School of Mathematics, University of Nairobi, Nairobi 30197, Kenya.
Int J Environ Res Public Health. 2021 Dec 30;19(1):399. doi: 10.3390/ijerph19010399.
Child mortality is high in Sub-Saharan Africa compared to other regions in the world. In Kenya, the risk of mortality is assumed to vary from county to county due to diversity in socio-economic and even climatic factors. Recently, the country was split into 47 different administrative regions called counties, and health care was delegated to those county governments, further aggravating the spatial differences in health care from county to county. The goal of this study is to evaluate the effects of spatial variation in under-five mortality in Kenya. Data from the Kenya Demographic Health Survey (KDHS-2014) consisting the newly introduced counties was used to analyze this risk. Using a spatial Cox Proportional Hazard model, an Intrinsic Conditional Autoregressive Model (ICAR) was fitted to account for the spatial variation among the counties in the country while the Cox model was used to model the risk factors associated with the time to death of a child. Inference regarding the risk factors and the spatial variation was made in a Bayesian setup based on the Markov Chain Monte Carlo (MCMC) technique to provide posterior estimates. The paper indicate the spatial disparities that exist in the country regarding child mortality in Kenya. The specific counties have mortality rates that are county-specific, although neighboring counties have similar hazards for death of a child. Counties in the central Kenya region were shown to have the highest hazard of death, while those from the western region had the lowest hazard of death. Demographic factors such as the sex of the child and sex of the household head, as well as social economic factors, such as the level of education, accounted for the most variation when spatial differences were factored in. The spatial Cox proportional hazard frailty model performed better compared to the non-spatial non-frailty model. These findings can help the country to plan health care interventions at a subnational level and guide social and health policies by ensuring that counties with a higher risk of Under Five Child Mortality (U5CM) are considered differently from counties experiencing a lower risk of death.
与世界其他地区相比,撒哈拉以南非洲地区的儿童死亡率较高。在肯尼亚,由于社会经济甚至气候因素的多样性,死亡率的风险被认为在各县之间有所不同。最近,该国被划分为 47 个不同的行政区域,称为县,并将医疗保健权下放给这些县政府,这进一步加剧了各县之间医疗保健的空间差异。本研究的目的是评估肯尼亚五岁以下儿童死亡率的空间变化的影响。本研究使用了肯尼亚人口与健康调查(KDHS-2014)的数据,该数据包含了新引入的县,以分析这种风险。本研究使用空间 Cox 比例风险模型,拟合了一个内在条件自回归模型(ICAR),以解释该国各县之间的空间变化,同时使用 Cox 模型来建模与儿童死亡时间相关的风险因素。基于马尔可夫链蒙特卡罗(MCMC)技术,在贝叶斯框架中对风险因素和空间变化进行推断,以提供后验估计。本文指出了肯尼亚在儿童死亡率方面存在的国家空间差异。具体的县有特定的死亡率,尽管相邻的县有相似的儿童死亡风险。肯尼亚中部地区的县显示出最高的死亡风险,而西部地区的县则显示出最低的死亡风险。人口因素,如儿童的性别和家庭户主的性别,以及社会经济因素,如教育水平,在考虑空间差异时,解释了最大的变化。与非空间非脆弱性模型相比,空间 Cox 比例风险脆弱性模型表现更好。这些发现可以帮助国家在国家以下一级规划医疗保健干预措施,并通过确保将面临更高的五岁以下儿童死亡率风险的县与死亡率风险较低的县区别对待,指导社会和卫生政策。