Racial-Ethnic Disparities in Benzodiazepine Prescriptions for Anxiety in US Emergency Departments

Influence of SSRI and SNRI co-prescription on benzodiazepine prescription trajectories

Purpose

This study examined whether co-prescription of selective serotonin reuptake inhibitors (SSRIs) or serotonin or norepinephrine reuptake inhibitors (SNRI)s with benzodiazepines is associated with differences in benzodiazepine prescriptions both within individual patients over time and between patients.

Methods

We analyzed deidentified electronic health records of patients prescribed a benzodiazepine between 2020 and 2022 (N = 847). Patients were categorized into three groups: those co-prescribed an SSRI, those co-prescribed an SNRI, and those not co-prescribed an SSRI or SNRI.

Results

Individuals co-prescribed an SSRI (M=6.63) or an SNRI (M=8.31) had more benzodiazepine prescription encounters than those who were not co-prescribed an SSRI/SNRI (M=5.08). Individuals co-prescribed an SSRI or SNRI also received a higher maximum benzodiazepine dosage than those who were not co-prescribed an SSRI/SNRI (SSRI M=2.41; SNRI M=2.30; No SSRI/SNRI M=1.91 diazepam milligram equivalent defined daily doses). Multilevel models indicated the SSRI co-prescription group received a higher initial benzodiazepine dosage (b=0.394), but showed no significant change in benzodiazepine dosage over time. When controlling for demographic and clinical correlates of benzodiazepine prescriptions, those who were not co-prescribed an SSRI showed an increase in benzodiazepine dose over time (b=0.075). Multilevel models revealed no relationship between SNRI co-prescription and starting benzodiazepine dosage or change in benzodiazepine dosage over time. An anxiety disorder diagnosis, younger age, and non-Black/African American race were associated with higher benzodiazepine dose.

Conclusions

Individuals who are co-prescribed an SSRI/SNRI may be vulnerable to longer treatment durations and higher prescribed doses of benzodiazepines, raising concerns about risk for dependence among individuals receiving combined benzodiazepine and SSRI/SNRI treatment.

Treating Benzodiazepine Withdrawal in a Bridge Clinic

BACKGROUND

Benzodiazepine-involved overdose deaths are rising, driven by increasing use of nonprescribed benzodiazepine pills. For patients who wish to stop nonprescribed benzodiazepine use, rapid inpatient tapers are typically the only option to treat benzodiazepine withdrawal. Substance use disorder bridge clinics can provide the high-touch care needed to manage outpatient benzodiazepine tapers in patients at high risk due to other substance use disorders.

OBJECTIVE

Describe the implementation and short-term outcomes of an outpatient benzodiazepine taper protocol to treat benzodiazepine withdrawal in a substance use disorder bridge clinic.

METHODS

The clinical team developed a 4- to 6-week intensive outpatient taper protocol using diazepam. Patients with benzodiazepine use disorder were eligible if they had benzodiazepine withdrawal, lacked a prescriber, wanted to stop benzodiazepines completely, and agreed to daily visits. For patients who initiated a taper between April 2021 and December 2022, we evaluated the proportion of patients who completed a taper (i.e., tapered to a last prescribed dose of diazepam 10 mg/d or less); likelihood of remaining on the taper over time; and seizure, overdose, or death documented at the study institution during or within 1 month of taper completion or discontinuation. Other secondary outcomes included HIV testing and prevention, hepatitis C testing, and referrals to recovery coaching or psychiatry.

RESULTS

Fifty-four patients initiated a total of 60 benzodiazepine tapers. The population was mostly male (61%) and non-Hispanic White (85%). Nearly all patients had opioid use disorder (96%), and most (80%) were taking methadone or buprenorphine for opioid use disorder before starting the taper. Patients reported using multiple substances in addition to benzodiazepines, most commonly fentanyl (75%), followed by cocaine (41%) and methamphetamine (21%). Fourteen patients (23%) completed a taper with a median duration of 34 days (IQR 27.8-43.5). Most tapers were stopped when the patient was lost to follow-up (57%), or the team recommended inpatient care (18%). Two patients had a seizure, and 4 had a presumed opioid-involved overdose during or within 1 month after the last taper visit, all individuals who did not complete a taper. No deaths occurred during or within 1 month of taper completion or discontinuation. Challenges included frequent loss to follow-up in the setting of other unstable substance use. Patients received other high-priority care during the taper including HIV testing (32%), PrEP initiation (6.7%), hepatitis C testing (30%), and referrals to recovery coaches (18%) and psychiatry (6.7%).

CONCLUSIONS

Managing benzodiazepine withdrawal with a 4- to 6-week intensive outpatient taper in patients with benzodiazepine and opioid use disorders is challenging. More work is needed to refine patient selection, balance safety risks with feasibility, and study long-term, patient-centered outcomes.

An Analysis of Benzodiazepine Prescribing to Primary Care Patients in a Large Healthcare System from 2019-2020

We sought to quantify benzodiazepine prescribing by primary care providers from 2019 to 2020 and identify correlates of prescribing. We hypothesized prescribing would increase post-COVID-19 lockdown. We conducted a retrospective cohort study of adult patients with primary care visits in 2019 or 2020 in a large Ohio healthcare system. Demographics, diagnosis codes, and receipt of benzodiazepine prescriptions were collected. Using multivariable logistic regression, we examined factors associated with benzodiazepine prescription receipt during the whole study period and post-lockdown. 455,537 adult patients had 1,643,473 visits. Benzodiazepines were prescribed in 3.2% (53,049/1,643,473) of visits. Effect sizes for positive associations with benzodiazepine prescription were largest for anxiety disorders. For negative associations, they were largest for Black patients and patients with cocaine use disorder. Benzodiazepine prescribing was positively associated with multiple groups having contraindications, though effect sizes were small. Contrary to our hypothesis, odds of receiving a prescription were 8.8% lower post-lockdown. Benzodiazepine prescribing rates in our system compared favorably to national rates. Year over year odds of receiving a prescription were slightly lower post-lockdown. Racial disparities were present and deserve further study. Strategies to reduce benzodiazepine prescribing to patients with anxiety may yield the largest reductions for benzodiazepine prescribing in primary care settings.

Opioid and benzodiazepine misuse in the United States: The impact of socio-demographic characteristics

BACKGROUND AND OBJECTIVES

In 2021, drug overdose deaths in the United States reached a new record of 107,622. Misuse of opioids and benzodiazepines accounts for a large portion of drug overdose deaths. However, the effects of socio-demographic characteristics on misuse of opioids and benzodiazepines are not evident. Thus, this study examines the socio-demographic characteristics associated with misuse of opioids and benzodiazepines among adults in the United States.

METHODS

Data from 2015-2019 National Survey on Drug Use and Health was utilized in the multinomial logistic regression analysis and included 202,935 adults ages ≥18 years.

RESULTS

During 2015-2019, 3.3% of the adults misused opioids, 1.2% misused benzodiazepines, and 0.9% misused both drugs in the preceding year of the survey. Those who were younger, bisexual, non-Hispanic White, had a history of delinquency in the past year, had alcohol dependence/abuse, marijuana dependence/abuse, nicotine dependence and use, and experienced major depressive episodes were more likely to misuse opioids, benzodiazepines, or both.

CONCLUSION AND SCIENTIFIC SIGNIFICANCE

A large portion of US adults are misusing opioids, benzodiazepines, and both drugs. Specifically, bisexual individuals experience higher odds of opioid misuse, benzodiazepine misuse and misuse of both drugs compared with heterosexuals, while males are experiencing lower odds of benzodiazepine misuse compared with females. Individuals aged 26-49 experience the highest odds of opioid misuse, though misuse of both drugs was higher among the 18-25 age group. Findings underscore the use of targeted preventive measures to reduce misuse of these drugs among at-risk populations identified in this study.

Exploring trends in benzodiazepine-positive fatal drug overdoses in Tennessee, 2019-2021

BACKGROUND

Benzodiazepine-positive overdoses increased between 2019 and 2021 in Tennessee. We sought to determine the changes in the number and characteristics of prescription and illicit benzodiazepine-positive fatal drug overdoses during this period.

MATERIALS AND METHODS

A statewide study was conducted to determine changes in the number and characteristics of benzodiazepine-positive drug overdose decedents using 2019-2021 data from the Tennessee State Unintentional Drug Overdose Reporting System. The analyses were limited to Tennessee residents aged ≥ 18 years. A benzodiazepine-positive overdose was defined as any benzodiazepine on toxicology, regardless of the presence of other substances. Frequencies were generated to compare demographics, circumstances, prescription history, and toxicology between 2019 and 2021 for illicit and prescription benzodiazepine-positive fatal overdoses.

RESULTS

Between 2019 and 2021, 1666 benzodiazepine-positive unintentional or undetermined fatal drug overdoses out of 5916 total overdoses that occurred among adult Tennessee residents with available toxicological information. Prescription benzodiazepines were identified in 80.7% of deaths, whereas illicit benzodiazepines were identified in 12.0% of deaths. Many decedents had an anxiety disorder (45.5%), while over half of all decedents had a history of substance use disorder (52.3%). Most benzodiazepine-positive overdoses involved fentanyl (71.3%).

CONCLUSIONS

This analysis can inform local and regional public health workers to implement focused prevention and intervention efforts for people with co-occurring mental health conditions and substance use disorders to curb overdose epidemics among persons using benzodiazepines in Tennessee. Public health campaigns should focus on educating people on appropriate prescription medication use and the dangers of obtaining substances illicitly. Given the high proportion of opioids in this population, further education also is needed on the dangers of polysubstance drug use. The differences between prescription and illicit benzodiazepine-positive fatal overdoses indicate the need to develop substance-specific prevention and treatment strategies.

Predicting benzodiazepine prescriptions: A proof-of-concept machine learning approach

Introduction

Benzodiazepines are the most commonly prescribed psychotropic medications, but they may place users at risk of serious adverse effects. Developing a method to predict benzodiazepine prescriptions could assist in prevention efforts.

Methods

The present study applies machine learning methods to de-identified electronic health record data, in order to develop algorithms for predicting benzodiazepine prescription receipt (yes/no) and number of benzodiazepine prescriptions (0, 1, 2+) at a given encounter. Support-vector machine (SVM) and random forest (RF) approaches were applied to outpatient psychiatry, family medicine, and geriatric medicine data from a large academic medical center. The training sample comprised encounters taking place between January 2020 and December 2021 (N = 204,723 encounters); the testing sample comprised data from encounters taking place between January and March 2022 (N = 28,631 encounters). The following empirically-supported features were evaluated: anxiety and sleep disorders (primary anxiety diagnosis, any anxiety diagnosis, primary sleep diagnosis, any sleep diagnosis), demographic characteristics (age, gender, race), medications (opioid prescription, number of opioid prescriptions, antidepressant prescription, antipsychotic prescription), other clinical variables (mood disorder, psychotic disorder, neurocognitive disorder, prescriber specialty), and insurance status (any insurance, type of insurance). We took a step-wise approach to developing a prediction model, wherein Model 1 included only anxiety and sleep diagnoses, and each subsequent model included an additional group of features.

Results

For predicting benzodiazepine prescription receipt (yes/no), all models showed good to excellent overall accuracy and area under the receiver operating characteristic curve (AUC) for both SVM (Accuracy = 0.868-0.883; AUC = 0.864-0.924) and RF (Accuracy = 0.860-0.887; AUC = 0.877-0.953). Overall accuracy was also high for predicting number of benzodiazepine prescriptions (0, 1, 2+) for both SVM (Accuracy = 0.861-0.877) and RF (Accuracy = 0.846-0.878).

Discussion

Results suggest SVM and RF algorithms can accurately classify individuals who receive a benzodiazepine prescription and can separate patients by the number of benzodiazepine prescriptions received at a given encounter. If replicated, these predictive models could inform system-level interventions to reduce the public health burden of benzodiazepines.

Sedative-hypnotic Co-prescribing with Opioids in a Large Network of Community Health Centers

OBJECTIVE

When prescribed with opioids, sedative-hypnotics substantially increase the risk of overdose. The objective of this paper was to describe characteristics and trends in opioid sedative-hypnotic co-prescribing in a network of safety-net clinics serving low-income, publicly insured, and uninsured individuals.

METHODS

This retrospective longitudinal analysis of prescription orders examined opioid sedative-hypnotic co-prescribing rates between 2009 and 2018 in the OCHIN network of safety-net community health centers. Sedative-hypnotics included benzodiazepine and non-benzodiazepine sedatives (eg, zolpidem). Co-prescribing patterns were assessed overall and across patient demographic and co-morbidity characteristics.

RESULTS

From 2009 to 2018, 240 587 patients had ≥1 opioid prescriptions. Most were White (65%), female (59%), and had Medicaid insurance (43%). One in 4 were chronic opioid users (25%). During this period, 55 332 (23%) were co-prescribed a sedative-hypnotic. The prevalence of co-prescribing was highest for females (26% vs 19% for males), non-Hispanic Whites (28% vs 13% for Hispanic to 20% for unknown), those over 44 years of age (25% vs 20% for <44 years), Medicare insurance (30% vs 21% for uninsured to 22% for other/unknown), and among those on chronic opioid therapy (40%). Co-prescribing peaked in 2010 (32%) and declined steadily through 2018 (20%). Trends were similar across demographic subgroups. Co-prescribed sedative-hypnotics remained elevated for those with chronic opioid use (27%), non-Hispanic Whites (24%), females (23%), and those with Medicare (23%) or commercial insurance (22%).

CONCLUSIONS

Co-prescribed sedative-hypnotic use has declined steadily since 2010 across all demographic subgroups in the OCHIN population. Concurrent use remains elevated in several population subgroups.