Postpartum mental illness during the COVID-19 pandemic: a population-based, repeated cross-sectional study
Bibliographic record
Abstract
BACKGROUND: It is unclear whether the clinical burden of postpartum mental illness has increased during the COVID-19 pandemic. We sought to compare physician visit rates for postpartum mental illness in Ontario, Canada, during the pandemic with rates expected based on prepandemic patterns. METHODS: In this population-based, repeated cross-sectional study using linked health administrative databases in Ontario, Canada, we used negative binomial regression to model expected visit rates per 1000 postpartum people for March-November 2020 based on prepandemic data (January 2016-February 2020). We compared observed visit rates to expected visit rates for each month of the pandemic period, generating absolute rate differences, incidence rate ratios (IRRs) and their 95% confidence intervals (CIs). The primary outcome was a visit to a primary care physician or a psychiatrist for any mental disorder. We stratified analyses by maternal sociodemographic characteristics. RESULTS: In March 2020, the visit rate was 43.5/1000, with a rate difference of 3.11/1000 (95% CI 1.25-4.89) and an IRR of 1.08 (95% CI 1.03-1.13) compared with the expected rate. In April, the rate difference (10.9/1000, 95% CI 9.14-12.6) and IRR (1.30, 95% CI 1.24-1.36) were higher; this level was generally sustained through November 2020. From April-November, we observed elevated visit rates across provider types and for diagnoses of anxiety, depressive and alcohol or substance use disorders. Observed increases from expected visit rates were greater for people 0-90 days postpartum compared with 91-365 days postpartum; increases were small among people living in low-income neighbourhoods. Public health units in the northern areas of the province did not see sustained elevations in visit rates after July; southern health units had elevated rates through to November. INTERPRETATION: Increased visits for mental health conditions among postpartum people during the first 9 months of the COVID-19 pandemic suggest an increased need for effective and accessible mental health care for this population as the pandemic progresses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".