Abstract
Background: In Ethiopia, still, malaria is killing and affecting a lot of people of any age group somewhere in the country at any time. However, due to limited research, little is known about the spatial patterns and correlated risk factors on the wards scale. Methods: In this research, we explored spatial patterns and evaluated related potential environmental risk factors in the distribution of malaria incidence in Ethiopia in 2015 and 2016. Hot Spot Analysis (Getis-Ord Gi* statistic) was used to assess the clustering patterns of the disease. The ordinary least square (OLS), geographically weighted regression (GWR), and semiparametric geographically weighted regression (s-GWR) models were compared to describe the spatial association of potential environmental risk factors with malaria incidence.Results: Our results revealed a heterogeneous and highly clustered distribution of malaria incidence in Ethiopia during the study period. The s-GWR model best explained the spatial correlation of potential risk factors with malaria incidence and was used to produce predictive maps. The GWR model revealed that the relationship between malaria incidence and elevation, temperature, precipitation, relative humidity, and normalized difference vegetation index (NDVI) varied significantly among the wards. During the study period, the s-GWR model provided a similar conclusion, except in the case of NDVI in 2015, and elevation and temperature in 2016, which were found to have a global relationship with malaria incidence. Hence, precipitation and relative humidity exhibited a varying relationship with malaria incidence among the wards in both years. Conclusions: This finding could be used in the formulation and execution of evidence-based malaria control and management program to allocate scare resources locally at the wards level. Moreover, these study results provide a scientific basis for malaria researchers in the country.
A Study on Risk Factors for the Prevalence of Dementia: Geographically Weighted Regression
