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.
