Gebreyesus Seifu Hagos, Mariam Damen H, Woldehanna Tasew, Lindtjørn Bernt
1Department of Reproductive Health and Health Service Management,School of Public Health,College of Health Sciences,Addis Ababa University,PO Box 2140,Addis Ababa,Ethiopia.
3Department of Economics,College of Business and Economics,Addis Ababa University,Addis Ababa,Ethiopia.
Public Health Nutr. 2016 Jun;19(8):1417-27. doi: 10.1017/S1368980015003377. Epub 2015 Dec 23.
The present study aimed to evaluate the clustering of undernutrition indicators of children under the age of 5 years in relation to different scales.
A community-based cross-sectional study design was employed. We collected anthropometric data, geographic locations/elevations of households and other data from visited households. We used a retrospective purely spatial Poisson probability model to identify and locate clusters (high rates) of stunting and wasting using the software SaTScan™ version 9·1·1. We ran a logistic regression model to help evaluate the causes of clustering. Settings Six villages in the Meskane Mareko District (38·45763°E, 8·042144°N) of southern Ethiopia.
We surveyed 2371 children aged <5 years, who were found in 1744 households.
We found a micro-level variation in the risk of stunting and wasting within the studied district. We found the most likely significant clusters for wasting and severe wasting in two of the six villages. For stunting, a single large cluster size of 390 cases (304·19 expected) in 756 households was identified (relative risk=1·48, P<0·01). For severe stunting, a single cluster size of 106 cases (69·39 expected) in 364 households was identified (relative risk=1·69, P=0·035).
We conclude that the distribution of wasting and stunting was partly spatially structured. We identified distinct areas within and between villages that have a higher risk than the underlying at-risk population. Our analysis identified the spatial locations of high-risk areas for stunting that could be an input for geographically targeting and optimizing nutritional interventions.
本研究旨在评估5岁以下儿童营养不良指标与不同尺度相关的聚集情况。
采用基于社区的横断面研究设计。我们收集了人体测量数据、家庭地理位置/海拔以及来自受访家庭的其他数据。我们使用回顾性纯空间泊松概率模型,通过SaTScan™ 9·1·1版软件识别和定位发育迟缓与消瘦的聚集区(高发病率区)。我们运行了逻辑回归模型以帮助评估聚集的原因。地点:埃塞俄比亚南部梅斯卡内马雷科区(东经38·45763°,北纬8·042144°)的6个村庄。
我们调查了1744户家庭中2371名5岁以下儿童。
我们发现在所研究的区域内,发育迟缓和消瘦风险存在微观层面的差异。我们在6个村庄中的2个村庄发现了最有可能存在消瘦和严重消瘦的显著聚集区。对于发育迟缓,在756户家庭中识别出一个单一的大聚集区,包含390例(预期304·19例)(相对风险=1·48,P<0·01)。对于严重发育迟缓,在364户家庭中识别出一个单一的聚集区,包含106例(预期69·39例)(相对风险=1·69,P=0·035)。
我们得出结论,消瘦和发育迟缓的分布在一定程度上具有空间结构。我们在村庄内部和村庄之间识别出了比潜在风险人群风险更高的不同区域。我们的分析确定了发育迟缓高风险区域的空间位置,这可为地理定位和优化营养干预措施提供依据。