Background In a context where the economic burden of HIV is increasing as HIV patients now have a close to normal lifespan, the availability of generic antiretrovirals commonly prescribed in 2017 and the imminence of patent expiration are expected to provide substantial savings in the coming years. This article aims to assess the economic impact of these generic antiretrovirals in France and specifically over a five-year period. Methods An agent-based model was developed to simulate patient trajectories and treatment use over a five-year period. By comparing the results of costs for trajectories simulated under different predefined scenarios, a budget impact model can be created and sensitivity analyses performed on several parameters of importance. Results The potential economic savings from 2019 to 2023 generated by generic antiretrovirals range from €309 million when the penetration rate of generics is set at 10% to €1.5 billion at 70%. These savings range from €984 million to €993 million as the delay between patent and generic marketing authorisation varies from 10 to 15 years, and from €965 million to €993 million as the Negotiated Price per Unit (NPU) of generics at market-entry varies from 40 to 50% of the NPU for patents. Discussion This economic savings simulation could help decision makers to anticipate resource allocations for further innovation in antiretrovirals therapies as well as prevention, especially by funding the Pre-Exposure Prophylaxis (PrEP) or HIV screening.

Although they remain little used in the field of Health Care Economics, Agent Based Models (ABM) are potentially powerful decision-making tools that open up great prospects. The reasons for this lack of popularity are essentially to be found in a methodology that should be further clarified. This article hence aims to illustrate the methodology by means of two applications to medical examples. The first example of ABM illustrates the construction of a Baseline Data Cohort by means of a Virtual Baseline Generator. The aim is to describe the prevalence of thyroid cancer in the French population over the long term according to different scenarios of evolution of this population. The second study considers a setting where the Baseline Data Cohort is an established cohort of (real) patients: the EVATHYR cohort. The aim of the ABM is to describe the long-term costs associated with different scenarios of thyroid cancer management. The results are evaluated using several simulation runs in order to observe the variability of simulations and to derive prediction intervals. The ABM approach is very flexible since several sources of data can be involved and a large variety of simulation models can be calibrated to generate observations according to different evolution scenarios.