M Kit Delgado1, Yanlan Huang2, Zachary Meisel3, Sean Hennessy4, Michael Yokell5, Daniel Polsky6, Jeanmarie Perrone7. 1. Department of Emergency Medicine, Center for Emergency Care Policy and Research, University of Pennsylvania, Philadelphia, PA; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA; Penn Injury Science Center, University of Pennsylvania, Philadelphia, PA; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA. Electronic address: kit.delgado@uphs.upenn.edu. 2. Department of Medicine, Division of General Internal Medicine, University of Pennsylvania, Philadelphia, PA; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA. 3. Department of Emergency Medicine, Center for Emergency Care Policy and Research, University of Pennsylvania, Philadelphia, PA; Penn Injury Science Center, University of Pennsylvania, Philadelphia, PA; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA. 4. Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA. 5. Department of Emergency Medicine, Center for Emergency Care Policy and Research, University of Pennsylvania, Philadelphia, PA. 6. Department of Medicine, Division of General Internal Medicine, University of Pennsylvania, Philadelphia, PA; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA; Department of Health Care Management and Economics, Wharton School, University of Pennsylvania, Philadelphia, PA. 7. Department of Emergency Medicine, Center for Emergency Care Policy and Research, University of Pennsylvania, Philadelphia, PA; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA.
Abstract
STUDY OBJECTIVE: To inform opioid stewardship efforts, we describe the variation in emergency department (ED) opioid prescribing for a common minor injury, ankle sprain, and determine the association between initial opioid prescription intensity and transition to prolonged opioid use. METHODS: We analyzed 2011 to 2015 US private insurance claims (Optum Clinformatics DataMart) for ED-treated ankle sprains among opioid-naive patients older than 18 years. We determined the patient- and state-level variation in the opioid prescription rate and characteristics, and the risk-adjusted association between total morphine milligram equivalents (MMEs) of the prescription and transition to prolonged use (filling 4 or more opioid prescriptions 30 to 180 days after the index visit). RESULTS: A total of 30,832 patients met inclusion criteria. Of these patients, 25.1% received an opioid prescription with a median total MME of 100 (interquartile range 75 to 113), tablet quantity of 15 (interquartile range 12 to 20), and days supplied of 3 (interquartile range 2 to 4). State-level prescribing rates ranged from 2.8% in North Dakota to 40.0% in Arkansas. Among patients who received a total MME of greater than 225 (equivalent to >30 tabs of oxycodone 5 mg), the adjusted rate of prolonged opioid use was 4.9% (95% CI 1.8% to 8.1%) compared with 1.1% (95% CI 0.7% to 1.5%) among those who received at total MME of 75 and 0.5% (95% CI 0.4% to 0.6%) among those who did not fill an opioid prescription. CONCLUSION: Opioid prescribing for ED patients treated for ankle sprains is common and highly variable. Although infrequent in this population, prescriptions greater than 225 MME were associated with higher rates of prolonged opioid use. This is concerning because these prescriptions could still fall within 5- or 7-day supply limit policies aimed at promoting safer opioid prescribing.
STUDY OBJECTIVE: To inform opioid stewardship efforts, we describe the variation in emergency department (ED) opioid prescribing for a common minor injury, ankle sprain, and determine the association between initial opioid prescription intensity and transition to prolonged opioid use. METHODS: We analyzed 2011 to 2015 US private insurance claims (Optum Clinformatics DataMart) for ED-treated ankle sprains among opioid-naive patients older than 18 years. We determined the patient- and state-level variation in the opioid prescription rate and characteristics, and the risk-adjusted association between total morphine milligram equivalents (MMEs) of the prescription and transition to prolonged use (filling 4 or more opioid prescriptions 30 to 180 days after the index visit). RESULTS: A total of 30,832 patients met inclusion criteria. Of these patients, 25.1% received an opioid prescription with a median total MME of 100 (interquartile range 75 to 113), tablet quantity of 15 (interquartile range 12 to 20), and days supplied of 3 (interquartile range 2 to 4). State-level prescribing rates ranged from 2.8% in North Dakota to 40.0% in Arkansas. Among patients who received a total MME of greater than 225 (equivalent to >30 tabs of oxycodone 5 mg), the adjusted rate of prolonged opioid use was 4.9% (95% CI 1.8% to 8.1%) compared with 1.1% (95% CI 0.7% to 1.5%) among those who received at total MME of 75 and 0.5% (95% CI 0.4% to 0.6%) among those who did not fill an opioid prescription. CONCLUSION: Opioid prescribing for ED patients treated for ankle sprains is common and highly variable. Although infrequent in this population, prescriptions greater than 225 MME were associated with higher rates of prolonged opioid use. This is concerning because these prescriptions could still fall within 5- or 7-day supply limit policies aimed at promoting safer opioid prescribing.
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