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Background and Aims: The Opioid Use Disorder (OUD) Cascade of Care is a public health model that has been used to measure population‐level OUD risk, treatment engagement, retention, service and outcome indicators. However, no studies have examined its relevance for American Indian and Alaska Native (AI/AN) communities. Thus, we aimed to understand (1) the utility of existing stages and (2) the relative 'fit' of the OUD Cascade of Care from a tribal perspective. Design, Setting, Participants and Measurements: Qualitative analysis of in‐depth interviews with 20 individuals who were knowledgeable regarding the treatment of OUD in an Anishinaabe tribal setting in Minnesota, USA. Community member roles included clinicians, peer support specialists and cultural practitioners, among others. Thematic analysis was used to analyze the data. Findings: Participants identified the key transition points of prevention, assessment, inpatient/outpatient pathways and recovery as relevant to their community. They re‐imagined an Aanji'bide (Changing our Paths) model of opioid recovery and change that was non‐linear; included developmental stage and individual pathways; and demonstrated resilience through connection to culture/spirituality, community and others. Conclusions: Community members living/working in a rural tribal nation in Minnesota, USA identified non‐linearity and cultural connection as key elements to include in an Anishinaabe‐centered model of opioid recovery and change. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background and aims There is evidence that low-risk drinking is possible during the course of alcohol treatment and can be maintained following treatment. Our aim was to identify characteristics associated with low-risk drinking during treatment in a large sample of individuals as they received treatment for alcohol dependence. Design Integrated analysis of data from the Combined Pharmacotherapies and Behavioral Intervention (COMBINE) study, Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity) and the United Kingdom Alcohol Treatment Trial (UKATT) using repeated-measures latent class analysis to identify patterns of drinking and predictors of low-risk drinking patterns during treatment. Setting United States and United Kingdom. Participants Patients ( n = 3589) with alcohol dependence receiving treatment in an alcohol clinical trial were primarily male (73.0%), white (82.0%) and non-married (41.7%), with an average age of 42.0 (standard deviation = 10.7). Measurements Self-reported weekly alcohol consumption during treatment was assessed using the Form-90 and validated with biological verification or collateral informants. Findings Seven patterns of drinking during treatment were identified: persistent heavy drinking (18.7% of the sample), increasing heavy drinking (9.6%), heavy and low-risk drinking (6.7%), heavy drinking alternating with abstinence (7.9%), low-risk drinking (6.8%), increasing low-risk drinking (10.5%) and abstinence (39.8%). Lower alcohol dependence severity and fewer drinks per day at baseline significantly predicted low-risk drinking patterns [e.g. each additional drink prior to baseline predicted a 27% increase in the odds of expected classification in heavy drinking versus low-risk drinking patterns; odds ratio = 1.27 (95% confidence interval (CI) = 1.10, 1.47, P = 0.002]. Greater negative mood and more heavy drinkers in the social network were significant predictors of expected membership in heavier drinking patterns. Conclusions Low-risk drinking is achievable for some individuals as they undergo treatment for alcohol dependence. Individuals with lower dependence severity, less baseline drinking, fewer negative mood symptoms and fewer heavy drinkers in their social networks have a higher probability of achieving low-risk drinking during treatment. [ABSTRACT FROM AUTHOR]

Background Alcohol use disorder (AUD) is a highly prevalent public health problem associated with considerable individual and societal costs. Abstinence from alcohol is the most widely accepted target of treatment for AUD, but it severely limits treatment options and could deter individuals who prefer to reduce their drinking from seeking treatment. Clinical validation of reduced alcohol consumption as the primary outcome of alcohol clinical trials is critical for expanding treatment options. One potentially useful measure of alcohol treatment outcome is a reduction in the World Health Organization (WHO, International Guide for Monitoring Alcohol Consumption and Related Harm. Geneva, Switzerland, 2000) risk levels of alcohol use (very high risk, high risk, moderate risk, and low risk). For example, a 2-shift reduction in WHO risk levels (e.g., high risk to low risk) has been used by the European Medicines Agency (2010, Guideline on the Development of Medicinal Products for the Treatment of Alcohol Dependence. UK) to evaluate nalmefene as a treatment for alcohol dependence (AD; Mann et al. 2013, Biol Psychiatry 73, 706-13). Methods The current study was a secondary data analysis of the COMBINE study ( n = 1,383; Anton et al., ) to examine the association between reductions in WHO risk levels and reductions in alcohol-related consequences and mental health symptoms during and following treatment in patients with AD. Results Any reduction in WHO risk drinking level during treatment was associated with significantly fewer alcohol-related consequences and improved mental health at the end of treatment and for up to 1 year posttreatment. A greater reduction in WHO risk drinking level predicted a greater reduction in consequences and greater improvements in mental health. Conclusions Changes in WHO risk levels appear to be a valid end point for alcohol clinical trials. Based on the current findings, reductions in WHO risk drinking levels during treatment reflect meaningful reductions in alcohol-related consequences and improved functioning. [ABSTRACT FROM AUTHOR]

Background: Diagnosis of alcohol use disorder (AUD) in primary care is critical for increasing access to alcohol treatment. However, AUD is underdiagnosed and may be inequitably diagnosed due to societal structures that determine access to resources (e.g., structural racism that limits opportunities for some groups and influences interpersonal interactions in and beyond health care). This study described patterns of provider-documented AUD in primary care across intersections of race, ethnicity, sex, and community-level socioeconomic status (SES)., Methods: This cross-sectional study used EHR data from a regional healthcare system with 35 primary care clinics that included adult patients who completed alcohol screenings between 3/1/2015 and 9/30/2020. The prevalence of provider-documented AUD in primary care based on International Classification of Diseases-9 (ICD-9) and ICD-10 diagnoses was compared across intersections of race, ethnicity, sex, and community-level SES., Results: Among 439,375 patients, 6.6% were Latine, 11.0% Asian, 5.4% Black, 1.3% Native Hawaiian/Pacific Islander (NH/PI), 1.5% American Indian/Alaska Native (AI/AN), and 74.2% White, and 58.3% women. The overall prevalence of provider-documented AUD was 1.0% and varied across intersecting identities. Among women, the prevalence was highest for AI/AN women with middle SES, 1.5% (95% CI 1.0-2.3), and lowest for Asian women with middle SES, 0.1% (95% CI 0.1-0.2). Among men, the prevalence was highest for AI/AN men with high and middle SES, 2.0% (95% CI 1.1-3.4) and 2.0% (95% CI 1.2-3.2), respectively, and lowest for Asian men with high SES, 0.5% (95% CI 0.3-0.7). Black and Latine patients tended to have a lower prevalence of AUD than White patients, across all intersections of sex and SES except for Black women with high SES. There were no consistent patterns of the prevalence of AUD diagnosis that emerged across SES., Conclusion: The prevalence of provider-documented AUD in primary care was highest in AI/AN men and women and lowest in Asian men and women. Findings of lower prevalence of provider-documented AUD in Black and Hispanic than White patients across most intersections of sex and SES differed from prior studies. Findings may suggest that differences in access to resources, which vary in effects across these identity characteristics and lived experiences, influence the diagnosis of AUD in clinical care., (© 2024. The Author(s).)