Objective: Morphine-standardized doses are used in clinical practice and research to account for molecular potency. Ninety milligrams of morphine equivalents (MME) per day are considered a “high dose” risk threshold in guidelines, laws, and by payers. Although ubiquitously cited, the “CDC definition” of daily MME lacks a clearly defined denominator. Our objective was to assess denominator-dependency on “high dose” classification across competing definitions. Methods: To identify definitional variants, we reviewed literature and electronic prescribing tools, yielding 4 unique definitions. Using Prescription Drug Monitoring Programs data (July to September 2018), we conducted a population-based cohort study of 3,916,461 patients receiving outpatient opioid analgesics in California (CA) and Florida (FL). The binary outcome was whether patients were deemed “high dose” (>90 MME/d) compared across 4 definitions. We calculated I ² for heterogeneity attributable to the definition. Results: 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). Definitional variation alone would impact a hypothetical surveillance study trying to establish how much more “high dose” prescribing was present in FL than CA: from 39% to 84% more. Meta-analyses revealed strong heterogeneity (I ² range: 86% to 99%). In sensitivity analysis, including unit interval 90.0 to 90.9 increased “high dose” population fraction by 15%. Discussion: 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.

Recurrent events—outcomes that an individual can experience repeatedly over the course of follow-up—are common in epidemiologic and health services research. Studies involving recurrent events often focus on time to first occurrence or on event rates, which assume constant hazards over time. In this paper, we contextualize recurrent event parameters of interest using counterfactual theory in a causal inference framework and describe an approach for estimating a target parameter referred to as the mean cumulative count. This approach leverages inverse probability weights to control measured confounding with an existing (and underutilized) nonparametric estimator of recurrent event burden first proposed by Dong et al. in 2015. We use simulations to demonstrate the unbiased estimation of the mean cumulative count using the weighted Dong-Yasui estimator in a variety of scenarios. The weighted Dong-Yasui estimator for the mean cumulative count allows researchers to use observational data to flexibly estimate and contrast the expected number of cumulative events experienced per individual by a given time point under different exposure regimens. We provide code to ease application of this method.