Perinatal depression in India: A narrative review of prevalence, risk factors, and healthcare provider awareness
Bibliographic record
Abstract
Background: Perinatal depression (PND), encompassing both antenatal and postnatal depression, affects 10%-20% women globally, with reported higher rates in low- and middle-income countries, like India. In India, PND prevalence varies widely (15%-45%), influenced by regional, cultural, and socioeconomic factors. Untreated PND has severe consequences for maternal health, child development, and family dynamics. Healthcare workers (HCWs) play a vital role in early detection, yet awareness and training in India are insufficient. Objective: This review aims to synthesize evidence on prevalence and risk factors of PND in India, assess awareness levels and preparedness among HCWs in managing PND and identify barriers to effective detection and management of PND. Methods: A comprehensive literature search was done in PubMed and Google Scholar for studies published between 2014 and 2024. Inclusion criteria involved studies from India focusing on PND prevalence, risk factors, and HCW awareness. Data extraction, involved study characteristics, prevalence rates, risk factors, HCW awareness. Quality for observational studies was assessed using Newcastle-Ottawa Scale and for systematic review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results: PND prevalence in India ranged from 15% to 45%, with significant regional variations. Major risk factors included poverty, domestic violence, unplanned pregnancies, and limited spousal support. HCWs demonstrated low awareness, with only 30% of nurses confident in identifying PND symptoms. Barriers to effective management, included cultural stigma, limited mental health training for HCWs, and inadequate healthcare infrastructure. Successful interventions included the use of validated screening tools like the Edinberg Postnatal Depression Scale (EPDS) and community-based support systems. Conclusion: PND remains a significant public health issue in India, exacerbated by sociocultural stigma and healthcare gaps. Integrated mental health services, HCW training, and culturally sensitive interventions are essential to improving PND care. Addressing these issues can reduce maternal and child health disparities and improve outcomes in resource-limited settings.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".