Priority setting of health interventions: the need for multi-criteria decision analysis

Abstract

Priority setting of health interventions is often ad-hoc and resources are not used to an optimal extent. Underlying problem is that multiple criteria play a role and decisions are complex. Interventions may be chosen to maximize general population health, to reduce health inequalities of disadvantaged or vulnerable groups, ad/or to respond to life-threatening situations, all with respect to practical and budgetary constraints. This is the type of problem that policy makers are typically bad at solving rationally, unaided. They tend to use heuristic or intuitive approaches to simplify complexity, and in the process, important information is ignored. Next, policy makers may select interventions for only political motives. This indicates the need for rational and transparent approaches to priority setting. Over the past decades, a number of approaches have been developed, including evidence-based medicine, burden of disease analyses, cost-effectiveness analyses, and equity analyses. However, these approaches concentrate on single criteria only, whereas in reality, policy makers need to make choices taking into account multiple criteria simultaneously. Moreover, they do not cover all criteria that are relevant to policy makers. Therefore, the development of a multi-criteria approach to priority setting is necessary, and this has indeed recently been identified as one of the most important issues in health system research. In other scientific disciplines, multi-criteria decision analysis is well developed, has gained widespread acceptance and is routinely used. This paper presents the main principles of multi-criteria decision analysis. There are only a very few applications to guide resource allocation decisions in health. We call for a shift away from present priority setting tools in health--that tend to focus on single criteria--towards transparent and systematic approaches that take into account all relevant criteria simultaneously.

Figure 1

Ad hoc priority setting and rational priority setting.

Figure 2

Example of a question in a discrete choice experiment.