Evaluating depression and anxiety throughout pregnancy and after birth: impact of the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic presents unique social, economic, and psychological challenges for individuals globally. Thus, women who are pregnant face unprecedented mental health challenges. OBJECTIVE: We sought to determine the impact of the pandemic on perinatal depression and anxiety in a longitudinal pregnancy cohort. We hypothesized increased depression and anxiety scores in women during pregnancy and after birth in the pandemic at all time points. STUDY DESIGN: Participants were enrolled in the Ontario Birth Study, a pregnancy cohort embedded in clinical care at Mount Sinai Hospital, Toronto, Canada. Perinatal depression and anxiety were assessed using the 2-Item Patient Health Questionnaire and 2-Item Generalized Anxiety Disorder Questionnaire in early pregnancy, whereas the Edinburgh Postnatal Depression Scale and 2-Item Generalized Anxiety Disorder Questionnaire were used in late pregnancy and after birth. Logistic regression models were created to examine the association of the pandemic with clinically elevated mental health scores in the prepandemic group vs pandemic group while adjusting for covariates. RESULTS: A total of 1159 survey responses from 649 participants between March 1, 2019, and February 28, 2021, were used to conduct this study. Participants were assessed in early pregnancy (n=416), in late pregnancy (n=373), and after birth (n=370). Responses received on or before February 29, 2020, were considered the "prepandemic" responses, whereas responses after the aforementioned date were considered the "pandemic" responses. Mean rank scores of depression and anxiety were significantly higher in the pandemic group (P=.02 and P=.003, respectively) in the postpartum period. There was no significant association between pandemic time and antenatal scores. However, postnatally, mothers were 2.6 times more likely to score ≥13 on the Edinburgh Postnatal Depression Scale during the pandemic than before the pandemic (95% confidence interval, 1.2-5.7; P=.02). Adjustment for ethnicity and income strengthened this association as the odds ratio increased to 3.3 (95% confidence interval, 1.4-8.0; P=.007). CONCLUSION: Pandemic-associated increases in depression and anxiety scores were confined to the postpartum period, highlighting a need for increased screening and interventions for perinatal mood and anxiety disorders postnatally as this pandemic continues.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".