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

University Of California-Irvine, Irvine CA

Investigators

Linked publications, trials & patents

Abstract

Study Type 3 (Implementation studies) PROJECT SUMMARY 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 Kenya and Ethiopia, the two focal countries of the current ICEMR. Because transmission settings and vector ecologies 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. We propose three specific aims: 1) measure and predict the risk of malaria using environmental, biological, social, and climatic features with a machine learning approach; 2) use a novel cluster-randomized sequential, multiple assignment randomized trial (SMART) design to develop an optimal intervention strategy adaptive to changing conditions; and 3) evaluate the cost-effectiveness and impact of the SMART design on transmission risk. The adaptive intervention strategy developed from this project will enable the optimization of existing malaria vector control tools and integration of future new control products or approaches. Because optimizing malaria control tools is an urgent issue that needs to be addressed globally, our approach can have broad and far-reaching implications for malaria prevention and control.