Methadone Matters: What the United States Can Learn from the Global Effort to Treat Opioid Addiction.

In the midst of an opioid epidemic, mortality related to opioid overdose continues to rise in the US. Medications to treat opioid use disorder, including methadone and buprenorphine, are highly effective in reducing the morbidity and mortality related to illicit opioid use. Despite the efficacy of these life-saving medications, the majority of people with an opioid use disorder lack access to treatment. This paper briefly reviews the evidence to support the use of medications to treat opioid use disorder with a specific focus on methadone. We discuss the current state of methadone therapy for the treatment of opioid use disorder in the US and present logistical barriers that limit its use. Next, we examine three international pharmacy-based models in which methadone dispensing to treat opioid use disorder occurs outside of an opioid treatment facility. We discuss current challenges and opportunities to incorporate similar methods of methadone dispensing for the treatment of opioid use disorder in the US. Finally, we present our vision to integrate pharmacy-based methadone dispensing into routine opioid use disorder treatment through collaboration between clinicians and pharmacies to improve local access to this life-saving medication.

Prediction of Future Chronic Opioid Use Among Hospitalized Patients.

BACKGROUND: Opioids are commonly prescribed in the hospital; yet, little is known about which patients will progress to chronic opioid therapy (COT) following discharge. We defined COT as receipt of ≥ 90-day supply of opioids with < 30-day gap in supply over a 180-day period or receipt of ≥ 10 opioid prescriptions over 1 year. Predictive tools to identify hospitalized patients at risk for future chronic opioid use could have clinical utility to improve pain management strategies and patient education during hospitalization and discharge. OBJECTIVE: The objective of this study was to identify a parsimonious statistical model for predicting future COT among hospitalized patients not on COT before hospitalization. DESIGN: Retrospective analysis electronic health record (EHR) data from 2008 to 2014 using logistic regression. PATIENTS: Hospitalized patients at an urban, safety net hospital. MAIN MEASUREMENTS: Independent variables included medical and mental health diagnoses, substance and tobacco use disorder, chronic or acute pain, surgical intervention during hospitalization, past year receipt of opioid or non-opioid analgesics or benzodiazepines, opioid receipt at hospital discharge, milligrams of morphine equivalents prescribed per hospital day, and others. KEY RESULTS: Model prediction performance was estimated using area under the receiver operator curve, accuracy, sensitivity, and specificity. A model with 13 covariates was chosen using stepwise logistic regression on a randomly down-sampled subset of the data. Sensitivity and specificity were optimized using the Youden's index. This model predicted correctly COT in 79% of the patients and no COT correctly in 78% of the patients. CONCLUSIONS: Our model accessed EHR data to predict 79% of the future COT among hospitalized patients. Application of such a predictive model within the EHR could identify patients at high risk for future chronic opioid use to allow clinicians to provide early patient education about pain management strategies and, when able, to wean opioids prior to discharge while incorporating alternative therapies for pain into discharge planning.

Trends in intentional abuse or misuse of benzodiazepines and opioid analgesics and the associated mortality reported to poison centers across the United States from 2000 to 2014.

CONTEXT: Prior works demonstrates an increased risk of death when opioid analgesics and benzodiazepines are used concomitantly to gain a high. Using poison center data, we described trends in abuse or misuse of benzodiazepines and opioid analgesics. We quantified mortality risk associated with abuse or misuse of benzodiazepines, opioid analgesics and the combination of opioid analgesics and benzodiazepines. METHODS: This was a retrospective chart review of data from the National Poison Data System which collects information from 55 poison centers located across the United States. We identified reported cases of "intentional abuse or misuse" of benzodiazepine and/or opioid analgesic exposures. Poisson regression was used to compare the number of cases from each year between 2001 and 2014 to the year 2000. Logistic regression was used to determine whether cases exposed to both benzodiazepines and opioids had greater odds of death relative to cases exposed to opioid analgesics alone. RESULTS: From 2000 to 2014, there were 125,485 benzodiazepine exposures and 84,627 opioid exposures among "intentional abuse or misuse" cases. Of the benzodiazepine exposures, 17.3% (n = 21,660) also involved an opioid. In 2010, exposures involving both opioids and benzodiazepines were 4.26-fold (95% CI: 3.87-4.70; p < .001) higher than in 2000. The risk of death was 1.55 (95% CI: 1.01-2.37; p = .04) times greater among those who used both an opioid and a benzodiazepine compared to opioids alone. This association held after adjusting for gender and age. CONCLUSION: Intentional abuse or misuse of benzodiazepines and opioids in combination increased significantly from 2000 to 2014. Benzodiazepine abuse or misuse far exceeded cases of opioid abuse or misuse. Death was greater with co-abuse or misuse of benzodiazepines and opioids. Population-level campaigns to inform the public about the risk of death with co-abuse or misuse of benzodiazepines and opioids are urgently needed to address this overdose epidemic.

Weight bearing as a measure of disease progression and efficacy of anti-inflammatory compounds in a model of monosodium iodoacetate-induced osteoarthritis.

OBJECTIVE: To describe an in vivo model in the rat in which change in weight distribution is used as a measure of disease progression and efficacy of acetaminophen and two nonsteroidal anti-inflammatory drugs (NSAIDs) in a model of monosodium iodoacetate (MIA)-induced osteoarthritis (OA). METHODS: Intra-articular injections of MIA and saline were administered to male Wistar rats (175-200 g) into the right and left knee joints, respectively. Changes in hind paw weight distribution between the right (osteoarthritic) and left (contralateral control) limbs were utilized as an index of joint discomfort. Acetaminophen and two archetypal, orally administered NSAIDs, naproxen and rofecoxib, were examined for their ability to decrease MIA-induced change in weight distribution. RESULTS: A concentration-dependent increase in change in hind paw weight distribution was noted after intra-articular injection of MIA. Both naproxen and rofecoxib demonstrated the capacity to significantly (P<0.05) decrease hind paw weight distribution in a dose-dependent fashion, indicating that the change in weight distribution associated with MIA injection is susceptible to pharmacological intervention. CONCLUSION: The determination of differences in hind paw weight distribution in the rat MIA model of OA is a technically straightforward, reproducible method that is predictive of the effects of anti-inflammatory and analgesic agents. This system may be useful for the discovery of novel pharmacologic agents in human OA.