1. Why Uncertainty in Global Health Interventions Matters—and What We Can Do About It
- Author:
- Kalipso Chalkidou, Anupama Dathan, and Francis Ruiz
- Publication Date:
- 09-2019
- Content Type:
- Policy Brief
- Institution:
- Center for Global Development (CGD)
- Abstract:
- Global health interventions, like many public policies, are rife with uncertainty. Will a program, such as a malaria prevention strategy that looks strong on paper, work as intended? Will a new technology, such as a specific drug or device that appears effective in clinical trial settings, work in practice and provide good value-for-money? In the case of programs made up of a complex interaction of multiple interventions, implementers often create a theory of change and then meticulously track whether it is being followed every step of the way, from each input translating into the prespecified activity, and the activities yielding the right outputs and the expected outcomes. When observational data is available that permits quantitative analysis (evaluation), it may also be possible to estimate causal impact in a given setting by applying experimental methods (such as a randomized controlled trial) or quasi-experimental techniques (such as difference-in-difference analysis). Such program evaluations generally consider outputs (e.g. the number of bed nets distributed) and relatively short-term outcomes (e.g. malaria infections following bed net distribution). Many eval- uations also collect data years after the program to identify longer-term impacts. Cost-effectiveness calculations are sometimes conducted after ascertaining the cost and impact of the program, but such analyses aren’t necessarily considered when determining whether to implement a certain program or technology—especially when politics and other concerns get in the way. Discrete clinical interventions and technologies (which are defined as including clinical interven- tions, drugs, diagnostics and even public health programs) are usually the subject of health technolo- gy assessment (HTA) to inform coverage decisions in many contexts. The underpinning evidence base for HTA typically involves a synthesis of randomized trial data, designed to reduce bias in estimating causal inference and relative effectiveness. Trial data is then combined with information from other sources and study designs to develop models of the technology’s long-term health and cost impact in a given context. A key feature of both programs and technologies is uncertainty.
- Topic:
- Health, International Cooperation, Science and Technology, Humanitarian Intervention, and Pharmaceuticals
- Political Geography:
- Global Focus