hand reaching for blue pills

Author Q&A: Reducing High-Dose Opioid Prescribing

Sara Heins, PhD
Sara Heins, PhD, Associate Policy Researcher, RAND Corporation

From 1999 to 2017, almost 218,000 people died in the United States from overdoses related to prescription opioids. Overdose deaths involving prescription opioids were five times higher in 2017 than in 1999, according to the CDC.

Previous research has indicated that patients who receive higher doses of prescription opioids have an increased risk of overdose and mortality. In response, several states have established Morphine Equivalent Daily Dose (MEDD) thresholds that convert opioid prescriptions to their equivalent dose in morphine and divides the total prescription by the number of days the prescription is intended to last, allowing for comparison among different opioid formulations and strengths. MEDD policies set thresholds for prescribers, which may only be exceeded in limited circumstances, such as when being prescribed to certain patient groups or as short-courses.

Sara Heins, PhD, an associate policy researcher at RAND Corporation, used policy surveillance to track MEDD policies through June 1, 2017 (data are available on LawAtlas.org). She published an article in Pain Medicine on March 13 that describes U.S. MEDD policies.

We asked Dr. Heins a few questions about her work and this recent publication.

Why did you track MEDD policies across all 50 states and DC?

I’ve been working in the opioid policy area for several years now and had been noticing that a lot of states were putting restrictions or making recommendations on overall MEDD dose limits. However, different states and even different agencies within the same state used different dose thresholds and the numbers selected often seemed quite arbitrary. Looking into the topic more deeply, I found commentaries to this effect, but no systematic list of all the different MEDD thresholds used in state policies. I was familiar with LawAtlas and their efforts to systematically collect information on state-level health policies, so I reached out to them with my idea to fill this important gap in the literature.

Are MEDD policies a potentially viable strategy for addressing the current opioid crisis? If so, what about them is promising, and how does this data support that?

We know from extensive prior research that patients who are prescribed higher doses of opioids are more likely to die of an overdose, so policies that reduce high dose prescribing make a lot of sense at the population level. However, I think some of these policies can be problematic when they are applied at an individual level. In the past, doctors have prescribed high doses of opioids to people who did not need them, putting them at unnecessary risk. MEDD policies may encourage doctors to start patients who have never taken opioids at lower doses, which is probably a good thing. However, there are some individuals who have been on high, but non-escalating doses of opioids for a long period of time and these dose restriction policies might harm them. For example, a patient who suddenly has their dose decreased may seek out illicit sources of opioids. Or, problematically, doctors may drop patients or refuse to take patients on high doses of opioids. These patients then have their pain management and other medical needs neglected. MEDD policies need to make sure that these patients are not abandoned.

How do you plan to use this data going forward?

I am currently working on evaluating how a selection of these policies have impacted prescribing behaviors. Knowing when the policies went into effect, which thresholds were used, and which populations the policies apply to has allowed me to set-up my analyses and examine the most relevant changes in prescribing. I also plan to evaluate how these policies might impact patient health outcomes or have unintended consequences for certain patient populations.

How do you hope others use this data?

I hope that researchers can use this data to evaluate the effectiveness of MEDD policies in different populations and against a variety of outcomes.

What surprised you when reviewing this data?

I expected there to be more consensus in threshold values over time, particularly after the Centers for Disease Control passed their own MEDD recommendations. However, this doesn’t appear to be the case. There is still a lot of disagreement between states.

What was your biggest “take-away” from this research?

There is a lot of diversity in state-level MEDD policies. Beyond the actual MEDD level used, states also differ on how much discretion they give prescribers and which patient populations are explicitly excluded from the policies (for example, patients with cancer or a terminal illness). There is a great opportunity here for researchers to look at these different policies options and figure out what is working and what isn’t.


You can read Dr. Heins’s article at Pain Medicine and explore her MEDD policies dataset at LawAtlas.org

Temple University Center for Public Health Law Research

Based at the Temple University Beasley School of Law, the Center for Public Health Law Research supports the widespread adoption of scientific tools and methods for mapping and evaluating the impact of law on health. It works by developing and teaching public health law research and legal epidemiology methods (including legal mapping and policy surveillance); researching laws and policies that improve health, increase access to care, and create or remove barriers to health (e.g., laws or policies that create or remove inequity); and communicating and disseminating evidence to facilitate innovation.

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