National and Subnational Level Estimates of Maternal Delivery at Home and Their Predictors in Bangladesh: Evidence from Population-Based Survey 2012-2022
Bibliographic record
Abstract
Abstract Background Despite significant progress in reducing maternal and neonatal mortality, home delivery remains a substantial public health challenge in Bangladesh and many other low- and middle-income countries. While the proportion of home deliveries has markedly decreased in the past decade, pronounced disparities persist across geographic, socioeconomic, and demographic groups. A nuanced understanding of the prevalence and determinants of maternal home delivery is key to designing targeted interventions. This study examines national and subnational variations in maternal home delivery and associated factors in Bangladesh. Methods We analyzed data from the Multiple Indicator Cluster Surveys (2012–13 and 2019) and the Bangladesh Demographic and Health Survey (2022), covering 20,770 ever-married women aged 15–49 who gave birth in the preceding two years. District-level prevalence, descriptive statistics, and multivariable logistic regression were used to assess trends and determinants. Results Home delivery prevalence declined from 68% in 2012–13 to 35% in 2022. Disparities remain: divisions such as Barisal (48.9%), Chattogram, Sylhet, and Mymensingh showed higher rates, while Dhaka and Khulna had the lowest. At the subnational level, remote areas like Bandarban, Rangamati, and Bhola exhibited higher prevalence. The logistic regression analysis identified several significant predictors, such as women with no formal education, limited ANC visits (≤3), rural residence, lower wealth status, multiparity (≥3 children), and lack of media exposure were more likely to deliver at home. Conclusions Despite marked improvement, persistent geographic and socioeconomic inequities highlight the need for targeted interventions. Strengthening healthcare infrastructure in underserved regions, promoting maternal health awareness, scaling up ANC utilization, and reducing financial barriers through subsidies and incentive programs can help further decrease home deliveries. Future research should explore cultural and religious factors to inform context-specific policies for equitable facility-based childbirth.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".