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All Journal Mikailalsys Journal of Mathematics and Statistics
P, Duruojinkeya
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Engineering Mathematics Mechanical Engineering

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Bayesian Hierarchical and Decomposition Analysis of Pregnancy-Related Mortality in Nigeria Using NDHS 2018-2023/24 S.O, Adeyemo; E.U, Ohaegbulam; P, Duruojinkeya
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 2 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i2.9166

Abstract

Nigeria continues to face one of the highest burdens of pregnancy-related mortality globally, yet recent measurement evidence remains constrained by gaps in direct reporting. This policy and measurement note examines the availability of direct sibling-history estimates in the Nigeria Demographic and Health Surveys (NDHS), with particular attention to the absence of an updated pregnancy-related mortality ratio (PRMR) in the 2023–24 reporting cycle. The NDHS 2018 reported a national PRMR of 512 per 100,000 live births (95% CI: 447–578). However, the NDHS 2023–24 Key Indicators and Summary Reports do not publish an updated sibling-history-based PRMR, thereby limiting direct assessment of mortality trends. Proxy indicators suggest modest progress, including a decline in the total fertility rate from 5.3 to 4.8 and an increase in skilled birth attendance from 43% to 46%, although rural–urban and zonal disparities persist. Exploratory Bayesian hierarchical modeling using NDHS 2023–24 microdata produced an illustrative national PRMR estimate of approximately 462 per 100,000 live births (CrI: 392–532), suggesting a possible modest decline, while rural estimates remain high at approximately 612. The findings indicate that the absence of a published direct PRMR weakens evidence-based monitoring of SDG 3.1 and limits independent assessment of progress in reducing pregnancy-related mortality. This note contributes to policy measurement by highlighting the need for routine inclusion of sibling-derived PRMR estimates in future DHS reports, alongside microdata access to support independent verification and more equitable maternal health planning.
Co-Authors E.U, Ohaegbulam S.O, Adeyemo