Substance use typologies among young people experiencing homelessness in seven cities across the United States: A latent class analysis.

BACKGROUND: Substance use and other health-risk risk factors, including mental health, trauma, and sexual-risk behaviors, often co-occur among youth experiencing homelessness (YEH). The present study aimed to identify subgroups of YEH based on polysubstance use and the linkages to sociodemographic and health-risk characteristics. METHODS: From June 2016 to July 2017, 1,426 YEH (aged 18-26 years) were recruited from seven cities (Houston, Los Angeles, Denver, Phoenix, New York City, St. Louis, San Jose). Participants provided information via a self-administered electronic survey on substance use, mental health, trauma, sexual risk behaviors, and sociodemographic characteristics. The majority of YEH identified as Black (37.3%), cisgender (92.8%), and heterosexual (69.2%). On average, YEH were 20.9 years (SD = 2.1). This study employed latent class analysis (LCA) to identify subgroups of YEH according to their substance use. Multinomial logistic regression analyses were conducted to identify sociodemographic and health-risk characteristics associated with class-membership. RESULTS: Four latent classes of YEH substance use were identified: (1) high polysubstance use; (2) moderate stimulant and high marijuana, alcohol, and prescription drug use; (3) high marijuana, alcohol, and prescription drug use; (4) low/moderate marijuana and alcohol use. Multinomial logistic regression models indicated that geographic location, gender, race/ethnicity, mental health, trauma history, and sexual risk behaviors were significant correlates of substance use class membership among YEH. CONCLUSIONS: These findings offer important implications for the prevention and treatment of substance use among YEH. Screening protocols should consider co-occurring risk factors such as traumatic experiences, sexual risk behaviors, and mental health history as indicators of polysubstance use.

Clinician and Patient Perspectives on the Use of Passive Mobile Monitoring and Self-Tracking for Patients With Serious Mental Illness: User-Centered Approach.

BACKGROUND: Early intervention in mental health crises can prevent negative outcomes. A promising new direction is remote mental health monitoring using smartphone technology to passively collect data from individuals to rapidly detect the worsening of serious mental illness (SMI). This technology may benefit patients with SMI, but little is known about health IT acceptability among this population or their mental health clinicians. OBJECTIVE: We used the Health Information Technology Acceptability Model to analyze the acceptability and usability of passive mobile monitoring and self-tracking among patients with serious mental illness and their mental health clinicians. METHODS: Data collection took place between December 2020 and June 2021 in 1 Veterans Administration health care system. Interviews with mental health clinicians (n=16) assessed the acceptability of mobile sensing, its usefulness as a tool to improve clinical assessment and care, and recommendations for program refinements. Focus groups with patients with SMI (n=3 groups) and individual usability tests (n=8) elucidated patient attitudes about engaging in health IT and perceptions of its usefulness as a tool for self-tracking and improving mental health assessments. RESULTS: Clinicians discussed the utility of web-based data dashboards to monitor patients with SMI health behaviors and receiving alerts about their worsening health. Potential benefits included improving clinical care, capturing behaviors patients do not self-report, watching trends, and receiving alerts. Clinicians' concerns included increased workloads tied to dashboard data review, lack of experience using health IT in clinical care, and how SMI patients' associated paranoia and financial instability would impact patient uptake. Despite concerns, all mental health clinicians stated that they would recommend it. Almost all patients with SMI were receptive to using smartphone dashboards for self-monitoring and having behavioral change alerts sent to their mental health clinicians. They found the mobile app easy to navigate and dashboards easy to find and understand. Patient concerns centered on privacy and "government tracking," and their phone's battery life and data plans. Despite concerns, most reported that they would use it. CONCLUSIONS: Many people with SMI would like to have mobile informatics tools that can support their illness and recovery. Similar to other populations (eg, older adults, people experiencing homelessness) this population presents challenges to adoption and implementation. Health care organizations will need to provide resources to address these and support successful illness management. Clinicians are supportive of technological approaches, with adapting informatics data into their workflow as the primary challenge. Despite clear challenges, technological developments are increasingly designed to be acceptable to patients. The research development-clinical deployment gap must be addressed by health care systems, similar to computerized cognitive training. It will ensure clinicians operate at the top of their skill set and are not overwhelmed by administrative tasks, data summarization, or reviewing data that do not indicate a need for intervention. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/39010.

Assessing longitudinal housing status using Electronic Health Record data: a comparison of natural language processing, structured data, and patient-reported history.

Introduction: Measuring long-term housing outcomes is important for evaluating the impacts of services for individuals with homeless experience. However, assessing long-term housing status using traditional methods is challenging. The Veterans Affairs (VA) Electronic Health Record (EHR) provides detailed data for a large population of patients with homeless experiences and contains several indicators of housing instability, including structured data elements (e.g., diagnosis codes) and free-text clinical narratives. However, the validity of each of these data elements for measuring housing stability over time is not well-studied. Methods: We compared VA EHR indicators of housing instability, including information extracted from clinical notes using natural language processing (NLP), with patient-reported housing outcomes in a cohort of homeless-experienced Veterans. Results: NLP achieved higher sensitivity and specificity than standard diagnosis codes for detecting episodes of unstable housing. Other structured data elements in the VA EHR showed promising performance, particularly when combined with NLP. Discussion: Evaluation efforts and research studies assessing longitudinal housing outcomes should incorporate multiple data sources of documentation to achieve optimal performance.

Promoting Safety and Connection During COVID-19: Tiny Homes as an Innovative Response to Homelessness in the USA.

Using an international human rights framework, this study explores how tiny home villages have provided unhoused individuals with critical shelter necessary for preserving health and safety during the COVID-19 pandemic, while attending to the needs of social connection and community maintenance. The gaps and inadequacies of conventional shelter systems in the USA for people experiencing homelessness have been highlighted by COVID-19. Physical distancing can be challenging for people experiencing homelessness due to the crowded congregate shelters and encampments. Furthermore, closed agencies and limited transportation can increase risk for isolation. People experiencing homelessness are more likely than the general population to have health risks, such as diabetes and heart disease, both of which can increase the risk of death for people who test positive for COVID-19. Through qualitative analysis of 32 open-ended survey responses of experts working and/or living in tiny home communities addressing homelessness, we explore how these leaders in the field responded to the COVID-19 pandemic. Two primary themes emerged through analysis: (1) villages prioritize safety through physical distancing, and (2) villages preserve social connection and combat isolation. This balance of being responsive to the safety guidelines of COVID-19, while also maintaining social connection and community building, highlights the need for social work to embrace new and innovative responses to homelessness and other social issues, particularly given the uncertainties and complexities of the future.