Depression and anxiety among pregnant women during COVID 19 pandemic in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Coronavirus Disease-19 pandemic had an adverse impact on the mental health of the public worldwide, but the problem is worst among pregnant women due to social distancing policies and mandatory lockdown, including prenatal care services. As a result, the prevalence of depression and anxiety could increase during the pandemic, particularly among pregnant women. Thus, the purpose of this review is to determine the magnitude of depression and anxiety and contributing factors among pregnant women during the pandemic in Ethiopia. Methods: Web of Science, Since Direct, PubMed, Google Scholar, and African Journals Online were the electronic databases searched, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines were followed in this review. The Newcastle-Ottawa Critical Appraisal Checklist was used to assess the quality of the included studies. A predefined data extraction sheet developed in Excel was used to extract the data. The pooled prevalence of anxiety and depression was determined by a random effect model meta-analysis. Results: 4,269 and 1,672 pregnant women were involved in depression and anxiety studies, respectively. The pooled prevalence of depression and anxiety among pregnant women during the COVID-19 pandemic in Ethiopia was 24.7% (95% CI: 18.52-30.87) and 35.19% (95% CI: 26.83-43.55), respectively. Single marital status (AOR = 2.22, 95% CI: 1.07-3.37), poor social support (AOR = 2.7, 95% CI: 1.06-4.35), unplanned pregnancies (AOR = 2.17, 95% CI: 1.34-3.0), and unsatisfied marital status (AOR = 2.16, 95% CI: 1.17-3.14) were risk factors for depression. Violence against intimate partners (AOR = 2.87, 95% CI: 1.97-3.77) and poor social support (AOR = 1.98, 95% CI: 1.24-2.71) were risk factors for anxiety. Conclusion: One-fourth and nearly one-third of pregnant women had depression and anxiety, respectively, during COVID-19 pandemic in Ethiopia. Single or unsatisfied marital status and unplanned pregnancies were risk factors for depression. Poor social support was significantly associated with depression and anxiety. Pregnant women who experienced violence against intimate partners had higher anxiety. After COVID-19 pandemic, mental health interventions are essential for reducing depression and anxiety. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=527148, PROSPERO (CRD42024527148).
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".