A Systematic Review of the Prevalence of Mental Health Disorders in Pregnant Women during the COVID-19 Pandemic
Bibliographic record
Abstract
Background There is not enough evidence to estimate the prevalence of depression and anxiety in pregnant women during the COVID-19 outbreak. This study aimed to investigate the prevalence of mental health disorders among pregnant women during the COVID-19 pandemic. Materials and Methods: In the present systematic review, a search process was conducted to screen the databases of ProQuest, Scopus, EMBASE, Web of Science, and MEDLINE for the relevant articles published between 2019 and 2020. The quality of the articles was assessed by the STROBE checklist. Results: From the relevant studies, 15 were selected for review. The results showed the prevalence of anxiety was between 3.8 to 17.5% in Asian countries, with the lowest in Iran (3.8%) and the highest in Sri Lanka (17.5 %). The prevalence of anxiety was from 23.9 to 72% in Western countries, with the lowest in the USA (23%) and the highest in Canada (72%). In two of the studies in China, the prevalence of anxiety was from 3.09 to 29.6% and of depression from 5.2 to 40%. The incidence rate of self-harm thoughts as a result of the epidemic was significantly high (RR=2.85, 95% CI= 1.70, 8.85, P=0.005). Conclusion The prevalence of anxiety was from 3.8 to 17.5% in Asian countries and from 23.9 to 72% in Western countries. The prevalence of depression was from 5.2 to 40%. Moderate levels of anxiety and depression were reported in Western countries compared with Asian countries. Depression and anxiety should be regularly screened in obstetrics and gynecology wards following the current epidemic to ensure optimal mental health during pregnancy and infancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".