PDF(Pubmed)
Abstract：
BACKGROUND: In Ethiopia, recent evidence revealed that over a quarter (27%) of households (HHs) defecated openly in bush or fields, which play a central role as the source of many water-borne infectious diseases, including cholera. Ethiopia is not on the best track to achieve the SDG of being open-defecation-free by 2030. Therefore, this study aimed to explore the spatial variation and geographical inequalities of open defecation (OD) among HHs in Ethiopia.
METHODS: This was a country-wide community-based cross-sectional study among a weighted sample of 8663 HHs in Ethiopia. The global spatial autocorrelation was explored using the global Moran\'s-I, and the local spatial autocorrelation was presented by Anselin Local Moran\'s-I to evaluate the spatial patterns of OD practice in Ethiopia. Hot spot and cold spot areas of OD were detected using ArcGIS 10.8. The most likely high and low rates of clusters with OD were explored using SaTScan 10.1. Geographical weighted regression analysis (GWR) was fitted to explore the geographically varying coefficients of factors associated with OD.
RESULTS: The prevalence of OD in Ethiopia was 27.10% (95% CI: 22.85-31.79). It was clustered across enumeration areas (Global Moran\'s I = 0.45, Z-score = 9.88, P-value ≤ 0.001). Anselin Local Moran\'s I analysis showed that there was high-high clustering of OD at Tigray, Afar, Northern Amhara, Somali, and Gambela regions, while low-low clustering of OD was observed at Addis Ababa, Dire-Dawa, Harari, SNNPR, and Southwest Oromia. Hotspot areas of OD were detected in the Tigray, Afar, eastern Amhara, Gambela, and Somali regions. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions were explored as having high rates of OD. The GWR model explained 75.20% of the geographical variation of OD among HHs in Ethiopia. It revealed that as the coefficients of being rural residents, female HH heads, having no educational attainment, having no radio, and being the poorest HHs increased, the prevalence of OD also increased.
CONCLUSIONS: The prevalence of OD in Ethiopia was higher than the pooled prevalence in sub-Saharan Africa. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions had high rates of OD. Rural residents, being female HH heads, HHs with no educational attainment, HHs with no radio, and the poorest HHs were spatially varying determinants that affected OD. Therefore, the government of Ethiopia and stakeholders need to design interventions in hot spots and high-risk clusters. The program managers should plan interventions and strategies like encouraging health extension programs, which aid in facilitating basic sanitation facilities in rural areas and the poorest HHs, including female HHs, as well as community mobilization with awareness creation, especially for those who are uneducated and who do not have radios.
METHODS: This was a country-wide community-based cross-sectional study among a weighted sample of 8663 HHs in Ethiopia. The global spatial autocorrelation was explored using the global Moran\'s-I, and the local spatial autocorrelation was presented by Anselin Local Moran\'s-I to evaluate the spatial patterns of OD practice in Ethiopia. Hot spot and cold spot areas of OD were detected using ArcGIS 10.8. The most likely high and low rates of clusters with OD were explored using SaTScan 10.1. Geographical weighted regression analysis (GWR) was fitted to explore the geographically varying coefficients of factors associated with OD.
RESULTS: The prevalence of OD in Ethiopia was 27.10% (95% CI: 22.85-31.79). It was clustered across enumeration areas (Global Moran\'s I = 0.45, Z-score = 9.88, P-value ≤ 0.001). Anselin Local Moran\'s I analysis showed that there was high-high clustering of OD at Tigray, Afar, Northern Amhara, Somali, and Gambela regions, while low-low clustering of OD was observed at Addis Ababa, Dire-Dawa, Harari, SNNPR, and Southwest Oromia. Hotspot areas of OD were detected in the Tigray, Afar, eastern Amhara, Gambela, and Somali regions. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions were explored as having high rates of OD. The GWR model explained 75.20% of the geographical variation of OD among HHs in Ethiopia. It revealed that as the coefficients of being rural residents, female HH heads, having no educational attainment, having no radio, and being the poorest HHs increased, the prevalence of OD also increased.
CONCLUSIONS: The prevalence of OD in Ethiopia was higher than the pooled prevalence in sub-Saharan Africa. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions had high rates of OD. Rural residents, being female HH heads, HHs with no educational attainment, HHs with no radio, and the poorest HHs were spatially varying determinants that affected OD. Therefore, the government of Ethiopia and stakeholders need to design interventions in hot spots and high-risk clusters. The program managers should plan interventions and strategies like encouraging health extension programs, which aid in facilitating basic sanitation facilities in rural areas and the poorest HHs, including female HHs, as well as community mobilization with awareness creation, especially for those who are uneducated and who do not have radios.
摘要：
背景：在埃塞俄比亚，最近的证据显示，超过四分之一（27％）的家庭（HHs）在布什或田野中公开排便，作为许多水传播传染病的源头，包括霍乱.埃塞俄比亚还没有走上到2030年实现开放式无排便可持续发展目标的最佳轨道。因此,这项研究旨在探讨埃塞俄比亚HHs之间开放式排便（OD）的空间变化和地理不等式。
方法：这是一项全国性的基于社区的横断面研究，在埃塞俄比亚的8663名HH加权样本中进行。使用全局Moran\s-I，探索了全局空间自相关，AnselinLocalMoran's-I提出了局部空间自相关，以评估埃塞俄比亚OD实践的空间格局。使用ArcGIS10.8检测OD的热点和冷点区域。使用SaTScan10.1探索具有OD的集群的最可能的高率和低率。拟合地理加权回归分析（GWR）以探索与OD相关的因素的地理变化系数。
结果：埃塞俄比亚的OD患病率为27.10％（95％CI：22.85-31.79）。它聚集在枚举区域（全球Moran'sI=0.45，Z评分=9.88，P值≤0.001）。AnselinLocalMoran\'sI分析表明，在Tigray处存在OD的高聚类，Afar,北阿姆哈拉,索马里，和甘贝拉地区，虽然在亚的斯亚贝巴观察到OD的低-低聚集，Dire-Dawa,Harari,SNNPR,和西南奥罗米亚。在提格雷中检测到OD的热点区域，Afar,阿姆哈拉东部,Gambela,索马里地区。提格雷,Afar,阿姆哈拉北部,奥罗米亚东部,索马里地区被认为OD率高。GWR模型解释了埃塞俄比亚HHs之间OD地理变化的75.20％。它揭示了作为农村居民的系数，女性HH头，没有教育程度，没有收音机,作为最贫穷的人，OD的患病率也增加。
结论：埃塞俄比亚的OD患病率高于撒哈拉以南非洲的合并患病率。提格雷,Afar,阿姆哈拉北部,奥罗米亚东部,索马里地区的OD率高。农村居民,作为女性HH头，没有受过教育的人，没有收音机的人，和最差的HH是影响OD的空间变化的决定因素。因此,埃塞俄比亚政府和利益相关者需要在热点和高风险集群中设计干预措施。项目经理应该计划干预措施和策略，如鼓励健康推广计划，这有助于促进农村地区和最贫穷的卫生工作者的基本卫生设施，包括女性HHs,以及社区动员和提高认识，特别是对于那些没有受过教育和没有收音机的人。
方法：这是一项全国性的基于社区的横断面研究，在埃塞俄比亚的8663名HH加权样本中进行。使用全局Moran\s-I，探索了全局空间自相关，AnselinLocalMoran's-I提出了局部空间自相关，以评估埃塞俄比亚OD实践的空间格局。使用ArcGIS10.8检测OD的热点和冷点区域。使用SaTScan10.1探索具有OD的集群的最可能的高率和低率。拟合地理加权回归分析（GWR）以探索与OD相关的因素的地理变化系数。
结果：埃塞俄比亚的OD患病率为27.10％（95％CI：22.85-31.79）。它聚集在枚举区域（全球Moran'sI=0.45，Z评分=9.88，P值≤0.001）。AnselinLocalMoran\'sI分析表明，在Tigray处存在OD的高聚类，Afar,北阿姆哈拉,索马里，和甘贝拉地区，虽然在亚的斯亚贝巴观察到OD的低-低聚集，Dire-Dawa,Harari,SNNPR,和西南奥罗米亚。在提格雷中检测到OD的热点区域，Afar,阿姆哈拉东部,Gambela,索马里地区。提格雷,Afar,阿姆哈拉北部,奥罗米亚东部,索马里地区被认为OD率高。GWR模型解释了埃塞俄比亚HHs之间OD地理变化的75.20％。它揭示了作为农村居民的系数，女性HH头，没有教育程度，没有收音机,作为最贫穷的人，OD的患病率也增加。
结论：埃塞俄比亚的OD患病率高于撒哈拉以南非洲的合并患病率。提格雷,Afar,阿姆哈拉北部,奥罗米亚东部,索马里地区的OD率高。农村居民,作为女性HH头，没有受过教育的人，没有收音机的人，和最差的HH是影响OD的空间变化的决定因素。因此,埃塞俄比亚政府和利益相关者需要在热点和高风险集群中设计干预措施。项目经理应该计划干预措施和策略，如鼓励健康推广计划，这有助于促进农村地区和最贫穷的卫生工作者的基本卫生设施，包括女性HHs,以及社区动员和提高认识，特别是对于那些没有受过教育和没有收音机的人。
