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Objective: The quality of alcohol-related prevention and treatment in US primary care is poor. The purpose of this study was to describe the extent to which Electronic Health Records (EHRs) used by 167 primary care practices across 7 states currently include the necessary prompts, clinical support, and performance reporting essential for improving alcohol-related prevention and treatment in primary care., Materials and Methods: Experts from five regional quality improvement programs identified basic EHR features needed to support evidence-based alcohol-related prevention (ie, screening and brief intervention) and treatment of alcohol use disorders (AUD). Data were collected regarding whether EHRs included these features., Results: EHRs from 21 vendors were used by the primary care practices. For prevention, 62% of the 167 practices' EHRs included a validated screening questionnaire, 46% automatically scored the screening instrument, 62% could report the percent screened, and 37% could report the percent screening positive. Only 7% could report the percent offered brief intervention. For alcohol treatment, 49% of practices could report the percent diagnosed with AUD, 58% and 91% allowed documentation of referral and treatment with AUD medication, respectively. Only 3% could report the percent of patients diagnosed with AUD who received treatment., Discussion: Most EHRs observed across 167 primary care practices across 7 US states lacked basic functionality necessary to support evidence-based alcohol-related prevention and AUD treatment. Only 3% and 7% of EHRs, respectively, included the ability to report widely recommended quality measures needed to improve the quality of recommended alcohol-related prevention and treatment in primary care., Conclusion: Improving EHR functionality is likely necessary before alcohol-related primary care can be improved., Competing Interests: The authors have no competing interests to declare., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objectives: Assessment and counseling are recommended for individuals with prenatal cannabis use. We examined characteristics that predict prenatal substance use assessment and counseling among individuals who screened positive for prenatal cannabis use in prenatal settings., Methods: Electronic health record data from Kaiser Permanente Northern California's Early Start perinatal substance use screening, assessment, and counseling program was used to identify individuals with ≥1 pregnancies positive for prenatal cannabis use. Outcomes included completion of a substance use assessment and among those assessed, attendance in Early Start counseling only or Addiction Medicine Recovery Services (AMRS) treatment. Predictors included demographics and past-year psychiatric and substance use disorder diagnoses evaluated with GEE multinomial logistic regression., Results: The sample included 17,782 individuals with 20,398 pregnancies positive for cannabis use (1/2011-12/2021). Most pregnancies (80.3%) had an assessment. Individuals with Medicaid, anxiety, depression and tobacco use disorders, compared to those without, had higher odds and those with greater parity, older age (≥35) and in later trimesters, had lower odds of assessment. Among 64% (n = 10,469) pregnancies needing intervention based on assessment, most (88%) attended Early Start counseling only or AMRS (with or without Early Start). Greater parity and later trimester assessment was associated with lower odds, while Medicaid was associated with higher odds of Early Start counseling. Nearly all diagnosed psychiatric and substance use disorders were associated with higher odds of AMRS treatment., Conclusions: A comprehensive prenatal substance use program engaged most pregnant individuals with prenatal cannabis use in substance use assessment and counseling. Opportunities to improve care gaps remain., Competing Interests: C.I.C. and M.B.D. have received support managed through their institution from the Industry PMR Consortium, a consortium of companies working together to conduct postmarketing studies required by the Food and Drug Administration that assess risks related to opioid analgesic use. The other authors report no conflicts of interest., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Society of Addiction Medicine.)

Importance: The Primary Care Opioid Use Disorders (PROUD) treatment trial was a 2-year implementation trial that demonstrated the Massachusetts office-based addiction treatment (OBAT) model of nurse care management for opioid use disorder (OUD) increased OUD treatment in the 2 years after implementation began (8.2 more patient-years of OUD treatment per 10 000 primary care patients). The intervention was continued for a third year, permitting evaluation of 3-year outcomes., Objective: To compare OUD medication treatment in intervention and usual care clinics over 3 years of implementation., Design, Setting, and Participants: This is a preplanned secondary analysis of a cluster randomized implementation trial, conducted in 6 health systems in 5 states (2 primary care clinics per health system) with clinic randomization stratified by system (assignment notification February 28, 2018 [August 31, 2018, in 1 system]). Data were obtained from electronic health records and insurance claims. Eligible patients were those aged 16 to 90 years visiting intervention or usual care clinics from 3 years before to 2 years after randomization. Patients new to clinics during the third year after randomization could not be included because COVID-19-era transitions to virtual care precluded assignment of patients to clinics. Data analysis occurred from November 2023 to September 2024., Intervention: Clinics were randomized to intervention or care as usual. Intervention included 3 implementation components: salary for 1 full-time OBAT nurse per intervention clinic; training and ongoing technical assistance for nurses; and 3 or more primary care buprenorphine prescribers., Main Outcome and Measures: Patient-years of OUD treatment (buprenorphine or extended-release naltrexone) per 10 000 primary care patients in the 3 years postrandomization. Mixed-effect models adjusted for baseline values of the outcome and included a health system-specific random intercept to account for correlation of clinic pairs within a system., Results: Prerandomization, a total of 290 071 primary care patients were seen, including 130 618 in intervention clinics (mean [SD] age, 48.6 [17.7] years; mean [SD] female, 59.3% [4.0%]) and 159 453 in usual care clinics (mean [SD] age, 47.2 [17.5] years; mean [SD] female, 64.0% [5.3%]). Over 3 years postrandomization, intervention clinics provided 19.7 (95% CI, 11.1-28.4) more patient-years of OUD treatment per 10 000 primary care patients compared with usual care clinics., Conclusions: In this secondary analysis of the PROUD cluster randomized trial, after an added year of the intervention, OUD treatment continued to increase in intervention clinics compared with usual care. The treatment increase over 3 years exceeded that of the first 2 years, suggesting that implementation of the Massachusetts OBAT model leads to ongoing increases in OUD treatment among primary care patients in the third year of implementation., Trial Registration: ClinicalTrials.gov Identifier: NCT03407638.

Aims: To estimate the strength of association between psychiatric disorders and substance use disorders (SUD), and cannabis use and cannabis use disorder (CUD) during early pregnancy., Design: Observational study., Setting: Kaiser Permanente Northern California, USA., Participants: 299 496 pregnancies from 227 555 individuals screened for cannabis use by self-report and a urine toxicology test at entrance to prenatal care in Kaiser Permanente Northern California during January 2011-December 2021 (excepting year 2020). The sample was 62.5% non-White, with a mean (standard deviation) age of 31.1 (5.5) years; 6.8% used cannabis; 0.2% had a CUD., Measurements: Exposure variables included electronic health record-based psychiatric diagnoses of attention deficit hyperactivity, anxiety, bipolar, depressive, personality, posttraumatic stress and psychotic disorders; and alcohol, opioid, stimulant and tobacco use disorders, during the two years prior to pregnancy up to the day before the prenatal substance use screening date. Outcome variables were any cannabis use, frequency of self-reported cannabis use and CUD during early pregnancy., Findings: Psychiatric disorder prevalence ranged from 0.2% (psychotic) to 14.3% (anxiety), and SUD ranged from 0.3% (stimulant/opioid) to 3.8% (tobacco). Psychiatric disorders were associated with cannabis use and CUD, with the strongest association for any use found for bipolar disorder (adjusted odds ratio [aOR] = 2.83; 95% confidence interval [CI] = 2.53-3.17) and the strongest association for CUD found for psychotic disorders (aOR = 10.01, 95% CI = 6.52-15.37). SUDs were associated with cannabis use and CUD, with the strongest association for any use found for tobacco use disorder (aOR = 4.03, 95% CI = 3.82-4.24) and the strongest association for CUD found for stimulant use disorder (aOR = 21.99, 95% CI = 16.53-29.26). Anxiety, bipolar, depressive disorders and tobacco use disorder were associated with greater odds of daily than monthly or less cannabis use., Conclusions: Psychiatric disorders and substance use disorders appear to be associated with elevated odds of any and frequent cannabis use as well as cannabis use disorder during early pregnancy. In most cases, the associations with cannabis outcomes were stronger for substance use disorders than other psychiatric disorders., (© 2024 Society for the Study of Addiction.)

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.)

This population-based cross-sectional study analyzed electronic health record data of pregnant individuals in an integrated health care delivery system in California to examine changes in prenatal cannabis use through self-report and urine toxicology testing during standard prenatal care between 2012 (n=33,546) and 2022 (n=43,415), and to test whether trends differed by race and ethnicity or age. The prevalence of prenatal cannabis use increased from 5.5% (95% CI, 5.3-5.8%) in 2012 to 9.0% (95% CI, 8.7-9.2%) in 2022 (adjusted prevalence ratio [aPR] 1.82, 95% CI, 1.72-1.92), with similar increases by toxicology test (aPR 1.70, 95% CI, 1.60-1.81) and self-report (aPR 2.12, 95% CI, 1.95-2.30). The increase in prevalence varied significantly across racial and ethnic and age groups, with the highest prevalence among Black individuals and those aged 13-24 across years. Although rates increased more slowly among groups with the highest prevalence of use, disparities persisted over time., Competing Interests: Financial Disclosure Cynthia I. Campbell and Monique B. Does have received support managed through their institution from the Industry PMR Consortium, a consortium of companies working together to conduct post-marketing studies required by the U.S. Food and Drug Administration that assess risks related to opioid analgesic use. The other authors did not report any potential conflicts of interest., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

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.)

Prescription opioid tapering has increased significantly over the last decade. Evidence suggests that tapering too quickly or without appropriate support may unintentionally harm patients. The aim of this analysis was to understand patients' experiences with opioid tapering, including support received or not received for pain control or mental health. Patients with evidence of opioid tapering from 6 health care systems participated in semi-structured, in-depth interviews; family members of suicide decedents with evidence of opioid tapering were also interviewed. Interviews were analyzed using thematic analysis. Participants included 176 patients and 16 family members. Results showed that 24% of the participants felt their clinicians checked in with them about their taper experiences while 41% reported their clinicians did not. A majority (68%) of individuals who experienced suicide behavior during tapering reported that clinicians did check in about mood and mental health changes specifically; however, 27% of that group reported no such check-in. More individuals reported negative experiences (than positive) with pain management clinics-where patients are often referred for tapering and pain management support. Patients reporting successful tapering experiences named shared decision-making and ability to adjust taper speed or pause tapering as helpful components of care. Fifty-six percent of patients reported needing more support during tapering, including more empathy and compassion (48%) and an individualized approach to tapering (41%). Patient-centered approaches to tapering include reaching out to monitor how patients are doing, involving patients in decision-making, supporting mental health changes, and allowing for flexibility in the tapering pace. PERSPECTIVE: Patients tapering prescription opioids desire more provider-initiated communication including checking in about pain, setting expectations for withdrawal and mental health-related changes, and providing support for mental health. Patients preferred opportunities to share decisions about taper speed and to have flexibility with pausing the taper as needed., (Copyright © 2024 United States Association for the Study of Pain, Inc. Published by Elsevier Inc. All rights reserved.)

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.)

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

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.

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.)

Importance: Unhealthy alcohol use is common and affects morbidity and mortality but is often neglected in medical settings, despite guidelines for both prevention and treatment., Objective: To test an implementation intervention to increase (1) population-based alcohol-related prevention with brief interventions and (2) treatment of alcohol use disorder (AUD) in primary care implemented with a broader program of behavioral health integration., Design, Setting, and Participants: The Sustained Patient-Centered Alcohol-Related Care (SPARC) trial was a stepped-wedge cluster randomized implementation trial, including 22 primary care practices in an integrated health system in Washington state. Participants consisted of all adult patients (aged ≥18 years) with primary care visits from January 2015 to July 2018. Data were analyzed from August 2018 to March 2021., Interventions: The implementation intervention included 3 strategies: practice facilitation; electronic health record decision support; and performance feedback. Practices were randomly assigned launch dates, which placed them in 1 of 7 waves and defined the start of the practice's intervention period., Main Outcomes and Measures: Coprimary outcomes for prevention and AUD treatment were (1) the proportion of patients who had unhealthy alcohol use and brief intervention documented in the electronic health record (brief intervention) for prevention and (2) the proportion of patients who had newly diagnosed AUD and engaged in AUD treatment (AUD treatment engagement). Analyses compared monthly rates of primary and intermediate outcomes (eg, screening, diagnosis, treatment initiation) among all patients who visited primary care during usual care and intervention periods using mixed-effects regression., Results: A total of 333 596 patients visited primary care (mean [SD] age, 48 [18] years; 193 583 [58%] female; 234 764 [70%] White individuals). The proportion with brief intervention was higher during SPARC intervention than usual care periods (57 vs 11 per 10 000 patients per month; P < .001). The proportion with AUD treatment engagement did not differ during intervention and usual care (1.4 vs 1.8 per 10 000 patients; P = .30). The intervention increased intermediate outcomes: screening (83.2% vs 20.8%; P < .001), new AUD diagnosis (33.8 vs 28.8 per 10 000; P = .003), and treatment initiation (7.8 vs 6.2 per 10 000; P = .04)., Conclusions and Relevance: In this stepped-wedge cluster randomized implementation trial, the SPARC intervention resulted in modest increases in prevention (brief intervention) but not AUD treatment engagement in primary care, despite important increases in screening, new diagnoses, and treatment initiation., Trial Registration: ClinicalTrials.gov Identifier: NCT02675777.

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.

Evidence suggests fetal risks are associated with cannabis use during pregnancy. Yet, insights into women's decision-making and cannabis use during pregnancy are limited. This study explored these concepts with postpartum women who used cannabis during and after pregnancy. We conducted interviews with 15 women (4 self-identifying a race other than White and 4 self-identifying Hispanic ethnicity) who: 1) lived in the Puget Sound region of Washington State, 2) reported past-year cannabis use on a routine screen, and 3) had documented pregnancy and delivery March 2015-May 2017. Semi-structured interviews asked about decision-making and cannabis use during pregnancy and postpartum. We used template analysis for coding and analysis. The key findings included that women: 1) gathered information about cannabis use during pregnancy primarily through internet searches and discussions with peers; 2) were reluctant to talk with health care providers about cannabis; 3) used cannabis while pregnant to treat health issues, including morning sickness, pain, and mental health conditions; 4) were comfortable with their decision to use cannabis while pregnant, but had questions about long-term effects; and 5) tried to mitigate transmission through breastmilk. Women decided about cannabis during pregnancy based on their experience, health symptoms, and information gathered from the internet and peers, often without guidance from their health care provider. Results point to opportunities for providers to become informed about and engage in discussion with patients about cannabis use during preconception, pregnancy, and postpartum., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This work was supported by funding from Kaiser Permanente Washington Health Research Institute. The sponsors were not involved in the study design; collection analysis and interpretation of the data; the writing of the manuscript; or the decision to submit the manuscript for publication., (© 2022 The Author(s).)

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.

Introduction: The design of integrated adolescent mental health care should address needs and preferences of patients and parents/guardians., Method: We conducted interviews and focus groups with adolescents aged 13-17 years who received care at Kaiser Permanente Washington in 2020 and interviews with parents of such adolescents. We sought to (1) understand the challenges of primary care-based mental health and substance use screening and care for adolescents and (2) identify program design solutions. Sessions were audio-recorded, transcribed, and coded. Thematic analysis was applied to identify key challenges and design solutions., Results: Emerging themes from interviews (n = 41) and focus groups (n = 10) were summarized in five overarching design principles: Engagement, Privacy, Communication, Choice, and Ease. Each design principle was expanded for operationalization within a new health system program., Discussion: Health systems serving adolescents in primary care may consider application of these design principles to the development of mental health integration programs., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

In 2016, the Agency for Healthcare Research and Quality (AHRQ) recommended seven domains for training and mentoring researchers in learning health systems (LHS) science. Health equity was not included as a competency domain. This commentary from scholars in the Consortium for Applied Training to Advance the Learning health system with Scholars/Trainees (CATALyST) K12 program recommends that competency domains be extended to reflect growing demands for evidence on health inequities and interventions to alleviate them. We present real-life case studies from scholars in an LHS research training program that illustrate facilitators, challenges, and potential solutions at the program, funder, and research community-level to receiving training and mentorship in health equity-focused LHS science. We recommend actions in four areas for LHS research training programs: (a) integrate health equity throughout the current LHS domains; (b) develop training and mentoring in health equity; (c) establish program evaluation standards for consideration of health equity; and (d) bring forth relevant, extant expertise from the areas of health disparities research, community-based participatory research, and community-engaged health services research. We emphasize that LHS research must acknowledge and build on the substantial existing contributions, mainly by scholars of color, in the health equity field., Competing Interests: All authors report no conflicts of interest including relevant financial interests, activities, relationships, and affiliations related to the submitted work., (© 2022 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.)

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.

Objective: This study examined past-year cannabis use prevalence and sociodemographic and clinical correlates of cannabis use among Veterans Health Administration (VHA) primary care patients in a state with legalized medical cannabis., Method: Participants were 1,072 predominantly white, male, VHA primary care patients enrolled in a drug screening validation study (2012-2014). Sociodemographic and clinical correlates were examined by past-year cannabis use status. Multivariate regression models, adjusted for demographics, estimated cannabis use prevalence and clinical correlates among recreational, medical, and both medical and recreational users., Results: Nearly one in five (18.7%) veterans endorsed past-year cannabis use, with 14.1% of the total sample reporting any recreational use and 7.0% reporting any medical use. Correlates of any past-year use included younger age, period of service, being unmarried, lower education, lower income, other substance use, meeting criteria for an alcohol or drug use disorder or posttraumatic stress disorder, higher pain rating, and lower self-reported well-being. Compared to veterans endorsing only recreational use, veterans endorsing only medical use reported more cannabis use days but had lower odds of other drug use, alcohol or drug-related problems, or alcohol or drug use disorders. Differences between recreational only users and those using medical and recreational cannabis were minimal., Conclusions: Veterans enrolled in VHA in states with legalized cannabis may be particularly likely to use cannabis. Veterans identifying as recreational users may be at increased risk for adverse clinical outcomes compared to medical-only users. Prevalence monitoring, assessment, and intervention services should be considered, particularly in states with legalized cannabis. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

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.

Background: Hepatitis C and HIV are associated with opioid use disorders (OUD) and injection drug use. Medications for OUD can prevent the spread of HCV and HIV., Objective: To describe the prevalence of documented OUD, as well as receipt of office-based medication treatment, among primary care patients with HCV or HIV., Design: Retrospective observational cohort study using electronic health record and insurance data., Participants: Adults ≥ 18 years with ≥ 2 visits to primary care during the study (2014-2016) at 6 healthcare systems across five states (CO, CA, OR, WA, and MN)., Main Measures: The primary outcome was the diagnosis of OUD; the secondary outcome was OUD treatment with buprenorphine or oral/injectable naltrexone. Prevalence of OUD and OUD treatment was calculated across four groups: HCV only; HIV only; HCV and HIV; and neither HCV nor HIV. In addition, adjusted odds ratios (AOR) of OUD treatment associated with HCV and HIV (separately) were estimated, adjusting for age, gender, race/ethnicity, and site., Key Results: The sample included 1,368,604 persons, of whom 10,042 had HCV, 5821 HIV, and 422 both. The prevalence of diagnosed OUD varied across groups: 11.9% (95% CI: 11.3%, 12.5%) for those with HCV; 1.6% (1.3%, 2.0%) for those with HIV; 8.8% (6.2%, 11.9%) for those with both; and 0.92% (0.91%, 0.94%) among those with neither. Among those with diagnosed OUD, the prevalence of OUD medication treatment was 20.9%, 16.0%, 10.8%, and 22.3%, for those with HCV, HIV, both, and neither, respectively. HCV was not associated with OUD treatment (AOR = 1.03; 0.88, 1.21), whereas patients with HIV had a lower probability of OUD treatment (AOR = 0.43; 0.26, 0.72)., Conclusions: Among patients receiving primary care, those diagnosed with HCV and HIV were more likely to have documented OUD than those without. Patients with HIV were less likely to have documented medication treatment for OUD.

Background: Most people with opioid use disorder (OUD) never receive treatment. Medication treatment of OUD in primary care is recommended as an approach to increase access to care. The PRimary Care Opioid Use Disorders treatment (PROUD) trial tests whether implementation of a collaborative care model (Massachusetts Model) using a nurse care manager (NCM) to support medication treatment of OUD in primary care increases OUD treatment and improves outcomes. Specifically, it tests whether implementation of collaborative care, compared to usual primary care, increases the number of days of medication for OUD (implementation objective) and reduces acute health care utilization (effectiveness objective). The protocol for the PROUD trial is presented here., Methods: PROUD is a hybrid type III cluster-randomized implementation trial in six health care systems. The intervention consists of three implementation strategies: salary for a full-time NCM, training and technical assistance for the NCM, and requiring that three primary care providers have DEA waivers to prescribe buprenorphine. Within each health system, two primary care clinics are randomized: one to the intervention and one to Usual Primary Care. The sample includes all patients age 16-90 who visited the randomized primary care clinics from 3 years before to 2 years after randomization (anticipated to be > 170,000). Quantitative data are derived from existing health system administrative data, electronic medical records, and/or health insurance claims ("electronic health records," [EHRs]). Anonymous staff surveys, stakeholder debriefs, and observations from site visits, trainings and technical assistance provide qualitative data to assess barriers and facilitators to implementation. The outcome for the implementation objective (primary outcome) is a clinic-level measure of the number of patient days of medication treatment of OUD over the 2 years post-randomization. The patient-level outcome for the effectiveness objective (secondary outcome) is days of acute care utilization [e.g. urgent care, emergency department (ED) and/or hospitalizations] over 2 years post-randomization among patients with documented OUD prior to randomization., Discussion: The PROUD trial provides information for clinical leaders and policy makers regarding potential benefits for patients and health systems of a collaborative care model for management of OUD in primary care, tested in real-world diverse primary care settings. Trial registration # NCT03407638 (February 28, 2018); CTN-0074 https://clinicaltrials.gov/ct2/show/NCT03407638?term=CTN-0074&draw=2&rank=1.

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.

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.)

Purpose: The opioid epidemic impacts both adolescents and adults, and overdose deaths continue to rise. Two medication treatments (buprenorphine and naltrexone) are effective for treating opioid use disorder (OUD) in office-based settings but are seldom prescribed to adolescents. The present study describes medication treatment for OUD and other care received by adolescents with OUD in a state at the center of the opioid epidemic., Methods: This retrospective cohort study included adolescents (aged 12-18 years) enrolled in Ohio Medicaid with an index OUD diagnosis (August 1, 2012, to May 31, 2016). Analyses describe the proportion of adolescents who received medication for OUD, health care (general medical outpatient, behavioral health, hospitalization, and emergency department), and other medications (attention deficit/hyperactivity disorder, antidepressants, antipsychotics, benzodiazepines, and opioid analgesics) within 3 months after OUD diagnosis. The results are compared by age group (12-15 and 16-18 years)., Results: Among 626,508 adolescents enrolled in Medicaid, 2,097 met inclusion criteria. Overall, 4.6% received medication for OUD, whereas 9.8% received an opioid analgesic, within 3 months after OUD diagnosis. The proportion of older adolescents receiving medication for OUD was significantly higher than younger adolescents (5.9% vs. 1.2%; p < .001), whereas a higher proportion of younger adolescents received attention deficit/hyperactivity disorder medications, antidepressants, and antipsychotics. Low proportions of adolescents in both age groups received benzodiazepines (2.4% and 3.6%). During the 3 months after OUD diagnosis, 48.5% of adolescents had general medical outpatient visits, and 29% received outpatient behavioral health services., Conclusions: Although a large proportion of adolescents with OUD have subsequent visits in outpatient settings, few receive medications to treat OUD., (Copyright © 2020. Published by Elsevier Inc.)

Background: Most patients with substance use disorders (SUDs) never receive treatment and SUDs are under-recognized in primary care (PC) where patients can be treated or linked to treatment. Asking PC patients to directly report SUD symptoms on questionnaires might help identify SUDs but to our knowledge, this approach is previously untested., Objective: To describe the prevalence and severity of DSM-5 SUD symptoms reported by PC patients as part of routine care., Design: Cross-sectional study using secondary data., Participants: A total of 241,265 adult patients who visited one of 25 PC sites in an integrated health system in Washington state and had alcohol, cannabis, or other drug use screening documented in their EHRs (March 2015-July 2018) were included in main analyses if they had a positive screen for high-risk substance use defined as AUDIT-C score 7-12 points, or report of past-year daily cannabis use or any other drug use., Main Measures: The main outcome was number of SUD symptoms based on Diagnostic and Statistical Manual, 5th edition (DSM-5), reported on Symptom Checklists (0-11) for alcohol or other drugs: 2-3 mild; 4-5 moderate; 6-11 severe., Results: Of screened patients, 16,776 (5.7%) reported high-risk use of alcohol (2.4%), cannabis (3.9%), and/or other drugs (1.7%), and 65.0-69.9% of those completed Symptom Checklists. Of those with high-risk alcohol use, 52.5% (95% CI 50.9-54.0%) reported ≥ 2 symptoms consistent with mild-severe alcohol use disorders. Of those reporting daily cannabis use, 29.8% (28.6-30.9%) reported ≥ 2 symptoms consistent with mild-severe SUDs. Of those reporting any other drug use, 37.5% (35.7-39.3%) reported ≥ 2 symptoms consistent with mild-severe SUDs., Conclusions and Relevance: Many PC patients who screened positive for high-risk substance use reported symptoms consistent with DSM-5 SUDs on self-report Symptom Checklists. Use of SUD Symptom Checklists could support PC providers in making SUD diagnoses and initiating discussions of substance use.

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.

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: This pilot study evaluated whether use of evidence-based implementation strategies to integrate care for cannabis and other drug use into primary care (PC) as part of Behavioral Health Integration (BHI) increased diagnosis and treatment of substance use disorders (SUDs)., Methods: Patients who visited the three pilot PC sites were eligible. Implementation strategies included practice coaching, electronic health record decision support, and performance feedback (3/2015-4/2016). BHI introduced annual screening for past-year cannabis and other drug use, a Symptom Checklist for DSM-5 SUDs, and shared decision-making about treatment options. Main analyses tested whether the proportions of PC patients diagnosed with, and treated for, new cannabis or other drug use disorders (CUDs and DUDs, respectively), differed significantly pre- and post-implementation., Results: Of 39,599 eligible patients, 57% and 59% were screened for cannabis and other drug use, respectively. Among PC patients reporting daily cannabis use (2%) or any drug use (1%), 51% and 37%, respectively, completed an SUD Symptom Checklist. The proportion of PC patients with newly diagnosed CUD increased significantly post-implementation (5 v 17 per 10,000 patients, p < 0.0001), but not other DUDs (10 vs 13 per 10,000, p = 0.24). The proportion treated for newly diagnosed CUDs did not increase post-implementation (1 vs 1 per 10,000, p = 0.80), but did for those treated for newly diagnosed other DUDs (1 vs 3 per 10,000, p = 0.038)., Conclusions: A pilot implementation of BHI to increase routine screening and assessment for SUDs was associated with increased new CUD diagnoses and a small increase in treatment of new other DUDs., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: Routine alcohol screening scores are increasingly available in electronic health records (EHRs). Changes in such scores could be useful for monitoring response to brief intervention or treatment of alcohol use disorder. However, it is unclear whether changes in clinically-documented AUDIT-C alcohol screening scores reflect true changes in drinking. This study evaluated associations between changes in EHR AUDIT-C scores and changes in high density lipoprotein cholesterol (HDL), a laboratory test that reflects average alcohol consumption., Methods: National U.S. Veterans Affairs EHR data (2004-2007) were used to identify patients screened with the AUDIT-C (0-12 points), on two occasions at least a year apart, who had HDL measured in the year after each screen. First differencing linear regression estimated associations between changes in AUDIT-C score (-12 to 12 points; modeled categorically to allow for non-linear associations) and subsequent changes in HDL (mg/dL), adjusting for baseline HDL. Additional analyses evaluated whether associations between changes in AUDIT-C and changes in HDL were modified by baseline AUDIT-C., Results: Among 316,712 patients, increases-but not decreases-in AUDIT-C scores were associated with commensurate changes in HDL. However, a significant interaction was observed with baseline AUDIT-C score (p < 0.00001), which revealed that decreases in AUDIT-C scores were also associated with commensurate decreases in HDL (p-values<0.05) except among the 1.5% of patients with the highest baseline AUDIT-C scores (10-12)., Conclusions: Findings suggest that changes in EHR AUDIT-C scores reflect changes in drinking. These results support the use of clinically-documented alcohol screening scores for monitoring patients' alcohol use over time., (Copyright © 2019. Published by Elsevier B.V.)

Background: Brief substance use screening questions for tobacco, alcohol, cannabis, and other drugs need further validation in adolescents. In particular, optimal age-specific screening cut-points are not known, and no study has been large enough to evaluate screening questions for noncannabis illicit drug use., Methods: Adolescent respondents to an annual national household survey were included (2008 to 2014; n = 169,986). Days of tobacco use in the past month, and days of alcohol, cannabis, other illicit drug use in the past year, were assessed as brief screens for tobacco dependence and DSM-IV alcohol (AUD), cannabis (CUD), and other illicit drug use disorders (DUD). Areas under receiver operating characteristics curves (AUCs), sensitivity and specificity were estimated separately by age group (12-15-, 16-17-, and 18-20-year-olds) and cut-points that maximized combined values of sensitivity and specificity were considered optimal., Results: The prevalence of tobacco dependence, AUD, CUD, and DUD was 5.8%, 7.1%, 4.5%, and 2.0%, respectively. AUCs ranged 0.84 to 0.99. The optimal cut-points for screening for tobacco dependence and DUDs was the same for all age groups: ≥1 day. The optimal cut-points for alcohol and cannabis varied by age: ≥3 days for 12-15-year-olds and ≥12 days for older adolescents., Conclusions: Brief measures of past-year use, or past-month use for tobacco, accurately identified adolescents with problematic substance use. However, health systems should use age-specific screening cut-points for alcohol and cannabis to optimize screening performance., Competing Interests: Conflict of interest: none declared., (© Copyright 2019 by the American Board of Family Medicine.)

Purpose: It is unknown whether receipt of evidence-based alcohol-related care varies by rurality among people living with HIV (PLWH) with unhealthy alcohol use-a population for whom such care is particularly important., Methods: All positive screens for unhealthy alcohol use (AUDIT-C ≥ 5) among PLWH were identified using Veterans Health Administration electronic health record data (10/1/09-5/30/13). Three domains of alcohol-related care were assessed: brief intervention (BI) within 14 days, and specialty addictions treatment or alcohol use disorder (AUD) medications (filled prescription for naltrexone, disulfiram, acamprosate, or topiramate) within 1 year of positive screen. Adjusted Poisson models and recycled predictions were used to estimate predicted prevalence of outcomes across rurality (urban, large rural, small rural), clustered on facility. Secondary analyses assessed outcomes in the subsample with documented AUD., Findings: 4,581 positive screens representing 3,458 PLWH (3,112 urban, 130 large rural, and 216 small rural) were included; 49.1% had diagnosed AUD. PLWH in large rural areas had highest receipt of BI (urban 56.6%, 95% CI: 55.0-58.2; large rural 66.0%, CI: 58.6-73.5; small rural 60.7%, CI: 54.6-67.0). PLWH in urban areas had highest receipt of specialty addictions treatment (urban 28.2%, CI: 26.7-29.8; large rural 19.7%, CI: 13.1-26.2; small rural 19.6%, CI: 14.1-25.0). There was no difference in receipt of AUD medications, although overall receipt was low (3%-4%). Results were similar in the subsample with AUD., Conclusion: Among PLWH with unhealthy alcohol use, those in rural areas may be vulnerable to under-receipt of specialty addictions treatment. Targeted interventions may help ensure PLWH receive recommended care regardless of rurality., (© 2019 National Rural Health Association.)

Background: Medical comorbidity may influence treatment initiation and engagement for alcohol and other drug (AOD) use disorders. We examined the association between medical comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) treatment initiation and engagement measures. Methods: We used electronic health record and insurance claims data from 7 US health care systems to identify patients with AOD use disorders between October 1, 2014, and August 15, 2015 ( N  = 86,565). Among patients identified with AOD use disorders in outpatient and emergency department (ED) settings, we examined how Charlson/Deyo comorbidity index scores and medical complications of AOD use were associated with treatment initiation. Among those who initiated treatment in inpatient and outpatient/ED settings, we also examined how comorbidity and AOD use-related medical complications were associated with treatment engagement. Analyses were conducted using generalized estimating equation logistic regression modeling. Results: Among patients identified as having an AOD diagnosis in outpatient and ED settings ( n  = 69,965), Charlson/Deyo comorbidity index scores of 2 or more were independently associated with reduced likelihood of initiation (risk ratio [RR] = 0.80, 95% confidence interval [CI] = 0.74, 0.86; reference score = 0), whereas prior-year diagnoses of cirrhosis (RR = 1.25, 95% CI = 1.12, 1.35) and pancreatic disease (RR = 1.34, 95% CI = 1.15, 1.56) were associated with greater likelihood of initiation. Among those who were identified in outpatient/ED settings and initiated, higher comorbidity scores were associated with lower likelihood of engagement (score 1: RR = 0.85, 95% CI = 0.76, 0.94; score 2+: RR = 0.61, 95% CI = 0.53, 0.71). Conclusion: Medical comorbidity was associated with lower likelihood of initiating or engaging in AOD treatment, but cirrhosis and pancreatic disease were associated with greater likelihood of initiation. Interventions to improve AOD treatment initiation and engagement for patients with comorbidities are needed, such as integrating medical and AOD treatment.

Background: The prevalence of opioid use disorder (OUD) has increased rapidly in the United States and improving treatment access is critical. Among patients with OUD, we examined factors associated with the Healthcare Effectiveness Data and Information Set (HEDIS) performance measures of alcohol and other drug (AOD) treatment initiation and engagement. Methods: Electronic health record and claims data between October 1, 2014, and August 15, 2015, from 7 health systems were used to identify patients ( n  = 11,490) with a new index OUD diagnosis (no AOD diagnosis prior <60 days) based on International Classification of Diseases (ICD)-9 codes. Multivariable generalized linear models with a logit link clustered on health system were used to examine the associations of patient demographic and clinical characteristics, and department of index diagnosis, with HEDIS measures of treatment initiation and engagement. Results: The prevalence of OUD among all AOD diagnoses varied across health systems, as did rates of AOD initiation (5.7%-21.6%) and engagement (7.6%-24.6%). Those diagnosed in the emergency department (adjusted odds ratio [aOR] = 1.58, 95% confidence interval [CI] = 1.27,1.97) or psychiatry/AOD treatment (aOR = 2.92, 95% CI = 2.47,3.46) were more likely to initiate treatment compared with primary care. Older patients were less likely to initiate (age 50-64 vs. age 18-29: aOR = 0.42, 95% CI = 0.35, 0.51; age 65+ vs. age 18-29: aOR = 0.34, 95% CI = 0.26, 0.43), as were women (aOR = 0.72, 95% CI = 0.62, 0.85). Patients diagnosed in psychiatry/AOD treatment (aOR = 2.67, 95% CI = 1.98, 3.60) compared with primary care were more likely to engage in treatment. Those identified in an inpatient setting (aOR = 0.19, 95% CI = 0.14, 0.27 vs. primary care), those with medical comorbidity (aOR = 0.70, 95% CI = 0.52, 0.95), and older patients (age 50-64 vs. 18-29: aOR = 0.64, 95% CI = 0.46, 0.88; age 65+ vs. 18-29: aOR = 0.36, 95% CI = 0.22, 0.57) were less likely to engage in treatment. Conclusions: Rates of initiation and engagement for OUD patients vary widely with noticeable room for improvement, particularly in this critical time of the opioid crisis. Targeting patient and system factors may improve health system performance, which is key to improving patient outcomes.

Background: Cannabis use disorders (CUDs) have increased with more individuals using cannabis, yet few receive treatment. Health systems have adopted the Healthcare Effectiveness Data and Information Set (HEDIS) quality measures of initiation and engagement in alcohol and other drug (AOD) dependence treatment, but little is known about the performance of these among patients with CUDs. Methods: This cohort study utilized electronic health records and claims data from 7 health care systems to identify patients with documentation of a new index CUD diagnosis (no AOD diagnosis ≤60 days prior) from International Classification of Diseases, Ninth revision, codes (October 1, 2014, to August 31, 2015). The adjusted prevalence of each outcome (initiation, engagement, and a composite of both) was estimated from generalized linear regression models, across index identification settings (inpatient, emergency department, primary care, addiction treatment, and mental health/psychiatry), AOD comorbidity (patients with CUD only and CUD plus other AOD diagnoses), and patient characteristics. Results: Among 15,202 patients with an index CUD diagnosis, 30.0% (95% confidence interval [CI]: 29.2-30.7%) initiated, 6.9% (95% CI: 6.2-7.7%) engaged among initiated, and 2.1% (95% CI: 1.9-2.3%) overall both initiated and engaged in treatment. The adjusted prevalence of outcomes varied across index identification settings and was highest among patients diagnosed in addiction treatment, with 25.0% (95% CI: 22.5-27.6%) initiated, 40.9% (95% CI: 34.8-47.0%) engaged, and 12.5% (95% CI: 10.0-15.1%) initiated and engaged. The adjusted prevalence of each outcome was generally highest among patients with CUD plus other AOD diagnosis at index diagnosis compared with those with CUD only, overall and across index identification settings, and was lowest among uninsured and older patients. Conclusion: Among patients with a new CUD diagnosis, the proportion meeting HEDIS criteria for initiation and/or engagement in AOD treatment was low and demonstrated variation across index diagnosis settings, AOD comorbidity, and patient characteristics, pointing to opportunities for improvement.

Background: Psychiatric comorbidity is common among patients with alcohol and other drug (AOD) use disorders. To better understand how psychiatric comorbidity influences AOD treatment access in health care systems, the present study examined treatment initiation and engagement among a large, diverse sample of patients with comorbid psychiatric and AOD use disorders. Methods: This study utilized data from a multisite observational study examining Healthcare Effectiveness Data and Information Set (HEDIS) measures of initiation and engagement in treatment (IET) among patients with AOD use disorders from 7 health care systems. Participants were aged 18 or older with at least 1 AOD index diagnosis between October 1, 2014, and August 15, 2015. Data elements extracted from electronic health records and insurance claims data included patient demographic characteristics, ICD-9 (International Classification of Diseases, Ninth Revision) diagnostic codes, and procedure codes. Descriptive analyses and multivariate logistic regression models were used to examine the relationship between patient-level factors and IET measures. Results: Across health care systems, out of a total of 86,565 patients who had at least 1 AOD index diagnosis during the study period, 66.2% ( n  = 57,335) patients also had a comorbid psychiatric disorder. Among patients with a comorbid psychiatric disorder, 34.9% ( n  = 19,998) initiated AOD treatment, and of those, 10.3% ( n  = 2,060) engaged in treatment. After adjusting for age, sex, and race/ethnicity, patients with comorbid psychiatric disorders were more likely to initiate (odds ratio [OR] = 3.20, 95% confidence interval [CI] = 3.08, 3.32) but no more likely to engage (OR = 0.56, 95% CI = 0.51, 0.61) in AOD treatment, compared with those without a comorbid psychiatric disorder. Conclusions: Findings suggest that identification of comorbid psychiatric disorders may increase initiation in AOD treatment. However, innovative efforts are needed to enhance treatment engagement both generally and especially for individuals without diagnosed psychiatric conditions.

Background: Problematic use of alcohol and other drugs (AOD) is highly prevalent among people living with the human immunodeficiency virus (PLWH), and untreated AOD use disorders have particularly detrimental effects on human immunodeficiency virus (HIV) outcomes. The Healthcare Effectiveness Data and Information Set (HEDIS) measures of treatment initiation and engagement are important benchmarks for access to AOD use disorder treatment. To inform improved patient care, we compared HEDIS measures of AOD use disorder treatment initiation and engagement and health care utilization among PLWH and patients without an HIV diagnosis. Methods: Patients with a new AOD use disorder diagnosis documented between October 1, 2014, and August 15, 2015, were identified using electronic health records (EHR) and insurance claims data from 7 health care systems in the United States. Demographic characteristics, clinical diagnoses, and health care utilization data were also obtained. AOD use disorder treatment initiation and engagement rates were calculated using HEDIS measure criteria. Factors associated with treatment initiation and engagement were examined using multivariable logistic regression models. Results: There were 469 PLWH (93% male) and 86,096 patients without an HIV diagnosis (60% male) in the study cohort. AOD use disorder treatment initiation was similar in PLWH and patients without an HIV diagnosis (10% vs. 11%, respectively). Among those who initiated treatment, few engaged in treatment in both groups (9% PLWH vs. 12% patients without an HIV diagnosis). In multivariable analysis, HIV status was not significantly associated with either AOD use disorder treatment initiation or engagement. Conclusions: AOD use disorder treatment initiation and engagement rates were low in both PLWH and patients without an HIV diagnosis. Future studies need to focus on developing strategies to efficiently integrate AOD use disorder treatment with medical care for HIV.

Background: Only 10% of patients with alcohol and other drug (AOD) disorders receive treatment. The AOD Initiation and Engagement in Treatment (AOD-IET) measure was added to the national Healthcare Effectiveness Data and Information Set (HEDIS) to improve access to care. This study identifies factors related to improving AOD-IET rates. Methods: We include data from 7 health systems with differing geographic, patient demographic, and organizational characteristics; all used a common Virtual Data Warehouse containing electronic health records and insurance claims data. Multilevel logistic regression models examined AOD-IET among adults (18+). Results: A total of 86,565 patients had an AOD diagnosis qualifying for the HEDIS denominator. The overall initiation rate was 27.9% with wide variation; the overall engagement rate was 11.5% and varied from 4.5% to 17.9%. Women versus men (odds ratio [OR] = 0.81, 95% confidence interval [CI] = 0.76-0.86); Hispanics (OR = 0.85, 95% CI = 0.79-0.91), black/African Americans (OR = 0.82, 95% CI = 0.75-0.90), and Asian Americans (OR = 0.83, 95% CI = 0.72-0.95) versus whites; and patients aged 65+ versus 18-29 (OR = 0.82, 95% CI = 0.74-0.90) had lower odds of initiation. Patients aged 30-49 versus 18-29 (OR = 1.11, 95% CI = 1.04-1.19) and those with prior psychiatric (OR = 1.26, 95% CI = 1.18-1.35) and medical (OR = 1.18, 95% CI = 1.10-1.26) conditions had higher odds of initiation. Identification in primary care versus other departments was related to lower odds of initiation (emergency department [ED]: OR = 1.55, 95% CI = 1.45-1.66; psychiatry/AOD treatment: OR = 3.58, 95% CI = 3.33-3.84; other outpatient: OR = 1.19, 95% CI = 1.06-1.32). Patients aged 30-49 versus 18-29 had higher odds of engagement (OR = 1.26, 95% CI = 1.10-1.43). Patients aged 65+ versus 18-29 (OR = 0.51, 95% CI = 0.43-0.62) and black/African Americans versus whites (OR = 0.64, 95% CI = 0.53-0.77) had lower odds. Those initiating treatment in psychiatry/AOD treatment versus primary care (OR = 7.02, 95% CI = 5.93-8.31) had higher odds of engagement; those in inpatient (OR = 0.40, 95% CI = 0.32-0.50) or other outpatient (OR = 0.73, 95% CI = 0.59-0.91) settings had lower odds. Discussion : Rates of initiation and engagement varied but were low. Findings identified age, race/ethnicity, co-occurring conditions, and department of identification as key factors associated with AOD-IET. Focusing on these could help programs develop interventions that facilitate AOD-IET for those less likely to receive care.

Background : Treatment initiation and engagement rates for alcohol and other drug (AOD) use disorders differ depending on where the AOD use disorder was identified. Emergency department (ED) and primary care (PC) are 2 common settings where patients are identified; however, it is unknown whether characteristics of patients who initiate and engage in treatment differ between these settings. Methods: Patients identified with an AOD disorder in ED or PC settings were drawn from a larger study that examined Healthcare Effectiveness Data and Information Set (HEDIS) AOD treatment initiation and engagement measures across 7 health systems using electronic health record data ( n  = 54,321). Multivariable generalized linear models, with a logit link, clustered on health system, were used to model patient factors associated with initiation and engagement in treatment, between and within each setting. Results: Patients identified in the ED had higher odds of initiating treatment than those identified in PC (adjusted odds ratio [aOR] = 1.89, 95% confidence interval [CI] = 1.73-2.07), with no difference in engagement between the settings. Among those identified in the ED, compared with patients aged 18-29, older patients had higher odds of treatment initiation (age 30-49: aOR = 1.25, 95% CI = 1.12-1.40; age 50-64: aOR = 1.42, 95% CI = 1.26-1.60; age 65+: aOR = 1.27, 95% CI = 1.08-1.49). However, among those identified in PC, compared with patients aged 18-29, older patients were less likely to initiate (age 30-49: aOR = 0.81, 95% CI = 0.71-0.94; age 50-64: aOR = 0.68, 95% CI = 0.58-0.78; age 65+: aOR = 0.47, 95% CI = 0.40-0.56). Women identified in ED had lower odds of initiating treatment (aOR = 0.80, 95% CI = 0.72-0.88), whereas sex was not associated with treatment initiation in PC. In both settings, patients aged 65+ had lower odds of engaging compared with patients aged 18-29 (ED: aOR = 0.61, 95% CI = 0.38-0.98; PC: aOR = 0.42, 95% CI = 0.26-0.68). Conclusion: Initiation and engagement in treatment differed by sex and age depending on identification setting. This information could inform tailoring of future AOD interventions.

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: Experts recommend that alcohol-related care be integrated into primary care (PC) to improve prevention and treatment of unhealthy alcohol use. However, few healthcare systems offer such integrated care. To address this gap, implementation researchers and clinical leaders at Kaiser Permanente Washington (KPWA) partnered to design a high-quality program of evidence-based care for unhealthy alcohol use: the Sustained Patient-centered Alcohol-related Care (SPARC) program. SPARC implements systems of clinical care designed to increase both prevention and treatment of unhealthy alcohol use. This clinical care for unhealthy alcohol use was implemented using three strategies: electronic health record (EHR) decision support, performance monitoring and feedback, and front-line support from external practice coaches with expertise in alcohol-related care ("SPARC implementation intervention" hereafter). The purpose of this report is to describe the protocol of the SPARC trial, a pragmatic, cluster-randomized, stepped-wedge implementation trial to evaluate whether the SPARC implementation intervention increased alcohol screening and brief alcohol counseling (so-called brief interventions), and diagnosis and treatment of alcohol use disorders (AUDs) in 22 KPWA PC clinics., Methods/design: The SPARC trial sample includes all adult patients who had a visit to any of the 22 primary care sites in the trial during the study period (January 1, 2015-July 31, 2018). The 22 sites were randomized to implement the SPARC program on different dates (in seven waves, approximately every 4 months). Primary outcomes are the proportion of patients with PC visits who (1) screen positive for unhealthy alcohol use and have documented brief interventions and (2) have a newly recognized AUD and subsequently initiate and engage in alcohol-related care. Main analyses compare the rates of these primary outcomes in the pre- and post-implementation periods, following recommended approaches for analyzing stepped-wedge trials. Qualitative analyses assess barriers and facilitators to implementation and required adaptations of implementation strategies., Discussion: The SPARC trial is the first study to our knowledge to use an experimental design to test whether practice coaches with expertise in alcohol-related care, along with EHR clinical decision support and performance monitoring and feedback to sites, increase both preventive care-alcohol screening and brief intervention-as well as diagnosis and treatment of AUDs., Trial Registration: The trial is registered at ClinicalTrials.Gov: NCT02675777. Registered February 5, 2016, https://clinicaltrials.gov/ct2/show/NCT02675777 .