Kawo Kemal N, Asfaw Zeytu G, Yohannes Negusse
Department of Statistics, Madda Walabu University, Robe, Ethiopia.
School of Mathematical and Statistical Sciences, Hawassa University, Hawassa, Ethiopia.
Anemia. 2018 Jun 3;2018:3087354. doi: 10.1155/2018/3087354. eCollection 2018.
Anemia is a widely spread public health problem and affects individuals at all levels. However, there is a considerable regional variation in its distribution.
Thus, this study aimed to assess and model the determinants of prevalence of anemia among children aged 6-59 months in Ethiopia.
Cross-sectional data from Ethiopian Demographic and Health Survey was used for the analysis. It was implemented by the Central Statistical Agency from 27 December 2010 through June 2011 and the sampling technique employed was multistage.
The statistical models that suit the hierarchical data such as variance components model, random intercept model, and random coefficients model were used to analyze the data. Likelihood and Bayesian approaches were used to estimate both fixed effects and random effects in multilevel analysis.
This study revealed that the prevalence of anemia among children aged between 6 and 59 months in the country was around 42.8%. The multilevel binary logistic regression analysis was performed to investigate the variation of predictor variables of the prevalence of anemia among children aged between 6 and 59 months. Accordingly, it has been identified that the number of children under five in the household, wealth index, age of children, mothers' current working status, education level, given iron pills, size of child at birth, and source of drinking water have a significant effect on prevalence of anemia. It is found that variances related to the random term were statistically significant implying that there is variation in prevalence of anemia across regions. From the methodological aspect, it was found that random intercept model is better compared to the other two models in fitting the data well. Bayesian analysis gave consistent estimates with the respective multilevel models and additional solutions as posterior distribution of the parameters.
The current study confirmed that prevalence of anemia among children aged 6-59 months in Ethiopia was severe public health problem, where 42.8% of them are anemic. Thus, stakeholders should pay attention to all significant factors mentioned in the analysis of this study but wealth index/improving household income and availability of pure drinking water are the most influential factors that should be improved anyway.
贫血是一个广泛存在的公共卫生问题，影响各阶层人群。然而，其分布存在显著的地区差异。
因此，本研究旨在评估和建立埃塞俄比亚6至59个月儿童贫血患病率的决定因素模型。
分析采用了埃塞俄比亚人口与健康调查的横断面数据。该调查由中央统计局于2010年12月27日至2011年6月实施，采用的抽样技术为多阶段抽样。
使用适用于分层数据的统计模型，如方差成分模型、随机截距模型和随机系数模型来分析数据。在多水平分析中，采用似然法和贝叶斯法估计固定效应和随机效应。
本研究表明，该国6至59个月儿童的贫血患病率约为42.8%。进行多水平二元逻辑回归分析以研究6至59个月儿童贫血患病率预测变量的变化。据此，已确定家庭中五岁以下儿童数量、财富指数、儿童年龄、母亲当前工作状况、教育水平、是否服用铁剂、出生时儿童体重和饮用水来源对贫血患病率有显著影响。发现与随机项相关的方差具有统计学意义，这意味着各地区贫血患病率存在差异。从方法学角度来看，发现随机截距模型在拟合数据方面比其他两个模型更好。贝叶斯分析给出了与相应多水平模型一致的估计，并作为参数的后验分布提供了额外的解决方案。
当前研究证实，埃塞俄比亚6至59个月儿童的贫血患病率是一个严重的公共卫生问题，其中42.8%的儿童贫血。因此，利益相关者应关注本研究分析中提到的所有重要因素，但财富指数/提高家庭收入和提供纯净饮用水是无论如何都应改善的最具影响力的因素。
