Christopher O St Clair1, Natalia I Golub2, Yong Ma1, Jaejoon Song1, Scott K Winiecki1, David L Menschik3. 1. Center for Drug Evaluation and Research, Food and Drug Administration, White Oak, Maryland. 2. Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 3. Center for Drug Evaluation and Research, Food and Drug Administration, White Oak, Maryland. Electronic address: david.menschik@fda.hhs.gov.
Abstract
INTRODUCTION: A considerable burden of prescription and illicit opioid-related mortality and morbidity in the U.S. is attributable to potentially unnecessary or excessive opioid prescribing, and co-prescribing gabapentinoids may increase risk of harm. Data are needed regarding physician and patient characteristics associated with opioid analgesic and opioid analgesic-gabapentinoid co-prescriptions to elucidate targets for reducing preventable harm. METHODS: Multiple logistic regression was utilized to examine patient and physician predictors of opioid analgesic prescriptions and opioid analgesic-gabapentinoid co-prescriptions in adult noncancer patients using the National Ambulatory Medical Care Survey 2015 public use data set. Potential predictors were selected based on literature review, clinical relevance, and random forest machine learning algorithms. RESULTS: Among the 11.8% (95% CI=9.8%, 13.9%) of medical encounters with an opioid prescription, 16.2% (95% CI=12.6%, 19.8%) had a gabapentinoid co-prescription. Among all gabapentinoid encounters, 40.7% (95% CI=32.6%, 48.7%) had an opioid co-prescription. Predictors of opioid prescription included arthritis (OR=1.87, 95% CI=1.30, 2.69). Predictors of new opioid prescription included physician status as an independent contractor (OR=3.67, 95% CI=1.38, 9.81) or part owner of the practice (OR=3.34, 95% CI=1.74, 6.42). Predictors of opioid-gabapentinoid co-prescription included patient age (peaking at age 55-64 years; OR=35.67, 95% CI=4.32, 294.43). CONCLUSIONS: Predictors of opioid analgesic prescriptions with and without gabapentinoid co-prescriptions were identified. These predictors can help inform and reinforce (e.g., educational) interventions seeking to reduce preventable harm, help identify populations for elucidating opioid-gabapentinoid risk-benefit profiles, and provide a baseline for evaluating subsequent public health measures. Published by Elsevier Inc.
INTRODUCTION: A considerable burden of prescription and illicit opioid-related mortality and morbidity in the U.S. is attributable to potentially unnecessary or excessive opioid prescribing, and co-prescribing gabapentinoids may increase risk of harm. Data are needed regarding physician and patient characteristics associated with opioid analgesic and opioid analgesic-gabapentinoid co-prescriptions to elucidate targets for reducing preventable harm. METHODS: Multiple logistic regression was utilized to examine patient and physician predictors of opioid analgesic prescriptions and opioid analgesic-gabapentinoid co-prescriptions in adult noncancer patients using the National Ambulatory Medical Care Survey 2015 public use data set. Potential predictors were selected based on literature review, clinical relevance, and random forest machine learning algorithms. RESULTS: Among the 11.8% (95% CI=9.8%, 13.9%) of medical encounters with an opioid prescription, 16.2% (95% CI=12.6%, 19.8%) had a gabapentinoid co-prescription. Among all gabapentinoid encounters, 40.7% (95% CI=32.6%, 48.7%) had an opioid co-prescription. Predictors of opioid prescription included arthritis (OR=1.87, 95% CI=1.30, 2.69). Predictors of new opioid prescription included physician status as an independent contractor (OR=3.67, 95% CI=1.38, 9.81) or part owner of the practice (OR=3.34, 95% CI=1.74, 6.42). Predictors of opioid-gabapentinoid co-prescription included patient age (peaking at age 55-64 years; OR=35.67, 95% CI=4.32, 294.43). CONCLUSIONS: Predictors of opioid analgesic prescriptions with and without gabapentinoid co-prescriptions were identified. These predictors can help inform and reinforce (e.g., educational) interventions seeking to reduce preventable harm, help identify populations for elucidating opioid-gabapentinoid risk-benefit profiles, and provide a baseline for evaluating subsequent public health measures. Published by Elsevier Inc.