Amy K. Lee

BACKGROUND Designed to provide peer support before concerns escalate to crisis, mental health warmlines play an important role in the mental health infrastructure. However, existing warmlines predominantly rely on voice calls, which may not align with changing communication preferences and practices. Warmlines also typically limit call frequency or duration, potentially restricting support for individuals who could benefit from longer-term peer support. Peer support services that are delivered via SMS text messaging and allow sustained engagement over time may address these limitations, yet little is known about who may utilize text messaging services or which individuals may utilize such services over longer-term periods (e.g., >1 day). OBJECTIVE This study characterized users of Mental Health America's Peer Bridger Program, a novel SMS text-based peer support service that allows individuals to engage over a short or longer-term period, and examined factors associated with program utilization and duration of engagement. METHODS We analyzed one year of data (January-December 2024) from website visitors who completed online mental health self-assessments after which they were invited to participate in the Peer Bridger Program. Demographic characteristics, self-assessment outcomes, and self-reported mental health factors were examined. Chi-square tests and logistic regression models identified characteristics associated with program engagement. Negative binomial regression identified characteristics associated with the length of time users engaged with the program. RESULTS Among 737,098 individuals who completed mental health screens, 1,535 engaged with the text-based peer support program. Relative to those who did not engage with the texting program, texters were more likely to identify as American Indian/Alaska Native, Black, or Hispanic/Latinx; report annual household income below $40,000; lack health insurance; and endorse identities as trauma survivors, healthcare workers, caregivers, or LGBTQ+ individuals. Texters completed more screens and were more likely to screen positive for depression, anxiety, bipolar disorder, and addiction. Those reporting financial problems or relationship difficulties had 88% and 50% higher odds of engagement, respectively. Duration of engagement ranged from under one minute to over nine months (median=78 minutes); 70.9% of exchanges concluded within 24 hours, while 17.3% continued for one week or longer. Individuals with more mental health concerns, as evidenced by completing a greater number of mental health screens, as well as those with limited financial resources, maintained longer engagement. CONCLUSIONS Confidential text-based peer support successfully reached diverse and clinically significant populations, including historically minoritized communities with substantial barriers to traditional mental health care. The sustained engagement model was found to be feasible, with extended engagement patterns suggesting that longer-term text-message based peer support services are more often utilized by individuals with more mental health concerns and limited resources. Findings support systems-level adoption of text-based peer support warmlines that permit ongoing engagement.

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.

Solid abdominal organ transplantation is fraught with variable rates of rejection and graft versus host disease (GVHD). We sought to determine the safety and efficacy of an advanced extracellular vesicle (EV) investigational product (IP) derived from mesenchymal stem cells (MSC) in the transplant patient population. Seven separate emergency investigational new drug (eNIDs) were filed with the Food and Drug Administration (FDA) for the emergency treatment of rejection of an isolated intestinal graft ( n = 2), liver allograft graft ( n = 2), modified multivisceral graft ( n = 3), and GVHD in isolated intestinal transplant patients ( n = 2). Fifteen milliliters of IP was administered intravenously on Day 0, 2, 4, and this treatment cycle was repeated up to four times in each patient depending on the treatment protocol allowed by the FDA. Safety (adverse event reporting) and efficacy (clinical status, serologies, and histopathology) were evaluated. There were no adverse events related to IP. All patients had improvement in clinical symptoms within 24 h, improved serologic laboratory evaluation, improved pulmonary symptoms and dermatologic manifestations of GVHD, and complete histologic resolution of graft inflammation/rejection within 7 days of IP administration. Systemic use of a MSC-derived EV IP was successful in achieving histological clearance of intestinal, liver, and multivisceral graft inflammation, and skin and pulmonary manifestations of GVHD.

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.

Importance: Unhealthy alcohol use is common and affects morbidity and mortality but is often neglected in medical settings, despite guidelines for both prevention and treatment.Objective: To test an implementation intervention to increase (1) population-based alcohol-related prevention with brief interventions and (2) treatment of alcohol use disorder (AUD) in primary care implemented with a broader program of behavioral health integration.Design, Setting, and Participants: The Sustained Patient-Centered Alcohol-Related Care (SPARC) trial was a stepped-wedge cluster randomized implementation trial, including 22 primary care practices in an integrated health system in Washington state. Participants consisted of all adult patients (aged ≥18 years) with primary care visits from January 2015 to July 2018. Data were analyzed from August 2018 to March 2021.Interventions: The implementation intervention included 3 strategies: practice facilitation; electronic health record decision support; and performance feedback. Practices were randomly assigned launch dates, which placed them in 1 of 7 waves and defined the start of the practice’s intervention period.Main Outcomes and Measures: Coprimary outcomes for prevention and AUD treatment were (1) the proportion of patients who had unhealthy alcohol use and brief intervention documented in the electronic health record (brief intervention) for prevention and (2) the proportion of patients who had newly diagnosed AUD and engaged in AUD treatment (AUD treatment engagement). Analyses compared monthly rates of primary and intermediate outcomes (eg, screening, diagnosis, treatment initiation) among all patients who visited primary care during usual care and intervention periods using mixed-effects regression.Results: A total of 333 596 patients visited primary care (mean [SD] age, 48 [18] years; 193 583 [58%] female; 234 764 [70%] White individuals). The proportion with brief intervention was higher during SPARC intervention than usual care periods (57 vs 11 per 10 000 patients per month; P < .001). The proportion with AUD treatment engagement did not differ during intervention and usual care (1.4 vs 1.8 per 10 000 patients; P = .30). The intervention increased intermediate outcomes: screening (83.2% vs 20.8%; P < .001), new AUD diagnosis (33.8 vs 28.8 per 10 000; P = .003), and treatment initiation (7.8 vs 6.2 per 10 000; P = .04).Conclusions and Relevance: In this stepped-wedge cluster randomized implementation trial, the SPARC intervention resulted in modest increases in prevention (brief intervention) but not AUD treatment engagement in primary care, despite important increases in screening, new diagnoses, and treatment initiation.Trial Registration: ClinicalTrials.gov Identifier: NCT02675777

Background: Digital interventions, such as websites and smartphone apps, can be effective in treating drug use disorders (DUDs). However, their implementation in primary care is hindered, in part, by a lack of knowledge on how patients might like these treatments delivered to them. Objective: This study aims to increase the understanding of how patients with DUDs prefer to receive app-based treatments to inform the implementation of these treatments in primary care. Methods: The methods of user-centered design were combined with qualitative research methods to inform the design of workflows for offering app-based treatments in primary care. Adult patients (n=14) with past-year cannabis, stimulant, or opioid use disorder from 5 primary care clinics of Kaiser Permanente Washington in the Seattle area participated in this study. Semistructured interviews were recorded, transcribed, and analyzed using qualitative template analysis. The coding scheme included deductive codes based on interview topics, which primarily focused on workflow design. Inductive codes emerged from the data. Results: Participants wanted to learn about apps during visits where drug use was discussed and felt that app-related conversations should be incorporated into the existing care whenever possible, as opposed to creating new health care visits to facilitate the use of the app. Nearly all participants preferred receiving clinician support for using apps over using them without support. They desired a trusting, supportive relationship with a clinician who could guide them as they used the app. Participants wanted follow-up support via phone calls or secure messaging because these modes of communication were perceived as a convenient and low burden (eg, no copays or appointment travel). Conclusions: A user-centered implementation of treatment apps for DUDs in primary care will require health systems to design workflows that account for patients’ needs for structure, support in and outside of visits, and desire for convenience.