The objective
Sanofi, the French multinational pharmaceutical company, has an innovative monoclonal antibody biotherapeutic – alemtuzumab (Lemtrada®) – on the market for the treatment of relapsing-remitting multiple sclerosis (RRMS). Through RWE, the company wanted to demonstrate that, compared to other products on the market, its introduction in 2013 had provided the Norwegian health care budget with long-term cost-savings.
We set out to develop a budget impact model (BIM), utilizing RWD to investigate the cost-savings of Lemtrada® for the Norwegian health care system, both since its introduction in 2013 and into the future. Generally, a BIM is a simple tool used to calculate the cost impact of introducing a new intervention to understand whether the intervention is affordable with respect to the health care budget. However, by introducing RWD into the model, a much deeper understanding of the underlying economics can be achieved. Using RWD, we aimed to investigate how many patients were treated with Lemtrada® each year, and how many patients were treated with competitors’ products. In addition, we aimed to calculate the total cost for the patient population in order to demonstrate potential evidence of cost-savings for the Norwegian health care sector with the use of Lemtrada® for patients with RRMS.
The challenge
All models are a simplification of a more complex reality. The challenge is to incorporate the relevant parameters in a way that makes the model useful. RWD has its limitations, as the data does not reflect all aspects of the patient’s life (e.g. other health problems and medication use, change of medications, or incorrect use of medications). In the analyses of RWD of patients treated for RRMS, we needed to convert the raw data in order to implement it into the model in a useful manner.
The models presented to the health care authorities need to be clear and transparent. With all these variables to consider, it is vital to not over-complicate the model.
The solution
We performed a retrospective analysis based on data from the Norwegian Patient Registry (NPR), the Norwegian Prescription Database (NorPD), and Information Medical Statistics (IMS) sales data. As data from NPR contains information on drugs provided at the hospital level, data from NorPD was necessary to include in order to cover prescription drugs dispensed at the pharmacy. IMS sales data was utilized to validate the data obtained from NPR and NorPD.
Using the data described above, a BIM was developed to investigate the savings for the Norwegian health care sector from 2013-2018, and to predict the future budget impact of Lemtrada® in 2019-2027. Two main scenarios were tested, one where the patient population was based on NPR and NorPD data, and one where the patient population was based on IMS sales data.
By using multiple sources, we were able to investigate the data through two different scenarios to validate the results of the analysis. Both scenarios presented similar results, which reduces uncertainty.
Results and benefits
Using the BIM together with RWD, it was possible to produce evidence that demonstrated significant cost savings for the Norwegian health care sector with the use of Lemtrada® for patients with RRMS. The analysis showed that since its launch in 2013, the drug had saved the Norwegian health care authorities approximately 60 million NOK (6 million USD). Future projections up to 2027 estimated that the drug would provide over 700 million NOK (72 million USD) in total cost savings.
The results provided by the model are based on RWD with its limitations and uncertainty. However, the magnitude of the results provides tangible evidence of cost savings, which Sanofi could present to strengthen their argument to the Norwegian health care authorities. Crucially, the data was analyzed and presented in a way that could be easily understood in order to generate the most impact.
This project was performed on behalf of Sanofi
The use of RWE provided Sanofi with the evidence required to demonstrate the value of Lemtrada®, from its introduction and in future projections. The project was presented in a poster at the ISPOR conference in Copenhagen, 2019.
View pdf poster
