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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., (© 2024. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Importance: Alcohol withdrawal syndrome (AWS) is an important cause and complication of hospitalizations. Although common and preventable, the incidence of AWS during hospitalizations is poorly described., Objective: To evaluate the incidence and proportional incidence of hospitalizations involving AWS in an adult primary care population overall and across patient characteristics., Design, Setting, and Participants: This retrospective cohort study used electronic health records and insurance claims from Kaiser Permanente Washington (KPWA) between July 1, 2018, and June 30, 2022. The study included adults with 1 or more primary care visits during this period or the year prior, where primary care included annual standardized alcohol screening using the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C)., Exposures: Age, sex, race, Hispanic ethnicity, AUDIT-C scores, and comorbid diagnoses., Main Outcome and Measures: Hospitalizations involving AWS were defined by diagnosis codes documented during hospitalizations (incidence numerator). Time enrolled in KPWA determined person-enrolled-years (incidence denominator). Proportional incidence was calculated as the incidence of hospitalizations involving AWS divided by the incidence of all-cause hospitalizations. Proportional incidence was also estimated for hospitalizations involving other common chronic conditions (chronic obstructive pulmonary disease, diabetes, heart failure, and hypertension), which were also defined using hospital diagnosis codes., Results: Among 544 825 adults engaged in primary care (mean [SD] age, 47.0 [17.9] years; 310 069 [56.9%] female; 3656 [0.7%] American Indian or Alaska Native, 55 206 [10.1%] Asian, 25 406 [4.7%] Black, 5204 [1.0%] Native Hawaiian or Other Pacific Islander, 365 780 [67.1%] White, 19 791 [3.6%] multiracial, 15 963 [2.9%] other races, and 53 819 [9.9%] unknown race; 33 987 [6.2%] Hispanic, 414 269 [76.0%] not Hispanic, and 96 569 [17.7%] unknown ethnicity), incidence of hospitalizations involving AWS was 169 (95% CI, 159-179) per 100 000 person-enrolled-years overall but as high as 15 347 (95% CI, 13 502-17 331) in patients with other alcohol-attributable diagnoses. The proportional incidence of hospitalizations involving AWS was 2.3% overall, with variation by age, sex, and AUDIT-C scores (eg, 9%-11% in male patients aged 30-49 years and 23%-44% in patients with high-risk AUDIT-C scores of 7-12 points). In most cases, among adults younger than 60 years, proportional incidence of hospitalizations involving AWS matched or surpassed that of other common chronic conditions (chronic obstructive pulmonary disease, diabetes, heart failure, and hypertension)., Conclusions and Relevance: In this cohort study of a large primary care population served by an integrated health system, AWS hospitalizations were common, especially in male patients, younger age groups, and individuals with high-risk alcohol use. During hospitalizations, the burden of AWS was similar to or exceeded complications of other chronic diseases that receive greater medical attention.

Background: 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., Objective: To test the psychometric properties of the Checklist among PC patients with OUD or long-term opioid therapy (LTOT) in Kaiser Permanente Washington (KPWA)., Design: Observational study using item response theory (IRT) and differential item functioning (DIF) analyses of measurement consistency across age, sex, race and ethnicity, and receipt of treatment., Patients: 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., Main Measure: 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)., Key Results: 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., (© 2024. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Background: The National Institute on Alcohol Abuse and Alcoholism (NIAAA) recommends the paper-based or computerized Alcohol Symptom Checklist to assess alcohol use disorder (AUD) symptoms in routine care when patients report high-risk drinking. However, it is unknown whether Alcohol Symptom Checklist response characteristics differ when it is administered online (eg, remotely via an online electronic health record [EHR] patient portal before an appointment) versus in clinic (eg, on paper after appointment check-in)., Objective: This study evaluated the psychometric performance of the Alcohol Symptom Checklist when completed online versus in clinic during routine clinical care., Methods: This cross-sectional, psychometric study obtained EHR data from the Alcohol Symptom Checklist completed by adult patients from an integrated health system in Washington state. The sample included patients who had a primary care visit in 2021 at 1 of 32 primary care practices, were due for annual behavioral health screening, and reported high-risk drinking on the behavioral health screen (Alcohol Use Disorder Identification Test-Consumption score ≥7). After screening, patients with high-risk drinking were typically asked to complete the Alcohol Symptom Checklist-an 11-item questionnaire on which patients self-report whether they had experienced each of the 11 AUD criteria listed in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) over a past-year timeframe. Patients could complete the Alcohol Symptom Checklist online (eg, on a computer, smartphone, or tablet from any location) or in clinic (eg, on paper as part of the rooming process at clinical appointments). We examined sample and measurement characteristics and conducted differential item functioning analyses using item response theory to examine measurement consistency across these 2 assessment modalities., Results: Among 3243 patients meeting eligibility criteria for this secondary analysis (2313/3243, 71% male; 2271/3243, 70% White; and 2014/3243, 62% non-Hispanic), 1640 (51%) completed the Alcohol Symptom Checklist online while 1603 (49%) completed it in clinic. Approximately 46% (752/1640) and 48% (764/1603) reported ≥2 AUD criteria (the threshold for AUD diagnosis) online and in clinic (P=.37), respectively. A small degree of differential item functioning was observed for 4 of 11 items. This differential item functioning produced only minimal impact on total scores used clinically to assess AUD severity, affecting total criteria count by a maximum of 0.13 criteria (on a scale ranging from 0 to 11)., Conclusions: Completing the Alcohol Symptom Checklist online, typically prior to patient check-in, performed similarly to an in-clinic modality typically administered on paper by a medical assistant at the time of the appointment. Findings have implications for using online AUD symptom assessments to streamline workflows, reduce staff burden, reduce stigma, and potentially assess patients who do not receive in-person care. Whether modality of DSM-5 assessment of AUD differentially impacts treatment is unknown., (©Theresa E Matson, Amy K Lee, Malia Oliver, Katharine A Bradley, Kevin A Hallgren. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.07.2024.)

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., Competing Interests: none, (© 2024 Published by Elsevier B.V.)

Introduction: 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., Methods: We 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., Results: 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., Competing Interests: Declaration of Competing Interest All authors declare no conflicts of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: The Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) is a three-item screening measure of unhealthy alcohol use that is widely used in healthcare settings. Evidence shows high test-retest reliability of the AUDIT-C in research samples, but most studies had limited external validity and used small samples that could not be used to evaluate reliability across demographic subgroups and/or screening modalities. This study evaluates the test-retest reliability of the AUDIT-C completed in routine care in a large primary care sample, including across demographic subgroups defined by age, sex, race, ethnicity, and screening modality (i.e., completed in-clinic or online)., Methods: We used electronic health record (EHR) data from Kaiser Permanente Washington. The sample included 18,491 adult primary care patients who completed two AUDIT-C screens 1-21 days apart as part of routine care in 2021. Test-retest reliability was evaluated for AUDIT-C total scores (0-12) and for a binary measure indicating unhealthy alcohol use (scores ≥3 women, ≥4 men). Using previously established cutoffs, we interpreted reliability coefficients >0.75 as indicating "excellent" reliability., Results: AUDIT-C screens completed in routine care and documented in EHRs had excellent test-retest reliability for total scores (ICC = 0.87, 95% CI: 0.87-0.87) and the binary indicator of unhealthy alcohol use (κ = 0.79, 95% CI: 0.78-0.80). Reliability coefficients were good to excellent across all demographic groups and for in-clinic and online modalities. Higher reliability was seen when both screens were completed through online patient portals (ICC = 0.93, 95% CI: 0.93-0.93) versus in-clinic (ICC = 0.81, 95% CI: 0.79-0.82) or when one screen was completed using each modality (ICC = 0.83, 95% CI: 0.82-0.83). Lower reliability was seen in American Indian/Alaska Native (ICC = 0.82, 95% CI: 0.75-0.87) and multiracial individuals (ICC = 0.82, 95% 0.80-0.84)., Conclusions: In real-world routine care conditions, AUDIT-C screens have excellent test-retest reliability across demographic subgroups and modalities (online and in-clinic). Future research should examine why reliability varies slightly across modalities and demographic subgroups., (© 2023 Research Society on Alcohol.)

Background: Medical cannabis is commonly used for chronic pain, but little is known about differences in characteristics, cannabis use patterns, and perceived helpfulness among primary care patients who use cannabis for pain versus nonpain reasons., Methods: Among 1688 patients who completed a 2019 cannabis survey administered in a health system in Washington state, where recreational use is legal, participants who used cannabis for pain (n = 375) were compared with those who used cannabis for other reasons (n = 558) using survey and electronic health record data. We described group differences in participant characteristics, use patterns, and perceptions and applied adjusted multinomial logistic and modified Poisson regression., Results: Participants who used cannabis for pain were significantly more likely to report using applied (50.7% vs 10.6%) and beverage cannabis products (19.2% vs 11.6%), more frequent use (47.1% vs 33.1% for use ≥2 times per day; 81.6% vs 69.7% for use 4 to 7 days per week), and smoking tobacco cigarettes (19.2% vs 12.2%) than those who used cannabis for other reasons. They were also significantly more likely to perceive cannabis as very/extremely helpful (80.5% vs 72.7%), and significantly less likely to use cannabis for nonmedical reasons (4.8% vs 58.8%) or report cannabis use disorder symptoms (51.7% vs 61.1%)., Discussion: Primary care patients who use cannabis for pain use it more frequently, often in applied and ingested forms, and have more co-use of tobacco, which may differentially impact safety and effectiveness. These findings suggest the need for different approaches to counseling in clinical care., Competing Interests: Conflict of interest: Ms. Ford, Dr. Lapham, Dr. Matson, Ms. Luce, and Ms. Oliver have no conflicts of interest to declare. Dr. Binswanger received royalties from UptoDate (Wolters Kluwer) and is employed by the Colorado Permanente Medical Group, which provides primary care and specialty care. She has no other conflicts of interest to declare., (© Copyright by the American Board of Family Medicine.)

Background: Brief cannabis screening followed by standardized assessment of symptoms may support diagnosis and treatment of cannabis use disorder (CUD). This study tested whether the probability of a medical provider diagnosing and treating CUD increased with the number of substance use disorder (SUD) symptoms documented in patients' EHRs., Methods: This observational study used EHR and claims data from an integrated healthcare system. Adult patients were included who reported daily cannabis use and completed the Substance Use Symptom Checklist, a scaled measure of DSM-5 SUD symptoms (0-11), during routine care 3/1/2015-3/1/2021. Logistic regression estimated associations between SUD symptom counts and: 1) CUD diagnosis; 2) CUD treatment initiation; and 3) CUD treatment engagement, defined based on Healthcare Effectiveness Data and Information Set (HEDIS) ICD-codes and timelines. We tested moderation across age, gender, race, and ethnicity., Results: Patients (N=13,947) were predominantly middle-age, male, White, and non-Hispanic. Among patients reporting daily cannabis use without other drug use (N=12,568), the probability of CUD diagnosis, treatment initiation, and engagement increased with each 1-unit increase in Symptom Checklist score (p's<0.001). However, probabilities of diagnosis, treatment, and engagement were low, even among those reporting ≥2 symptoms consistent with SUD: 14.0% diagnosed (95% CI: 11.7-21.6), 16.6% initiated treatment among diagnosed (11.7-21.6), and 24.3% engaged in treatment among initiated (15.8-32.7). Only gender moderated associations between Symptom Checklist and diagnosis (p=0.047) and treatment initiation (p=0.012). Findings were similar for patients reporting daily cannabis use with other drug use (N=1379)., Conclusion: Despite documented symptoms, CUD was underdiagnosed and undertreated in medical settings., Competing Interests: Declaration of Competing Interest Matson reports support from Veteran Affairs Puget Sound Health Services Research & Development outside the submitted work. Remaining authors have no affiliation with or financial involvement in any organization or entity with a direct financial interest in the subject matter or materials discussed in the manuscript., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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.

Background: Alcohol use disorders (AUD) are prevalent and often go untreated. Patients are commonly screened for AUD in primary care, but existing treatment programs are failing to meet demand. Digital therapeutics include novel mobile app-based treatment approaches which may be cost-effective treatment options to help fill treatment gaps. The goal of this study was to identify implementation needs and workflow design considerations for integrating digital therapeutics for AUD into primary care., Methods: We conducted qualitative interviews with clinicians, care delivery leaders, and implementation staff (n = 16) in an integrated healthcare delivery system in the United States. All participants had experience implementing digital therapeutics for depression or substance use disorders in primary care. Interviews were designed to gain insights into adaptations needed to optimize existing clinical processes, workflows, and implementation strategies for use with alcohol-focused digital therapeutics. Interviews were recorded and transcribed and then analyzed using a rapid analysis process and affinity diagramming., Results: Qualitative themes were well represented across health system staff roles. Participants were enthusiastic about digital therapeutics for AUD, anticipated high patient demand for such a resource, and made suggestions for successful implementation. Key insights regarding the implementation of digital therapeutics for AUD and unhealthy alcohol use from our data include: (1) implementation strategy selection must be driven by digital therapeutic design and target population characteristics, (2) implementation strategies should seek to minimize burden on clinicians given the large numbers of patients with AUD who are likely to be interested in and eligible for digital therapeutics, and (3) digital therapeutics should be offered alongside many other treatment options to accommodate individual patients' AUD severity and treatment goals. Participants also expressed confidence that previous implementation strategies used with other digital therapeutics such as clinician training, electronic health record supports, health coaching, and practice facilitation would be effective for the implementation of digital therapeutics for AUD., Conclusions: The implementation of digital therapeutics for AUD would benefit from careful consideration of the target population. Optimal integration requires tailoring workflows to meet anticipated patient volume and designing workflow and implementation strategies to meet the unique needs of patients with varying AUD severity., (© 2023. The Author(s).)

Importance: 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., Objective: 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., Design, Setting, and Participants: This cross-sectional study 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., Main Outcomes and Measures: 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., Results: 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 and Relevance: 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.

The aim of this exploratory analysis was to evaluate cannabis exposure, reasons for use and problematic cannabis use among adult primary care patients in Washington state (United States) who co-use cannabis and nicotine (tobacco cigarettes and/or nicotine vaping) compared to patients who endorse current cannabis use only. As part of a NIDA Clinical Trials Network (CTN) parent study, patients who completed a cannabis screen as part of routine primary care were randomly sampled (N = 5,000) to a receive a confidential cannabis survey. Patients were stratified and oversampled based on the frequency of past-year cannabis use and for Black, indigenous, or other persons of color. Patients who endorsed past 30-day cannabis use are included here (N = 1388). Outcomes included; prevalence of cannabis use, days of cannabis use per week and times used per day, methods of use, THC:CBD content, non-medical and/or medical use, health symptoms managed, and cannabis use disorder (CUD) symptom severity. We conducted unadjusted bivariate analyses comparing outcomes between patients with cannabis and current nicotine co-use to patients with cannabis-only use. Nicotine co-use (n = 352; 25.4 %) was associated with differences in method of cannabis use, THC:CBD content, days of use per week and times used per day, number of health symptoms managed, and CUD severity (all p < 0.001), compared to primary care patients with cannabis-only use (n = 1036). Interventions targeting cannabis and nicotine co-use in primary care are not well-established and further research is warranted given findings of more severe cannabis use patterns and the adverse health outcomes associated with co-use., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective: The authors sought to characterize the 3-year prevalence of mental disorders and nonnicotine substance use disorders among male and female primary care patients with documented opioid use disorder across large U.S. health systems., Methods: This retrospective study used 2014-2016 data from patients ages ≥16 years in six health systems. Diagnoses were obtained from electronic health records or claims data; opioid use disorder treatment with buprenorphine or injectable extended-release naltrexone was determined through prescription and procedure data. Adjusted prevalence of comorbid conditions among patients with opioid use disorder (with or without treatment), stratified by sex, was estimated by fitting logistic regression models for each condition and applying marginal standardization., Results: Females (53.2%, N=7,431) and males (46.8%, N=6,548) had a similar prevalence of opioid use disorder. Comorbid mental disorders among those with opioid use disorder were more prevalent among females (86.4% vs. 74.3%, respectively), whereas comorbid other substance use disorders (excluding nicotine) were more common among males (51.9% vs. 60.9%, respectively). These differences held for those receiving medication treatment for opioid use disorder, with mental disorders being more common among treated females (83% vs. 71%) and other substance use disorders more common among treated males (68% vs. 63%). Among patients with a single mental health condition comorbid with opioid use disorder, females were less likely than males to receive medication treatment for opioid use disorder (15% vs. 20%, respectively)., Conclusions: The high rate of comorbid conditions among patients with opioid use disorder indicates a strong need to supply primary care providers with adequate resources for integrated opioid use disorder treatment.

Introduction: Transgender persons are vulnerable to under-receipt of recommended health care due to chronic exposure to systemic stressors (e.g., discriminatory laws and health system practices). Scant information exists on receipt of alcohol-related care for transgender populations, and whether structural interventions to reduce transgender discrimination in health care improve receipt of recommended treatment. This study evaluated the effect of the Veteran Health Administration (VA) Transgender Healthcare Directive-a national policy to reduce structural discrimination-on receipt of evidence-based alcohol-related care for transgender VA patients with unhealthy alcohol use., Methods: The study used an interrupted time series with control design to compare monthly receipt of alcohol-related care among transgender patients with unhealthy alcohol use (Alcohol Use Disorders Identification Test Consumption ≥5) documented in their electronic health record before (10/1/2009-5/31/2011) and after (7/1/2011-7/31/2017) implementation of VA's Transgender Healthcare Directive. A propensity-score matched sample of non-transgender patients with unhealthy alcohol use served as a comparison group to control for concurrent secular trends. Mixed effects segmented logistic regression models estimated changes in level and slope (i.e., rate of change) in receipt of any evidence-based alcohol-related care, including brief intervention, specialty addictions treatment, and alcohol use disorder medications., Results: The matched sample (mean age = 47.5 [SD = 15.0]; 75% non-Hispanic White race/ethnicity) included 2074 positive alcohol screens completed by 1377 transgender patients and 6,l99 positive alcohol screens completed by 6185 non-transgender patients. Receipt of alcohol-related care increased for transgender patients from 78.5% (95% CI: 71.3%-85.6%) at the start of study to 83.0% (75.9%-90.1%) immediately before the directive and decreased slightly from 81.6% (77.4%-85.9%) immediately after the directive to 80.1% (76.8-85.4) at the end of the study. Changes in level and slope comparing periods before and after the directive were not statistically significant, nor were they statistically significantly different from the matched sample of non-transgender patients., Conclusions: Health systems must urgently employ and evaluate policies to address structural stigma that produces and reproduces disparities in health and health care. Although VA's directive was not associated with increased receipt of alcohol-related care, that receipt of alcohol-related care among transgender patients is comparable to non-transgender patients is promising., Competing Interests: Declaration of competing interest None., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Digital interventions, such as smartphone apps, can be effective in treating alcohol use disorders (AUD). However, efforts to integrate digital interventions into primary care have been challenging. To inform successful implementation, we sought to understand how patients and clinicians preferred to use apps in routine primary care., Methods: This study combined user-centered design and qualitative research methods, interviewing 18 primary care patients with AUD and nine primary care clinicians on topics such as prior experiences with digital tools, and design preferences regarding approaches for offering apps for AUD in primary care. Interviews were recorded and transcribed for template analysis whereby a priori codes were based on interview topics and refined through iterative coding. New codes and cross-cutting themes emerged from the data., Results: Patient participants with AUD indicated they would be more likely to engage in treatment if primary care team members were involved in their use of apps. They also preferred to see clinicians "invested" and recommended that clinicians ask about app use and progress during follow-up appointments or check-ins. Clinician participants valued the opportunity to offer apps to their patients but noted that workflows would need to be tailored to individual patient needs. Time pressures, implementation complexity, and lack of appropriate staffing were cited as barriers. Clinicians proposed concrete solutions (e.g., education, tools, and staffing models) that could improve their ability to use apps within the constraints of primary care and suggested that some patients could potentially use apps without clinician support., Conclusions: A user-centered approach to engaging patients in digital alcohol interventions in primary care may require personalized support for both initiation and follow-up. Meeting patients' needs likely require increased staffing and efficient workflows in primary care. Health systems should consider offering multiple pathways for enrolling patients in apps to accommodate individual preferences and contextual barriers., Plain Language Summary: Healthcare systems have begun using app-based treatments to help patients manage their health conditions, including alcohol use disorders. Some apps have been tested in research studies and appear to be effective. However, it is difficult for healthcare teams to offer apps to patients. Clinicians must engage in new activities that they have not done before, such as "teaching" patients to use apps and checking in on their use of the apps. Identifying how to use apps in routine healthcare is critical to their successful implementation. This study interviewed 27 people, including healthcare providers and patients in primary care, to uncover the most optimal ways to offer apps to patients with alcohol use disorders. The interviews combined the use of qualitative research methods and user-centered design. Results suggest that to use to address alcohol use disorders, primary care teams should be prepared to offer personalized support to patients. Both patient and clinician interviewees said that the steps required to use apps must be intuitive and simple. Patients could gain more benefits if clinicians introduced the apps and guided patients to use them, as opposed to making apps available for patients to download and use on their own. However, the exact approach to offering apps would depend on a given patient's preferences and the extent that staffing was available in the clinic to support patients. Health systems should be prepared to offer and support patients in their use of apps, which should accommodate patient preferences and the constraints of the clinic., Competing Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Several authors (JEG, RMC, KAB) are investigators on a trial funded by the National Institute on Drug Abuse (R01DA047954; PI: Glass), which evaluates the implementation of reSET and reSET-O, which are digital therapeutics for substance use disorder marketed by Pear Therapeutics, Inc. Pear Therapeutics Inc provided digital therapeutic prescriptions at no cost to Kaiser Permanente Washington during a quality improvement pilot study that preceded the implementation trial. JEG is a coinvestigator on a Small Business Innovation Award funded awarded to Pear Therapeutics, Inc by the National Institute on Drug Abuse, which evaluates potential improvements to reSET-O (R44DA042652). This study is informing our research on the implementation of digital therapeutics, but Pear Therapeutics, Inc was not involved in this study and does not provide funding to the authors, (© The Author(s) 2022.)

Importance: Cannabis use is prevalent and increasing, and frequent use intensifies the risk of cannabis use disorder (CUD). CUD is underrecognized in medical settings, but a validated single-item cannabis screen could increase recognition., Objective: To evaluate the Single-Item Screen-Cannabis (SIS-C), administered and documented in routine primary care, compared with a confidential reference standard measure of CUD., Design, Setting, and Participants: This diagnostic study included a sample of adult patients who completed routine cannabis screening between January 28 and September 12, 2019, and were randomly selected for a confidential survey about cannabis use. Random sampling was stratified by frequency of past-year use and race and ethnicity. The study was conducted at an integrated health system in Washington state, where adult cannabis use is legal. Data were analyzed from May 2021 to March 2022., Exposures: The SIS-C asks about frequency of past-year cannabis use with responses (none, less than monthly, monthly, weekly, daily or almost daily) documented in patients' medical records., Main Outcomes and Measures: The Diagnostic and Statistical Manual, Fifth Edition (DSM-5) Composite International Diagnostic Interview-Substance Abuse Module (CIDI-SAM) for past-year CUD was completed on a confidential survey and considered the reference standard. The SIS-C was compared with 2 or more criteria on the CIDI-SAM, consistent with CUD. All analyses were weighted, accounting for survey design and nonresponse, to obtain estimates representative of the health system primary care population., Results: Of 5000 sampled adult patients, 1688 responded to the cannabis survey (34% response rate). Patients were predominantly middle-aged (weighted mean [SD] age, 50.7 [18.1]), female or women (weighted proportion [SE], 55.9% [4.1]), non-Hispanic (weighted proportion [SE], 96.7% [1.0]), and White (weighted proportion [SE], 74.2% [3.7]). Approximately 6.6% of patients met criteria for past-year CUD. The SIS-C had an area under receiver operating characteristic curve of 0.89 (95% CI, 0.78-0.96) for identifying CUD. A threshold of less than monthly cannabis use balanced sensitivity (0.88) and specificity (0.83) for detecting CUD. In populations with a 6% prevalence of CUD, predictive values of a positive screen ranged from 17% to 34%, while predictive values of a negative screen ranged from 97% to 100%., Conclusions and Relevance: In this diagnostic study, the SIS-C had excellent performance characteristics in routine care as a screen for CUD. While high negative predictive values suggest that the SIS-C accurately identifies patients without CUD, low positive predictive values indicate a need for further diagnostic assessment following positive results when screening for CUD in primary care.

Background: Alcohol use disorder (AUD) is highly prevalent but underrecognized and undertreated in primary care settings. Alcohol Symptom Checklists can engage patients and providers in discussions of AUD-related care. However, the performance of Alcohol Symptom Checklists when they are used in routine care and documented in electronic health records (EHRs) remains unevaluated., Objective: To evaluate the psychometric performance of an Alcohol Symptom Checklist in routine primary care., Design: Cross-sectional study using item response theory (IRT) and differential item functioning analyses of measurement consistency across age, sex, race, and ethnicity., Patients: Patients seen in primary care in the Kaiser Permanente Washington Healthcare System who reported high-risk drinking on the Alcohol Use Disorder Identification Test Consumption screening measure (AUDIT-C ≥ 7) and subsequently completed an Alcohol Symptom Checklist between October 2015 and February 2020., Main Measure: Alcohol Symptom Checklists with 11 items assessing AUD criteria defined in the Diagnostic and Statistical Manual for Mental Disorders, 5th edition (DSM-5), completed by patients during routine medical care and documented in EHRs., Key Results: Among 11,464 patients who screened positive for high-risk drinking and completed an Alcohol Symptom Checklist (mean age 43.6 years, 30.5% female), 54.1% reported ≥ 2 DSM-5 AUD criteria (threshold for AUD diagnosis). IRT analyses demonstrated that checklist items measured a unidimensional continuum of AUD severity. Differential item functioning was observed for some demographic subgroups but had minimal impact on accurate measurement of AUD severity, with differences between demographic subgroups attributable to differential item functioning never exceeding 0.42 points of the total symptom count (of a possible range of 0-11)., Conclusions: Alcohol Symptom Checklists used in routine care discriminated AUD severity consistently with current definitions of AUD and performed equitably across age, sex, race, and ethnicity. Integrating symptom checklists into routine care may help inform clinical decision-making around diagnosing and managing AUD., (© 2021. Society of General Internal Medicine.)

Importance: Patients who use cannabis for medical reasons may benefit from discussions with clinicians about health risks of cannabis and evidence-based treatment alternatives. However, little is known about the prevalence of medical cannabis use in primary care and how often it is documented in patient electronic health records (EHR)., Objective: To estimate the primary care prevalence of medical cannabis use according to confidential patient survey and to compare the prevalence of medical cannabis use documented in the EHR with patient report., Design, Setting, and Participants: This study is a cross-sectional survey performed in a large health system that conducts routine cannabis screening in Washington state where medical and nonmedical cannabis use are legal. Among 108 950 patients who completed routine cannabis screening (between March 28, 2019, and September 12, 2019), 5000 were randomly selected for a confidential survey about cannabis use, using stratified random sampling for frequency of past-year use and patient race and ethnicity. Data were analyzed from November 2020 to December 2021., Exposures: Survey measures of patient-reported past-year cannabis use, medical cannabis use (ie, explicit medical use), and any health reason(s) for use (ie, implicit medical use)., Main Outcomes and Measures: Survey data were linked to EHR data in the year before screening. EHR measures included documentation of explicit and/or implicit medical cannabis use. Analyses estimated the primary care prevalence of cannabis use and compared EHR-documented with patient-reported medical cannabis use, accounting for stratified sampling and nonresponse., Results: Overall, 1688 patients responded to the survey (34% response rate; mean [SD] age, 50.7 [17.5] years; 861 female [56%], 1184 White [74%], 1514 non-Hispanic [97%], and 1059 commercially insured [65%]). The primary care prevalence of any past-year patient-reported cannabis use on the survey was 38.8% (95% CI, 31.9%-46.1%), whereas the prevalence of explicit and implicit medical use were 26.5% (95% CI, 21.6%-31.3%) and 35.1% (95% CI, 29.3%-40.8%), respectively. The prevalence of EHR-documented medical cannabis use was 4.8% (95% CI, 3.45%-6.2%). Compared with patient-reported explicit medical use, the sensitivity and specificity of EHR-documented medical cannabis use were 10.0% (95% CI, 4.4%-15.6%) and 97.1% (95% CI, 94.4%-99.8%), respectively., Conclusions and Relevance: These findings suggest that medical cannabis use is common among primary care patients in a state with legal use, and most use is not documented in the EHR. Patient report of health reasons for cannabis use identifies more medical use compared with explicit questions about medical use.

Background: Alcohol use disorder (AUD) is underdiagnosed and undertreated in medical settings, in part due to a lack of AUD assessment instruments that are reliable and practical for use in routine care. This study evaluates the test-retest reliability of a patient-report Alcohol Symptom Checklist questionnaire when it is used in routine care, including primary care and mental health specialty settings., Methods: We performed a pragmatic test-retest reliability study using electronic health record (EHR) data from Kaiser Permanente Washington, an integrated health system in Washington state. The sample included 454 patients who reported high-risk drinking on a behavioral health screen and completed two Alcohol Symptom Checklists 1 to 21 days apart. Subgroups of these patients who completed both checklists in primary care (n = 271) or mental health settings (n = 79) were also examined. The primary measure was an Alcohol Symptom Checklist on which patients self-reported whether they experienced each of the 11 AUD criteria within the past year, as defined by the Diagnostic and Statistical Manual of Mental Disorders-5 th edition (DSM-5)., Results: Alcohol Symptom Checklists completed in routine care and documented in EHRs had excellent test-retest reliability for measuring AUD criterion counts (ICC = 0.79, 95% CI: 0.76 to 0.82). Test-retest reliability estimates were also high and not significantly different for the subsamples of patients who completed both checklists in primary care (ICC = 0.82, 95% CI: 0.77 to 0.85) or mental health settings (ICC = 0.74, 95% CI: 0.62 to 0.83). Test-retest reliability was not moderated by having a past two-year AUD diagnosis, nor by the age or sex of the patient completing it., Conclusions: Alcohol Symptom Checklists can reliably and pragmatically assess AUD criteria in routine care among patients who screen positive for high-risk drinking. The Alcohol Symptom Checklist may be a valuable tool in supporting AUD-related care and monitoring AUD criteria longitudinally in routine primary care and mental health settings., (© 2022 by the Research Society on Alcoholism.)

Background: Most people with alcohol use disorder do not receive treatment, and primary care (PC)-based management of alcohol use disorder is a key strategy to close this gap. Understanding PC patients' perspectives on changing drinking and receiving alcohol-related care is important for this goal, particularly among those who decline alcohol-related care. This study examined perspectives on barriers and facilitators to changing drinking and receiving alcohol-related care among Veterans Health Administration (VA) PC patients who indicated interest but did not enroll in the Choosing Healthier drinking Options In primary CarE trial (CHOICE), which tested a PC-based alcohol care management intervention. Methods: VA PC patients with frequent heavy drinking who indicated interest in CHOICE but did not enroll were invited to participate. Twenty-seven patients completed in-person, semi-structured interviews. Interview transcripts were analyzed using iterative deductive and inductive content analysis. Results: Participants were mostly men (96%) and White (59%), and the mean age was 48. Seventy-four percent met criteria for alcohol use disorder, and the median number of past-week standard drinks was 41.5. Participants reported fewer alcohol-related problems, lower importance of/readiness to change drinking, and higher confidence in their ability to change than patients who enrolled in the CHOICE trial. Barriers fell into 5 domains: drinking fulfills need(s); reducing drinking or treatment is not needed; treatment is not effective/not acceptable; alcohol-related stigma; and practical barriers. Facilitators fell into 4 domains: reasons to change drinking; social support; treatment is acceptable/meets patients' needs; and practical facilitators. Participants discussed how Veteran identity and military experiences impacted drinking and willingness to receive care, which amplified multiple barriers/facilitators. Conclusions: This study identified barriers and facilitators to changing drinking and receiving alcohol-related care among VA PC patients who indicated interest but did not enroll in an alcohol care management trial. Findings can inform patient-centered interventions and support clinicians in engaging patients in care.

Background: Most states have legalized medical cannabis, yet little is known about how medical cannabis use is documented in patients' electronic health records (EHRs). We used natural language processing (NLP) to calculate the prevalence of clinician-documented medical cannabis use among adults in an integrated health system in Washington State where medical and recreational use are legal. Methods: We analyzed EHRs of patients ≥18 years old screened for past-year cannabis use (November 1, 2017-October 31, 2018), to identify clinician-documented medical cannabis use. We defined medical use as any documentation of cannabis that was recommended by a clinician or described by the clinician or patient as intended to manage health conditions or symptoms. We developed and applied an NLP system that included NLP-assisted manual review to identify such documentation in encounter notes. Results: Medical cannabis use was documented for 16,684 (5.6%) of 299,597 outpatient encounters with routine screening for cannabis use among 203,489 patients seeing 1,274 clinicians. The validated NLP system identified 54% of documentation and NLP-assisted manual review the remainder. Language documenting reasons for cannabis use included 125 terms indicating medical use, 28 terms indicating non-medical use and 41 ambiguous terms. Implicit documentation of medical use (e.g., "edible THC nightly for lumbar pain ") was more common than explicit (e.g., "continues medical cannabis use"). Conclusions: Clinicians use diverse and often ambiguous language to document patients' reasons for cannabis use. Automating extraction of documentation about patients' cannabis use could facilitate clinical decision support and epidemiological investigation but will require large amounts of gold standard training data.

Background/objective: Evidence-based alcohol-related care-brief intervention for all patients with unhealthy alcohol use and specialty addictions treatment and/or pharmacotherapy for patients with alcohol use disorder (AUD)-should be routinely offered. Transgender persons may be particularly in need of alcohol-related care, given common experiences of social and economic hardship that may compound the adverse effects of unhealthy alcohol use. We examined receipt of alcohol-related care among transgender patients compared to non-transgender patients in a large national sample of Veterans Health Administration (VA) outpatients with unhealthy alcohol use., Methods: We extracted electronic health record data for patients from all VA facilities who had an outpatient visit 10/1/09-7/31/17 and a documented positive screen for unhealthy alcohol use (AUDIT-C ≥ 5). We identified transgender patients with a validated approach using transgender-related diagnostic codes. We fit modified Poisson models, adjusted for demographics and comorbidities, to estimate the average predicted prevalence of brief intervention (documented 0-14 days following most recent positive screening), specialty addictions treatment for AUD (documented 0-365 days following screening), and filled prescriptions for medications to treat AUD (documented 0-365 days following screening) for transgender patients, and compared to that of non-transgender patients., Results: Among transgender Veterans with unhealthy alcohol use (N = 1392), the adjusted prevalence of receiving brief intervention was 75.4% (95% CI 72.2-78.5), specialty addictions treatment for AUD was 15.7% (95% CI 13.7-17.7), and any AUD pharmacotherapy was 19.0% (95% CI 17.1-20.8). Receipt of brief intervention did not differ for transgender relative to non-transgender patients (Prevalence Ratio [PR] 1.01, 95% CI 0.98-1.04, p = 0.574). However, transgender patients were more likely to receive specialty addictions treatment (PR 1.24, 95% CI 1.12-1.37, p < 0.001) and pharmacotherapy (PR 1.16, 95% CI 1.06-1.28, p = 0.002)., Conclusions: Findings suggest the majority of transgender VHA patients with unhealthy alcohol use receive brief intervention, though a quarter still do not. Nonetheless, rates of specialty addictions treatment and pharmacotherapy are low overall, although transgender patients may be receiving this care at greater rates than non-transgender patients. Further research is needed to investigate these findings and to increase receipt of evidence-based care overall., (Copyright © 2021. Published by Elsevier Inc.)

Shared decision making is an important implementation "pull" strategy for increasing uptake of evidence-based mental health practices. In this qualitative study, we explored provider perspectives on implementing shared decision making at the point of mental health treatment initiation using a publicly available, patient-facing decision support tool for post-traumatic stress disorder (PTSD). We conducted semi-structured interviews with 22 mental health providers (psychiatrists, nurses, psychologists, and social workers) working in one of five VA primary care clinics. Interviewed were analyzed using thematic analysis. Provider were enthusiastic about using decision aids as a source of high quality information that could improve patient experience and confidence in treatment. However, providers had concerns about decision aid accessibility, time constraints to conduct shared decision making in-session, and patient motivation to engage in shared decision making. Providers stated they would prefer to use shared decision making with patients that they felt were most likely to follow through with treatment. While providers believed that shared decision making could improve PTSD treatment planning, they thought it most appropriate for patients with the highest levels of motivation and fewest barriers to care. These beliefs may limit widespread adoption and reflect missed opportunities to reach difficult-to-engage patients., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Background: Digital interventions, such as websites and smartphone apps, can be effective in treating drug use disorders (DUDs). However, their implementation in primary care is hindered, in part, by a lack of knowledge on how patients might like these treatments delivered to them., Objective: This study aims to increase the understanding of how patients with DUDs prefer to receive app-based treatments to inform the implementation of these treatments in primary care., Methods: The methods of user-centered design were combined with qualitative research methods to inform the design of workflows for offering app-based treatments in primary care. Adult patients (n=14) with past-year cannabis, stimulant, or opioid use disorder from 5 primary care clinics of Kaiser Permanente Washington in the Seattle area participated in this study. Semistructured interviews were recorded, transcribed, and analyzed using qualitative template analysis. The coding scheme included deductive codes based on interview topics, which primarily focused on workflow design. Inductive codes emerged from the data., Results: Participants wanted to learn about apps during visits where drug use was discussed and felt that app-related conversations should be incorporated into the existing care whenever possible, as opposed to creating new health care visits to facilitate the use of the app. Nearly all participants preferred receiving clinician support for using apps over using them without support. They desired a trusting, supportive relationship with a clinician who could guide them as they used the app. Participants wanted follow-up support via phone calls or secure messaging because these modes of communication were perceived as a convenient and low burden (eg, no copays or appointment travel)., Conclusions: A user-centered implementation of treatment apps for DUDs in primary care will require health systems to design workflows that account for patients' needs for structure, support in and outside of visits, and desire for convenience., (©Joseph E Glass, Theresa E Matson, Catherine Lim, Andrea L Hartzler, Kilian Kimbel, Amy K Lee, Tara Beatty, Rebecca Parrish, Ryan M Caldeiro, Angela Garza McWethy, Geoffrey M Curran, Katharine A Bradley. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 06.07.2021.)

Importance: Many people use cannabis for medical reasons despite limited evidence of therapeutic benefit and potential risks. Little is known about medical practitioners' documentation of medical cannabis use or clinical characteristics of patients with documented medical cannabis use., Objectives: To estimate the prevalence of past-year medical cannabis use documented in electronic health records (EHRs) and to describe patients with EHR-documented medical cannabis use, EHR-documented cannabis use without evidence of medical use (other cannabis use), and no EHR-documented cannabis use., Design, Setting, and Participants: This cross-sectional study assessed adult primary care patients who completed a cannabis screen during a visit between November 1, 2017, and October 31, 2018, at a large health system that conducts routine cannabis screening in a US state with legal medical and recreational cannabis use., Exposures: Three mutually exclusive categories of EHR-documented cannabis use (medical, other, and no use) based on practitioner documentation of medical cannabis use in the EHR and patient report of past-year cannabis use at screening., Main Outcomes and Measures: Health conditions for which cannabis use has potential benefits or risks were defined based on National Academies of Sciences, Engineering, and Medicine's review. The adjusted prevalence of conditions diagnosed in the prior year were estimated across 3 categories of EHR-documented cannabis use with logistic regression., Results: A total of 185 565 patients (mean [SD] age, 52.0 [18.1] years; 59% female, 73% White, 94% non-Hispanic, and 61% commercially insured) were screened for cannabis use in a primary care visit during the study period. Among these patients, 3551 (2%) had EHR-documented medical cannabis use, 36 599 (20%) had EHR-documented other cannabis use, and 145 415 (78%) had no documented cannabis use. Patients with medical cannabis use had a higher prevalence of health conditions for which cannabis has potential benefits (49.8%; 95% CI, 48.3%-51.3%) compared with patients with other cannabis use (39.9%; 95% CI, 39.4%-40.3%) or no cannabis use (40.0%; 95% CI, 39.8%-40.2%). In addition, patients with medical cannabis use had a higher prevalence of health conditions for which cannabis has potential risks (60.7%; 95% CI, 59.0%-62.3%) compared with patients with other cannabis use (50.5%; 95% CI, 50.0%-51.0%) or no cannabis use (42.7%; 95% CI, 42.4%-42.9%)., Conclusions and Relevance: In this cross-sectional study, primary care patients with documented medical cannabis use had a high prevalence of health conditions for which cannabis use has potential benefits, yet a higher prevalence of conditions with potential risks from cannabis use. These findings suggest that practitioners should be prepared to discuss potential risks and benefits of cannabis use with patients.

Alcohol use increases non-adherence to antiretroviral therapy (ART) among persons living with HIV (PLWH). Dynamic longitudinal associations are understudied. Veterans Aging Cohort Study (VACS) data 2/1/2008-7/31/16 were used to fit linear regression models estimating changes in adherence (% days with ART medication fill) associated with changes in alcohol use based on annual clinically-ascertained AUDIT-C screening scores (range - 12 to + 12, 0 = no change) adjusting for demographics and initial adherence. Among 21,275 PLWH (67,330 observations), most reported no (48%) or low-level (39%) alcohol use initially, with no (55%) or small (39% ≤ 3 points) annual change. Mean initial adherence was 86% (SD 21%), mean annual change was - 3.1% (SD 21%). An inverted V-shaped association was observed: both increases and decreases in AUDIT-C were associated with greater adherence decreases relative to stable scores [p < 0.001, F (4, 21,274)]. PLWH with dynamic alcohol use (potentially indicative of alcohol use disorder) should be considered for adherence interventions.

Objective: Alcohol use is understudied among transgender persons--persons whose sex differs from their gender identity. We compare patterns of alcohol use between Veterans Health Administration (VA) transgender and nontransgender outpatients., Method: National VA electronic health record data were used to identify all patients' last documented Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) screen (October 1, 2009-July 31, 2017). Transgender patients were identified using diagnostic codes. Logistic regression models estimated four past-year primary outcomes: (a) alcohol use (AUDIT-C > 0); (b) unhealthy alcohol use (AUDIT-C ≥ 5); (c) high-risk alcohol use (AUDIT-C ≥ 8); and (d) heavy episodic drinking (HED; ≥6 drinks on ≥1 occasion). Two secondary diagnostic-based outcomes, alcohol use disorder (AUD) and alcohol-specific conditions, were also examined., Results: Among 8,872,793 patients, 8,619 (0.10%) were transgender. For transgender patients, unadjusted prevalence estimates were as follows: 52.8% for any alcohol use, 6.6% unhealthy alcohol use, 2.8% high-risk use, 10.4% HED, 8.6% AUD, and 1.3% alcohol-specific conditions. After adjustment for demographic characteristics, transgender patients had lower odds of patient-reported alcohol use but higher odds of alcohol-related diagnoses compared with nontransgender patients. Differences in alcohol-related diagnoses were attenuated after adjustment for comorbid conditions and utilization., Conclusions: This is the largest study of patterns of alcohol use among transgender persons and among the first to directly compare patterns to nontransgender persons. Findings suggest nuanced associations with patterns of alcohol use and provide a base for further disparities research to explore alcohol use within the diverse transgender community. Research with self-reported measures of gender identity and sex-at-birth and structured assessment of alcohol use and disorders is needed.

Background: Cannabis and other drug use is associated with adverse health events, but little is known about the association of routine clinical screening for cannabis or other drug use and acute care utilization. This study evaluated whether self-reported frequency of cannabis or other drug use was associated with subsequent acute care., Method: This retrospective cohort study used EHR and claims data from 8 sites in Washington State that implemented annual substance use screening. Eligible adult primary care patients (N = 47,447) completed screens for cannabis (N = 45,647) and/or other drug use, including illegal drug use and prescription medication misuse, (N = 45,255) from 3/3/15-10/1/2016. Separate single-item screens assessed frequency of past-year cannabis and other drug use: never, less than monthly, monthly, weekly, daily/almost daily. An indicator of acute care utilization measured any urgent care, emergency department visits, or hospitalizations ≤19 months after screening. Adjusted Cox proportional hazards models estimated risk of acute care., Results: Patients were predominantly non-Hispanic White. Those reporting cannabis use less than monthly (Hazard Ratio [HR] = 1.12, 95 % CI = 1.03-1.21) or daily (HR = 1.24; 1.10-1.39) had greater risk of acute care during follow-up than those reporting no use. Patients reporting other drug use less than monthly (HR = 1.34; 1.13-1.59), weekly (HR = 2.21; 1.46-3.35), or daily (HR = 2.53; 1.86-3.45) had greater risk of acute care than those reporting no other drug use., Conclusion: Population-based screening for cannabis and other drug use in primary care may have utility for understanding risk of subsequent acute care. It is unclear whether findings will generalize to U.S. states with broader racial/ethnic diversity., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: The authors tested the efficacy of the "I-STAND" intervention for reducing sitting time, a novel and potentially health-promoting approach, in older adults with obesity., Methods: The authors recruited 60 people (mean age = 68 ± 4.9 years, 68% female, 86% White; mean body mass index = 35.4). The participants were randomized to receive the I-STAND sitting reduction intervention (n = 29) or healthy living control group (n = 31) for 12 weeks. At baseline and at 12 weeks, the participants wore activPAL devices to assess sitting time (primary outcome). Secondary outcomes included fasting glucose, blood pressure, and weight. Linear regression models assessed between-group differences in the outcomes., Results: The I-STAND participants significantly reduced their sitting time compared with the controls (-58 min per day; 95% confidence interval [-100.3, -15.6]; p = .007). There were no statistically significant changes in the secondary outcomes., Conclusion: I-STAND was efficacious in reducing sitting time, but not in changing health outcomes in older adults with obesity.

Background: The CHOICE care management intervention did not improve drinking relative to usual care (UC) for patients with frequent heavy drinking at high risk of alcohol use disorders. Patients with alcohol dependence were hypothesized to benefit most. We conducted preplanned secondary analyses to test whether the CHOICE intervention improved drinking relative to UC among patients with and without baseline DSM-IV alcohol dependence., Methods: A total of 304 patients reporting frequent heavy drinking from 3 VA primary care clinics were randomized (stratified by DSM-IV alcohol dependence, sex, and site) to UC or the patient-centered, nurse-delivered, 12-month CHOICE care management intervention. Primary outcomes included percent heavy drinking days (%HDD) using 28-day timeline follow-back and a "good drinking outcome" (GDO)-abstaining or drinking below recommended limits and no alcohol-related symptoms on the Short Inventory of Problems at 12 months. Generalized estimating equation binomial regression models (clustered on provider) with interaction terms between dependence and intervention group were fit., Results: At baseline, 59% of intervention and UC patients had DSM-IV alcohol dependence. Mean drinking outcomes improved for all subgroups. For participants with dependence, 12-month outcomes did not differ for intervention versus UC patients (%HDD 37% versus 38%, p = 0.76 and GDO 16% versus 16%, p = 0.77). For participants without dependence, %HDD did not differ between intervention (41%) and UC (31%) patients (p = 0.12), but the proportion with GDO was significantly higher among UC participants (26% versus 13%, p = 0.046). Neither outcome was significantly modified by dependence (interaction p values 0.19 for %HDD and 0.10 for GDO)., Conclusions: Among participants with frequent heavy drinking, care management had no benefit relative to UC for patients with dependence, but UC may have had benefits for those without dependence., Trial Registration: ClinicalTrials.gov NCT01400581.

Purpose: To estimate changes in self-reported health and psychosocial factors associated with a 12-week sedentary behavior intervention for older adults., Design: Exploratory secondary analysis of pilot randomized controlled trial., Setting: Kaiser Permanente Washington., Subjects: Sixty adults aged 60 to 89 with body mass index ≥30 kg/m 2 ., Intervention: Participants were randomized to the I-STAND intervention or control group. I-STAND involved 6 coaching sessions, a study workbook, Jawbone UP activity tracker to prompt breaks from sitting, and activPAL feedback on objective sitting time., Measures: At baseline and 12-week follow-up, participants completed a survey with validated measures of self-reported health outcomes (depression, stress, memory/concentration, sleep, pain, ability to do daily activities, energy, and quality of life) and modified scales measuring psychosocial factors (perceived benefits/barriers, social support, self-efficacy, and sedentary habit strength) regarding sedentary behavior., Analysis: Generalized linear models assessed associations between group assignment and change in each self-reported health and psychosocial score, adjusting for baseline scores., Results: I-STAND participants demonstrated improvements in self-efficacy (β = 0.35, 95% confidence interval [CI]: 0.10 to 0.60) and reduced habit strength (β= -0.23, 95% CI: -0.42 to -0.04) compared to control participants. There were no significant differences in self-reported health outcomes, although power was limited in this exploratory analysis., Conclusion: A sedentary behavior reduction intervention for older adults resulted in improvements for some psychosocial factors. Health outcomes may require longer than 12 weeks to observe improvements.

Background: Alcohol use influences HIV disease severity through multiple mechanisms. Whether HIV disease severity is sensitive to changes in alcohol use among people with HIV (PWH) is understudied., Setting: National Veterans Health Administration., Methods: Pairs of AUDIT-C screens within 9-15 months (February 1, 2008-September 30, 2014) were identified among PWH from the Veterans Aging Cohort Study (VACS). Initial and follow-up VACS Index 2.0 pairs obtained 0-270 days after initial and follow-up AUDIT-Cs, respectively, determined change in VACS Index 2.0, a composite HIV severity measure. Change in VACS Index 2.0 was regressed on AUDIT-C change scores (-12 to +12) adjusted for demographics, initial VACS Index 2.0, and days between VACS Index measures., Results: Among 23,297 PWH (76,202 observations), most had no (51%) or low-level (38%) alcohol use initially. Most (54%) had no subsequent change; 21% increased and 24% decreased drinking. Initial VACS Index 2.0 scores ranged from 0 to 134, change scores ranged from -65 to +73, with average improvement of 0.76 points (SD 9.48). AUDIT-C change was associated with VACS Index 2.0 change (P < 0.001). Among those with stable alcohol use (AUDIT-C change ≤ │1│ point), VACS Index 2.0 improvements ranged 0.36-0.60 points. For those with maximum AUDIT-C increase (change from 0 to 12), VACS Index 2.0 worsened 3.74 points (95% CI: -4.71 to -2.78); for those with maximum AUDIT-C decrease (change from 12 to 0), VACS Index 2.0 changed minimally [-0.60 (95% CI: -1.43 to 0.23)]., Conclusions: In this national sample, improvement in HIV severity was generally greatest among those with stable alcohol use (primarily those with no use).

Background: Effective medications for treating alcohol use disorders (AUD) are available but underutilized. Multiple barriers to their provision have been identified, and optimal strategies for addressing and overcoming barriers to use of medications for AUD treatment remain elusive. We conducted a structured review of published care delivery and implementation studies evaluating interventions that aimed to increase medication treatment for patients with AUD to identify interventions and component strategies that were most effective., Methods: We reviewed literature through May 2018 and used networking to identify intervention studies with AUD medication receipt reported as a primary or secondary outcome. Studies were identified as care delivery studies, characterized by patient-level recruitment and willingness to be randomized to candidate treatment options, and implementation studies, characterized by inclusion of all patients treated at sites involved in the study. Each identified study was independently coded by two investigators for strategies used, guided by a published taxonomy of implementation strategies. All authors reviewed coding discrepancies and revised codes based on consensus. After reaching internal consensus, we solicited feedback from lead investigators on studies to code additional strategies. We reviewed implementation strategies used across studies to assess their relationship with medication receipt, as well as alcohol use outcomes, as available., Results: Nine studies were identified: four RCTs of care delivery interventions, four quasi-experimental evaluations of large-scale implementation interventions, and one quasi-experimental evaluation of a targeted single-site implementation intervention. Implementation strategies used were variable across studies; no strategy was universally used. Effects of the interventions on receipt of AUD pharmacotherapy and alcohol use outcomes also varied. Three of four care delivery interventions resulted in increased receipt of AUD medications, but only one of these three improved alcohol use outcomes. One large-scale and one single-site implementation intervention were associated with increased AUD medication receipt, and these studies did not assess alcohol use outcomes. Patterns of implementation strategies did not clearly distinguish studies that successfully increased use of pharmacotherapy versus those that did not., Conclusions: Our review did not reveal strategies most effective for implementing AUD medications. Interventions designed to overcome identified barriers may have missed the mark, or differences in the intensity or targets of strategies may matter more than differences in strategies. Further research is needed to understand effective implementation methods and to better understand patient-level perspective, preferences and barriers to receipt of medications.

We evaluated associations between levels of alcohol use and HIV care continuum components using national Veterans Aging Cohort Study data for all patients with HIV and AUDIT-C screening (2/1/2008-9/30/2014). Poisson regression models evaluated associations between alcohol use levels (non-drinking, low-, medium-, high-, and very high-level drinking) and: (1) engagement with care (documented CD4 cells/µl or viral load copies/ml labs), (2) ART treatment (≥ 1 prescription), and (3) viral suppression (HIV RNA < 500 copies/ml) within one year. Among 33,224 patients, alcohol use level was inversely associated with all care continuum outcomes (all p < 0.001). Adjusted prevalence of care engagement ranged from 77.8% (95% CI 77.1-78.4%) for non-drinking to 69.1% (66.6-71.6%) for high-level drinking. The corresponding range for ART treatment was 74.0% (73.3-74.7%) to 60.1% (57.3-62.9%) and for viral suppression was 57.3% (56.5-58.1%) to 38.3% (35.6-41.1%). Greater alcohol use is associated with suboptimal HIV treatment across the HIV care continuum.

Background: Alcohol use is risky for patients with hepatitis C virus (HCV) and/or human immunodeficiency virus (HIV) infection, but alcohol use disorder (AUD) treatment is underutilized in these populations. Comorbid drug use disorders (DUD) are common, but their influence on AUD treatment receipt is understudied. We evaluated the association between DUD and AUD treatment receipt in two national samples of patients with AUD, those with HIV and those with HCV, in the U.S. Veterans Health Administration., Methods: Samples included patients with AUD and HCV and/or HIV among positive alcohol screens (AUDIT-C≥5) documented 10/01/09-5/30/13 in the national electronic health record. Poisson regression models estimated incidence rate ratios for receiving specialty treatment (stop codes) and pharmacotherapy (filled prescription for naltrexone, disulfiram, acamprosate, or topiramate) within 365 days of positive alcohol screening for patients with DUD versus those without. Models were clustered on patient and adjusted for potential confounders., Results: Among 22,039 patients with HCV/AUD, 45.2% (N = 9,964) had DUD, which was associated with receiving specialty treatment [adjusted incidence rate ratio: 1.89 (95% confidence interval 1.82-1.96)] and pharmacotherapy [aIRR: 1.50 (1.37-1.65)]. Among 1,834 patients with HIV/AUD, 56.9% (N = 1,043) had DUD, which was associated with receiving specialty treatment [aIRR: 1.94 (1.68-2.24)], but not pharmacotherapy., Conclusions: Rates of AUD treatment receipt among patients with AUD and HCV and/or HIV were low overall, but likelihood of treatment receipt was generally higher among those with comorbid DUD. Future research should investigate mechanisms underlying these associations, such as enhanced readiness for treatment or differential provider prescribing or referral practices., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objectives: To investigate whether gender is associated with three recommended stages of the HIV care continuum and whether gender modifies known associations between level of alcohol use and HIV care among US veterans., Design: Retrospective cohort., Methods: Veterans Aging Cohort Study data were used to identify Veterans Health Administration (VA) patients with HIV and AUDIT-C alcohol screening from 1 February 2008 to 30 September 2014. Modified Poisson regression models estimated the relative risk and predicted prevalences of engagement in HIV care (documented CD4 cells/μl or viral load copies/ml lab values), ART treatment (at least one prescription), and viral suppression (HIV RNA <500 copies/ml) in the year following AUDIT-C (1) for women compared to men, and (2) for each level of alcohol use compared to nondrinking among women and among men. A multiplicative interaction between gender and alcohol use was tested., Results: Among 33 224 patients, women (n = 971) were less likely than men (n = 32 253) to receive HIV care (P values <0.001). Respective predicted prevalences for women and men were 71.9% (95% CI 69.1-74.7%) and 77.9% (77.5-78.4%) for engagement, 60.0% (57.0-73.14%) and 73.8% (73.4-74.3%) for ART treatment, and 46.4% (43.3-49.6%) and 55.8% (55.3-56.3%) for viral suppression. Although the interaction between gender and alcohol use was not statistically significant, stratified analyses suggested worse outcomes for women than men at higher levels of alcohol use., Conclusion: In this large national cohort, women were less likely than men to be engaged in HIV medical care, prescribed ART, and virally suppressed. Interventions to improve HIV care for women are needed at all levels of alcohol use.

Background: Older adults spend more time sitting than any other age group, contributing to poor health outcomes. Effective behavioral interventions are needed to encourage less sitting among older adults, specifically those with obesity, but these programs must be acceptable to the target population. We explored participant acceptance of a theory-based and technology-enhanced sitting reduction intervention designed for older adults (I-STAND)., Methods: The 12-week I-STAND intervention consisted of 6 health coaching contacts, a study workbook, a Jawbone UP band to remind participants to take breaks from sitting, and feedback on sitting behaviors (generated from wearing an activPAL device for 7 days at the beginning and mid-point of the study). Semi-structured interviews were conducted with 22 participants after they completed the intervention. Interview transcripts were iteratively coded by a team, and thematic analysis was used to identify and refine emerging themes., Results: Overall, participants were satisfied with the I-STAND intervention, thought the sedentary behavior goals of the intervention were easy to incorporate, and found the technologies to be helpful additions to (but not substitutes for) health coaching. Barriers to standing more included poor health, ingrained sedentary habits, lack of motivation to change sedentary behavior, and social norms that dictate when it is appropriate to sit/stand. Facilitators to standing more included increased awareness of sitting, a sense of accountability, daily activities that involved standing, social support, and changing ways of interacting in the home environment. Participants reported that the intervention improved physical health, increased energy, increased readiness to engage in physical activity, improved mood, and reduced stress., Conclusions: The technology-enhanced sedentary behavior reduction intervention was acceptable, easy to incorporate, and had a positive perceived health impact on older adults with obesity., Trial Registration: The I-STAND study was registered at clinicaltrials.gov (ID: NCT02692560 ) February 2016.

Background: Older adults have high rates of obesity and are prone to chronic health conditions. These conditions are in part due to high rates of sedentary time (ST). As such, reducing ST could be an innovative strategy for improving health outcomes among obese older adults. To test this theory, we developed a novel, technology-enhanced intervention to reduce sitting time (I-STAND) and pilot tested it to assess the feasibility, acceptability, and preliminary effects of the intervention on ST and biometric outcomes., Objective: The current paper aims to describe the rationale, design, and methods of the I-STAND sitting reduction pilot trial., Methods: Older adults with obesity (n=60) were recruited from a large health care system and randomized to receive I-STAND or a healthy living intervention. I-STAND combined personal coaching with a technology-enhanced intervention (Jawbone UP band) to cue breaks from sitting. Participants completed self-report and biometric assessments at baseline and 3 months. Additional qualitative results were collected from a subset of I-STAND participants (n=22) to further inform the feasibility and acceptability of the interventions. The primary outcome was total hours of daily sitting time measured by the activPAL device. Secondary outcomes included sit-to-stand transitions, bouts of sitting longer than 30 minutes, physical function, blood pressure, fasting glucose, cholesterol, and depressive symptoms., Results: Study enrollment has ended and data processing is underway., Conclusions: Data from randomized trials on sitting reduction are needed to inform novel approaches to health promotion among older adults with obesity. Our trial will help fill this gap. The methods used in our study can guide future research on using technology-based devices to assess or prompt sedentary behavior reduction, or those interested in behavioral interventions targeting obese older adults with novel approaches., Trial Registration: ClinicalTrials.gov: NCT02692560; https://clinicaltrials.gov/ct2/show/NCT02692560 (Archived by WebCite at http://www.webcitation.org/6wppLTWAl)., (©Dori E Rosenberg, Amy K Lee, Melissa Anderson, Anne Renz, Theresa E Matson, Jacqueline Kerr, David Arterburn, Jennifer B McClure. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 12.02.2018.)

Background: Most patients with alcohol use disorders (AUDs) never receive alcohol treatment, and experts have recommended management of AUDs in primary care. The Choosing Healthier Drinking Options In primary CarE (CHOICE) trial was a randomized controlled effectiveness trial of a novel intervention for primary care patients at high risk for AUDs. This report describes the conceptual and scientific foundation of the CHOICE model of care, critical elements of the CHOICE trial design consistent with the Template for Intervention Description and Replication (TIDieR), results of recruitment, and baseline characteristics of the enrolled sample., Methods: The CHOICE intervention is a multi-contact, extended counseling intervention, based on the Chronic Care Model, shared decision-making, motivational interviewing, and evidence-based options for managing AUDs, designed to be practical in primary care. Outpatients who received care at 3 Veterans Affairs primary care sites in the Pacific Northwest and reported frequent heavy drinking (≥4 drinks/day for women; ≥5 for men) were recruited (2011-2014) into a trial in which half of the participants would be offered additional alcohol-related care from a nurse. CHOICE nurses offered 12 months of patient-centered care, including proactive outreach and engagement, repeated brief motivational interventions, monitoring with and without alcohol biomarkers, medications for AUDs, and/or specialty alcohol treatment as appropriate and per patient preference. A CHOICE nurse practitioner was available to prescribe medications for AUDs., Results: A total of 304 patients consented to participate in the CHOICE trial. Among consenting participants, 90% were men, the mean age was 51 (range 22-75), and most met DSM-IV criteria for alcohol abuse (14%) or dependence (59%). Many participants also screened positive for tobacco use (44%), depression (45%), anxiety disorders (30-41%) and non-tobacco drug use disorders (19%). At baseline, participants had a median AUDIT score of 18 [Interquartile range (IQR) 14-24] and a median readiness to change drinking score of 5 (IQR 2.75-6.25) on a 1-10 Likert scale., Conclusion: The CHOICE trial tested a patient-centered intervention for AUDs and recruited primary care patients at high risk for AUDs, with a spectrum of severity, co-morbidity, and readiness to change drinking. Trial registration The trial is registered at clinicaltrial.gov (NCT01400581).