Racial Inequality in Receipt of Medications for Opioid Use Disorder.

BACKGROUND: Since 2010, Black persons in the United States have had a greater increase in opioid overdose-related mortality than other groups, but national-level evidence characterizing racial and ethnic disparities in the use of medications for opioid use disorder (OUD) is limited. METHODS: We used Medicare claims data from the 2016-2019 period for a random 40% sample of fee-for-service beneficiaries who were Black, Hispanic, or White; were eligible for Medicare owing to disability; and had an index event related to OUD (nonfatal overdose treated in an emergency department or inpatient setting, hospitalization with injection drug use-related infection, or inpatient or residential rehabilitation or detoxification care). We measured the receipt of medications to treat OUD (buprenorphine, naltrexone, and naloxone), the receipt of high-risk medications (opioid analgesics and benzodiazepines), and health care utilization, all in the 180 days after the index event. We estimated differences in outcomes according to race and ethnic group with adjustment for beneficiary age, sex, index event, count of chronic coexisting conditions, and state of residence. RESULTS: We identified 25,904 OUD-related index events among 23,370 beneficiaries, with 3937 events (15.2%) occurring among Black patients, 2105 (8.1%) among Hispanic patients, and 19,862 (76.7%) among White patients. In the 180 days after the index event, patients received buprenorphine after 12.7% of events among Black patients, after 18.7% of those among Hispanic patients, and after 23.3% of those among White patients; patients received naloxone after 14.4%, 20.7%, and 22.9%, respectively; and patients received benzodiazepines after 23.4%, 29.6%, and 37.1%, respectively. Racial differences in the receipt of medications to treat OUD did not change appreciably from 2016 to 2019 (buprenorphine receipt: after 9.1% of index events among Black patients vs. 21.6% of those among White patients in 2016, and after 14.1% vs. 25.5% in 2019). In all study groups, patients had multiple ambulatory visits in the 180 days after the index event (mean number of visits, 6.6 after events among Black patients, 6.7 after events among Hispanic patients, and 7.6 after events among White patients). CONCLUSIONS: Racial and ethnic differences in the receipt of medications to treat OUD after an index event related to this disorder among patients with disability were substantial and did not change over time. The high incidence of ambulatory visits in all groups showed that disparities persisted despite frequent health care contact. (Funded by the National Institute on Drug Abuse and the National Institute on Aging.).

Growing Role of Gabapentin in Opioid-Related Overdoses Highlights Misuse Potential and Off-label Prescribing Practices.

This Medical News article discusses a trend of gabapentin misuse and harms, including fatal overdoses.

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.

Coprescription of Opioids With Other Medications and Risk of Opioid Overdose.

Polypharmacy is common among patients taking prescription opioids long-term, and the codispensing of interacting medications may further increase opioid overdose risk. To identify nonopioid medications that may increase opioid overdose risk in this population, we conducted a case-crossover-based screening of electronic claims data from IBM MarketScan and Optum Clinformatics Data Mart spanning 2003 through 2019. Eligible patients were 18 years of age or older and had at least 180 days of continuous enrollment and 90 days of prescription opioid use immediately before an opioid overdose resulting in an emergency room visit or hospitalization. The main analysis quantified the odds ratio (OR) between opioid overdose and each nonopioid medication dispensed in the 90 days immediately before the opioid overdose date after adjustment for prescription opioid dosage and benzodiazepine codispensing. Additional analyses restricted to patients without cancer diagnoses and individuals who used only oxycodone for 90 days immediately before the opioid overdose date. The false discovery rate (FDR) was used to account for multiple testing. We identified 24,866 individuals who experienced opioid overdose. Baclofen (OR 1.56; FDR < 0.01; 95% confidence interval (CI), 1.29 to 1.89), lorazepam (OR 1.53; FDR < 0.01; 95% CI, 1.25 to 1.88), and gabapentin (OR 1.16; FDR = 0.09; 95% CI, 1.04 to 1.28), among other nonopioid medications, were associated with opioid overdose. Similar patterns were observed in noncancer patients and individuals who used only oxycodone. Interventions may focus on prescribing safer alternatives when a potential for interaction exists.

Assessing opioid overdose risk: a review of clinical prediction models utilizing patient-level data.

Drug, and specifically opioid-related, overdoses remain a major public health problem in the United States. Multiple studies have examined individual risk factors associated with overdose risk, but research developing clinical risk prediction tools for overdose has only emerged in the last few years. We conducted a comprehensive review of the literature on patient-level factors associated with opioid-related overdose risk, with an emphasis on clinical risk prediction models for opioid-related overdose in the United States. Studies that developed and/or validated clinical prediction models were closely reviewed and evaluated to determine the state of the field. We identified 12 studies that reported risk prediction models for opioid-related overdose risk. Published models were developed from a variety of data sources, including Veterans Health Administration data, Medicare data, commercial insurance data, and statewide linked datasets. Studies reported model performance using measures of discrimination, usually at good-to-excellent levels, though they did not always assess calibration. C-statistics were better for models that included clinical predictors (c-statistics: 0.75-0.95) compared to models without them (c-statistics: 0.69-0.82). External validation of models was rare, and we found no studies evaluating implementation of models or risk prediction tools into clinical practice. A common feature of these models was a high rate of false positives, largely because opioid-related overdose is rare in the general population. Thus, efforts to implement prediction models into practice should take into account that published models overestimate overdose risk for many low-risk patients. Future prediction models assessing overdose risk should employ external validation and address model calibration. In order to translate findings from prediction models into clinical public health benefit, future studies should focus on developing clinical prediction tools based on prediction models, implementing these tools into clinical practice, and evaluating the impact of these models on treatment decisions, patient outcomes, and, ultimately, opioid overdose rates.

Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach.

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877-0.892 vs. C-statistic = 0.871; 95%CI = 0.863-0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.

Patterns of clinic switching and continuity of medication for opioid use disorder in a Medicaid-enrolled population.

BACKGROUND: Many persons with opioid use disorder (OUD) initiate medication for opioid use disorder (MOUD) with one clinic and switch to another clinic during their course of treatment. These switches may occur for referrals or for unplanned reasons. It is unknown, however, what effect switching MOUD clinics has on continuity of MOUD treatment or on overdoses. OBJECTIVE: To examine patterns of switching MOUD clinics and its association with the proportion of days covered (PDC) by MOUD, and opioid-related overdose. DESIGN: Cross-sectional retrospective analysis of Pennsylvania Medicaid claims data. MAIN MEASURES: MOUD clinic switches (i.e., filling a MOUD prescription from a prescriber located in a different clinic than the previous prescriber), PDC, and opioid-related overdose. RESULTS: Among 14,107 enrollees, 43.2 % switched clinics for MOUD at least once during the 270 day period. In multivariate regression results, enrollees who were Non-Hispanic black (IRR = 1.43; 95 % CI = 1.24-1.65; p < 0.001), had previous methadone use (IRR = 1.32; 95 % CI = 1.13-1.55; p < 0.001), and a higher total number of office visits (IRR = 1.01; CI = 1.01-1.01; p < 0.001) had more switches. The number of clinic switches was positively associated with PDC (OR = 1.12; 95 % CI = 1.10-1.13). In secondary analyses, we found that switches for only one MOUD fill were associated with lower PDC (OR = 0.97; 95 % CI = 0.95-0.99), while switches for more than one MOUD fill were associated with higher PDC (OR = 1.40; 95 % CI = 1.36-1.44). We did not observe a relationship between opioid-related overdose and clinic switches. CONCLUSIONS: Lack of prescriber continuity for receiving MOUD may not be problematic as it is for other conditions, insofar as it is related to overdose and PDC.

[Postoperative opioid overdose due to patient-controlled analgesia by proxy]. / Postoperatieve overdosering morfine door familielid.

Patient-controlled analgesia (PCA) is a popular and efficacious form of postoperative pain relief that, however, is not without complications. Here we describe a 73-year-old Somalian male patient that underwent abdominal surgery and received intravenous morphine PCA for postoperative pain relief. Due to his inability to speak the native language, his son served as interpreter. On the day after surgery, the patient was found unresponsive by the nursing staff with an oxygen saturation of 91%. He was treated with naloxone and transferred to a medium care facility. The son indicated that he had operated the PCA system at regular intervals over the last 12 hours. The dangers of PCA and PCA by proxy in particular are discussed. In this case, the language barrier, and possibly cultural differences and health illiteracy may have contributed to the PCA by proxy.

Association of Current Opioid Use With Serious Adverse Events Among Older Adult Survivors of Breast Cancer.

Importance: National efforts to improve safe opioid prescribing focus on preventing misuse, overdose, and opioid use disorder. This approach overlooks opportunities to better prevent other serious opioid-related harms in complex populations, such as older adult survivors of cancer. Little is known about the rates and risk factors for comprehensive opioid-related harms in this population. Objective: To determine rates of multiple opioid-related adverse drug events among older adults who survived breast cancer and estimate the risk of these events associated with opioid use in the year after completing cancer treatment. Design, Setting, and Participants: This retrospective cohort study used 2007 to 2016 Surveillance, Epidemiology and End Results-Medicare data from fee-for-service Medicare beneficiaries with first cancer diagnosis of stage 0 to III breast cancer at age 66 to 90 years from January 1, 2008, through December 31, 2015, who completed active breast cancer treatment. Data were analyzed from October 31, 2019, to June 10, 2020. Exposures: Repeated daily measure indicating possession of any prescription opioid supply in Medicare Part D prescription claims. Main Outcomes and Measures: Adjusted risk ratios (aRRs), estimated using modified Poisson generalized estimating equation models, for adverse drug events related to substance misuse (ie, diagnosed opioid abuse, dependence, or poisoning), other adverse drug events associated with opioid use (ie, gastrointestinal events, infections, falls and fractures, or cardiovascular events), and all-cause hospitalization associated with opioid supply the prior day, controlling for patient characteristics. Results: Among 38 310 women included in the study (mean [SD] age, 74.3 [6.3] years), there were 0.010 (95% CI, 0.008-0.011) adverse drug events related to substance misuse per 1000 person-days, 0.237 (95% CI, 0.229-0.245) other adverse drug events associated with opioid use per 1000 person-days, and 0.675 (95% CI, 0.662-0.689) all-cause hospitalizations per 1000 person-days. Opioid use was associated with increased risk of adverse drug events related to substance misuse (aRR, 14.62; 95% CI, 9.69-22.05; P < .001), other adverse drug events related to opioid use (aRR, 2.50; 95% CI, 2.11-2.96; P < .001), and all-cause hospitalization (aRR, 2.77; 95% CI, 2.55-3.02; P < .001). In a dose-response effect, individuals with high daily opioid doses had consistently higher risks of all study outcomes compared with individuals who had low opioid doses. Compared with days with no opioid exposure, the risk of any adverse drug event related to substance misuse was 3.4-fold higher for individuals with a current opioid supply ≥50 mg morphine equivalent dose per day (aRR, 3.40; 95% CI, 2.47-4.68; P < .001), while the risk was 2.3-fold higher for individuals with 1 to 49 mg morphine equivalent dose per day (aRR, 2.29; 95% CI, 1.89-2.77; P < .001). Conclusions and Relevance: These findings suggest that among older adults who survived breast cancer, continued prescription opioid use in the year after completing active cancer treatment was associated with an immediate increased risk of a broad range of serious adverse drug events related to substance misuse and other adverse drug events associated with opioid use. Clinicians should consider the comprehensive risks of managing cancer pain with long-term opioid therapy.

Incidence rates of and risk factors for opioid overdose in new users of prescription opioids among US Medicaid enrollees: A cohort study.

PURPOSE: To measure incidence rates of and risk factors for opioid overdose among new users of prescription opioids in the Medicaid population. METHODS: A cohort study using Medicaid claims from four states (1999-2012) among adults continuously enrolled in Medicaid for ≥3 years free of opioid prescriptions and opioid overdose before cohort entry. Exposure and outcome of interest were prescription opioid use and apparent incident opioid overdose identified in inpatient and outpatient claims (sensitivity ≈ 97%; positive predictive value ≈ 87%), respectively. RESULTS: Among new prescription opioid users (1 336 140 persons; 246 466 person-years), the overall opioid overdose incidence rate per 100 000 person-years was 247.1 (95% confidence interval [CI], 227.5-266.7), with 251.0 (CI, 188.6-313.5) in 2002 and 225.5 (CI, 142.0-309.0) in 2012. A lower hazard for opioid overdose was seen for age 65-80 years (adjusted hazard ratio [HR], 0.50; CI, 0.37-0.66) and 80-100 years (0.35; 0.23-0.52) vs 18-35 years; females (0.79; 0.67-0.93) vs males; and other/unknown race/ethnicity (0.71; 0.54-0.93) vs whites. A higher hazard was seen for initial opioid dose in morphine milligram equivalents (MMEs), 50-100 MME/day (1.52; 1.24-1.86) and >100 MME/day (1.98; 1.55-2.53), vs <50 MME/day; prior diagnosis of substance use disorders (2.30; 1.91-2.79) or mental health conditions (1.75; 1.47-2.08); and prior prescriptions for benzodiazepines (1.43; 1.13-1.81). CONCLUSION: In Medicaid enrollees in four study states during 2002 to 2012, opioid overdose incidence rate per 100 000 person-years among apparent new users of prescription opioids was 247.1, with 251.0 in 2002 and 225.5 in 2012. Younger ages, white race/ethnicity, higher MME opioid daily doses, prior substance use disorders, mental health conditions, and benzodiazepine prescriptions were associated with a higher risk of opioid overdose incidence.

Incidence of Postoperative Opioid Overdose and New Diagnosis of Opioid Use Disorder Among US Veterans.

BACKGROUND AND OBJECTIVES: Perioperative exposure to opioids is associated with adverse outcomes. We aim to determine the associations between surgery and subsequent opioid overdose, an acute event, and a new diagnosis of opioid use disorder (OUD), a chronic relapsing disease, in parallel. METHODS: This retrospective cohort study of US veterans used surgery as exposure and the two outcomes were (1) occurrence of overdose and (2) new diagnosis of OUD in the first postoperative year. Surgical group was matched to the reference controls based on the propensity score of having surgery, and matched logistic regression was used to calculate the odds ratio (OR). RESULTS: A total of 261 208 surgical patients were matched to 479 531 controls. Overdose occurred in 1893 (0.7%) of the surgical patients and in 518 (0.1%) of the matched controls in the first postoperative year (OR, 6.71; 95% confidence interval [CI], 5.80-7.75; P < .001). Among patients with no history of OUD, surgery was also associated with a new diagnosis of OUD in the first postoperative year (OR, 1.13; 95% CI, 1.02-1.24; P = .015). DISCUSSION AND CONCLUSIONS: The postoperative period is strongly associated with opioid overdose, but only weakly associated with new diagnosis of OUD. This is likely due to the difficulty of diagnosing OUD in the postoperative period. SCIENTIFIC SIGNIFICANCE: This is the first study that has examined opioid overdose and new-onset OUD in the postoperative period in parallel. Our analysis suggests different risk factors for each, as well as different strengths of association with surgery. More sensitive diagnostic criteria for postoperative OUD are needed to promptly diagnose and treat this condition. (Am J Addict 2020;00:00-00).

A population-based study of sociodemographic and clinical factors among children and adolescents with opioid overdose.

STUDY OBJECTIVE: An upsurge of high-risk opioid misuse has contributed to the epidemic of opioid overdose in the United States. The primary aim was to report the rate of opioid overdose among the pediatric population and to report demographic and medical differences among POD versus IOD populations. DESIGN: Retrospective descriptive analysis of opioid overdose using the largest pediatric inpatient database in the United States. We performed a Pearson chi-square and Wilcoxon rank sum test to compare differences between cohorts. SETTING: Multi-institutional. PATIENTS: Data were obtained from the Kids' Inpatient Database of the Healthcare Cost and Utilization Project. We used the International Classification of Disease, Ninth Revision codes to extract records of pediatric patients who were admitted for POD or IOD from 2000 to 2012. INTERVENTIONS: None. MEASUREMENTS: None. MAIN RESULTS: The final analysis included 15,884 patients admitted to a United States hospitals with opioid overdose. The rate of POD and IOD has increased steadily from 2000 to 2012. Black, Asian or Pacific Islander, Native American, Multi-race, and Unknown race had higher proportion of POD versus IOD (p < 0.001). Compared to POD, the rate of IOD was highest in Northeast (29.2% versus 14.3%, p < 0.001) and Midwest (31.6%versus 26.1%, (p < 0.001) regions of the country. CONCLUSIONS: Our findings reinforce existing studies that report a continued rise in opioid morbidity and mortality while providing new insights into sociodemographic patterns and comorbidities associated with POD versus IOD.