Prevalence and risk factors of perinatal depression among mothers and fathers in Pakistan: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Perinatal mental health issues affect approximately 10% of women in high-income countries and 30% in low- or middle-income countries. This review aims to determine the prevalence of perinatal depression among mothers and fathers in Pakistan and identify associated risk factors. Methods: We conducted a systematic review and meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We included quantitative studies on the prevalence or incidence of maternal and paternal perinatal depression, including antenatal or postnatal depression in Pakistan, with or without associated risk factors. We performed an electronic search, dual-title/abstract and full-text screening, and data extraction. Analysis was conducted on Revman and JBI SUMARI software. The quality of the included studies was assessed with the NHLBI tool. This review updated a previously published review that included 43 studies, with the last search date of 31st May 2019, now extended to literature published up to June 30, 2023. Results: Consistent with the previous review, our analysis of 61 studies indicated a pooled prevalence of 37% (95% confidence interval (CI): 30.6-43.6) for maternal antenatal depression. Postnatal depression at different time points, revealed rates of 34.2% (95% CI: 22.7-46.7), 40.9% (95% CI: 0-97.4), and 43.1% (95% CI: 24.4-62.9) at 3, 6 and 12 months, respectively. Paternal postnatal depression was observed at 40.5% (95% CI: 14.9-69) based on two studies. Risk factors for maternal perinatal depression include multiparity, contraceptive failure, inadequate antenatal care, pregnancy-induced hypertension, previous psychiatric illness, passive smoking, drug abuse, low socio-economic status, marital problems, family hardships, recent bereavement, housing difficulties, food insecurity, husband's illiteracy, his unemployment, and being blamed for child disability. Conclusion: The findings reveal a high prevalence of perinatal depression among mothers with very limited evidence of fathers residing in Pakistan, emphasising the need for prospective studies addressing mental health challenges. Registration: This review is registered on PROSPERO (CRD42023442581).
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".