Opioid-related overdose and chronic use following an initial prescription of hydrocodone versus oxycodone.

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.

Evaluation of a Medicaid performance improvement project to reduce high-dose opioid prescriptions.

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.

Factors Associated With Opioid Overdose After an Initial Opioid Prescription.

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.

Patient outcomes after opioid dose reduction among patients with chronic opioid therapy.

ABSTRACT: The net effects of prescribing initiatives that encourage dose reductions are uncertain. We examined whether rapid dose reduction after high-dose chronic opioid therapy (COT) associates with suicide, overdose, or other opioid-related adverse events. This retrospective cohort study included Oregon Medicaid recipients with high-dose COT. Claims were linked with prescription data from the prescription drug monitoring program and death data from vital statistics, 2014 to 2017. Participants were placed into 4 mutually exclusive dose trajectory groups after the high-dose COT period, and Cox proportional hazard models were used to examine the effect of dose changes on patient outcomes in the following year. Of the 14,596 high-dose COT patients, 4191 (28.7%) abruptly discontinued opioid prescriptions, 1648 (11.3%) reduced opioid dose before discontinuing, 6480 (44.4%) had a dose reduction but never discontinued, and 2277 (15.6%) had a stable or increasing dose. Discontinuation, whether abrupt (adjusted hazard ratio [aHR] 3.63; 95% confidence interval [CI] 1.42-9.25) or with dose reduction (aHR 4.47, 95% CI 1.68-11.88) significantly increased risk of suicide compared with those with stable or increasing dose. By contrast, discontinuation or dose reduction reduced the risk of overdose compared with those with a stable or increasing dose (aHR 0.36-0.62, 95% CI 0.20-0.94). Patients with an abrupt discontinuation were more likely to overdose on heroin (vs. prescription opioids) than patients in other groups (P < 0.0001). Our study suggests that patients on COT require careful risk assessment and supportive interventions when considering opioid discontinuation or continuation at a high dose.

Characteristics and health care events of patients admitted to treatment for both heroin and methamphetamine compared to patients admitted for heroin only.

INTRODUCTION: Co-occurring heroin and methamphetamine use is a growing public health problem. This study assessed the characteristics of Medicaid patients admitted to substance use disorder (SUD) treatment programs for heroin and methamphetamine use compared with patients admitted for heroin only. METHODS: The study identified patients who entered treatment for heroin and methamphetamine and those admitted for heroin only between 2014 and 2017 from the Oregon Treatment Episode Data Set linked with Medicaid enrollment, and medical and pharmacy claims. We used a cross-sectional design to compare demographics, type of treatment, and substance use characteristics between the two groups. We used logistic regression models to assess differences in the odds of opioid-related and all-cause adverse events. RESULTS: Among the 3802 study sample, 2004 (53%) were admitted for both heroin and methamphetamine use. The heroin and methamphetamine group were more likely to be younger, female, White or American Indian/Alaska Native; and had more comorbidities than patients admitted for heroin only. Patients admitted for heroin and methamphetamine treatment were less likely to receive any medication for opioid use disorder (MOUD) (56% vs 75%, p < 0.001) and received fewer days of MOUD treatment (mean 188 vs. 265 days, p < 0.001) compared to the heroin only group. The heroin and methamphetamine group were more likely to receive buprenorphine (28.1% vs 24.2%) and less likely to receive methadone (39.9% vs 62.5%). The heroin and methamphetamine group began use at a younger age, used and injected more frequently than those admitted for heroin only. Patients treated for heroin and methamphetamine had 17% lower odds of OUD-related adverse events (aOR 0.83; 95% CI 0.70-0.99) and 52% higher odds of all-cause adverse events (aOR 1.52; 95% CI 1.14-2.03) relative to the heroin only group. CONCLUSION: Patients admitted for both heroin and methamphetamine reported greater addiction severity (more frequent use, earlier onset of use, and injection use), yet less commonly received MOUD compared to those who were admitted for heroin only. These findings indicate substantial missed opportunities for MOUD treatment even among people who successfully engage with the SUD treatment system.

COVID-19-related adaptations to the implementation and evaluation of a clinic-based intervention designed to improve opioid safety.

The United States faces an opioid crisis with an unprecedented and increasing death rate from opioid overdose. Successfully reducing the rates of opioid use disorder (OUD) and overdose will require the engagement of frontline clinicians to prescribe opioids more safely and to build their capacity to treat patients with OUD using evidence-based approaches. The COVID-19 pandemic has created significant challenges for patients, clinicians and health systems and has been associated with increasing risks of overdoses and deaths. Herein, we review a multidisciplinary project designed to implement and evaluate clinic-based interventions in Oregon, USA, to improve pain management, opioid prescribing and treatment of OUD. The intervention, called Improving PaIn aNd OPiOId MaNagemenT in Primary Care (PINPOINT), combines practice facilitation, academic detailing and education through the Oregon ECHO Network. Implementation of PINPOINT has occurred across the Oregon Rural Practice-based Research Network and has involved 49 clinic sites to date. To evaluate the impact of the intervention, the research team created the Provider Results of Opioid Management and Prescribing Training (PROMPT), a dataset that links information from the state prescription drug monitoring program, all-payer claims database, emergency medical services, vital records and substance use disorder treatment system. The PROMPT dataset will allow evaluation of the impact of the intervention at both the clinician and clinic levels. Due to the constraints of the COVID-19 pandemic, elements of both implementation and evaluation required significant adaptations to continue to meet the original project goals.

Linkage of public health and all payer claims data for population-level opioid research.

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 2013-2018. 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.

Identifying opioid dose reductions and discontinuation among patients with chronic opioid therapy.

PURPOSE: To identify and systematically categorize opioid dose reductions and discontinuations in large administrative datasets. METHODS: Using a dataset of Oregon Medicaid beneficiaries linked with prescription drug monitoring program (PDMP) data between 2014 and 2017, we identified patients with high-dose chronic opioid therapy (COT), ≥84 consecutive days with an average daily MME of ≥50 on each of those days. We categorized patients into four mutually exclusive groups based on the trajectory of opioid use in the year after COT: abrupt discontinuation, dose reduction and discontinuation, dose reduction without discontinuation, and stable or increasing dose. Finally, we examined prescription patterns in each category. RESULTS: Among individuals with high-dose COT, 7636 (37.1%) had an abrupt discontinuation, 2577 (12.5%) had a dose reduction and discontinuation, 7739 (37.6%) had a dose reduction without discontinuation, and 2623 (12.8%) had a stable or increasing dose in the year following the COT episode. Among those who discontinued opioid use (n = 10 213, 49.6%), three in four (74.8%) did so without evidence of tapering. Patients who discontinued opioid use were younger, had higher daily MME during COT, and were more likely to have filled a benzodiazepine or had a multiple provider or multiple pharmacy episode compared to patients who did not discontinue opioid use. CONCLUSIONS: Dose reductions and discontinuations after a COT episode can be identified in large administrative datasets. Those with a discontinuation were more likely to have riskier prescription profiles during their COT episode.

Patterns of Prescription Opioid Use Prior to Self-reported Heroin Initiation.

OBJECTIVES: To determine the association between self-reported heroin initiation and patterns of prescription opioid use. METHODS: Using linked Oregon Medicaid, prescription drug monitoring program, and Treatment Episodes Data Set data, we conducted a case-control study of individuals reporting heroin initiation between 2015 and 2017 during treatment intake. Prescription drug monitoring program data provided prescription opioid use patterns, including long-term prescription opioid therapy, in the year before self-reported heroin initiation. Four controls were matched to each case on aggregate prescription opioid use and demographics. RESULTS: About half (49%) of individuals who reported heroin initiation filled an opioid in the year before initiation. Individuals who initiated heroin (n = 306) were more likely to receive prescriptions from multiple prescribers (24% vs 18%, P = 0.007) and pharmacies (12% vs 5%, P < 0.001) compared with matched controls (n = 1224). Long-term opioid therapy (13% vs 14%, P = 0.74) was uncommon and did not differ between groups. CONCLUSIONS: Although prescription opioid use commonly preceded self-reported heroin initiation, long-term opioid therapy was not common. Although this study did not find an association between opioid discontinuation and heroin initiation, sample size and follow-up limitations preclude definitive conclusions. Efforts to limit prescription opioids should continue to evaluate for unintended harms.

Prescription and Prescriber Specialty Characteristics of Initial Opioid Prescriptions Associated with Chronic Use.

OBJECTIVE: This study evaluated the characteristics of opioid prescriptions, including prescriber specialty, given to opioid-naïve patients and their association with chronic use. DESIGN: Cross-sectional analysis of the Ohio prescription drug monitoring program from January 2010 to November 2017. SETTING: Ohio, USA. SUBJECTS: Patients who had no opioid prescriptions from 2010 to 2012 and a first-time prescription from January 2013 to November 2016. METHODS: Chronic use was defined as at least six opioid prescriptions in one year and either one or more years between the first and last prescription or an average of ≤30 days not covered by an opioid during that year. RESULTS: A total of 4,252,809 opioid-naïve patients received their first opioid prescription between 2013 and 2016; 364,947 (8.6%) met the definition for chronic use. Those who developed chronic use were older (51.7 vs 45.6 years) and more likely to be female (53.6% vs 52.8%), and their first prescription had higher pill quantities (44.9 vs 30.2), higher morphine milligram equivalents (MME; 355.3 vs 200.0), and was more likely to be an extended-release formulation (2.9% vs 0.7%, all P < 0.001). When compared with internal medicine, the adjusted odds of chronic use were highest with anesthesiology (odds ratio [OR] = 1.46) and neurology (OR = 1.43) and lowest with ophthalmology (OR = 0.33) and gynecology (OR = 0.37). CONCLUSIONS: Eight point six percent of opioid-naïve individuals who received an opioid prescription developed chronic use. This rate varied depending on the specialty of the provider who wrote the prescription. The risk of chronic use increased with higher MME content of the initial prescription and use of extended-release opioids.

A comparison of trends in opioid dispensing patterns between Medicaid pharmacy claims and prescription drug monitoring program data.

PURPOSE: Public and private payers have implemented benefit limitations to reduce high-risk opioid prescriptions. The effect of these policies on the increase of out-pocket payment is unclear. To understand this gap, we compared the discrepancies in trends between opioid prescription fills vs claims among Medicaid beneficiaries. METHODS: Data from the Oregon Prescription Drug Monitoring Program (PDMP) and Oregon Medicaid administrative claims were used to identify Medicaid beneficiaries 18 years and older enrolled at least one full month from 2015 to 2017. Generalized linear models assessed the trends in the monthly rates of opioid PDMP prescription fills and pharmacy claims per 1000 eligible members. Rates by morphine equivalent dose (MED) tier (<50, 50-89, 90-120, >120 MED) and co-prescribed opioid and benzodiazepine were also assessed. RESULTS: During the study period, an average of 495 355 Medicaid members had 2 797 054 opioid PDMP fills and 2 472 155 opioid Medicaid pharmacy claims. Study participants had 15.4 (95% confidence interval [CI] 13.6 to 17.0; P < .001) more prescriptions per 1000 member per month in the PDMP data (114.1 [SD 7.4]) compared with the Medicaid claims data (98.7 [SD 7.9]). Similarly, there were 1.9 more co-occurring opioid/benzodiazepine prescriptions per 1000 members per month observed in the PDMP data than the Medicaid claims data (95% CI 1.7 to 2.1; P < .001). At each MED tier, the PDMP fills were consistently higher than the claims (P < .001). CONCLUSIONS: Higher rate of fills in the PDMP compared to pharmacy claims suggests that there may be an increasing trend of out-of-pocket payment among Medicaid beneficiaries.

Prescription Opioid Dispensing Patterns Prior to Heroin Overdose in a State Medicaid Program: a Case-Control Study.

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 < 0.001). For both heroin- and non-heroin-involved overdose cases, the likelihood of receiving an opioid increased with age. Among heroin overdose cases with an opioid dispensed, the rate of multiple pharmacy use was the only high-risk opioid pattern that was greater than non-overdose controls (adjusted odds ratio 3.2; 95% confidence interval 1.48 to 6.95). Discontinuation of long-term opioid use was not common prior to heroin overdose and not higher than discontinuation rates among non-overdose controls. CONCLUSIONS: Although individuals with a heroin-involved overdose were less likely to receive prescribed opioids in the year preceding their overdose relative to non-heroin opioid overdose cases, prescription opioid use was relatively common and increased with age. Discontinuation of long-term prescription opioid use was not associated with heroin-involved overdose.

Opioid-Prescribing Continuity and Risky Opioid Prescriptions.

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.

Use of prescription opioids before and after an operation for chronic pain (lumbar fusion surgery).

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.

Association of Prescription Drug Monitoring Program Use With Opioid Prescribing and Health Outcomes: A Comparison of Program Users and Nonusers.

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.

High-risk prescribing and opioid overdose: prospects for prescription drug monitoring program-based proactive alerts.

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.

Opioid Prescribing Patterns and Patient Outcomes by Prescriber Type in the Oregon Prescription Drug Monitoring Program.

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.