Ideas to Action:
Independent research for global prosperity
Rwanda’s performance-based incentives were effective for some indicators, but unconditional financing also induced improvements. The incentive effects persisted in the mediumrun and as the program was scaled-up.
We examine alternative strategies for targeted sampling of health clinics for independent verification. Our results indicate that machine learning methods, particularly Random Forest, outperform other approaches and can increase the cost-effectiveness of verification activities.
In this paper we combine fourteen years of high-resolution satellite data on forest loss with individual-level survey data on malaria in more than 60,000 rural children in 17 countries in Africa, and fever in more than 470,000 rural children in 41 countries in Latin America, Africa, and Asia. We did not find that deforestation increases malaria prevalence nor that intermediate levels of forest cover have higher malaria prevalence.
Founded in 2002, the Global Fund to Fight AIDS, Tuberculosis and Malaria (the Global Fund) is one of the world’s largest multilateral health funders, disbursing $3–$4 billion a year across 100-plus countries. Many of these countries rely on Global Fund monies to finance their respective disease responses—and for their citizens, the efficient and effective use of Global Fund monies can be the difference between life and death.
Commentary Menu