Evidence map›Paper›PMID 37279238›Full record

ArticlePloS one2023

Using geographically weighted regression analysis to assess predictors of home birth hot spots in Ethiopia.

Samuel Hailegebreal, Firehiwot Haile, Yosef Haile, Atsedu Endale Simegn, Ermias Bekele Enyew

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Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Samuel HailegebrealSchool of Public Health Department of Health Informatics, College of Medicine and Health Sciences, Wachemo University, Hosaena, Ethiopia.ORCID 0000-0003-0887-7803
Firehiwot HaileSchool of Public Health, College of Medicine and Health Sciences, Arba Minch University, Arba Minch, Ethiopia.
Yosef HaileSchool of Public Health, College of Medicine and Health Sciences, Arba Minch University, Arba Minch, Ethiopia.
Atsedu Endale SimegnDepartment of Anesthesia, Wachemo University, Hosaena, Ethiopia.
Ermias Bekele EnyewDepartment of Health Informatics, Institute of Public Health, Mettu University, Mettu, Ethiopia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAnnually, 30 million women in Africa become pregnant, with the majority of deliveries taking place at home without the assistance of skilled healthcare personnel. In Ethiopia the proportion of home birth is high with regional disparity. Also limited evidence on spatial regression and deriving predictors. Therefore, this study aimed to assess the predictors of home birth hot spots using geographically weighted regression in Ethiopia.

methodsThis study used secondary data from the 2019 Ethiopian Mini Demographic and Health Survey. First, Moran's I and Getis-OrdGi* statistics were used to examine the geographic variation in home births. Further, spatial regression was analyzed using ordinary least squares regression and geographically weighted regression to predict hotspot area of home delivery.

resultAccording to this result, Somalia, Afar, and the SNNPR region were shown to be high risk locations for home births. Women from rural residence, women having no-education, poorest wealth index, Muslim religion follower, and women with no-ANC visit were predictors of home delivery hotspot locations.

conclusionThe spatial regression revealed women from rural resident, women having no-education, women being in the household with a poorest wealth index, women with Muslim religion follower, and women having no-ANC visit were predictors of home delivery hotspot regions. Therefore, governmental and other stakeholders should remain the effort to decrease home childbirth through access to healthcare services especially for rural resident, strengthen the women for antenatal care visits.

Indexed as

Home ChildbirthSpatial RegressionEducational StatusEthiopiaFemaleHumansPregnancyPrenatal CareSpatial Analysis

Identifiers

PMID37279238
PMCPMC10243638

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