Equations for Calculating MMEs

Theory
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What we are studying

There is no standard or accepted method in the literature for calculating average daily milligrams of morphine equivalents (MME). MMEs are a way to standardize across opioids of different potencies in epidemiology studies. In particular, overlapping prescriptions change the picture of MME if they are not accounted for properly.

There is a belief that 90 MME/day is a threshold for overdose risk in patients treated with opioids, perpetuated by the CDC Guideline for Prescribing Opioids for Chronic Pain. However, the Food and Drug Administration has pointed out that there is actually a gradient of risk and the concept of a 90 MME/day threshold is an artifact of how studies measured opioid use.

Consider the following scenario:
A patient receives 30 mg extended-release oxycodone twice a day for around-the-clock pain for 30 days (60 tab- 43 lets), and one 5 mg oxycodone twice a day as needed for breakthrough pain for 7 days (14 tablets). Both prescriptions are dispensed on the first day of a 30-day month, with no subsequent dispensing. Assume 1.5 as the conversion factor for oxycodone-to-morphine. Alarmingly, for this simple scenario, 4 definitional 49 variants return daily MME inconsistently: 31.2mg, 75.8mg, 93.5mg, 105mg per day. Are any of these correct?

see the lab notebook

Why it matters

Health insurers, lawmakers, clinical policymakers, and others are using 90 MME/day to limit patient benefits or prescriber practice.

For example, one state Medicaid policy reads: “for chronic non-cancer pain beneficiaries receiving an opioid… the total daily MME allowed limit will be ≤90 MME/day.”

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What we learned

We identified 4 definitions from the 18 papers used as the evidence base in the CDC Guideline for chronic non-cancer pain management with opioids. We used data from California and Florida to examine how these previously unknown 4 definitions impact who gets considered to be a “high dose” patient. We also wanted to see if studies comparing these states would come up with the same findings using the 4 equations. While 90 MME may have cautionary mnemonic benefits, without harmonization of calculation, its utility is limited. Comparison between studies using daily MME requires explicit attention to definitional variation. Among 9,436,640 prescriptions, 42% overlapped, which led denominator definitions to impact daily MME values. Across definitions, average daily MME varied 3-fold (range: 17 to 52 [CA] and 23 to 65 mg [FL]). Across definitions, prevalence of “high dose” individuals ranged 5.9% to 14.2% (FL) and 3.5% to 10.3% (CA). A definitional variation would impact a hypothetical surveillance study trying to establish how much more “high dose” prescribing 93 was present in FL than CA: from 34% to 79% more. Meta-analyses revealed strong heterogeneity (I2 range: 86% to 99%). In sensitivity analysis, including unit interval 90.0 to 90.9 increased “high dose” population fraction by 15%.

How to use the results

All the code and equations are being made public so others can explicitly decide which to use for research studied involving daily MME. validity of a single numerical risk threshold. When measuring with inches, centimeters, and yards, the absolute number of units is arbitrary. The mix of clinical and research metrics
used to calculate the 90 MME threshold is similarly convoluted. As providers, we struggle to do what we feel is right for our patients in the midst of increasing outside pressure with serious ramifications. Our findings call into question state laws and third-party payer MME threshold mandates. Without harmonization, the scientific basis for these man dates may need to be revisited. As the CDC Guideline is revised, and clinical decision tools are developed, it is critically important to reassess the evidence base in light of this previously unknown MME definitional variability.

Who is conducting and supporting the study

This study was supported by funding from the US Food and Drug Administration and the US Department of Justice (Bureau of Justice Assistance). Funding agencies had no involvement in design, analysis, or interpretation. Views presented are not necessarily those of the funders.Awards: HHSF223201810183C and 2017-PM-BX-K038. We are grateful to generations of taxpayers in North Carolina, Kentucky, and Florida for supporting public universities. We are also grateful to US taxpayers for safeguarding public health by supporting FDA and this research project.

Nabarun Dasgupta
Epidemiologist, Factotum
Yanning Wang
Analyst
June Bae
Analyst
Toska Cooper
Project Manager
Chris Delcher
Epidemiologist

Bethany DiPrete
Epidemiologist
Brooke Chidgey
Pain Physician
Alan Kinlaw
Pharmacoepidemiologist