Epidemiology of antenatal depression in Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Antenatal depression is a serious problem worldwide that has devastating consequences not only for the mother but also for the child and family. The pooled evidence regarding the prevalence and associated factors of antenatal depression is rare in Africa. Hence this review aimed to investigate the prevalence and associated factors of antenatal depression in Africa. Methods We searched CINHAL, MEDLINE, PsycINFO, Psychiatry online, PubMed, SCOPES, and Emcare databases for English written observational studies conducted in Africa from 2007 to 2018.Quality of studies was assessed using the Newcastle Ottawa Scale (NOS), and studies with good quality were included in the final review. Heterogeneity across studies was assessed using the I 2 and Higgins test. Publication bias was checked using Funnel plot symmetry, and Egger’s regression test and adjustment was made by using Duval and Tweedie’s Trim and Fill analysis. A random effect Meta-analysis was employed to determine the pooled estimates with 95% confidence interval (CI). Stata 14 was used for analysis. The review protocol has been registered in PROSPERO number CRD42018106717. Result Of the 175 studies identified, 28 studies with an overall sample size of 17,938 were included. According to the random effect model following trim and fill analysis, the pooled prevalence of antenatal depression in Africa was 26.3% (95%CI: 22.2, 30.4%). Economic difficulties [POR = 1.87;95%CI:1.25,2.78, I 2 = 88.1%], unfavorable marital condition [POR = 4.17;95% CI:1.75, 9.94, I 2 = 81.2%], poor support from relatives [POR = 1.36;95% CI:1.18, 1.56, I 2 = 78.0%], bad obstetric history [POR = 2.30;95% CI:1.81, 2.92), I 2 = 81.7%], and history of mental health problem [POR = 2.97; 95% CI:1.74, 5.06, I 2 = 92.0%]were the factors associated with antenatal depression. Conclusion The prevalence of antenatal depression is high in Africa, which showed that one in four pregnant women had depression. Pregnant mothers who had economic difficulties, bad obstetric history, poor support from relatives, previous mental health problems, and unfavorable marital conditions were at higher risk of antenatal depression. Therefore these factors should be considered while designing mental health care services for pregnant mothers.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".