PARALLEL LATENT CHANGE MODELING FOR DEPRESSION AND PAIN TO PREDICT RELAPSE DURING BUPRENORPHINE AND SUBOXONE TREATMENT
Vest, Noel Adam
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Much of what is currently known regarding treatment for opioid use disorder has been derived from heroin users, with very few investigations into the unique processes that may be involved with individuals who primarily misuse prescription opioids. Relapse is common in treatment for opioid use disorder, making the parallel processes related to relapse during treatment critical for examination. Pain and depression often co-occur in substance use disorder treatment, including opioid substitution treatments. Advanced statistical analyses that can simultaneously model these two conditions may lead to targeted clinical interventions. The objective of this dissertation was to utilize a discrete survival analysis with a growth mixture model to test time to prescription opioid relapse, predicted by parallel growth trajectories of depression and pain, in a clinical sample of patients in buprenorphine/naloxone treatment for primary prescription opioid use disorder. The latent class analysis characterized heterogeneity among patients (n=359) in the Prescription Opioid Addiction Study, a Clinical Trials Network project collected from 2006-2009. The results from this secondary analysis suggested that a 4-class solution was the most parsimonious based on global fit indices and clinical relevance. In order of class size, the 4 classes identified were: 1) typical treatment, 2) high depression and moderate pain, 3) high pain, and 4) low treatment motivation. Odds ratios for time-to-first use indicated no statistically significant difference in relapse between the high pain and the high depression classes, but all other classes differed significantly. These results emphasize the need to monitor the influence of pain and depression during stabilization on buprenorphine and naloxone. Future work may identify appropriate interventions that can be introduced to extend time-to-first prescription opioid use among patients in this clinical population.