Uncovering the Reasons Behind Maternal Care Dropout in Bangladesh: Cross-sectional study (Preprint)
Bibliographic record
Abstract
Background: Utilization of the maternal continuum of care (CoC)-comprising adequate antenatal care (ANC), skilled birth attendance, and postnatal care (PNC)-is critical for improving maternal and child health outcomes. However, dropout from the CoC remains substantial in Bangladesh, with women discontinuing services at different stages of pregnancy, delivery, and postpartum care. Objective: This study aimed to quantify maternal dropout at each stage of the CoC and identify socioeconomic and demographic factors associated with discontinuity, comparing two nationally representative survey rounds. Methods: Data were drawn from the Bangladesh Demographic and Health Surveys (BDHS) 2017-2018 and 2022. Women aged 15 to 49 years with a live birth in the preceding 2 to 3 years were included. Completion of full CoC was defined as receiving at least 4 ANC visits, delivering with a skilled birth attendant, and obtaining at least 1 PNC contact within 48 hours of delivery. Predisposing (age, education, parity, religion, and division), enabling (wealth index, media exposure, health care access, and residence), and need factors (terminated pregnancy and desired pregnancy status) were identified using the Andersen Behavioral Model. Survey-weighted multivariable logistic regression models were fitted for each CoC component and overall CoC completion, with interaction terms to assess whether associations differed between survey rounds. Results: Among 8424 mothers, 27.9% (n=2350) failed to complete all components of the maternal CoC. Dropout was highest at the ANC stage (n=4962, 55.7%), followed by PNC (n=3976, 47.2%) and skilled birth attendant-assisted delivery (n=3378, 40.1%). Between survey rounds, overall CoC dropout decreased significantly from 31.9% (BDHS 2017-2018) to 22.4% (BDHS 2022), reflecting modest improvements in service continuity. Factors significantly associated with higher odds of CoC dropout included lower maternal education (adjusted odds ratio [AOR] 2.70, 95% CI 1.94-3.77; P<.001), higher parity (AOR 2.73, 95% CI 2.12-3.50; P<.001), lower wealth quintiles (AOR 4.04, 95% CI 3.02-5.41; P<.001), and rural residence (AOR 1.40, 95% CI 1.18-1.67; P<.001). Protective factors included older maternal age at delivery (AOR 0.56, 95% CI 0.42-0.74; P<.001) and history of ever-terminated pregnancy (AOR 0.74, 95% CI 0.63-0.86; P<.001). Significant temporal interactions (all P<.05) indicated that the strength of associations for education, parity, religion, wealth, media exposure, health care access barriers, residence, and pregnancy desire differed between survey rounds, reflecting changing determinants of CoC engagement amid policy reforms and pandemic disruptions. Conclusions: Maternal, socioeconomic, and geographic factors are strongly associated with discontinuity along the maternal health care continuum in Bangladesh. Statistically significant temporal variations underscore the impact of evolving health policies and system disruptions on maternal service utilization patterns. Targeted, area-specific interventions addressing these determinants across all CoC components are essential to improve maternal health care retention and achieve better maternal and child health outcomes.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".