Evaluation of IR3535 As a Spatial Repellent for Malaria Control. (Avaliação Da Eficácia Do Repelente Ambiental 3-(n-acetyl-n-butyl) Aminopropionic Acid Ethyl Ester (IR3535) Como Ferramenta Para O Controlo Da Malária).

Mozambique contributes with 5% of global malaria cases, and despite control efforts the Sofala province continues to experience a high burden of malaria. The resistance to insecticides and changes in vector habits can compromise the use of common vector control tools. The use of spatial repellents is thus an interesting alternative, as it does not exert selective pressure on resistance genes or eliminate other insects with impact on biodiversity. IR3535 is a non-toxic repellent and if used at community level can extend protection to outdoor biting.

Hypothesis: Using the IR3535 repellent for indoor and outdoor spraying will reduce the prevalence of malaria and reduce vector density and infection.

An experimental Before-After-Control-Intervention will be carried out with two groups: a) Intervention (Tambai Q2 and Q6): with intra and extra-household spraying with IR3535 and b) Control (Tambai Q3 and Q4): without spraying. Tambai is acommunity of Bebedo, Nhamatanda, Sofala, Mozambique. The mosquito distribution, diversity, density and sporozoite rate will be monitored indoors and outdoors in both communities for 2 years. The prevalence of malaria will be determined in under five years old children at the beginning, the end of the 1st year and at the end of the study. Additionally, cross-sectional studies with a mixed approach assessing the community knowledge, attitudes and practices (KAP) will be carried out to detect gaps that persist at the community level. Heads of households and health professionals will be interviewed at the beginning of the study, the end of 1st year and at the end of the study. The data will be analyzed using SPSS and R software packages. For matching situations (before and after), the McNemar test will be used to ascertain statistical significance. Generalized Linear Models (GLM) will be used to jointly analyze several explanatory variables. Linear Mixed Models (LMM) and Generalized Estimation Equation (GEE) models will be used to compare longitudinal data. The prevalence of malaria and entomological indices relevant for transmission are expected to decrease with the intervention while community knowledge on malaria and its control are expected to increase.