How should we define “unmet need”?

Many health technology assessment (HTA) agencies give additional consideration to treatments if there is unmet need. But what really defines unmet need?  According to an article by Vreman et al. (2019), there are three key elements.  First, there should be no or limited treatment alternatives.  If there are lots of good treatment options available, then there is not an unmet need.  Second, the disease should be severe.  If there is a disease with very mild symptoms, even if there are no treatments available, then this is probably not a huge issue for patients.  If both of these factors are relevant (i.e., limited treatment and high disease severity), then there is individual unmet need. 

The authors also bring up a third component: disease incidence or prevalence.  If a disease is uncommon, it may not be commercially viable for pharmaceutical manufacturers to fund the R&D to create treatments for that disease.  Thus, if the disease is rare (e.g., orphan indication) and there are limited treatments for a severe disease, the authors call this scenario societal unmet need.

The authors also asked a number of different stakeholders (i.e., patients, medicine developers, regulators, HTA bodies, and payers) to weigh in on these three dimensions and there was little consensus. For instance,

UMN [Unmet need] would be quantified if some alternative treatments exist but these are not satisfactory to patients or if patients cannot access them. Counting the sheer number of alternative treatments was considered inadequate. For medicine prioritization, alternative treatments should be taken into account relative to the benefits of the technology under assessment. If a new technology could provide significant benefits for patients over existing treatments, this would contribute to fulfilling the UMN more. 

There was general support that large incremental improvements over existing products could represent addressing an unmet need. It was unclear, however, how one would incorporate this conception of unmet need, without double-counting health benefits within existing value frameworks.  Also, for disease with significant severity, it is unlikely that one would consider the size of health gains differently.  One may, however, value these gains differently depending on whether there is unmet need.

In the Netherlands, for instance, HTA agencies place a higher willing to pay more per quality-adjusted life year (QALY).  In Sweden, the acceptable cost per QALY is depends on disease severity [see Svensson et al. 2015].

Despite these varied opinions, one could use a variety of approaches to rank order diseases with the highest unmet need.  Multi-criterion decision analysis (MCDA) is one way to incorporate these additional value considerations. More broadly, the authors recommend a 3-step process to evaluate treatment benefits with unmet need.  First, stakeholders need to determine how much unmet need there is for a given disease.  Second, stakeholders need to evaluate how well a given treatment fulfills this unmet need.  Finally, decision-makers need to evaluate to what extend meeting a significant unmet need should be reflected in a value framework approach.

Overall, the article brings up more questions than it answers. Nevertheless, quantifying the degree to which treatments address unmet need and how this would be reflected in different value frameworks is clearly a challenge that needs to be addressed.


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