Prevalence and correlates of DSM-5 opioid withdrawal syndrome in U.S. adults with non-medical use of prescription opioids: results from a national sample.
Zachary L MannesOfir LivneJustin KnoxDeborah S HasinHenry R KranzlerPublished in: The American journal of drug and alcohol abuse (2023)
Background: In the U.S. non-medical use of prescription opioids (NMOU) is prevalent and often accompanied by opioid withdrawal syndrome (OWS). OWS has not been studied using nationally representative data. Objectives: We examined the prevalence and clinical correlates of OWS among U.S. adults with NMOU. Methods: We used data from 36,309 U.S. adult participants in the 2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions-III, 1,527 of whom reported past 12-month NMOU. Adjusted linear and logistic regression models examined associations between OWS and its clinical correlates, including psychiatric disorders, opioid use disorder (OUD; excluding the withdrawal criterion), medical conditions, and healthcare utilization among people with regular (i.e. ≥3 days/week) NMOU ( n = 534). Results: Over half (50.4%) of the sample was male. Approximately 9% of people with NMOU met criteria for DSM-5 OWS, with greater prevalence of OWS (∼20%) among people with regular NMOU. Individuals with bipolar disorder, dysthymia, panic disorder, and borderline personality disorder had greater odds of OWS (aOR range = 2.71-4.63). People with OWS had lower mental health-related quality of life (β=-8.32, p < .001). Individuals with OUD also had greater odds of OWS (aOR range = 26.02-27.77), an association that increased with more severe OUD. People using substance use-related healthcare services also had greater odds of OWS (aOR range = 6.93-7.69). Conclusion: OWS was prevalent among people with OUD and some psychiatric disorders. These findings support screening for OWS in people with NMOU and suggest that providing medication- assisted treatments and behavioral interventions could help to reduce the burden of withdrawal in this patient population.
Keyphrases
- healthcare
- chronic pain
- pain management
- risk factors
- bipolar disorder
- case report
- quality improvement
- borderline personality disorder
- electronic health record
- big data
- primary care
- emergency department
- tyrosine kinase
- clinical trial
- affordable care act
- deep learning
- artificial intelligence
- placebo controlled
- neural network