Modeling and Determining Risk Factors of Malaria Among Children in Kenya

Abstract

Malaria is a life threatening disease that has adverse e ects on child development. The e ects include absenteeism from school and pains associated with malaria. This paper investigates risk factors associated with malaria in children using logistic regression and generalized linear mixed e ect model(GLMM).The study used secondary data derived from Kenya Malaria Indicator Survey (KMIS) conducted in 2015. Based on Akaike information criterion (AIC), GLMM model results to a better t compared to logistic regression. The study revealed that age, place of residence, level of anemia, wealth quintile, availability of electricity and cluster altitude were signi cant predictors of malaria.In addition, the ndings revealed that access to radio and television by households result to reduction in malaria prevalence