Trajectories of depressive and anxiety symptoms from pregnancy to 24-months postpartum during the COVID-19 pandemic
Bibliographic record
Abstract
Background: As many as 1 in 5 pregnant individuals will develop symptoms consistent with perinatal mood and anxiety disorders (PMADs), making PMADs one of the most common obstetrical complications. The COVID-19 pandemic is a unique stressor which has increased burden on the mental health of pregnant and postpartum individuals. Most studies examining the impact of the pandemic on perinatal mental health have been cross-sectional, while existing longitudinal studies are limited and don’t span beyond 15-months postpartum or have a representative pan-Canadian sample. Examining symptom trajectories would allow for a fuller understanding of the course of depression and anxiety symptoms and tailoring of screening and referral guidelines. Despite being initially high at the beginning of the pandemic, it is unclear if anxiety and depression symptoms have persisted or diminished as the pandemic progressed, and which factors may contribute to trajectories of long-term adverse mental health outcomes. Methods: The current study recruited 9463 pregnant people between 8-35-weeks of pregnancy from the pan-Canadian Pregnancy during the Pandemic (PdP) Cohort. Each participant completed a baseline survey between April 2020 and April 2021, and completed mental health measures at 6-months, 12-months, and 24-months postpartum. Using latent class mixed models, group-based trajectory analysis was used to determine trajectories of anxiety and depression symptoms. Model fit was evaluated using Bayesian and Akaike model criterion. Multinomial logistic regression analysis was conducted to compare trajectory characteristics across groups. Results: A three-class depression symptomology model (moderate-stable 60.9%; elevated-decreasing 26.7%; low-stable 12.4%) and a three-class anxiety model (elevated-increasing 20.2%; elevated-decreasing 65.85%; low-stable 14%) was identified and considered the best fitting model. Common risk factors of depression and anxiety across groups with elevated symptoms include identifying as non-White (odds ratios [ORs] varied from 1.22 to 1.50), low household income (odds ratios [ORs] varied from 1.67 to 2.34), being single (odds ratios [ORs] varied from 1.64 to 2.29), having a history of pre-pregnancy anxiety and/or depression (odds ratios [ORs] varied from 2.53 to 3.06), poor sleep quality (odds ratios [ORs] varied from 1.07 to 1.13), unplanned pregnancy (odds ratios [ORs] varied from 1.40 to 1.82), and elevated baseline anxiety and depression at intake (odds ratios [ORs] varied from 1.27 to 9.84). Common COVID-19 pandemic-related risk factors of depression and anxiety across groups with elevated symptoms include fear their life or their unborn baby’s life was in danger (odds ratios [ORs] varied from 1.01 to 1.02), changes to birth plan due to the pandemic (odds ratios [ORs] varied from 1.82 to 1.88), decreased income due to the pandemic (odds ratios [ORs] varied from 1.44 to 1.58) and feeling more alone than usual (odds ratios [ORs] varied from 1.02 to 1.04).Conclusion: The current study is the first to describe mental health trajectories in a large pan-Canadian sample that began at the beginning of the COVID-19 pandemic. Findings indicate clinically elevated levels of anxiety and depression from pregnancy to the postpartum period, that declined for some groups or persisted throughout the perinatal period. The COVID-19 pandemic was a unique stressor, with consequences ranging far beyond pregnancy. Understanding the depressive and anxiety trajectories of pregnant and postpartum individuals in the context of the pandemic may help to identify individuals who are at greater risk for developing PMADs. These findings could aid in the development of targeted screening and intervention strategies to prevent and mitigate the detrimental lasting impacts perinatal anxiety and depression for birthing individuals and their children
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".