Prevalence of Cannabis Use Disorder Among Primary Care Patients with Varying Frequency of Past-Year Cannabis Use.

BACKGROUND: Valid, single-item cannabis screens for the frequency of past-year use (SIS-C) can identify patients at risk for cannabis use disorder (CUD); however, the prevalence of CUD for patients who report varying frequencies of use in the clinical setting remains unexplored. OBJECTIVE: Compare clinical responses about the frequency of past-year cannabis use to typical use and CUD severity reported on a confidential survey. PARTICIPANTS: Among adult patients in an integrated health system who completed the SIS-C as part of routine care (3/28/2019-9/12/2019; n = 108,950), 5000 were selected for a confidential survey using stratified random sampling. Among 1688 respondents (34% response rate), 1589 who reported past-year cannabis use on the SIS-C were included. MAIN MEASURES: We compared patients with varying frequency of cannabis use on the SIS-C (< monthly, monthly, weekly, daily) to survey responses on the Composite International Diagnostic Interview Substance Abuse Module for CUD (any and moderate-severe CUD) and cannabis exposure measures (typical use per-week, per-day). Adjusted multinomial (categorical) and logistic regression (binary), weighted for population estimates, estimated the prevalence of outcomes across frequencies. KEY RESULTS: Patients were predominantly middle-aged (mean = 43.3 years [SD = 16.9]), male (51.8%), white (78.2%), non-Hispanic (94.0%), and commercially insured (68.9%). The prevalence of any and moderate-severe CUD increased with greater frequency of past-year cannabis use reported on the SIS-C (p-values < 0.001) and ranged from 12.7% (6.3-19.2%) and 0.9% (0.0-2.7%) for < monthly to 44.6% (41.4-47.7%) and 20.3% (17.8-22.9%) for daily use, respectively. Greater frequency of use on the SIS-C in the clinical setting corresponded with greater per-week and per-day use on the confidential survey. CONCLUSIONS: Among patients who reported past-year cannabis use as part of routine screening, the prevalence of CUD and other cannabis exposure measures increased with greater frequency of cannabis use, underscoring the utility of brief cannabis screens for identifying patients at risk for CUD.

Use of an Inverted Synthetic Control Method to Estimate Effects of Recent Drug Overdose Good Samaritan Laws, Overall and by Black/White Race/Ethnicity.

Overdose Good Samaritan laws (GSLs) aim to reduce mortality by providing limited legal protections when a bystander to a possible drug overdose summons help. Most research into the impact of these laws is dated or potentially confounded by coenacted naloxone access laws. Lack of awareness and trust in GSL protections, as well as fear of police involvement and legal repercussions, remain key deterrents to help-seeking. These barriers may be unequally distributed by race/ethnicity due to racist policing and drug policies, potentially producing racial/ethnic disparities in the effectiveness of GSLs for reducing overdose mortality. We used 2015-2019 vital statistics data to estimate the effect of recent GSLs on overdose mortality, overall (8 states) and by Black/White race/ethnicity (4 states). Given GSLs' near ubiquity, few unexposed states were available for comparison. Therefore, we generated an "inverted" synthetic control method (SCM) to compare overdose mortality in new-GSL states with that in states that had GSLs throughout the analytical period. The estimated relationships between GSLs and overdose mortality, both overall and stratified by Black/White race/ethnicity, were consistent with chance. An absence of effect could result from insufficient protection provided by the laws, insufficient awareness of them, and/or reticence to summon help not addressable by legal protections. The inverted SCM may be useful for evaluating other widespread policies.

Baseline representativeness of patients in clinics enrolled in the PRimary care Opioid Use Disorders treatment (PROUD) trial: comparison of trial and non-trial clinics in the same health systems.

BACKGROUND: Pragmatic primary care trials aim to test interventions in "real world" health care settings, but clinics willing and able to participate in trials may not be representative of typical clinics. This analysis compared patients in participating and non-participating clinics from the same health systems at baseline in the PRimary care Opioid Use Disorders treatment (PROUD) trial. METHODS: This observational analysis relied on secondary electronic health record and administrative claims data in 5 of 6 health systems in the PROUD trial. The sample included patients 16-90 years at an eligible primary care visit in the 3 years before randomization. Each system contributed 2 randomized PROUD trial clinics and 4 similarly sized non-trial clinics. We summarized patient characteristics in trial and non-trial clinics in the 2 years before randomization ("baseline"). Using mixed-effect regression models, we compared trial and non-trial clinics on a baseline measure of the primary trial outcome (clinic-level patient-years of opioid use disorder (OUD) treatment, scaled per 10,000 primary care patients seen) and a baseline measure of the secondary trial outcome (patient-level days of acute care utilization among patients with OUD). RESULTS: Patients were generally similar between the 10 trial clinics (n = 248,436) and 20 non-trial clinics (n = 341,130), although trial clinics' patients were slightly younger, more likely to be Hispanic/Latinx, less likely to be white, more likely to have Medicaid/subsidized insurance, and lived in less wealthy neighborhoods. Baseline outcomes did not differ between trial and non-trial clinics: trial clinics had 1.0 more patient-year of OUD treatment per 10,000 patients (95% CI: - 2.9, 5.0) and a 4% higher rate of days of acute care utilization than non-trial clinics (rate ratio: 1.04; 95% CI: 0.76, 1.42). CONCLUSIONS: trial clinics and non-trial clinics were similar regarding most measured patient characteristics, and no differences were observed in baseline measures of trial primary and secondary outcomes. These findings suggest trial clinics were representative of comparably sized clinics within the same health systems. Although results do not reflect generalizability more broadly, this study illustrates an approach to assess representativeness of clinics in future pragmatic primary care trials.

Discriminative validity of a substance use symptom checklist for moderate-severe DSM-5 cannabis use disorder (CUD) in primary care settings.

Background: The prevalence of cannabis use disorder (CUD) is increasing in the US and primary care providers need tools to identify patients with moderate-severe CUD to facilitate treatment. A single-item screen for cannabis (SIS-C) has outstanding discriminative validity for CUD. However, because the prevalence of moderate-severe CUD is typically low, the probability that an average patient who screens positive for daily cannabis has moderate-severe cannabis use disorder is low, making follow-up assessment important. Methods: This study reports the discriminative validity of a DSM-5 Substance Use Symptom Checklist ("Checklist") for moderate-severe CUD among 498 primary care patients who reported daily cannabis use on the SIS-C. We evaluated the performance of the Checklist (score 0-11) completed during routine care, compared to ≥4 DSM-5 CUD symptoms (moderate-severe CUD) on the Composite International Diagnostic Interview Substance Abuse Module from a confidential survey (reference standard). We estimated areas under receiver operating curve (AUROC), sensitivities, specificities, and post-test probabilities. Results: Of 498 eligible patients, 17 % met diagnostic criteria for moderate-severe CUD. The Checklist's AUROC for moderate-severe CUD was 0.77 (95 % CI: 0.71-0.83), and Checklist scores of 1-2 balanced sensitivity and specificity. Among patients from a population with average prevalence of CUD before screening (~6 % prevalence) and daily use on the SIS-C, a Checklist score of 3 indicated a post-test probability of 82.1 %. Conclusion: Overall performance of the Checklist was good and the high specificity made it useful for identifying patients likely to have moderate-severe CUD among those at average risk.

Prevalence of Cannabis Use Disorder and Reasons for Use Among Adults in a US State Where Recreational Cannabis Use Is Legal.

Importance: Medical and nonmedical cannabis use and cannabis use disorders (CUD) have increased with increasing cannabis legalization. However, the prevalence of CUD among primary care patients who use cannabis for medical or nonmedical reasons is unknown for patients in states with legal recreational use. Objective: To estimate the prevalence and severity of CUD among patients who report medical use only, nonmedical use only, and both reasons for cannabis use in a state with legal recreational use. Design, Setting, and Participants: This cross-sectional survey study took place at an integrated health system in Washington State. Among 108 950 adult patients who completed routine cannabis screening from March 2019 to September 2019, 5000 were selected for a confidential cannabis survey using stratified random sampling for frequency of past-year cannabis use and race and ethnicity. Among 1688 respondents, 1463 reporting past 30-day cannabis use were included in the study. Exposure: Patient survey-reported reason for cannabis use in the past 30 days: medical use only, nonmedical use only, and both reasons. Main Outcomes and Measures: Patient responses to the Composite International Diagnostic Interview-Substance Abuse Module for CUD, corresponding to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition CUD severity (0-11 symptoms) were categorized as any CUD (≥2 symptoms) and moderate to severe CUD (≥4 symptoms). Adjusted analyses were weighted for survey stratification and nonresponse for primary care population estimates and compared prevalence of CUD across reasons for cannabis use. Results: Of 1463 included primary care patients (weighted mean [SD] age, 47.4 [16.8] years; 748 [weighted proportion, 61.9%] female) who used cannabis, 42.4% (95% CI, 31.2%-54.3%) reported medical use only, 25.1% (95% CI, 17.8%-34.2%) nonmedical use only, and 32.5% (95% CI, 25.3%-40.8%) both reasons for use. The prevalence of CUD was 21.3% (95% CI, 15.4%-28.6%) and did not vary across groups. The prevalence of moderate to severe CUD was 6.5% (95% CI, 5.0%-8.6%) and differed across groups: 1.3% (95% CI, 0.0%-2.8%) for medical use, 7.2% (95% CI, 3.9%-10.4%) for nonmedical use, and 7.5% (95% CI, 5.7%-9.4%) for both reasons for use (P = .01). Conclusions and Relevance: In this cross-sectional study of primary care patients in a state with legal recreational cannabis use, CUD was common among patients who used cannabis. Moderate to severe CUD was more prevalent among patients who reported any nonmedical use. These results underscore the importance of assessing patient cannabis use and CUD symptoms in medical settings.

A patient-centered nurse-supported primary care-based collaborative care program to treat opioid use disorder and depression: Design and protocol for the MI-CARE randomized controlled trial.

BACKGROUND: Opioid use disorder (OUD) contributes to rising morbidity and mortality. Life-saving OUD treatments can be provided in primary care but most patients with OUD don't receive treatment. Comorbid depression and other conditions complicate OUD management, especially in primary care. The MI-CARE trial is a pragmatic randomized encouragement (Zelen) trial testing whether offering collaborative care (CC) to patients with OUD and clinically-significant depressive symptoms increases OUD medication treatment with buprenorphine and improves depression outcomes compared to usual care. METHODS: Adult primary care patients with OUD and depressive symptoms (n ≥ 800) from two statewide health systems: Kaiser Permanente Washington and Indiana University Health are identified with computer algorithms from electronic Health record (EHR) data and automatically enrolled. A random sub-sample (50%) of eligible patients is offered the MI-CARE intervention: a 12-month nurse-driven CC intervention that includes motivational interviewing and behavioral activation. The remaining 50% of the study cohort comprise the usual care comparison group and is never contacted. The primary outcome is days of buprenorphine treatment provided during the intervention period. The powered secondary outcome is change in Patient Health Questionnaire (PHQ)-9 depression scores. Both outcomes are obtained from secondary electronic healthcare sources and compared in "intent-to-treat" analyses. CONCLUSION: MI-CARE addresses the need for rigorous encouragement trials to evaluate benefits of offering CC to generalizable samples of patients with OUD and mental health conditions identified from EHRs, as they would be in practice, and comparing outcomes to usual primary care. We describe the design and implementation of the trial, currently underway. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05122676. Clinical trial registration date: November 17, 2021.

Nurse Care Management for Opioid Use Disorder Treatment: The PROUD Cluster Randomized Clinical Trial.

Importance: Few primary care (PC) practices treat patients with medications for opioid use disorder (OUD) despite availability of effective treatments. Objective: To assess whether implementation of the Massachusetts model of nurse care management for OUD in PC increases OUD treatment with buprenorphine or extended-release injectable naltrexone and secondarily decreases acute care utilization. Design, Setting, and Participants: The Primary Care Opioid Use Disorders Treatment (PROUD) trial was a mixed-methods, implementation-effectiveness cluster randomized clinical trial conducted in 6 diverse health systems across 5 US states (New York, Florida, Michigan, Texas, and Washington). Two PC clinics in each system were randomized to intervention or usual care (UC) stratified by system (5 systems were notified on February 28, 2018, and 1 system with delayed data use agreement on August 31, 2018). Data were obtained from electronic health records and insurance claims. An implementation monitoring team collected qualitative data. Primary care patients were included if they were 16 to 90 years old and visited a participating clinic from up to 3 years before a system's randomization date through 2 years after. Intervention: The PROUD intervention included 3 components: (1) salary for a full-time OUD nurse care manager; (2) training and technical assistance for nurse care managers; and (3) 3 or more PC clinicians agreeing to prescribe buprenorphine. Main Outcomes and Measures: The primary outcome was a clinic-level measure of patient-years of OUD treatment (buprenorphine or extended-release injectable naltrexone) per 10 000 PC patients during the 2 years postrandomization (follow-up). The secondary outcome, among patients with OUD prerandomization, was a patient-level measure of the number of days of acute care utilization during follow-up. Results: During the baseline period, a total of 130 623 patients were seen in intervention clinics (mean [SD] age, 48.6 [17.7] years; 59.7% female), and 159 459 patients were seen in UC clinics (mean [SD] age, 47.2 [17.5] years; 63.0% female). Intervention clinics provided 8.2 (95% CI, 5.4-∞) more patient-years of OUD treatment per 10 000 PC patients compared with UC clinics (P = .002). Most of the benefit accrued in 2 health systems and in patients new to clinics (5.8 [95% CI, 1.3-∞] more patient-years) or newly treated for OUD postrandomization (8.3 [95% CI, 4.3-∞] more patient-years). Qualitative data indicated that keys to successful implementation included broad commitment to treat OUD in PC from system leaders and PC teams, full financial coverage for OUD treatment, and straightforward pathways for patients to access nurse care managers. Acute care utilization did not differ between intervention and UC clinics (relative rate, 1.16; 95% CI, 0.47-2.92; P = .70). Conclusions and Relevance: The PROUD cluster randomized clinical trial intervention meaningfully increased PC OUD treatment, albeit unevenly across health systems; however, it did not decrease acute care utilization among patients with OUD. Trial Registration: ClinicalTrials.gov Identifier: NCT03407638.

A modified Delphi process to identify experts' perceptions of the most beneficial and harmful laws to reduce opioid-related harm.

BACKGROUND: States have enacted multiple types of laws, with a variety of constituent provisions, in response to the opioid epidemic, often simultaneously. This temporal proximity and variation in state-to-state operationalization has resulted in significant challenges for empirical research on their effects. Thus, expert consensus can be helpful to classify laws and their provisions by their degree of helpfulness and impact. METHODS: We conducted a four-stage modified policy Delphi process to identify the top 10 most helpful and 5 most harmful provisions from eight opioid-related laws. This iterative consultation with six types of opioid experts included a preliminary focus group (n=12), two consecutive surveys (n=56 and n=40, respectively), and a final focus group feedback session (n=5). RESULTS: On a scale of very harmful (0) to very helpful (4), overdose Good Samaritan laws received the highest average helpfulness rating (3.62, 95% CI: 3.48-3.75), followed by naloxone access laws (3.37, 95% CI: 3.22-3.51), and pain management clinic laws (3.08, 95% CI: 2.89-3.26). Drug-induced homicide (DIH) laws were rated the most harmful (0.88, 95% CI: 0.66-1.11). Impact ratings aligned similarly, although Medicaid laws received the second highest overall impact rating (3.71, 95% CI: 3.45, 3.97). The two most helpful provisions were naloxone standing orders (3.94, 95% CI: 3.86-4.02) and Medicaid coverage of medications for opioid use disorder (MOUD) (3.89, 95% CI: 3.82). Mandatory minimum DIH laws were the most harmful provision (0.73, 95% CI 0.53-0.93); followed by requiring prior authorization for Medicaid coverage of MOUD (1.00 95% CI: 0.72-1.27). CONCLUSION: Overall, experts rated laws and provisions that facilitated harm reduction efforts and access to MOUD as most helpful. Laws and provisions rated as most harmful criminalized substance use and placed restrictions on access to MOUD. These ratings provide a foundation for evaluating the overall overdose policy environment for each state.

A two-dimensional model that employs explicit and implicit attitudes to characterize prejudice.

In the authors' 2-dimensional model of prejudice, explicit and implicit attitudes are used to create 4 profiles: truly low prejudiced (TLP: double lows), aversive racists (AR: low explicit modern racism/high implicit prejudice), principled conservatives (PC: high explicit modern racism/low implicit prejudice), and modern racists (MR: double highs). Students completed an Asian Modern Racism Scale and an Asian/White Implicit Association Test. The authors compared the 4 groups' prejudice-related ideologies (i.e., egalitarianism/humanism and social conservatism) and economic/political conservatism (Study 1, N=132). The authors also tested whether MR but not PC (Study 2, N=65) and AR but not TLP (Study 3, N=143) are more likely to negatively evaluate an Asian target when attributional ambiguity is high (vs. low). In support of the model, TLP did not hold prejudice-related ideologies and did not discriminate; AR were low in conservatism and demonstrated the attributional-ambiguity effect; PC did not strongly endorse prejudice-related ideologies and did not discriminate; MR strongly endorsed prejudice-related ideologies, were conservative, and demonstrated the attributional-ambiguity effect. The authors discuss implications for operationalizing and understanding the nature of prejudice.

The reciprocal lagged effects of substance use and recidivism in a prisoner reentry context.

BACKGROUND: Much work has investigated the association between substance use, crime, and recidivism, yet little scholarship has examined these associations longitudinally among samples of recently released prisoners. We examine the lagged reciprocal effects of hard substance use and crime, among other covariates, in the context of the prisoner reentry process. METHODS: We rely on data from the Serious and Violent Offender Reentry Initiative (SVORI) evaluation and employ cross-lagged panel models to examine short-term changes in substance use and crime over time among a large sample of high-risk, former prisoners (N = 1697). RESULTS: Substance use marginally predicted increased odds of rearrest at one wave, and rearrest significantly (p < .05) predicted increased odds of substance use at another. As such, the results provide limited evidence for a degree of lagged mutual causation; associations vary over the reentry process and are complicated by other realities of life after prison. A key finding is that both behaviors are more consistently influenced by other factors, such as service needs and instrumental and emotional supports. CONCLUSIONS: Although there are relationships between drug use and criminal behavior, these behaviors alone are insufficient explanations for one another in an adult reentry population. Alternatively, the compounding social and personal needs of the reentry population, and the extent to which they received support or services to address these needs, appear to have the strongest influence on both behaviors in the reentry context.