Prevalence of documented alcohol and opioid use disorder diagnoses and treatments in a regional primary care practice-based research network

Medication-based treatment among rural, primary care patients diagnosed with opioid use disorder and alcohol use disorder

INTRODUCTION

Opioid and alcohol use disorders are increasingly being addressed in primary care, yet how medications to treat these disorders are prescribed in rural regions is unknown.

METHODS

We determined prevalence, types, and duration of medication prescription for opioid and/or alcohol use disorder among adult patients in rural primary clinics. The sample included 1874 adult patients who visited one of six rural primary care sites in the Northeastern and Northwestern United States at least once from October 2019 to January 2021 and had a diagnosis code for opioid use disorder (OUD), alcohol use disorder (AUD), or co-occurring opioid and alcohol use disorder (OUD + AUD) during that time.

RESULTS

Patients with OUD + AUD were more likely to be prescribed medication for at least one of these disorders (85.3 %) than patients with OUD only (63.7 %) or AUD only (10.3 %). Further, the OUD + AUD group had the highest number of days on medication (M = 264.7), followed by OUD only (M = 220.5), then the AUD only group (M = 62.5). Only 8.8 % of patients with OUD + AUD were prescribed naltrexone or medication for OUD + AUD to treat both substance use disorders.

CONCLUSIONS

Medications for treating AUD as well as OUD are available, but few patients with OUD + AUD and even fewer with AUD received pharmacological treatment for AUD. The current work highlights the need for rural clinicians to consider medications for AUD as an important treatment method for patients with AUD only or OUD + AUD.

Sensitivity and specificity of International Classification of Diseases algorithms (ICD-9 and ICD-10) used to identify opioid-related overdose cases: A systematic review and an example of estimation using Bayesian latent class models in the absence of gold standards

OBJECTIVES

This study aimed to summarize validity estimates of International Classification of Diseases (ICD) codes in identifying opioid overdose (OOD) among patient data from emergency rooms, emergency medical services, inpatient, outpatient, administrative, medical claims, and mortality, and estimate the sensitivity and specificity of the algorithms in the absence of a perfect reference standard.

METHODS

We systematically reviewed studies published before December 8, 2023, and identified with Medline and Embase. Studies reporting sufficient details to recreate a 2 × 2 table comparing the ICD algorithms to a reference standard in diagnosing OOD-related events were included. We used Bayesian latent class models (BLCM) to estimate the posterior sensitivity and specificity distributions of five ICD-10 algorithms and of the imperfect coroner's report review (CRR) in detecting prescription opioid-related deaths (POD) using one included study.

RESULTS

Of a total of 1990 studies reviewed, three were included. The reported sensitivity estimates of ICD algorithms for OOD were low (range from 25.0% to 56.8%) for ICD-9 in diagnosing non-fatal OOD-related events and moderate (72% to 89%) for ICD-10 in diagnosing POD. The last included study used ICD-9 for non-fatal and fatal and ICD-10 for fatal OOD-related events and showed high sensitivity (i.e. above 97%). The specificity estimates of ICD algorithms were good to excellent in the three included studies. The misclassification-adjusted ICD-10 algorithm sensitivity estimates for POD from BLCM were consistently higher than reported sensitivity estimates that assumed CRR was perfect.

CONCLUSION

Evidence on the performance of ICD algorithms in detecting OOD events is scarce, and the absence of bias correction for imperfect tests leads to an underestimation of the sensitivity of ICD code estimates.

Equivalence of Alcohol Use Disorder Symptom Assessments in Routine Clinical Care When Completed Remotely via Online Patient Portals Versus In Clinic via Paper Questionnaires: Psychometric Evaluation

BACKGROUND

The National Institute on Alcohol Abuse and Alcoholism (NIAAA) recommends the paper-based or computerized Alcohol Symptom Checklist to assess alcohol use disorder (AUD) symptoms in routine care when patients report high-risk drinking. However, it is unknown whether Alcohol Symptom Checklist response characteristics differ when it is administered online (eg, remotely via an online electronic health record [EHR] patient portal before an appointment) versus in clinic (eg, on paper after appointment check-in).

OBJECTIVE

This study evaluated the psychometric performance of the Alcohol Symptom Checklist when completed online versus in clinic during routine clinical care.

METHODS

This cross-sectional, psychometric study obtained EHR data from the Alcohol Symptom Checklist completed by adult patients from an integrated health system in Washington state. The sample included patients who had a primary care visit in 2021 at 1 of 32 primary care practices, were due for annual behavioral health screening, and reported high-risk drinking on the behavioral health screen (Alcohol Use Disorder Identification Test-Consumption score ≥7). After screening, patients with high-risk drinking were typically asked to complete the Alcohol Symptom Checklist-an 11-item questionnaire on which patients self-report whether they had experienced each of the 11 AUD criteria listed in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) over a past-year timeframe. Patients could complete the Alcohol Symptom Checklist online (eg, on a computer, smartphone, or tablet from any location) or in clinic (eg, on paper as part of the rooming process at clinical appointments). We examined sample and measurement characteristics and conducted differential item functioning analyses using item response theory to examine measurement consistency across these 2 assessment modalities.

RESULTS

Among 3243 patients meeting eligibility criteria for this secondary analysis (2313/3243, 71% male; 2271/3243, 70% White; and 2014/3243, 62% non-Hispanic), 1640 (51%) completed the Alcohol Symptom Checklist online while 1603 (49%) completed it in clinic. Approximately 46% (752/1640) and 48% (764/1603) reported ≥2 AUD criteria (the threshold for AUD diagnosis) online and in clinic (P=.37), respectively. A small degree of differential item functioning was observed for 4 of 11 items. This differential item functioning produced only minimal impact on total scores used clinically to assess AUD severity, affecting total criteria count by a maximum of 0.13 criteria (on a scale ranging from 0 to 11).

CONCLUSIONS

Completing the Alcohol Symptom Checklist online, typically prior to patient check-in, performed similarly to an in-clinic modality typically administered on paper by a medical assistant at the time of the appointment. Findings have implications for using online AUD symptom assessments to streamline workflows, reduce staff burden, reduce stigma, and potentially assess patients who do not receive in-person care. Whether modality of DSM-5 assessment of AUD differentially impacts treatment is unknown.

Assessing Substance Use Disorder Symptoms with a Checklist among Primary Care Patients with Opioid Use Disorder and/or Long-Term Opioid Treatment: An Observational Study

BACKGROUND

Primary care (PC) offers an opportunity to treat opioid use disorders (OUD). The Substance Use Symptom Checklist ("Checklist") can assess DSM-5 substance use disorder (SUD) symptoms in PC.

OBJECTIVE

To test the psychometric properties of the Checklist among PC patients with OUD or long-term opioid therapy (LTOT) in Kaiser Permanente Washington (KPWA).

DESIGN

Observational study using item response theory (IRT) and differential item functioning (DIF) analyses of measurement consistency across age, sex, race and ethnicity, and receipt of treatment.

PATIENTS

Electronic health records (EHR) data were extracted for all adult PC patients visiting KPWA 3/1/15-8/30/2020 who had ≥ 1 Checklist documented and indication of either (a) clinically-recognized OUD (i.e., documented OUD diagnosis and/or OUD medication treatment) or (b) LTOT in the year prior to the checklist.

MAIN MEASURE

The Checklist includes 11 items reflecting DSM-5 criteria for SUD. We described the prevalence of 2 SUD symptoms reported on the Checklist (consistent with mild-severe DSM-5 SUD). Analyses were conducted in the overall sample and in two subsamples (clinically-recognized OUD and LTOT only).

KEY RESULTS

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

CONCLUSIONS

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

Quantifying diagnosis and treatment practices of opioid use disorder in primary care practices using chart review data

BACKGROUND

Opioid misuse is a significant public health crisis. The aim sought to identify potential gaps in opioid care in primary care practices.

METHODS

American Academy of Family Physicians (AAFP) offered a monthly online educational series to seven U.S. practices. Practices were asked to complete up to 50 chart reviews for visits during two periods: February-April, 2019, and February-April, 2022. Each chart had to have an ICD-10 diagnosis of opioid misuse, opioid dependence, or opioid use. Chart reviews consisted of 14 questions derived from an American Academy of Addiction Psychiatry (AAAP) Performance in Practice activity, and then, scored based on practices' responses. Descriptive statistics and binary logistic and multinomial regressions were used.

RESULTS

Both periods had 173 chart reviews (total: 346) from the six practices. Most chart reviews were for patients with a diagnosis of opioid dependence (2019: 90.2%; 2022: 83.2%). Three questions for assessing OUD treatment behaviors had high levels of documentation across both time periods (>85%): other drug use, treatment readiness, and treatment discussion.

DISCUSSION

Results show a gap in the treatment of patients with OUD in primary care across several clinical practice recommendations.

CONCLUSIONS

Expanding OUD treatment integration to primary care remains the most promising effort to combat the opioid crisis.

Effect of Behavioral Health Disorders on Surgical Outcomes in Cancer Patients

BACKGROUND

Behavioral health disorders (BHDs) can often be exacerbated in the setting of cancer. We sought to define the prevalence of BHD among cancer patients and characterize the association of BHD with surgical outcomes.

STUDY DESIGN

Patients diagnosed with lung, esophageal, gastric, liver, pancreatic, and colorectal cancer between 2018 and 2021 were identified within Medicare Standard Analytic Files. Data on BHD defined as substance abuse, eating disorder, or sleep disorder were obtained. Postoperative textbook outcomes (ie no complications, prolonged length of stay, 90-day readmission, or 90-day mortality), as well as in-hospital expenditures and overall survival were assessed.

RESULTS

Among 694,836 cancer patients, 46,719 (6.7%) patients had at least 1 BHD. Patients with BHD were less likely to undergo resection (no BHD: 23.4% vs BHD: 20.3%; p < 0.001). Among surgical patients, individuals with BHD had higher odds of a complication (odds ratio [OR] 1.32 [1.26 to 1.39]), prolonged length of stay (OR 1.36 [1.29 to 1.43]), and 90-day readmission (OR 1.57 [1.50 to 1.65]) independent of social vulnerability or hospital-volume status resulting in lower odds to achieve a TO (OR 0.66 [0.63 to 0.69]). Surgical patients with BHD also had higher in-hospital expenditures (no BHD: $16,159 vs BHD: $17,432; p < 0.001). Of note, patients with BHD had worse long-term postoperative survival (median, no BHD: 46.6 [45.9 to 46.7] vs BHD: 37.1 [35.6 to 38.7] months) even after controlling for other clinical factors (hazard ratio 1.26 [1.22 to 1.31], p < 0.001).

CONCLUSIONS

BHD was associated with lower likelihood to achieve a postoperative textbook outcome, higher expenditures, as well as worse prognosis. Initiatives to target BHD are needed to improve outcomes of cancer patients undergoing surgery.

Association between pre-dementia psychiatric diagnoses and all-cause dementia is independent from polygenic dementia risks in the UK Biobank

BACKGROUND

Psychiatric disorders have been associated with higher risk for future dementia. Understanding how pre-dementia psychiatric disorders (PDPD) relate to established dementia genetic risks has implications for dementia prevention.

METHODS

In this retrospective cohort study, we investigated the relationships between polygenic risk scores for Alzheimer's disease (AD PRS), PDPD, alcohol use disorder (AUD), and subsequent dementia in the UK Biobank (UKB) and tested whether the relationships are consistent with different causal models.

FINDINGS

Among 502,408 participants, 9352 had dementia. As expected, AD PRS was associated with greater risk for dementia (odds ratio (OR) 1.62, 95% confidence interval (CI), 1.59-1.65). A total of 94,237 participants had PDPD, of whom 2.6% (n = 2519) developed subsequent dementia, compared to 1.7% (n = 6833) of 407,871 participants without PDPD. Accordingly, PDPD were associated with 73% greater risk of incident dementia (OR 1.73, 1.65-1.83). Among dementia subtypes, the risk increase was 1.5-fold for AD (n = 3365) (OR 1.46, 1.34-1.59) and 2-fold for vascular dementia (VaD, n = 1823) (OR 2.08, 1.87-2.32). Our data indicated that PDPD were neither a dementia prodrome nor a mediator for AD PRS. Shared factors for both PDPD and dementia likely substantially account for the observed association, while a causal role of PDPD in dementia could not be excluded. AUD could be one of the shared causes for PDPD and dementia.

INTERPRETATION

Psychiatric diagnoses were associated with subsequent dementia in UKB participants, and the association is orthogonal to established dementia genetic risks. Investigating shared causes for psychiatric disorders and dementia would shed light on this dementia pathway.

FUNDING

US NIH (K08AG054727).

Medication treatment for opioid use disorder among rural primary care patients

PURPOSE

To investigate the prevalence of opioid use disorder (OUD) and medication treatment for OUD (MOUD) receipt in rural primary care settings and identify characteristics associated with MOUD among patients with OUD.

METHODS

Secondary analyses based on electronic health records of all adult patients who visited 1 of the 6 rural primary care clinic sites from October 2019 to January 2021. Mixed effects logistic regression was conducted to assess MOUD receipt (Y/N) in relation to patient characteristics (eg, demographics, other substance use disorders [SUDs], mental health disorders, and chronic pain) and the number of MOUD prescribers per clinic.

FINDINGS

The prevalence of OUD varied from 0.7% to 8.2% (Mean [SD] = 3.3% [95% CI: 0.4, 6.1]) among 36,762 primary care patients across 6 clinic sites. Among 1,164 patients with OUD, on average 50.1% received MOUD (95% CI: 28.0, 72.3). Patients in clinics with more than 3 MOUD prescribers had more than 3 times the odds of receiving MOUD (OR = 3.42; 95% CI, 1.22-9.62) as those in clinics with fewer than 3 prescribers. MOUD was positively associated with younger age (18-30 [OR = 6.97; 95% CI, 3.37-14.42], 31-64 [OR = 5.03; 95% CI, 2.64-9.57], relative to those 65 and older), having other co-occurring SUDs (OR = 3.77; 95% CI, 2.57-5.52), being male (OR = 1.50; 95% CI, 1.12-2.01), and negatively associated with having chronic pain disorders (OR = 0.69; 95% CI, 0.50-0.94).

CONCLUSIONS

The prevalence of OUD and MOUD are high but vary considerably across rural primary care clinics; primary care MOUD prescribers play a key role on MOUD access in rural settings.

Neurobiology and the Treatment of Alcohol Use Disorder: A Review of the Evidence Base

Alcohol use disorder (AUD) is a significant public health concern, accounting for a majority of substance use disorder cases in the United States. Treatment for AUD is complex, with multiple intervention points that may be further complicated by genotype and phenotype, resulting in diverse outcomes. In order to better understand the current landscape of AUD treatment, the present review considers different etiological models of AUD and assesses the evidence base of current treatment options. The first section of this review summarizes various etiological models of AUD and presents different approaches to classifying the disorder. Various theories, including neurobiological models, are discussed. The second section presents a comprehensive analysis of available treatment options for AUD, encompassing behavioral and pharmacological interventions and their current evidence base. Finally, this review discusses the ongoing treatment gap and significant factors contributing to low treatment utilization. Together, this review provides an overview of different etiological processes and mechanisms of AUD, as well as summarizes the literature on key treatment approaches. By integrating historical, theoretical, and empirical data, this review aims to inform both researchers and providers with valuable insights to advance AUD treatment approaches and narrow the treatment gap.

Practice-Level Documentation of Alcohol-Related Problems in Primary Care

Importance

Rates of alcohol-associated deaths increased over the past 20 years, markedly between 2019 and 2020. The highest rates are among individuals aged 55 to 64 years, primarily attributable to alcoholic liver disease and psychiatric disorders due to use of alcohol. This study investigates potential geographic disparities in documentation of alcohol-related problems in primary care electronic health records, which could lead to undertreatment of alcohol use disorder.

Objective

To identify disparities in documentation of alcohol-related problems by practice-level social deprivation.

Design, Setting, and Participants

A cross-sectional study using secondary data from the Integrating Behavioral Health and Primary Care clinical trial (September 21, 2017, to January 8, 2021) was performed. A national sample of 44 primary care practices with co-located behavioral health services was included in the analysis. Patients with 2 primary care visits within 2 years and at least 1 chronic medical condition and 1 behavioral health condition or at least 3 chronic medical conditions were included.

Exposure

The primary exposure was practice-level Social Deprivation Index (SDI), a composite measure based on county income, educational level, employment, housing, single-parent households, and access to transportation (scores range from 0 to 100; 0 indicates affluent counties and 100 indicates disadvantaged counties).

Main Outcomes and Measures

Documentation of an alcohol-related problem in the electronic health record was determined by International Classification of Diseases, 9th Revision, Clinical Modification and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Clinical Modification codes or use of medications for alcohol use disorder in past 2 years. Multivariable models adjusted for alcohol consumption, screening for a substance use disorder, urban residence, age, sex, race and ethnicity, income, educational level, and number of chronic health conditions.

Results

A total of 3105 participants (mean [SD] age, 63.7 [13.0] years; 64.1% female; 11.5% Black, 7.0% Hispanic, 76.7% White, and 11.9% other race or chose not to disclose; 47.8% household income <$30 000; and 80.7% urban residence). Participants had a mean (SD) of 4.0 (1.7) chronic conditions, 9.1% reported higher-risk alcohol consumption, 4% screened positive for substance use disorder, and 6% had a documented alcohol-related problem in the electronic health record. Mean (SD) practice-level SDI score was 45.1 (20.9). In analyses adjusted for individual-level alcohol use, demographic characteristics, and health status, practice-level SDI was inversely associated with the odds of documentation (odds ratio for each 10-unit increase in SDI, 0.89; 95% CI, 0.80 to 0.99; P = .03).

Conclusions and Relevance

In this study, higher practice-level SDI was associated with lower odds of documentation of alcohol-related problems, after adjusting for individual-level covariates. These findings reinforce the need to address primary care practice-level barriers to diagnosis and documentation of alcohol-related problems. Practices located in high need areas may require more specialized training, resources, and practical evidence-based tools that are useful in settings where time is especially limited and patients are complex.

Implementation of substance use screening in rural federally-qualified health center clinics identified high rates of unhealthy alcohol and cannabis use among adult primary care patients

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Development and validation of an electronic health records-based opioid use disorder algorithm by expert clinical adjudication among patients with prescribed opioids

BACKGROUND

In the US, over 200 lives are lost from opioid overdoses each day. Accurate and prompt diagnosis of opioid use disorders (OUD) may help prevent overdose deaths. However, international classification of disease (ICD) codes for OUD are known to underestimate prevalence, and their specificity and sensitivity are unknown. We developed and validated algorithms to identify OUD in electronic health records (EHR) and examined the validity of OUD ICD codes.

METHODS

Through four iterations, we developed EHR-based OUD identification algorithms among patients who were prescribed opioids from 2014 to 2017. The algorithms and OUD ICD codes were validated against 169 independent "gold standard" EHR chart reviews conducted by an expert adjudication panel across four healthcare systems. After using 2014-2020 EHR for validating iteration 1, the experts were advised to use 2014-2017 EHR thereafter.

RESULTS

Of the 169 EHR charts, 81 (48%) were reviewed by more than one expert and exhibited 85% expert agreement. The experts identified 54 OUD cases. The experts endorsed all 11 OUD criteria from the Diagnostic and Statistical Manual of Mental Disorders-5, including craving (72%), tolerance (65%), withdrawal (56%), and recurrent use in physically hazardous conditions (50%). The OUD ICD codes had 10% sensitivity and 99% specificity, underscoring large underestimation. In comparison our algorithm identified OUD with 23% sensitivity and 98% specificity.

CONCLUSIONS AND RELEVANCE

This is the first study to estimate the validity of OUD ICD codes and develop validated EHR-based OUD identification algorithms. This work will inform future research on early intervention and prevention of OUD.

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.

Screening for opioid use disorder and co-occurring depression and post-traumatic stress disorder in primary care in New Mexico

BACKGROUND

Identifying patients in primary care services with opioid use disorder and co-occurring mental health disorders is critical to providing treatment. Objectives of this study were to (1) assess the feasibility of recruiting people to screen in-person for opioid use disorder and co-occurring mental health disorders (depression and/or post-traumatic stress disorder) in primary care clinic waiting rooms in preparation for a randomized controlled trial, and (2) compare results of detecting these disorders by universal in-person screening compared to electronic health record (EHR) diagnoses.

METHODS

This cross-sectional feasibility and pilot study recruited participants from four primary care clinics, two rural and two urban, from three health care organizations in New Mexico. Inclusion criteria were adults (≥ 18 years), attending one of the four clinics as a patient, and who spoke English or Spanish. Exclusion criteria were people attending the clinic for a non-primary care visit (e.g., dental, prescription pick up, social support). The main outcomes and measures were (1) recruitment feasibility which was assessed by frequencies and proportions of people approached and consented for in-person screening, and (2) relative differences of detecting opioid use disorder and co-occurring mental health disorders in waiting rooms relative to aggregate EHR data from each clinic, measured by prevalence and prevalence ratios.

RESULTS

Over two-weeks, 1478 potential participants were approached and 1145 were consented and screened (77.5% of patients approached). Probable opioid use disorder and co-occurring mental health disorders were identified in 2.4% of those screened compared to 0.8% in EHR. Similarly, universal screening relative to EHR identified higher proportions of probable opioid use disorder (4.5% vs. 3.4%), depression (17.5% vs. 12.7%) and post-traumatic stress disorder (19.0% vs. 3.6%).

CONCLUSIONS

Universal screening for opioid use disorder, depression, and post-traumatic stress disorder was feasible, and identified three times as many patients with these co-occurring disorders compared to EHR. Higher proportions of each condition were also identified, especially post-traumatic stress disorder. Results support that there are likely gaps in identification of these disorders in primary care services and demonstrate the need to better address the persistent public health problem of these co-occurring disorders.

The Association Between Depression and Substance Use Among Primary Care Patients With Comorbid Medical and Behavioral Health Conditions

INTRODUCTION

The scope of primary care increasingly encompasses patient behavioral health problems, manifest typically through depression screening and treatment. Although substance use is highly comorbid with depression, it is not commonly identified and addressed in the primary care context. This study aimed to examine the association between the likelihood of substance use disorder and increased depression severity, both cross-sectionally and longitudinally, among a sample of 2409 patients from 41 geographically dispersed and diverse primary care clinics across the US.

METHODS

This is secondary analysis of data obtained from a multi-site parent study of integrated behavioral health in primary care, among patients with both chronic medical and behavioral health conditions. Patient reported outcome surveys were gathered from patients at 3 time points. The primary care practices were blind to which of their patients completed surveys. Included were standardized measures of depression severity (Patient Health Questionnaire-9) [PHQ-9] and substance use disorder likelihood (Global Appraisal of Individual Needs-Short Screener [GSS]).

RESULTS

Four percent of the study population screened positive for substance use disorder. PHQ-9 scores indicated depression among 43% of all patients. There was a significant association between the likelihood of substance use disorder and depression initially, at a 9-month follow-up, and over time. These associations remained significant after adjusting for age, gender, race, ethnicity, education, income, and other patient and contextual characteristics.

CONCLUSIONS

The findings suggest that substance use disorder is associated with depression severity cross-sectionally and over time. Primary care clinics and health systems might consider implementing substance use screening in addition to the more common screening strategies for depression. Especially for patients with severe depression or those who do not respond to frontline depression treatments, the undermining presence of a substance use disorder should be explored.

Gabapentin increases the abuse liability of alcohol alone and in combination with oxycodone in participants with co-occurring opioid and alcohol use disorder

BACKGROUND

People who have co-occurring Alcohol Use Disorder (AUD) and Opioid Use Disorder (OUD) carry a higher risk of adverse outcomes, including drug overdose. Early clinical and preclinical studies suggested that gabapentin may be effective in treating both disorders. The present study was designed to assess the effects of gabapentin on the subjective and physiological effects of oxycodone (OXY) and alcohol (ALC), alone and in combination.

METHODS

During an 8-week, inpatient, within-subject, randomized, double-blind, placebo-controlled crossover study, non-treatment seeking participants (N = 13; 12 M/1F; 44.1 ± 3 years of age) with OUD and AUD were maintained on oral morphine (120 mg daily). Under gabapentin (1800 mg/day) and placebo (0 mg/day) maintenance, participants completed nine separate test sessions (three sessions per week) during which they received an oral solution containing 0, 15, or 30 mg/70 kg OXY in combination with 0, 0.5, or 0.75 g/kg ALC. During test sessions, subjective effects and physiological responses were assessed repeatedly on 100-mm visual analog scales (VAS). The primary outcome variable was the VAS rating of drug liking after receiving the drug challenge.

RESULTS

Alcohol alone (but not oxycodone alone) produced dose-related increases in several positive subjective responses, including drug liking. Gabapentin significantly increased drug liking when given in combination with ALC and OXY + ALC (p < 0.05). Gabapentin did not clinically compromise respiration or other vital functions.

CONCLUSIONS

Gabapentin may increase the abuse liability of ALC and OXY + ALC in those with co-occurring OUD and AUD.

Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data

Introduction

Opioid use disorders (OUDs) constitute a major public health issue, and we urgently need alternative methods for characterizing risk for OUD. Electronic health records (EHRs) are useful tools for understanding complex medical phenotypes but have been underutilized for OUD because of challenges related to underdiagnosis, binary diagnostic frameworks, and minimally characterized reference groups. As a first step in addressing these challenges, a new paradigm is warranted that characterizes risk for opioid prescription misuse on a continuous scale of severity, i.e., as a continuum.

Methods

Across sites within the PsycheMERGE network, we extracted prescription opioid data and diagnoses that co-occur with OUD (including psychiatric and substance use disorders, pain-related diagnoses, HIV, and hepatitis C) for over 2.6 million patients across three health registries (Vanderbilt University Medical Center, Mass General Brigham, Geisinger) between 2005 and 2018. We defined three groups based on levels of opioid exposure: no prescriptions, minimal exposure, and chronic exposure and then compared the comorbidity profiles of these groups to the full registries and to those with OUD diagnostic codes.

Results

Our results confirm that EHR data reflects known higher prevalence of substance use disorders, psychiatric disorders, medical, and pain diagnoses in patients with OUD diagnoses and chronic opioid use. Comorbidity profiles that distinguish opioid exposure are strikingly consistent across large health systems, indicating the phenotypes described in this new quantitative framework are robust to health systems differences.

Conclusion

This work indicates that EHR prescription opioid data can serve as a platform to characterize complex risk markers for OUD using existing data.

Healthcare Utilization of Patients with Opioid Use Disorder in US Hospitals from 2016 to 2019: Focusing on Racial and Regional Variances

BACKGROUND

There is a lack of US population-based research on healthcare utilization differences caused by opioid misuse.

OBJECTIVE

The aim of this study was to explore disparities in healthcare utilization by type of opioid use disorder, race, region, and other patient factors for a more targeted prevention and treatment program.

METHODS

The National Inpatient Sample of the United States was used to identify patients with opioid use disorder (n = 101,231, weighted n = 506,155) from 2016 to 2019. Type of opioid use disorder was defined as opioid dependence/unspecified use, adverse effects of opioids, opioid misuse, and opioid poisoning (also known as overdose). We examined the sample characteristics and the association between type of disorder, racial and regional variables, and healthcare utilization, measured by hospital charges and length of stay. The multivariate survey linear regression model was used.

RESULTS

Among 506,155 patients, most were categorized as opioid dependence/unspecified use (56.3%) and opioid poisoning (42.7%). The number of opioid use disorder patients during the study decreased; however, overall total charges and length of stay continuously increased. Survey linear results showed that opioid poisoning, adverse effects, and abuse were associated with higher hospital charges than opioid dependence; however, length of stay was significantly lower for these groups. White patients compared with minorities, and West, Northeast, and South regions were associated with higher hospital charges and length of stay.

CONCLUSION

Significant differences in healthcare utilization exist between type of disorder, race, and region. Such findings illustrate that tailored treatment regimens are required to bridge the gaps in care and combat the opioid crisis. Minorities with opioid use disorder utilize healthcare the least, possibly because of affordability, and need culturally sensitive and financially feasible treatment options.

Novel digital approaches to the assessment of problematic opioid use

The opioid epidemic continues to contribute to loss of life through overdose and significant social and economic burdens. Many individuals who develop problematic opioid use (POU) do so after being exposed to prescribed opioid analgesics. Therefore, it is important to accurately identify and classify risk factors for POU. In this review, we discuss the etiology of POU and highlight novel approaches to identifying its risk factors. These approaches include the application of polygenic risk scores (PRS) and diverse machine learning (ML) algorithms used in tandem with data from electronic health records (EHR), clinical notes, patient demographics, and digital footprints. The implementation and synergy of these types of data and approaches can greatly assist in reducing the incidence of POU and opioid-related mortality by increasing the knowledge base of patient-related risk factors, which can help to improve prescribing practices for opioid analgesics.

Practical Assessment of Alcohol Use Disorder in Routine Primary Care: Performance of an Alcohol Symptom Checklist

BACKGROUND

Alcohol use disorder (AUD) is highly prevalent but underrecognized and undertreated in primary care settings. Alcohol Symptom Checklists can engage patients and providers in discussions of AUD-related care. However, the performance of Alcohol Symptom Checklists when they are used in routine care and documented in electronic health records (EHRs) remains unevaluated.

OBJECTIVE

To evaluate the psychometric performance of an Alcohol Symptom Checklist in routine primary care.

DESIGN

Cross-sectional study using item response theory (IRT) and differential item functioning analyses of measurement consistency across age, sex, race, and ethnicity.

PATIENTS

Patients seen in primary care in the Kaiser Permanente Washington Healthcare System who reported high-risk drinking on the Alcohol Use Disorder Identification Test Consumption screening measure (AUDIT-C ≥ 7) and subsequently completed an Alcohol Symptom Checklist between October 2015 and February 2020.

MAIN MEASURE

Alcohol Symptom Checklists with 11 items assessing AUD criteria defined in the Diagnostic and Statistical Manual for Mental Disorders, 5th edition (DSM-5), completed by patients during routine medical care and documented in EHRs.

KEY RESULTS

Among 11,464 patients who screened positive for high-risk drinking and completed an Alcohol Symptom Checklist (mean age 43.6 years, 30.5% female), 54.1% reported ≥ 2 DSM-5 AUD criteria (threshold for AUD diagnosis). IRT analyses demonstrated that checklist items measured a unidimensional continuum of AUD severity. Differential item functioning was observed for some demographic subgroups but had minimal impact on accurate measurement of AUD severity, with differences between demographic subgroups attributable to differential item functioning never exceeding 0.42 points of the total symptom count (of a possible range of 0-11).

CONCLUSIONS

Alcohol Symptom Checklists used in routine care discriminated AUD severity consistently with current definitions of AUD and performed equitably across age, sex, race, and ethnicity. Integrating symptom checklists into routine care may help inform clinical decision-making around diagnosing and managing AUD.

Brief report: Cause of death among people discharged from infective endocarditis related hospitalization-West Virginia, 2016-2019

BACKGROUND AND OBJECTIVES

Compare proportion of all-cause and cause-specific mortality among West Virginia Medicaid enrollees who were discharged from infective endocarditis (IE) hospitalization with and without opioid use disorder (OUD) diagnosis.

METHODS

The proportions of cause-specific deaths among those who were discharged from IE-related hospitalizations were compared by OUD diagnosis.

RESULTS

The top three underlying causes of death discharged from IE hospitalization were accidental drug poisoning, mental and behavioral disorders due to polysubstance use, and cardiovascular diseases. Of the total deaths occurring among patients discharged after IE-related hospitalization, the proportion has increased seven times from 2016 to 2019 among the OUD deaths while it doubled among the non-OUD deaths.

DISCUSSION AND CONCLUSIONS

Of the total deaths occurring among patients discharged after IE-related hospitalization, the increase is higher in those with OUD diagnosis. OUD is becoming a significantly negative impactor on the survival outcome among IE patients. It is of growing importance to deliver medication for OUD treatment and harm reduction efforts to IE patients in a timely manner, especially as the COVID-19 pandemic persists.

Association of Opioid Dose Reduction With Opioid Overdose and Opioid Use Disorder Among Patients Receiving High-Dose, Long-term Opioid Therapy in North Carolina

IMPORTANCE

Rapid reduction or discontinuation of long-term opioid therapy may increase risk of opioid overdose or opioid use disorder (OUD). Current guidelines for chronic pain management caution against rapid dose reduction but are based on limited evidence.

OBJECTIVE

To characterize the association between rapid reduction or abrupt discontinuation of opioid therapy (vs maintained or gradual reduction) and incidence of opioid overdose and OUD among patients prescribed high-dose, long-term opioid therapy (HDLTOT).

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study was conducted among patients aged 18 to 64 years who were prescribed HDLTOT (≥90 daily morphine milligram equivalents for ≥90% of 90 days) from January 2006 to September 2018, with follow-up up to 4 years after cohort entry. Claims data were drawn from a large private health insurer in North Carolina and analyzed from March 1, 2006, to September 30, 2018.

EXPOSURES

Time-varying exposure of rapid dose reduction or discontinuation (>10% dose reduction/week) vs maintenance, increase, or gradual reduction or discontinuation.

MAIN OUTCOMES AND MEASURES

The main outcome was incident opioid overdose (fatal or nonfatal) or diagnosed OUD. Inverse probability-weighted cumulative incidence of outcomes were estimated using the cumulative incidence function and hazard ratios (HRs) using marginal structural Fine-Gray models as a function of rapid dose tapering or discontinuation (vs gradual reduction or discontinuation or maintained or increased), accounting for competing risks.

RESULTS

A total of 19 443 patients (median [IQR] age, 49 [41-55] years; 10 073 [51.8%] men) who received HDLTOT were identified. Rapid reduction or discontinuation was associated with higher risk of fatal and nonfatal overdoses compared with gradual reduction after the first year (year 1: HR, 1.43; 95% CI, 0.94-2.18; years 2-4: HR, 1.95; 95% CI, 1.31-2.90). There was no association between rapid reduction or discontinuation and diagnosed OUD through 2 years of follow-up; however, the hazard of incident OUD among patients exposed to rapid tapering or discontinuation was greater 25 to 48 months after the start of follow-up (HR, 1.28; 95% CI, 1.01-1.63).

CONCLUSIONS AND RELEVANCE

In this cohort study, rapid dose reduction or discontinuation was associated with increased risk of opioid overdose and OUD during long-term follow-up. These findings reinforce prior concerns about safety of rapid dose reductions for patients receiving HDLTOT and highlight the need for caution when reducing opioid doses.

Practical assessment of DSM-5 alcohol use disorder criteria in routine care: High test-retest reliability of an Alcohol Symptom Checklist

BACKGROUND

Alcohol use disorder (AUD) is underdiagnosed and undertreated in medical settings, in part due to a lack of AUD assessment instruments that are reliable and practical for use in routine care. This study evaluates the test-retest reliability of a patient-report Alcohol Symptom Checklist questionnaire when it is used in routine care, including primary care and mental health specialty settings.

METHODS

We performed a pragmatic test-retest reliability study using electronic health record (EHR) data from Kaiser Permanente Washington, an integrated health system in Washington state. The sample included 454 patients who reported high-risk drinking on a behavioral health screen and completed two Alcohol Symptom Checklists 1 to 21 days apart. Subgroups of these patients who completed both checklists in primary care (n = 271) or mental health settings (n = 79) were also examined. The primary measure was an Alcohol Symptom Checklist on which patients self-reported whether they experienced each of the 11 AUD criteria within the past year, as defined by the Diagnostic and Statistical Manual of Mental Disorders-5^th edition (DSM-5).

RESULTS

Alcohol Symptom Checklists completed in routine care and documented in EHRs had excellent test-retest reliability for measuring AUD criterion counts (ICC = 0.79, 95% CI: 0.76 to 0.82). Test-retest reliability estimates were also high and not significantly different for the subsamples of patients who completed both checklists in primary care (ICC = 0.82, 95% CI: 0.77 to 0.85) or mental health settings (ICC = 0.74, 95% CI: 0.62 to 0.83). Test-retest reliability was not moderated by having a past two-year AUD diagnosis, nor by the age or sex of the patient completing it.

CONCLUSIONS

Alcohol Symptom Checklists can reliably and pragmatically assess AUD criteria in routine care among patients who screen positive for high-risk drinking. The Alcohol Symptom Checklist may be a valuable tool in supporting AUD-related care and monitoring AUD criteria longitudinally in routine primary care and mental health settings.

Cervical cancer screening, abnormal results, and follow-up in women with substance use-related diagnoses

Background: Substance use-related diagnoses are common and associated with poor health outcomes. The objective of this analysis was to compare rates of cervical cancer screening, screening abnormalities, and follow-up care in women with and without a substance use-related diagnosis seen for primary care between January 1, 2016 and December 31, 2019 in the University of Washington healthcare system. Methods: This study included women aged 21-65 years of age who had at least one outpatient visit between January 1, 2016 and December 31, 2019 within one of 45 primary care or women's health clinics in the academic healthcare system. Exposure status was defined using ICD10 codes for substance-use related diagnoses or no substance-use related diagnoses. Only first cervical cancer screening was included. Generalized linear models with a binomial family and log link were used to estimate risk ratios. Results: 3845 women had a substance use-related diagnosis and 89214 did not. Women with a substance use-related diagnosis were less likely to be screened for cervical cancer (44%, 1675/3845) compared to women without a substance use-related diagnosis (49%, 43338/89214; relative risk [RR] 0.90, 95% CI 0.86-0.93). Women with a substance use-related diagnosis were also more likely to have an abnormal screening result (18%, 304/1675) compared to women without a substance use-related diagnosis (10%, 4528/43338; RR 1.74, 95% CI 1.56-1.93). Follow-up for abnormal screens did not differ significantly between groups (24 vs 25%; RR 0.80, 95% CI 0.55-1.17). Conclusion: To combat disparities in cervical cancer screening for women with substance use-related diagnoses, public health efforts should expand access to screening where women with substance use-related diagnoses are seen, including acute care, inpatient hospitalizations, and addiction treatment settings.

Screening for Unhealthy Alcohol and Drug Use in General Medicine Settings

Unhealthy alcohol and drug use are among the top 10 causes of preventable death in the United States, but they are infrequently identified and addressed in medical settings. Guidelines recommend screening adult primary care patients for alcohol and drug use, and routine screening should be a component of high-quality clinical care. Brief, validated screening tools accurately detect unhealthy alcohol and drug use, and their thoughtful implementation can facilitate adoption and optimize the quality of screening results. Recommendations for implementation include patient self-administered screening tools, integration with electronic health records, and screening during routine primary care visits.

Treatment for alcohol use disorder among persons with and without HIV in a clinical care setting in the United States

BACKGROUND

Alcohol use disorders (AUD) can lead to poor health outcomes. Little is known about AUD treatment among persons with HIV (PWH). In an integrated health system in Northern California, 2014-2017, we compared AUD treatment rates between PWH with AUD and persons without HIV (PWoH) with AUD.

METHODS

Using Poisson regression with GEE, we estimated prevalence ratios (PRs) comparing the annual probability of receiving AUD treatment (behavioral intervention or dispensed medication), adjusted for sociodemographics, psychiatric comorbidities, insurance type, and calendar year. Among PWH, we examined independent AUD treatment predictors using PRs adjusted for calendar year only.

RESULTS

PWH with AUD (N = 633; 93% men, median age 49) were likelier than PWoH with AUD (N = 7006; 95% men, median age 52) to have depression (38% vs. 21%) and a non-alcohol substance use disorder (SUD, 48% vs. 25%) (both P < 0.01). Annual probabilities of receiving AUD treatment were 45.4% for PWH and 34.4% for PWoH. After adjusting, there was no difference by HIV status (PR 1.02 [95% CI 0.94-1.11]; P = 0.61). Of treated PWH, 59% received only a behavioral intervention, 5% only a medication, and 36% both, vs. 67%, 4%, 30% for treated PWoH, respectively. Irrespective of HIV status, the most common medication was gabapentin. Among PWH, receiving AUD treatment was associated with having depression (PR 1.78 [1.51-2.10]; P < 0.01) and another SUD (PR 2.68 [2.20-3.27]; P < 0.01).

CONCLUSIONS

PWH with AUD had higher AUD treatment rates than PWoH with AUD in unadjusted but not adjusted analyses, which may be explained by higher psychiatric comorbidity burden among PWH.

Service utilization and chronic condition outcomes among primary care patients with substance use disorders and co-occurring chronic conditions

BACKGROUND

Patients with a substance use disorder (SUD) often present with co-occurring chronic conditions in primary care. Despite the high co-occurrence of chronic medical conditions and SUD, little is known about whether chronic condition outcomes or related service utilization in primary care varies between patients with versus without documented SUDs. This study examined whether having a SUD influenced the use of primary care services and common chronic condition outcomes for patients with diabetes, hypertension, and obesity.

METHODS

A longitudinal cohort observational study examined electronic health record data from 21 primary care clinics in Washington and Idaho to examine differences in service utilization and clinical outcomes for diabetes, hypertension, and obesity in patients with and without a documented SUD diagnosis. Differences between patients with and without documented SUD diagnoses were compared over a three-year window for clinical outcome measures, including hemoglobin A1c, systolic and diastolic blood pressure, and body mass index, as well as service outcome measures, including number of encounters with primary care and co-located behavioral health providers, and orders for prescription opioids. Adult patients (N = 10,175) diagnosed with diabetes, hypertension, or obesity before the end of 2014, and who had ≥2 visits across a three-year window including at least one visit in 2014 (baseline) and at least one visit occurring 12 months or longer after the 2014 visit (follow-up) were examined.

RESULTS

Patients with SUD diagnoses and co-occurring chronic conditions were seen by providers more frequently than patients without SUD diagnoses (p's < 0.05), and patients with SUD diagnoses were more likely to be prescribed opioid medications. Chronic condition outcomes were no different for patients with versus without SUD diagnoses.

DISCUSSION

Despite the higher visit rates to providers in primary care, a majority of patients with SUD diagnoses and chronic medical conditions in primary care did not get seen by co-located behavioral health providers, who can potentially provide and support evidence informed care for both SUD and chronic conditions. Patients with chronic medical conditions also were more likely to get prescribed opioids if they had an SUD diagnosis. Care pathway innovations for SUDs that include greater utilization of evidence-informed co-treatment of SUDs and chronic conditions within primary care settings may be necessary for improving care overall for patients with comorbid SUDs and chronic conditions.

A cascade of care for alcohol use disorder: Using 2015-2019 National Survey on Drug Use and Health data to identify gaps in past 12-month care

BACKGROUND

Although effective treatments exist, alcohol use disorder (AUD) is undertreated. We used a cascade of care framework to understand gaps in care for persons with AUD.

METHODS

Using 2015-2019 National Survey on Drug Use and Health data, we evaluated the following steps in the cascade of care: (1) adult prevalence of AUD; (2) proportion of adults with AUD who utilized health care in the past 12 months; (3) proportion with AUD screened about their alcohol use; (4) proportion with AUD who received a brief intervention about their alcohol misuse; (5) proportion with AUD who received information about treatment for alcohol misuse; and (6) proportion with AUD who received treatment. Analyses were stratified by AUD severity.

RESULTS

Of the 214,505 persons included in the sample, the weighted prevalence of AUD was 7.8% (95% CI 7.6-8.0%). Cascades of care showed the majority of individuals with AUD utilized health care in the past 12 months [81.4% (95% CI 80.7-82.1%)] and were screened about alcohol use [69.9% (95% CI 68.9-70.8%)]. However, only a minority of individuals received subsequent steps of care, including 11.6% (95% CI 11.0-12.2%) who reported receiving a brief intervention, 5.1% (95% CI 4.6-5.6%) who were referred to treatment, and 5.8% (95% CI 5.4-6.3%) who received treatment. Similar patterns were observed when cascades of care were stratified by AUD severity.

CONCLUSIONS

Persons with AUD commonly utilize health care and are often screened about alcohol use, but few receive treatment. Healthcare settings-particularly primary care settings-represent a prime opportunity to implement AUD treatment to improve outcomes in this high-risk population.

Comparison of Methods for Alcohol and Drug Screening in Primary Care Clinics

IMPORTANCE

Guidelines recommend that adult patients receive screening for alcohol and drug use during primary care visits, but the adoption of screening in routine practice remains low. Clinics frequently struggle to choose a screening approach that is best suited to their resources, workflows, and patient populations.

OBJECTIVE

To evaluate how to best implement electronic health record (EHR)-integrated screening for substance use by comparing commonly used screening methods and examining their association with implementation outcomes.

DESIGN, SETTING, AND PARTICIPANTS

This article presents the outcomes of phases 3 and 4 of a 4-phase quality improvement, implementation feasibility study in which researchers worked with stakeholders at 6 primary care clinics in 2 large urban academic health care systems to define and implement their optimal screening approach. Site A was located in New York City and comprised 2 clinics, and site B was located in Boston, Massachusetts, and comprised 4 clinics. Clinics initiated screening between January 2017 and October 2018, and 93 114 patients were eligible for screening for alcohol and drug use. Data used in the analysis were collected between January 2017 and October 2019, and analysis was performed from July 13, 2018, to March 23, 2021.

INTERVENTIONS

Clinics integrated validated screening questions and a brief counseling script into the EHR, with implementation supported by the use of clinical champions (ie, clinicians who advocate for change, motivate others, and use their expertise to facilitate the adoption of an intervention) and the training of clinic staff. Clinics varied in their screening approaches, including the type of visit targeted for screening (any visit vs annual examinations only), the mode of administration (staff-administered vs self-administered by the patient), and the extent to which they used practice facilitation and EHR usability testing.

MAIN OUTCOMES AND MEASURES

Data from the EHRs were extracted quarterly for 12 months to measure implementation outcomes. The primary outcome was screening rate for alcohol and drug use. Secondary outcomes were the prevalence of unhealthy alcohol and drug use detected via screening, and clinician adoption of a brief counseling script.

RESULTS

Patients of the 6 clinics had a mean (SD) age ranging from 48.9 (17.3) years at clinic B2 to 59.1 (16.7) years at clinic B3, were predominantly female (52.4% at clinic A1 to 64.6% at clinic A2), and were English speaking. Racial diversity varied by location. Of the 93,114 patients with primary care visits, 71.8% received screening for alcohol use, and 70.5% received screening for drug use. Screening at any visit (implemented at site A) in comparison with screening at annual examinations only (implemented at site B) was associated with higher screening rates for alcohol use (90.3%-94.7% vs 24.2%-72.0%, respectively) and drug use (89.6%-93.9% vs 24.6%-69.8%). The 5 clinics that used a self-administered screening approach had a higher detection rate for moderate- to high-risk alcohol use (14.7%-36.6%) compared with the 1 clinic that used a staff-administered screening approach (1.6%). The detection of moderate- to high-risk drug use was low across all clinics (0.5%-1.0%). Clinics with more robust practice facilitation and EHR usability testing had somewhat greater adoption of the counseling script for patients with moderate-high risk alcohol or drug use (1.4%-12.5% vs 0.1%-1.1%).

CONCLUSIONS AND RELEVANCE

In this quality improvement study, EHR-integrated screening was feasible to implement in all clinics and unhealthy alcohol use was detected more frequently when self-administered screening was used at any primary care visit. The detection of drug use was low at all clinics, as was clinician adoption of counseling. These findings can be used to inform the decision-making of health care systems that are seeking to implement screening for substance use.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02963948.

Identifying individuals with opioid use disorder: Validity of International Classification of Diseases diagnostic codes for opioid use, dependence and abuse

BACKGROUND

Policy evaluations and health system interventions often utilize International Classification of Diseases (ICD) codes of opioid use, dependence, and abuse to identify individuals with opioid use disorder (OUD) and assess receipt of evidence-based treatments. However, ICD codes may not map directly onto the Diagnostic and Statistical Manual of Mental Disorder (DSM-5) OUD criteria. This study investigates the positive predictive value of ICD codes in identifying patients with OUD.

METHODS

We conducted a clinical chart review on a national sample of 520 Veterans assigned ICD-9 or ICD-10 codes for opioid use, dependence, or abuse from 2012 to 2017. We extracted evidence of DSM-5 OUD criteria and opioid misuse from clinical documentation in the month preceding and three months following initial ICD code listing, and categorized patients into: 1) high likelihood of OUD, 2) limited aberrant opioid use, 3) prescribed opioid use without evidence of aberrant use, and 4) insufficient information. Positive predictive value was calculated as the percentage of individuals with these ICD codes meeting high likelihood of OUD criteria upon chart review.

RESULTS

Only 57.7 % of patients were categorized as high likelihood of OUD; 16.5 % were categorized as limited aberrant opioid use, 18.9 % prescribed opioid use without evidence of aberrant use, and 6.9 % insufficient information.

CONCLUSIONS

Patients assigned ICD codes for opioid use, dependence, or abuse often lack documentation of meeting OUD criteria. Many receive long-term opioid therapy for chronic pain without evidence of misuse. Robust methods of identifying individuals with OUD are crucial to improving access to clinically appropriate treatment.

A Cascade of Care for Alcohol Use Disorder: Using 2015-2018 National Survey on Drug Use and Health Data to Identify Gaps in Care

BACKGROUND

Although effective treatments exist, alcohol use disorder (AUD) is undertreated. We used a cascade of care framework to understand gaps in care between diagnosis and treatment for persons with AUD.

METHODS

Using 2015-2018 National Survey on Drug Use and Health data, we evaluated the following steps in the cascade of care: 1) prevalence of adults with AUD; 2) proportion of adults who utilized health care in the past 12 months; 3) were screened about alcohol use; 4) received a brief intervention about alcohol misuse; 5) received information about treatment for alcohol misuse; and 6) proportion of persons with AUD who received treatment. Analyses were stratified by AUD severity.

RESULTS

Of the 171,766 persons included in the sample, weighted prevalence of AUD was 7.9% (95% CI 7.7-8.0%). Persons with AUD utilized health care settings at similar rates as those without AUD. Cascades of care showed the majority of individuals with AUD utilized health care and were screened about alcohol use, but the percent who received the subsequent steps of care decreased substantially. For those with severe AUD, 83.5% (CI: 78.3%-88.7%) utilized health care in the past 12 months, 73.5% (CI: 68.1%-78.9%) were screened for alcohol use, 22.7% (CI: 19.4%-26.0%) received a brief intervention, 12.4% (CI: 10%-14.7%) received information about treatment, and 20.5% (CI: 18%-23.1%) were treated for AUD. The greatest decrease in the care continuum occurred from screening to brief intervention and referral to treatment. More persons with severe AUD received treatment than were referred, indicating other pathways to treatment outside of the healthcare system.

CONCLUSIONS

Persons with AUD utilize health care at high rates and are frequently screened about alcohol use, but few receive treatment. Health care settings-particularly primary care settings-represent a prime opportunity to implement pharmacologic treatment for AUD to improve outcomes in this high-risk population.