The impact of relaxing restrictions on take-home doses during the COVID-19 pandemic on program effectiveness and client experiences in opioid agonist treatment: a mixed methods systematic review

Time for Australia to increase take away doses in opioid agonist treatment

Opioid Agonist Treatment is the cornerstone of minimising harms related to opioid use, however its uptake is limited by a tightly regulated and stigmatising treatment environment. The COVID-19 pandemic necessitated relaxation of some treatment restrictions, with global evidence pointing to more patient-centred care in this time. In light of local evidence to support the safety of increased access to takeaway doses and a precedent set by the Substance Abuse and Mental Health Administration, we recommend adoption of the Australian Interim Medication Assisted Treatment of Opioid Dependence guidance in Australia.

Examining inequities in access to opioid agonist treatment (OAT) take-home doses (THD): A Canadian OAT guideline synthesis and systematic review

BACKGROUND

Daily supervised Opioid Agonist Treatment (OAT) medication has been identified as a barrier to treatment retention. Canadian OAT guidelines outline take-home dose (THD) criteria, yet, OAT prescribers use their clinical judgement to decide whether an individual is 'clinically stable' to receive THD. There is limited information regarding whether these decisions may result in inequitable access to THD, including in the context of updated COVID-19 guidance. The current Canadian OAT THD guideline synthesis and systematic review aimed to address this knowledge gap.

METHODS

This systematic review included a two-pronged approach. First, we searched available academic literature in Embase, Medline, and PsychINFO up until October 12th, 2022, to identify studies that compared characteristics of individuals on OAT who had and had not been granted access to THD to explore potential inequities in access. Next, we identified all Canadian national and provincial OAT guidelines through a semi-structured grey literature search (conducted between September-October 2022) and extracted all THD 'stability' and allowances/timeline criteria to compare against characteristics identified in the literature search. Data from both review arms were synthesized and narratively presented.

RESULTS

A total of n = 56 guidelines and n = 7 academic studies were included. The systematic review identified a number of patient characteristics such as age, sex, race/ethnicity, marital status, housing, employment, neighborhood income, drug use, mental health, health service utilization, as well as treatment duration that were associated with differential access to THD. The Canadian OAT THD guideline synthesis identified many of these same characteristics as 'stability' criteria, underscoring the potential for Canadian OAT guidelines to result in inequitable access to THD.

CONCLUSIONS

This two-pronged literature review demonstrated that current guidelines likely contribute to inequitable OAT THD access due primarily to inconsistent 'stability' criteria across guidelines. More research is needed to understand differential OAT THD access with a focus on prescriber decision-making and evaluating associated treatment and safety outcomes. The development of a client-centered, equity-focused, and evidence-informed decision making framework that incorporates more clear definitions of 'stability' criteria and indications for prescriber discretion is warranted.

Optimizing methadone dose adjustment in patients with opioid use disorder

Introduction

Opioid use disorder is a cause for concern globally. This study aimed to optimize methadone dose adjustments using mixed modeling and machine learning.

Methods

This retrospective study was conducted at Taichung Veterans General Hospital between January 1, 2019, and December 31, 2020. Overall, 40,530 daily dosing records and 1,508 urine opiate test results were collected from 96 patients with opioid use disorder. A two-stage approach was used to create a model of the optimized methadone dose. In Stage 1, mixed modeling was performed to analyze the association between methadone dose, age, sex, treatment duration, HIV positivity, referral source, urine opiate level, last methadone dose taken, treatment adherence, and likelihood of treatment discontinuation. In Stage 2, machine learning was performed to build a model for optimized methadone dose.

Results

Likelihood of discontinuation was associated with reduced methadone doses (β = 0.002, 95% CI = 0.000-0.081). Correlation analysis between the methadone dose determined by physicians and the optimized methadone dose showed a mean correlation coefficient of 0.995 ± 0.003, indicating that the difference between the methadone dose determined by physicians and that determined by the model was within the allowable range (p < 0.001).

Conclusion

We developed a model for methadone dose adjustment in patients with opioid use disorders. By integrating urine opiate levels, treatment adherence, and likelihood of treatment discontinuation, the model could suggest automatic adjustment of the methadone dose, particularly when face-to-face encounters are impractical.