Branded prescription drug spending: A framework to evaluate policy options

Background: High drug spending is a concern for policy makers due to limits on access for patients. Numerous policies have been proposed to address high drug spending. The existence of multifarious proposals makes it difficult for policy makers to consider all the alternatives. We developed an approach to select the most viable options to present to policy makers. Methods: We identified 41 different proposals in the peer-reviewed literature to reduce the level of spending or change the incentives for branded prescription drugs; ten of which we identified as promising proposals. Based on criterion used to assess various legislative proposals regarding branded pharmaceuticals we developed a framework to evaluate the ten promising proposals. We then used a modified Delphi technique to iteratively evaluate these ten proposals starting with the initial criterion. During each iteration, five researchers independently evaluated the ten policies based on available criterion and assessed how to modify the criterion to achieve consensus on what attributes the criterion were intended to measure. We highlight areas of disagreement to show where modifications to existing criterion are needed. Results: We found general agreement for most policy-criterion combinations after three iterations. Areas with the greatest remaining disagreement include possible unintended consequences, the concept of value implied by many of the policies, and secondary effects by the pharmaceutical industry, insurers, and the FDA. Conclusions: Our analysis provides an approach that can be applied to evaluate policy proposals. It also suggests factors that policy analysts and researchers should consider when they propose policy options and where additional research is needed to assess policy impacts. Developing an objective approach to compare alternatives may facilitate the adoption of policies for branded prescription drugs in the U.S. by allowing policy makers to focus on the most viable options.