Shruti Zaveri1, Tamar B Nobel1, Prerna Khetan1, Maya Srinivasan1, Celia M Divino2. 1. Department of Surgery, Icahn School of Medicine, Mount Sinai Hospital, 1 Gustave L Levy Place, New York, 10023, USA. 2. Department of Surgery, Icahn School of Medicine, Mount Sinai Hospital, 1 Gustave L Levy Place, New York, 10023, USA. celia.divino@mountsinai.org.
Abstract
BACKGROUND: The misuse of opioids is a serious national crisis that is fueled by prescriptions medications. Opioid prescribing habits are known to be highly varied amongst providers. The purpose of this study is to identify patient and surgeon characteristics that predict postoperative opioid prescribing patterns. METHODS: This is a serial cross-sectional analysis of 20,497 patients who underwent general surgical procedures at a large academic center. Our primary outcome was the total amount of opioids prescribed within 30 days of the surgery. Univariate and multivariate linear regression models were used to identify patient and provider characteristics that were associated with increased opioids prescribed. RESULTS: Among patient characteristics studied, patient age, sex, ethnicity, and insurance status were found to have a significant association with the amount of opioids prescribed. Younger patients and male patients received higher morphine milligram equivalents (MMEs) on discharge (p < 0.05). Patients of Hispanic background were prescribed significantly lower opioids compared to Non-Hispanic patients (p < 0.0001). Among the provider characteristics studied, surgeon sex and years in practice were significantly predictive of the amount of opioids prescribed, with surgeons in practice for <15 years prescribing the highest MMEs (p < 0.0001). CONCLUSION: While opioid prescribing habits after surgery seem highly varied and arbitrary, we have identified key predictors that highlight biases in surgeon opioid prescribing patterns. Surgeons tend to prescribe significantly larger amounts of opioids to younger, male patients and those of certain ethnic backgrounds, and surgeons with fewer years in practice are more likely to prescribe more opioids.
BACKGROUND: The misuse of opioids is a serious national crisis that is fueled by prescriptions medications. Opioid prescribing habits are known to be highly varied amongst providers. The purpose of this study is to identify patient and surgeon characteristics that predict postoperative opioid prescribing patterns. METHODS: This is a serial cross-sectional analysis of 20,497 patients who underwent general surgical procedures at a large academic center. Our primary outcome was the total amount of opioids prescribed within 30 days of the surgery. Univariate and multivariate linear regression models were used to identify patient and provider characteristics that were associated with increased opioids prescribed. RESULTS: Among patient characteristics studied, patient age, sex, ethnicity, and insurance status were found to have a significant association with the amount of opioids prescribed. Younger patients and male patients received higher morphine milligram equivalents (MMEs) on discharge (p < 0.05). Patients of Hispanic background were prescribed significantly lower opioids compared to Non-Hispanic patients (p < 0.0001). Among the provider characteristics studied, surgeon sex and years in practice were significantly predictive of the amount of opioids prescribed, with surgeons in practice for <15 years prescribing the highest MMEs (p < 0.0001). CONCLUSION: While opioid prescribing habits after surgery seem highly varied and arbitrary, we have identified key predictors that highlight biases in surgeon opioid prescribing patterns. Surgeons tend to prescribe significantly larger amounts of opioids to younger, male patients and those of certain ethnic backgrounds, and surgeons with fewer years in practice are more likely to prescribe more opioids.
Authors: Cornelius A Thiels; Stephanie S Anderson; Daniel S Ubl; Kristine T Hanson; Whitney J Bergquist; Richard J Gray; Halena M Gazelka; Robert R Cima; Elizabeth B Habermann Journal: Ann Surg Date: 2017-10 Impact factor: 12.969
Authors: Maureen V Hill; Ryland S Stucke; Sarah E Billmeier; Julia L Kelly; Richard J Barth Journal: J Am Coll Surg Date: 2017-11-30 Impact factor: 6.113
Authors: Rebecca E Scully; Andrew J Schoenfeld; Wei Jiang; Stuart Lipsitz; Muhammad Ali Chaudhary; Peter A Learn; Tracey Koehlmoos; Adil H Haider; Louis L Nguyen Journal: JAMA Surg Date: 2018-01-01 Impact factor: 14.766