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Primary care encounters are common among patients at risk for suicide. Prior suicide care programs that incorporate depression screening, suicide risk assessment, and collaborative safety planning show promise in nonprimary care settings. This secondary analysis of a stepped-wedge, cluster randomized implementation trial examines the effectiveness of a primary care–based suicide care program embedded alongside a substance use care program in reducing suicide attempts within 90 days of the primary care visit. Visual Abstract. Effectiveness of Integrating Suicide Care in Primary Care: Primary care encounters are common among patients at risk for suicide. Prior suicide care programs that incorporate depression screening, suicide risk assessment, and collaborative safety planning show promise in nonprimary care settings. This secondary analysis of a stepped-wedge, cluster randomized implementation trial examines the effectiveness of a primary care–based suicide care program embedded alongside a substance use care program in reducing suicide attempts within 90 days of the primary care visit. Background: Primary care encounters are common among patients at risk for suicide. Objective: To evaluate the effectiveness of implementing population-based suicide care (SC) in primary care for suicide attempt prevention. Design: Secondary analysis of a stepped-wedge, cluster randomized implementation trial. (ClinicalTrials.gov: NCT02675777) Setting: 19 primary care practices within a large health care system in Washington State, randomly assigned launch dates. Patients: Adult patients (aged ≥18 years) with primary care visits from January 2015 to July 2018. Intervention: Practice facilitators, electronic medical record (EMR) clinical decision support, and performance monitoring supported implementation of depression screening, suicide risk assessment, and safety planning. Measurements: Clinical practice and patient measures relied on EMR and insurance claims data to compare usual care (UC) and SC periods. Primary outcomes included documented safety planning after population-based screening and suicide risk assessment and suicide attempts or deaths (with self-harm intent) within 90 days of a visit. Mixed-effects logistic models regressed binary outcome indicators on UC versus SC, adjusted for randomization stratification and calendar time, accounting for repeated outcomes from the same site. Monthly outcome rates (percentage per 10 000 patients) were estimated by applying marginal standardization. Results: During UC, 255 789 patients made 953 402 primary care visits and 228 255 patients made 615 511 visits during the SC period. The rate of safety planning was higher in the SC group than in the UC group (38.3 vs. 32.8 per 10 000 patients; rate difference, 5.5 [95% CI, 2.3 to 8.7]). Suicide attempts within 90 days were lower in the SC group than in the UC group (4.5 vs. 6.0 per 10 000 patients; rate difference, −1.5 [CI, −2.6 to −0.4]). Limitation: Suicide care was implemented in combination with care for depression and substance use. Conclusion: Implementation of population-based SC concurrent with a substance use program resulted in a 25% reduction in the suicide attempt rate in the 90 days after primary care visits. Primary Funding Source: National Institute of Mental Health. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: The authors tested the efficacy of the "I-STAND" intervention for reducing sitting time, a novel and potentially health-promoting approach, in older adults with obesity. Methods: The authors recruited 60 people (mean age = 68 ± 4.9 years, 68% female, 86% White; mean body mass index = 35.4). The participants were randomized to receive the I-STAND sitting reduction intervention (n = 29) or healthy living control group (n = 31) for 12 weeks. At baseline and at 12 weeks, the participants wore activPAL devices to assess sitting time (primary outcome). Secondary outcomes included fasting glucose, blood pressure, and weight. Linear regression models assessed between-group differences in the outcomes. Results: The I-STAND participants significantly reduced their sitting time compared with the controls (–58 min per day; 95% confidence interval [–100.3, –15.6]; p =.007). There were no statistically significant changes in the secondary outcomes. Conclusion: I-STAND was efficacious in reducing sitting time, but not in changing health outcomes in older adults with obesity. [ABSTRACT FROM AUTHOR]

The article focuses on the increase in common behavioral health conditions due to cannabis use disorders (CUD) in the U.S. Topics discussed include collection of data from annual behavioral health screening and questionnaires, and primary care of adult patients with diagnosed mental health and substance use disorder.