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dc.contributor.authorFagbamigbe, A F
dc.contributor.authorSalawu, M M
dc.contributor.authorAbatan, S M
dc.contributor.authorAjumobi, O
dc.date.accessioned2021-07-07T13:30:15Z
dc.date.available2021-07-07T13:30:15Z
dc.date.issued2021-06-29
dc.identifier.citationFagbamigbe , A F , Salawu , M M , Abatan , S M & Ajumobi , O 2021 , ' Approximation of the Cox survival regression model by MCMC Bayesian hierarchical Poisson modelling of factors associated with childhood mortality in Nigeria ' , Scientific Reports , vol. 11 , 13497 . https://doi.org/10.1038/s41598-021-92606-0en
dc.identifier.issn2045-2322
dc.identifier.otherPURE: 274947192
dc.identifier.otherPURE UUID: afaa1119-b960-43e3-a9ce-ba0466a9a970
dc.identifier.otherRIS: urn:C5A1ED8FC3DB8A685D59E61EC68FEF60
dc.identifier.otherWOS: 000671782600010
dc.identifier.otherScopus: 85108999404
dc.identifier.urihttp://hdl.handle.net/10023/23481
dc.description.abstractThe need for more pragmatic approaches to achieve sustainable development goal on childhood mortality reduction necessitated this study. Simultaneous study of the influence of where the children live and the censoring nature of children survival data is scarce. We identified the compositional and contextual factors associated with under-five (U5M) and infant (INM) mortality in Nigeria from 5 MCMC Bayesian hierarchical Poisson regression models as approximations of the Cox survival regression model. The 2018 DHS data of 33,924 under-five children were used. Life table techniques and the Mlwin 3.05 module for the analysis of hierarchical data were implemented in Stata Version 16. The overall INM rate (INMR) was 70 per 1000 livebirths compared with U5M rate (U5MR) of 131 per 1000 livebirth. The INMR was lowest in Ogun (17 per 1000 live births) and highest in Kaduna (106), Gombe (112) and Kebbi (116) while the lowest U5MR was found in Ogun (29) and highest in Jigawa (212) and Kebbi (248). The risks of INM and U5M were highest among children with none/low maternal education, multiple births, low birthweight, short birth interval, poorer households, when spouses decide on healthcare access, having a big problem getting to a healthcare facility, high community illiteracy level, and from states with a high proportion of the rural population in the fully adjusted model. Compared with the null model, 81% vs 13% and 59% vs 35% of the total variation in INM and U5M were explained by the state- and neighbourhood-level factors respectively. Infant- and under-five mortality in Nigeria is influenced by compositional and contextual factors. The Bayesian hierarchical Poisson regression model used in estimating the factors associated with childhood deaths in Nigeria fitted the survival data.
dc.format.extent18
dc.language.isoeng
dc.relation.ispartofScientific Reportsen
dc.rightsCopyright © The Author(s) 2021. This article is licensed under a Copyright © The Author(s) 2021. Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.en
dc.subjectEpidemiologyen
dc.subjectHealth careen
dc.subjectRisk factorsen
dc.subjectStatisticsen
dc.subjectHA Statisticsen
dc.subjectRA Public aspects of medicineen
dc.subject3rd-DASen
dc.subject.lccHAen
dc.subject.lccRAen
dc.titleApproximation of the Cox survival regression model by MCMC Bayesian hierarchical Poisson modelling of factors associated with childhood mortality in Nigeriaen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.Population and Behavioural Science Divisionen
dc.contributor.institutionUniversity of St Andrews.School of Medicineen
dc.identifier.doihttps://doi.org/10.1038/s41598-021-92606-0
dc.description.statusPeer revieweden


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