Briony Larance1, Natacha Carragher2, Richard P Mattick2, Nicholas Lintzeris3, Robert Ali4, Louisa Degenhardt5. 1. National Drug and Alcohol Research Centre, UNSW Australia, Randwick Campus, 22-32 King Street, Sydney NSW 2052, Australia. Electronic address: b.larance@unsw.edu.au. 2. National Drug and Alcohol Research Centre, UNSW Australia, Randwick Campus, 22-32 King Street, Sydney NSW 2052, Australia. 3. The Langton Centre, South Eastern Sydney Local Health District (SESLHD), 591 South Dowling Street, Surry Hills NSW 2010, Australia; Discipline of Addiction Medicine, The University of Sydney, Drug Health Services, Royal Prince Alfred Hospital, Level 6 KGV Building, 83-117 Missenden Road, Camperdown, Sydney NSW 2050, Australia. 4. Discipline of Pharmacology, The University of Adelaide, Medical School South Building, Frome Road, Adelaide SA 5005, Australia; Drug and Alcohol Services South Australia, 161 Greenhill Road, Parkside SA 5063, Australia. 5. National Drug and Alcohol Research Centre, UNSW Australia, Randwick Campus, 22-32 King Street, Sydney NSW 2052, Australia; School of Population and Global Health, University of Melbourne, Australia; Murdoch Children's Research Institute, Australia; Department of Global Health, School of Public Health, University of Washington, USA.
Abstract
AIMS: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
AIMS: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Harry Jin; Brandon D L Marshall; Louisa Degenhardt; John Strang; Matt Hickman; David A Fiellin; Robert Ali; Julie Bruneau; Sarah Larney Journal: Addiction Date: 2020-05-19 Impact factor: 6.526
Authors: Ali Jalali; Danielle A Ryan; Philip J Jeng; Kathryn E McCollister; Jared A Leff; Joshua D Lee; Edward V Nunes; Patricia Novo; John Rotrosen; Bruce R Schackman; Sean M Murphy Journal: Drug Alcohol Depend Date: 2020-08-05 Impact factor: 4.492