Description of the study variables.
malaria, malaria infection
MALARIA, REGRESSION, INTERACTIONS, INTERACTION
For categorical variables, numbers and proportions (percentages) in each level were calculated. For continuous variable median and inter-quartile range (IQR) were calculated.Given that malaria prevalence was more than 40% in the study area; Poisson regression was used as an alternative to logistic regression to determine factors associated with malaria infection. Poisson regression estimates risk ratio (prevalence ratio) instead of odd ratio. For rare outcome (less than 10%) odds ratio approximates prevalence ratio or risks ratio, that’s why odds are used to present the risk. However when the outcome is very common odds tend to overestimate the risk [A Multilevel modelling technique was used to estimate the risk ratio, given the hierarchical nature of the data (as the children are nested within a household and households are nested within clusters/villages). Household and village level variation in malaria prevalence was assessed from a null multilevel model that included only a random intercept term. The likelihood showed no variability at the household level; hence only village/cluster level was considered as random effect during this analysis. Multilevel mixed-effect generalized liner mixed model (meglm) with a Poisson distribution (family) and log link function was used in Stata.A step-wise by backward elimination method was used for all factors associated with malaria infection with a p-value of 0.2 in the univariate model into a multivariable multilevel Poisson regression model. Interactions between study arm with LLIN usage and survey rounds were investigated. Interaction between household IRS status and survey round was also included. Akaike Information Criteria (AIC) was used to assess the model with and without interaction. The between cluster variability and Intracluster correlation coefficient (ICC) were also calculated and presented. Complete case analysis was considered.