Patient perspectives on mental health and pain management support needed versus received during opioid deprescribing

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 six 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 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.

Comparing Cannabis Use for Pain to Use for Other Reasons in Primary Care Patients

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.

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

Importance

Few primary care (PC) practices treat patients with medications for opioid use disorder (OUD) despite availability of effective treatments.

Objective

To assess whether implementation of the Massachusetts model of nurse care management for OUD in PC increases OUD treatment with buprenorphine or extended-release injectable naltrexone and secondarily decreases acute care utilization.

Design, Setting, and Participants

The Primary Care Opioid Use Disorders Treatment (PROUD) trial was a mixed-methods, implementation-effectiveness cluster randomized clinical trial conducted in 6 diverse health systems across 5 US states (New York, Florida, Michigan, Texas, and Washington). Two PC clinics in each system were randomized to intervention or usual care (UC) stratified by system (5 systems were notified on February 28, 2018, and 1 system with delayed data use agreement on August 31, 2018). Data were obtained from electronic health records and insurance claims. An implementation monitoring team collected qualitative data. Primary care patients were included if they were 16 to 90 years old and visited a participating clinic from up to 3 years before a system's randomization date through 2 years after.

Intervention

The PROUD intervention included 3 components: (1) salary for a full-time OUD nurse care manager; (2) training and technical assistance for nurse care managers; and (3) 3 or more PC clinicians agreeing to prescribe buprenorphine.

Main Outcomes and Measures

The primary outcome was a clinic-level measure of patient-years of OUD treatment (buprenorphine or extended-release injectable naltrexone) per 10 000 PC patients during the 2 years postrandomization (follow-up). The secondary outcome, among patients with OUD prerandomization, was a patient-level measure of the number of days of acute care utilization during follow-up.

Results

During the baseline period, a total of 130 623 patients were seen in intervention clinics (mean [SD] age, 48.6 [17.7] years; 59.7% female), and 159 459 patients were seen in UC clinics (mean [SD] age, 47.2 [17.5] years; 63.0% female). Intervention clinics provided 8.2 (95% CI, 5.4-∞) more patient-years of OUD treatment per 10 000 PC patients compared with UC clinics (P = .002). Most of the benefit accrued in 2 health systems and in patients new to clinics (5.8 [95% CI, 1.3-∞] more patient-years) or newly treated for OUD postrandomization (8.3 [95% CI, 4.3-∞] more patient-years). Qualitative data indicated that keys to successful implementation included broad commitment to treat OUD in PC from system leaders and PC teams, full financial coverage for OUD treatment, and straightforward pathways for patients to access nurse care managers. Acute care utilization did not differ between intervention and UC clinics (relative rate, 1.16; 95% CI, 0.47-2.92; P = .70).

Conclusions and Relevance

The PROUD cluster randomized clinical trial intervention meaningfully increased PC OUD treatment, albeit unevenly across health systems; however, it did not decrease acute care utilization among patients with OUD.

Trial Registration

ClinicalTrials.gov Identifier: NCT03407638.

Association between cannabis use disorder symptom severity and probability of clinically-documented diagnosis and treatment in a primary care sample

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.

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

Importance

Medical and nonmedical cannabis use and cannabis use disorders (CUD) have increased with increasing cannabis legalization. However, the prevalence of CUD among primary care patients who use cannabis for medical or nonmedical reasons is unknown for patients in states with legal recreational use.

Objective

To estimate the prevalence and severity of CUD among patients who report medical use only, nonmedical use only, and both reasons for cannabis use in a state with legal recreational use.

Design, Setting, and Participants

This cross-sectional survey study took place at an integrated health system in Washington State. Among 108 950 adult patients who completed routine cannabis screening from March 2019 to September 2019, 5000 were selected for a confidential cannabis survey using stratified random sampling for frequency of past-year cannabis use and race and ethnicity. Among 1688 respondents, 1463 reporting past 30-day cannabis use were included in the study.

Exposure

Patient survey-reported reason for cannabis use in the past 30 days: medical use only, nonmedical use only, and both reasons.

Main Outcomes and Measures

Patient responses to the Composite International Diagnostic Interview-Substance Abuse Module for CUD, corresponding to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition CUD severity (0-11 symptoms) were categorized as any CUD (≥2 symptoms) and moderate to severe CUD (≥4 symptoms). Adjusted analyses were weighted for survey stratification and nonresponse for primary care population estimates and compared prevalence of CUD across reasons for cannabis use.

Results

Of 1463 included primary care patients (weighted mean [SD] age, 47.4 [16.8] years; 748 [weighted proportion, 61.9%] female) who used cannabis, 42.4% (95% CI, 31.2%-54.3%) reported medical use only, 25.1% (95% CI, 17.8%-34.2%) nonmedical use only, and 32.5% (95% CI, 25.3%-40.8%) both reasons for use. The prevalence of CUD was 21.3% (95% CI, 15.4%-28.6%) and did not vary across groups. The prevalence of moderate to severe CUD was 6.5% (95% CI, 5.0%-8.6%) and differed across groups: 1.3% (95% CI, 0.0%-2.8%) for medical use, 7.2% (95% CI, 3.9%-10.4%) for nonmedical use, and 7.5% (95% CI, 5.7%-9.4%) for both reasons for use (P = .01).

Conclusions and Relevance

In this cross-sectional study of primary care patients in a state with legal recreational cannabis use, CUD was common among patients who used cannabis. Moderate to severe CUD was more prevalent among patients who reported any nonmedical use. These results underscore the importance of assessing patient cannabis use and CUD symptoms in medical settings.

Cannabis and nicotine co-use among primary care patients in a state with legal cannabis access

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.

Psychometric Performance of a Substance Use Symptom Checklist to Help Clinicians Assess Substance Use Disorder in Primary Care

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.

Integrating Alcohol-Related Prevention and Treatment Into Primary Care: A Cluster Randomized Implementation Trial

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.

Sex Differences in Comorbid Mental and Substance Use Disorders Among Primary Care Patients With Opioid Use Disorder

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.

Perspectives from women who engaged in prenatal and postpartum cannabis use in a U.S. State with legal non-medical use

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.

What Do Adolescents and Their Parents Need From Mental Health Integration in Primary Care? A Qualitative Exploration of Design Insights

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.

Validity of the Single-Item Screen-Cannabis (SIS-C) for Cannabis Use Disorder Screening in Routine Care

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.

A call to integrate health equity into learning health system research training

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.

Comparison of Medical Cannabis Use Reported on a Confidential Survey vs Documented in the Electronic Health Record Among Primary Care Patients

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.

Clinical documentation of patient-reported medical cannabis use in primary care: Toward scalable extraction using natural language processing methods

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.

Prevalence of medical and nonmedical cannabis use among veterans in primary care

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) 2021 APA, all rights reserved).

Prevalence of Medical Cannabis Use and Associated Health Conditions Documented in Electronic Health Records Among Primary Care Patients in Washington State

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.

Prevalence and Medication Treatment of Opioid Use Disorder Among Primary Care Patients with Hepatitis C and HIV

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.

PRimary Care Opioid Use Disorders treatment (PROUD) trial protocol: a pragmatic, cluster-randomized implementation trial in primary care for opioid use disorder treatment

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 and Antiretroviral Adherence Among Patients Living with HIV: Is Change in Alcohol Use Associated with Change in Adherence?

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.

Medication Treatment and Health Care Use Among Adolescents With Opioid Use Disorder in Ohio

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.

Routine Assessment of Symptoms of Substance Use Disorders in Primary Care: Prevalence and Severity of Reported Symptoms

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.

Effect of a Care Management Intervention on 12-Month Drinking Outcomes Among Patients With and Without DSM-IV Alcohol Dependence at Baseline

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.

Integration of screening, assessment, and treatment for cannabis and other drug use disorders in primary care: An evaluation in three pilot sites

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.

Utility of routine alcohol screening for monitoring changes in alcohol consumption

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.

Differences in Receipt of Alcohol-Related Care Across Rurality Among VA Patients Living With HIV With Unhealthy Alcohol Use

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.

Healthcare Effectiveness Data and Information Set (HEDIS) measures of alcohol and drug treatment initiation and engagement among people living with the human immunodeficiency virus (HIV) and patients without an HIV diagnosis

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.

Patient characteristics associated with treatment initiation and engagement among individuals diagnosed with alcohol and other drug use disorders in emergency department and primary care settings

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.

Psychiatric comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) measures of alcohol and other drug treatment initiation and engagement across 7 health care systems

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.

Predictors of Healthcare Effectiveness Data and Information Set (HEDIS) treatment initiation and engagement among patients with opioid use disorder across 7 health systems

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.

Factors associated with Healthcare Effectiveness Data and Information Set (HEDIS) alcohol and other drug measure performance in 2014-2015

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.

The association between medical comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) measures of treatment initiation and engagement for alcohol and other drug use disorders

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.

The prevalence of Healthcare Effectiveness Data and Information Set (HEDIS) initiation and engagement in treatment among patients with cannabis use disorders in 7 US health systems

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.

Study protocol: a cluster-randomized trial implementing Sustained Patient-centered Alcohol-related Care (SPARC trial)

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.

Electronic supplementary material

The online version of this article (10.1186/s13012-018-0795-9) contains supplementary material, which is available to authorized users.

Alcohol-Related Nurse Care Management in Primary Care: A Randomized Clinical Trial

IMPORTANCE

Experts recommend that alcohol use disorders (AUDs) be managed in primary care, but effective approaches are unclear.

OBJECTIVE

To test whether 12 months of alcohol care management, compared with usual care, improved drinking outcomes among patients with or at high risk for AUDs.

DESIGN, SETTING, AND PARTICIPANTS

This randomized clinical trial was conducted at 3 Veterans Affairs (VA) primary care clinics. Between October 11, 2011, and September 30, 2014, the study enrolled 304 outpatients who reported heavy drinking (≥4 drinks per day for women and ≥5 drinks per day for men).

INTERVENTIONS

Nurse care managers offered outreach and engagement, repeated brief counseling using motivational interviewing and shared decision making about treatment options, and nurse practitioner-prescribed AUD medications (if desired), supported by an interdisciplinary team (CHOICE intervention). The comparison was usual primary care.

MAIN OUTCOMES AND MEASURES

Primary outcomes, assessed by blinded telephone interviewers at 12 months, were percentage of heavy drinking days in the prior 28 days measured by timeline follow-back interviews and a binary good drinking outcome, defined as abstinence or drinking below recommended limits in the prior 28 days (according to timeline follow-back interviews) and no alcohol-related symptoms in the past 3 months as measured by the Short Inventory of Problems.

RESULTS

Of 304 participants, 275 (90%) were male, 206 (68%) were white, and the mean (SD) age was 51.4 (13.8) years. At baseline, both the CHOICE intervention (n = 150) and usual care (n = 154) groups reported heavy drinking on 61% of days (95% CI, 56%-66%). During the 12-month intervention, 137 of 150 patients in the intervention group (91%) had at least 1 nurse visit, and 77 of 150 (51%) had at least 6 nurse visits. A greater proportion of patients in the intervention group than in the usual care group received alcohol-related care: 42% (95% CI, 35%-49%; 63 of 150 patients) vs 26% (95% CI, 19%-35%; 40 of 154 patients). Alcohol-related care included more AUD medication use: 32% (95% CI, 26%-39%; 48 of 150 patients in the intervention group) vs 8% (95% CI, 5%-13%; 13 of 154 patients in the usual care group). No significant differences in primary outcomes were observed at 12 months between patients in both groups. The percentages of heavy drinking days were 39% (95% CI, 32%-47%) and 35% (95% CI, 28%-42%), and the percentages of patients with a good drinking outcome were 15% (95% CI, 9%-22%; 18 of 124 patients) and 20% (95 % CI, 14%-28%; 27 of 134 patients), in the intervention and usual care groups, respectively (P = .32-.44). Findings at 3 months were similar.

CONCLUSIONS AND RELEVANCE

The CHOICE intervention did not decrease heavy drinking or related problems despite increased engagement in alcohol-related care.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT01400581.

Barriers to and Facilitators of Alcohol Use Disorder Pharmacotherapy in Primary Care: A Qualitative Study in Five VA Clinics

BACKGROUND

Three medications are FDA-approved and recommended for treating alcohol use disorders (AUD) but they are not offered to most patients with AUD. Primary care (PC) may be an optimal setting in which to offer and prescribe AUD medications, but multiple barriers are likely.

OBJECTIVE

This qualitative study used social marketing theory, a behavior change approach that employs business marketing techniques including "segmenting the market," to describe (1) barriers and facilitators to prescribing AUD medications in PC, and (2) beliefs of PC providers after they were segmented into groups more and less willing to prescribe AUD medications.

DESIGN

Qualitative, interview-based study.

PARTICIPANTS

Twenty-four providers from five VA PC clinics.

APPROACH

Providers completed in-person semi-structured interviews, which were recorded, transcribed, and analyzed using social marketing theory and thematic analysis. Providers were divided into two groups based on consensus review.

KEY RESULTS

Barriers included lack of knowledge and experience, beliefs that medications cannot replace specialty addiction treatment, and alcohol-related stigma. Facilitators included training, support for prescribing, and behavioral staff to support follow-up. Providers more willing to prescribe viewed prescribing for AUD as part of their role as a PC provider, framed medications as a potentially effective "tool" or "foot in the door" for treating AUD, and believed that providing AUD medications in PC might catalyze change while reducing stigma and addressing other barriers to specialty treatment. Those less willing believed that medications could not effectively treat AUD, and that treating AUD was the role of specialty addiction treatment providers, not PC providers, and would require time and expertise they do not have.

CONCLUSIONS

We identified barriers to and facilitators of prescribing AUD medications in PC, which, if addressed and/or capitalized on, may increase provision of AUD medications. Providers more willing to prescribe may be the optimal target of a customized implementation intervention to promote changes in prescribing.

Frequency of Cannabis Use Among Primary Care Patients in Washington State

INTRODUCTION

Over 12% of US adults report past-year cannabis use, and among those who use daily, 25% or more have a cannabis use disorder. Use is increasing as legal access expands. Yet, cannabis use is not routinely assessed in primary care, and little is known about use among primary care patients and relevant demographic and behavioral health subgroups. This study describes the prevalence and frequency of past-year cannabis use among primary care patients assessed for use during a primary care visit.

METHODS

This observational cohort study included adults who made a visit to primary care clinics with annual behavioral health screening, including a single-item question about frequency past-year cannabis use (March 2015 to February 2016; n = 29,857). Depression, alcohol and other drug use were also assessed by behavioral health screening. Screening results, tobacco use, and diagnoses for past-year behavioral health conditions (e.g., mental health and substance use disorders) were obtained from EHRs.

RESULTS

Among patients who completed the cannabis use question (n = 22,095; 74% of eligible patients), 15.3% (14.8% to 15.8%) reported any past-year use: 12.2% (11.8% to 12.6%) less than daily, and 3.1% (2.9%-3.3%) daily. Among 2228 patients age 18 to 29 years, 36.0% (34.0% to 38.0%) reported any cannabis use and 8.1% (7.0% to 9.3%) daily use. Daily cannabis use was common among men age 18 to 29 years who used tobacco or screened positive for depression or used tobacco: 25.5% (18.8% to 32.1%) and 31.7% (23.3% to 40.0%), respectively.

CONCLUSIONS

Cannabis use was common in adult primary care patients, especially among younger patients and those with behavioral health conditions. Results highlight the need for primary care approaches to address cannabis use.

Influence of a targeted performance measure for brief intervention on gender differences in receipt of brief intervention among patients with unhealthy alcohol use in the Veterans Health Administration

AIMS

Women are less likely than men to receive brief intervention (BI) for unhealthy alcohol use. In 2007, the U.S. Veterans Health Administration (VA) used a national performance measure to implement BI. Although AUDIT-C scores ≥3 for women and ≥4 for men optimize sensitivity and specificity for identifying unhealthy alcohol use, VA's performance measure required BI only among a targeted subgroup defined by a non-gender-specific score threshold (AUDIT-C ≥5). This may have influenced gender differences in receipt of BI among those optimally eligible for BI. Therefore, we evaluate differences in proportions of women and men offered BI before and after BI implementation.

METHODS

National secondary chart review data (7/06-6/10) identified all outpatients with unhealthy alcohol use for whom BI would be indicated (AUDIT-C ≥3 women, ≥4 men). Logistic regression, including a time-by-gender interaction, estimated the prevalence and 95% confidence interval (CI) of BI for women and men pre- and post-implementation.

FINDINGS

Among patients optimally eligible for BI (n=51,272, 8206 women and 43,066 men), the prevalence of BI increased more steeply for men than women after implementation (interaction p-value <0.0001). Pre-implementation rates of BI were 21% (95% CI, 18-24) for women and 26% (95% CI, 24-29) for men, and post-implementation rates were 32% (95% CI, 30-34) for women and 47% (95% CI, 45-49) for men.

CONCLUSIONS

Healthcare systems implementing BI with performance measures may wish to consider that specifying a single alcohol screening threshold for men and women may increase gender differences in receipt of BI among patients likely to benefit.

Documented brief intervention not associated with resolution of unhealthy alcohol use one year later among VA patients living with HIV

OBJECTIVE

Unhealthy alcohol use is particularly risky for patients living with HIV (PLWH). Brief interventions reduce drinking among patients with unhealthy alcohol use, but whether its receipt in routine outpatient settings is associated with reduced drinking among PLWH with unhealthy alcohol use is unknown. We assessed whether PLWH who screened positive for unhealthy alcohol use were more likely to resolve unhealthy drinking one year later if they had brief alcohol intervention (BI) documented in their electronic health record in a national sample of PLWH from the Veterans Health Administration.

METHODS

Secondary VA clinical and administrative data from the electronic medical record (EMR) were used to identify all positive alcohol screens (AUDIT-C score≥5) documented among PLWH (10/01/09-5/30/13) followed by another alcohol screen documented 9-15months later. Unadjusted and adjusted Poisson regression models assessed the association between brief intervention (advice to reduce drinking or abstain documented in EMR) and resolution of unhealthy alcohol use (follow-up AUDIT-C<5 with ≥2 point reduction).

RESULTS

Overall 2101 PLWH with unhealthy drinking (10/01/09-5/30/13) had repeat alcohol screens 9-15months later. Of those, 77% had brief intervention documented after their first screen, and 61% resolved unhealthy alcohol use at follow-up. Documented brief intervention was not associated with resolution [Adjusted incidence rate ratio 0.96, (95% CI 0.90-1.02)].

CONCLUSIONS

Documented brief intervention was not associated with resolving unhealthy alcohol use at follow-up screening among VA PLWH with unhealthy alcohol use. Effective methods of resolving unhealthy alcohol use in this vulnerable population are needed.

Predictive validity of clinical AUDIT-C alcohol screening scores and changes in scores for three objective alcohol-related outcomes in a Veterans Affairs population

AIMS

To evaluate the association between Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) alcohol screening scores, collected as part of routine clinical care, and three outcomes in the following year (Aim 1), and the association between changes in AUDIT-C risk group at 1-year follow-up and the same outcomes in the subsequent year (Aim 2).

DESIGN

Cohort study.

SETTING

Twenty-four US Veterans Affairs (VA) healthcare systems (2004-07), before systematic implementation of brief intervention.

PARTICIPANTS

A total of 486 115 out-patients with AUDIT-Cs documented in their electronic health records (EHRs) on two occasions ≥ 12 months apart ('baseline' and 'follow-up').

MEASUREMENTS

Independent measures were baseline AUDIT-C scores and change in standard AUDIT-C risk groups (no use, low-risk use and mild, moderate, severe misuse) from baseline to follow-up. Outcome measures were (1) high-density lipoprotein cholesterol (HDL), (2) alcohol-related gastrointestinal hospitalizations ('GI hospitalizations') and (3) physical trauma, each in the years after baseline and follow-up.

FINDINGS

Baseline AUDIT-C scores had a positive association with outcomes in the following year. Across AUDIT-C scores 0-12, mean HDL ranged from 41.4 [95% confidence interval (CI) = 41.3-41.5] to 53.5 (95% CI = 51.4-55.6) mg/l, and probabilities of GI hospitalizations from 0.49% (95% CI = 0.48-0.51%) to 1.8% (95% CI = 1.3-2.3%) and trauma from 3.0% (95% CI = 2.95-3.06%) to 6.0% (95% CI = 5.2-6.8%). At follow-up, patients who increased to moderate or severe alcohol misuse had consistently higher mean HDL and probabilities of subsequent GI hospitalizations or trauma compared with those who did not (P-values all < 0.05). For example, among those with baseline low-risk use, in those with persistent low-risk use versus severe misuse at follow-up, the probabilities of subsequent trauma were 2.65% (95% CI = 2.54-2.75%) versus 5.15% (95% CI = 3.86-6.45%), respectively. However, for patients who decreased to lower AUDIT-C risk groups at follow-up, findings were inconsistent across outcomes, with only mean HDL decreasing in most groups that decreased use (P-values all < 0.05).

CONCLUSIONS

When AUDIT-C screening is conducted in clinical settings, baseline AUDIT-C scores and score increases to moderate-severe alcohol misuse at follow-up screening appear to have predictive validity for HDL cholesterol, alcohol-related gastrointestinal hospitalizations and physical trauma. Decreasing AUDIT-C scores collected in clinical settings appear to have predictive validity for only HDL.

Binge drinking and perceived ethnic discrimination among Hispanics/Latinos: Results from the Hispanic community health study/study of Latinos sociocultural ancillary study

The study assessed whether overall perceived ethnic discrimination and four unique discrimination types were associated with binge drinking in participants from the Hispanic Community Health Study/Study of Latinos who also completed the Hispanic Community Health Study/Study of Latinos Sociocultural Ancillary Study (n = 5,313). In unadjusted analyses that were weighted for sampling strategy and design, each unit increase in discrimination type was associated with a 12-63% increase in odds of binge drinking; however, after adjusting for important demographic variables including age, sex, heritage group, language, and duration of U.S. residence, there was no longer an association between discrimination and binge drinking. Further research still needs to identify the salient factors that contribute to increased risk for binge drinking among Hispanics/Latinos.

Changes in Patient-Reported Alcohol-Related Advice Following Veterans Health Administration Implementation of Brief Alcohol Interventions

OBJECTIVE

Brief alcohol interventions are recommended for primary care patients who screen positive for alcohol misuse, but implementation is challenging. The U.S. Veterans Health Administration (Veterans Affairs [VA]) implemented brief interventions for patients with alcohol misuse in 2008, and rates of brief interventions documented in the electronic medical record increased from 24% to 78% (2008-2011). This study examined whether an independent measure of brief interventions-patient-reported alcohol-related advice-also increased among VA outpatients who screened positive for alcohol misuse on a mailed survey.

METHOD

This retrospective cross-sectional study included VA outpatient respondents to the VA's Survey of Healthcare Experiences of Patients (SHEP; 2007-2011) who reported past-year alcohol use and answered a question about alcohol-related advice. Alcohol-related advice was defined as a report of past-year advice from a VA clinician to abstain from or reduce drinking. The adjusted prevalence of alcoholrelated advice among patients who screened positive for alcohol misuse (SHEP AUDIT-C ≥ 5) was estimated for each year.

RESULTS

Among patients with alcohol misuse (n = 61,843), the adjusted prevalence of alcohol-related advice increased from 40.4% (95% CI [39.3%, 41.5%]) in 2007 to 55.5% (95% CI [53.3%, 57.8%]) in 2011. Rates of alcoholrelated advice increased significantly each year except the last.

CONCLUSIONS

The VA's efforts to implement brief interventions were associated with increased patient-reported alcohol-related advice over time, with a majority of patients with alcohol misuse reporting its receipt. Other systems considering similar approaches to implementation may benefit from collecting patient-reported measures of brief interventions for an additional perspective on implementation.

Unhealthy alcohol use in older adults: Association with readmissions and emergency department use in the 30 days after hospital discharge

BACKGROUND

Unhealthy alcohol use could impair recovery of older patients after medical or surgical hospitalizations. However, no prior research has evaluated whether older patients who screen positive for unhealthy alcohol use are at increased risk of readmissions or emergency department (ED) visits within 30 days after discharge. This study examined the association between AUDIT-C alcohol screening results and 30-day readmissions or ED visits.

METHODS

Veterans Affairs (VA) patients age 65 years or older, were eligible if they were hospitalized for a medical or surgical condition (2/1/2009-10/1/2011) and had an AUDIT-C score documented in their VA electronic medical record in the year before they were hospitalized. VA and Medicare data identified VA or non-VA index hospitalizations, readmissions, and ED visits. Primary analyses adjusted for demographics, comorbid conditions, and past-year health care utilization.

RESULTS

Among 579,330 hospitalized patients, 13.7% were readmitted and 12.0% visited an ED within 30 days of discharge. In primary analyses, high-risk drinking (n=7,167) and nondrinking (n=357,086) were associated with increased probability of readmission (13.8%, 95% CI 13.0-14.6%; and 14.2%, 95% CI 14.1-14.3%, respectively), relative to low-risk drinking (12.9%; 95% CI 12.7-13.0%). Only nondrinkers had increased risk for ED visits.

CONCLUSIONS

Alcohol screening results indicating high-risk drinking that were available in medical records were modestly associated with risk for 30-day readmissions and were not associated with risk for ED visits.