Statistical analysis
anaemia, infection, malaria infection
ANAEMIA, INFECTION, REGRESSION, INTERACTION
A total of 48 clusters of 80 individuals per cluster provided 80% power to detect a 28% relative reduction in malaria infection prevalence between the combined IRS treatment groups (standard LLIN + IRS and PBO-Pyrethroid-LLIN + IRS groups, 24 clusters in total) and the two groups not receiving IRS (standard LLIN & PBO-Pyrethroid-LLIN, 24 clusters in total), and between the two PBO-Pyrethroid-LLIN groups (24 clusters of which 12 received IRS) compared to the two standard LLIN groups (24 clusters of which 12 received IRS), assuming a mean infection prevalence of 20% in the reference groups, and a coefficient of variation of 0.3 [Differences in malaria infection prevalence in children under 15 years old and anaemia prevalence in children from 6 months to 4 years old between IRS groups versus no IRS groups, and PBO-Pyrethroid-LLIN groups versus standard pyrethroid-LLIN groups was conducted in an intention to treat (ITT) basis at each time point (28 and 33 months), using logistic regression allowing for within cluster correlation of responses by using a robust variance estimator to calculate standard errors. Interaction between IRS and PBO-Pyrethroid-LLINs was assessed during each survey. As study net usage was low, differences in malaria infection prevalence and anaemia prevalence were also analysed using a per-protocol (PP) analysis, restricted to only children using the allocated LLINs, for both outcomes. For the entomological outcomes, vector density and EIR, negative binomial regression with a robust variance estimator was used to estimate density rate ratios between groups after adjusting for baseline. EIR was estimated as the mean number of sporozoite infected To assess reduction in malaria infection prevalence over the 3 years, PBO-Pyrethroid-LLIN groups (with and without prior IRS) and standard pyrethroid-LLIN groups (with and without IRS) were compared. A post hoc ITT analysis was done using a logistic regression with a robust variance estimator. The model included fixed effects for survey time, study group, IRS/no IRS and the interaction time by study group. Data from all 6 malaria infection prevalence surveys conducted in June and November between 2015 and 2017 were included in this analysis.