Applying the net-benefit framework for assessing cost-effectiveness of interventions towards universal health coverage

Economic evaluation in general, and cost-effectiveness analysis in particular is deemed by scholars, health administration and policy experts a centerpiece of the decision making process by balancing health gains against costs of interventions [1]. The results of cost effectiveness analysis provide a rationale to enhance the efficiency of resource allocation. Traditionally, the use of cost effectiveness analysis as a tool to inform and guide resources allocation has revolved around the Incremental Cost Effectiveness Ratio (ICER), which indicates the additional amount of money needed to obtain an extra unit of health gain or to prevent an adverse event compared to alternatives. However, apart from few situations with a clearly dominant intervention (less effective and more costly or more effective and less costly) compared to alternatives, most interventions will present a situation in which there is a need of a threshold value, a ceiling that the decision maker is willing to pay as a good value for money (Figure 1). New interventions in clinical medicine are likely to be more effective and more costly because breakthroughs in medical procedures and new technologies are typically more expensive than existing practices. In these cases, not only is there a need to estimate the maximum a provider (society, or the health system for example) is willing to pay for an additional unit of health gain, but it is also difficult to reliably build confidence intervals around the ICER estimates for inferential analysis. The value of the maximum a provider is willing to pay for an additional unit of health gain is often estimated through extensive willingness to pay surveys and is not always available to the analysts. The use of the net-benefit framework, a recently developed approach and mostly applied in pharmacoeconomics and clinical interventions [2,3], presents the potential to overcome the current limitations of handling of uncertainties and reliance on the maximum value a pr