Trump Administration’s Blueprint to Address Drug Prices

 

On May 11th 2018, the Trump Administration released an outline of their plan to address rising pharmaceutical prices in the U.S. The plan intends to increase competition, improve negotiations, change incentives, and decrease out-of-pocket costs. However, it has been criticized as being too moderate and ignores issues that experts have identified as key problems in the healthcare market.

Dr. Jonathan H. Watanabe, an alumnus of The CHOICE Institute and associate professor at UC San Diego Skaggs School of Pharmacy and Pharmaceutical Sciences, was recently interviewed by Mari Payton of NBC7 in San Diego, CA about the Trump Administration proposed plans. Dr. Watanabe stated that the U.S. can’t sustain the current prices on healthcare and argued that we need to have the ability to negotiate more reasonable prices from the pharmaceutical industry. Currently, Medicare does not have the power to negotiate lower prices with drug makers like other countries and private healthcare plans do.

Thus, the absence of Medicare’s ability to negotiate for lower prices highlights an important limitation of the agency. Other federal agencies such as the Department of Veterans Affairs are able to directly negotiate for lower drug prices; however, Medicare is unable to do so. The Trump Administration’s plan doesn’t address price control or allow Medicare to leverage their market power to negotiate prices directly with pharmaceutical manufacturers, a key problem in today’s healthcare industry. Instead, the plan intends to reform Medicare Part D to allow the plan sponsors, instead of Medicare, to negotiate lower priceswith drug makers.

Of concern is the dangerous pattern of increased federal spending with increased out-of-pocket costs for pharmaceuticals. As patients are burdened with higher drug costs, they are less likely to adhere to their medications, which can result in poor outcomes. According to Dr Watanabe:

What we’re seeing with the medications that Medicare spends the most on is a troubling pattern of higher federal spend in constant dollars coupled with increased out-of-pocket spend by patients. Yet, fewer patients receiving the high-spend medications, because these drugs are often for less common conditions.”

Dr. Watanabe was on the committee of the National Academies of Sciences, Engineering, and Medicine that drafted the report Making Medicines Affordable: A National Imperative. One of the committee’s key recommendations to address the high cost of pharmaceuticals is that government agencies (e.g., Medicare) should be allowed to use their market power and negotiate lower drug prices. Other recommendations include reducing incentives to use costly pharmaceuticals, eliminating direct-to-consumer advertising, reforming health insurance plan structure, and re-evaluating discount programs (e.g., 340B) to ensure that participating facilities are meeting the program’s goal of helping vulnerable patients. Although the Trump Administration’s plan reflected some of the proposals from the National Academies document, they fall short of being firm resolutions. Dr. Watanabe stated that:

The key elements required are transparencyand informed public dialogue.  If we could shed light on the actual flow of the dollars and the practices used that absorb spending, then rational approaches can be taken to help patients better get the care they deserve and society to devise a sustainable system for delivering care by medications. It’s too hard to measure in the dark.

Despite the criticisms, the Trump Administration’s announcement indicates that the nation is finally beginning to address the problem of unchecked increases in drug costs. The challenge will be to implement effective policies that continue to encourage innovation while addressing rising costs in a timely manner. However, Secretary of Health and Human Services, Alex M. Azar, cautioned that any dramatic change would take months if not years to implement.

As drug prices continue to increase, U.S. citizens have to continue shouldering the economic burden of an inefficient health care market. Health care policy makers agree that this is not sustainable, and that wide-scale reform is needed.Additionally, more nonpartisan discussion is needed to develop health care reforms that benefit the vast majority of U.S. citizens. Whether the Trump Administration’s plan is going to make an impact remains to be seen.

 

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.

Generating Survival Curves from Study Data: An Application for Markov Models

By Mark Bounthavong

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CHOICE Student Mark Bounthavong

In cost-effectiveness analysis (CEA), a life-time horizon is commonly used to simulate the overall costs and health effects of a chronic disease. Data for mortality comparing therapeutic treatments are normally derived from survival curves or Kaplan-Meier curves published in clinical trials. However, these Kaplan-Meier curves may only provide survival data up to a few months to a few years, reflecting the length of the trial.

In order to adapt these clinical trial data to a lifetime horizon for use in cost-effectiveness modeling, modelers must make assumptions about the curve and extrapolate beyond what was seen empirically. Luckily, extrapolation to a lifetime horizon is possible using a series of methods based on parametric survival models (e.g., Weibull, exponential). Performing these projections can be challenging without the appropriate data and software, which is why I wrote a tutorial that provides a practical, step-by-step guide to estimate a parameter method (Weibull) from a survival function for use in CEA models.

I split my tutorial into two parts, as described below.

Part 1 begins by providing a guide to:

  • Capture the coordinates of a published Kaplan-Meier curve and export the results into a *.CSV file
  • Estimate the survival function based on the coordinates from the previous step using a pre-built template
  • Generate a Weibull curve that closely resembles the survival function and whose parameters can be easily incorporated into a simple three-state Markov model

Part 2 concludes with a step-by-step guide to:

  • Describe how to incorporate the Weibull parameters into a Markov model
  • Compare the survival probability of the Markov model to the reference Kaplan-Meier curve to validate the method and catch any errors
  • Extrapolate the survival curve across a lifetime horizon

The tutorial requires using and transferring data across a couple of different software. You will need to have some familiarity with Excel to perform these parametric simulations. You should download and install the open source software “Engauge Digitizer” developed by Mark Mitchell, which can be found here. You should also download and install the latest version of R and RStudio to generate the parametric survival curve parameters.

Hoyle and Henley wrote a great paper on using data from a Kaplan-Meier curve to generate parameters for a parametric survival model, which can be found here. The tutorial makes use of their methods and supplemental file. Specifically, you will need to download their Excel Template to generate the parametric survival curve parameters.

I have created a public folder with the relevant files used in the tutorial here.

If you have any comments or notice any errors, please contact me at mbounth@uw.edu