A spotlight featuring the three projects of our 2025 CHIPTS Rapid Projects awardees that includes project titles and project abstracts. Dr. Wei-Ti Chen tests a compassion-based intervention to reduce stigma and improve mental health. Dr. Huyen Pham uses evidence synthesis and machine learning to improve care for people with HIV and co-occurring mental health or substance use disorders. Beimnet Taye examines how transportation, crime, and neighborhood factors shape care access in Los Angeles. Together, they drive innovative strategies to strengthen engagement and outcomes for people living with HIV.
Project Title: The impact of a culturally adapted stigma-reduction intervention on mental health and care engagement
Awardee: Wei-Ti Chen, PhD
Project Abstract: This project evaluates the impact of a spiritually grounded, compassion-based intervention on mental health and HIV care engagement among adults living with HIV. The primary aim is to generate evidence to inform innovative, adaptable strategies for reducing stigma and improving outcomes among people living with HIV (PLWH) in the United States. HIV-related stigma—manifested as negative societal attitudes and internalized shame—remains a major barrier to mental health, social support, and treatment adherence, especially within marginalized communities. The intervention integrates mindfulness, nonattachment, and self-compassion, drawing from cognitive-behavioral frameworks and culturally-informed practices. Participants are randomized to an enhanced treatment group or a standard care group. The enhanced group receives four weekly, facilitator-led sessions focused on coping with stigma and building peer support. Outcomes assessed include depressive symptoms, anxiety, antiretroviral therapy adherence, and viral load. Mediators such as internalized stigma, self-compassion, and perceived social support are measured at multiple time points. Generalized Estimating Equations (GEE) will compare changes over time between groups, controlling for demographics and baseline stigma, and mediation analyses will test whether improvements in these mediators account for better mental health and care engagement.
Project Title: Integrating Predictive Modeling and Evidence Synthesis to Improve Care for Individuals with Co-occurring HIV, SUD, and MHD
Awardee: Huyen Pham, PhD
Project Abstract: People with HIV (PWH) often experience co-occurring mental health disorders (MHD) and substance use disorders (SUD), which negatively impact HIV treatment outcomes. Integrated care models are essential but remain poorly understood. This study has two aims: (1) to conduct a scoping review of integrated care models for individuals with HIV and co-occurring SUD/MHD, and (2) to use machine learning (ML) to predict SUD/MHD comorbidity, treatment utilization, and identify key drivers of treatment utilization among PWH, using 2010–2019 National Survey on Drug Use and Health (NSDUH) data. The first aim will follow Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping review (PRISMAScR) framework, drawing from electronic databases and focusing on studies published between 2015 and 2024. Integrated care models will be characterized by setting, provider type, services offered, and evaluation metrics (e.g., accessibility, effectiveness, quality, cost). Additionally, patient characteristics associated with positive outcomes will be identified. For the second aim, the study will analyze data from 409,521 adult respondents from NSDUH data, including 702 individuals who self-reported an HIV diagnosis (representing a weighted estimate of 488,000 annually). Key drivers of SUD/MHD comorbidity and treatment utilization among individuals with HIV and co-occurring SUD and/or MHD will be identified. Findings from this project will improve understanding of integrated care strategies and inform healthcare policy to better address service needs and treatment engagement among individuals with comorbid HIV, SUD, and/or MHD.
Project Title: Transportation Vulnerability & HIV Care Engagement in Los Angeles County
Awardee: Beimnet Taye, MPH
Project Abstract: Although effective treatment and testing options exist for HIV care, people still face issues in fully utilizing these care options. External neighborhood level factors such as crime prevalence, public transportation access, and traffic congestion could all affect a person living with HIV’s ability and willingness to seek HIV care by impacting their ability to commute to and from HIV clinics and pharmacies. In Los Angeles County these factors vary significantly between neighborhoods. Thus, this project provides an opportunity to explore how these factors at the census tract level are associated with individual HIV care engagement and how these factors affecting residential mobility modify the association between individual behaviors such as frequency of substance use and care engagement among a cohort of men who are living with HIV (The mSTUDY). By linking publicly available crime and arrest data from the Los Angeles Sheriff and Police departments, Public transport stops density data, and daily average traffic data to the individual level cohort data by census tract of residence we can leverage hierarchal mixed models to quantify cross level associations and interactions with HIV related appointment adherence and viral suppression (≤ 200 copies/mL) among those living with HIV. Quantifying these relationships will not only identify important residential based issues facing this population in Los Angeles, but can also inform future specifically tailored interventions for this population that can attempt to ameliorate these issues and better HIV care engagement for all.