Abstract
The conventional method of assessing the value of higher prices of new drugs is the ratio of investment in research and development (R&D) sourced from internal funds to the additional health for the population due to additional future New Molecular Entities (NMEs). There is evidence that the conventionally defined ratio is significantly greater than one. But does this mean that higher prices are necessarily in the population’s interest? A positive correlation between additional future NMEs and future health for the population is a central premise of the prevailing political economy of new drug price. In this chapter I show that a positive correlation is not axiomatic; it is a testable hypothesis and its direction depends on the economic context of the health budget. I present a general expression for the estimate of a return on increased drug prices and public funding for private pharmaceutical R&D that accommodates this context. I distinguish between three types of health budget constraints: (1) a fixed budget that cannot be expanded; (2) a constrained budget that can be expanded incrementally, but only by foregoing the best alternative strategy; and (3) an unconstrained budget that expands to fund every programme that is “cost-effective” in the lay sense of the term. I use this general expression to show that if the health budget is constrained or fixed, even a very high ratio of conventionally defined return to investment does not exclude the possibility that today’s health could have been better had historic prices and R&D been lower.
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- 1.
See page 273 in Sloan and Hsieh (2007).
- 2.
See page 14 in Lichtenberg and Duflos (2008).
- 3.
This would be the case if the additional services displaced to finance the additional new drugs had a lower ICER compared to the ICER of the new drugs.
- 4.
A review of this literature is presented in Pekarsky (2012, Appendix 2). The summary presented in this chapter is primarily concerned with evidence of the social return to consumers’ investment in Pharma R&D via higher prices.
- 5.
See footnote 2, Chap. 1.
- 6.
This is analogous to calculating the clinical value of innovation by comparing it to placebo rather than the best available existing care. This approach results in an overestimate of the clinical innovation of a new drug, where this overestimate increases as the clinical innovation of the comparator increases (see Chap. 2).
- 7.
See page 1207 in Comanor (1986).
- 8.
This review is presented in Pekarsky (2012, Appendix 2).
- 9.
For countries that use CEA to inform drug reimbursement decisions, there would appear to be little value in retrospective uncontrolled studies such as these to inform pricing decisions on future drugs.
- 10.
Claxton et al. (2000) distinguish between a shadow price of a budget constraint that is determined from a positive empirical question and a normative decision by a social decision maker. The exogenous shadow price is derived in a situation where the health budget is defined by a policy maker exogenous to the health care system and the shadow price of the budget constraint is defined as the marginal benefit (additional QALYs) from marginal expansion of the budget. However, when the health budget is able to be expanded, the authors argue that “there is no reason to regarding existing budgets for health care as fixed.” In these cases the budget is expanded to fund all services that have a net benefit of zero or more and hence the budget can be defined as endogenous.
- 11.
The evidence for this adjustment is that around 20–30 % of the additional profit from a higher price with constant quantity purchased will be invested into R&D for new drugs hence \( \frac{1}{0.3}<\omega <\frac{1}{0.2} \) (ITA 2004 p. 30).
- 12.
The IPER is arithmetically identical to the ICER and therefore includes adjustments to account for any net additional costs or savings elsewhere in the health system. Conceptually the IPE recognises the endogeneity of price of new drugs to the reimbursement process. Unlike the ICER of say a smoking cessation program where the only costs are the salary of a counsellor, the IPER of a drug behaves more like the price in a bilateral monopoly—it is the product of negotiation.
- 13.
The average ICER is an average of the average.
- 14.
The question of whether the economic rent to the firm should be included in the estimate of the impact of new drug R&D is discussed in detail in Chap. 10. In this chapter the question is: what is the return to the consumer of their investment in new Drug R&D via higher prices?
- 15.
Significantly, institutions such as NICE, PBAC and the Australian Medical Services Advisory Committee (MSAC) with their preference for financing programmes and technologies that have demonstrated cost-effectiveness could be increasing the probability that \( d \) < \( f \).
First, the availability of evidence of cost and effect is biased towards programs for which the market and institutions provide incentives for an evidence base. A focus on HTA/CEA generated evidence to inform decisions can be justified as consistent with Evidence Based Medicine (EBM). However, EBM does not recognise the failure of markets and institutions to provide incentives for the development of evidence for non-patented or unpatentable technologies. Hence, it is likely that there are programs that are cost-effective, but are not funded because the market has failed to provide incentives to develop evidence of cost and effect.
Second, the process of displacement to finance new drugs is biased towards programs that have not been approved as part of formal evidence based reimbursement process. If a program is recommended or reimbursed via an EBM decision process, then the capacity of the system to displace this program in order to access the funds for a newly reimbursed service is limited. This is particularly relevant if there is a financial incentive to prevent this displacement (a patent for example). Additionally, a program for which there is no evidence of cost and effect can be displaced more easily than one for which there is evidence of “cost-effectiveness”.
Therefore, to the extent that increased probability of being displaced is correlated with less available evidence rather than the underlying cost-effectiveness of the program, it is feasible that there are situations where \( d \) < \( f \) and hence the net effect of reimbursement on a population is negative. Furthermore, the greater the bias in funding technologies for which there is no failure in the market for evidence, the greater the probability that \( d \) < \( f \).
- 16.
See Pekarsky (2012, Appendix 3).
- 17.
The US pharma-economic literature is not consistent in this position. Some of the US pharma-economic analysis implicitly assumes that the budget is unconstrained.
- 18.
A number of studies have shown that the US has shorter delay times for new drug adoption (Danzon et al. 2005).
- 19.
The Health Minister said this to the Reimburser after she approved a very high cost drug with a significant additional cost to the health budget. The Health Minister expressed his relief at being able to provide Treasury with a “solid economic rationale” for this unexpected increase in the drug budget, namely that it was “cost-effective” and “value for money”.
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Pekarsky, B.A.K. (2015). The Social Rate of Return on Investment in Pharmaceutical Research and Development. In: The New Drug Reimbursement Game. Adis, Cham. https://doi.org/10.1007/978-3-319-08903-4_3
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