Understanding the potential risks and opportunities with naloxone

According to the Centers for Disease Control and Prevention (CDC) Annual Surveillance Report of Drug-Related Risks and Outcomes report, opioid-related overdose mortality has increase from 2.9 per 100,000 in 1999 to 10.4 in 2015. Several strategies have been implemented to address this opioid crisis, which including federal regulation on the drug supply through Prescription Drug Management Programs (PDMP), opioid overdose education and naloxone distribution programs, and Good Samaritan Laws to prevent bystanders from being arrested for possessing illicit drugs. Despite these, the rising rate of opioid-overdose mortality continues to increase.

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A key strategy to help patients and their family/friends reverse opioid overdose is naloxone, an opioid reversal agent. However, debate about its use among non-emergency medical services handicaps its ability to make a greater impact on opioid-related mortality. As part of the United States (U.S.) Department of Veterans Affairs (VA), I’ve observed the struggles and rewards of getting naloxone into the hands of patients and their family and friends; educating providers about opioid overdose risk, recognition, and response; and promoting a culture of patient-centered care.

As a PhD candidate in the Comparative Health Outcomes, Policy, & Economics (CHOICE) Institute working on behavior changes regarding naloxone prescribing, I have been exposed to a number of research studies associated with naloxone safety and efficacy. Although there is ample evidence that naloxone is effective and safe in reversing opioid overdose, several limitations exists. In a recent paperin the Annals of Internal Medicine, Chou and colleagues identified several knowledge gaps about naloxone use by emergency medical services such as the best route of administration, titration to respiration versus consciousness, repeat dosing, and transportation after an opioid overdose event. These gaps do not, however, indicate that naloxone is ineffective. In fact, they highlight the importance of our limited understanding of the opioid overdose epidemic that plagues the United States.

My colleague Elizabeth M. Oliva, program director of the U.S. Veterans Health Administration Opioid Overdose Education and Naloxone Distribution (OEND) Program, and I co-authored an accompanying editorialon the findings from Chou and colleagues. In addition to identifying the limitations of Chou and colleagues paper, we reminded the readers that naloxone is still a necessary and critical strategy in preventing opioid overdose mortality, which should incorporate the patient’s caregivers and the layperson. Specifically, we write that “[F]uture investigations should examine whether naloxone delivery by [caregivers and laypersons] may have outcomes that are similar to, if not better than, waiting for EMS to arrive ‘in the nick of time.’” The role of caregivers and layperson in preventing opioid overdose remains controversial. Some states still do not have naloxone distribution programsand providers continue to harbor stigmaassociated with naloxone and illicit drug, which combines to aggravate the opioid crisis.

The potential for moral hazard behavior among patients at-risk for opioid-related overdose continually fuels the stigma regarding naloxone use. Moral hazard is the phenomenon where subjects assume a reckless/risky behavior with the knowledge that they are not responsible for the consequences of their actions. Hence, providers are unlikely to write for naloxone thinking that their patients, uninhibited by the consequences of opioid-related overdose, will adopt riskier behavior with opioids and illicit drugs.

Debate about the moral hazard issues generated from state laws on naloxone access continues to be fueled by conflicting evidence. Doleac and Mukherjee recently released an unpublished studythat implicated naloxone as a potential cause of increased opioid-related events, misuse, and social harm. Their findings indicate that state naloxone distribution laws and Good SamaritanLaws are causes of a moral hazard issue associated with naloxone. In other words, policies that are liberal in naloxone distribution induce risky behavior resulting in increased opioid-related events. Their findings are in direct conflict with other reports. In an unpublished National Bureau of Economic Research paper, Rees, et al reported no association between policies associated with naloxone distribution and opioid-related events despite using similar methods. Moreover, a recently accepted manuscriptby McClellan and colleagues reported that states with naloxone access laws and Good Samaritan Laws were significantly associated with reduced incidence of opioid overdose mortality.

These conflicting findings have sparked debate about the role of naloxone in the opioid crisis and the distribution of papers unvetted by peer review. Critics of the report by Doleac and Mukherjee have pointed out that the treatment variable (passage of state laws associated with naloxone distribution) have several limitations. Frank, Humphreys, and Pollack argued that the state laws regarding naloxone have different goals or intentions (e.g., providing naloxone to anyone, immunity laws associate with naloxone use), may not have immediate effects, and do not capture other policy-level effects such as Medicaid expansion, federal grants to increase naloxone purchases, and increased mental health services for substance use disorder. Critics conclude that naloxone laws have little to no effect on naloxone use and further discussion are required to understand this phenomenon.

As the debate regarding the use of naloxone to prevent opioid overdose mortality continues, it is clear that treatment of opioid use disorder has become a priority and a burden to the U.S. healthcare system. One thing is certain, naloxone is effective at saving lives threatened by opioid overdose and is an essential strategy for addressing the opioid crisis. Withholding this life-saving medication is antithesis to the overall goal of public health.

Is there still value in the p-value?

not sure if significantDoing science is expensive, so a study that reveals significant results yet cannot be replicated by other investigators, represents a lost opportunity to invest those resources elsewhere. At the same time, the pressure on researchers to publish is immense.

These are the tensions that underlie the current debate about how to resolve issues surrounding the use of the p-value and the infamous significance threshold of 0.05. This measurement was adopted in the early 20th century to indicate the probability that the observed results are obtained by chance variation, and the 0.05 threshold has been with it since the beginning, allowing researchers to declare as significant any effect they find that can cross that threshold.

This threshold was selected for convenience in a time when computation of the p-value was difficult to calculate. Our modern scientific tools have made calculation so easy, however, that it is hard to defend a 0.05 threshold as anything but arbitrary. A group of statisticians and researchers is trying to rehabilitate the p-value, at least for the time being, so that we can improve the reliability of results with minimal disruption to the scientific production system. They hope to do this by changing the threshold for statistical significance to 0.005.

In a new editorial in JAMA, Stanford researcher John Ioannidis, a famous critic of bias and irreproducibility in research, has come out in favor of this approach. His argument is pragmatic. In it, he acknowledges that misunderstandings of the p-value are common: many people believe that a result is worth acting on if it is supported by a significant p-value, without regard for the size of the effect or the uncertainty surrounding it.

Rather than reeducating everyone who ever needs to interpret scientific research, then, it is preferable to change our treatment of the threshold signaling statistical significance. Ioannidis also points to the success of genome-wide association studies, which improved in reproducibility after moving to a statistical significance threshold of p < 5 x 10-5.

As Ioannidis admits, this is an imperfect solution. The proposal has set off substantial debate within the American Statistical Association. Bayesians, for example, see it as perpetuating the same flawed practices that got us into the reproducibility crisis in the first place. In an unpublished but widely circulated article from 2017 entitled Abandon Statistical Significance [pdf warning], Blakely McShane, Andrew Gelman, and others point to several problems with lowering the significance threshold that make it unsuitable for medical research.

First, they point out that the whole idea of the null hypothesis is poorly suited to medical research. Virtually anything ingested by or done to the body has downstream effects on other processes, almost certainly including the ones that any given trial hopes to measure. Therefore, using the null hypothesis as a straw man takes away the focus on what a meaningful effect size might be and how certain we are about the effect size we calculate for a given treatment.

They also argue that the reporting of a single p-value hides important decisions made in the analytic process itself, including all the different ways that the data could have been analyzed. They propose reporting all analyses attempted, in an attempt to capture the “researcher degrees of freedom” – the choices made by the analyst that affect how the results are calculated and interpreted.

Beyond these methodological issues, lowering the significance threshold could increase the costs of clinical trials. If our allowance for Type I error is reduced by an order of magnitude, our required sample size roughly doubles, holding all other parameters equal. In a regulatory environment where it costs over a billion dollars to bring a drug to market, this need for increased recruitment could drive up costs (which would need to be passed on to the consumer) and delay the health benefits of market release for good drugs. It is unclear whether these potential cost increases will be offset by the savings of researchers producing more reliable, reproducible studies earlier in the development process.

It also remains to be seen whether the lower p-value’s increased sample size requirement might dissuade pharmaceutical companies from bringing products to market that have a low marginal benefit. After all, you need a larger sample size to detect smaller effects, and that would only be amplified under the new significance thresholds. Overall, the newly proposed significance threshold interacts with value considerations in ways that are hard to predict but potentially worth watching.

Trends for Performance-based Risk-sharing Arrangements

Author: Shuxian Chen

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CHOICE Student Shuxian Chen

When considering the approval of new drugs, devices and diagnostic products, there’s always a tension between making the product’s benefits available to more people and collecting more information in trials. The restrictive design of randomized-controlled trials (RCTs) mean that their indications of effectiveness don’t always hold in the real world. They’re also unlikely to detect long-term adverse events. This uncertainty and risk make it hard for payers to make coverage decisions for new interventions.

Performance-based risk-sharing arrangements (PBRSAs), also known as patient access schemes (PAS), managed entry arrangements, and coverage with evidence development (CED), help to reduce such risk. These are arrangements between a payer and a pharmaceutical, device, or diagnostic manufacturer where the price level and/or nature of reimbursement is related to the actual future performance of the product in either the research or ‘real world’ environment rather than the expected future performance [1].

I recently developed a review paper with CHOICE faculty Josh Carlson and Lou Garrison that gave an update of the trends in PBRSAs both in the US and globally. Using the University of Washington Performance-Based Risk-Sharing Database, we have identified 437 eligible cases between 1993 and 2016 from that contains information obtained by searching Google, PubMed, and government websites. Eighteen cases have been added to the database in 2017 and 2018. Seventy-two cases are from the US.

Figure 1. Eligible cases between 1993-2016 by country

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Australia, Italy, the US, Sweden and the UK are the five countries that have the largest number of PBRSAs. (Distribution of cases from different countries can be seen in Graph 1.) Except for the US, cases from the other four countries are identified from their government programs: the Pharmaceutical Benefits Scheme (PBS) in Australia, the Italian Medicines Agency (AIFA) in Italy, the Swedish Dental and Pharmaceutical Benefits Agency (TLV) in Sweden, and the National Institute for Health and Care Excellence (NICE) in the UK. These single-payer systems have more power in negotiating drug price with the manufacturer than we do in the US.

Cases in the US are more heterogeneous, with both public (federal/state-level) and private payers involved. The US Centers for Medicare and Medicaid Services (CMS) contributes to 25 (37%) of the 72 US cases. Among these, most arrangements involve medical devices and diagnostic products and originate in the CED program at CMS [2]. This program is used to generate additional data to support national coverage decisions for potentially innovative medical technologies and procedures, as coverage for patients is provided only in the context of approved clinical studies [3]. For pharmaceuticals, there have been few PBRSAs between CMS and manufacturers – no cases established between 2006 and 2016. However, in August 2017, Novartis announced that a first-of-its-kind collaboration with the CMS has been made: a PBRSA for Kymriah™ (tisagenlecleucel), their novel cancer treatment for B-cell acute lymphoblastic leukemia that uses the body’s own T cells to fight cancer [4]. The arrangement allows for payment only when participants respond to Kymriah™ by the end of the first month. It can be categorized as performance-linked reimbursement (PLR), as reimbursement is only provided to the manufacturer if the patient meets the pre-specified measure of clinical outcomes. This recent collaboration may lead to a larger number and more variety of PBRSAs between pharmaceutic manufacturers and the CMS.

Please refer to our article for more detailed analyses regarding the trends in PBRSAs.

References:

[1] Carlson JJ, Sullivan SD, Garrison LP, Neumann PJ, Veenstra DL. Linking payment to health outcomes: a taxonomy and examination of performance-based reimbursement schemes between healthcare payers and manufacturers. Health Policy. 2010;96(3): 179–90. doi:10.1016/j.healthpol.2010.02.005.

[2] CMS. Coverage with Evidence Development. Available at: https://www.cms.gov/Medicare/Coverage/Coverage-with-Evidence-Development/

[3] Neumann PJ, Chambers J. Medicare’s reset on ‘coverage with evidence development’. Health Affairs Blog. 2013 Apr 1. http://healthaffairs.org/blog/2013/04/01/medicares-reset-on-coverage- with-evidence-development/

[4] Novatis. Novartis receives first ever FDA approval for a CAR-T cell therapy, Kymriah(TM) (CTL019), for children and young adults with B-cell ALL that is refractory or has relapsed at least twice. 2017. Available at: https://www.novartis.com/news/media-releases/novartis-receives-first-ever-fda-approval-car-t-cell-therapy-kymriahtm-ctl019

ISPOR’s Special Task Force on US Value Assessment Frameworks: A summary of dissenting opinions from four stakeholder groups

By Elizabeth Brouwer


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The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) recently published an issue of their Value in Health (VIH) journal featuring reports on Value Assessment Frameworks. This marks the culmination of a Spring 2016 initiative “to inform the shift toward a value-driven health care system by promoting the development and dissemination of high-quality, unbiased value assessment frameworks, by considering key methodological issues in defining and applying value frameworks to health care resource allocation decisions.” (VIH Editor’s note) The task force summarized and published their findings in a 7-part series, touching on the most important facets of value assessment. Several faculty of the CHOICE Institute at the University of Washington authored portions of the report, including Louis Garrison, Anirban Basu and Scott Ramsey.

In the spirit of open dialogue, the journal also published commentaries representing the perspectives of four stakeholder groups: payers (in this case, private insurance groups), patient advocates, academia, and the pharmaceutical industry. While supportive of value assessment in theory, each commentary critiqued aspects of the task force’s report, highlighting the contentious nature of value assessment in the US health care sector.

Three common themes emerged, however, among the dissenting opinions:

  1. Commenters saw CEA as a flawed tool, on which the task force placed too much emphasis

All commentaries except the academic perspective bemoaned the task force’s reliance on cost-effectiveness analysis. Payers, represented in an interview of two private insurance company CEOs, claimed that they do not have a choice on whether to cover most new drugs. If it’s useful at all, then, CEA informs the ways that payers distinguish between drugs of the same class. The insurers went on to claim that they are more interested in the way that CEA can highlight high-value uses for new drugs, as most are expected to be expensive regardless.

Patient advocates also saw CEA as a limited tool and were opposed to any value framework overly dependent on the cost per QALY paradigm.  The commentary equated CEAs to clinical trials—while informative, they imperfectly reflect how a drug will fare in the real world. Industry representatives, largely representing the PhRMA Foundation, agreed that the perspective provided by CEAs is too narrow and shouldn’t be the cornerstone for value assessment, at least in the context of coverage and reimbursement decisions.

  1. Commenters disagreed with how the task force measured benefits (the QALY)

All four commentaries noted the limitations the quality-adjusted life-year (QALY). The patient advocates and the insurance CEOs both claimed that the QALY did not reflect their definition of health benefits. The insurance representatives reminded us that their businesses don’t give weight to societal value because it is not in their business model. Similarly, the patient advocate said the QALY did not reflect patient preferences, where value is more broadly defined. The QALY, for example, does not adequately capture the influence of health care on functionality, ability to work, or family life. The patient advocate noted that while the task force identified these flaws and their methodological difficulties, it stopped short of recommending or taking any action to address them.

Industry advocates wrote that what makes the QALY useful—it’s ability to make comparisons across most health care conditions and settings—is also what makes it ill-suited for use in a complex health care system. Individual parts of the care continuum cannot be considered in isolation. They also noted that the QALY is discriminatory to vulnerable populations and was not reflective of their customers’ preferences.

Mark Sculpher, Professor at the University of York representing health economic theory and academia, defended the QALY to an extent, noting that the measure is the most suitable available unit for measuring health. He acknowledged the QALY’s limitations in capturing all the benefits of health care, however, and noted that decision makers and not economists should be the ones defining benefit.

 

  1. Commenters noticed a disconnect between the reports and social/political realities

Commenters seemed disappointed that the task force did not go further in directing the practical application of value assessment frameworks within the US health care sector. The academic representative wrote that, while economic underpinnings are important, ultimately value frameworks need to be useful to, and reflect the values of, the decision makers. He argued that decision-makers’ buy-in is invaluable, as they hold the power to implement and execute resource allocation. Economics can provide a foundation for this but should not be the source of judgement relating to value if the US is going to take-up value assessment frameworks to inform decisions.

Patient advocates and industry representatives went further in their criticism, saying the task force seemed disconnected from the existing health care climate. The patient advocate author felt the task force ignored the social and political realities in which health care decisions are made. Industry representatives pointed out that current policy, written in the Patient Protection and Affordable Care Act (PPACA), prohibited a QALY-based CEA because most decision makers in the US believe it inappropriate for use in health care decision making. Both groups wondered why the task force continued to rely on CEA methodology when it had been prohibited by the public sector.

 

The United States will continue to grapple with value assessment as it seeks to balance innovation with budgetary constraints. The ISPOR task force ultimately succeeded in its mission, which was never to specify a definitive and consensual value assessment framework, but instead to consider “key methodological issues in defining and applying value frameworks to health care resource allocation decisions.”

The commentaries also succeeded in their purpose: highlighting the ongoing tensions in creating value assessment frameworks that stakeholders can use. There is a need to improve tools that value health care to assure broader uptake, along with a need to accept flawed tools until we have better alternatives. The commentaries also underscore a chicken-and-egg phenomenon within health care policy. Value assessment frameworks need to align with the goals of decision-makers, but decision-makers also need value frameworks to help set goals.

Ultimately, Mark Sculpher may have summarized it best in his commentary. Value assessment frameworks ultimately seek to model the value of health care technology and services. But as Box’s adage reminds us: although all models are wrong, some are useful. How to make value assessment frameworks most useful moving forward remains a lively, complex conversation.

CHOICE Institute Director Discusses Amazon Health Care Announcement

You may have heard the big news that came out of Seattle recently: Amazon is partnering with Berkshire Hathaway and JPMorgan Chase to address health care costs and quality by creating an independent health care company for their employees. Further details of their plan remain a secret to the general public, and the companies are likely still working out logistics amongst themselves. Given the 1.2 million employees involved in the three companies, however, many in the health care industry are thinking through the likely impact of this new partnership.

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Director of the CHOICE Institute and professor of health economics at the University of Washington, Anirban Basu was recently referenced in two regional blogs describing the potential significance of the proposed plan:

According to Anirban Basu, a health care economist at the University of Washington, the trio could do a number of things to reform the health care system just by their sheer size and power alone. While most small and individual health care buyers have little power when it comes to directly negotiating with either health care providers or pharmaceutical companies, this partnership could change that—at least for those who qualify for it. Currently, price negotiating falls on third-party pharmacy benefit managers, at a cost then passed on to consumers.

Besides taking on bargaining power, Basu says Amazon may even open primary care clinics for their employees, but this could expand beyond their base.

It is important to note that while the new health plan may eventually have industry-wide effects, its scope will be limited to the companies’ employees at the beginning. And it is hardly a new phenomenon for employer groups to choose self-insurance as a means to control costs.

Henry Ford was one of the first industry giants to start his own health care insurance and delivery system in 1915, and America’s largest managed care organization, Kaiser Permanente, originally started as a health care program for employees of the Kaiser steel mills and shipyards.

Another important item to note is that America’s health care system has already been undergoing fundamental changes. While the United States Congress remains divided about how to move forward with the Affordable Care Act and improve the nation’s health care system overall, private health care companies are making their own moves. Hospital and insurance markets are becoming increasingly consolidated (with less competition to control prices), and some health care stakeholders are partnering and consolidating in innovative ways to capture market share (for example, the pharmacy company CVS Health just bought insurance company Aetna in January 2018).

Amazon’s new health care company could simply be joining these trends: historic trends of self-insuring companies to cut costs or newer trends of consolidating aspects of American health care for increased market power. However, it is entirely conceivable that the potent combination of Amazon (a technology industry giant), JPMorgan Chase (a banking industry giant), and Berkshire Hathaway (an investment giant) will bring something new to the table. Vox and StaTECHery are among many media outlets offering interesting predictions.

After the announcement, stock prices for major health care industries (e.g., Anthem, UnitedHealth, CVS, and Walgreens) experienced a sell-off as investors worry about the implications. However, experts believe that the current market would weather the storm due to the massive operational costs necessary for the partnership to enter the health care market. Moreover, the scale of Amazon, Berkshire Hathaway, and JPMorgan Chase will not be enough to compete with larger health care industry giants that already have purchasing power.

Will this health care partnership be a game changer? Perhaps, perhaps not. But as health care economists and health policy enthusiasts, students at the CHOICE Institute will certainly be watching our neighbors with interest.

[Written with the assistance of Mark Bounthavong and Nathaniel Hendrix.]