Development of a Brief Patient-Administered Screening Tool for Prescription Opioid Dependence for Primary Care Settings

Development and validation of a brief screening tool for over-the-counter codeine dependence

BACKGROUND

Low-dose codeine is sold without a prescription in countries like the UK, Ireland, and South Africa. Due to misuse concerns, exploring pharmacy screening tools to identify those at risk and needing additional support is vital.

OBJECTIVES

The study aims to develop and validate a brief screening tool that assesses the risk of codeine dependence with language appropriate for routine use in community pharmacies.

METHOD

Scale development and validation occurred over two studies. In Study 1, scale item generation was based on structured analyses of psychosocial and pharmacy variables from frequent over-the-counter codeine consumers (N = 795). CFA was used to assess the cohesiveness of the resultant four-item Codeine Dependence Scale (CDS). ROC analyses were used to assess the performance of the CDS against risk cases identified by the Severity of Dependence Scale; identifying an optimal cut-off value of ≥2 as representing individuals at risk of codeine dependence. In Study 2, this CDS threshold was assessed against positive DSM-5 Opioid Use Disorder (OUD) cases related to codeine use assessed using the AUDADIS-IV.

RESULTS

With a cut-off score of ≥2, the CDS has sensitivity and specificity of 76% and 48%, respectively, against a DSM-5 codeine-related OUD diagnosis using the AUDADIS-IV. For identification of any codeine-related OUD (as measured by the AUDADIS-IV) 15 months after baseline, the CDS achieved an overall correct classification rate of 52%; 72% for positive cases.

CONCLUSIONS

The CDS exhibits reasonable cross-sectional and longitudinal sensitivity but low specificity, partly due to its brevity. However, the inclusive nature of the CDS is not a negative for application as a screening tool in a pharmacy setting as individual CDS items represent critical conversation points with a pharmacist, regardless of the screening outcome. The non-confronting nature of CDS items make the scale a viable option for pharmacy-based SBI in countries where codeine remains OTC.

Measuring the importance of different barriers to opioid agonist treatment using best-worst scaling in an Australian setting

OBJECTIVE

Opioid agonist treatment (OAT) is an effective treatment for opioid use disorder (OUD), however several client barriers to OAT are reported. Client importance of these barriers using economic preference elicitation measures have not been identified. This paper determines the most important OAT barriers using best-worst scaling (BWS) and compares the results of BWS to Likert scale.

METHODS

Cross-sectional self-completed survey with 191 opioid dependent clients who attended Australian needle and syringe sites. Participants were presented 15 Likert scale barriers and 15 BWS barrier scenarios. The BWS data was presented using count analysis, multinomial logit and mixed logit models. The ranking of barrier items was completed using three BWS methods and one Likert scale method, with share preference results (BWS) or mean scores (Likert) used to rank the 15 barriers.

RESULTS

The most important client barriers were 'enjoy using opioids', 'lack of support services' and 'hard to access'. The four ranking methods produced different barrier rankings for the most important barriers, but similar results for the least important barriers.

CONCLUSION

Policies around OAT as a harm reduction approach, increased support services and increased availability of OAT services would be beneficial in improving OAT uptake. Comparing BWS and Likert methods produced different highest ranked barriers, indicating the method used to identify preferences has significant implications on the type of intervention prioritised.

Identifying the most common barriers to opioid agonist treatment in an Australian setting

BACKGROUND

Opioid use disorder is a public health concern in Australia. Opioid agonist treatment (OAT) is effective at treating and minimising harm from opioid use disorder, yet is underused in Australia due to client barriers. Although these barriers have been reported, the barriers that are most important to clients is unclear. The aim of this paper was to determine the most important OAT barriers to Australian clients.

METHODS

A cross-sectional, self-completed survey was given to 204 opioid-dependent clients who attended needle and syringe sites in Australia. Participants were given 15 OAT barrier statements, which they answered using a 5-point Likert scale (1=strongly disagree, 2=disagree, 3=neutral, 4=agree and 5=strongly agree). The Likert scale data are presented using the count method and the mean Likert scores (for the whole sample and for subgroups).

RESULTS

The two methods determined that the four most important barriers to OAT were stigma, lack of support services, no flexibility and enjoy using opioids. Furthermore, those who used prescription opioids (compared with heroin) were female or non-binary (compared with male), were not currently using OAT (compared with current OAT), were younger (compared with older) and had high dependence scores (compared with low dependence scores) were impacted more by certain OAT barriers.

CONCLUSIONS

Policies around improving support services, reducing stigma and increasing flexibility would be beneficial to reduce barriers to OAT in Australia. Second, certain groups were more vulnerable to OAT barriers, emphasising the importance to better tailor opioid treatment programs to these specific populations to increase treatment engagement.

Algorithms to Identify Nonmedical Opioid Use

The rise in nonmedical opioid overdoses over the last two decades necessitates improved detection technologies. Manual opioid screening exams can exhibit excellent sensitivity for identifying the risk of opioid misuse but can be time-consuming. Algorithms can help doctors identify at-risk people. In the past, electronic health record (EHR)-based neural networks outperformed Drug Abuse Manual Screenings in sparse studies; however, recent data shows that it may perform as well or less than manual screenings. Herein, a discussion of several different manual screenings and recommendations is contained, along with suggestions for practice. A multi-algorithm approach using EHR yielded strong predictive values of opioid use disorder (OUD) over a large sample size. A POR (Proove Opiate Risk) algorithm provided a high sensitivity for categorizing the risk of opioid abuse within a small sample size. All established screening methods and algorithms reflected high sensitivity and positive predictive values. Neural networks based on EHR also showed significant effectiveness when corroborated with Drug Abuse Manual Screenings. This review highlights the potential of algorithms for reducing provider costs and improving the quality of care by identifying nonmedical opioid use (NMOU) and OUD. These tools can be combined with traditional clinical interviewing, and neural networks can be further refined while expanding EHR.

What predicts pharmacists' engagement with opioid-outcome screening? Secondary analysis from an implementation study in community pharmacy

Background Pharmacists have a key role to play in identifying and responding to emerging clinical problems with prescribed opioids. A pilot study in Australia examined the implementation of screening and brief intervention (Routine Opioid Outcome Monitoring [ROOM]) to identify and respond to opioid-related problems in community pharmacies. In this implementation study, the rate of screening varied considerably between pharmacies. Objective The aim of this study was to examine pharmacist characteristics associated with implementation of ROOM. Setting Community pharmacies in Victoria and New South Wales, Australia. Methods We implemented a validated computer-facilitated screening (ROOM), combined with brief intervention for opioid-related problems based on a widely accepted framework for monitoring outcomes. In this analysis, we examined the correlates of ROOM completion for individual pharmacists. Negative binomial regression was used to identify baseline predictors of greater screening, with the number of ROOM screens as the dependent (outcome) variable and pharmacist demographics, knowledge, confidence and comfort responding to prescription opioids problems, and attitudes towards evidence based practice examined as independent (predictor) variables. Main outcome measure Number of screens completed by an individual pharmacist as reported in follow-up surveys by pharmacist. Results Fewer years of practice was associated with a greater number of screenings conducted. On average, each additional decade of practice was associated with a 31% (95% CI 0%, 53%) reduction in the number of screenings undertaken by pharmacists. A multivariable analysis revealed that each additional decade practicing, lower knowledge of naloxone and lower confidence in identifying unmanaged pain were all independently associated with reduced engagement in screening after controlling for other variables. Conclusion Findings from this pilot study identified potential barriers to implementing opioid outcome monitoring. Further studies could test different groups of community pharmacists' experience of different barriers when implementing monitoring outcomes with prescribed opioids, to inform future implementation and clinical practice.