Comment on: “Healthy Decisions: Towards Uncertainty Tolerance in Healthcare Policy”

Abstract

The article by Grutters et al. [1] discusses the important issue of considering uncertainty in cost-effectiveness analysis (CEA) of healthcare programmes. Particularly, it highlights the fact that, at present, most CEAs restrict themselves to considering known uncertainty (called ‘risk’ in the seminal textbook by Luce and Raiffa [2]). In this situation, there is complete probabilistic knowledge. In contrast, most CEAs do not consider what the authors call ‘non-statistical uncertainty’ and Luce and Raiffa [2] denote as ‘ambiguity’, i.e. there is no probabilistic knowledge. An important source of this ambiguity was discussed by Salomon et al. [3] more than a decade ago: the potential arrival of new treatments in the future. At that time Salomon et al. [3] recommended the conduct of scenario analysis, similar to what the authors of the present paper suggest. Yet, the point I would like to make is the following: the problem identified by the authors depends on the framework used for the CEA. Some healthcare systems and health technology assessment agencies may use a framework that does not require considering certain sources of (known and unknown) uncertainty because they cancel out. An example is the German healthcare system. In Germany, new legislation regulating the reimbursement of drugs within the statutory healthcare system (Arzneimittelmarktneuordnungsgesetz) was introduced on 1 January 2011. According to this law, new products are subject to a rapid assessment to determine whether there is sufficient evidence of added clinical benefits compared with the existing standard of treatment. If such added benefits are confirmed, then manufacturers and representatives of the statutory health insurance are expected to agree on an appropriate reimbursement price within 6 months, starting from the completion of the benefit assessment by the German Federal Joint Committee. If drug makers and health insurers cannot agree on the price, a final decision on the reimbursement price will be made by an arbitration body. If one of the parties involved wishes so, then the Institute for Quality and Efficiency in Health Care (Institut fur Qualitat und Wirtschaftlichkeit im Gesundheitswesen [IQWiG]) will be commissioned with a formal evaluation of costs and benefits of the product in question. To determine reimbursement prices, IQWiG suggests the following criterion (called proportional rule [4, 5]): the ratio between the difference in costs and effectiveness of a new drug compared with the next effective intervention (for the same indication) should not be higher than that of the difference in the costs and effectiveness of the next effective intervention compared with its next effective intervention [6]. The basic idea behind the proportional decision rule is as follows [7]. Suppose a new drug—if applied to a given population—avoids twice as many strokes as the next effective treatment. In this case, the new drug provides exactly twice as much value or health benefit to the population as the next effective treatment. The question then is how much more resources should decision makers spend on this drug in order to avoid the additional strokes. If decision makers intend to spend resources according to the value of an intervention, then no more than twice as many resources should be allocated to the new drug. In turn, this means that the cost-effectiveness ratio of the new drug should not be higher than that of the comparator. & Afschin Gandjour a.gandjour@fs.de