Exploring the burden of postpartum depression in urban Bangladesh: Prevalence and its associations with pregnancy‐related factors from a cross‐sectional study
Bibliographic record
Abstract
Abstract Background and Aims Postpartum depression (PPD) is a globally recognized public health concern, yet research focusing on women in urban areas of Bangladesh remains unexplored. This study aimed to address this research gap by investigating the prevalence and associated factors of PPD within the first 2 years after childbirth. Methods A cross‐sectional study was conducted, enrolling 259 women (26.66 ± 4.57 years) residing in urban areas who were attending healthcare delivery centers. Sociodemographic factors, child‐related issues, pregnancy‐related complications, and PPD using the Edinburgh Postnatal Depression Scale (EPDS) were used for data collection. Data analysis involved the application of χ 2 tests and logistic regression analysis using SPSS software. Results This study found a 60.6% prevalence of PPD using a cutoff of 10 (out of 30) on the EPDS scale. Logistic regression analysis identified several significant factors associated with PPD, including high monthly family income (odds ratio [OR] = 47.51, 95% confidence interval [CI]: 8.34–270.54, p < 0.001), income dissatisfaction (OR = 14.28, 95% CI: 4.75–42.87, p < 0.001), up to two gravidities (OR = 2.94, 95% CI = 1.25–6.90, p = 0.013), pregnancy‐related complications (OR = 2.70, 95% CI = 1.05–6.96, p = 0.039), increased antenatal care visits, and higher childbirth expenses. Conclusion This study underscores the high prevalence of PPD among urban mothers in Bangladesh. The identified risk factors emphasize the need for targeted mental health initiatives, specifically tailored to support the vulnerable group. Implementing such initiatives can effectively address the challenges posed by PPD and enhance the well‐being of postpartum women in urban areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".