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Hydrocodone and oxycodone are prescribed commonly to treat pain. However, differences in risk of opioid-related adverse outcomes after an initial prescription are unknown. This study aims to determine the risk of opioid-related adverse events, defined as either chronic use or opioid overdose, following a first prescription of hydrocodone or oxycodone to opioid naïve patients. A retrospective analysis of multiple linked public health datasets in the state of Oregon. Adult patients ages 18 and older who a) received an initial prescription for oxycodone or hydrocodone between 2015-2017 and b) had no opioid prescriptions or opioid-related hospitalizations or emergency department visits in the year preceding the prescription were followed through the end of 2018. First-year chronic opioid use was defined as ≥6 opioid prescriptions (including index) and average ≤30 days uncovered between prescriptions. Fatal or non-fatal opioid overdose was indicated from insurance claims, hospital discharge data or vital records. After index prescription, 2.8% (n = 14,458) of individuals developed chronic use and 0.3% (n = 1,480) experienced overdose. After adjustment for patient and index prescription characteristics, patients receiving oxycodone had lower odds of developing chronic use relative to patients receiving hydrocodone (adjusted odds ratio = 0.95, 95% confidence interval (CI) 0.91-1.00) but a higher risk of overdose (adjusted hazard ratio (aHR) = 1.65, 95% CI 1.45-1.87). Oxycodone monotherapy appears to greatly increase the hazard of opioid overdose (aHR 2.18, 95% CI 1.86-2.57) compared with hydrocodone with acetaminophen. Oxycodone combined with acetaminophen also shows a significant increase (aHR 1.26, 95% CI 1.06-1.50), but not to the same extent. Among previously opioid-naïve patients, the risk of developing chronic use was slightly higher with hydrocodone, whereas the risk of overdose was higher after oxycodone, in combination with acetaminophen or monotherapy. With a goal of reducing overdose-related deaths, hydrocodone may be the favorable agent.

Background In 2015, Oregon’s Medicaid program implemented a performance improvement project to reduce high-dose opioid prescribing across its 16 coordinated care organizations (CCOs). The objective of this study was to evaluate the effect of that program on prescription opioid use and outcomes. Methods Using Medicaid claims data from 2014 to 2017, we conducted interrupted time-series analyses to examine changes in the prescription opioid use and overdose rates before (July 2014 to June 2015) and after (January 2016 to December 2017) implementation of Oregon’s high-dose policy initiative (July 2015 to December 2015). Prescribing outcomes were: 1) total opioid prescriptions 2) high-dose [> 90 morphine milligram equivalents per day] opioid prescriptions, and 3) proportion of opioid prescriptions that were high-dose. Opioid overdose outcomes included emergency department visits or hospitalizations that involved an opioid-related poisoning (total, heroin-involved, non-heroin involved). Analyses were performed at the state and CCO level. Results There was an immediate reduction in high dose opioid prescriptions after the program was implemented (− 1.55 prescription per 1000 enrollee; 95% CI − 2.26 to − 0.84; p  < 0.01). Program implementation was also associated with an immediate drop (− 1.29 percentage points; 95% CI − 1.94 to − 0.64 percentage points; p  < 0.01) and trend reduction (− 0.23 percentage point per month; 95% CI − 0.33 to − 0.14 percentage points; p  < 0.01) in the monthly proportion of high-dose opioid prescriptions. The trend in total, heroin-involved, and non-heroin overdose rates increased significantly following implementation of the program. Conclusions Although Oregon’s high-dose opioid performance improvement project was associated with declines in high-dose opioid prescriptions, rates of opioid overdose did not decrease. Policy efforts to reduce opioid prescribing risks may not be sufficient to address the growing opioid crisis.

BackgroundLong-term efficacy of opioids for non-cancer pain is unproven, but risks argue for cautious prescribing. Few data suggest how long or how much opioid can be prescribed for opioid-naïve patients without inadvertently promoting long-term use.ObjectiveTo examine the association between initial opioid prescribing patterns and likelihood of long-term use among opioid-naïve patients.DesignRetrospective cohort study; data from Oregon resident prescriptions linked to death certificates and hospital discharges.ParticipantsPatients filling opioid prescriptions between October 1, 2012, and September 30, 2013, with no opioid fills for the previous 365 days. Subgroup analyses examined patients under age 45 who did not die in the follow-up year, excluding most cancer or palliative care patients.Main MeasuresExposure: Numbers of prescription fills and cumulative morphine milligram equivalents (MMEs) dispensed during 30 days following opioid initiation (“initiation month”). Outcome: Proportion of patients with six or more opioid fills during the subsequent year (“long-term users”).Key ResultsThere were 536,767 opioid-naïve patients who filled an opioid prescription. Of these, 26,785 (5.0 %) became long-term users. Numbers of fills and cumulative MMEs during the initiation month were associated with long-term use. Among patients under age 45 using short-acting opioids who did not die in the follow-up year, the adjusted odds ratio (OR) for long-term use among those receiving two fills versus one was 2.25 (95 % CI: 2.17, 2.33). Compared to those who received < 120 total MMEs, those who received between 400 and 799 had an OR of 2.96 (95 % CI: 2.81, 3.11). Patients initiating with long-acting opioids had a higher risk of long-term use than those initiating with short-acting drugs.ConclusionsEarly opioid prescribing patterns are associated with long-term use. While patient characteristics are important, clinicians have greater control over initial prescribing. Our findings may help minimize the risk of inadvertently initiating long-term opioid use.

Abstract Objective Prescription drug monitoring programs (PDMPs) were created to facilitate responsible use of controlled substances. In Oregon, physicians, physician’s assistants (MDs/DOs/PAs), dentists, nurse practitioners (NPs), and naturopathic physicians (NDs) may prescribe opioids, but differences in prescribing practices, patient mix, and patient outcomes among prescriber types have not been characterized. Methods De-identified Oregon PDMP data from October 2011 through October 2014 were linked with vital records and a statewide hospital discharge registry. The disciplines of registered prescribers were identified by board affiliations. Prescription profiles associated with opioid overdose risk were tabulated for patients with at least one registered prescriber. Opioid-related hospitalizations and deaths were identified using ICD-9 and ICD-10 codes. Results There were 5,935 prescribers registered during the study period. Patients of NPs or NDs received more high-risk opioid prescriptions than patients of MDs/DOs/PAs. For example, they received greater proportions of high-dose prescriptions (NP 12.9%, ND 15%, MD/DO/PA 11.1%), and had greater opioid-related hospitalization (NP 1.7%, ND 3.1%, MD/DO/PA 1.2%; P < 0.005 for all). However, patients of NPs or NDs were also more likely to have four or more prescribers (NP 45.3%, ND 58.5%, MD/DO/PA 27.1%), and most of their patients’ high-risk opioid prescriptions came from prescribers in other disciplines. Conclusion Our analysis suggests significant differences in opioid prescription profiles and opioid-related hospitalization and mortality among patients receiving opioid prescriptions from nurse practitioners, naturopathic physicians, or medical clinicians in Oregon. However, these differences appear largely due to differences in patient mix between provider types rather than discipline-specific prescribing practices.

The opioid epidemic continues to be a public health crisis in the US. To assess the patient factors and early time-varying prescription-related factors associated with opioid-related fatal or nonfatal overdose. This cohort study evaluated opioid-naive adult patients in Oregon using data from the Oregon Comprehensive Opioid Risk Registry, which links all payer claims data to other health data sets in the state of Oregon. The observational, population-based sample filled a first (index) opioid prescription in 2015 and was followed up until December 31, 2018. Data analyses were performed from March 1, 2020, to June 15, 2021. Overdose after the index opioid prescription. The outcome was an overdose event. The sample was followed up to identify fatal or nonfatal opioid overdoses. Patient and prescription characteristics were identified. Prescription characteristics in the first 6 months after the index prescription were modeled as cumulative, time-dependent measures that were updated monthly through the sixth month of follow-up. A time-dependent Cox proportional hazards regression model was used to assess patient and prescription characteristics that were associated with an increased risk for overdose events. The cohort comprised 236 921 patients (133 839 women [56.5%]), of whom 667 (0.3%) experienced opioid overdose. Risk of overdose was highest among individuals 75 years or older (adjusted hazard ratio [aHR], 3.22; 95% CI, 1.94-5.36) compared with those aged 35 to 44 years; men (aHR, 1.29; 95% CI, 1.10-1.51); those who were dually eligible for Medicaid and Medicare Advantage (aHR, 4.37; 95% CI, 3.09-6.18), had Medicaid (aHR, 3.77; 95% CI, 2.97-4.80), or had Medicare Advantage (aHR, 2.18; 95% CI, 1.44-3.31) compared with those with commercial insurance; those with comorbid substance use disorder (aHR, 2.74; 95% CI, 2.15-3.50), with depression (aHR, 1.26; 95% CI, 1.03-1.55), or with 1 to 2 comorbidities (aHR, 1.32; 95% CI, 1.08-1.62) or 3 or more comorbidities (aHR, 1.90; 95% CI, 1.42-2.53) compared with none. Patients were at an increased overdose risk if they filled oxycodone (aHR, 1.70; 95% CI, 1.04-2.77) or tramadol (aHR, 2.80; 95% CI, 1.34-5.84) compared with codeine; used benzodiazepines (aHR, 1.06; 95% CI, 1.01-1.11); used concurrent opioids and benzodiazepines (aHR, 2.11; 95% CI, 1.70-2.62); or filled opioids from 3 or more pharmacies over 6 months (aHR, 1.38; 95% CI, 1.09-1.75). This cohort study used a comprehensive data set to identify patient and prescription-related risk factors that were associated with opioid overdose. These findings may guide opioid counseling and monitoring, the development of clinical decision-making tools, and opioid prevention and treatment resources for individuals who are at greatest risk for opioid overdose.

Abstract Objectives. Little is known about clinicians’ use of prescription drug monitoring program (PDMP) profiles in decision-making. The objective of this qualitative study was to understand how clinicians use, interpret, and integrate PDMP profiles with other information in making clinical decisions. Design. Qualitative interviews of clinician PDMP users. Setting. Oregon registrants in the state’s PDMP. Subjects. Thirty-three clinicians practicing in primary care, emergency medicine, pain management, psychiatry, dentistry, and surgery. Methods. We conducted semistructured telephone interviews with PDMP users. A multidisciplinary team used a grounded theory approach to identify patterns of PDMP use and how PDMP profiles influence clinical decisions. Results. PDMP use varied from consistent monitoring to checking the PDMP only on suspicion of misuse, with inconsistent use reported particularly among short-term prescribers. Primary care clinicians reported less routine use with existing pain patients than with new patients. In response to worrisome PDMP profiles with new patients, participants reported declining to prescribe, except in the case of acute, verifiable conditions. Long-term prescribers reported sometimes continuing prescriptions for existing patients depending on perceived patient intent, honesty, and opioid misuse risk. Some long-term prescribers reported discharging patients from their practices due to worrisome PDMP profiles; others expressed strong ethical grounds for retaining patients but discontinuing controlled substances. Conclusion. Greater consistency is needed in use of PDMP in monitoring existing patients and in conformity to guidelines against discharging patients from practice. Research is needed to determine optimal approaches to interpreting PDMP profiles in relation to clinical judgment, patient screeners, and other information.

Objectives. Clinician communication with patients regarding worrisome findings in Prescription Drug Monitoring Programs (PDMPs) may influence patient responses and subsequent care. The authors studied the range of approaches clinicians report when communicating with patients in this situation and how practice policies and procedures may influence this communication. Design. Qualitative interviews of clinician PDMP users. Setting: Oregon registrants in the state's PDMP. Subjects. Thirty-three clinicians practicing in pain management, emergency medicine, primary care, psychiatry, dentistry, and surgery. Methods. The authors conducted semi-structured interviews via telephone with clinicians who routinely used the PDMP. A multidisciplinary team used a grounded theory approach to identify ways clinicians reported using information from the PDMP when communicating with patients, and policies that influenced that communication. Results. Clinicians reported using a range of approaches for communicating about PDMP results, from openly sharing, to questioning patients without disclosing access to the PDMP, to avoiding the conversation. Clinicians also reported practice policies and procedures that influenced communication with their patients about prescribing and ongoing care, including policies that normalized use of the PDMP with all patients and those that facilitated difficult conversations by providing a rationale not to prescribe in certain circumstances. Conclusion. Clinicians' self-reported approaches to sharing PDMP findings and communicating prescribing decisions with patients vary and may be facilitated by appropriate practice policies. Such communication may have implications for patient engagement and alliance building. More research is needed to identify best practices and potential guidelines for effectively communicating about PDMP findings, as this may enhance health outcomes.

Subjects. Thirty-three clinicians practicing in primary care, emergency medicine, pain management, psychiatry, dentistry, and surgery.