Objective: The Alcohol Addiction Research Domain Criteria (AARDoC) is an organizational framework for assessing heterogeneity in addictive disorders organized across the addiction cycle domains of ...incentive salience, negative emotionality, and executive functioning and may have benefits for precision medicine. Recent work found pretreatment self-report items mapped onto the addiction cycle domains and predicted 1- and 3-year alcohol use disorder treatment outcomes. Given the potential utility of the addiction cycle domains for predicting relevant treatment outcomes, this study sought to evaluate the longitudinal measurement invariance of the domains. Method: We conducted a secondary analysis of individuals with alcohol use disorder (n = 1,383, 30.9% female, 76.8% non-Hispanic White, 11.2% Hispanic) who participated in the COMBINE study. Eleven items assessed at pre- and posttreatment were included in exploratory structural equation modeling (ESEM) and longitudinal invariance analyses. Results: The pre- and posttreatment ESEM models had factor loadings consistent with the three addiction cycle domains and fit the data well. The ESEM factor structure was invariant from pre- to posttreatment (representing configural invariance) and metric invariance (factor loadings) was largely supported, but analyses failed to support scalar invariance (item-level thresholds) of the addiction cycle domains. Conclusions: A three-factor structure representing addiction cycle domains can be modeled using brief self-report measures pre- and posttreatment. Individuals demonstrated a downward shift in the level of item endorsement, indicating improvement with treatment. Although this 11-item measure might be useful at baseline for informing treatment decisions, results indicate the need to exercise caution in comparing the addiction cycle domains pre- to posttreatment within persons.
Public Health Significance Statement
The addiction cycle domains of reward/incentive salience, relief/negative emotionality, and executive functioning, which are based on a neurobiological model of alcohol use disorder, may have clinical utility in predicting alcohol use disorder treatment response and recovery outcomes, but more work is needed before domain scores can be used to assess changes within individuals across time.
Clinical decision making encompasses a broad set of processes that contribute to the effectiveness of depression treatments. There is emerging interest in using digital technologies to support ...effective and efficient clinical decision making. In this paper, we provide "snapshots" of research and current directions on ways that digital technologies can support clinical decision making in depression treatment. Practical facets of clinical decision making are reviewed, then research, design, and implementation opportunities where technology can potentially enhance clinical decision making are outlined. Discussions of these opportunities are organized around three established movements designed to enhance clinical decision making for depression treatment, including measurement‐based care, integrated care, and personalized medicine. Research, design, and implementation efforts may support clinical decision making for depression by (1) improving tools to incorporate depression symptom data into existing electronic health record systems, (2) enhancing measurement of treatment fidelity and treatment processes, (3) harnessing smartphone and biosensor data to inform clinical decision making, (4) enhancing tools that support communication and care coordination between patients and providers and within provider teams, and (5) leveraging treatment and outcome data from electronic health record systems to support personalized depression treatment. The current climate of rapid changes in both healthcare and digital technologies facilitates an urgent need for research, design, and implementation of digital technologies that explicitly support clinical decision making. Ensuring that such tools are efficient, effective, and usable in frontline treatment settings will be essential for their success and will require engagement of stakeholders from multiple domains.
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.
Objectives:
The purpose of this study was to examine associations between psychotherapy session attendance, alcohol treatment outcomes, and Alcoholics Anonymous (AA) attendance.
Method:
Using data ...from Project MATCH, repeated measures latent class analyses of psychotherapy session attendance were conducted among participants in the outpatient arm who were randomly assigned to complete 12-session cognitive-behavioral therapy (CBT; n = 301), 12-session twelve-step facilitation (TSF; n = 335), or 4-session motivational enhancement therapy (MET; n = 316). Associations between psychotherapy attendance classes, heavy drinking, alcohol-related consequences, psychosocial functioning, and AA attendance were examined at posttreatment (97% retention), 1-year posttreatment (92% retention), and 3-years posttreatment (85% retention).
Results:
In general, participants who attended all 12 CBT/TSF sessions had significantly fewer heavy drinking days and alcohol-related consequences at all posttreatment time points than participants who attended 0-2 CBT/TSF sessions. Participants who attended all four MET sessions generally had significantly fewer heavy drinking days and alcohol-related consequences at posttreatment and 1-year posttreatment than participants who attended 0-1 MET sessions. Participants who attended more TSF and MET sessions generally attended more AA meetings, and participants who attended less CBT sessions generally attended fewer AA meetings.
Conclusions:
With some exceptions, attending all sessions in CBT, TSF, and MET was related to the most favorable heavy drinking and alcohol-related consequences outcomes. Alcoholics' Anonymous and other mutual help groups may be attended differently based on the form and dose of psychotherapy
What is the public health significance of this article?
Attending 12 sessions of cognitive-behavioral therapy, 12 sessions of twelve-step facilitation, and 4 sessions of motivational enhancement therapy is generally related to significant reductions in heavy drinking and alcohol-related consequences at treatment end and up to 3 years following treatment. Clinicians should talk to clients about the function of AA attendance during CBT, TSF, and MET, as well as after psychotherapy termination.
Objective:
People living with severe mental illness are at increased risk of medical comorbidity as well as poverty, food insecurity, and inadequate social support in managing their mental and ...physical health conditions. Lack of access to sufficient food negatively affects a person's ability to manage health conditions, in particular diabetes, which is twice as common among people with severe mental illness as the general population. This study aimed to explore associations among food insecurity, social support, and psychiatric symptoms among adults with severe mental illness and diabetes.
Method:
A cross-sectional survey was conducted between January and May 2021 among adults (N = 156) with severe mental illness and type 2 diabetes who received primary care through a large academic health-care system (26% response rate). Valid and reliable questionnaires were implemented to measure food insecurity, social support, and mental health. Regression analysis was applied to examine the associations between food security status, social support, and mental health.
Results:
Food insecurity and social support are both correlated with psychiatric symptom severity. Specifically, support from family members has the largest protective role against food insecurity.
Conclusions and Implications for Practice:
This study found food insecurity is likely a critical issue to address whenever it is present in adults with severe mental illness (SMI) and type 2 diabetes. The presence of family support mitigates the need for addressing food insecurity. Practices and policies aimed at both addressing health inequities such as food insecurity and strengthening family support among people living with SMI and comorbid medical conditions are important adjuncts to self-management interventions.
Impact and Implications
Medical care for diabetes for people who have SMI must address the impact of social determinants of health, including food insecurity and social support. For adults with SMI and type 2 diabetes, family support may have important effects on the link between food insecurity and adverse mental health outcomes.
Measurement-based care (MBC) is an evidence-based practice in which patients routinely complete standardized measures throughout treatment to help monitor clinical progress and inform clinical ...decision-making. Despite its potential benefits, MBC is rarely used in community-based substance use disorder (SUD) treatment. In this pilot study, we evaluated the feasibility of incorporating a digital and remotely delivered MBC system into SUD treatment within a community setting by characterizing patients' and clinicians' engagement with and usability ratings toward the MBC system that was piloted.
A pilot study was conducted with 30 patients receiving SUD treatment and eight clinicians providing SUD treatment in a large, publicly funded addiction and mental health treatment clinic. Services as usual within the clinic included individual psychotherapy, case management, group therapy, peer support, and medication management for mental health and SUD, including buprenorphine. Patients who enrolled in the pilot continued to receive services as usual and were automatically sent links to complete a 22-item questionnaire, called
,
text message or email weekly for 24 weeks. Results of the weekly check-in were summarized on a clinician-facing web-based dashboard. Engagement was characterized by calculating the mean number of weekly check-ins completed by patients and the mean number times clinicians logged into the MBC system. Ratings of the MBC system's usability and clinical utility were provided by patients and clinicians.
Patient participants (53.3% male, 56.7% white, 90% Medicaid enrolled) completed a mean of 20.60 weekly check-ins (i.e., 85.8% of the 24 expected per patient). All but one participating clinician with a patient enrolled in the study logged into the clinician-facing dashboard at least once, with an average of 12.20 logins per clinician. Patient and clinician ratings of usability and clinical utility were favorable: most patients agreed with statements that the weekly check-in was easy to navigate and aided self-reflection. All clinicians who completed usability questionnaires agreed with statements indicating that the dashboard was easy to navigate and that it provided meaningful information for SUD treatment.
A digital and remotely delivered MBC system can yield high rates of patient and clinician engagement and high ratings of usability and clinical utility when added into SUD treatment as usual. The success of this clinical pilot may be attributable, in part, to the user-centered design processes that were used to develop and refine the MBC system that was piloted. Future efforts may focus on strategies to test whether MBC can be sustainably implemented and offers clinical benefits to patients in community SUD treatment settings.
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‐5th 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.
Alcohol use disorder (AUD) is underdiagnosed in healthcare settings, in part due to a lack of assessment measures that are reliable and practical to use in routine care. Alcohol Symptom Checklists were recently implemented in routine care at Kaiser Permanente Washington for patients who report high‐risk drinking on an annual behavioral health screen. We found that Alcohol Symptom Checklists completed by patients in routine care had high test‐retest reliability, suggesting the checklists can support AUD‐related care, including longitudinal clinical monitoring.
Objective:
Adolescents' drinking is influenced by their friends' drinking. However, it is unclear whether individually-targeted alcohol interventions reduce drinking in the friends of individuals who ...receive the intervention. This study used simulations of drinking in simulated longitudinal social networks to test whether individually-targeted alcohol interventions may be expected to spread to non-targeted individuals. Method: Stochastic actor-based models simulated longitudinal social networks where changes in drinking and friendships were modeled using parameters from a meta-analysis of high school 10th grade social networks. Social influence (i.e., how much one's friends' drinking affects their own drinking) and social selection (i.e., how much one's drinking affects who they select as friends) were manipulated at several levels. At the midpoint of each simulation, a randomly-selected heavy-drinking individual was experimentally assigned to an intervention (changing their drinking status to non-drinking) or a control condition (no change in drinking status) and the drinking statuses of that individual's friends were recorded at the end of the simulation.
Results:
Friends of individuals who received the intervention significantly reduced their drinking, with higher reductions occurring in networks with greater social influence. However, all effect sizes were small (e.g., average per-friend reduction of .07 on a 5-point drinking scale).
Conclusions:
Individually-targeted alcohol interventions may have small effects on reducing the drinking of non-targeted adolescents, with social influence being a mechanism that drives such effects. Due to small effect sizes, many adolescents may need to receive alcohol interventions to produce measurable effects on drinking outcomes for non-targeted individuals.
Public Health Significance
This study illustrates the potential for alcohol interventions to have "contagion" effects, where exposing an individual to an alcohol intervention may reduce their friends' drinking, despite those friends not directly receiving the intervention themselves. Although treatment contagion effects in computer simulations of social networks were generally small on a per-person basis, they may still reflect a significant public health benefit due to the large number of individuals who may be reached within friendship social networks.
•Average and momentary craving interact in a mutually amplifying fashion to predict cannabis use.•Non-judgment of and non-reactivity to inner experience were negatively associated with use.•Negative ...craving beliefs interacted with momentary craving to confer a buffering effect on use.
Young adult frequent cannabis use has increased in prevalence and some frequent users have problems reducing their use. A strong link between momentary craving and subsequent use behaviors among individuals with problematic cannabis use has been reported in the literature, including young adults. In treatment contexts, interventions based on associative learning and reinforcement aim to reduce the prevalence of problematic substance use by altering the association between craving and use by increasing craving management skills such as mindfulness and reducing unhelpful responding such as avoidance or suppression. However, this model has not been tested among young adult cannabis users. The current study examined the influence of trait and state craving management strategies (mindfulness, coping style, experiential avoidance, and craving beliefs) on the link between momentary craving and use, using ecological momentary assessment in a sample of young adults with problematic use interested in reducing their use. Results demonstrated that two craving management constructs were associated with use: non-reactivity (p = 0.02) and non-judgment (p < 0.01). Interactions with momentary craving were observed for two constructs: non-judgmentalness (p = 0.02) and craving beliefs (p < 0.01). Findings suggest that treatments that increase non-reactivity and non-judgmentalness may reduce the occurrence of cannabis use for young adults contemplating reduction during an important period of biopsychosocial development by mitigating the impact of craving or directly reducing use. Additionally, negative beliefs about craving may serve a protective function during acute periods of elevation in momentary craving, an unexpected finding deserving further investigation.
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.
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