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.

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.

Predictors of US states' adoption of naloxone access laws, 2001-2017.

BACKGROUND: The opioid crisis has put an increasing strain on US states over the last two decades. In response, all states have passed legislation to implement a portfolio of policies to address the crisis. Although effects of some of these policies have been studied, research into factors associated with state policy adoption decisions has largely been lacking. We address this gap by focusing on factors associated with adoption of naloxone access laws (NAL), which aim to increase the accessibility and availability of naloxone in the community as a harm reduction strategy to reduce opioid-related morbidity and mortality. METHODS: We used event history analysis (EHA) to identify predictors of the diffusion of naloxone access laws (NAL) from 2001, when the first NAL was passed, to 2017, when all states had adopted NAL. A variety of state characteristics were included in the model as potential predictors of adoption. RESULTS: We found that state adoption of NAL increased gradually, then more rapidly starting in 2013. Consistent with this S-shaped diffusion process, the strongest predictor of adoption was prior adoption by neighboring states. Having a more conservative political ideology and having a higher percentage of residents who identified as evangelical Protestants were associated with later adoption of NAL. CONCLUSION: States appear to be influenced by their neighbors in deciding whether and when to adopt NAL. Advocacy for harm reduction policies like NAL should take into account the political and religious culture of a state.

Opioid Prescribing Behaviors - Prescription Behavior Surveillance System, 11 States, 2010-2016.

PROBLEM/CONDITION: In 2017, a total of 70,237 persons in the United States died from a drug overdose, and 67.8% of these deaths involved an opioid. Historically, the opioid overdose epidemic in the United States has been closely associated with a parallel increase in opioid prescribing and with widespread misuse of these medications. National and state policy makers have introduced multiple measures to attempt to assess and control the opioid overdose epidemic since 2010, including improvements in surveillance systems. PERIOD COVERED: 2010-2016 DESCRIPTION OF SYSTEM: The Prescription Behavior Surveillance System (PBSS) was created in 2011. Its goal was to track rates of prescribing of controlled substances and possible misuse of such drugs using data from selected state prescription drug monitoring programs (PDMP). PBSS data measure prescribing behaviors for prescription opioids using multiple measures calculated from PDMP data including 1) opioid prescribing, 2) average daily opioid dosage, 3) proportion of patients with daily opioid dosages ≥90 morphine milligram equivalents, 4) overlapping opioid prescriptions, 5) overlapping opioid and benzodiazepine prescriptions, and 6) multiple-provider episodes. For this analysis, PBSS data were available for 2010-2016 from 11 states representing approximately 38.0% of the U.S. POPULATION: Average quarterly percent changes (AQPC) in the rates of opioid prescribing and possible opioid misuse measures were calculated for each state. RESULTS AND INTERPRETATION: Opioid prescribing rates declined in all 11 states during 2010-2016 (range: 14.9% to 33.0%). Daily dosage declined least (AQPC: -0.4%) in Idaho and Maine, and most (AQPC: -1.6%) in Florida. The percentage of patients with high daily dosage had AQPCs ranging from -0.4% in Idaho to -2.3% in Louisiana. Multiple-provider episode rates declined by at least 62% in the seven states with available data. Variations in trends across the 11 states might reflect differences in state policies and possible differential effects of similar policies. PUBLIC HEALTH ACTIONS: Use of PDMP data from individual states enables a more detailed examination of trends in opioid prescribing behaviors and indicators of possible misuse than is feasible with national commercially available prescription data. Comparison of opioid prescribing trends among states can be used to monitor the temporal association of national or state policy interventions and might help public health policymakers recognize changes in the use or possible misuse of controlled prescription drugs over time and allow for prompt intervention through amended or new opioid-related policies.

Effects of mandatory prescription drug monitoring program (PDMP) use laws on prescriber registration and use and on risky prescribing.

BACKGROUND: Comprehensive mandatory use laws for prescription drug monitoring programs (PDMPs) have been implemented in a number of states to help address the opioid overdose epidemic. These laws may reduce opioid-related overdose deaths by increasing prescribers' use of PDMPs and reducing high-risk prescribing behaviors. METHODS: We used state PDMP data to examine the effect of these mandates on prescriber registration, use of the PDMP, and on prescription-based measures of patient risk in three states-Kentucky, Ohio, and West Virginia-that implemented mandates between 2010 and 2015. We conducted comparative interrupted time series analyses to examine changes in outcome measures after the implementation of mandates in the mandate states compared to control states. RESULTS: Mandatory use laws increased prescriber registration and utilization of the PDMP in the mandate states compared to controls. The multiple provider episode rate, rate of opioid prescribing, rate of overlapping opioid prescriptions, and rate of overlapping opioid/benzodiazepine prescriptions decreased in Kentucky and Ohio. Nevertheless, the magnitude of changes in these measures varied among mandates states. CONCLUSIONS: These findings indicate that PDMP mandates have the potential to reduce risky opioid prescribing practices. Variation in the laws may explain why the effectiveness varied between states.

Opioid Prescriptions by Specialty in Ohio, 2010-2014.

Background: The current US opioid epidemic is attributed to the large volume of prescribed opioids. This study analyzed the contribution of different medical specialties to overall opioids by evaluating the pill counts and morphine milligram equivalents (MMEs) of opioid prescriptions, stratified by provider specialty, and determined temporal trends. Methods: This was an analysis of the Ohio prescription drug monitoring program database, which captures scheduled medication prescriptions filled in the state as well as prescriber specialty. We extracted prescriptions for pill versions of opioids written in the calendar years 2010 to 2014. The main outcomes were the number of filled prescriptions, pill counts, MMEs, and extended-released opioids written by physicians in each specialty, and annual prescribing trends. Results: There were 56,873,719 prescriptions for the studied opioids dispensed, for which 41,959,581 (73.8%) had prescriber specialty type available. Mean number of pills per prescription and MMEs were highest for physical medicine/rehabilitation (PM&R; 91.2 pills, 1,532 mg, N = 1,680,579), anesthesiology/pain (89.3 pills, 1,484 mg, N = 3,261,449), hematology/oncology (88.2 pills, 1,534 mg, N = 516,596), and neurology (84.4 pills, 1,230 mg, N = 573,389). Family medicine (21.8%) and internal medicine (17.6%) wrote the most opioid prescriptions overall. Time trends in the average number of pills and MMEs per prescription also varied depending on specialty. Conclusions: The numbers of pills and MMEs per opioid prescription vary markedly by prescriber specialty, as do trends in prescribing characteristics. Pill count and MME values define each specialty's contribution to overall opioid prescribing more accurately than the number of prescriptions alone.

Unsolicited Reporting to Prescribers of Opioid Analgesics by a State Prescription Drug Monitoring Program: An Observational Study with Matched Comparison Group.

Objective: State prescription drug monitoring programs (PDMPs) can help detect individuals with multiple provider episodes (MPEs; also referred to as doctor/pharmacy shopping), an indicator of prescription drug abuse and/or diversion. Although unsolicited reporting by PDMPs to prescribers of opioid analgesics is thought to be an important practice in reducing MPEs and the potential harm associated with them, evidence of its effectiveness is mixed. This exploratory research evaluates the impact of unsolicited reports sent by Massachusetts' PDMP to the prescribers of persons with MPEs. Methods: Individuals with MPEs were identified from PDMP records between January 2010 and July 2011 as individuals having Schedule II prescriptions (at least one prescription being an opioid) from four or more distinct prescribers and four or more distinct pharmacies within six months. Based on available MA-PDMP resources, an unsolicited report containing the patient's 12-month prescription history was sent to prescribers of a subset of patients who met the MPE threshold; a comparison group closely matched on demographics and baseline prescription history, whose prescribers were not sent a report, was generated using propensity score matching. The prescription history of each group was examined for 12 months before and after the intervention. Results: There were eighty-four patients (intervention group) whose prescribers received an unsolicited report and 504 matched patients (comparison group) whose prescribers were not sent a report. Regression analyses indicated significantly greater decreases in the number of Schedule II opioid prescriptions (P < 0.01), number of prescribers visited (P < 0.01), number of pharmacies used (P < 0.01), dosage units (P < 0.01), total days' supply (P < 0.01), total morphine milligram equivalents (MME; P < 0.01), and average daily MME (P < 0.05) for the intervention group relative to the comparison group. A post hoc analysis suggested that the observed intervention effects were greater for individuals with an average daily dose of less than 100 MMEs. Conclusions: This study suggests that PDMP unsolicited reporting to prescribers can help reduce risk measures in patients' prescription histories, which may improve health outcomes for patients receiving opioid analgesics from multiple providers.

Prescribing patterns of buprenorphine waivered physicians.

BACKGROUND: DATA 2000 enabled physicians with approved training to be waivered to prescribe buprenorphine for the treatment of opioid use disorders (OUD) for a limited number of patients. A rule change in 2016 increased the patient limit for certain buprenorphine waivered physicians from 100 to 275. This study examines the prescribing patterns of buprenorphine prescribers by waiver limit status (30- or 100-patient limit). METHODS: Prescription Monitoring Program (PMP) data from Ohio, California, and Maine were used to identify prescriptions for buprenorphine for OUD from January 2010 to April 2015. Analysis of prescribing patterns by prescriber waiver status included monthly patient censuses and treatment episode duration by state, year, and the frequency with which prescribers were near their respective patient limits. RESULTS: In the three states, 8638 physicians initiated 468,148 buprenorphine episodes. The adjusted mean monthly patient census was 42.9 for 100-patient waivered prescribers, 13.6 patients for 30-patient waivered prescribers, and 7.6 patients for prescribers unassociated with a waiver. Half (48.5%) of episodes were associated with 100-patient waivered prescribers, 26.9% with 30-patient waivered prescribers, and 24.4% with non-waivered prescribers. 30-patient waivered physicians were more likely to have no buprenorphine treatment episodes in a given month than 100-patient waivered prescribers. CONCLUSIONS: Most buprenorphine prescribers practice well under their current patient limit and have numerous months with no patient episodes. For the few high prescribers, increasing the maximum patient limit beyond 100 has the potential to improve access but alone may not have widespread impact unless integrated into complementary approaches toward increasing prescriber capacity.

Validation of prescriber risk indicators obtained from prescription drug monitoring program data.

BACKGROUND: Prescription opioids are commonly overprescribed. However, validated measures of inappropriate controlled substance prescribing are lacking. This study examined associations between prescriber risk indicators developed as part of a public health surveillance project and medical board disciplinary actions against prescribers. METHODS: We compiled 12 prescriber risk indicators using data from the Maine prescription drug monitoring program (PDMP) for 2010. We used logistic regression models to assess the relative likelihood of the top 1%, 2%, 5%, and 10% of prescribers on each risk indicator having been subject to medical board disciplinary actions, those citing inappropriate prescribing, or those involving license suspension or revocation, during 2010-2014, controlling for prescriber medical specialty and gender. RESULTS: The top 1% of prescribers for number of patients, opioid prescriptions per day, and opioid dosage prescribed per day had a greater likelihood of medical board disciplinary actions citing inappropriate prescribing, relative to a matched sample of other (non-top 1%) prescribers. Of the 56 prescribers in the top 1% for opioid prescriptions per day, nine (16.1%) were sanctioned for inappropriate prescribing, compared with 11 of 224 (0.5%) in the comparison group. The top 2% of prescribers for opioid dosage per day, and average distance patients travel to prescriber, had a greater likelihood of actions involving license suspension, revocation, or denial for renewal. CONCLUSIONS: Measures derived from PDMP data may be useful in assessing levels of inappropriate prescribing of controlled substances in a population of prescribers, and in evaluating changes associated with efforts to influence prescriber behavior.

Multiple Sources of Prescription Payment and Risky Opioid Therapy Among Veterans.

BACKGROUND: Opioid overdose and other related harms are a major source of morbidity and mortality among US Veterans, in part due to high-risk opioid prescribing. OBJECTIVES: We sought to determine whether having multiple sources of payment for opioids-as a marker for out-of-system access-is associated with risky opioid therapy among veterans. RESEARCH DESIGN: Cross-sectional study examining the association between multiple sources of payment and risky opioid therapy among all individuals with Veterans Health Administration (VHA) payment for opioid analgesic prescriptions in Kentucky during fiscal year 2014-2015. MEASURES: Source of payment categories: (1) VHA only source of payment (sole source); (2) sources of payment were VHA and at least 1 cash payment [VHA+cash payment(s)] whether or not there was a third source of payment; and (3) at least one other noncash source: Medicare, Medicaid, or private insurance [VHA+noncash source(s)]. Our outcomes were 2 risky opioid therapies: combination opioid/benzodiazepine therapy and high-dose opioid therapy, defined as morphine equivalent daily dose ≥90 mg. RESULTS: Of the 14,795 individuals in the analytic sample, there were 81.9% in the sole source category, 6.6% in the VHA+cash payment(s) category, and 11.5% in the VHA+noncash source(s) category. In logistic regression, controlling for age and sex, persons with multiple payment sources had significantly higher odds of each risky opioid therapy, with those in the VHA+cash having significantly higher odds than those in the VHA+noncash source(s) group. CONCLUSIONS: Prescribers should examine the prescription monitoring program as multiple payment sources increase the odds of risky opioid therapy.

Controlled Substance Prescribing Patterns--Prescription Behavior Surveillance System, Eight States, 2013.

PROBLEM/CONDITION: Drug overdose is the leading cause of injury death in the United States. The death rate from drug overdose in the United States more than doubled during 1999-2013, from 6.0 per 100,000 population in 1999 to 13.8 in 2013. The increase in drug overdoses is attributable primarily to the misuse and abuse of prescription drugs, especially opioid analgesics, sedatives/tranquilizers, and stimulants. Such drugs are prescribed widely in the United States, with substantial variation by state. Certain patients obtain drugs for nonmedical use or resale by obtaining overlapping prescriptions from multiple prescribers. The risk for overdose is directly associated with the use of multiple prescribers and daily dosages of >100 morphine milligram equivalents (MMEs) per day. PERIOD COVERED: 2013. DESCRIPTION OF SYSTEM: The Prescription Behavior Surveillance System (PBSS) is a public health surveillance system that allows public health authorities to characterize and quantify the use and misuse of prescribed controlled substances. PBSS began collecting data in 2012 and is funded by CDC and the Food and Drug Administration. PBSS uses standard metrics to measure prescribing rates per 1,000 state residents by demographic variables, drug type, daily dose, and source of payment. Data from the system can be used to calculate rates of misuse by certain behavioral measures such as use of multiple prescribers and pharmacies within specified time periods. This report is based on 2013 de-identified data (most recent available) that represent approximately one fourth of the U.S. POPULATION: Data were submitted quarterly by prescription drug monitoring programs (PDMPs) in eight states (California, Delaware, Florida, Idaho, Louisiana, Maine, Ohio, and West Virginia) that routinely collect data on every prescription for a controlled substance to help law enforcement and health care providers identify misuse or abuse of such drugs. RESULTS: In all eight states, opioid analgesics were prescribed approximately twice as often as stimulants or benzodiazepines. Prescribing rates by drug class varied widely by state: twofold for opioids, fourfold for stimulants, almost twofold for benzodiazepines, and eightfold for carisoprodol, a muscle relaxant. Rates for opioids and benzodiazepines were substantially higher for females than for males in all states. In most states, opioid prescribing rates peaked in either the 45-54 years or the 55-64 years age group. Benzodiazepine prescribing rates increased with age. Louisiana ranked first in opioid prescribing, and Delaware and Maine had relatively high rates of use of long-acting (LA) or extended-release (ER) opioids. Delaware and Maine ranked highest in both mean daily opioid dosage and in the percentage of opioid prescriptions written for >100 MMEs per day. The top 1% of prescribers wrote one in four opioid prescriptions in Delaware, compared with one in eight in Maine. For the five states whose PDMPs collected the method of payment, the percentage of controlled substance prescriptions paid for in cash varied almost threefold, and the percentage paid by Medicaid varied sixfold. In West Virginia, for 1 of every 5 days of treatment with an opioid, the patient also was taking a benzodiazepine. Multiple-provider episode rates were highest in Ohio and lowest in Louisiana. INTERPRETATION: This report presents rates of population-based prescribing and behavioral measures of drug misuse in the general population that have not been available previously for comparison among demographic groups and states. The higher prescribing rates for opioids among women compared with men are consistent with a higher self-reported prevalence of certain common types of pain, such as lower back pain among women. The trend in opioid prescribing rates with age is consistent with an increase in the prevalence of chronic pain with age, but the increasing prescribing rates of benzodiazepines with age is not consistent with the fact that anxiety is most common among persons aged 30-44 years. The variation among states in the type of opioid or benzodiazepine of choice is unexplained. Most opioid prescribing occurs among a small minority of prescribers. Most of the prescriptions by top-decile prescribers probably are written by general, family medicine, internal medicine, and midlevel practitioners. The source of payment varied by state, for reasons that are unclear. Persons who are prescribed opioids also are commonly prescribed benzodiazepine sedatives despite the risk for additive depressant effects. PUBLIC HEALTH ACTIONS: States can use their prescription drug monitoring programs to generate population-based measures for the prescribing of controlled substances and for behaviors that suggest their misuse. Comparing data with other states and tracking changes in these measures over time can be useful in measuring the effect of policies designed to reduce prescription drug misuse.

Early experience with electronic prescribing of controlled substances in a community setting.

BACKGROUND: In 2010, the US Drug Enforcement Administration issued regulations allowing electronic prescribing of controlled substances (EPCS), a practice previously prohibited. OBJECTIVE: To carry out a survey of the experience of prescribers in the nation's first study of EPCS implementation. MATERIALS AND METHODS: Prescribers were surveyed in a community setting before and after implementation of EPCS, to assess adoption, attitudes, and challenges. RESULTS: Of the 102 prescribers enabled to use EPCS and who responded to surveys before and after implementation, 70 had sent at least one controlled substance prescription electronically. Most users reported that EPCS was significantly less burdensome than expected. Over half reported that EPCS was easy to use and improved work flow, accuracy of prescriptions (69.5%), monitoring of medications (59.3%), and coordination with pharmacists, though high prior expectations for improved efficiency were not met. EPCS users reported a significant decrease in the perceived frequency of medication errors and drug diversion, compared with controls. Barriers to use of EPCS included limited pharmacy participation and instances of unreliability of the technology. DISCUSSION: Interest in adoption of EPCS is considerable among providers, pharmacies, and vendors. The results suggest that while most EPCS security features may be more acceptable to providers than expected, barriers such as the limited participation by pharmacies may also partly explain slow adoption rates for EPCS nationally. CONCLUSIONS: EPCS was a better experience for many providers than they had expected, but related improvements in practice efficiency and quality of care will depend upon implementation strategies.