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

Cangadis-Douglass, Helena; Xia, Ting; Buchbinder, Rachelle; Lalic, Samanta; Russell, Grant; Andrew, Nadine E.; Pearce, Christopher; Bell, John; Ilomäki, Jenni; Nielsen, Suzanne

doi:10.1016/j.sapharm.2022.07.041

Cited by 8 publications

(15 citation statements)

References 55 publications

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“…Further details of the SNOMED codes used for diagnosis identification are comprehensively documented elsewhere. 19 Unique identifiers are available for prescribers within practices, and a variable captures prescriber type, enabling analyses on prescribing patterns that consider these variables.…”

Section: Cohort Descriptionmentioning

confidence: 99%

“… 21 Further details of the SNOMED codes used for mental health medications are comprehensively documented elsewhere. 19 In the OPPICO cohort, one in two people were identified as having mental health conditions, with the most common diagnoses being depression (16.8%) and anxiety (10.0%). A further 24.9% of the cohort were prescribed a medication for a mental health condition, without a mental health diagnosis being recorded.…”

Section: Cohort Descriptionmentioning

confidence: 99%

“…For example, we plan to examine trends of benzodiazepine and opioid coprescription, and high dose opioid prescribing as part of planned analyses related to this work. 19 Next, as an extension of this already funded work, we will link the person-level records with hospital data across the same three health regions to determine if there are increased or decreased harms (eg, heroin or other drug-related poisonings, or suicide-related attendances) resulting from prescribing changes as captured by emergency department and hospital admissions. There are clear examples where opioid policy has had serious unintended consequences in other locations, resulting in rapid tapering or opioid cessation, with devastating outcomes.…”

Section: Cohort Descriptionmentioning

confidence: 99%

“…Diagnoses are derived from the patients’ historical and current records and are coded using systemised nomenclature of medicine (SNOMED) codes,17 18 with the OPPICO study focussing on pain conditions, mental health and substance use disorders. Further details of the SNOMED codes used for diagnosis identification are comprehensively documented elsewhere 19. Unique identifiers are available for prescribers within practices, and a variable captures prescriber type, enabling analyses on prescribing patterns that consider these variables.…”

Section: Cohort Descriptionmentioning

confidence: 99%

“…The validated RxRisk-V tool21 and WHO Anatomical Therapeutic Chemical (ATC) classification system for mental health-related medications were used for identifying medications commonly used in Australian mental health reporting 21. Further details of the SNOMED codes used for mental health medications are comprehensively documented elsewhere 19. In the OPPICO cohort, one in two people were identified as having mental health conditions, with the most common diagnoses being depression (16.8%) and anxiety (10.0%).…”

Section: Cohort Descriptionmentioning

confidence: 99%

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

Nielsen

Buchbinder

Pearce³

et al. 2023

BMJ Open

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PurposeThe 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.ParticipantsThe 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 dateIn 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 plansThe 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 numberEU PAS Register (EUPAS43218, prospectively registered).

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Section: Cohort Descriptionmentioning

confidence: 99%

Section: Cohort Descriptionmentioning

confidence: 99%

Section: Cohort Descriptionmentioning

confidence: 99%

Section: Cohort Descriptionmentioning

confidence: 99%

Section: Cohort Descriptionmentioning

confidence: 99%

See 3 more Smart Citations

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

Nielsen

Buchbinder

Pearce³

et al. 2023

BMJ Open

Self Cite

View full text Add to dashboard Cite

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Trajectories of prescription opioid tapering in patients with chronic non-cancer pain: a retrospective cohort study, 2015–2020

Jung,

Xia,

Ilomäki

et al. 2024

Pain Medicine

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Objective To identify common opioid tapering trajectories among patients commencing taper from long-term opioid therapy for chronic non-cancer pain, and examine patient-level characteristics associated with these different trajectories. Design A retrospective cohort study. Setting. Australian primary care. Subjects Patients prescribed opioid analgesics between 2015 and 2020. Methods Group-based trajectory modelling and multinomial logistic regression analysis were conducted to determine taper trajectories and examine demographic and clinical factors associated with the different trajectories. Results A total of 3,369 patients commenced taper from long-term opioid therapy. Six distinct opioid taper trajectories were identified: low dose, completed taper (12.9%); medium dose, faster taper (12.2%); medium dose, gradual taper (6.5%); low dose, non-completed taper (21.3%); medium dose, non-completed taper (30.4%); high dose, non-completed taper (16.7%). A completed taper trajectory from high opioid dose was not identified. For patients prescribed medium opioid doses, those that completed taper were more likely to have higher geographically-derived socio-economic status (relative risk ratio [RRR], 1.067; 95% confidence intervals [CI], 1.001-1.137) and less likely to have sleep disorders (RRR, 0.661; 95% CI, 0.463-0.945), compared to those that didn’t complete taper. Patients who didn’t complete taper were more likely to be prescribed strong opioids (e.g. morphine, oxycodone), regardless of whether they were tapered from low (RRR, 1.444; 95% CI, 1.138-1.831) or high (RRR, 1.344; 95% CI, 1.027-1.760) doses. Conclusions Those prescribed strong opioids and high doses appear less likely to complete taper. Further studies are needed to evaluate the clinical outcomes associated with the identified trajectories.

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Predictors of persistent opioid use in Australian primary care: a retrospective cohort study, 2018–2022

Jung,

Xia,

Ilomäki

et al. 2024

Pain Medicine

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Objective To examine the predictors of persistent opioid use (‘persistence’) in people initiating opioids for non-cancer pain in Australian primary care. Design A retrospective cohort study. Setting Australian primary care. Subjects People prescribed opioid analgesics between 2018–2022, identified through the Population Level Analysis and Reporting (POLAR) database. Methods Persistence was defined as receiving opioid prescriptions for at least 90 days with a gap of less than 60 days between subsequent prescriptions. Multivariable logistic regression was used to examine the predictors of persistent opioid use. Results The sample consisted of 343,023 people initiating opioids for non-cancer pain; of these, 16,527 (4.8%) developed persistent opioid use. Predictors of persistence included older age (≥75 vs 15–44 years: Adjusted odds ratio: 1.67, 95% CI: 1.58–1.78), concessional beneficiary status (1.78, 1.71–1.86), diagnosis of substance use disorder (1.44, 1.22–1.71) and chronic pain (2.05, 1.85–2.27), initiation of opioid therapy with buprenorphine (1.95, 1.73–2.20) and long-acting opioids (2.07, 1.90–2.25), provision of higher quantity of opioids prescribed at initiation (total OME of ≥ 750mg vs < 100mg: 7.75, 6.89–8.72), provision of repeat/refill opioid prescriptions at initiation (2.94, 2.77–3.12), and prescription of gabapentinoids (1.59, 1.50–1.68), benzodiazepines (1.43, 1.38–1.50) and z-drugs (e.g., zopiclone, zolpidem; 1.61, 1.46–1.78). Conclusions These findings add to the limited evidence of individual-level factors associated with persistent opioid use. Further research is needed to understand the clinical outcomes of persistent opioid use in people with these risk factors to support the safe and effective prescribing of opioids.

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

Cited by 8 publications

References 55 publications

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

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

Trajectories of prescription opioid tapering in patients with chronic non-cancer pain: a retrospective cohort study, 2015–2020

Predictors of persistent opioid use in Australian primary care: a retrospective cohort study, 2018–2022

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