Adaptive Interventions for Optimizing Malaria Control: a Cluster-Randomized SMART Trial

In the past decade, massive scale-up of long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) have led to significant reductions in malaria mortality and morbidity. Nonetheless, malaria burden remains high, and a dozen countries in Africa show a trend of increasing malaria incidence over the past several years. The high malaria burden in many areas of Africa underscores the need to improve the effectiveness of intervention tools by optimizing first-line intervention tools and integrating newly approved products into control programs. Vector control is an important component of the national malaria control strategy in Africa. Because transmission settings and vector ecology vary among countries or among districts within a country, interventions that work in one setting may not work well in all settings. Malaria interventions should be adapted and re-adapted over time in response to evolving malaria risks and changing vector ecology and behavior. The central objective of this application is to design optimal adaptive combinations of vector control interventions to maximize reductions in malaria burden based on local malaria transmission risks, changing vector ecology, and available mix of interventions approved by the Ministry of Health in each target country. The central hypothesis is that an adaptive approach based on local malaria risk and changing vector ecology will lead to significant reductions in malaria incidence and transmission risk. The aim of this study is to use a cluster-randomized sequential, multiple assignment randomized trial (SMART) design to compare various vector control methods implemented by the Ministry of Health of Kenya in reducing malaria incidence and infection, and develop an optimal intervention strategy tailored toward to local epidemiological and vector conditions.