Document Type

Article

Publication Date

4-2026

Keywords

antenatal care, Bangladesh, endogeneity, machine learning, maternal health, regional disparities

Abstract

Inadequate utilization of antenatal care (ANC) services remains a major contributor to maternal and child morbidity globally, posing barriers to achieving Sustainable Development Goal 3.1 in developing countries such as Bangladesh. Despite notable national progress, regional disparities continue to constrain timely ANC access for pregnant women. This study identifies the region-specific determinants of ANC access using advanced econometric modeling. A nonparametric copula control function approach is applied to correct for endogeneity in Poisson regression, with robustness validated through machine learning techniques. Using nationally representative data from the 2022 Bangladesh Demographic and Health Survey, findings show that only 41% of pregnant women achieved the recommended >= 4 visits, with the highest coverage in Dhaka and the lowest in Khulna. Determinants varied across divisions: higher education, household wealth, family-planning knowledge, financial inclusion, prior caesarean delivery, decision-making autonomy, and media exposure significantly increased ANC use, while higher birth order, more young children, large household size, agricultural employment, and rural residence reduced utilization. Unique contextual findings, such as the positive role of women's land ownership in Sylhet and the potential of ICT-based counseling in remote regions, underscore the need for region-specific interventions. Policy recommendations include re-targeting health budgets to underserved regions, reforming inheritance and land-distribution laws, expanding mobile banking and community insurance, and scaling digital health counseling platforms. The study highlights that achieving universal ANC coverage requires empowerment-driven, evidence-based, and cross-sectoral strategies integrating social, financial, and infrastructural dimensions into the regional maternal health planning framework.

Comments

Web of Science

Wiley

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