Making measurement-based care for addictions a reality in primary care

Association between change in alcohol use reported during routine healthcare screening and change in subsequent hospitalization: A retrospective cohort study

BACKGROUND AND AIMS

Primary care systems often screen for unhealthy alcohol use with brief self-report tools such as the 3-item Alcohol Use Disorders Identification Test for consumption (AUDIT-C). There is little research examining whether change in alcohol use measured on the AUDIT-C captures meaningful change in outcomes affected by alcohol use. This study aimed to measure the association between change in AUDIT-C and change in all-cause hospitalization risk, measured in the year after each AUDIT-C.

DESIGN

Retrospective cohort study.

SETTING

Health system in the state of Washington, USA, that conducts annual screening with the AUDIT-C in outpatient care.

PARTICIPANTS

Adults (n = 165 101) who had completed at least two AUDIT-Cs 11-24 months apart (2016-2020).

MEASUREMENTS

AUDIT-C scores were grouped into five risk categories reflecting no drinking (0), drinking without unhealthy alcohol use [1-2 (female)/1-3 (male)] and unhealthy alcohol use with moderate risk [3-6 (female)/4-6 (male)], high risk (7-8), and very high risk (9-12). Changes in AUDIT-C were based on the number of category levels that changed (0-4). Hospitalizations were binary, reflecting one or more hospitalizations in the 365 days after each AUDIT-C, identified from insurance claims.

FINDINGS

Of 165 101 eligible patients, 5.7% and 6.1% were hospitalized the year after the first and second AUDIT-C, respectively. Decreases in AUDIT-C risk category of 1 or ≥2 levels were associated with statistically significant decreases in risk of hospitalization, compared with the change in hospitalization risk for those with no change in AUDIT-C [1-level decrease: ratio of adjusted risk ratios (aRR) = 0.92, 95% confidence interval (CI) = 0.86-0.99; ≥2-level decrease: ratio of aRR = 0.68, 95% CI = 0.58-0.81]. Increases in AUDIT-C risk category of 1 or ≥2 levels were not associated with statistically significant differences in risk of hospitalization, compared with those with no change in AUDIT-C.

CONCLUSIONS

A decrease in AUDIT-C score risk category is associated with a decreased risk of both all-cause hospitalizations and hospitalizations with conditions directly or potentially attributable to alcohol. An increase in AUDIT-C score does not appear to be associated with a change in risk of hospitalization in the following year.

Post Void Residuals: Medications for Opioid Use Disorder, Patient Outcomes, and How Not to Get Fooled by Urine Toxicology Results

As opioid-related mortality has increased in the United States, greater emphasis has been placed on ensuring access to medications for opioid use disorder(MOUD) and improving outcomes. While abstinence using urine toxicology testing was a mainstay of assessing MOUD treatment outcomes, there have been major shifts in clinical, research, and public health communities away from placing untoward emphasis on these metrics. Herein we challenge the purported objective truth of urine testing and highlight how we can be easily fooled into thinking a patient is or is not abstinent. We highlight the potential value of patient-reported outcomes (PROMs) that are easily obtained and may more accurately reflect the benefits of MOUD, such as decreases in overdose, retention in treatment, and improved health outcomes.

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.

Test-retest reliability of DSM-5 substance use symptom checklists used in primary care and mental health care settings

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

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.

Common procedures of remote measurement-based care in an integrated behavioural health context: protocol for a scoping review

INTRODUCTION

Integrated behavioural health, a model of care that embeds mental health services in primary care, can potentially increase access to mental healthcare. With the increase in health information technologies, remote measurement-based care (RMBC) presents an opportunity to improve support of integrated care. This scoping review will comprehensively examine what common procedures are followed when RMBC for mental health is tested in integrated care settings.

METHODS AND ANALYSIS

Based on an established six-step framework for conducting scoping reviews, we will search PubMed, Embase, PsycINFO, Cochrane, EBSCOhost and Web of Science with search terms related to 'integrated care' and 'RMBC'. Articles published from 2015 onwards, in English, including an intervention that meets our definition of RMBC, and are conducted in collaboration with primary care or in a primary care setting will be included. After data extraction, we will categorise key findings along the following dimensions: (1) common delivery practices of RMBC; (2) common technologies and instruments used and (3) most common barriers and facilitators when implementing RMBC in an integrated care model.

ETHICS AND DISSEMINATION

Ethics approval is not required for this scoping review. For maximum impact, we will disseminate the findings to the scientific community (via publication in a peer-reviewed journal and at national conferences) and to the broader healthcare community. We will share findings with the broader healthcare community through our research centre's existing stakeholder communication structures and through guidance from our multidisciplinary research team. These key stakeholder relationships will continue to guide our subsequent RMBC research following the review.

An Alcohol Symptom Checklist identifies high rates of alcohol use disorder in primary care patients who screen positive for depression and high-risk drinking

Background

Although alcohol use disorder can complicate depression management, there is no standard process for assessing AUD symptoms (i.e., AUD diagnostic criteria) in primary care for patients who screen positive for depression. This study characterizes the association between depressive symptoms and high-risk drinking reported by primary care patients on screening measures in routine care. Then, using data from a novel clinical program, this study characterizes the association between depressive symptoms and AUD symptoms reported by primary care patients with high-risk drinking via an Alcohol Symptom Checklist.

Methods

In this cross-sectional study, electronic health record data were obtained from patients who visited 33 Kaiser Permanente Washington primary care clinics between 03/2018 and 02/2020 and completed depression (PHQ-2) and alcohol consumption (AUDIT-C) screening measures as part of routine care (N = 369,943). Patients who reported high-risk drinking (AUDIT-C scores 7–12) also completed an Alcohol Symptom Checklist where they reported the presence or absence of 11 AUD criteria as defined by the DSM-5 (N = 8,184). Generalized linear models estimated and compared the prevalence of high-risk drinking (AUDIT-C scores 7–12) and probable AUD (2–11 AUD symptoms on Alcohol Symptom Checklists) for patients with and without positive depression screens.

Results

Patients who screened positive for depression had a 131% higher prevalence of high-risk drinking than those who screened negative (5.2% vs. 2.2%; p < 0.001). Among patients with high-risk drinking, positive depression screens were associated with a significantly higher prevalence of probable AUD (69.8% vs. 48.0%; p < 0.001), with large differences in the prevalence of probable AUD observed with increasing PHQ-2 scores (e.g., probable AUD prevalence of 37.6%, 55.3% and 65.2%, for PHQ-2 scores of 0, 1, and 2, respectively). Although the overall prevalence of high-risk drinking was higher for male patients, similar patterns of association between depression screens, high-risk drinking, and AUD symptoms were observed for male and female patients.

Conclusions

Patients with positive depression screens are more likely to have high-risk drinking. Large percentages of patients with positive depression screens and high-risk drinking report symptoms consistent with AUD to healthcare providers when given the opportunity to do so using an Alcohol Symptom Checklist.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08408-1.

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.

Bathing in Terminal Care of Cancer Patients and Its Relation to Perceptions of a “Good Death”: A Nationwide Bereavement Survey in Japan

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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.

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

BACKGROUND

Most people with alcohol or opioid use disorders (AUD or OUD) are not diagnosed or treated for these conditions in primary care. This study takes a critical step toward quantifying service gaps and directing improvement efforts for AUD and OUD by using electronic health record (EHR) data from diverse primary care organizations to quantify the extent to which AUD and OUD are underdiagnosed and undertreated in primary care practices.

METHODS

We extracted and integrated diagnosis, medication, and behavioral health visit data from the EHRs of 21 primary care clinics within four independent healthcare organizations representing community health centers and rural hospital-associated clinics in the Pacific Northwest United States. Rates of documented AUD and OUD diagnoses, pharmacological treatments, and behavioral health visits were evaluated over a two-year period (2015-2016).

RESULTS

Out of 47,502 adult primary care patients, 1476 (3.1%) had documented AUD; of these, 115 (7.8%) had orders for AUD medications and 271 (18.4%) had at least one documented visit with a non-physician behavioral health specialist. Only 402 (0.8%) patients had documented OUD, and of these, 107 (26.6%) received OUD medications and 119 (29.6%) had at least one documented visit with a non-physician behavioral health specialist. Rates of AUD diagnosis and AUD and OUD medications were higher in clinics that had co-located non-physician behavioral health specialists.

CONCLUSIONS

AUD and OUD are underdiagnosed and undertreated within a sample of independent primary care organizations serving mostly rural patients. Primary care organizations likely need service models, technologies, and workforces, including non-physician behavioral health specialists, to improve capacities to diagnose and treat AUD and OUD.