Bayesian geo-additive model to analyze spatial pattern and determinants of maternal mortality in Ethiopia

Abstract Background and Aims Maternal mortality is defined as the death of a woman from any cause associated to or made worse by her pregnancy, either during her pregnancy or within 42 days of the pregnancy's termination, regardless of the length of the pregnancy or its location. The objective...

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Main Authors: Yitagesu Eshetu, Tigist Getachew
Format: Article
Language:English
Published: BMC 2024-11-01
Series:BMC Public Health
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Online Access:https://doi.org/10.1186/s12889-024-20812-2
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author Yitagesu Eshetu
Tigist Getachew
author_facet Yitagesu Eshetu
Tigist Getachew
author_sort Yitagesu Eshetu
collection DOAJ
description Abstract Background and Aims Maternal mortality is defined as the death of a woman from any cause associated to or made worse by her pregnancy, either during her pregnancy or within 42 days of the pregnancy's termination, regardless of the length of the pregnancy or its location. The objective of this study is to determine the factors influencing maternal mortality as well as to examine the regional distribution of maternal deaths in Ethiopia. Method This study was conducted in Ethiopia and the data was basically secondary which is obtained from 2016 Ethiopian Demographic and Health survey (EDHS). The Bayesian Geo-additive regression model is used to identify the major risk factors and spatial effects (spatial pattern) on maternal death in Ethiopia. Result Pregnancy-related problems or childbirth were the cause of death for 1.43% of the 10,009 women in the research, whose ages ranged from 15 to 49. In contrast to the semi-parametric and generalized linear models, the Bayesian Geo-additive regression model is based on the DIC and better fits the data. According to the Bayesian Geo-additive regression model's results, maternal death is significantly affected by the place of delivery, the number of prenatal care visits, marital status, wealth index, mother's age and the number of birth orders. The Afar, Somali, Benishangul Gumuz, and Gambela regions have higher rates of maternal death, according to evidence of geographic variation in a model. Conclusion The findings of the study revealed that maternal mortality is influenced by numerous social, demographic, and geographic variables. Geographic variations exist in the patterns of maternal mortality.
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spelling doaj-art-d0dbc17d153d48e098c9daaf3c5f509b2024-12-01T12:49:00ZengBMCBMC Public Health1471-24582024-11-0124111010.1186/s12889-024-20812-2Bayesian geo-additive model to analyze spatial pattern and determinants of maternal mortality in EthiopiaYitagesu Eshetu0Tigist Getachew1Department of Statistics, College of Natural and Computational Sciences, Dambi Dollo UniversityDepartment of Statistics, College of Natural and Computational Sciences, Dambi Dollo UniversityAbstract Background and Aims Maternal mortality is defined as the death of a woman from any cause associated to or made worse by her pregnancy, either during her pregnancy or within 42 days of the pregnancy's termination, regardless of the length of the pregnancy or its location. The objective of this study is to determine the factors influencing maternal mortality as well as to examine the regional distribution of maternal deaths in Ethiopia. Method This study was conducted in Ethiopia and the data was basically secondary which is obtained from 2016 Ethiopian Demographic and Health survey (EDHS). The Bayesian Geo-additive regression model is used to identify the major risk factors and spatial effects (spatial pattern) on maternal death in Ethiopia. Result Pregnancy-related problems or childbirth were the cause of death for 1.43% of the 10,009 women in the research, whose ages ranged from 15 to 49. In contrast to the semi-parametric and generalized linear models, the Bayesian Geo-additive regression model is based on the DIC and better fits the data. According to the Bayesian Geo-additive regression model's results, maternal death is significantly affected by the place of delivery, the number of prenatal care visits, marital status, wealth index, mother's age and the number of birth orders. The Afar, Somali, Benishangul Gumuz, and Gambela regions have higher rates of maternal death, according to evidence of geographic variation in a model. Conclusion The findings of the study revealed that maternal mortality is influenced by numerous social, demographic, and geographic variables. Geographic variations exist in the patterns of maternal mortality.https://doi.org/10.1186/s12889-024-20812-2Bayesian Geo-additive regression modelMaternal deathSpatial pattern
spellingShingle Yitagesu Eshetu
Tigist Getachew
Bayesian geo-additive model to analyze spatial pattern and determinants of maternal mortality in Ethiopia
BMC Public Health
Bayesian Geo-additive regression model
Maternal death
Spatial pattern
title Bayesian geo-additive model to analyze spatial pattern and determinants of maternal mortality in Ethiopia
title_full Bayesian geo-additive model to analyze spatial pattern and determinants of maternal mortality in Ethiopia
title_fullStr Bayesian geo-additive model to analyze spatial pattern and determinants of maternal mortality in Ethiopia
title_full_unstemmed Bayesian geo-additive model to analyze spatial pattern and determinants of maternal mortality in Ethiopia
title_short Bayesian geo-additive model to analyze spatial pattern and determinants of maternal mortality in Ethiopia
title_sort bayesian geo additive model to analyze spatial pattern and determinants of maternal mortality in ethiopia
topic Bayesian Geo-additive regression model
Maternal death
Spatial pattern
url https://doi.org/10.1186/s12889-024-20812-2
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AT tigistgetachew bayesiangeoadditivemodeltoanalyzespatialpatternanddeterminantsofmaternalmortalityinethiopia