Nigussie Teshager Zerihun, Zewotir Temesgen T, Muluneh Essey Kebede
Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
Department of Statistics, Faculty of Natural and Computational Sciences, Debre Tabor University, Debre Tabor, Ethiopia.
Heliyon. 2023 Apr 5;9(4):e15252. doi: 10.1016/j.heliyon.2023.e15252. eCollection 2023 Apr.
The impacts of climate change and environmental predictors on malaria epidemiology remain unclear and not well investigated in the Sub-Sahara African region. This study was aimed to investigate the nonlinear effects of climate and environmental factors on monthly malaria cases in northwest Ethiopia, considering space-time interaction effects. The monthly malaria cases and populations sizes of the 152 districts were obtained from the Amhara public health institute and the central statistical agency of Ethiopia. The climate and environmental data were retrieved from US National Oceanic and Atmospheric Administration. The data were analyzed using a spatiotemporal generalized additive model. The spatial, temporal, and space-time interaction effects had higher contributions in explaining the spatiotemporal distribution of malaria transmissions. Malaria transmission was seasonal, in which a higher number of cases occurred from September to November. The long-term trend of malaria incidence has decreased between 2012 and 2018 and has turned to an increased pattern since 2019. Areas neighborhood to the Abay gorge and Benshangul-Gumuz, South Sudan, and Sudan border have higher spatial effects. Climate and environmental predictors had significant nonlinear effects, in which their effects are not stationary through the ranges of values of variables, and they had a smaller contributions in explaining the variabilities of malaria incidence compared to seasonal, spatial and temporal effects. Effects of climate and environmental predictors were nonlinear and varied across areas, ecology, and landscape of the study sites, which had little contribution to explaining malaria transmission variabilities with an account of space and time dimensions. Hence, exploring and developing an early warning system that predicts the outbreak of malaria transmission would have an essential role in controlling, preventing, and eliminating malaria in areas with lower and higher transmission levels and ultimately lead to the achievement of malaria GTS milestones.
气候变化和环境预测因素对疟疾流行病学的影响仍不明确,且在撒哈拉以南非洲地区尚未得到充分研究。本研究旨在探讨气候和环境因素对埃塞俄比亚西北部每月疟疾病例的非线性影响,并考虑时空交互效应。152个地区的每月疟疾病例和人口规模数据来自阿姆哈拉公共卫生研究所和埃塞俄比亚中央统计局。气候和环境数据取自美国国家海洋和大气管理局。数据采用时空广义相加模型进行分析。空间、时间和时空交互效应在解释疟疾传播的时空分布方面具有更高的贡献。疟疾传播具有季节性,9月至11月病例数较多。2012年至2018年期间疟疾发病率的长期趋势有所下降,自2019年以来转为上升趋势。靠近阿巴伊峡谷以及与南苏丹和苏丹接壤的本尚古勒-古穆兹地区具有较高的空间效应。气候和环境预测因素具有显著的非线性效应,其效应在变量值范围内并非固定不变,与季节、空间和时间效应相比,它们在解释疟疾发病率变异性方面的贡献较小。气候和环境预测因素的效应是非线性的,且在研究地点的不同区域、生态和景观中有所不同,在考虑空间和时间维度的情况下,它们对解释疟疾传播变异性的贡献很小。因此,探索和开发一个预测疟疾传播爆发的预警系统,对于控制、预防和消除疟疾传播水平较低和较高地区的疟疾至关重要,并最终有助于实现全球疟疾防治技术战略的里程碑目标。