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.
Sci Rep. 2021 Jun 29;11(1):13497. doi: 10.1038/s41598-021-92606-0.
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.
为了实现儿童死亡率降低的可持续发展目标，需要更加务实的方法，因此开展了这项研究。同时研究儿童居住地点和儿童生存数据的删失性质的研究很少。我们从 5 个 MCMC 贝叶斯层次泊松回归模型中确定了与尼日利亚五岁以下儿童（U5M）和婴儿（INM）死亡率相关的构成和背景因素，这些模型近似于 Cox 生存回归模型。使用了 2018 年 DHS 数据中的 33924 名五岁以下儿童。采用生命表技术和 Mlwin 3.05 模块对分层数据进行分析，在 Stata 版本 16 中实现。总的 INM 率（INMR）为每 1000 例活产 70 例，而 U5MR 为每 1000 例活产 131 例。INMR 最低的州是奥贡州（每 1000 例活产 17 例），最高的州是卡杜纳州（每 1000 例活产 106 例）、贡贝州（每 1000 例活产 112 例）和凯比州（每 1000 例活产 116 例），而 U5MR 最低的州是奥贡州（每 1000 例活产 29 例），最高的州是吉加瓦州（每 1000 例活产 212 例）和凯比州（每 1000 例活产 248 例）。在完全调整后的模型中，母亲受教育程度低/无、多胎、低出生体重、出生间隔短、家庭贫困、配偶决定医疗保健的获取方式、难以获得医疗保健设施、社区文盲率高、农村人口比例高的州，儿童 INM 和 U5M 的风险最高。与零模型相比，州和社区层面因素分别解释了 INM 和 U5M 总变异的 81%和 13%，59%和 35%。尼日利亚婴儿和五岁以下儿童的死亡率受到构成和背景因素的影响。用于估计与尼日利亚儿童死亡相关因素的贝叶斯层次泊松回归模型拟合了生存数据。
