Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
Schools of Mathematics, Statistics and Computer Science, College of Agriculture Engineering and Science, University of KwaZulu-Natal, Durban, South Africa.
PLoS One. 2022 May 20;17(5):e0268186. doi: 10.1371/journal.pone.0268186. eCollection 2022.
Malaria is a severe health threat in the World, mainly in Africa. It is the major cause of health problems in which the risk of morbidity and mortality associated with malaria cases are characterized by spatial variations across the county. This study aimed to investigate the spatial patterns and predictors of malaria distribution in Ethiopia.
A weighted sample of 15,239 individuals with rapid diagnosis test obtained from the Central Statistical Agency and Ethiopia malaria indicator survey of 2015. Global Moran's I and Moran scatter plots were used in determining the distribution of malaria cases, whereas the local Moran's I statistic was used in identifying exposed areas. The auto logistics spatial binary regression model was used to investigate the predictors of malaria.
The final auto logistics regression model was reported that male clients had a positive significant effect on malaria cases as compared to female clients [AOR = 2.401, 95% CI: (2.125-2.713) ]. The distribution of malaria across the regions was different. The highest incidence of malaria was found in Gambela [AOR = 52.55, 95%CI: (40.54-68.12)] followed by Beneshangul [AOR = 34.95, 95%CI: (27.159-44.963)]. Similarly, individuals in Amhara [AOR = 0.243, 95% CI:(0.195-0.303], Oromiya [AOR = 0.197, 955 CI: (0.158-0.244)], Dire Dawa [AOR = 0.064, 95%CI(0.049-0.082)], Addis Ababa[AOR = 0.057,95%CI:(0.044-0.075)], Somali[AOR = 0.077,95%CI:(0.059-0.097)], SNNPR[OR = 0.329, 95%CI: (0.261-0.413)] and Harari [AOR = 0.256, 95%CI:(0.201-0.325)] were less likely to had low incidence of malaria as compared with Tigray. Furthermore, for one meter increase in altitude, the odds of positive rapid diagnostic test (RDT) decreases by 1.6% [AOR = 0.984, 95% CI: (0.984-0.984)]. The use of a shared toilet facility was found as a protective factor for malaria in Ethiopia [AOR = 1.671, 95% CI: (1.504-1.854)]. The spatial autocorrelation variable changes the constant from AOR = 0.471 for logistic regression to AOR = 0.164 for auto logistics regression.
This study found that the incidence of malaria in Ethiopia had a spatial pattern which is associated with socio-economic, demographic, and geographic risk factors. Spatial clustering of malaria cases had occurred in all regions, and the risk of clustering was different across the regions. The risk of malaria was found to be higher for those who live in soil floor-type houses as compared to those who lived in cement or ceramics floor type. Similarly, households with thatched, metal and thin, and other roof-type houses have a higher risk of malaria than ceramics tiles roof houses. Moreover, using a protected anti-mosquito net was reducing the risk of malaria incidence.
疟疾是世界范围内的严重健康威胁，主要在非洲。它是导致健康问题的主要原因，与疟疾病例相关的发病率和死亡率风险的特点是全县的空间差异。本研究旨在调查埃塞俄比亚疟疾分布的空间模式和预测因素。
使用从中央统计局获得的 15239 名快速诊断检测的加权样本和 2015 年埃塞俄比亚疟疾指标调查。全局 Moran's I 和 Moran 散点图用于确定疟疾病例的分布，而局部 Moran's I 统计量用于识别暴露区域。使用自回归逻辑斯谛空间二元回归模型来研究疟疾的预测因素。
最终的自回归逻辑斯谛回归模型报告称，与女性客户相比，男性客户对疟疾病例有积极的显著影响[优势比（AOR）=2.401，95%置信区间（95%CI）：（2.125-2.713）]。疟疾在各地区的分布不同。疟疾发病率最高的是甘贝拉[AOR=52.55，95%CI：（40.54-68.12）]，其次是本尚古勒-古马兹[AOR=34.95，95%CI：（27.159-44.963）]。同样，阿姆哈拉[AOR=0.243，95%CI：（0.195-0.303）]、奥罗莫[AOR=0.197，955 CI：（0.158-0.244）]、德雷达瓦[AOR=0.064，95%CI：（0.049-0.082）]、亚的斯亚贝巴[AOR=0.057，95%CI：（0.044-0.075）]、索马里[AOR=0.077，95%CI：（0.059-0.097）]、南方各族州[AOR=0.329，95%CI：（0.261-0.413）]和哈拉里[AOR=0.256，95%CI：（0.201-0.325）]的疟疾发病率较低。此外，海拔每增加一米，快速诊断检测（RDT）呈阳性的几率就会降低 1.6%[AOR=0.984，95%CI：（0.984-0.984）]。埃塞俄比亚共用厕所设施被发现是疟疾的保护因素[AOR=1.671，95%CI：（1.504-1.854）]。空间自相关变量将逻辑回归的常数从 AOR=0.471 变为自回归逻辑斯谛回归的 AOR=0.164。
本研究发现，埃塞俄比亚的疟疾发病率存在空间模式，与社会经济、人口和地理风险因素有关。所有地区都出现了疟疾病例的空间聚集，各地区的聚集风险不同。与居住在水泥或陶瓷地板类型房屋的人相比，居住在土坯房的人患疟疾的风险更高。同样，茅草、金属和薄屋顶和其他屋顶类型的房屋比陶瓷瓦屋顶房屋患疟疾的风险更高。此外，使用有保护作用的蚊帐可以降低疟疾发病率。
