Data analysis
malaria
REGRESSION, MALARIA
The data collection and analysis methods used were described elsewhere [Univariable mixed effect logistic regression using glmer function of R version 4.0.4 statistical software was used to assess the effect of MPE on outcome variables by taking schools as a random variable to account for the cluster effect. Odds ratio (OR) and corresponding 95% confidence interval (CI) were used to assess the strength of association between the outcome variables and the intervention. For bed net utilization and reported incidence of malaria, whether there is a confounding effect or not is analyzed by using multivariable mixed-effect logistic regression. Therefore, apart from the intervention, other factors predicting the bed net utilization and reported cumulative incidence were assessed. The fit of the model in predicting the outcome variables was checked by Akaike Information Criterion (AIC). The selection of variables for multivariable mixed-effect logistic regression was made through the backward stepwise variable selection method in which the variable with the largest P-value is removed from the model and checked for AIC. If the removal of a variable from the model improves the AIC value, it is removed from the model, otherwise, it is retained in the final model. For multivariable mixed-effects models, P-values less than 0.05 were considered statistically significant.