Prevalence and associated factors of skilled birth attendants in Bangladesh: A combined statistical and machine learning analysis.

Authors

Jakia Sultana Mim, Md Shihab Mostafa, Bodrunnahar Barna, Ahmed Nuzat Sadia, Raisul Islam, Abdullah Al Islam

Published in

PloS one. Volume 21. Issue 9. Pages e0358556. Epub Sep 21, 2026.

Abstract

Bangladesh has achieved notable progress in maternal and child health; however, maternal and neonatal mortality remain high, partly due to inadequate access to skilled birth attendants (SBA) during delivery. This study aims to identify key socioeconomic and demographic factors influencing SBA utilization in Bangladesh.
Data were obtained from the 2022 Bangladesh Demographic and Health Survey (BDHS), a nationally representative cross-sectional survey which was conducted from June 27 to December 12, 2022. The outcome variable was skilled birth attendants during delivery, defined as attendants provided by a doctor, nurse, or midwife. Descriptive statistics and chi-square tests were used for initial analysis, followed by binary logistic regression to identify significant determinants. Additionally, the inclusion of machine learning models provides an additional classification framework that improves classification performance and helps identify important predictors, complementing traditional regression analysis.
From the study, 67.94% of women utilized skilled birth attendants during childbirth. Higher maternal education, household wealth, antenatal care utilization, and urban residence were significantly associated with greater SBA utilization. Women with higher education had substantially higher odds of using SBA than those with no education (AOR = 4.10, 95% CI: 1.94-8.66, p < 0.001), while rural women had lower odds than urban women (AOR = 0.64, 95% CI: 0.47-0.88, p = 0.006). Women who attended four or more ANC visits were also more likely to use SBA than those with no ANC visits (AOR = 2.92, 95% CI: 1.72-4.96, p < 0.001). Women from the richest households had higher odds of SBA utilization compared with those from the poorest households (AOR = 1.77, 95% CI: 1.11-2.81, p = 0.016). Among the machine-learning models, Random Forest achieved the highest numerical accuracy (0.82).
Education, economic status, ANC utilization, and place of residence were identified as key factors associated with skilled birth attendants during delivery in Bangladesh using the binary logistic regression model. Moreover, the machine learning models were used separately to classify SBA. Targeted interventions focusing on disadvantaged and rural populations can help improve equitable access to maternal healthcare. However, differences related to religion and region should be interpreted cautiously, as they may reflect broader socioeconomic and cultural factors rather than direct effects.

PMID:
42766565
Bibliographic data and abstract were imported from PubMed on 22 Sep 2026.

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