Spatiotemporal Variation of Malaria and Risk Factors in West Gojjam Zone from 1 July 2013- 30 June 2018, Northwest Ethiopia

Abstract

Abstract Background: Malaria is a life-threatening acute febrile illness in many tropical and subtropical areas, which is affecting the lives of millions globally. Its distribution is characterized by spatial, temporal, and spatiotemporal heterogeneity making detection of the space-time distribution and mapping high-risk areas useful to effectively targeting hot spots of malaria for intervention. Objective: This study aimed to assess spatiotemporal variation of malaria and risk factors in West Gojjam Zone from 1 July 2013- 30 June 2018, Northwest Ethiopia. Methods: Time series cross sectional study was conducted using data was obtained from weekly malaria surveillance reports stored in the Amhara Public Health Institute from 1 July 2013-30 June 2018. Climatic variables were obtained from West Amhara Meteorological Agency. All districts were included and geo-coded and the spatial data was created in ArcGIS10.2.2 software. Global and local spatial autocorrelation were used to test the hypothesis and to detect hot spots respectively. The Poisson model was fitted to determine the purely spatial, temporal, and space-time clusters using SaTScan™9.6 software. Spearman correlation, bivariate, and multivariable negative binomial regressions were used to analyze the relation of the climatic factors to count of malaria incidence. Result: The study revealed spatial, temporal, and spatiotemporal heterogeneity of malaria distribution. Jabitenan, Quarit, Sekela, Bure, and Wonberma were high rate spatial cluster of malaria incidence hierarchically. Spatiotemporal clusters were detected. A temporal scan statistic identified one risk period from 1 July 2013 to 30 June 2015. Monthly average temperature was positively but monthly average rainfall and monthly average relative humidity were negatively correlated to count of malaria incidence at all lag-months. The adjusted incidence rate ratio showed that monthly average temperature and monthly average rainfall were independent predictors for malaria incidence at all lag-months. Monthly average relative humidity was significant at 2 months lag.