Does community pressure matter in cesarean deliveries in Bangladesh? An analysis of nationally representative surveys
Bibliographic record
Abstract
Cesarean delivery plays a significant role in reducing maternal and child mortality. However, unjustified cesarean section (C-section) delivery is rising worldwide, including in Bangladesh. C-section delivery rates in Bangladesh have increased from 2.9% in 1999 to 45% in 2022, which is particularly high for first-order births (51%). This study aims to describe the prevalence and determinants of births by C-section for institutional deliveries in Bangladesh's private and public health facilities. Data from the Bangladesh Demographic and Health Surveys (BDHS) for 2011, 2014, 2017-18, and 2022 are used in this study. Besides the common socio-economic determinants of C-sections, adequate antenatal care (ANC) visits, place of delivery (public/private), and community-level factors including level of illiteracy and prevalence of C-sections in the community were found to have a significant association. After controlling the effect of other variables, women from a community with a high prevalence of C-sections were found to be 11.68 times more likely to have a C-section in their last birth compared to women from a community with a low prevalence of C-sections. Also, the women who had their last birth in private facilities were 8.16 times more likely to have C-sections than women who delivered in public facilities. These findings suggest that the increased rate of C-sections in Bangladesh may be driven by both individual-level and provider-level factors where community pressure plays a vital role. Close monitoring, particularly in private hospitals, and community-level awareness programs about the adversity of C-sections are the proposed policy strategies to avoid unnecessary cesarean deliveries in Bangladesh.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".