The Cost-Effectiveness of Neurotech

It is time to address the elephant in the room. Amid all the exciting advances in medical neurotechnologies, there are the looming questions of “What is this going to cost, and is it worth it?”

We’re all aware that healthcare costs continue to rise. Indeed, healthcare professionals are sensitive to healthcare costs. For example, in a survey of over 2500 physicians, 56% believed that pharmaceutical and device manufacturers have a ‘major responsibility’ for reducing health care costs, and 76% reported being ‘aware of the costs of the tests/treatments they recommend’.

Neurotech development can be expensive. The average development cost for a novel, complex therapeutic medical device is about $54 million, excluding any post-approval studies that might be required. After accounting for the cost of failed studies and the cost of capital, the average capitalized cost of bringing such a device to the U.S. market is about $522 million.

However, spending on medical devices constitutes only about 5% of total U.S. health expenditures, and prices for medical devices have grown more slowly than the Medical Consumer Price Index, a well-accepted benchmark of price increases. There’s also growing evidence that medical neurotechnologies are not only clinically effective but also cost-effective. This evidence can give confidence to patients and emerging companies, and it can help overcome potential fears among physicians and payers.

What’s Cost-Effectiveness Analysis (CEA)?

CEA compares oneintervention to another intervention by estimating how much it costs to gain a unit of a health outcome, like a year of life gained. Because CEA is comparative, an intervention can only be considered cost-effective compared to other interventions. It is being increasingly used to inform public and private organizations’ reimbursement decisions, benefit designs, and price negotiations worldwide.

In 2012, the Second Panel on Cost-Effectiveness in Health and Medicine was formed and developed recommendations for the conduct of CEAs. They recommended the concept of a ‘reference case’ and a set of standard methodological practices that all cost-effectiveness analyses should follow to improve quality and comparability. According to the recommendations, all cost-effectiveness analyses should report two reference case analyses: one based on a healthcare sector perspective and another based on a societal perspective.

There is now a growing body of CEA data available. The CEA Registry is a comprehensive database of over 10,000 analyses on a wide variety of diseases and treatments published since 1976.

Let’s take a closer look at cost-effectiveness data for the use of a specific kind of neurotech for a specific clinical condition.

Case Study: Spinal Cord Stimulation to Treat Pain

Overview

Chronic pain impacts more than 100 million Americans and has a significant impact on the economy. New cases of chronic pain occur more often in the United States than cases of some other chronic conditions, like diabetes, depression, and high blood pressure. In response to the opioid epidemic in the United States, doctors are moving away from medication as the first response to treating chronic pain and instead are using a multimodal approach of different therapies.

Spinal cord stimulation (SCS) has demonstrated efficacy in managing a growing number of chronic pain conditions. In addition, data has shown that among patients who continued SCS therapy for at least two years, a significant proportion were able to reduce or discontinue opioid use, with costs after the start of therapy significantly reduced relative to baseline. This data has resulted in significant changes in the usage and overall cost of SCS, in combination with an increasing number of physicians trained in SCS placement.

The FDA has approved SCS for the treatment of pain in the trunk and extremities, including for patients with failed back surgery syndrome. One of the most significant technical advances of recent years has been the creation of wireless SCS systems. With their use, the trial period and the risk of complications associated with the implantable pulse generator and extension cables can be avoided.

Cost and cost-effectiveness

The market for SCS treatment for pain is large, and sales are predicted to grow from $2.4 billion in 2020 to $4.1 billion globally by 2027. At the same time, there is increasing scrutiny around the utilization of this therapy related to cost, complications, long-term efficacy, and explant rates. These concerns have the potential to impact access to this therapy in the future.

A study to assess utilization and expenditures for providers in the fee-for-service Medicare population from 2009 to 2018 in the United States showed that spinal cord stimulation trials increased nearly 200%, with an annual increase of 12%, and expenditures increased nearly 300%, with an annual increase of 16%.

Following the above discussion of CEA methodology, a systematic review of fourteen economic evaluations found that in ten of the studies, SCS was cost-effective compared with the alternative strategies, particularly when considering a long-term time horizon. Methodologies varied, and the evaluations spanned a range of clinical conditions including refractory angina pectoris, failed back surgery syndrome, complex regional pain syndrome, diabetic peripheral neuropathy, and peripheral arterial disease.

To conclude this case study, there is now substantial evidence that SCS for the treatment of pain is safe, clinically effective, and cost-effective. Of course, SCS is a family of related technologies, not just a single tool, and chronic pain has diverse causes and manifestations. In addition, the cost-effectiveness of any intervention depends on the healthcare setting in which it is assessed. How will this translate to future clinical indications? For example, there is growing evidence that SCS is effective in restoring motor function in people paralyzed by spinal cord injury, though the cost side of the equation remains to be determined, based on manufacturer pricing, physician prescribing, and other factors. Nonetheless, it is hoped that payers do not unnecessarily delay reimbursement if similar cost-effectiveness begins to emerge.

Cost-effectiveness of other neurotechnologies

There is compelling evidence in favor of using SCS to treat pain. What about other technologies and indications?

Deep brain stimulation

The clinical use of deep brain stimulation (DBS) has been called “among the most important advances in the clinical neurosciences in the past two decades”. Increased DBS utilization for adult movement disorders in the United States has been attributed to rapid adoption by teaching hospitals. DBS is a procedure with low overall complications, and inpatient costs have been stable. Complication risks vary by type of movement disorder, and although rare, complications increase the cost of care.

Unfortunately, cost-effectiveness evidence has been mixed. A 2016 systematic review of nine studies concluded that DBS is a cost-effective intervention for patients with advanced Parkinson’s disease (PD), but it has a high initial cost compared with the best medical treatment. However, DBS reduces pharmacologic treatment costs and should also reduce direct, indirect, and social costs of PD in the long term. In contrast, a 2019 systematic review of 17 studies found that the ability to report robust cost-effectiveness summaries was limited due to the infrequent use of randomized controlled trials to evaluate DBS efficacy, the lack of data reporting the long-term effectiveness and utility of DBS, and the uncertainty surrounding cost data.

Vagus nerve stimulation

Vagus nerve stimulation (VNS) is a treatment option for drug-resistant epilepsy. An analysis of a large US healthcare claims database to identify patients with epilepsy who underwent neurostimulation between 2012 and 2019 found that VNS was associated with significantly lower costs for the two years following implantation. All-cause and epilepsy-related costs remained significantly lower for VNS even after costs of implantation were excluded. A similar study agreed that VNS is a cost-effective therapy yielding measurable clinical and therapeutic outcomes over the long term, but highlighted the poor consensus of methodological approaches.

Another study evaluated whether VNS is cost-effective for the treatment of cluster headache, a debilitating condition that is generally associated with substantial health care costs. Results suggested that adjunctive treatment with a novel non-invasive VNS device led to decreased attack frequency and abortive medication and that VNS was cost-effective compared with standard of care. Treatment with VNS may also promote further economic benefit when other potential sources of cost savings — like reduced frequency of clinic visits — are considered.

Next steps

Growing evidence is now available for the efficacy, safety, and cost-effectiveness of many medical devices. A study examining the health benefits and costs for pre-market approved (PMA) devices approved by the FDA from 1999 to 2015 found that cost-effectiveness ratios were more favorable than standard benchmarks of Quality Adjusted Life Years. Roughly one-quarter of the major PMA medical device categories have published cost-effectiveness evidence indicating that devices generally offer good value.

However, it is essential that academic and industry researchers continue to generate cost-effectiveness data that can be trusted — because some studies have found evidence of bias. One showed that many published analyses report favorable incremental cost effectiveness ratios, particularly studies funded by industry, whereas studies of higher methodological quality and those conducted in Europe and the US rather than elsewhere were less likely to report cost-effectiveness. Another assessed the association between industry sponsorship and cost effectiveness results, and found that “sponsorship bias in CEAs is significant and systemic”. That study noted that use of CEAs conducted by independent bodies could provide payers with more ability to negotiate lower prices. This impartiality is especially important for countries that rely on published CEAs to inform policy-making for insurance coverage because of the limited capacity for independent economic analysis.

Data should also be generated in a timely manner. A study of cost-effectiveness analyses of medical devices in the United States published between 2002 and 2020 found that CEA studies were published about four years after the device received FDA approval, so decision-makers often do not have early evidence of cost-effectiveness. Although there may be limited data available at the time of approval, there are methods to handle uncertainty in economic evaluation, such as the cost-effectiveness acceptability curve.

Cost-effectiveness data has the opportunity to support reimbursement decisions and accelerate patient access to new devices. A literature review of barriers and facilitators of patient access to medical devices in Europe revealed that data on current reimbursement procedures and practices was both heterogeneous and incomplete. Barriers were caused by unclear legislation, complex market approval procedures, inconsistent evidence requirements between countries and regional reimbursement — as well as factors influencing physicians’ prescription decisions, including device costs and hospital-physician relationships.

Conclusions

Although all cost-effectiveness data should be scrutinized, there is growing evidence that neurotechnologies can be cost-effective. Academic and industry researchers should continue to generate unbiased, high-quality data. Payers and physicians can use this information to provide treatments for their patients that are not only clinically beneficial but will also reduce healthcare costs for society.

Written by Marco Sorani, edited by Muhammad Ali Haidar, Simon Geukes and Lars Olsen, with original artwork by Lina Cortéz.

Marco Sorani works in biopharma and has a PhD in Bioinformatics. He sustained a spinal cord injury in 1994 and advocates for innovative research approaches.

Muhammad Ali Haidar is a PhD student working on the origin of individuality at the Freie Universität Berlin. His focus is deciphering the differences in the neuronal circuitry involved in sleep cycle and memory.

Simon Geukes works at the UMC Utrecht Brain Center. His work evolves around BCI participants, fMRI and ECoG.

Lars Olsen is a regulatory medical writer. He works in the pharmaceutical industry writing submission-level documents, and has additional experience with medical devices and pharmacovigilance.

Lina Cortéz is an electronics engineer and neurotechnology enthusiast who is highly interested in the application of brain-computer interface in robotics control.