Michele Buonora1, Hector R Perez2, Moonseong Heo3, Chinazo O Cunningham2, Joanna L Starrels2. 1. Albert Einstein College of Medicine, Bronx, New York. 2. Department of Medicine, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, New York. 3. Department of Epidemiology and Population Health, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, New York, USA.
Abstract
OBJECTIVE: Among patients with chronic pain, risk of opioid use is elevated with high opioid dose or concurrent benzodiazepine use. This study examined whether these clinical factors, or sociodemographic factors of race and gender, are associated with opioid dose reduction. DESIGN AND SETTING: A retrospective cohort study of outpatients prescribed chronic opioid therapy between 2007 and 2012 within a large, academic health care system in Bronx, New York, using electronic medical record data. Included patients were prescribed a stable dose of chronic opioid therapy over a one-year "baseline period" and did not have cancer. METHODS: The primary outcome was opioid dose reduction (≥30% reduction from baseline) within two years. Multivariable logistic regression tested the associations of two clinical variables (baseline daily opioid dose and concurrent benzodiazepine prescription) and two sociodemographic variables (race/ethnicity and gender) with opioid dose reduction. RESULTS: Of 1,097 patients, 463 (42.2%) had opioid dose reduction. High opioid dose (≥100 morphine-milligram equivalents [MME]) was associated with lower odds of opioid dose reduction compared with an opioid dose <100 MME (adjusted odds ratio [AOR] = 0.69, 95% confidence interval [CI] = 0.54-0.89). Concurrent benzodiazepine prescription was not associated with opioid dose reduction. Black (vs white) race and female (vs male) gender were associated with greater odds of opioid dose reduction (AOR = 1.82, 95% CI = 1.22-2.70; and AOR = 1.43, 95% CI = 1.11-1.83, respectively). CONCLUSIONS: Black race and female gender were associated with greater odds of opioid dose reduction, whereas clinical factors of high opioid dose and concurrent benzodiazepine prescription were not. Efforts to reduce opioid dose should target patients based on clinical factors and address potential biases in clinical decision-making.
OBJECTIVE: Among patients with chronic pain, risk of opioid use is elevated with high opioid dose or concurrent benzodiazepine use. This study examined whether these clinical factors, or sociodemographic factors of race and gender, are associated with opioid dose reduction. DESIGN AND SETTING: A retrospective cohort study of outpatients prescribed chronic opioid therapy between 2007 and 2012 within a large, academic health care system in Bronx, New York, using electronic medical record data. Included patients were prescribed a stable dose of chronic opioid therapy over a one-year "baseline period" and did not have cancer. METHODS: The primary outcome was opioid dose reduction (≥30% reduction from baseline) within two years. Multivariable logistic regression tested the associations of two clinical variables (baseline daily opioid dose and concurrent benzodiazepine prescription) and two sociodemographic variables (race/ethnicity and gender) with opioid dose reduction. RESULTS: Of 1,097 patients, 463 (42.2%) had opioid dose reduction. High opioid dose (≥100 morphine-milligram equivalents [MME]) was associated with lower odds of opioid dose reduction compared with an opioid dose <100 MME (adjusted odds ratio [AOR] = 0.69, 95% confidence interval [CI] = 0.54-0.89). Concurrent benzodiazepine prescription was not associated with opioid dose reduction. Black (vs white) race and female (vs male) gender were associated with greater odds of opioid dose reduction (AOR = 1.82, 95% CI = 1.22-2.70; and AOR = 1.43, 95% CI = 1.11-1.83, respectively). CONCLUSIONS: Black race and female gender were associated with greater odds of opioid dose reduction, whereas clinical factors of high opioid dose and concurrent benzodiazepine prescription were not. Efforts to reduce opioid dose should target patients based on clinical factors and address potential biases in clinical decision-making.
Authors: Marc R Larochelle; Jane M Liebschutz; Fang Zhang; Dennis Ross-Degnan; J Frank Wharam Journal: Ann Intern Med Date: 2016-09-06 Impact factor: 25.391
Authors: Shannon M Smith; Richard C Dart; Nathaniel P Katz; Florence Paillard; Edgar H Adams; Sandra D Comer; Aldemar Degroot; Robert R Edwards; David J Haddox; Jerome H Jaffe; Christopher M Jones; Herbert D Kleber; Ernest A Kopecky; John D Markman; Ivan D Montoya; Charles O'Brien; Carl L Roland; Marsha Stanton; Eric C Strain; Gary Vorsanger; Ajay D Wasan; Roger D Weiss; Dennis C Turk; Robert H Dworkin Journal: Pain Date: 2013-06-20 Impact factor: 6.961
Authors: Jane A Gwira Baumblatt; Caleb Wiedeman; John R Dunn; William Schaffner; Leonard J Paulozzi; Timothy F Jones Journal: JAMA Intern Med Date: 2014-05 Impact factor: 21.873
Authors: Mark A Ilgen; Amy S B Bohnert; Dara Ganoczy; Matthew J Bair; John F McCarthy; Frederic C Blow Journal: Pain Date: 2016-05 Impact factor: 7.926
Authors: Hector R Perez; Michele Buonora; Chinazo O Cunningham; Moonseong Heo; Joanna L Starrels Journal: J Gen Intern Med Date: 2019-08-19 Impact factor: 5.128
Authors: Michele Buonora; Hector R Perez; Jordan Stumph; Robert Allen; Shadi Nahvi; Chinazo O Cunningham; Jessica S Merlin; Joanna L Starrels Journal: Pain Med Date: 2020-10-01 Impact factor: 3.750
Authors: Joshua J Fenton; Alicia L Agnoli; Guibo Xing; Lillian Hang; Aylin E Altan; Daniel J Tancredi; Anthony Jerant; Elizabeth Magnan Journal: JAMA Netw Open Date: 2019-11-01
Authors: Alexander C Tsai; Mathew V Kiang; Michael L Barnett; Leo Beletsky; Katherine M Keyes; Emma E McGinty; Laramie R Smith; Steffanie A Strathdee; Sarah E Wakeman; Atheendar S Venkataramani Journal: PLoS Med Date: 2019-11-26 Impact factor: 11.069