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?
Introduction
Opioid substitution therapy is the most effective treatment for opioid dependence, and involves prescribing long-acting opioid medications such as methadone, buprenorphine or buprenorphine–naloxone (Mattick et al., 2009, Mattick et al., 2008). Opioid substitution therapy reduces morbidity (Ward et al., 1998), mortality (Degenhardt et al., 2011), HIV transmission (Gowing et al., 2011), criminality (Hall, 1996), other drug use (Amato et al., 2005, Ward et al., 1998), and substantially improves the quality of life among people who are opioid dependent (Padaiga et al., 2007). However, there are significant risks associated with the extra-medical use of the medications used in opioid substitution therapy, including dependence, injection-related injuries and diseases, overdose, and mortality (Degenhardt et al., 2008, Strang et al., 2010). Unsupervised doses (“take-home medication” or “takeaways”), in particular, are susceptible to diversion and injection (e.g., Haskew et al., 2008, Ritter and Di Natale, 2005).
The supervised administration of medications in opioid substitution therapy, where the dose is consumed under direct observation of a pharmacist/clinician, is recommended in international guidelines (World Health Organization, 2009) and is standard practice in Australia (Department of Health and Ageing, 2007). The primary aims of supervised dosing are to maximise both medication adherence and programmatic adherence (see Larance et al., 2011b for definitions), and minimise the risks, particularly those associated with diversion and injection. However, supervised dosing has disadvantages; it is labour-intensive and costly, travel may be difficult and daily attendance can be counterproductive to leading a ‘normalised’ life, such as working or having a holiday (Bell et al., 2007, Deering et al., 2011, Madden et al., 2008). For these reasons, the provision of some unsupervised dosing is supported internationally (World Health Organization, 2009), but the identification of patients who are ‘suitable’ for receiving this more flexible treatment is crucial. Careful patient selection for unsupervised dosing is important not only for ensuring the safety and efficacy of opioid substitution therapy, but also for avoiding criticisms and erosion of public and government support for such programmes.
The literature on treatment adherence highlights many ways to measure medication and programmatic adherence, including pill counts, patient self-report, third-party reports, use of electronic monitoring devices, length of treatment episode, and reviews of prescription records and claims (e.g. Ahn et al., 2008, Dunbar, 1984, Hansen et al., 2009, Haynes et al., 2008, Julius et al., 2009). The use of multiple rather than single indicators to evaluate adherence is preferable (Ahn et al., 2008). However, a key challenge facing clinicians and researchers is that it is not always apparent which indicators are more salient for particular patient groups.
In opioid substitution therapy, ‘stability assessments’ are used to determine which patients have stabilised on their medication and may therefore be suitable for unsupervised dosing. Assessing patient ‘stability’ involves identifying when a pharmacological equilibrium has been reached (i.e., the patient is no longer fluctuating between the physical states of intoxication and withdrawal) and examining psychosocial functioning (Australasian Chapter of Addiction Medicine, 2006). Multiple indicators for monitoring treatment progress are recommended, including medication and programmatic adherence, drug use, physical and psychosocial wellbeing (Australasian Chapter of Addiction Medicine, 2006, Department of Health and Ageing, 2007, World Health Organization, 2009). The main contraindications to unsupervised dosing include hazardous use of drugs, injection of prescribed medication, diversion and/or unsanctioned use of medication, and child welfare concerns (Department of Health and Ageing, 2007).
Most guidelines for assessing patient suitability for unsupervised dosing are based on indicators of psychosocial stability, adherence and risk defined through clinician consensus. The extent to which the assessment domains match the actual practices and risk profiles of opioid substitution therapy patients has not been previously examined in the literature. Therefore, the over-arching aims of this study are to: (a) identify a stability typology based on a comprehensive range of adherence indicators spanning drug use, medication and programmatic adherence, psychosocial stability, comorbidity, and child welfare; (b) examine whether this typology predicts the number of unsupervised doses received by patients, controlling for key demographic and treatment characteristics; and (c) identify the independent predictors of unsupervised dosing.
Section snippets
Opioid substitution therapy patient sample
Data were derived from the first Australian large-scale post-marketing surveillance studies of buprenorphine–naloxone (Larance et al., 2011a). Face-to-face interviews were conducted between June 2007 and November 2008 with 768 opioid substitution therapy patients in three Australian jurisdictions: South Australia (n = 239), Victoria (n = 277) and New South Wales (n = 252). Further details about the study background and methodology are detailed elsewhere (Larance et al., 2011a, Larance et al., 2011b).
Sample characteristics
The sample was comprised of 286 methadone patients, 251 buprenorphine patients and 231 buprenorphine–naloxone patients. Participants reported being enrolled in their current treatment episode for a median of 16 months prior to interview (range 1–286 months). The mean daily doses of methadone, buprenorphine and buprenorphine–naloxone were similar to the average daily doses found in other Australian studies (i.e., approximately 70 mg methadone and 12 mg buprenorphine/buprenorphine–naloxone;
Discussion
This study represents the first time LCA has been used to empirically examine patterns of adherence and stability among patients. In addition, the extent to which the clinical guidelines for assessing patient suitability for unsupervised dosing match the self-reported stability profiles of opioid substitution therapy patients has not been previously examined in the literature.
Two latent classes of patient stability were identified: (i) a higher-adherence group who had low-moderate probabilities
Role of the funding source
These studies were funded by Reckitt Benckiser by way of an untied educational grant. This study's design, conduct and interpretation of findings are the work of the investigators. The funder played no role in the conception or writing of this paper.
Contributors
BL undertook the analyses and wrote the first draft of the paper. All authors contributed to the manuscript and approved the final version.
Conflict of interest
BL, LD and RPM have received untied educational grants from Reckitt Benckiser for the post-marketing surveillance of buprenorphine–naloxone tablets (2006–2008) and film (2012–2013), and the development of an opioid-related behaviour scale (2010). NL and RA have also received untied educational grants from Reckitt Benckiser. All such studies’ design, conduct and interpretation of findings are the work of the investigators; Reckitt Benckiser had no role in these.
Acknowledgements
BL, LD, and RPM are supported by National Health and Medical Research Council (NHMRC) research fellowships (#1073858, #1041472, and #1045318). The National Drug and Alcohol Research Centre at the University of NSW is supported by funding from the Australian Government under the Substance Misuse Prevention and Service Improvements Grants Fund. This study would not have been possible without the generous participation of people who receive treatment for opioid dependence. This study utilised data
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2020, Drug and Alcohol DependenceCitation Excerpt :Statistical significance was assessed at the standard 5 % level. No standardized procedure of identifying the optimal number of latent classes in a regression mixture model has been proposed (Nylund et al., 2007); therefore, we developed a decision algorithm synthesized from approaches reported in recently published studies that conducted latent class analyses of HRQoL and patterns of drug use among similar populations (Larance et al., 2014; Dowsey et al., 2015; De Maeyer et al., 2013; Green et al., 2010; Krebs et al., 2011; Nosyk et al., 2011). Details on the selection algorithm can be found in the online supplement, but are briefly described here.
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2017, Drug and Alcohol DependenceCitation Excerpt :However, our study is the first study to examine the longitudinal association between supervised consumption over the course of multiple treatment episodes and retention in treatment. While a number of authors have suggested flexibly timed discontinuation of supervision once a patient is stabilised (Holland et al., 2014; Notley et al., 2014), recent Australian studies have highlighted the challenges involved in assessing patient stability with many OST prescribers reporting high levels of uncertainty in identifying whether their patients are diverting or injecting their medication (Larance et al., 2011, 2014). Further work is needed to determine how to optimally stratify patients’ suitability for unsupervised dosing, with the aim of retaining patients in treatment and simultaneously reducing diversion and injecting.
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2016, Drug and Alcohol DependenceCitation Excerpt :Even higher rates of effectiveness were seen when take-home therapy was used in previously stabilised patients, namely those with negative urine tests (Gerra et al., 2011). Some patients subgroups are receiving more unsupervised doses, especially those who are older, currently employed without a prison history, and undergoing treatment for longer period (Larance et al., 2014). Stable patients with good adherence to treatment could benefit by unsupervised prescription of OST preparations, especially with the combination of buprenorphine plus naloxone, which is safe and has a small diversion potential (Mauger et al., 2014).