Particular considerations for rare diseases

There are a number of challenges when building economic models in rare diseases. Firstly, there is often very limited background information on the disease; for example, the prevalence of the disease may not be known, which means it is hard to make accurate estimates about the cost of treating the patient population.

Secondly, rare diseases are often very heterogeneous, meaning that patients with the same disease can present with different symptoms at different times. This means that it can be difficult to model a “typical” patient, both in terms of the treatments they receive and the way the disease progresses.

Finally, it is likely that no other economic studies will have been carried out in this disease, which means there will be few (if any) previous economic models to use as a starting point. In addition, it is unlikely that the cost of treating patients will have been calculated before, meaning that costs will need to be sourced from cost databases such as the NHS reference costs and added together based on the treatment pathway.

In general, the lack of published clinical and economic information about the disease and/or treatment means that more of the model inputs may need to be based on assumptions, or informed by expert clinicians, than you would typically expect for a more common condition. It is important that the assumptions are clearly noted, and inputs tested in sensitivity analyses to make sure the model results are as robust as they can be considering the limitations of the data.

How is economic modelling revelant for rare disease organisations?

Economic models can be used to help rare disease organisations in a variety of ways. For instance, models can be used to show the financial burden a rare disease is having on a healthcare system, and highlight the importance of developing treatments that not only benefit patients but reduce costs to the healthcare system. In addition, economic models can be used to show the potential cost savings of introducing a new intervention for the treatment of a rare disease, which could transform the way rare diseases are treated in the future.

Information on rare diseases is often based on limited datasets and trials with small sample sizes that were run over a short period of time, so extrapolation will be required to provide information on long term outcomes. Economic modelling can provide this extrapolation and this is really valuable to policy and decision-makers, who can then make informed decisions about the provision of healthcare in the future. To overcome the issue of small sample sizes, models can be used to test a variety of scenarios, which allows a decision-maker to understand the effects of the uncertainty associated with small datasets.

What help is available for economic modelling for rare diseases?

Modelling for rare diseases can be difficult and appear a daunting task. However, there are many sources of help available. Although literature for rare diseases is often limited, it is always worth searching the literature for existing economic models either in the disease you are interested in or similar conditions. Agencies familiar with rare diseases, like Costello Medical, can build economic models and may be able to provide support on a pro bono basis. Clinical experts and hospitals might have useful literature or economic data that they are happy to share. Patient groups and charities, like Findacure, can provide valuable insights and may be able to introduce you to additional contacts who might be able to help.