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Abstract: While approximately one in five Americans with substance use disorder (SUD) receives treatment in addiction treatment programs, a majority have seen a primary care medical provider in the past year. Recognizing the critical role of primary care in addressing prevention and treatment of unhealthy substance use, for over a decade the National Drug Abuse Treatment Clinical Trials Network (CTN) has supported research to build the tools and evidence needed to support the integration of SUD care, while remaining realistic about the barriers to doing so. Authored by primary care and addiction medicine physician researchers, this commentary provides an overview of CTN primary care-focused research, from developing and implementing substance use screening tools to advancing evidence-based SUD treatment delivery in primary care settings. We identify three priority areas for research and practice innovations: 1) identifying effective treatment interventions to address polysubstance use; 2) improved screening and treatment for cannabis use; and 3) building the evidence base for substance use interventions among non-treatment seeking patients who have unhealthy drug use identified through screening. Addressing these areas can help primary care fulfill its potential as a key component of the substance use services continuum of care.

Abstract:

Background: The Tobacco, Alcohol, Prescription Medication, and Other Substance (TAPS) tool is a screening and brief assessment instrument to identify unhealthy tobacco, alcohol, drug use, and prescription medication use in primary care patients. This secondary analysis compares the TAPS tool to the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) for alcohol screening.

Methods: Adult primary care patients (1124 female, 874 male) completed the TAPS tool followed by AUDIT-C. Performance of each instrument was evaluated against a reference standard measure, the modified World Mental Health Composite International Diagnostic Interview, to identify problem use and alcohol use disorder (AUD). Area under the curve (AUC) appraised discrimination, and sensitivity and specificity were calculated for Youden optimal score thresholds.

Results: For identifying problem use: On the AUDIT-C, AUC was 0.90 (95% Confidence Interval: 0.86-0.92) for females and 0.91 (0.89-0.93) for males. Sensitivity and specificity for females were 0.89 (0.83-0.93) and 0.78 (0.75-0.80), respectively, and for males were 0.84 (0.79-0.88) and 0.82 (0.79-0.85). On the TAPS tool, AUC was 0.82 (0.79-0.86) for females and 0.81 (0.78-0.84) for males. Sensitivity and specificity for females were 0.78 (0.72-0.84) and 0.78 (0.75-0.81), respectively, and for males were 0.76 (0.71-0.81) and 0.76 (0.72-0.79). For AUD: On the AUDIT-C, AUC was 0.90 (0.88-0.93) for both females and males. Sensitivity and specificity for females were 0.83 (0.74-0.90) and 0.83 (0.80-0.85), respectively, while for males, they were 0.81 (0.74-0.87) and 0.84 (0.81-0.87). On the TAPS tool, AUC was 0.84 (0.80-0.89) for females and 0.82 (0.78-0.86) for males. Sensitivity and specificity for females were 0.73 (0.63-0.81) and 0.85 (0.83-0.88), respectively, while for males, they were 0.75 (0.68-0.81) and 0.84 (0.81-0.86).

Conclusions: The AUDIT-C performed somewhat better than the TAPS tool for alcohol screening. However, the TAPS tool had an acceptable level of performance for alcohol screening and may be advantageous in practice settings seeking to identify alcohol and other substance use with a single instrument.

Related protocols: CTN-0059

Abstract:

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.

Related protocols: CTN-0077-Ot

Abstract:

Objective: This secondary analysis evaluated the validation results of the Tobacco, Alcohol, Prescription Medication, and Other Substance Use (TAPS) tool for older adults.

Methods: Researchers performed a subgroup analysis of older adults aged =65 (n = 184) from the TAPS tool validation study conducted in 5 primary care clinics (CTN-0059). They compared the interviewer and self-administered versions of the TAPS tool at a cutoff of =1 for identifying problem use with a reference standard measure, the modified World Mental Health Composite International Diagnostic Interview.

Results: The mean age was 70.6 ± 5.9 years, 52.7% were female, and 49.5% were non-Hispanic Black. For identifying problem use, the self-administered TAPS tool had sensitivity of 0.91 (95% CI: 0.75–0.98) and specificity of 0.91 (95% CI: 0.85–0.95) for tobacco; sensitivity of 0.68 (95% CI: 0.45–0.86) and specificity of 0.88 (95% CI: 0.82–0.93) for alcohol; and sensitivity 0.86 (95% CI: 0.42–1.00) and specificity 0.94 (95% CI: 0.90–0.97) for cannabis. The interviewer-administered TAPS tool had similar results. Researchers were unable to evaluate its performance for identifying problem use of individual classes of drugs other than cannabis in this population due to small sample sizes.

Conclusions: While the TAPS had excellent sensitivity and specificity for identifying tobacco use among older adults, the results for other substances lack precision, and we were unable to evaluate its performance for prescription medications and individual illicit drugs in this sample. This analysis underlines the critical need to adapt and validate screening tools for unhealthy substance use, specifically for older populations who have unique risks.

Related protocols: CTN-0059

Abstract:

As the opioid epidemic continues to have a major negative impact across the US, community pharmacies have come under scrutiny from legal systems attempting to hold them accountable for their role in over-dispensing and lack of patient intervention. While the most available tool for monitoring patients’ opioid use is Prescription Drug Monitoring Programs (PDMP), these do not provide pharmacists with actionable information and decision support. Our study addresses this gap through three objectives: [1] incorporate validated opioid risk metric thresholds into a PDMP platform to create the Opioid Risk Reduction Clinical Decision Support (ORRCDS) tool; [2] assess ORRCDS’ ability to reduce patient opioid risk; [3] assess ORRCDS’ sustainability and viability for broader dissemination in community pharmacy.

For objective 1, our team is partnering with leadership from the largest US PDMP organization and a top-five pharmacy chain to implement ORRCDS into the pharmacy chain’s workflow following the Guideline Implementation with Decision Support (GUIDES) framework. For objective 2, our team will conduct a type-1 implementation mixed methods study using a 2-arm parallel group clustered randomized design. We anticipate enrolling ~6,600 patients with moderate and high opioid use risk during the 6-month enrollment phase across 80 pharmacies. This sample size will provide 96.3% power to detect a 5% or greater difference in responder rate between the intervention and control arm. Responders are patients with moderate-risk at baseline who reduce to low-risk or those with high-risk at baseline who reduce to moderate or low-risk at 180 days post last intervention. To accomplish objective 3, we will use the Consolidated Framework for Implementation Research (CFIR) to develop and execute cross-sectional qualitative interviews with pharmacists (n=15), pharmacy leaders (n=15), and PDMP leaders (n=15) regarding long term adoption and sustainability of the ORRCDS tool.

Conclusions: A PDMP tool that addresses moderate- and high-risk opioid use is not available in community pharmacy. This study will implement ORRCDS in a large retail pharmacy chain that will include additional screening and guidance to pharmacy staff to address risky opioid medication use. Our results will make critical advancements for protecting patient health and addressing the opioid epidemic.

Related protocols: CTN-0138

Abstract:

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.

This study, part of CTN-0077-Ot, 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.

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%.

Conclusions: 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.

Related protocols: CTN-0077-Ot

Abstract:

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.

PCCs and leaders (N = 13) from clinics in an integrated health system in which an OUD-CDS tool was implemented participated in semi-structured 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.

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.

Related protocols: CTN-0076-Ot

Abstract:

Primary care (PC) offers an opportunity to treat opioid use disorders (OUD). The Substance Use Symptom Checklist (“Checklist”) can assess DSM-5 substance use disorder (SUD) symptoms in PC. The objective of this study, part of CTN-0113, was to test the psychometric properties of the Checklist among PC patients with OUD or long-term opioid therapy (LTOT) in Kaiser Permanente Washington (KPWA).

Electronic health records (EHR) data were extracted for all adult PC patients visiting KPWA 3/1/15-8/30/2020 who had = 1 Checklist documented and indication of either (a) clinically-recognized OUD (i.e., documented OUD diagnosis and/or OUD medication treatment) or (b) LTOT in the year prior to the checklist. The Checklist includes 11 items reflecting DSM-5 criteria for SUD. We described the prevalence of 2 SUD symptoms reported on the Checklist (consistent with mild-severe DSM-5 SUD). Analyses were conducted in the overall sample and in two subsamples (clinically-recognized OUD and LTOT only).

Among 2007 eligible patients, 39.9% endorsed = 2 SUD symptoms (74.3% in the clinically-recognized OUD subsample and 13.1% in LTOT subsample). IRT indicated that a unidimensional model for the 11 checklist items had excellent fit (comparative fit index = 0.998) with high item-level discrimination parameters for the overall sample and both subsamples. DIF across age, race and ethnicity, and treatment was observed for one item each, but had minimal impact on expected number of criteria (0-11) patients endorse.

Conclusions: The Substance Use Symptom Checklist measured SUD symptoms consistent with DSM-5 conceptualization (scaled, unidimensional) in patients with clinically-recognized OUD and LTOT and had similar measurement properties across demographic subgroups. The Checklist may support symptom assessment in patients with OUD and diagnosis in patients with LTOT.

Related protocols: CTN-0113

Abstract:

Substance use disorders (SUDs) are underdiagnosed in healthcare settings. The Substance Use Symptom Checklist (SUSC) is a practical, patient-report questionnaire that has been used to assess SUD symptoms based on Diagnostic and Statistical Manual of Mental Disorders-5th edition (DSM-5) criteria. This study evaluates the test-retest reliability of SUSCs completed in primary and mental health care settings.

In this study, researchers identified 1194 patients who completed two SUSCs 1–21 days apart as part of routine care after reporting daily cannabis use and/or any past-year other drug use on behavioral health screens. Test-retest reliability of SUSC scores was evaluated within the full sample, subsamples who completed both checklists in primary care (n=451) or mental health clinics (n=512) where SUSC implementation differed, and subgroups defined by sex, insurance status, age, and substance use reported on behavioral health screens.

In the full sample, test-retest reliability was high for indices reflecting the number of SUD symptoms endorsed (ICC=0.75, 95% CI:0.72–0.77) and DSM-5 SUD severity (kappa=0.72, 95% CI:0.69–0.75). These reliability estimates were higher in primary care (ICC=0.81, 95% CI:0.77–0.84; kappa=0.79, 95% CI:0.75–0.82, respectively) than in mental health clinics (ICC=0.74, 95% CI:0.70–0.78; kappa=0.73, 95% CI:0.68–0.77). Reliability differed by age and substance use reported on behavioral health screens, but not by sex or insurance status.

Conclusions: The SUSC has good-to-excellent test-retest reliability when completed as part of routine primary or mental health care. Symptom checklists can reliably measure symptoms consistent with DSM-5 SUD criteria, which may aid SUD-related care in primary care and mental health settings.

Related protocols: CTN-0113

Abstract:

The Brief Addiction Monitor-Revised (BAM-R) is a widely used, 17-item assessment of substance use, risk, and protective factors associated with recovery from substance use disorders. Despite wide adoption in the U.S. Department of Veterans Affairs (VA) and recommendations for use in measurement-based care (MBC), administration may not be feasible in many MBC settings due to time constraints. The purpose of this study, part of CTN-0106 (Derivation and Validation of New Measurement-Based Care Tools Derived from the Brief Addiction Monitor), was to derive a shortened version of the BAM-R for use in fast-paced healthcare settings.

BAM-R data from 32,002 Veterans were obtained through the VA’s Corporate Data Warehouse. We used logistic regression models to identify items for removal based on prediction of two clinical outcomes (90-day substance use disorder (SUD) treatment retention and 12-month mortality) and item-level sensitivity to change during substance use treatment.

Although no intake BAM-R items predicted SUD treatment retention or mortality, effect sizes for item-level sensitivity to change during substance use treatment varied from small to large. Seven items were judged as relevant for MBC of SUD. Among all BAM-R items, Heavy Alcohol Use, Self-Help, Drug Use, Craving, and Mood items demonstrated the greatest magnitude of sensitivity to change.

Conclusions: Although additional research is recommended before a shortened BAM-R can be implemented in non-specialty MBC settings, we identified 5 BAM-R items with perceived clinical utility and scores that demonstrated evidence of sensitivity to change. Shortening the BAM-R increases feasibility of use, though more work is needed to optimize measurement for SUD MBC.

Related protocols: CTN-0106

Abstract:

Opioid use disorder (OUD) is a deadly illness that remains undertreated, despite effective pharmacological treatments. Barriers, such as stigma, treatment affordability, and a lack of training and prescribing within medical practices result in low access to treatment. Software-delivered measurement-based care (MBC) is one way to increase treatment access. MBC uses systematic patient symptom assessments to inform an algorithm to support clinicians at critical decision points.

Focus groups of faculty clinicians (N=33) from 3 clinics were conducted to understand perceptions of OUD diagnosis and treatment and whether a computerized MBC model might assist with diagnosis and treatment. Themes from the transcribed focus groups were identified in two phases: (1) content analysis focused on uncovering general themes; and (2) systematic coding and interpretation of the data.

Analysis revealed six major themes utilized to develop the coding terms: “distinguishing between chronic pain and OUD,” “current practices with patients using prescribed or illicit opioids or other drugs,” “attitudes and mindsets about providing screening or treatment for OUD in your practice,” “perceived resources needed for treating OUD,” “primary care physician role in patient care not specific to OUD,” and “reactions to implementation of proposed clinical decision support tool.”

Conclusions: Results revealed that systemic and attitudinal barriers to screening, diagnosing, and treating OUD continue to persist. Providers tended to view the software-based MBC program favorably, indicating that it may be a solution to increasing accessibility to OUD treatment; however, further interventions to combat stigma would likely be needed prior to implementation of these programs.

Related protocols: CTN-0090

Abstract:

Efficient screening tools that effectively identify substance use disorders (SUDs) among youths are needed. The objective of this study, part of CTN-0060-A-1, was to evaluate the psychometric properties of 3 brief substance use screening tools (Screening to Brief Intervention [S2BI]; Brief Screener for Tobacco, Alcohol, and Drugs [BSTAD]; and Tobacco, Alcohol, Prescription Medication, and Other Substances [TAPS]) with adolescents aged 12 to 17 years.

This cross-sectional validation study was conducted from July 1, 2020, to February 28, 2022. Participants aged 12 to 17 years were recruited virtually and in person from 3 health care settings in Massachusetts: (1) an outpatient adolescent SUD treatment program at a pediatric hospital, (2) an adolescent medicine program at a community pediatric practice affiliated with an academic institution, and (3) 1 of 28 participating pediatric primary care practices. Participants were randomly assigned to complete 1 of the 3 electronic screening tools via self-administration, followed by a brief electronic assessment battery and a research assistant-administered diagnostic interview as the criterion standard measure for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnoses of SUDs. Data were analyzed from May 31 to September 13, 2022.

The main outcome was a DSM-5 diagnosis of tobacco/nicotine, alcohol, or cannabis use disorder as determined by the criterion standard World Mental Health Composite International Diagnostic Interview Substance Abuse Module. Classification accuracy of the 3 substance use screening tools was assessed by examining the agreement between the criterion, using sensitivity and specificity, based on cut points for each tool for use disorder, chosen a priori from previous studies.

This study included 798 adolescents, with a mean (SD) age of 14.6 (1.6) years. The majority of participants identified as female (415 [52.0%]) and were White (524 [65.7%]). High agreement between screening results and the criterion standard measure was observed, with area under the curve values ranging from 0.89 to 1 for nicotine, alcohol, and cannabis use disorders for each of the 3 screening tools.

Conclusions: These findings suggest that screening tools that use questions on past-year frequency of use are effective for identifying adolescents with SUDs. Future work could examine whether these tools have differing properties when used with different groups of adolescents in different settings.

Related protocols: CTN-0060-A-1

Abstract:

Screening for substance use in rural primary care clinics faces unique challenges due to limited resources, high patient volumes, and multiple demands on providers. To explore the potential for electronic health record (EHR)-integrated screening in this context, researchers conducted an implementation feasibility study with a rural federally-qualified health center (FQHC) in Maine. This was an ancillary study to a NIDA Clinical Trials Network study of screening in urban primary care clinics (CTN-0062).

Researchers worked with stakeholders from three FQHC clinics to define and implement their optimal screening approach. Clinics used the Tobacco, Alcohol, Prescription Medication, and Other Substance (TAPS) Tool, completed on tablet computers in the waiting room, and results were immediately recorded in the EHR. Adult patients presenting for annual preventive care visits, but not those with other visit types, were eligible for screening. Data were analyzed for the first 12 months following implementation at each clinic to assess screening rates and prevalence of reported unhealthy substance use, and documentation of counseling using an EHR-integrated clinical decision support tool, for patients screening positive for moderate-high risk alcohol or drug use.

Screening was completed by 3749 patients, representing 93.4% of those with screening-eligible annual preventive care visits, and 18.5% of adult patients presenting for any type of primary care visit. Screening was self-administered in 92.9% of cases. The prevalence of moderate-high risk substance use detected on screening was 14.6% for tobacco, 30.4% for alcohol, 10.8% for cannabis, 0.3% for illicit drugs, and 0.6% for non-medical use of prescription drugs. Brief substance use counseling was documented for 17.4% of patients with any moderate-high risk alcohol or drug use.

Conclusions: Self-administered EHR-integrated screening was feasible to implement, and detected substantial alcohol, cannabis, and tobacco use in rural FQHC clinics. Counseling was documented for a minority of patients with moderate-high risk use, possibly indicating a need for better support of primary care providers in addressing substance use. There is potential to broaden the reach of screening by offering it at routine medical visits rather than restricting to annual preventive care visits, within these and other rural primary care clinics.

Related protocols: CTN-0062-Ot

Abstract:

The co-occurrence of suicidality and substance use disorders has been well established, but rating scales to examine suicidal behavior and risk are sparse among participants with substance use disorders. In this study, part of the CTN ADAPT-2 protocol (CTN-0068), researchers examined the psychometric properties of the 16-item Concise Health Risk Tracking Scale – Self Report (CHRT-SR16) to measure suicidality among adults with moderate-to-severe methamphetamine use disorder.

Participants (n = 403) with moderate-to-severe methamphetamine use disorder completed the CHRT-SR16 as part of a randomized, double-blind, placebo-controlled pharmacotherapy trial. The CHRT-SR16 factor structure was assessed using confirmatory factor analysis (CFA). Internal consistency was estimated with coefficients alpha (a) and omega ( ), test-retest reliability with intraclass correlation coefficient (ICC) and standard error of measurement, and convergent validity using Spearman’s rank order correlation coefficient test between CHRT-SR16 factors and the Patient Health Questionnaire (PHQ-9). The analyses utilized baseline and week 1 data (for test-retest reliability only).

CFA revealed a seven-factor model of Pessimism, Helplessness, Social Support, Despair, Impulsivity, Irritability, and Suicidal Thoughts as the best-fitting model. The CHRT-SR16 also exhibited strong internal consistency (a = 0.89; = 0.89), test-retest reliability (ICC = 0.78) and convergent validity with the PHQ-9 total score ( = 0.62).

Conclusions: The results of this study offer evidence for strong reliability and convergent validity of the CHRT-SR16, among the first validated instruments to measure Suicidal Thoughts and behavior in a population that is disproportionately experiencing overdose and death due to methamphetamine use. The CHRT-SR16 builds upon prior work with the CHRT-SR and allows for the novel assessment of impulsivity and irritability as possible risk factors for suicidal behavior.

Related protocols: CTN-0068

Abstract:

This is the primary outcomes paper for CTN-0113.

Substance use disorders (SUDs) are underrecognized in primary care, where structured clinical interviews are often infeasible. A brief, standardized substance use symptom checklist could help clinicians assess SUD. This project aimed to evaluate the psychometric properties of the Substance Use Symptom Checklist (hereafter symptom checklist) used in primary care among patients reporting daily cannabis use and/or other drug use as part of population-based screening and assessment.

This cross-sectional study, part of NIDA Clinical Trials Network study CTN-0113, was conducted among adult primary care patients who completed the symptom checklist during routine care between March 1, 2015, and March 1, 2020, at an integrated health care system. Data analysis was conducted from June 1, 2021, to May 1, 2022.

The symptom checklist included 11 items corresponding to SUD criteria in the Diagnostic and Statistical Manual for Mental Disorders (Fifth Edition) (DSM-5). Item response theory (IRT) analyses tested whether the symptom checklist was unidimensional and reflected a continuum of SUD severity and evaluated item characteristics (discrimination and severity). Differential item functioning analyses examined whether the symptom checklist performed similarly across age, sex, race, and ethnicity. Analyses were stratified by cannabis and/or other drug use.

A total of 23,304 screens were included (mean [SD] age, 38.2 [5.6] years; 12 554 [53.9%] male patients; 17 439 [78.8%] White patients; 20 393 [87.5%] non-Hispanic patients). Overall, 16,140 patients reported daily cannabis use only, 4791 patients reported other drug use only, and 2373 patients reported both daily cannabis and other drug use. Among patients with daily cannabis use only, other drug use only, or both daily cannabis and other drug use, 4242 (26.3%), 1446 (30.2%), and 1229 (51.8%), respectively, endorsed 2 or more items on the symptom checklist, consistent with DSM-5 SUD. For all cannabis and drug subsamples, IRT models supported the unidimensionality of the symptom checklist, and all items discriminated between higher and lower levels of SUD severity. Differential item functioning was observed for some items across sociodemographic subgroups but did not result in meaningful change (<1 point difference) in the overall score (0-11).

Conclusions: In this cross-sectional study, a symptom checklist, administered to primary care patients who reported daily cannabis and/or other drug use during routine screening, discriminated SUD severity as expected and performed well across subgroups. Findings support the clinical utility of the symptom checklist for standardized and more complete SUD symptom assessment to help clinicians make diagnostic and treatment decisions in primary care.

Related protocols: CTN-0113