Stocking and supplying naloxone: Findings from a representative sample of community pharmacies in Victoria, Australia.

INTRODUCTION: Naloxone is an opioid receptor antagonist, which can rapidly reverse the effects of an opioid overdose. Community pharmacists may experience several barriers to stocking and supplying naloxone including a lack of confidence or knowledge and time constraints. The current study aimed to examine the extent to which Victorian community pharmacies stock and supply naloxone and determine specific characteristics associated with stocking naloxone. METHODS: A representative sample of community pharmacists (n = 558) in Victoria, Australia, were contacted between October and November 2020 and invited to participate in an online survey. Data related to pharmacy- and pharmacist-related characteristics, including stocking and frequency of supplying naloxone in the past year. Multivariate logistic regression analysis was performed to examine the effect of various covariates on stocking naloxone. RESULTS: The sample comprised 265 pharmacists (response rate 47%). Most pharmacies were located in Melbourne (the capital city of Victoria, 59.6%) and were part of a pharmacy chain (61.5%). In total, 100 (38%) pharmacies stocked naloxone, a third of whom did not supply it in the past year. Pharmacies that provided opioid agonist treatment had 2.4 times higher odds of stocking naloxone (95% confidence interval 1.425-4.136; p = 0.001). DISCUSSION AND CONCLUSION: Less than half of Victorian community pharmacies stock naloxone, with even fewer actually supplying it in the past year. Future efforts are needed to increase the number of pharmacies that stock naloxone and the frequency in which it is supplied, while also addressing possible barriers to stocking and supplying naloxone among community pharmacists.

Association of state-level prescription drug monitoring program implementation with opioid prescribing transitions in primary care in Australia.

AIMS: This study aimed to evaluate whether voluntary and mandatory prescription drug monitoring program (PDMP) use in Victoria, Australia, had an impact on prescribing behaviour, focusing on individual patients' prescribed opioid doses and transition to prescribing of nonmonitored medications. METHODS: This was a retrospective cross-sectional study using routinely collected primary healthcare data. A 90-day moving average prescribed opioid dose in oral morphine equivalents was used to estimate opioid dosage. A Markov transition matrix was used to describe how patients prescribed medications transitioned between opioid dose groups and other nonopioid treatment options during 3 transition periods: transition between 2 control periods prior to PDMP implementation (T1 to T2); during the voluntary PDMP implementation (T2 to T3); and during mandatory PDMP implementation (T3 to T4). RESULTS: Among patients prescribed opioids in our study, we noted an increased probability of transitioning to not being prescribed opioids during the mandatory PDMP period (T3 to T4). This increase was attributed mainly to the ceasing of low-dose opioid prescribing. Membership in an opioid dose group remained relatively stable for most patients who were prescribed high opioid doses. For those who were only prescribed nonmonitored medications initially, the probability of being prescribed opioids increased during the mandatory PDMP when compared to other transition periods. CONCLUSION: The introduction of PDMP mandates appeared to have an impact on the prescribing for patients who were prescribed low-dose opioids, while its impact on individuals prescribed higher opioid doses was comparatively limited.

Determining the Impact of Opioid Policy on Substance Use and Mental Health-Related Harms: Protocol for a Data Linkage Study.

BACKGROUND: Increasing harms related to prescription opioids over the past decade have led to the introduction of a range of key national and state policy initiatives across Australia. These include introducing a mandatory real-time prescription drug-monitoring program in the state of Victoria from April 2020 and a series of changes to subsidies for opioids on the Pharmaceutical Benefit Scheme from June 2020. Together, these changes aim to influence opioid supply and reduce harms related to prescription opioids, yet few studies have specifically explored how these policies have influenced opioid prescribing and related harms in Australia. OBJECTIVE: The aim of this study is to examine the impact of a range of opioid-related policies on hospital admissions and emergency department (ED) presentations in Victoria, Australia. In particular, the study aims to understand the effect of various opioid policies and opioid-prescribing changes on (1) the number and rates of ED presentations and hospital admissions attributed to substance use (ie, opioid and nonopioid related) or mental ill-health (eg, suicide, self-harm, anxiety, and depression), (2) the association between differing opioid dose trajectories and the likelihood of ED presentations and hospital admissions related to substance use and mental ill-health, and (3) whether changes in an individual's opioid prescribing change the risk related to ED presentations and hospital admissions related to substance use and mental ill-health. METHODS: We will conduct a population-level linked data study. General practice health records obtained from the Population Level Analysis and Reporting platform are linked with person-level data from 3 large hospital networks in Victoria, Australia. Interrupted time series analysis will be used to examine the impact of opioid policies on a range of harms, including the rates of presentations related to substance use (opioid and nonopioid) and mental ill-health among the primary care cohort. Group-based trajectory modeling and a case-crossover design will be used to further explore the impact of changes in opioid dosage and other covariates on opioid and nonopioid poisonings and mental ill-health-related presentations at the patient level. RESULTS: Given that this paper serves as a protocol, there are currently no results available. The deidentified primary health data were sourced from electronic medical records of approximately 4,717,000 patients from 542 consenting general practices over a 6-year period (2017-2022). The submission of results for publication is planned for early 2024. CONCLUSIONS: This study will add to the limited evidence base to help understand the impact of opioid policies in Australia, including whether intended or unintended outcomes are occurring as a result. TRIAL REGISTRATION: EU PAS Register EUPAS104005; https://www.encepp.eu/encepp/viewResource.htm?id=104006. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/51825.

Identifying Prescription-Opioid-Related Risks Using Prescription Drug Monitoring Programs' Algorithms and Clinical Screening Tools.

BACKGROUND: Pharmacists adopt various approaches to identifying prescription-opioid-related risks and harms, including prescription drug monitoring programs (PDMPs) and clinical screening tools. This study aims to compare 'at-risk' patients according to the published Australian PDMP algorithms with the validated Routine Opioid Outcome Monitoring (ROOM) clinical screening tool. METHODS: Data were used from an implementation study amongst people who had been prescribed regular opioids. We examined the results from ROOM and the patients' dispensing history over the previous 90 days. A chi-squared test was used to examine the association between risk according to (i) a PDMP alert and a clinical risk per ROOM; (ii) a PDMP alert and positive screening for opioid use disorder; and (iii) a PDMP 'high-dose' alert (average of >100 mg OME/day in the past 90 days) and any ROOM-validated risk. RESULTS: No significant associations were found between being 'at-risk' according to any of the PDMP alerts and clinical risk as identified via the ROOM tool (x2 = 0.094, p = 0.759). There was only minimal overlap between those identified as 'at-risk' via PDMP alerts and those meeting the clinical risk indicators; most patients who were 'at-risk' of clinical opioid-related risk factors were not identified as 'at-risk' based on PDMP alerts. CONCLUSIONS: PDMP alerts were not predictive of clinical risk (as per the ROOM tool), as many people with well-established clinical risks would not receive a PDMP alert. Pharmacists should be aware that PDMPs are limited to identifying medication-related risks which are derived using algorithms; therefore, augmenting PDMP information with clinical screening tools can help create a more detailed narrative of patients' opioid-related risks.

Changes in opioid agonist treatment initiation among people prescribed opioids for pain following voluntary and mandatory prescription drug monitoring program implementation: A time series analysis.

INTRODUCTION: Prescription drug monitoring programs (PDMP) are increasingly used to identify people prescribed high-dose opioids. However, little is known about whether PDMPs impact opioid agonist treatment (OAT) uptake, the gold standard for opioid use disorder. This study examined the impact of PDMP implementation on OAT initiation among people prescribed opioids, in Victoria, Australia. METHODS: De-identified electronic records from all 464 Victorian general practices included in the POLAR database were used. OAT initiation was defined as a new OAT prescription between 1 April 2017 and 31 December 2020, with no OAT prescriptions in the year prior. Interrupted time series analyses were used to compare outcomes before (April 2017 to March 2019) and after (April 2019 to December 2020) PDMP implementation. Binary logistic regression was used to examine differences in patients' characteristics associated with OAT initiation prior to and after PDMP implementation. RESULTS: In total, 1610 people initiated OAT, 946 before and 664 after PDMP implementation. No significant immediate (step) or longer-term (slope) changes in the rates of OAT initiation were identified following PDMP implementation, after adjusting for seasonality. A high opioid dose (>100 mg oral morphine equivalent) in the 6-months prior to OAT initiation was the only significant characteristic associated with reduced odds of OAT initiation post-PDMP implementation (odds ratio 0.29; 0.23-0.37). DISCUSSION AND CONCLUSIONS: PDMP implementation did not have a significant impact on OAT initiation among people prescribed opioids. Findings suggest additional clinical initiatives that support OAT initiation are required to ensure PDMPs meet their intended target of reducing opioid-related harms.

Building knowledge in opioid prescribing in post-operative treatment.

INTRODUCTION: Opioids prescribed in hospital are a key risk factor for harm in the community. This study aimed to gain an in-depth understanding of factors affecting post-operative opioid prescribing amongst clinicians using the capability, opportunity, motivation generate behaviour framework, more commonly known as COM-B. METHODS: Focus groups and semi-structured interviews were used to gain an in-depth understanding of factors affecting optimal practice when prescribing opioids for post-operative patients at discharge. A topic guide was written using the COM-B behaviour change model to ensure the full range of possible factors influencing prescribing behaviours were explored. RESULTS: We found barriers and facilitators of optimal opioid prescribing practice across all three domains of capability, opportunity and motivation. Capability among junior doctors could be increased in the areas of risk assessment and prescribing appropriate discharge analgesia, though education and training were not key barriers to improving practice. Findings indicated that opportunity to practice optimal prescribing was hindered by a lack of time at discharge and technology. Beliefs about one's own and others' responsibilities also impacted motivation to practice optimal prescribing behaviours. Pharmacists were identified as key supports for patient education and appropriate prescribing. CONCLUSIONS: Educating prescribers about opioid risks and clinical practice guidelines are necessary interventions, however, our findings indicate that if implemented in isolation, they may not have the desired impact. Interventions also need to address discharge time pressures and presumptions that GPs are aware of whether opioids should be ceased or continued after surgical discharge.

Cohort profile: Using primary care data to understand Opioid Prescribing, Policy Impacts and Clinical Outcomes (OPPICO) in Victoria, Australia.

PURPOSE: The OPPICO cohort is a population-based cohort based on non-identifiable electronic health records routinely collected from 464 general practices in Victoria, Australia, created with the aim of understanding opioid prescribing, policy impacts and clinical outcomes. The aim of this paper is to provide a profile of the study cohort by summarising available demographic, clinical and prescribing characteristics. PARTICIPANTS: The cohort described in this paper comprises people who were aged at least 14 years at cohort entry, and who were prescribed an opioid analgesic at least once at participating practices for a total of 1 137 728 person-years from 1 January 2015 to 31 December 2020. The cohort was formed using the data collected from electronic health records through the Population Level Analysis and Reporting (POLAR) system. The POLAR data primarily consist of patient demographics, clinical measurements, Australian Medicare Benefits Scheme item numbers, diagnoses, pathology testing and prescribed medications. FINDING TO DATE: In total, the cohort consists of 676 970 participants with 4 389 185 opioid prescription records from 1 January 2015 to 31 December 2020. Approximately half (48.7%) received a single opioid prescription, and 0.9% received more than 100 opioid prescriptions. The mean number of opioid prescriptions per patient was 6.5 (SD=20.9); prescriptions for strong opioids accounted for 55.6% of all opioid prescriptions. FUTURE PLANS: The OPPICO cohort data will be used for various types of pharmacoepidemiological research, including examining the impact of policy changes on coprescription of opioids with benzodiazepines and gabapentin, and monitoring trends and patterns of other medication utilisation. Through data-linkage between our OPPICO cohort and hospital outcome data, we will examine whether policy changes for opioid prescribing lead to changes in prescription opioid-related harms, and other drug and mental health-related outcomes. TRIAL REGISTRATION NUMBER: EU PAS Register (EUPAS43218, prospectively registered).

Changes in opioid and other analgesic prescribing following voluntary and mandatory prescription drug monitoring program implementation: A time series analysis of early outcomes.

BACKGROUND: Australian prescription drug monitoring programs (PDMPs) provide information about a patient's recent medication history for controlled drugs at the point of prescribing and dispensing. Despite their increasing use, the evidence for PDMPs is mixed, and is almost exclusively from the United States. This study examined the impact of PDMP implementation on opioid prescribing among general practitioners in Victoria, Australia. METHOD: We examined data on analgesic prescribing using electronic records of 464 medical practices in the Australian state of Victoria between 01/04/2017 and 31/12/ 2020. We used interrupted time series analyses, to examine immediate and longer-term trends in medication prescribing following voluntary (from April 2019) and mandatory PDMP implementation (from April 2020). We examined changes in three outcomes (i) 'high' opioid dose (50-100mg oral morphine equivalent daily dose (OMEDD) and over 100mg (OMEDD) prescribing (ii) prescribing of high-risk medication combinations (opioids with either benzodiazepines or pregabalin), and (iii) initiation of non-controlled pain medications (tricyclic antidepressants, pregabalin and tramadol). RESULTS: We found no effect of voluntary or mandatory PDMP implementation on 'high-dose' opioid prescribing with reductions only seen in those prescribed <20mg OMEDD (i.e., the lowest dose category). Co-prescribing of opioids with benzodiazepines (additional 11.87 [95%CI 2.04 to 21.67] patients/10,000 and pregabalin (additional 3.54 [95% CI 0.82 to 6.26] patients/10,000 increased following mandatory PDMP implementation among those prescribed opioids. In contrast to trends of reduced initiation prior to PDMP implementation, we found increased new initiation of non-monitored medications following PDMP implementation (e.g., an immediate increase of 2.32 [95%CI 0.02 to 4.54], patients/10,000 received pregabalin and 3.06 [95%CI 0.54 to 5.5] patients/10,000 received tricyclic antidepressants after mandatory PDMP implementation), and increased tramadol initiation during the voluntary PDMP period (an increase of 11.26 [95%CI: 5.84, 16.67] patients /10,000). CONCLUSION: PDMP implementation did not appear to reduce prescribing of high opioid doses or high-risk combinations. Increased initiation of tricyclic antidepressants, pregabalin and tramadol may indicate a possible unintended effect.

Factors associated with prescription drug monitoring program utilisation: a cross-sectional survey of community pharmacists.

BACKGROUND: Prescription drug monitoring programs (PDMP) are databases which collect prescribing and dispensing information for high-risk medicines, and are one approach to mitigate prescription opioid-related risks. AIM: To examine correlates of PDMP use under voluntary and mandatory conditions, among a representative sample of community pharmacists in Victoria, Australia. METHOD: An online anonymous survey was conducted and collected data in relation to pharmacist and pharmacy characteristics, comfort in performing certain tasks, PDMP training and the frequency of PDMP use under voluntary and mandatory conditions. Multivariate logistic regression models were performed to determine the effect of each covariate on voluntary and mandatory PDMP use. RESULTS: In total, 265 pharmacists participated (response rate 47%). Under voluntary conditions, a quarter of pharmacists (24.9%) used the PDMP all the time, while half (51.7%) used the PDMP all of the time, once mandated. Pharmacies that stocked naloxone (OR: 1.96; 95% CI 1.11-3.45) and pharmacists that had attended formal PDMP training (OR: 1.78; 95% CI 1.05-3.05), were significantly associated with regular PDMP use under voluntary conditions. Under mandatory conditions, increased odds of PDMP use were associated with pharmacies that stocked naloxone (OR: 1.88; 95% CI 1.06-3.34). Pharmacists working in regional and rural areas had significantly lower odds (OR: 0.35; 95% CI 0.20-0.63) of always using the PDMP, as did pharmacists with > 15 years' experience (OR: 0.24; 95% CI 0.11-0.51) once use was mandated. CONCLUSION: Given that PDMP utilisation was slower or less regular amongst pharmacists located in regional and rural areas, pharmacists with more years of experience and those not already supplying naloxone, targeted training aimed at these sub-populations may be beneficial.

How do patient, pharmacist and medication characteristics and prescription drug monitoring program alerts influence pharmacists' decisions to dispense opioids? A randomised controlled factorial experiment.

BACKGROUND: Prescription drug monitoring programs (PDMP) are electronic databases that track the prescribing and dispensing of high-risk medicines such as opioids. They have the ability to provide clinicians with alerts, which identify medication-related risks, and are used to help inform decisions to supply. This study aimed to determine to what extent patient, pharmacist, and medication related characteristics and PDMP alerts influence decisions to dispense opioids and take other action, using a randomised controlled factorial design. METHODS: Pharmacists completed an online factorial experiment, comprising six randomly generated vignettes, describing a hypothetical pharmacy patient. Pharmacists ranked the likelihood of dispensing an opioid prescription and indicated other actions, if any, they would make. Mixed-effects linear and logistical models were used to examine the association between the vignette (patient, medication and alerts), and pharmacist characteristics and the likelihood to dispense and take other actions. RESULTS: 241 pharmacists were included in the analysis (n = 1353 vignettes). The PDMP alert for high dose and multiple prescriber episodes were significant predicators of reduced likelihood to dispense, with a respective 2.73- and 4.1-unit decrease in likelihood to dispense (p < 0.001). Alerts had the strongest association with other actions such as contacting the prescriber, talking to the patient and recommending naloxone, though patient and medication characteristics including age, opioid dose, benzodiazepine use and co-morbidity were also associated with increased odds of engaging in some actions. CONCLUSION: PDMP alerts were the most significant predictor of reduced likelihood to dispense and were associated with the greatest odds of taking other actions. Well-established risk factors such as high dose and high-risk drug combinations, in the absence of PDMP alerts, were associated with some actions, though to a lesser degree than PDMP alerts. These findings have significant policy implications and suggest PDMP alerts are a greater driver of decisions to dispense opioids and take other actions, compared with other known clinical risk factors.

Opioid-sparing effect of cannabinoids for analgesia: an updated systematic review and meta-analysis of preclinical and clinical studies.

Cannabinoid co-administration may enable reduced opioid doses for analgesia. This updated systematic review on the opioid-sparing effects of cannabinoids considered preclinical and clinical studies where the outcome was analgesia or opioid dose requirements. We searched Scopus, Cochrane Central Registry of Controlled Trials, Medline, and Embase (2016 onwards). Ninety-two studies met the search criteria including 15 ongoing trials. Meta-analysis of seven preclinical studies found the median effective dose (ED50) of morphine administered with delta-9-tetrahydrocannabinol was 3.5 times lower (95% CI 2.04, 6.03) than the ED50 of morphine alone. Six preclinical studies found no evidence of increased opioid abuse liability with cannabinoid administration. Of five healthy-volunteer experimental pain studies, two found increased pain, two found decreased pain and one found reduced pain bothersomeness with cannabinoid administration; three demonstrated that cannabinoid co-administration may increase opioid abuse liability. Three randomized controlled trials (RCTs) found no evidence of opioid-sparing effects of cannabinoids in acute pain. Meta-analysis of four RCTs in patients with cancer pain found no effect of cannabinoid administration on opioid dose (mean difference -3.8 mg, 95% CI -10.97, 3.37) or percentage change in pain scores (mean difference 1.84, 95% CI -2.05, 5.72); five studies found more adverse events with cannabinoids compared with placebo (risk ratio 1.13, 95% CI 1.03, 1.24). Of five controlled chronic non-cancer pain trials; one low-quality study with no control arm, and one single-dose study reported reduced pain scores with cannabinoids. Three RCTs found no treatment effect of dronabinol. Meta-analyses of observational studies found 39% reported opioid cessation (95% CI 0.15, 0.64, I2 95.5%, eight studies), and 85% reported reduction (95% CI 0.64, 0.99, I2 92.8%, seven studies). In summary, preclinical and observational studies demonstrate the potential opioid-sparing effects of cannabinoids in the context of analgesia, in contrast to higher-quality RCTs that did not provide evidence of opioid-sparing effects.

The Impact of Stigma on People with Opioid Use Disorder, Opioid Treatment, and Policy.

Illicit drug use disorders are the most stigmatised health conditions worldwide, and stigma acts as a meaningful barrier to treatment entry and treatment provision. In the context of dramatically rising opioid-related harms, it is critical that we understand the drivers of stigma and how it affects opioid use disorder treatment and policy. The aim of this narrative review is to discuss how opioid-related stigma impacts treatment provision and harm reduction, and provide potential strategies to reduce stigma at a social and structural level. We used the Framework for Integrating Normative Influences on Stigma (FINIS) to identify sources of opioid-related stigma at the macro (structural stigma), meso (public stigma) and micro (internalised stigma) levels. Reducing stigma requires strategies that target multiple levels, however addressing inequity in the laws, regulations, and rules that segregate people with opioid and other substance use disorders from mainstream society is essential.

A prospective, multisite implementation-efficacy trial of a collaborative prescriber-pharmacist model of care for Medication Assisted Treatment for Opioid Dependence: Protocol for the EPIC-MATOD study.

BACKGROUND: Medication Assisted Treatment for Opioid Dependence (MATOD) is clinically effective and cost effective, yet a lack of MATOD prescribers in the community limits access to this treatment in Australia. These shortages are often greatest in regional and rural areas. OBJECTIVE(S): The Enhancing Pharmacist Involvement in Care (EPIC)-MATOD study will evaluate clinical and implementation outcomes among people with opioid dependence receiving MATOD through a collaborative pharmacist-prescriber model of care across multiple sites in a regional location (encompassing a mix of metropolitan and non-metropolitan areas) of Victoria, Australia. METHODS AND ANALYSIS: The EPIC-MATOD study is a prospective, multisite, implementation trial of collaborative MATOD care. Pharmacists and prescribers will be recruited through the local network of opioid pharmacotherapy providers. Patients will be recruited through participating healthcare providers. After induction into the collaborative care model, patients and healthcare professionals will be followed up over 6- (patients) and 12-months (pharmacists and prescribers) in a hybrid implementation-efficacy study, with outcomes mapped to the RE-AIM framework. The primary clinical efficacy endpoint is patient retention in treatment at 26 weeks. The primary implementation outcome is treatment capacity, based on prescriber time required to provide treatment through collaborative care compared with traditional care. Secondary clinical endpoints include attendance for dosing and clinical reviews, substance use, mental and physical health and overall well-being. Implementation costs, acceptability, and provider engagement in collaborative care will be used as secondary implementation outcome indicators. Time and costs associated with collaborative care, and health service utilisation, will also be estimated. PROJECT IMPACT: The study will provide important information on outcomes and acceptability of collaborative care for MATOD, as well as the cost and key considerations in delivering a collaborative model of care in Australia and other countries where similar treatment barriers exist.

Does naloxone provision lead to increased substance use? A systematic review to assess if there is evidence of a 'moral hazard' associated with naloxone supply.

BACKGROUND: Take home naloxone (THN) programs have been rapidly upscaled in response to increasing opioid-related mortality. One often cited concern is that naloxone provision could be associated with increased opioid use, due to the availability of naloxone to reverse opioid overdose. We conducted a systematic review to determine whether THN provision is associated with changes in substance use by participants enrolled in THN programs. METHODS: We conducted a systematic review of the literature to assess changes in heroin or other substance use by people who use opioids following THN provision. RESULTS: Seven studies with 2578 participants were included. Of the seven studies, there were two quasi-experimental studies and five cohort studies. Based on the Joanna Briggs Institute quality assessment, four studies were of moderate quality and three studies were of high quality. Of the five studies that reported on the primary outcome of heroin use, no study found evidence of increased heroin use across the study population. Five studies reported on other substance use (benzodiazepines, alcohol, cocaine, amphetamine, cannabis, prescription opioids), none of which found evidence of an increase in other substance use associated with THN provision. Four studies reported on changes in overdose frequency following THN provision: three studies reporting no change, and one study of people prescribed opioids finding a reduction in opioid-related emergency department attendances for participants who received naloxone. CONCLUSION: We found no evidence that THN provision was associated with increased opioid use or overdose. Concerns that THN supply may lead to increased substance use were not supported by data from reviewed studies.

How prescription drug monitoring programs influence clinical decision-making: A mixed methods systematic review and meta-analysis.

BACKGROUND: Opioid prescribing, for chronic non-cancer pain, has increased substantially in the past two decades and is associated with significant harms. Various public health approaches have been adopted to address these harms including the implementation of prescription drug monitoring programs (PDMPs). This systematic review aims to identify how PDMP use influences healthcare providers' clinical decision-making. METHODS: Six databases were searched for literature up until April 2021. Empirical studies were included, with no restrictions placed on year, location, language or study design. Thematic analysis of the identified articles was conducted and where possible, meta-analyses were conducted using a random effect model in Stata. RESULTS: Forty-one articles related to 39 studies were included. All studies were conducted in the United States, published between 2005 and 2021 and most (n = 28) related to one state-level PDMP. PDMP utilisation influenced healthcare providers' clinical decision-making across seven broad themes: (i) the supply of controlled substances, (ii) refusal to prescribe or treat, (iii) risk mitigation strategies, (iv) communication, (v) education and counselling, (vi) referrals and care coordination and (vii) stigma. CONCLUSIONS: PDMP use influenced healthcare providers' clinical decision-making, resulting in both intended and unintended outcomes for patients. PDMPs are a public health initiative designed to reduce harms associated with increased opioid prescribing, yet their use is associated with multiple unintended outcomes. Targeted research is needed to understand the impact of healthcare providers' clinical decision-making after PDMP utilisation, and the clinical outcomes for patients identified through these tools.

Community pharmacists' preparedness to intervene with concerns around prescription opioids: findings from a nationally representative survey.

Background Prescription opioid use and related harms have dramatically increased in many countries. Objective To investigate pharmacists' preparedness and confidence to intervene when concerned about supplying prescription opioids and strategies used when concerned about supplying these opioids. Setting Online survey among a representative sample of Australian community pharmacists. Method Pharmacists completed an online survey about their concerns, comfort and strategies used when supplying prescription opioids. Correlates of comfort to intervene and active intervention strategies were explored using multivariable ordered logistic regression and adjusted odd ratios (aOR) and 95% confidence intervals were reported. Main outcome measures Comfort to intervene when concerned about supplying prescription opioids and pharmacists' discussing these concerns with the patient, and the prescriber. Results Most pharmacists were concerned about supplying prescription opioids to patients in the past week. Being female [adjusted odds ratio (aOR) 0.63; 95% confidence interval (CI) 0.47-0.85] was associated with reduced comfort, while practicing within a large chain pharmacy (aOR 1.52, 95% CI 1.08-2.15) was associated with greater comfort to intervene when concerned about prescription opioid supply. Pharmacists practicing in rural areas were significantly less likely than those in capital cities to discuss concerns with patients (aOR 0.66, 95% CI 0.45-0.97). Post-graduate education about substance use disorders was associated with increased likelihood of discussing concerns with patients (aOR 1.54, 95% CI 1.12-2.13). Pharmacists that indicated greater comfort in intervening when concerned about prescription opioids were more likely to discuss concerns with both patients and prescribers. Females were significantly more likely to discuss concerns with prescribers (aOR 1.67, 95% CI 1.22-2.29), whereas years of practice reduced the odds of discussing concerns with prescribers (aOR 0.98, 95% CI 0.97-0.99). Conclusion Considering specific factors such as gender and years of practice to help target pharmacist training may lead to increased comfort in discussing concerns related to prescription opioids, which in turn may improve communication with prescribers and patients.

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.

Validity and Reliability of the Computer-Administered Routine Opioid Outcome Monitoring (ROOM) Tool.

OBJECTIVE: The Routine Opioid Outcome Monitoring (ROOM) tool measures outcomes with opioids using an established framework which includes domains such as pain, mood, opioid use disorder, alcohol use, and constipation. This study aims to validate and establish the test-retest reliability of the computer-administered ROOM tool. DESIGN AND SETTING: Cross-sectional analysis of an online sample. SUBJECTS: Participants comprised those with chronic noncancer pain who regularly used prescription opioids. METHODS: Participants self-completed the online ROOM tool along with other validated measures (validation questionnaire), and those who were agreeable also completed the online test-retest questionnaire approximately two weeks later. Subcomponents of the ROOM tool (i.e., pain, mood, alcohol use, opioid use disorder, and constipation) were validated against longer measures of the same construct using Pearson correlation coefficients. Intraclass correlation coefficients were used to assess the stability of the ROOM tool over time. RESULTS: A total of 324 participants completed the validation questionnaire, of whom 260 also completed the test-retest questionnaire. The opioid use disorder domain showed good sensitivity (73.6) and specificity (75.8) against the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, any opioid use disorder. All ROOM components showed moderate correlation (r = 0.55-0.73) with their longer counterparts. Test-retest reliability was fair (0.58-0.75), indicating that responses were relatively stable over time. Reliability did vary, however, based on the components being measured and how certain tools were scored. CONCLUSION: The computer-administered ROOM tool is a valid approach for brief monitoring of outcomes with prescribed opioids in primary care settings and appears to be acceptable to people who are using prescribed opioids for chronic pain.

Validation of the OWLS, a Screening Tool for Measuring Prescription Opioid Use Disorder in Primary Care.

OBJECTIVE: The OWLS is a screening tool for prescription opioid use disorder designed for use in primary care. This study aimed to confirm the optimal wording, scoring methods, and cutoff for the OWLS. DESIGN AND SETTING: Cross-sectional analysis of an online sample. SUBJECTS: Participants comprised those with chronic noncancer pain who regularly used prescription opioids. METHODS: Eligible participants self-completed an online version of the OWLS prescription opioid use disorder screening tool and the Composite International Diagnostic Interview Substance Abuse module. Receiver operating characteristics were calculated for three scoring methods for the OWLS, and these were compared with DSM-5 classification of any use disorder and moderate to severe opioid use disorder. RESULTS: Among the sample (N = 324), utilizing scoring method (i) (i.e., positive endorsement ≥ response option "a little bit") and a cutoff of 3 increased the percentage of correctly classified participants, with concurrent increases in specificity and decreases in false discovery rate, and false positive rate. CONCLUSION: OWLS utilizing scoring method (i) with a cutoff of 3 was shown to be the optimal version and scoring method of this tool. This represents a time-efficient, simple scoring method, allowing for quick and accurate screening for opioid use disorder to occur.