postpartum, and rates have been rising. However, data describing these admissions is limited. In this retrospective, population-based study of all deliveries between 2015-2019 in British Columbia (BC), Canada, we linked BC perinatal data registry with other health services data, including all hospital admissions. Rates, characteristics, and risk factors for postpartum psychiatric hospital admissions were analyzed using descriptive statistics and logistic regression modeling. Following 189,530 deliveries, there were 812 postpartum psychiatric admissions (∼4/1000 deliveries). The most common indications for postpartum admission were depression (n=233[28.7%]) and substance-related disorders (n=234[28.8%]). These hospitalizations were short (median=3 days, IQR(Q1-Q3)=1-8), and relatively late (median=5.5 months postpartum, IQR(Q1-Q3)=68.5-251 days). Hospitalizations for psychosis and bipolar disorder occurred much later than expected (psychosis: median=111 days postpartum (IQR=32-213); bipolar disorder: median=118 days (IQR=42-230)). The risk factors most associated with these admissions were younger age, especially age<20 (aOR=5.01, 95% CI:3.60-6.98), receiving treatment for schizophrenia during pregnancy (aOR=4.21, 95% CI:2.42-7.34), and a psychiatric diagnosis during pregnancy (aOR=3.58, 95% CI:2.30-5.58). These results show that those at high risk of severe postpartum psychiatric illness were young, took antidepressants during pregnancy, and had a recent psychiatric illness. This knowledge could support early intervention and avoid the need for hospitalization. This research should be replicated and expanded to explore gaps in the healthcare system for severe postpartum psychiatric illness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".