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The market for new drugs is changing: personalised drugs will increase the heterogeneity in patients' responses and, possibly, costs. In this context, price regulation will play an increasingly important role. In this article, we argue that personalised medicine opens new scenarios in the relationship between value-based prices, regulation and industry listing strategies. Our focus is on the role of asymmetry of information and competition. We show that, if the firm has more information than the payer on the heterogeneity in patients' responses and it adopts a profit-maximising listing strategy, the outcome may be independent of the choice of the type of value-based price. In this case, the information advantage that the manufacturer has prevents the payer from using marginal value-based prices to extract part of the surplus. However, in a dynamic setting where competition by a new entrant is possible, the choice of the type of value-based price may matter. We suggest that more research should be devoted to the dynamic analysis of price regulation for personalised medicines.

Managed entry agreements (MEAs) have been used for several years, with the aim of curbing the growth of pharmaceutical expenditure and enhancing patient access to innovation. Yet, much remains to be understood about their economic implications. This paper studies the impact of MEAs on list prices, that is, prices before the deduction of any discount. Using a theoretical model, we show that, under most price setting regimes, the introduction of an MEA leads to a higher list price. This is confirmed by our empirical analysis of a sample of 156 medicines in six countries, providing a conservative estimate of the increase in price due to the MEA of 5.9%. A relevant policy implication is that payers may overestimate the financial gains that can be achieved through this tool.

We utilize a large administrative dataset of sickness leave in Italy (a) to investigate whether private firms are more effective than the public insurer in choosing who to monitor and (b) to study the correlation between potentially opportunistic behavior and the observable characteristics of the employee. We find that private employers are more likely to select into monitoring employees who are fit for work despite being on sick leave, if the public insurer is not supported by any data-driven tool. However, the use of a scoring mechanism, based on past records, allows the public insurer to be as effective as the employer. This result suggests that the application of machine learning to appropriate databases may improve the targeting of public monitoring to detect opportunistic behavior. Concerning the association between observable characteristics and potentially opportunistic behavior, we find that males, employees younger than 50, those on short leaves, or without a history of illness are more likely to be found fit for work.

Since the early 80s, incentives have been introduced to stimulate R&D for rare diseases. We develop a theoretical model to study the impact of push and pull incentives on the intensive and extensive margin of optimal R&D investments. The model describes the mechanisms by which the type of incentives provided may favor R&D for orphan diseases with comparatively high prevalence. In our empirical analysis, we merge data on orphan drug designations by the Food and Drug Administration with Orphanet data on disease characteristics. In line with the theoretical results, we find evidence supporting the idea that the incentives adopted may have contributed substantially to widening the gap between more and less rare diseases classified as orphan. Our theoretical and empirical findings together suggest that, if providing some therapeutic option to patients with very rare diseases is a priority, a revision of the current system of incentives should be considered.

We use a simple model to study the static and dynamic efficiency of alternative regulation regimes for the reimbursement of medical innovations when responses to a new treatment (effectiveness) are heterogeneous across the eligible population. When the rational behavior of profit-maximizing firms is taken into account, only average value-based prices can ensure both static and dynamic efficiency, but they imply higher expenditure and lower consumer surplus. Ignoring dynamic efficiency, if patients' responses are sufficiently homogeneous, marginal value-based prices may dominate from the payer's perspective. We also present a refinement of average value-based prices that could reverse this result. Overall, the cost of ensuring static and dynamic efficiency is increasing in the degree of heterogeneity. A real-world example is used to illustrate these results.

© 2016 Elsevier B.V.We present a model combining the two regulatory stages relevant to the approval of a new health technology: the authorisation of its commercialisation and the insurer's decision about whether to reimburse its cost. We show that the degree of uncertainty concerning the true value of the insurer's maximum willingness to pay for a unit increase in effectiveness has a non-monotonic impact on the optimal price of the innovation, the firm's expected profit and the optimal sample size of the clinical trial. A key result is that there exists a range of values of the uncertainty parameter over which a reduction in uncertainty benefits the firm, the insurer and patients. We consider how different policy parameters may be used as incentive mechanisms, and the incentives to invest in R&D for marginal projects such as those targeting rare diseases. The model is calibrated using data on a new treatment for cystic fibrosis.

We present a two-period framework, which combines real option and decision-theoretic approaches to health technology assessment under uncertainty. By viewing adoption, treatment and research decisions as a single economic project, we illustrate how their key dimensions affect optimal rules. We consider the results in relation to the existing literature and argue that developments in this direction could contribute substantially to efficiency gains in resource allocation.

The paper studies the incentive for providers to invest in new health care technologies under alternative payment systems, when the patients' benefits are uncertain. If the reimbursement by the purchaser includes both a variable (per patient) and a lump-sum component, efficiency can be ensured both in the timing of adoption (dynamic) and the intensity of use of the technology (static). If the second instrument is unavailable, a trade-off may emerge between static and dynamic efficiency. In this context, we also discuss how the regulator could use control of the level of uncertainty faced by the provider as an instrument to mitigate the trade-off between static and dynamic efficiency. Finally, we calibrate the model to study a specific technology and estimate the cost of a regulatory failure.

Recent years have witnessed much progress in the incorporation of economic considerations into the evaluation of public health interventions. In England, the Centre for Public Health Excellence within the National Institute for Health and Care Excellence works to develop guidance for preventing illness and assessing which public health interventions are most effective and provide best value for money...

The paper studies the impact of alternative reimbursement systems on two provider decisions: whether to adopt a technology whose provision requires a sunk investment cost and how many patients to treat with it. Using a simple economic model we show that the optimal pricing policy involves a two-part payment: a price equal to the marginal cost of the patient whose benefit of treatment equals the cost of provision, and a separate payment for the partial reimbursement of capital costs. Departures from this scheme, which are frequent in DRG tariff systems designed around the world, lead to a trade-off between the objective of making effective technologies available to patients and the need to ensure appropriateness in use.

Background After implementation of the PREDICA annual chest X-ray (CXR) screening program in smokers in the general practice setting of Varese-Italy a significant reduction in lung cancer-specific mortality (18 %) was observed. The objective of this study covering July 1997 through December 2006 was to estimate the cost-effectiveness of this intervention. Methods We examined detailed information on lung cancer (LC) cases that occurred among smokers invited to be screened in the PREDICA study (Invitation-to-screening Group, n = 5815 subjects) to estimate costs and quality-adjusted life-years (QALYs) from LC diagnosis until death. The control group consisted of 156 screening-eligible smokers from the same area, uninvited and unscreened, who developed LC and were treated by usual care. We calculated the incremental net monetary benefit (INMB) by comparing LC management in screening participants (n = 1244 subjects) and in the Invitation-to-screening group versus control group. Results The average number of QALYs since LC diagnosis was 1.7, 1.49 and 1.07, respectively, in screening participants, the invitation-to-screening group, and the control group. The average total cost (screening + management) per LC case was higher in screening participants (€17,516) and the Invitation-to-screening Group (€16,167) than in the control group (€15,503). Assuming a maximum willingness to pay of €30,000/QALY, we found that the intervention was cost-effective with high probability: 79 % for screening participation (screening participants vs. control group) and 95 % for invitation-to-screening (invitation-to-screening group vs. control group). Conclusions Based on the PREDICA study, annual CXR screening of high-risk smokers in a general practice setting has high probability of being cost-effective with a maximum willingness to pay of €30,000/QALY.