Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa.
PLoS One. 2024 Feb 28;19(2):e0282463. doi: 10.1371/journal.pone.0282463. eCollection 2024.
There are a number of previous studies that investigated undernutrition and its determinants in Ethiopia. However, the national average in the level of undernutrition conceals large variation across administrative zones of Ethiopia. Hence, this study aimed to determine the geographic distribution of composite index for anthropometric failure (CIAF) and identify the influencing factors it' might be more appropriate.
We used the zonal-level undernutrition data for the under-five children in Ethiopia from the Ethiopian Demographic and Health Survey (EDHS) dataset. Different spatial models were applied to explore the spatial distribution of the CIAF and the covariates.
The Univariate Moran's I statistics for CIAF showed spatial heterogeneity of undernutrition in Ethiopian administrative zones. The spatial autocorrelation model (SAC) was the best fit based on the AIC criteria. Results from the SAC model suggested that the CIAF was positively associated with mothers' illiteracy rate (0.61, pvalue 0.001), lower body mass index (0.92, pvalue = 0.023), and maximum temperature (0.2, pvalue = 0.0231) respectively. However, the CIAF was negatively associated with children without any comorbidity (-0.82, pvalue = 0.023), from families with accessibility of improved drinking water (-0.26, pvalue = 0.012), and minimum temperature (-0.16).
The CIAF across the administrative zones of Ethiopia is spatially clustered. Improving women's education, improving drinking water, and improving child breast feeding can reduce the prevalence of undernutrition (CIAF) across Ethiopian administrative zones. Moreover, targeted intervention in the geographical hotspots of CIAF can reduce the burden of CIAF across the administrative zones.
此前有多项研究调查了埃塞俄比亚的营养不良及其决定因素。然而，全国营养不良平均水平掩盖了埃塞俄比亚各行政区之间的巨大差异。因此，本研究旨在确定复合人体测量失败指数（CIAF）的地理分布，并确定其可能更合适的影响因素。
我们使用了来自埃塞俄比亚人口与健康调查（EDHS）数据集的埃塞俄比亚五岁以下儿童的区域营养不良数据。应用了不同的空间模型来探索 CIAF 及其协变量的空间分布。
CIAF 的单变量 Moran's I 统计量显示，埃塞俄比亚行政区的营养不良存在空间异质性。基于 AIC 标准，空间自相关模型（SAC）是最佳拟合模型。SAC 模型的结果表明，CIAF 与母亲文盲率（0.61，p 值 0.001）、较低的体重指数（0.92，p 值=0.023）和最高温度（0.2，p 值=0.0231）呈正相关。然而，CIAF 与没有任何合并症的儿童（-0.82，p 值=0.023）、家庭可获得改良饮用水（-0.26，p 值=0.012）和最低温度（-0.16）呈负相关。
埃塞俄比亚各行政区的 CIAF 呈空间聚类分布。提高妇女教育水平、改善饮用水质量和促进儿童母乳喂养可以降低埃塞俄比亚各行政区的营养不良（CIAF）患病率。此外，在 CIAF 的地理热点地区进行有针对性的干预，可以减轻各行政区的 CIAF 负担。
