A latent class analysis of self-reported clinical indicators of psychosocial stability and adherence among opioid substitution therapy patients: do stable patients receive more unsupervised doses?

Abstract

To develop a stability typology among opioid substitution therapy patients using a range of adherence indicators derived from clinical guidelines, and determine whether stable patients receive more unsupervised doses. An interviewer-administered cross-sectional survey was used in opioid substitution therapy programmes in three Australian jurisdictions, totalling 768 patients in their current treatment episode for ≥4 weeks. A structured questionnaire collated data from patients about their demographics, treatment characteristics, past 6-month drug use and medication adherence, psychosocial stability, comorbidity, child welfare concerns and levels of supervised dosing. Latent class analysis (LCA) was used to derive a stability typology. Linear regression models examined predictors of unsupervised dosing in the past month. LCA identified two classes: (i) a higher-adherence group (67%) who had low-moderate probabilities of endorsing the opioid substitution therapy stability indicators and (ii) a lower-adherence group (33%) who had moderate-high probabilities of endorsing the stability indicators. There was no association between adherence profile and the number of unsupervised doses. Significant predictors of receiving larger numbers of unsupervised doses included being older, living in New South Wales or South Australia (vs. Victoria), receiving methadone (vs. mono-buprenorphine), being prescribed in private clinic or general practice (vs. public clinic), reporting a longer current treatment episode, not receiving a urine drug screen in the past month, being currently employed and not having a prison history. This study suggested that system-level factors and observable indicators of social functioning were more strongly associated with the receipt of less supervised treatment. Future research should examine this issue using prospectively collected data.