Measuring spillover effects of reactive, focal malaria elimination interventions

项目摘要/摘要

PROJECT SUMMARY / ABSTRACT This proposed K01 award will support the career development of Dr. Jade Benjamin-Chung, an Epidemiologist in the Division of Epidemiology & Biostatistics at the University of California (UC), Berkeley. Dr. Benjamin- Chung’s career goal is to become a leader in the application of rigorous biostatistical methods to infectious disease control and elimination. To support her career development, this application proposes a study she will lead to fill an important gap in research on malaria elimination interventions. As malaria transmission declines it becomes more heterogeneous and is characterized by focal hot spots of transmission. Blanket coverage of interventions becomes impractical and is not cost-effective. Reactive, focal interventions target hot spots by delivering antimalarials to people residing near to a symptomatic malaria case that presents to a surveillance site. Focally delivered interventions aim to reduce transmission to those outside focal treatment zones, including to asymptomatic malaria cases, who are thought to be responsible for the majority of transmission in elimination settings. Thus, information about whether interventions reduce illness among intervention recipients (“direct effects”) vs. non-intervention recipients in intervention clusters (i.e., “spillover effects”) is critical to understanding whether these interventions can eliminate malaria, yet current studies have not estimated such spillover effects. This study will estimate site-specific and pooled direct effects and spillover effects in three cluster-randomized trials of reactive, focal malaria elimination interventions in low malaria transmission settings in Namibia, Swaziland, and Zambia. The specific aims are to (1) estimate direct effects and spillover effects of reactive, focal malaria elimination interventions on Plasmodium falciparum malaria incidence and prevalence and (2) assess whether direct effects and spillover effects of reactive, focal malaria elimination interventions vary by distance to intervention, intervention coverage, and time from incident case detection. Evidence of spillover effects would suggest that reactive, focal interventions hold promise for malaria elimination when scaled up. The absence of spillover effects would suggest that interventions did not interrupt transmission; if so, information about the spatial configuration of infections would inform who and how many people to treat using redesigned interventions. This study will apply novel machine learning-based methods for estimation of causal effects appropriate for infectious disease data. This application proposes a 4-year training plan including mentorship from two leading biostatisticians at UC Berkeley and two malaria epidemiologists at UC San Francisco. Dr. Benjamin-Chung’s training goals are to (1) develop skills in machine learning and causal inference methods for dependent data, (2) learn about malaria biology and epidemiology, and (3) enhance her software development and spatial analysis skills. UC Berkeley is the optimal place for Dr. Benjamin-Chung to advance her career goals because of its leading faculty in biostatistics, strong track record of research and teaching in infectious diseases and biostatistics, and close proximity to UCSF’s Malaria Elimination Initiative.