Impact of codeine rescheduling on prescribing of codeine and other opioids: Interrupted time series analyses using Australian general practice data.

AIMS: We aimed to determine the impact of codeine rescheduling on prescribing of codeine and other opioids, with a focus on demographic and diagnoses associated with codeine prescribing before and after rescheduling of codeine to prescription-only in February 2018. METHODS: We used interrupted time series analysis (February 2016-February 2020) and probit regression to examine prescribing of codeine and other opioids according to primary care data from 464 general practice clinics in Victoria, Australia. RESULTS: The rate of codeine prescribing increased in the month following rescheduling (additional 76 people/10000, 95% confidence interval [CI] 49-103), then declined to baseline rates (slope -2.02, 95% CI 3.79, -0.25). Prescribing of other opioids did not change. Post rescheduling, females were more likely to receive codeine prescriptions compared to males (ß = 0.094, 95% CI 0.08-0.108) and those aged 70-79 years were more likely to receive codeine compared to those aged <30 years. Those residing in the least disadvantaged areas had a greater probability of being prescribed codeine than those in more disadvantaged areas after rescheduling (ß = 0.154, 95% CI 0.129-0.179). A documented mental health diagnosis (ß = 0.067, 95% CI 0.052-0.082) or migraine diagnosis (ß = 0.057, 95% CI 0.037-0.078) was associated with increased likelihood of receiving a codeine prescription after rescheduling compared to before in contrast to those without such a diagnosis. CONCLUSION: Codeine rescheduling did not result in a sustained increase in codeine prescribing nor a change in the prescribing of other opioids. Patient factors associated with increased codeine prescribing after compared to before rescheduling included female sex, older age, migraine diagnosis and comorbid mental health conditions. REGISTRATION: EU PAS Register (EUPAS43218).

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.

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).

What are the research priorities for optimising the safe and effective use of opioids in Australian general practice?

INTRODUCTION: Persistent high rates of prescription opioid use and harms remain a concern in Australia, Europe and North America. Research priority setting can inform the research agenda, strategic responses and evidence-based interventions. The objective of this study was to establish research priorities related to the safe and effective use of prescription opioids in general practice. METHODS: Consumers, clinicians and policy makers were invited to participate in a structured consensus workshop in May 2021. A modified nominal group technique was used to explore research priorities for the safe and effective use of opioids in Australian general practice. Research priorities were identified, consolidated and prioritised using a structured process. RESULTS: Seventeen consumer, medical, pharmacy, nursing, allied health and policy participants generated 26 consolidated priorities across three domains: (i) consumer-focused priorities; (ii) clinician and practice-focused priorities; and (iii) system and policy-focused priorities. The highest ranked research priorities in each of the domains were consumer characteristics that influence opioid prescribing and outcomes, opioid deprescribing strategies, and system-level barriers to prescribing alternatives to opioids, in the consumer, clinician and practice, and system and policy domains, respectively. DISCUSSION AND CONCLUSION: The priorities reflect opportunities for research priority setting within Australian general practice. The priorities provide a map for future qualitative and quantitative research that will inform safe and effective opioid prescribing.

Using primary care data to understand opioid prescribing, policy impacts and clinical outcomes: A protocol for the OPPICO study.

INTRODUCTION: Prescription opioid use in Australia has increased over the last 3 decades. The majority of opioids are prescribed and dispensed in primary care, however, there are few studies that are specific to opioid prescribing in this setting. Evidence about the impact of key government policy strategies to optimize opioid prescribing in primary care is limited. The aim of this study is to examine the impact of recent policy changes and clinical guidelines on opioid prescribing in primary care. METHODS AND ANALYSIS: Longitudinal analysis of people prescribed opioid analgesics using Population Level Analysis and Reporting (POLAR) data. POLAR is a primary care dataset comprising 464 primary health care practices in Victoria, Australia. People prescribed opioid analgesics between 2015 and 2020 will be included. The impact of opioid policies and guideline recommendations will be evaluated using interrupted time series models. Group- based trajectory modelling and multivariate regression will be used to identify patterns of opioid cessation and the provision of corresponding non-opioid interventions. ETHICS AND DISSEMINATION: The study has received Monash University Human Research Ethics Committee approval (ID 24139). Permission to access, collate and use POLAR data is granted from Outcome Health as the data custodians. The results of this study will be disseminated through publication in international journals, presented at national and international scientific conferences, and disseminated to consumers, policy makers, primary care providers and primary health networks. PROTOCOL REGISTRATION DETAILS: EU PAS Register (EUPAS43218).