Marc R. Larochelle

Opioid prescribing after nonfatal overdose and association with repeated overdose: a cohort study

Context Hospitalization or presentation to an emergency department for nonfatal opioid overdose represents an opportunity to identify and refer patients with opioid abuse for substance abuse treatment. Contribution Data from a large U.S. health insurer were used to identify patients presenting to a hospital or an emergency department with a nonfatal opioid overdose between 2000 and 2012. Investigators examined whether the patients continued to receive opioid prescriptions and the time to repeated overdose. Caution Out-of-hospital overdoses and opioid overdoserelated deaths were excluded. Implication Nearly all patients having a nonfatal opioid overdose continued to receive prescription opioids, and 7% had a repeated overdose. Treatment of chronic noncancer pain with prescription opioids has increased dramatically in recent decades (13). Opioid misuse and overdose have increased in parallel, and deaths due to prescription opioids quadrupled to 16651 between 1999 and 2010 (4, 5). However, overdose deaths alone do not capture the full morbidity from prescription opioids. More than 300000 patients visited an emergency department in 2008 due to nonmedical use of prescription opioidsmore than double the total from 5 years earlier (6). Presentation to an emergency department or hospital with a nonfatal opioid overdose is an opportunity to identify and refer patients who may be misusing opioids. Prescribing guidelines specify that misuse of opioids and related adverse events are indications to discontinue long-term therapy (7, 8). However, patterns of treatment, including rates of continued prescribing, after an opioid overdose are unknown. Research suggests that opioid overdose is associated with substance use disorders or high opioid dosages (9, 10), but the association between opioid analgesia treatment after an overdose and subsequent overdose is unknown. In this study, we sought to characterize opioid use after an overdose among patients receiving long-term opioid therapy for noncancer pain. We also aimed to determine whether patients who continued to receive prescription opioids after the index overdose switched providers and whether opioid dosage after an overdose was associated with risk for a subsequent overdose. Methods Study Design and Data Source We did a retrospective cohort study of persons having a nonfatal opioid overdose during an episode of long-term opioid use. We used the Optum database (Optum), comprising complete inpatient, outpatient, and pharmacy claims for patients from a large U.S. health insurer with members in all 50 states. We drew our cohort from 50 million commercially enrolled patients between May 2000 and December 2012 with a median follow-up of 15 months. We obtained study approval through the Harvard Pilgrim Health Care Institutional Review Board. Patient Selection We identified 14725 patients aged 18 to 64 years who had an index opioid overdose, defined as the first emergency department or inpatient claim with an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), diagnosis code of overdose due to a prescription opioid (965.00, 965.02, 965.09, E850.1, or E850.2) or heroin (965.01 or E850.0). This coding classification is consistent with past studies using administrative data and consensus recommendations from the Injury Surveillance Workgroup (1113). We combined emergency department or inpatient episodes separated by fewer than 2 days into a single episode. We excluded 1711 patients without 90 days of continuous enrollment before the start date of the index overdose to ensure sufficient data to identify prescription opioid dispensing patterns and patient characteristics at baseline (Appendix Figure 1). We limited the cohort to 3379 patients (26% of the remaining 13014) with evidence of long-term opioid therapy at the time of the index overdose. We defined opioid episodes as consecutive dispensings with 60 or fewer days between the run-out date (fill date plus days supplied) and the fill date of the next dispensing (Figure 1, A). We defined opioid episodes as long-term if they consisted of 3 or more dispensings at least 21 days apart and lasted at least 84 days (12 weeks) with at least 84 days supplied (Figure 1, B). We used First Databank (San Francisco, California) drug summary tables to identify National Drug Codes for opiate agonists using American Hospital Formulary Service classification 28080800. We included the following opioids: codeine, dihydrocodeine, meperidine, morphine, oxycodone, hydrocodone, hydromorphone, fentanyl, oxymorphone, propoxyphene, methadone, tramadol, and levorphanol. Appendix Figure 1. Study flow diagram. Figure 1. Definition of long-term opioid episodes and daily opioid dosage attribution. MED = morphine-equivalent dose. We excluded 479 patients with a diagnosis of cancer (except nonmelanoma skin cancer) during their enrollment based on ICD-9-CM diagnosis codes (140.X-208.X, 209.0-209.3, or V10.X [except V10.83]). We excluded 50 patients (2% of the 2900 remaining) for whom we could not confirm that the overdose was nonfatal by proxy of continued health plan enrollment or a subsequent pharmacy or medical claim. We excluded 2 patients with missing demographic variables, yielding a final cohort of 2848 patients (Appendix Figure 1). Patients were followed from 90 days before the index overdose until 1 of the following 5 stopping criteria was met: a second inpatient or emergency department claim for opioid overdose; disenrollment from the health plan; age of 65 years (Medicare eligible); 730 days (2 years) after the index overdose; or December 2012, the end of the study period. Outcomes The primary outcome measure was daily opioid dosage. We calculated the morphine-equivalent dosage (MED) for each opioid dispensing using established conversion tables (14). We calculated a daily MED by distributing the total MED for each dispensing over the days supplied and summing the total of overlapping dispensings on each day (Figure 1, C). For dispensings that spanned the index overdose, we stopped attribution of opioid dosage on the day of the overdose. We set daily dosage to 0 from the first day after overdose until the date of the first opioid dispensing after the overdose, if any (Figure 1, D). We believe that this attribution scheme best reflects prescriber intent before the overdose and modifiable prescriber behavior after the overdose. We assessed whether the primary opioid prescriber changed between the 90-day periods before and after the index overdose. We used standard provider identifiers to determine who prescribed each opioid dispensing. We identified the patients primary prescriber before and after the index overdose as the provider associated with the most opioid dispensings in both periods. Provider identifiers were available for 95% of the prescriptions. We excluded patients whose primary prescriber could not be determined due to missing data for 1 or more opioid dispensings. We also assessed daily availability of benzodiazepines and buprenorphine after the overdose based on the date of dispensing and days supplied. Benzodiazepines are commonly involved in fatal opioid overdoses and are associated with an increased risk for overdose when combined with opioids (4, 9). Buprenorphine has demonstrated efficacy as an office-based treatment of opioid dependence (15). We examined time to repeated opioid overdose; patients were censored through use of the criteria identified previously. We sought to determine whether changes in opioid dosage (in particular, opioid discontinuation) after the index overdose led to opioid withdrawal visits. We examined time to a medical claim for drug withdrawal, which was identified with ICD-9-CM diagnosis code 292.0. We examined characteristics of patients and their treatments in the 90 days before the index opioid overdose. We identified those who had 1 or more claims with a diagnosis of substance use disorder (ICD-9-CM codes 303.X-305.X), mental health disorder (schizophrenic disorder, 295.X; mood disorder, 296.X, 311.X; or anxiety disorder, 300.X), or dispensing for a benzodiazepine (American Hospital Formulary Service classification 28240800). We identified whether patients received immediate-release opioids, extended-release/long-acting opioids (Appendix Table 1), or both. We created clinically relevant categories of pain indications through review of ICD-9-CM diagnosis codes for medical claims in the 90 days before the overdose (Appendix Table 2). Appendix Table 1. Opioid Formulations Classified as Extended-Release/Long-Acting Appendix Table 2. All ICD-9-CM Diagnosis Codes From 90 d Before the Index Overdose That Were Reviewed by 2 Physicians* and Categorized Into Clinically Relevant Groupings Statistical Analysis We depicted opioid dosage before and after the index overdose in 3 ways. First, we calculated the mean dosage for the cohort on each day of the study period. Second, we examined the percentages of patients in the cohort with daily opioid dosages of none (0 mg MED), low (>0 to <50 mg MED), moderate (50 to <100 mg MED), or large (100 mg MED), consistent with previous studies demonstrating an association between increasing opioid dosage and overdose risk (9, 10, 15). Finally, to examine patterns of change in dosage in individual patients, we examined the category of average daily dosage for the following 3 periods: 60 days before the overdose, days 31 to 90 after the overdose, and days 91 to 365 after the overdose. We then identified the proportion of patients in each dosage category who moved to a different dosage category in the next period. We excluded the first 30 days before and after the index overdose because we could not correctly classify the dosage before the first prescription in each respective period. We compared benzodiazepine and buprenorphine use daily after the overdose, stratified by whether patients had an active opioid dispensing. To assess the association

Improving Adherence to Long-term Opioid Therapy Guidelines to Reduce Opioid Misuse in Primary Care: A Cluster-Randomized Clinical Trial

Importance Prescription opioid misuse is a national crisis. Few interventions have improved adherence to opioid-prescribing guidelines. Objective To determine whether a multicomponent intervention, Transforming Opioid Prescribing in Primary Care (TOPCARE; http://mytopcare.org/), improves guideline adherence while decreasing opioid misuse risk. Design, Setting, and Participants Cluster-randomized clinical trial among 53 primary care clinicians (PCCs) and their 985 patients receiving long-term opioid therapy for pain. The study was conducted from January 2014 to March 2016 in 4 safety-net primary care practices. Interventions Intervention PCCs received nurse care management, an electronic registry, 1-on-1 academic detailing, and electronic decision tools for safe opioid prescribing. Control PCCs received electronic decision tools only. Main Outcomes and Measures Primary outcomes included documentation of guideline-concordant care (both a patient-PCC agreement in the electronic health record and at least 1 urine drug test [UDT]) over 12 months and 2 or more early opioid refills. Secondary outcomes included opioid dose reduction (ie, 10% decrease in morphine-equivalent daily dose [MEDD] at trial end) and opioid treatment discontinuation. Adjusted outcomes controlled for differing baseline patient characteristics: substance use diagnosis, mental health diagnoses, and language. Results Of the 985 participating patients, 519 were men, and 466 were women (mean [SD] patient age, 54.7 [11.5] years). Patients received a mean (SD) MEDD of 57.8 (78.5) mg. At 1 year, intervention patients were more likely than controls to receive guideline-concordant care (65.9% vs 37.8%; P < .001; adjusted odds ratio [AOR], 6.0; 95% CI, 3.6-10.2), to have a patient-PCC agreement (of the 376 without an agreement at baseline, 53.8% vs 6.0%; P < .001; AOR, 11.9; 95% CI, 4.4-32.2), and to undergo at least 1 UDT (74.6% vs 57.9%; P < .001; AOR, 3.0; 95% CI, 1.8-5.0). There was no difference in odds of early refill receipt between groups (20.7% vs 20.1%; AOR, 1.1; 95% CI, 0.7-1.8). Intervention patients were more likely than controls to have either a 10% dose reduction or opioid treatment discontinuation (AOR, 1.6; 95% CI, 1.3-2.1; P < .001). In adjusted analyses, intervention patients had a mean (SE) MEDD 6.8 (1.6) mg lower than controls (P < .001). Conclusions and Relevance A multicomponent intervention improved guideline-concordant care but did not decrease early opioid refills. Trial Registration clinicaltrials.gov Identifier: NCT01909076

Trends in Receipt of Buprenorphine and Naltrexone for Opioid Use Disorder Among Adolescents and Young Adults, 2001-2014

Importance Opioid use disorder (OUD) frequently begins in adolescence and young adulthood. Intervening early with pharmacotherapy is recommended by major professional organizations. No prior national studies have examined the extent to which adolescents and young adults (collectively termed youth) with OUD receive pharmacotherapy. Objective To identify time trends and disparities in receipt of buprenorphine and naltrexone among youth with OUD in the United States. Design, Setting, and Participants A retrospective cohort study was conducted using deidentified data from a national commercial insurance database. Enrollment and complete health insurance claims of 9.7 million youth, aged 13 to 25 years were analyzed, identifying individuals who received a diagnosis of OUD between January 1, 2001, and June 30, 2014, with final follow-up date December 31, 2014. Analysis was conducted from April 25 to December 31, 2016. Time trends were identified and multivariable logistic regression was used to determine sociodemographic factors associated with medication receipt. Exposures Sex, age, race/ethnicity, neighborhood education and poverty levels, geographic region, census region, and year of diagnosis. Main Outcomes and Measures Dispensing of a medication (buprenorphine or naltrexone) within 6 months of first receiving an OUD diagnosis. Results Among 20 822 youth diagnosed with OUD (0.2% of the 9.7 million sample), 13 698 (65.8%) were male and 17 119 (82.2%) were non-Hispanic white. Mean (SD) age was 21.0 (2.5) years at the first observed diagnosis. The diagnosis rate of OUD increased nearly 6-fold from 2001 to 2014 (from 0.26 per 100 000 person-years to 1.51 per 100 000 person-years). Overall, 5580 (26.8%) youth were dispensed a medication within 6 months of diagnosis, with 4976 (89.2%) of medication-treated youth receiving buprenorphine and 604 (10.8%) receiving naltrexone. Medication receipt increased more than 10-fold, from 3.0% in 2002 (when buprenorphine was introduced) to 31.8% in 2009, but declined in subsequent years (27.5% in 2014). In multivariable analyses, younger individuals were less likely to receive medications, with adjusted probability for age 13 to 15 years, 1.4% (95% CI, 0.4%-2.3%); 16 to 17 years, 9.7% (95% CI, 8.4%-11.1%); 18 to 20 years, 22.0% (95% CI, 21.0%-23.0%); and 21 to 25 years, 30.5% (95% CI, 30.0%-31.5%) (P < .001 for difference). Females (7124 [20.3%]) were less likely than males (13 698 [24.4%]) to receive medications (P < .001), as were non-Hispanic black (105 [14.8%]) and Hispanic (1165 [20.0%]) youth compared with non-Hispanic white (17 119 [23.1%]) youth (P < .001). Conclusions and Relevance In this first national study of buprenorphine and naltrexone receipt among youth, dispensing increased over time. Nonetheless, only 1 in 4 commercially insured youth with OUD received pharmacotherapy, and disparities based on sex, age, and race/ethnicity were observed.

Medication for Opioid Use Disorder After Nonfatal Opioid Overdose and Association With Mortality: A Cohort Study

The United States is in the midst of a crisis of opioid-related harms (1). Some efforts to address this crisis focus on expanding access to effective treatment of opioid use disorders (OUDs) (2). Prior nonfatal opioid overdose is a known risk factor for subsequent nonfatal and fatal overdoses (37), and engaging persons in treatment who survive an overdose may be effective in limiting subsequent fatalities. However, data on the association between treatment of OUD and mortality after a nonfatal overdose are limited to a single retrospective cohort study that analyzed enrollment in methadone maintenance treatment (MMT) at a single time point and found no association (3). The 3 medications for OUD (MOUD) approved by the U.S. Food and Drug Administration are methadone, buprenorphine, and naltrexone. Randomized controlled trials of these medications have shown consistent benefits across many outcomes, including increased treatment retention and suppression of illicit opioid use (810). A recent systematic review and meta-analysis of 19 observational cohort studies identified substantial reductions in all-cause and overdose mortality for methadone and buprenorphine (11). However, the mortality benefit in this analysis was limited to time actively retained in treatment, and the 4-week period after discontinuation was associated with an especially high risk for death. The few studies that examined mortality among patients receiving naltrexone show an unclear effect (1215). Massachusetts has been particularly affected by the opioid crisis: Opioid overdose deaths more than tripled between 2010 and 2016 (16). Through Chapter 55 of the Acts of 2015, the state legislature permitted individual-level linkage of data from 16 Massachusetts government agencies to gain a deeper understanding of the circumstances that influence fatal and nonfatal opioid overdoses (17). For this analysis, we identified a cohort of persons in the Chapter 55 data set who survived an opioid overdose and described any episodes of treatment with MOUD before and after that overdose. Specifically, we sought to determine whether treatment with MOUD, including receipt of MMT, buprenorphine, or naltrexone, was associated with reduced risk for all-cause and opioid-related mortality. Methods Study Design and Data Source We did a retrospective cohort study using the Massachusetts Chapter 55 data set, which includes data between 2011 and 2015 on residents aged 11 years or older with health insurance (as reported in the Massachusetts All-Payer Claims Database [APCD]) and represents more than 98% of Massachusetts residents. Data from APCD were linked at the individual level with records from other data sets using a multistage deterministic linkage technique described elsewhere (18). For this study, we used 7 linked Massachusetts databases: APCD, the Registry of Vital Records and Statistics, the prescription monitoring program, the Acute Hospital Case Mix, the Ambulance Trip Record Information System, the Bureau of Substance Addiction Services licensed treatment encounters, and the cancer registry. This work was mandated by Massachusetts law and conducted by a public health authority that required no institutional board review. The Boston University Medical Campus Institutional Review Board also determined that this study was not human subjects research. Cohort Selection We identified persons who had had a nonfatal opioid overdose between January 2012 and December 2014 to allow 12 months of observation before and after the overdose. We restricted the cohort to persons aged 18 years or older because access to OUD treatment substantially differs in adolescents versus adults (19). We identified opioid overdose in 2 ways. First, we identified emergency department, observation, or inpatient encounters with a medical claim containing a diagnosis code for opioid poisoning from the International Classification of Diseases, Ninth Revision, Clinical Modification (codes 965.00, 965.01, 965.02, 965.09, E850.0, E850.1, and E850.2). A study validated these codes by showing positive predictive values of 81% for identifying fatal or nonfatal opioid overdose and 94% for an opioid overdose or opioid-related adverse event (20). Second, we identified persons with an ambulance encounter for opioid overdose (available in 2013 and 2014 only). In collaboration with the Centers for Disease Control and Prevention, the Massachusetts Department of Public Health created and refined an algorithm to use with emergency medical services data to identify opioid-related overdoses; this algorithm was previously validated against internal emergency medical services data on opioid overdose events (Supplement). Supplement. Supplementary Material We examined the first qualifying event (nonfatal opioid overdose) for each person, hereafter called the index overdose. Of 20155 persons with an event, we excluded 1203 who died within 30 days after the overdose using dates of death from the Registry of Vital Records and Statistics. We excluded 1338 persons with evidence of cancer at any time in the 5 years of Chapter 55 data because of high competing risk for death. Cancer was identified using International Classification of Diseases, Ninth Revision, diagnosis codes in APCD (Supplement) or entry in the state-based cancer registry. We also excluded 46 persons whose age or sex was unknown, yielding a final cohort of 17568 persons. Key Variables We identified exposure to MOUD in monthly intervals. Exposure to MMT was identified in 2 ways: a medical claim from APCD for methadone administration via Healthcare Common Procedure Coding System code H0020 or a record of treatment with methadone in data from the Bureau of Substance Addiction Services. We used the prescription monitoring program to identify dispensing of buprenorphine or buprenorphine and naloxone combined. Naltrexone was identified via a pharmacy claim for injectable or oral naltrexone in APCD. We examined all-cause and opioid-related mortality as identified in death files. Classification of opioid-related death was based on medical examiner determination or standardized assessment by the Massachusetts Department of Public Health (Supplement). We examined potential confounding variables. We obtained patient sex and age from APCD and categorized age as 18 to 29 years, 30 to 44 years, or 45 years or older. We identified monthly dispensings of opioid analgesics and benzodiazepines from the prescription monitoring program. We identified diagnosis of anxiety or depression using International Classification of Diseases, Ninth and Tenth Revisions, diagnosis codes from APCD (Supplement). We identified OUD treatment services, including inpatient detoxification episodes and postdetoxification treatment in short- and long-term residential facilities, through the Bureau of Substance Addiction Services. Statistical Analysis To compare baseline characteristics by receipt of MOUD, we developed the following 5 categories of MOUD receipt in the 12 months after the index overdose: no MOUD during follow-up, 1 or more months of buprenorphine, 1 or more months of methadone, 1 or more months of naltrexone, and 1 or more months of 2 or 3 MOUDs combined. We compared baseline characteristics among these mutually exclusive treatment groups using 2 tests. We did time-to-event analyses for all-cause and opioid-related mortality using MOUD as a monthly time-varying exposure variable. We used 2 dichotomous classifications for MOUD exposure, with discontinuation and on treatment. Several studies have shown an increased risk for all-cause and opioid-related mortality in the 4 weeks immediately after MOUD discontinuation (11, 21). Thus, we defined a with discontinuation exposure variable, which we considered the primary classification, to attribute any effect of MOUD discontinuation on mortality to the MOUD. For this classification, persons were considered exposed to MOUD in any month in which they received it and in the month after last receipt. We defined an on treatment exposure variable as the secondary classification, in which persons were considered exposed to MOUD only in months in which they received it (Figure 1). Figure 1. MOUD exposure classification. For the primary classification (with discontinuation), MOUD exposure extends through the month after discontinuation (light and dark-green months). For the secondary classification (on treatment), exposure is limited to months in which treatment is received (light-green months only). In the illustrative examples, participant 1 is not exposed to MOUD through follow-up; participant 2 is exposed in months 12 and 712 for the primary classification and months 1 and 712 for the secondary classification. In the month of death, participant 3 would be considered exposed in the primary classification only, participant 4 would be considered exposed in both primary and secondary exposure classifications, and participant 5 would be considered not exposed to MOUD. MOUD= medication for opioid use disorder. We used an extended KaplanMeier estimator allowing for time-varying exposure to MOUD to generate cumulative incidence curves (Supplement) (22). We developed a multivariable Cox regression model of time to all-cause and opioid-related mortality. The predictors of interest were monthly receipt of MMT, buprenorphine, and naltrexone as time-varying exposure variables. Covariates were age; sex; monthly time-varying receipt of prescription opioids, benzodiazepines, and OUD treatment services; baseline characteristics, including mental health diagnoses; and prior receipt of medication or OUD treatment services. We calculated the E-value to identify the minimum strength of association that an unmeasured confounder would need to have with both treatment and outcome, conditional on the measured covariates, to explain away the observed associations between MOUD and mortality (23). We used SAS Studio, version 3.5 (SAS Institute), for analyses (Supplemen

Access to Prescription Opioids-Primum Non Nocere: A Teachable Moment.

Story From the Front Lines A 14-year-old boy found acetaminophen-hydrocodone in his parents’ medicine cabinet and took it out of curiosity. He liked how the pills made him feel and progressed to daily use of prescription opioids. At age 15 years, he was prescribed a short course of acetaminophen-oxycodone for a back injury due to wrestling. After the prescription ended, he continued to seek prescription opioids from illicit sources, taking them almost daily. He was briefly sent to juvenile detention after being caught selling opioids. For the next 3 years he abstained from opioids but drank alcohol socially and smoked cigarettes. At age 19 years, he tried heroin with a friend and began using the drug daily. Today, he describes the experience as “spiritual... when I took the drug, it felt like I had found a deep calling.” He began selling heroin to fund his habit. At age 21 years, he overdosed on heroin. He was initially revived by emergency medical services with intranasal naloxone, but owing to compartment syndrome of the right thigh, he developed hyperkalemia and experienced cardiac arrest. After 18 minutes of advanced cardiac life support, a pulse returned. He was intubated and admitted to the intensive care unit. His course was complicated by renal failure requiring renal replacement therapy, stress cardiomyopathy, deep venous thrombosis, and anoxic brain injury. His cardiac and renal function normalized. Although his left-sided motor function is now normal, he still has spastic movements, weakness, and limited range of motion in the right upper and lower extremities. He can now ambulate without a walker or cane, and he has normal thought and speech. Since leaving the hospital, he has been attending weekly Alcoholics Anonymous, Heroin Anonymous, and Narcotics Anonymous meetings. Unfortunately, he has had several relapses, and contracted hepatitis C after sharing injection paraphernalia.

Physician Prescribing of Opioids to Patients at Increased Risk of Overdose From Benzodiazepine Use in the United States

Importance Recent increases in US opioid-related deaths underscore the need to understand drivers of fatal overdose. The initial prescription of opioids represents a critical juncture because it increases the risk of future opioid use disorder and is preventable. Objective To examine new opioid prescribing patterns in US patients at increased risk of overdose from benzodiazepine use. Design, Setting, and Participants This study used publicly available data from the National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey from January 1, 2005, through December 31, 2015, to identify adults 20 years or older receiving new opioid prescriptions and concurrently using a benzodiazepine. Main Outcomes and Measures Population-based rates of new opioid prescriptions stratified by use of benzodiazepines. Results This study analyzed 13 146 visits, representing 214 million visits nationally, with a new opioid prescription. Rates of new opioid prescriptions among adults using a benzodiazepine increased from 189 to 351 per 1000 persons between 2005 and 2010 (rate difference, 162; 95% CI, 29-295; P = .02) and decreased to 172 per 1000 persons by 2015 (rate difference, −179; 95% CI, −310 to −48; P = .008). New opioid prescriptions in the general population not using benzodiazepines increased nonsignificantly from 78 to 93 per 1000 US persons between 2005 and 2010 (rate difference, 15; 95% CI, −3 to 33; P = .10) and decreased nonsignificantly to 79 per 1000 persons by 2015 (rate difference, −14; 95% CI, −38 to 11; P = .28). The likelihood of receiving a new opioid prescription during an ambulatory visit remained higher for patients concurrently using benzodiazepines compared with the general population after adjusting for demographic characteristics, comorbidities, and diagnoses associated with pain (adjusted relative risk, 1.83; 95% CI, 1.56-2.15; P < .001). Naloxone was coprescribed in less than 1% of visits when a patient concurrently used a benzodiazepine. Conclusions and Relevance In 2010, new opioid prescriptions for US adults stopped increasing and began to decrease among higher-risk patients who used benzodiazepines. These patterns suggest that the recent increase in opioid-related deaths may be associated with factors other than physicians writing new opioid prescriptions. Nevertheless, prescribing among higher-risk patients still occurred at rates higher than rates in the general population, representing an important opportunity to improve quality of care for patients experiencing pain.

The U.S. opioid epidemic: One disease, diverging tales

To date, the stigma associated with opioid dependence has had the untoward effect of discouraging treatment-seeking behavior before the occurrence of an adverse event.

Reasons for Opioid Discontinuation and Unintended Consequences Following Opioid Discontinuation Within the TOPCARE Trial

Objective To identify reasons for opioid discontinuation and post-discontinuation outcomes among patients in the Transforming Opioid Prescribing in Primary Care (TOPCARE) study. Design In TOPCARE, an intervention to improve adherence to opioid prescribing guidelines, randomized intervention primary care providers (PCPs) received nurse care manager support, an electronic registry, academic detailing, and electronic tools, and control PCPs received electronic tools only. Setting Four Boston safety net primary care practices. Subjects Patients in both TOPCARE study arms who discontinued opioid therapy during the trial. Methods Through chart review, we examined the reason for discontinuation and post-discontinuation outcomes: one or more PCP visits, one or more pain-related emergency department (ED) visits, evidence of opioid use disorder (OUD), and referral for OUD treatment. Results Opioid discontinuations occurred in 83/586 (14.2%) intervention and 42/399 (10.5%) control patients (P = 0.09). Among patients who discontinued opioids, 81 (65%) discontinued for misuse, with no difference by group (P = 0.38). Aberrancy in monitoring (e.g., discordant urine drug test results) was the most common type of misuse prompting discontinuation (occurring in (51/83 [61%] of intervention patients vs 19/42 [45%, P = 0.08] of control patients). Intervention patients who discontinued opioids had less PCP follow-up (65% vs 88%, P < 0.01) compared with control patients. We found no differences between groups for pain-related ED visits, evidence of OUD, or OUD treatment referral following discontinuation. Conclusions The decreased follow-up among TOPCARE intervention patients who discontinued opioids highlights the need to understand unintended consequences of involuntary opioid discontinuations resulting from interventions to reduce opioid risk.

Receipt of Timely Addiction Treatment and Association of Early Medication Treatment With Retention in Care Among Youths With Opioid Use Disorder

Importance Retention in addiction treatment is associated with reduced mortality for individuals with opioid use disorder (OUD). Although clinical trials support use of OUD medications among youths (adolescents and young adults), data on timely receipt of buprenorphine hydrochloride, naltrexone hydrochloride, and methadone hydrochloride and its association with retention in care in real-world treatment settings are lacking. Objectives To identify the proportion of youths who received treatment for addiction after diagnosis and to determine whether timely receipt of OUD medications is associated with retention in care. Design, Setting, and Participants This retrospective cohort study used enrollment data and complete health insurance claims of 2.4 million youths aged 13 to 22 years from 11 states enrolled in Medicaid from January 1, 2014, to December 31, 2015. Data analysis was performed from August 1, 2017, to March 15, 2018. Exposures Receipt of OUD medication (buprenorphine, naltrexone, or methadone) within 3 months of diagnosis of OUD compared with receipt of behavioral health services alone. Main Outcomes and Measures Retention in care, with attrition defined as 60 days or more without any treatment-related claims. Results Among 4837 youths diagnosed with OUD, 2752 (56.9%) were female and 3677 (76.0%) were non-Hispanic white. Median age was 20 years (interquartile range [IQR], 19-21 years). Overall, 3654 youths (75.5%) received any treatment within 3 months of diagnosis of OUD. Most youths received only behavioral health services (2515 [52.0%]), with fewer receiving OUD medications (1139 [23.5%]). Only 34 of 728 adolescents younger than 18 years (4.7%; 95% CI, 3.1%-6.2%) and 1105 of 4109 young adults age 18 years or older (26.9%; 95% CI, 25.5%-28.2%) received timely OUD medications. Median retention in care among youths who received timely buprenorphine was 123 days (IQR, 33-434 days); naltrexone, 150 days (IQR, 50-670 days); and methadone, 324 days (IQR, 115-670 days) compared with 67 days (IQR, 14-206 days) among youths who received only behavioral health services. Timely receipt of buprenorphine (adjusted hazard ratio, 0.58; 95% CI, 0.52-0.64), naltrexone (adjusted hazard ratio, 0.54; 95% CI, 0.43-0.69), and methadone (adjusted hazard ratio, 0.32; 95% CI, 0.22-0.47) were each independently associated with lower attrition from treatment compared with receipt of behavioral health services alone. Conclusions and Relevance Timely receipt of buprenorphine, naltrexone, or methadone was associated with greater retention in care among youths with OUD compared with behavioral treatment only. Strategies to address the underuse of evidence-based medications for youths with OUD are urgently needed.

Non-fatal opioid-related overdoses among adolescents in Massachusetts 2012–2014

BACKGROUND Opioid-related overdoses and deaths among adolescents in the United States continue to increase, but little is known about adolescents who experience opioid-related non-fatal overdose (NFOD). Our objective was to describe (1) the characteristics of adolescents aged 11-17 who experienced NFOD and (2) their receipt of medications for opioid use disorder (MOUD) in the 12 months following NFOD, compared with adults. METHODS We created a retrospective cohort using six Massachusetts state agency datasets linked at the individual level, with information on 98% of state residents. Individuals entered the cohort if they experienced NFOD between January 1, 2012 and December 31, 2014. We compared adolescents to adults experiencing NFOD, examining individual characteristics and receipt of medications for opioid use disorder (MOUD)-methadone, buprenorphine, or naltrexone. RESULTS Among 22,506 individuals who experienced NFOD during the study period, 195 (0.9%) were aged 11-17. Fifty-two percent (102/195) of adolescents were female, whereas only 38% of adults were female (P < 0.001). In the year prior to NFOD, 11% (21/195) of adolescents received a prescription opioid, compared to 43% of adults (P < 0.001), and <5% (<10/195) received any MOUD compared to 23% of adults (P < 0.001). In the 12 months after NFOD, only 8% (15/195) of adolescents received MOUD, compared to 29% of adults. CONCLUSION Among individuals experiencing NFOD, adolescents were more likely to be female and less likely to have been prescribed opioids in the year prior. Few adolescents received MOUD before or after NFOD. Non-fatal overdose is a missed opportunity for starting evidence-based treatment in adolescents.