Statistical modeling of health system causes of maternal mortality among pregnant women

The problem of maternal death remains a serious universal concern, with nearly 295,000 women dying annually from pregnancy-related complications, the majority of which happen in countries that are not financially balanced. Many African countries accounts for about 60% of these deaths, where the incident of maternal loss proportions exceed 500 of 100,000 living childbirths in many countries. This study examines the influence of health system causes of motherly losses at Mulago Specialized Women and Neonatal Hospital in Kampala, Uganda, using statistical modeling techniques. Adopting logistic regression model, the effect of antenatal care, parity, gravidity, and age conditions on pregnant women death was assessed. Outcome of the model revealed that the significant factors identified in this model, parity (p=0.001) and antenatal care attendance (p=0.018), form a robust predictive model of maternal mortality. The findings confirm that advanced maternal age and high parity, further exacerbate risks, particularly among older women with multiple previous pregnancies. The study's predictive model combines health system factors, providing a valuable tool for early identification of high-risk cases. By incorporating regular ANC services and targeted support for high-risk groups, MSWNH can improve maternal care and reduce mortality.

Authors

Publication Details

Journal
Journal of applied science, information and computing.
Published
2025-05-29
DOI
https://doi.org/10.59568/jasic-2025-6-1-06
Primary Topic
Global Maternal and Child Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Statistical modeling of health system causes of maternal mortality among pregnant women

Osayomore Ikpotokin, Dumitrita Sorostinean M, Nwaka R. N
Journal of applied science, information and computing.
Global Maternal and Child Health
article

Statistical modeling of health system causes of maternal mortality among pregnant women

Osayomore Ikpotokin, Dumitrita Sorostinean M, Nwaka R. N
article en

Abstract

The problem of maternal death remains a serious universal concern, with nearly 295,000 women dying annually from pregnancy-related complications, the majority of which happen in countries that are not financially balanced. Many African countries accounts for about 60% of these deaths, where the incident of maternal loss proportions exceed 500 of 100,000 living childbirths in many countries. This study examines the influence of health system causes of motherly losses at Mulago Specialized Women and Neonatal Hospital in Kampala, Uganda, using statistical modeling techniques. Adopting logistic regression model, the effect of antenatal care, parity, gravidity, and age conditions on pregnant women death was assessed. Outcome of the model revealed that the significant factors identified in this model, parity (p=0.001) and antenatal care attendance (p=0.018), form a robust predictive model of maternal mortality. The findings confirm that advanced maternal age and high parity, further exacerbate risks, particularly among older women with multiple previous pregnancies. The study's predictive model combines health system factors, providing a valuable tool for early identification of high-risk cases. By incorporating regular ANC services and targeted support for high-risk groups, MSWNH can improve maternal care and reduce mortality.

Journal of applied science, information and computing.Vol. 6(1)
Good health and well-being
Openalex Percentile: Top 70%
Global Maternal and Child Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.