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Fagbamigbe AF, Salawu MM, Abatan SM, Ajumobi O. Approximation of the Cox survival regression model by MCMC Bayesian Hierarchical Poisson modelling of factors associated with childhood mortality in Nigeria. Sci Rep 2021; 11:13497. [PMID: 34188083 PMCID: PMC8241837 DOI: 10.1038/s41598-021-92606-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 06/07/2021] [Indexed: 11/26/2022] Open
Abstract
The 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.
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Affiliation(s)
- A F Fagbamigbe
- Department of Epidemiology and Medical Statistics, Faculty of Public Health, College of Medicine, University of Ibadan, Ibadan, Nigeria.
- Health Data Science Group, Division of Population and Behavioural Sciences, School of Medicine, University of St Andrews, St Andrews, UK.
| | - M M Salawu
- Department of Epidemiology and Medical Statistics, Faculty of Public Health, College of Medicine, University of Ibadan, Ibadan, Nigeria
- Health Data Science Group, Division of Population and Behavioural Sciences, School of Medicine, University of St Andrews, St Andrews, UK
| | - S M Abatan
- Health Data Science Group, Division of Population and Behavioural Sciences, School of Medicine, University of St Andrews, St Andrews, UK
- Department of Demography and Social Statistics, Federal University Oye, Oye, Ekiti, Nigeria
| | - O Ajumobi
- Health Data Science Group, Division of Population and Behavioural Sciences, School of Medicine, University of St Andrews, St Andrews, UK
- School of Community Health Sciences, University of Nevada, Reno, USA
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Log-Burr XII Gamma–Weibull Regression Model with Random Effects and Censored Data. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2019. [DOI: 10.1007/s42519-018-0026-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Tawiah R, Yau KKW, McLachlan GJ, Chambers SK, Ng SK. Multilevel model with random effects for clustered survival data with multiple failure outcomes. Stat Med 2018; 38:1036-1055. [PMID: 30474216 DOI: 10.1002/sim.8041] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 10/18/2018] [Accepted: 10/27/2018] [Indexed: 12/27/2022]
Abstract
We present a multilevel frailty model for handling serial dependence and simultaneous heterogeneity in survival data with a multilevel structure attributed to clustering of subjects and the presence of multiple failure outcomes. One commonly observes such data, for example, in multi-institutional, randomized placebo-controlled trials in which patients suffer repeated episodes (eg, recurrent migraines) of the disease outcome being measured. The model extends the proportional hazards model by incorporating a random covariate and unobservable random institution effect to respectively account for treatment-by-institution interaction and institutional variation in the baseline risk. Moreover, a random effect term with correlation structure driven by a first-order autoregressive process is attached to the model to facilitate estimation of between patient heterogeneity and serial dependence. By means of the generalized linear mixed model methodology, the random effects distribution is assumed normal and the residual maximum likelihood and the maximum likelihood methods are extended for estimation of model parameters. Simulation studies are carried out to evaluate the performance of the residual maximum likelihood and the maximum likelihood estimators and to assess the impact of misspecifying random effects distribution on the proposed inference. We demonstrate the practical feasibility of the modeling methodology by analyzing real data from a double-blind randomized multi-institutional clinical trial, designed to examine the effect of rhDNase on the occurrence of respiratory exacerbations among patients with cystic fibrosis.
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Affiliation(s)
- Richard Tawiah
- School of Medicine and Menzies Health Institute Queensland, Griffith University, Queensland, Australia
| | - Kelvin K W Yau
- Department of Management Sciences, City University of Hong Kong, Hong Kong
| | | | - Suzanne K Chambers
- School of Medicine and Menzies Health Institute Queensland, Griffith University, Queensland, Australia
| | - Shu-Kay Ng
- School of Medicine and Menzies Health Institute Queensland, Griffith University, Queensland, Australia
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