Spatial heterogeneity and risk factors for stunting among children under age five in Ethiopia: A Bayesian geo-statistical model.
Spatial heterogeneity and risk factors for stunting among children under age five in Ethiopia: A Bayesian geo-statistical model.
复制标题
DOI：
10.1371/journal.pone.0170785
复制
发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Lindtjørn B
中科院分区：
文献类型：
--
作者：
Hagos S;Hailemariam D;WoldeHanna T;Lindtjørn B
了解发育迟缓的空间分布和中尺度的潜在因素对干预措施的设计和实施至关重要。然而，对发育迟缓的空间分布知之甚少，而且在所报告的风险因素的相对重要性方面存在一些差异。因此，本研究的目的是探索在中（区）尺度的空间分布发育迟缓，并评估空间依赖性的影响，识别的风险因素及其相对贡献的发生发育迟缓和严重发育迟缓在埃塞俄比亚农村地区。进行了一项以社区为基础的横断面研究，以衡量0-59个月儿童发育迟缓和严重发育迟缓的发生率。此外，我们还收集了人体测量、饮食习惯、父母和儿童相关的人口统计学和社会经济状况等相关信息。还记录了被调查家庭的纬度和经度。计算当地Anselin Moran's I，以调查发育迟缓患病率的空间变化，并确定潜在的高患病率局部区域（热点）。最后，我们采用了贝叶斯地质统计模型，占空间依赖结构的数据，以确定潜在的风险因素发育迟缓的研究领域。总体而言，该地区发育迟缓和严重发育迟缓的患病率分别为43.7% [95%CI：40.9，46.4]和21.3% [95%CI：19.5，23.3]。我们确定了统计上显着的集群发育迟缓的高患病率（热点）在东部地区和集群的低患病率（冷点）在西部。我们发现，将数据的空间结构包含到贝叶斯模型中可以提高发育迟缓模型的拟合度。贝叶斯地理统计模型表明，发育迟缓的风险随着儿童年龄的增加而增加（OR 4.74; 95%Bayesian可信区间[BCI]：3.35-6.58），在男孩中（OR 1.28; 95%BCI; 1.12-1.45）。然而，产妇教育和家庭粮食安全被认为是防止发育迟缓和严重发育迟缓的保护措施。在不同的空间尺度上，发育迟缓的发生率可能各不相同。为此，重要的是，营养研究，更重要的是，控制干预措施考虑到这种空间异质性的营养缺陷及其潜在的相关因素的分布。这项研究的结果还表明，在该地区的营养计划中纳入家庭粮食不安全的干预措施可能有助于避免发育迟缓的负担。
Understanding the spatial distribution of stunting and underlying factors operating at meso-scale is of paramount importance for intervention designing and implementations. Yet, little is known about the spatial distribution of stunting and some discrepancies are documented on the relative importance of reported risk factors. Therefore, the present study aims at exploring the spatial distribution of stunting at meso- (district) scale, and evaluates the effect of spatial dependency on the identification of risk factors and their relative contribution to the occurrence of stunting and severe stunting in a rural area of Ethiopia. A community based cross sectional study was conducted to measure the occurrence of stunting and severe stunting among children aged 0–59 months. Additionally, we collected relevant information on anthropometric measures, dietary habits, parent and child-related demographic and socio-economic status. Latitude and longitude of surveyed households were also recorded. Local Anselin Moran's I was calculated to investigate the spatial variation of stunting prevalence and identify potential local pockets (hotspots) of high prevalence. Finally, we employed a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data, to identify potential risk factors for stunting in the study area. Overall, the prevalence of stunting and severe stunting in the district was 43.7% [95%CI: 40.9, 46.4] and 21.3% [95%CI: 19.5, 23.3] respectively. We identified statistically significant clusters of high prevalence of stunting (hotspots) in the eastern part of the district and clusters of low prevalence (cold spots) in the western. We found out that the inclusion of spatial structure of the data into the Bayesian model has shown to improve the fit for stunting model. The Bayesian geo-statistical model indicated that the risk of stunting increased as the child’s age increased (OR 4.74; 95% Bayesian credible interval [BCI]:3.35–6.58) and among boys (OR 1.28; 95%BCI; 1.12–1.45). However, maternal education and household food security were found to be protective against stunting and severe stunting. Stunting prevalence may vary across space at different scale. For this, it's important that nutrition studies and, more importantly, control interventions take into account this spatial heterogeneity in the distribution of nutritional deficits and their underlying associated factors. The findings of this study also indicated that interventions integrating household food insecurity in nutrition programs in the district might help to avert the burden of stunting.