Buprenorphine use and courses of care for opioid use disorder treatment within the Veterans Health Administration

Are gaps in rates of retention on buprenorphine for treatment of opioid use disorder closing among veterans across different races and ethnicities? A retrospective cohort study

INTRODUCTION

The U.S. Veterans Health Administration has undertaken several initiatives to improve veterans' access to and retention on buprenorphine because it prevents overdose and reduces drug-related morbidity. We aimed to determine whether improvements in retention duration over time was equitable across veterans of different races and ethnicities.

METHODS

This retrospective cohort study was conducted among veterans who initiated buprenorphine from federal fiscal years (FY) 2006 to 2020 after diagnosis of opioid use disorder. Using an accelerated failure time model, we estimated the association between time to buprenorphine discontinuation and FY of initiation, race and ethnicity, and other control covariates. We followed veterans from buprenorphine initiation until they discontinued or had a censoring event. We then estimated the predicted median days retained on buprenorphine, the average marginal effect of initiating in a later FY, the same measure by race and ethnicity, the incremental effect of the various racial and ethnic identities in contrast to non-Hispanic White, and the total change in the size of the gap over the 15 years of the study between veterans with a minoritized racial or ethnic identity compared to non-Hispanic White veterans.

RESULTS

Most of the 31,797 veterans in the sample were non-Hispanic White (74.5 %), from urban areas (83.5 %), male (92.0 %), and had significant comorbidities, most frequently anxiety disorders (51.0 %) and depression (63.0 %). Overall, 49.8 % of veterans were retained at least 180 days. The average marginal effect of FY was 7.0 days [95%CI:5.3, 8.8] but was significantly smaller among veterans identifying as Black or African American [3.2 days; 95%CI:2.4, 4.1] or Asian [3.6 days; 95%CI:1.6, 5.7] compared to veterans who identify as non-Hispanic White [7.9 days; 95%CI:5.9, 9.9]. Additional measures of change were significant for veterans identifying as Hispanic White or with two or more races.

CONCLUSION

Although buprenorphine retention in OUD treatment improved for all veterans over the 15-year study period, veterans from most minoritized racial and ethnic groups fell further behind as gains in duration on therapy accrued primarily to non-Hispanic White veterans. Targeted interventions addressing specific challenges experienced by veterans with minoritized identities are needed to close gaps in retention on buprenorphine.

Predicting buprenorphine adherence among patients with opioid use disorder in primary care settings

BACKGROUND

Medications for opioid use disorder (MOUD), including buprenorphine, are effective treatments for opioid use disorder (OUD) and reduce risk for overdose and death. Buprenorphine can be prescribed in outpatient primary care settings to treat OUD; however, prior research suggests adherence to buprenorphine in these settings can be low. The purpose of this study was to identify the rates of and factors associated with buprenorphine adherence among patients with OUD in the first six months after a new start of buprenorphine.

METHODS

Data were extracted from the electronic health record (EHR) from a large integrated health system in the upper Midwest. Patients with OUD (N = 345; Mean age = 37.6 years, SD 13.2; 61.7% male; 78% White) with a new start of buprenorphine between March 2019 and July 2021 were included in the analysis. Buprenorphine adherence in the first six months was defined using medication orders; the proportion of days covered (PDC) with a standard cut-point of 80% was used to classify patients as adherent or non-adherent. Demographic (e.g., age, sex, race and ethnicity, geographic location), service (e.g., encounters, buprenorphine formulations and dosage) and clinical (e.g., diagnoses, urine toxicology screens) characteristics were examined as factors that could be related to adherence. Analyses included logistic regression with adherence group as a binary outcome.

RESULTS

Less than half of patients were classified as adherent to buprenorphine (44%). Adjusting for other factors, male sex (OR = 0.34, 95% CI = 0.20, 0.57, p < .001) and having an unexpected positive for opioids on urine toxicology (OR = 0.42, 95% CI = 0.21, 0.83, p < .014) were associated with lower likelihood of adherence to buprenorphine, whereas being a former smoker (compared to a current smoker; OR = 1.82, 95% CI = 1.02, 3.27, p = .014) was associated with greater likelihood of being adherent to buprenorphine.

CONCLUSIONS

These results suggest that buprenorphine adherence in primary care settings may be low, yet male sex and smoking status are associated with adherence rates. Future research is needed to identify the mechanisms through which these factors are associated with adherence.

Cognitive processing therapy (CPT) versus individual drug counseling (IDC) for PTSD for veterans with opioid use disorder maintained on buprenorphine

BACKGROUND AND OBJECTIVES

There are high rates of comorbidity between posttraumatic stress disorder (PTSD) and opioid use disorder (OUD). Evidence-based trauma-focused psychotherapies such as Cognitive Processing Therapy (CPT) are a first-line treatment for PTSD. Veterans with OUD are treated primarily in substance use disorder (SUD) clinics where the standard of care is drug counseling; they often do not have access to first-line PTSD treatments. This study tested whether CPT can be conducted safely and effectively in veterans with comorbid OUD treated with buprenorphine.

METHODS

This 12-week, 2-site, randomized clinical trial (RCT) included open-label randomization to two groups: (a) CPT versus (b) Individual Drug Counselling (IDC) in veterans with PTSD and comorbid OUD who were maintained on buprenorphine (N = 38).

RESULTS

Veterans randomized to either IDC (n = 18) or CPT (n = 20) showed a significant reduction in self-reported PTSD symptoms over time as measured by the PTSD checklist (PCL-5) but there were no treatment group differences; there was some indication that reduction in PTSD symptoms in the CPT group were sustained in contrast to the IDC group. Recruitment was significantly impacted by COVID-19 pandemic, so this study serves as a proof-of-concept pilot study.

DISCUSSION AND CONCLUSIONS

Veterans with OUD and PTSD can safely and effectively participate in evidence-based therapy for PTSD; further work should confirm that trauma-focused treatment may be more effective in leading to sustained remission of PTSD symptoms than drug counseling.

SCIENTIFIC SIGNIFICANCE

This is the first study to evaluate CPT for PTSD in the context of buprenorphine treatment for OUD.

Barriers and Facilitators to Using a Clinical Decision Support Tool for Opioid Use Disorder in Primary Care

PURPOSE

Clinical decision support (CDS) tools are designed to help primary care clinicians (PCCs) implement evidence-based guidelines for chronic disease care. CDS tools may also be helpful for opioid use disorder (OUD), but only if PCCs use them in their regular workflow. This study's purpose was to understand PCC and clinic leader perceptions of barriers to using an OUD-CDS tool in primary care.

METHODS

PCCs and leaders (n = 13) from clinics in an integrated health system in which an OUD-CDS tool was implemented participated in semistructured qualitative interviews. Questions aimed to understand whether the CDS tool design, implementation, context, and content were barriers or facilitators to using the OUD-CDS in primary care. Recruitment stopped when thematic saturation was reached. An inductive thematic analysis approach was used to generate overall themes.

RESULTS

Five themes emerged: (1) PCCs prefer to minimize conversations about OUD risk and treatment; (2) PCCs are enthusiastic about a CDS tool that addresses a topic of interest but lack interest in treating OUD; (3) contextual barriers in primary care limit PCCs' ability to use CDS to manage OUD; (4) CDS needs to be simple and visible, save time, and add value to care; and (5) CDS has value in identifying and screening patients and facilitating referrals.

CONCLUSIONS

This study identified several factors that impact use of an OUD-CDS tool in primary care, including PCC interest in treating OUD, contextual barriers, and CDS design. These results may help others interested in implementing CDS for OUD in primary care.

Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder

BACKGROUND

Buprenorphine for opioid use disorder (B-MOUD) is essential to improving patient outcomes; however, retention is essential.

OBJECTIVE

To develop and validate machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans initiating B-MOUD.

METHODS

Veterans initiating B-MOUD from fiscal years 2006-2020 were identified. Veterans' B-MOUD episodes were randomly divided into training (80%;n = 45,238) and testing samples (20%;n = 11,309). Candidate algorithms [multiple logistic regression, least absolute shrinkage and selection operator regression, random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN)] were used to build and validate classification models to predict six binary outcomes: 1) B-MOUD retention, 2) any overdose, 3) opioid-related overdose, 4) overdose death, 5) opioid overdose death, and 6) all-cause mortality. Model performance was assessed using standard classification statistics [e.g., area under the receiver operating characteristic curve (AUC-ROC)].

RESULTS

Episodes in the training sample were 93.0% male, 78.0% White, 72.3% unemployed, and 48.3% had a concurrent drug use disorder. The GBM model slightly outperformed others in predicting B-MOUD retention (AUC-ROC = 0.72). RF models outperformed others in predicting any overdose (AUC-ROC = 0.77) and opioid overdose (AUC-ROC = 0.77). RF and GBM outperformed other models for overdose death (AUC-ROC = 0.74 for both), and RF and DNN outperformed other models for opioid overdose death (RF AUC-ROC = 0.79; DNN AUC-ROC = 0.78). RF and GBM also outperformed other models for all-cause mortality (AUC-ROC = 0.76 for both). No single predictor accounted for >3% of the model's variance.

CONCLUSIONS

Machine-learning algorithms can accurately predict OUD-related outcomes with moderate predictive performance; however, prediction of these outcomes is driven by many characteristics.

Patients' Perspectives on Discontinuing Buprenorphine for the Treatment of Opioid Use Disorder

OBJECTIVES

Buprenorphine and other medications for opioid use disorder (OUD) are recommended as standard of care in the treatment of OUD and are associated with positive health and addiction-related outcomes. Despite benefits, discontinuation is common, with half of patients discontinuing in the first year of treatment. Addressing OUD is a major clinical priority, yet little is known about the causes of medication discontinuation from the patient perspective.

METHODS

From March 2021 to April 2022, we conducted qualitative interviews with patients who had discontinued buprenorphine for the treatment of OUD within the past 12 months. Eligible participants were selected from 2 Veterans Health Administration Health Care Systems in Oregon. Coding and analysis were guided by conventional qualitative content analysis.

RESULTS

Twenty participants completed an interview; 90% were White and 90% were male, and the mean age was 54.2 years. Before discontinuation, participants had received buprenorphine for 8.3 months on average (range, 1-40 months); 80% had received buprenorphine for less than 12 months. Qualitative analysis identified the following themes relating to discontinuation: health system barriers (eg, logistical hurdles, rules and policy violations), medication effects (adverse effects; attributed adverse effects, lack of efficacy in treating chronic pain) and desire for opioid use. Patient description of decisions to discontinue buprenorphine could be multicausal, reflecting provider or system-level barriers in interaction with patient complexity or medication ambivalence.

CONCLUSIONS

Study results identify several actionable ways OUD treatment could be modified to enhance patient retention.

A Systematic Review of State Office-Based Buprenorphine Treatment Laws Effective During 2022: Counseling, Dosage, and Visit Frequency Requirements

BACKGROUND

Buprenorphine is among the most effective treatments for opioid use disorder. Even though the federal government recently eliminated the waiver requirement and patient limits applicable to office-based buprenorphine treatment (OBBT), among other settings, some states may still have policies imposing requirements on OBBT providers not required by federal law.

METHODS

We collected statutes and regulations from 50 US states and the District of Columbia (ie, 51 jurisdictions) between August 11 and November 30, 2022 using the Nexis Uni legal database and search terms related to OBBT counseling, dosage, and/or frequency of visits. We then used template analysis, a mixed deductive-inductive qualitative method, to analyze legal content.

RESULTS

Ten jurisdictions (20%) in 2022 had an OBBT counseling, dosage, and/or visit frequency requirement. Four jurisdictions had at least one law in each OBBT policy category examined. One-fifth of jurisdictions have OBBT policies not required under federal law. Five of these jurisdictions are among those with the highest overdose death rates per capita, according to publicly available data from 2021. Some OBBT requirements could potentially limit clinician interest in offering buprenorphine treatment or result in inadequate care (eg, if dosage limitations are too low.).

CONCLUSIONS

Even though a federal waiver is no longer required for OBBT, our results suggests that at least some jurisdictions have other OBBT requirements, such as counseling, dosage, and/or frequency requirements. Given the severity of the ongoing opioid overdose crisis, policymakers should carefully consider the extent to which OBBT requirements are evidence based.

Expert Views on State Policies to Improve Engagement and Retention in Treatment for Opioid Use Disorder: A Qualitative Analysis of an Online Modified Delphi Process

OBJECTIVES

The aim of this study was to examine expert views on the effectiveness and implementability of state policies to improve engagement and retention in treatment for opioid use disorder (OUD).

METHODS

We conducted a 3-round modified Delphi process using the online ExpertLens platform. Participants included 66 experts on OUD treatment policies. Experts commented on 14 hypothetical state policies targeting treatment engagement and quality of care. Using the GRADE Evidence-to-Decision framework, we conducted reflexive thematic analysis to develop patterns of meaning from the dataset.

RESULTS

Only policies for providing continued access to evidence-based treatment for highly at-risk populations, settings, and periods were seen as effective in meaningfully reducing population-level opioid-related overdose mortality. Experts commented that, although the general public increasingly supports policies expanding medications for OUD and evidence-based care, ongoing stigma about OUD encourages public acceptance of punitive and paternalistic policies. Experts viewed all policies as at least moderately feasible given the current infrastructure and resources, with affordability reliant on long-term cost savings from reduced opioid-related harms. Equitability depended on whether experts perceived a policy as inherently equitable in its design as well as concerns about the potential for inequitable implementation due to structural oppression and interpersonal biases in criminal-legal, healthcare, and other systems.

CONCLUSIONS

Experts believe that supportive (rather than punitive) policies improve engagement and retention in OUD treatment. States could prioritize implementing supportive policies that are patient-centered and take a harm-reduction approach to enhance medications for OUD access and utilization. States could consider deimplementing punitive policies that are coercive, take an abstinence-only approach, and use punitive and restrictive measures.

Differences in adverse outcomes across race and ethnicity among Veterans with similar predicted risks of an overdose or suicide-related event

OBJECTIVE

To evaluate the degree to which differences in incidence of mortality and serious adverse events exist across patient race and ethnicity among Veterans Health Administration (VHA) patients receiving outpatient opioid prescriptions and who have similar predicted risks of adverse outcomes. Patients were assigned scores via the VHA Stratification Tool for Opioid Risk Mitigation (STORM), a model used to predict the risk of experiencing overdose- or suicide-related health care events or death. Individuals with the highest STORM risk scores are targeted for case review.

DESIGN

Retrospective cohort study of high-risk veterans who received an outpatient prescription opioid between 4/2018-3/2019.

SETTING

All VHA medical centers.

PARTICIPANTS

In total, 84 473 patients whose estimated risk scores were between 0.0420 and 0.0609, the risk scores associated with the top 5%-10% of risk in the STORM development sample.

METHODS

We examined the expected probability of mortality and serious adverse events (SAEs; overdose or suicide-related events) given a patient's risk score and race.

RESULTS

Given a similar risk score, Black patients were less likely than White patients to have a recorded SAE within 6 months of risk score calculation. Black, Hispanic, and Asian patients were less likely than White patients with similar risk scores to die within 6 months of risk score calculation. Some of the mortality differences were driven by age differences in the composition of racial and ethnic groups in our sample.

CONCLUSIONS

Our results suggest that relying on the STORM model to identify patients who may benefit from an interdisciplinary case review may identify patients with clinically meaningful differences in outcome risk across race and ethnicity.

Impact of Potential Case Misclassification by Administrative Diagnostic Codes on Outcome Assessment of Observational Study for People Who Inject Drugs

Introduction

Initiation of medications for opioid use disorder (MOUD) within the hospital setting may improve outcomes for people who inject drugs (PWID) hospitalized because of an infection. Many studies used International Classification of Diseases (ICD) codes to identify PWID, although these may be misclassified and thus, inaccurate. We hypothesized that bias from misclassification of PWID using ICD codes may impact analyses of MOUD outcomes.

Methods

We analyzed a cohort of 36 868 cases of patients diagnosed with Staphylococcus aureus bacteremia at 124 US Veterans Health Administration hospitals between 2003 and 2014. To identify PWID, we implemented an ICD code-based algorithm and a natural language processing (NLP) algorithm for classification of admission notes. We analyzed outcomes of prescribing MOUD as an inpatient using both approaches. Our primary outcome was 365-day all-cause mortality. We fit mixed-effects Cox regression models with receipt or not of MOUD during the index hospitalization as the primary predictor and 365-day mortality as the outcome.

Results

NLP identified 2389 cases as PWID, whereas ICD codes identified 6804 cases as PWID. In the cohort identified by NLP, receipt of inpatient MOUD was associated with a protective effect on 365-day survival (adjusted hazard ratio, 0.48; 95% confidence interval, .29-.81; P < .01) compared with those not receiving MOUD. There was no significant effect of MOUD receipt in the cohort identified by ICD codes (adjusted hazard ratio, 1.00; 95% confidence interval, .77-1.30; P = .99).

Conclusions

MOUD was protective of all-cause mortality when NLP was used to identify PWID, but not significant when ICD codes were used to identify the analytic subjects.

Identification of opioid use disorder using electronic health records: Beyond diagnostic codes

BACKGROUND

We used structured and unstructured electronic health record (EHR) data to develop and validate an approach to identify moderate/severe opioid use disorder (OUD) that includes individuals without prescription opioid use or chronic pain, an underrepresented population.

METHODS

Using electronic diagnosis grouper text from EHRs of ~1 million patients (2012-2020), we created indicators of OUD-with "tiers" indicating OUD likelihood-combined with OUD medication (MOUD) orders. We developed six sub-algorithms with varying criteria (multiple vs single MOUD orders, multiple vs single tier 1 indicators, tier 2 indicators, tier 3 and 4 indicators). Positive predictive values (PPVs) were calculated based on chart review to determine OUD status and severity. We compared demographic and clinical characteristics of cases identified by the sub-algorithms.

RESULTS

In total, 14,852 patients met criteria for one of the sub-algorithms. Five sub-algorithms had PPVs ≥0.90 for any severity OUD; four had PPVs ≥0.90 for moderate/severe OUD. Demographic and clinical characteristics differed substantially between groups. Of identified OUD cases, 31.3% had no past opioid analgesic orders, 79.7% lacked evidence of chronic prescription opioid use, and 43.5% lacked a chronic pain diagnosis.

DISCUSSION

Incorporating unstructured data with MOUD orders yielded an approach that adequately identified moderate/severe OUD, identified unique demographic and clinical sub-groups, and included individuals without prescription opioid use or chronic pain, whose OUD may stem from illicit opioids. Findings show that incorporating unstructured data strengthens EHR algorithms for identifying OUD and suggests approaches limited to populations with prescription opioid use or chronic pain exclude many individuals with OUD.