Brief HIV assessment for screening CJ offenders in addiction treatment

Abstract

their non-opioid substance use andmultinomial logistic regression was used to predict these substance use groups based on type of opioid drugs used and other characteristics (demographics, pain, treatment history). Results: The most frequently used non-opioid drugs were cannabis (40%), antianxiety medications (34%) and cocaine (25%). LCA identified a 5-class (C) model. C1 (58.7%), had low risk of using non-opioid drugs. C2 (23.5%) had high risk of using anti-anxiety drugs and moderate risk of cannabis. C3 (8.8%) had high risk of using non-opioid prescription drugs (anti-anxiety, sleep, andmuscle relaxant drugs) and cannabis. C4 (6.2%) had high risk of using marijuana and cocaine. C5 (2.8%) had high risk of using all nonopioid drugs. Compared toC1 (the low-using group), participants in the other classes were younger, female, tobacco users, had chronic pain, inject opioids, and used both prescription opioids and heroin. The two non-opioid prescription drug groups (C2, C3) were more likely to report chronic pain and use prescription opioids. C4 (marijuana/cocaine users) and C5 (polydrug users) were more likely to report injection. Conclusions: Aggregation of substance usemay obscure important subgroup differences in patterns of illicit non-opioid drug use. The identification of two groups that primarily misuse prescription drugs and that have comparatively high rates of chronic pain suggests that self-medicationmay play a role among sub-groups of OTP patients. Financial support: Denver Health is part of the Researched Abuse Diversion and Addiction-Related Surveillance (RADARS®) System.