Géoinformatique et géostatistique : un aperçu

Comparison of Spatial and Conventional Regression Models in Determination of Trachoma Prevalence and Associated Risk Factors

Pius Kipngetich Kirui, Benson Kipkemboi Kenduiywo and Edward Hunja Waithaka

Trachoma is a neglected tropical disease and leading infectious cause of blindness, In Kenya it accounts for 19% of blindness. Past research on associated risk factors in Kenya have relied on traditional impact survey data only however non uniform distribution of prevalence in suspected endemic areas despite similar interventions measures calls for the need to include environmental and climatic potential risk factors in modeling trachoma transmission. Our study therefore aims at determining the prevalence of trachoma and its associated risks factors by use of spatial regression models in variable selection, estimation and prediction compared to conventional regression models. Through use of data from trachoma surveys and remotely sensed environmental and climatic data, spatial and non-spatial regression models were implemented. Regression results were then utilized in spatial interpolation using kriging and geographically weighted regression. Rainfall, presence of flies in children’s face, dirty faces of children and aridity were found out to be the significant variables that contributes towards trachoma transmission. Spatial lag model had the least value of akaike information criterion of 385.08 hence performed relatively better compared to the rest of the regressions models. In estimation of prevalence in places where data was not collected, multivariate regression kriging did slightly better than the geographically weighted regression. The study shows that Spatial regression models performs better compared to conventional regression models both in variable selection and in spatial prediction of trachoma prevalence. Among the spatial regressions the significant variables as obtained were similar though spatial lag performed relatively better compared to other regression models in variable selection based on AIC value and R -squared. There was minimal variation between the two spatial interpolation methods.