Malaria is one of the greatest health challenges facing the developing world. World Health Organization data indicate that malaria causes over 1 million deaths per year, with over 90% of those deaths occurring in sub-Saharan Africa. A number of strategies are available to prevent malaria, including the use of insecticide treated nets and indoor residual spraying for vector control and a variety of diagnostic strategies and drug therapies for both prophylaxis and treatment. However, it is often difficult for decision-makers to determine the best combination of interventions, given the constraints within local health care delivery systems, as well as in vector control infrastructure. Thus, the central objective of this proposal is to improve malaria control outcomes through an implementation science approach that integrates health delivery experiments and decision support modeling to promote joint optimization of vector control and disease management strategies. We propose three specific aims: (1) perform randomized experiments of vector control and disease management strategies in Tanzania to elucidate which intervention strategy combinations are most effective in real world settings, using an implementation science approach;(2) use analytical insights from the health delivery experiments to refine an existing decision support model that includes both vector control and disease management;and (3) develop approaches for replicating the decision support tool in other parts of Tanzania and other countries in sub-Saharan Africa. Accomplishing these combined specific aims will provide a framework for designing and implementing multi-pronged malaria control strategies that have the best chances for sustained effectiveness in an operational setting. We will test the effectiveness of different combinations of vector and disease management interventions over time based on field experiments. We will then use the experimental results in conjunction with decision support modeling to improve a new tool that will allow decision-makers to jointly optimize vector and disease management strategies. The tool is flexible enough to incorporate new therapies and interventions as they are developed. This proposal has been developed in a collaborative process with critical partners in Tanzania and, thus, leverages an active and vibrant set of professional relationships. Through these in-country partnerships, we have direct access to key decision-makers at both the local and national levels. In addition, our previous work across Africa provides a strong platform from which to launch future replication in areas beyond the primary study site.
Malaria is one of the greatest health challenges facing the developing world. This project evaluates malaria control interventions under real world conditions. By harnessing new insights from decision science and implementation science, we are helping decision-makers to jointly optimize vector and disease management strategies. In so doing, we will provide a clear mechanism for improving malaria outcomes.
| Kim, Dohyeong; Brown, Zachary; Anderson, Richard et al. (2017) The Value of Information in Decision-Analytic Modeling for Malaria Vector Control in East Africa. Risk Anal 37:231-244 |
| Rahman, Rifat; Lesser, Adriane; Mboera, Leonard et al. (2016) Cost of microbial larviciding for malaria control in rural Tanzania. Trop Med Int Health 21:1468-1475 |
| Mboera, Leonard E G; Kramer, Randall A; Miranda, Marie Lynn et al. (2014) Community knowledge and acceptance of larviciding for malaria control in a rural district of east-central Tanzania. Int J Environ Res Public Health 11:5137-54 |
| Kramer, Randall A; Mboera, Leonard E G; Senkoro, Kesheni et al. (2014) A randomized longitudinal factorial design to assess malaria vector control and disease management interventions in rural Tanzania. Int J Environ Res Public Health 11:5317-32 |
| Kim, Dohyeong; Fedak, Kristen; Kramer, Randall (2012) Reduction of malaria prevalence by indoor residual spraying: a meta-regression analysis. Am J Trop Med Hyg 87:117-24 |
