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In this narrative review, we outline the literature describing the history, training, certification, and role of peer recovery support specialists working with people with substance use disorders at different stages of active use and recovery. We explore the impact of peer recovery support specialists serving people in various settings, including the community, hospitals and emergency departments, jails and prisons, and treatment and recovery agencies; and describes considerations for future expansion of peer recovery support services, including supervision needs, compassion fatigue and burnout, and scope of practice. Finally, we make recommendations to support the broad implementation of peer recovery support services as a sustainable, cohesive, and replicable component of harm reduction and addiction services. We also make recommendations for research to continue to evaluate peer recovery support specialist interventions across settings and outcomes.

BACKGROUND: A large proportion of individuals who use heroin report initiating opioid use with prescription opioids. However, patterns of prescription opioid use preceding heroin-related overdose have not been described. OBJECTIVE: To describe prescription opioid use in the year preceding heroin overdose. DESIGN: Case-control study comparing prescription opioid use with a heroin-involved overdose, non-heroin-involved opioid overdose, and non-overdose controls from 2015 to 2017. PARTICIPANTS: Oregon Medicaid beneficiaries with linked administrative claims, vital statistics, and prescription drug monitoring program data. MAIN MEASURES: Opioid, benzodiazepine, and other central nervous system depressant prescriptions preceding overdose; among individuals with one or more opioid prescription, we assessed morphine milligram equivalents per day, overlapping prescriptions, prescriptions from multiple prescribers, long-term use, and discontinuation of long-term use. KEY RESULTS: We identified 1458 heroin-involved overdoses (191 fatal) and 2050 non-heroin-involved opioid overdoses (266 fatal). In the 365 days prior to their overdose, 45% of individuals with a heroin-involved overdose received at least one prescribed opioid compared with 78% of individuals who experienced a non-heroin-involved opioid overdose (p

Background: All Payer Claims Databases (APCD) are a rich source of health information, however, race and ethnicity (R&E) data are largely missing. Bayesian Improved Surname Geocoding (BISG) is a common R&E imputation method, yet, validation of BISG in APCDs is lacking. We used the BISG to impute missing R&E in the Oregon APCD. Methods: BISG imputed R&E for Asian Pacific Islanders (API), Blacks, Hispanics and Whites were contrasted to the gold standard (vital statistics) and sensitivity and specificity improvements were assessed. Logistic regression examined whether missing R&E was random across patient characteristics. Results: Among 85,857 individuals in the study, 32.1% (n=27,594) had missing R&E. Missing R&E was not randomly distributed. There were higher odds of missingness among males, Whites, those age 65 and older, and commercially insured individuals. Differences in the percent missing were also found by co-morbid conditions and mortality causes. Imputing the missing R&E with BISG method improved the sensitivity to identify White, Black, API, and Hispanics. Conclusions: APCDs can benefit from enhancing missing R&E with BISG imputation to perform more robust population-health level analyses and identify inequities according to R&E without losing power or dropping non-random records with missing R&E data.

Background 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. Methods 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. Results 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. Conclusions 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 Residential treatment is a common approach for treating opioid use disorder (OUD), however, few studies have directly compared it to outpatient treatment. The objective of this study was to compare OUD outcomes among individuals receiving residential and outpatient treatment. Methods A retrospective cohort study used linked data from a state Medicaid program, vital statistics, and the Substance Abuse and Mental Health Services Administration (SAMHSA) Treatment Episodes Dataset (TEDS) to compare OUD-related health outcomes among individuals treated in a residential or outpatient setting between 2014 and 2017. Multivariable Cox proportional hazards and logistic regression models examined the association between treatment setting and outcomes (i.e., opioid overdose, non-overdose opioid-related and all-cause emergency department (ED) visits, hospital admissions, and treatment retention) controlling for patient characteristics, co-morbidities, and use of medications for opioid use disorders (MOUD). Interaction models evaluated how MOUD use modified associations between treatment setting and outcomes. Results Of 3293 individuals treated for OUD, 957 (29%) received treatment in a residential facility. MOUD use was higher among those treated as an outpatient (43%) compared to residential (19%). The risk of opioid overdose (aHR 1.39; 95% CI 0.73–2.64) or an opioid-related emergency department encounter or admission (aHR 1.02; 95% CI 0.80–1.29) did not differ between treatment settings. Independent of setting, MOUD use was associated with a significant reduction in overdose risk (aHR 0.45; 95% CI 0.23–0.89). Residential care was associated with greater odds of retention at 6-months (aOR 1.71; 95% CI 1.32–2.21) but not 1-year. Residential treatment was only associated with improved retention for individuals not receiving MOUD (6-month aOR 2.05; 95% CI 1.56–2.71) with no benefit observed in those who received MOUD (aOR 0.75; 95% CI 0.46–1.29; interaction p = 0.001). Conclusions Relative to outpatient treatment, residential treatment was not associated with reductions in opioid overdose or opioid-related ED encounters/hospitalizations. Regardless of setting, MOUD use was associated with a significant reduction in opioid overdose risk.

OBJECTIVE. Our objective is to describe how we combine, at an individual level, multiple administrative datasets to create a Comprehensive Opioid Risk Registry (CORR). The CORR will characterize the role that individual characteristics, household characteristics, and community characteristics have on an individual’s risk of opioid use disorder or opioid overdose. DATA SOURCES. Study data sources include the voluntary Oregon All Payer Claims Database (APCD), American Community Survey Census Data, Oregon Death Certificate data, Oregon Hospital Discharge Data (HDD), and Oregon Prescription Drug Monitoring (PDMP) Data in 2013 through 2018. STUDY DESIGN. To create the CORR we first prepared the APCD data set by cleaning and geocoding addresses, creating a community grouper and adding census indices, creating household grouper, and imputing patient race. Then we deployed a probabilistic linkage methodology to incorporate other data sources maintaining compliance with strict data governance regulations. DATA COLLECTION / EXTRACTION METHODS. Administrative datasets were obtained through an executed data use agreement with each data owner. The APCD served as the population universe to which all other data sources were linked. PRINCIPAL FINDINGS. There were 3,628,992 unique people in the APCD over the entire study period. We identified 968,767 unique households in 2013 and 1,209,236 in 2018, and geocoded patient addresses representing all census tracts in Oregon. Census, death certificate, HDD, and PDMP datasets were successfully linked to this population universe. CONCLUSIONS. This methodology can be replicated in other states and may also apply to a broad array of health services research topics.

OBJECTIVES: The objective of this study was to investigate methamphetamine use among people who use opioids in rural Oregon communities to explore reasons for use and perceptions of methamphetamine consequences. METHODS: We conducted interviews and surveys with participants who inject drugs or misuse prescription opioids in 2 rural Oregon counties with high opioid overdose rates. Survey participants were identified through participant-driven sampling initiated in syringe service programs and field outreach (n = 144). Semi-structured interviews with participants were recruited from the same locations (n = 52). RESULTS: Of 144 surveys completed, 112 reported using opioids in the past 30 days; 96% of the 112 also report methamphetamine use. Among the 124 reporting injection drug use, 50% indicated they injected both methamphetamine and heroin in the past 30 days. Interview participants reported early exposure to methamphetamine and indicated that methamphetamine was more widely available, less expensive, and less stigmatized compared to heroin. Participants reported using methamphetamine to improve work-life functioning and because they enjoy the high produced from simultaneous use. Several participants reported a conscious effort to shift to methamphetamine from heroin as a harm reduction strategy. Some participants reported being involuntarily discharged from treatment for opioid use disorder due to methamphetamine use. Several participants perceived methamphetamine as conveying overdose prevention or reversal benefits, while fentanyl contamination in methamphetamine was reported or suspected. CONCLUSION: As rural communities respond to evolving drug supply and demand, there is increasing need for public health efforts to address the emerging issue of concurrent methamphetamine and opioid use.

This cohort study investigates patient- and prescription-related factors associated with opioid-related fatal or nonfatal overdose among opioid-naive individuals receiving an initial opioid prescription., Key Points Question What factors are associated with an increased risk for opioid overdose after the initial opioid prescription to a previously opioid-naive individual? Findings In this cohort study of 236 921 individuals who received a first opioid prescription, 667 experienced an incident opioid overdose. Patient risk factors included being aged 75 years or older, being male, receiving Medicaid or Medicare Advantage coverage, having a comorbid substance use disorder or depression, and having medical comorbidities. Prescription-related risk factors included an initial prescription of oxycodone or tramadol, concurrent use of benzodiazepines, and filling opioid prescriptions from 3 or more pharmacies. Meaning Findings from this study suggest that several patient- and prescription-related risk factors are associated with opioid overdose; prescribers, researchers, policy makers, and insurers can apply this information to guide opioid counseling and monitoring, develop clinical decision-making tools, and provide additional opioid prevention and treatment resources to individuals who are at greatest risk for opioid overdose., Importance The opioid epidemic continues to be a public health crisis in the US. Objective To assess the patient factors and early time-varying prescription-related factors associated with opioid-related fatal or nonfatal overdose. Design, Setting, and Participants 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. Exposures Overdose after the index opioid prescription. Main Outcomes and Measures 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. Results 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). Conclusions and Relevance 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.

Background Oregon's Medicaid program is delivered through 16 Coordinated Care Organizations (CCOs) participating in a statewide performance improvement program to reduce high-dose opioid prescribing. CCOs were allowed flexibility to develop their own dose targets and any policies, trainings, guidelines, and/or materials to meet these targets. In this study, we characterize CCO strategies to reduce high-dose opioid prescribing across the 16 CCOs. Methods We reviewed relevant CCO documents and conducted semi-structured interviews with CCO administrators to acquire opioid-related policies, practices, timelines and contextual factors. We applied a systematic coding procedure to develop a comprehensive description of each CCO's strategy. We used administrative data from the state to summarize contextual utilization data for each CCO. Results Most CCOs selected a target daily morphine milligram equivalent (MME) dose of 90 mg. Sixteen issued quantity limits related to dose, eight restricted specific drug formulations (short-acting or long-acting), and 11 allowed for time-limited taper plan periods for patients over threshold. Many CCOs also employed provider trainings, feedback reports, and/or onsite technical assistance. Other innovations included incentive measures, electronic health record alerts, and toolkits with materials on local alternative therapy resources and strategies for patient communication. CCOs leveraging collaborations with regional partners appeared to mount a greater intensity of interventions than independently operating CCOs. Conclusions CCOs developed a diversity of interventions to confront high-risk opioid prescribing within their organization. As healthcare systems mount interventions to reduce risky opioid prescribing, it is critical to carefully describe these activities and examine their impact on process and health outcomes.

Lumbar fusion surgery is usually prompted by chronic back pain, and many patients receive long-term preoperative opioid analgesics. Many expect surgery to eliminate the need for opioids. We sought to determine what fraction of long-term preoperative opioid users discontinue or reduce dosage postoperatively; what fraction of patients with little preoperative use initiate long-term use; and what predicts long-term postoperative use. This retrospective cohort study included 2491 adults undergoing lumbar fusion surgery for degenerative conditions, using Oregon's prescription drug monitoring program to quantify opioid use before and after hospitalization. We defined long-term postoperative use as ≥4 prescriptions filled in the 7 months after hospitalization, with at least 3 occurring >30 days after hospitalization. Overall, 1045 patients received long-term opioids preoperatively, and 1094 postoperatively. Among long-term preoperative users, 77.1% continued long-term postoperative use, and 13.8% had episodic use. Only 9.1% discontinued or had short-term postoperative use. Among preoperative users, 34.4% received a lower dose postoperatively, but 44.8% received a higher long-term dose. Among patients with no preoperative opioids, 12.8% became long-term users. In multivariable models, the strongest predictor of long-term postoperative use was cumulative preoperative opioid dose (odds ratio of 15.47 [95% confidence interval 8.53-28.06] in the highest quartile). Cumulative dose and number of opioid prescribers in the 30-day postoperative period were also associated with long-term use. Thus, lumbar fusion surgery infrequently eliminated long-term opioid use. Opioid-naive patients had a substantial risk of initiating long-term use. Patients should have realistic expectations regarding opioid use after lumbar fusion surgery.

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

We aimed to better understand the association between opioid-prescribing continuity, risky prescribing patterns, and overdose risk. For this retrospective cohort study, we included patients with long-term opioid use, pulling data from Oregon's Prescription Drug Monitoring Program (PDMP), vital records, and hospital discharge registry. A continuity of care index (COCI) score was calculated for each patient, and we defined metrics to describe risky prescribing and overdose. As prescribing continuity increased, likelihood of filling risky opioid prescriptions and overdose hospitalization decreased. Prescribing continuity is an important factor associated with opioid harms and can be calculated using administrative pharmacy data.

Objective Many states have begun implementing enhancements to PDMP patient profiles such as summaries or graphics to highlight issues of concern and enhance comprehension. The purpose of this study was to examine how physicians respond to sample enhanced PDMP profiles based on patient vignettes. Design Brief semistructured interviews with physicians. Setting Three national medical conferences for targeted specialties. Subjects Ninety-three physicians practicing in primary care, emergency medicine, or pain management. Methods We presented participants with one of three patient vignettes with corresponding standard and enhanced PDMP profiles and conducted brief interviews. Results Findings indicated that enhanced profiles could increase ease of comprehension, reduce time burden, and aid in communicating with patients about opioid risks. Physicians also expressed concern about liability for prescribing when the enhanced profile indicates risk and cautioned against any implication that risk warnings should override clinical judgment based on the patient’s complete medical history or presenting condition. Physicians emphasized the need for transparency of measures and evidence of validation of risk scores. We found little indication that enhanced profiles would change opioid prescribing decisions, though decisions varied by physician. Conclusions Our study underscores the importance of involving prescribers in developing and testing PDMP profile enhancements, as well as providing guidance in the interpretation and clinical use of enhanced profiles. Reduced time burden is an important benefit to consider as the number of states mandating PDMP use increases.

Purpose To develop a complete and consistent prescription drug monitoring program (PDMP) data set for use by drug safety researchers in evaluating patterns of high-risk use and potential abuse of scheduled drugs. Methods Using publically available data references from the US Food and Drug Administration and the Centers for Disease Control and Prevention, we developed a strategic methodology to assign drug categories based on pharmaceutical class for the majority of prescriptions in the PDMP data set. We augmented data elements required to calculate morphine milligram equivalents and assigned duration of action (short-acting or long acting) properties for a majority of opioids in the data set. Results About 10% of prescriptions in the PDMP data set did not have a vendor-assigned drug category, and 20% of opioid prescriptions were missing data needed to calculate risk metrics. Using inclusive methods, 19 133 167 (>99.9%) of prescriptions in the PDMP data set were assigned a drug category. For the opioid category, augmenting data elements resulted in 10 760 669 (99.8%) having required values to calculate morphine milligram equivalents and evaluate duration of action properties. Conclusions Drug safety researchers who require a complete and consistent PDMP data set can use the methods described here to ensure that prescriptions of interest are assigned consistent drug categories and complete opioid risk variable values. Copyright © 2016 John Wiley & Sons, Ltd.

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.

Objective The pharmacist’s role and responsibilities in addressing the opioid epidemic have yet to be clearly defined, particularly from the patient’s point of view. This qualitative study explores the pharmacist’s role in promoting opioid safety from the perspective of pharmacists and patients. Design Focus groups. Setting Patient groups were held in person, and pharmacist groups were held online. Subjects Oregon pharmacists (N = 19, Mage = 39.0 years, range = 26–57 years, 58% female) and patients (N = 18, Mage = 60.1 years, range = 30–77 years, 71% female) with current experience dispensing or receiving opioid medications. Methods Pharmacists were asked about the challenges and opportunities for opioid safety monitoring and prescription dispensing. Patients were asked about their experiences accessing care, medications, and safety information. Focus group data were analyzed by a multidisciplinary team using an immersion-crystallization approach. Results Pharmacists and patients agreed that pharmacists are responsible for medication safety. Pharmacists expressed discomfort filling potentially high-risk opioid prescriptions and noted barriers such as lack of clinical information and discomfort policing high-risk prescribing. Patients were concerned about pharmacists potentially overstepping their professional responsibilities by interfering with prescribers’ clinical decisions. Conclusions Feedback from both pharmacists and patient participants suggests that there is uncertainty in the degree to which pharmacists can and should confront the prescription opioid epidemic directly. Ambiguities in the pharmacist’s role may be best clarified through structured training promoting enhanced between-party communication.

To develop a simple, valid model to identify patients at high risk of opioid overdose-related hospitalization and mortality, Oregon prescription drug monitoring program, Vital Records, and Hospital Discharge data were linked to estimate 2 logistic models; a first model that included a broad range of risk factors from the literature and a second simplified model. Receiver operating characteristic curves, sensitivity, and specificity of the models were analyzed. Variables retained in the final model were categories such as older than 35 years, number of prescribers, number of pharmacies, and prescriptions for long-acting opioids, benzodiazepines or sedatives, or carisoprodol. The ability of the model to discriminate between patients who did and did not overdose was reasonably good (area under the receiver operating characteristic curve = 0.82, Nagelkerke R = 0.11). The positive predictive value of the model was low. Computationally simple models can identify high-risk patients based on prescription history alone, but improvement of the predictive value of models may require information from outside the prescription drug monitoring program. Patient or prescription features that predict opioid overdose may differ from those that predict diversion.

Prescription drug monitoring programs (PDMPs) are a response to the prescription opioid epidemic, but their effects on prescribing and health outcomes remain unclear, with conflicting reports. We sought to determine if prescriber use of Oregon's PDMP led to fewer high-risk opioid prescriptions or overdose events. We conducted a retrospective cohort study from October 2011 through October 2014, using statewide PDMP data, hospitalization registry, and vital records. Early PDMP registrants (n = 927) were matched with clinicians who never registered during the study period, using baseline prescribing metrics in a propensity score. Generalized estimating equations were used to examine prescribing trends after PDMP registration, using 2-month intervals. We found a statewide decline in measures of per capita opioid prescribing. However, compared with nonregistrants, PDMP registrants did not subsequently have significantly fewer patients receiving high-dose prescriptions, overlapping opioid and benzodiazepine prescriptions, inappropriate prescriptions, prescriptions from multiple prescribers, or overdose events. At baseline, frequent PDMP users wrote fewer high-risk opioid prescriptions than infrequent users; this persisted during follow-up with few significant group differences in trend. Thus, although opioid prescribing declined statewide after implementing the PDMP, registrants did not show greater declines than nonregistrants. Perspective Factors other than PDMP use may have had greater influence on prescribing trends. Refinements in the PDMP program and related policies may be necessary to increase PDMP effects.

Objectives Prescription drug monitoring programs (PDMPs) are now active in most states to assist clinicians in identifying potential controlled drug misuse, diversion, or excessive prescribing. Little is still known about the ways in which they are incorporated into workflow and clinical decision making, what barriers continue to exist, and how clinicians are sharing PDMP results with their patients. Design Qualitative data were collected through online focus groups and telephone interviews. Setting Clinicians from pain management, emergency and family medicine, psychiatry/behavioral health, rehabilitation medicine, internal medicine and dentistry participated. Patients Thirty-five clinicians from nine states participated. Methods We conducted two online focus groups and seven telephone interviews. A multidisciplinary team then used a grounded theory approach coupled with an immersion–crystallization strategy for identifying key themes in the resulting transcripts. Results Some participants, mainly from pain clinics, reported checking the PDMP with every patient, every time. Others checked only for new patients, for new opioid prescriptions, or for patients for whom they suspected abuse. Participants described varied approaches to sharing PDMP information with patients, including openly discussing potential addiction or safety concerns, avoiding discussion altogether, and approaching discussion confrontationally. Participants described patient anger or denial as a common response and noted the role of patient satisfaction surveys as an influence on prescribing. Conclusion Routines for accessing PDMP data and how clinicians respond to it vary widely. As PDMP use becomes more widespread, it will be important to understand what approaches are most effective for identifying and addressing unsafe medication use.

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.Qualitative interviews of clinician PDMP users.Oregon registrants in the state's PDMP.Thirty-three clinicians practicing in primary care, emergency medicine, pain management, psychiatry, dentistry, and surgery.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.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.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.

Long-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-naive patients without inadvertently promoting long-term use. To examine the association between initial opioid prescribing patterns and likelihood of long-term use among opioid-naive patients. Retrospective cohort study; data from Oregon resident prescriptions linked to death certificates and hospital discharges. Patients 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. Exposure: 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”). There were 536,767 opioid-naive 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

To develop a complete and consistent prescription drug monitoring program (PDMP) data set for use by drug safety researchers in evaluating patterns of high-risk use and potential abuse of scheduled drugs.Using publically available data references from the US Food and Drug Administration and the Centers for Disease Control and Prevention, we developed a strategic methodology to assign drug categories based on pharmaceutical class for the majority of prescriptions in the PDMP data set. We augmented data elements required to calculate morphine milligram equivalents and assigned duration of action (short-acting or long acting) properties for a majority of opioids in the data set.About 10% of prescriptions in the PDMP data set did not have a vendor-assigned drug category, and 20% of opioid prescriptions were missing data needed to calculate risk metrics. Using inclusive methods, 19 133 167 (99.9%) of prescriptions in the PDMP data set were assigned a drug category. For the opioid category, augmenting data elements resulted in 10 760 669 (99.8%) having required values to calculate morphine milligram equivalents and evaluate duration of action properties.Drug safety researchers who require a complete and consistent PDMP data set can use the methods described here to ensure that prescriptions of interest are assigned consistent drug categories and complete opioid risk variable values. Copyright © 2016 John WileySons, Ltd.

OBJECTIVES Prescription Drug Monitoring Programs (PDMPs) can help inform patient management, coordinate care, and identify drug safety risks, abuse, or diversion. However, many clinicians are not registered to use these systems, and use may be suboptimal. We sought to describe outreach efforts in 1 state (Oregon); quantify uptake of system use; identify barriers; and identify potential system improvements. METHODS Program reports of outreach efforts and operational metrics provided rates of registration and use. A statewide survey identified perceived barriers and potential improvements from users and nonusers of the system. RESULTS Even with extensive registration efforts

Prescription drug monitoring programs (PDMPs) are now active in most states to assist clinicians in identifying potential controlled drug misuse, diversion, or excessive prescribing. Little is still known about the ways in which they are incorporated into workflow and clinical decision making, what barriers continue to exist, and how clinicians are sharing PDMP results with their patients.Qualitative data were collected through online focus groups and telephone interviews.Clinicians from pain management, emergency and family medicine, psychiatry/behavioral health, rehabilitation medicine, internal medicine and dentistry participated.Thirty-five clinicians from nine states participated.We conducted two online focus groups and seven telephone interviews. A multidisciplinary team then used a grounded theory approach coupled with an immersion-crystallization strategy for identifying key themes in the resulting transcripts.Some participants, mainly from pain clinics, reported checking the PDMP with every patient, every time. Others checked only for new patients, for new opioid prescriptions, or for patients for whom they suspected abuse. Participants described varied approaches to sharing PDMP information with patients, including openly discussing potential addiction or safety concerns, avoiding discussion altogether, and approaching discussion confrontationally. Participants described patient anger or denial as a common response and noted the role of patient satisfaction surveys as an influence on prescribing.Routines for accessing PDMP data and how clinicians respond to it vary widely. As PDMP use becomes more widespread, it will be important to understand what approaches are most effective for identifying and addressing unsafe medication use.

Prescription drug monitoring programs (PDMPs) are relatively new but potentially useful tools to enhance prudent prescribing of controlled substances. However, little is known about the types of clinicians who make the most use of PDMPs, how these programs are incorporated into clinicians' work flow, or how clinicians and patients respond to the information. We therefore surveyed a random sample of Oregon providers, with 1,065 respondents. Clinicians in emergency medicine, primary care, and pain and addiction specialties were the largest number of registrants, but many frequent prescribers of controlled substances were not registered to use the PDMP. Among users, 95% reported accessing the PDMP when they suspected a patient of abuse or diversion, but fewer than half would check it for every new patient or every time they prescribe a controlled drug. Nearly all PDMP users reported that they discuss worrisome PDMP data with patients; 54% reported making mental health or substance abuse referrals, and 36% reported sometimes discharging patients from the practice. Clinicians reported frequent patient denial or anger and only occasional requests for help with drug dependence. More research is needed to optimize how clinicians use PDMPs across settings and how clinicians and patients respond to the data. Perspective This study examined differences between PDMP users and nonusers and how clinicians in various specialties use PDMPs in practice. A better understanding of effective PDMP use will facilitate access to treatment for patients with pain while curbing the prescription drug epidemic and may ultimately reduce abuse, misuse, and overdose death.

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.