Depression during pregnancy and associated factors among women in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Pregnancy is one of the most remarkable experiences in a woman's life. Prenatal depression, characterized by stress and worry associated with pregnancy, can reach severe levels. On a global scale, mental and addictive disorders affect more than one billion people, causing 19% of years lived with disability. It is estimated that 25-35% of pregnant women experience depressive symptoms, with 20% meeting the diagnostic criteria for major depression. METHODS: A systematic review and meta-analysis were conducted to examine depression during pregnancy in Ethiopia. The search was conducted from March 1-31, 2023. Data extraction used Microsoft Excel, and analysis was performed using STATA version 17. The New Castle-Ottawa Scale quality assessment tool was employed to evaluate the methodological quality of included studies. The Cochrane Q test and I2 statistics were used to assess heterogeneity. A weighted inverse variance random-effects model estimated the pooled level of antenatal depression (APD). Publication bias was detected using a funnel plot and Begg's and Egger's tests. RESULTS: Out of 350 studies searched, 18 were included in the analysis. The overall pooled prevalence of depression in Ethiopia was 27.85% (95% CI: 23.75-31.96). Harari region reported the highest prevalence (37.44%), while Amhara region had the lowest (23.10%). Factors significantly associated with depression included unplanned pregnancies, low social support, low income, previous history of depression, intimate partner violence, and history of abortion. CONCLUSION: This systematic review and meta-analysis demonstrate that approximately one-quarter of pregnant women in Ethiopia experience depression during pregnancy. Unplanned pregnancy, low social support, low income, previous history of depression, history of abortion, and intimate partner violence are determinants of depression. To address this high prevalence, the Ethiopian government and stakeholders should develop policies that incorporate counseling during pregnancy follow-ups. Improving the quality of life for pregnant women is crucial for the well-being of families, communities, and the nation as a whole.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".