This project aims to understand how concerns about pregnancy, whether planning for a child or trying to avoid an unintended one, affect women’s sexual well-being. Despite public health goals to reduce unintended pregnancies and STDs (Higgins et al., 2009), and the importance of sexual health to overall quality of life (Flynn et al., 2016), little research has explored the specific impact of pregnancy-related worries on sexual satisfaction and experience in women who are not currently pregnant (Bond et al., 2023a; Higgins et al., 2009). We know that sexual function issues are prevalent even among women planning pregnancy. We propose to explore pregnancy concerns and sexual quality of life among women (Bond et al., 2023b). We will then provide targeted education (depending on the responses in the survey and can differ for each individual) addressing their specific concerns and measure their sexual quality of life again to see if this information helps improve their sexual experience. The findings will highlight the sexual health needs related to pregnancy concerns and inform future educational programs.
Funding Type: State/UW
Engagement patterns in pediatric integrated behavioral health: investigating service utilization and attrition among children with mental health and neurodevelopmental disorders
Children with comorbid mental health and neurodevelopmental disorders (NDDs) often require more intensive and coordinated care than those with mental health disorders alone. These two categories of disorders often present with comorbidity and engagement disparities in integrated behavioral health programs among this population remain poorly understood. This study examines differences in service utilization and attrition rates between these two populations using retrospective electronic health record (EHR) data from the SCCN study population (ages 6-18) across multiple primary care sites.
We will analyze visit frequency over a 6-12 month period to assess whether children with comorbid NDDs have higher service utilization. Additionally, early dropout rates will be examined using Kaplan-Meier survival analysis and Cox proportional hazards models to identify risk factors for attrition. Findings will provide critical insights into engagement patterns, informing strategies to enhance retention, reduce access disparities, and improve care coordination for children with complex behavioral needs. This study will contribute to investigating further integrated care model improvements in order to ensure more equitable and sustained mental health treatment for vulnerable pediatric populations.
Discovering how a task-shifted Care Manager workforce of community health workers can address geriatric mental health
Older adults are less likely to receive the recommended standard of care for preventative services, chronic diseases and geriatric concerns such as complex care navigation. Late-life depression is a common chronic disease, and older adults face multiple barriers obtaining depression care from healthcare settings, especially if things like fragility, social needs, and transportation limit access to primary care. Offering depression care in non-traditional healthcare settings is one way to increase access. Community health workers (CHWs) are trusted community members who increase the health of communities through care coordination, health education and outreach. One approach is to task-shift the Care Manager (CM) role of a Collaborative Care framework to CHWs in the community. Global health work has demonstrated that non-clinicians can conduct low-intensity psychosocial interventions for depression. However, task-shifting the Care Manager role in a non-clinical setting requires additional skills and poses added challenges. We have gathered prior formative work among CHWs on what they think about being trained and supported in the skills of CM. We now seek to understand Collaborative Care stakeholders’ perspectives on this proposed role expansion of CHWs to CHW Care Managers (CHW-CMs) to understand how to design this role.
Subtyping the neurobiology of PTSD through novel methods for the alpha- and beta-adrenergic autonomic nervous system components: a pilot study
Trauma and posttraumatic stress disorder (PTSD) are common in veteran and civilian populations. Meanwhile, observationally estimated half of people cared for at UW autonomic nervous system (ANS) disorder clinic have a history of trauma. Increasingly, research explains this observation through an evolving understanding of the complex interplay of peripheral and central catecholamine signaling that appear to underlie much of the persistent impact of trauma. An improved understanding of ANS changes in trauma and their relationship to the complex symptoms people experience is an important research frontiers to improve a) our understanding of PTSD, b) our ability to predict effective treatment for a given person, and c) the development of new treatments for PTSD. We propose implementing a novel analysis method for non-invasive autonomic testing in context of several clinical studies at VA Puget Sound. The proposed research aims to 1) make use of ANS testing results already available to provide quantitative data for adrenergic signaling, 2) test associations of extracted biomarkers with symptoms of PTSD, and 3) collect prospective data to test the relationship of peripheral autonomic signaling to quantitative measures of central nervous system (CNS) catecholamine signaling measured by event-related potential (ERP).
Developing person-specific signatures of momentary risk for alcohol use
Alcohol use disorder remains a major public health concern, with persistent disparities in treatment outcomes. Traditional interventions often fail to account for the heterogeneity of drinking triggers, limiting their effectiveness. This study aims to develop and evaluate an idiographic, mobile-based clinical tool to identify personalized triggers for alcohol use. Idiographic methods allow for individualized assessments of momentary risk factors, providing tailored insights into when a person is most vulnerable to drinking.
The study will allow for customization of a given participant’s data collection process, such that individuals can track what is clinically meaningful to them (e.g. one individual may track feelings of loneliness following divorce, while another may track experiences of racial microaggressions) through diverse data collection techniques including ecological momentary assessment (EMA), audio diaries, and GPS. We will evaluate the feasibility and acceptability of this approach by pilot testing the data collection process, analyzing each participant’s data, and providing personalized feedback on the momentary conditions that influence one’s drinking.
This approach leverages current advances in mobile monitoring and precision idiographic machine learning analysis to pilot a novel clinical tool. If successful, this tool could enhance treatment equity and effectiveness by empowering individuals to recognize their unique drinking triggers.
How fentanyl changes the brain: assessing mood, cognition, and withdrawal using animal models of addiction and brain-wide neural activity markers
Fentanyl overdose is responsible for nearly 75,000 deaths each year in the U.S. and causes severe psychological, physical, financial, and social harm. Despite existing treatments, fentanyl addiction remains difficult to overcome due to the chronic and complex nature of fentanyl addiction which contributes to patterns of chronic use and high relapse rates. This is partly due to fentanyl’s ability to rewire the brain’s reward and executive cognitive system to cause lasting changes in mood and cognition while also triggering intense withdrawal symptoms that drive continued use. To better understand the widespread impact of fentanyl on the brain, this project uses mouse models of addiction to explore the effects of fentanyl on various brain regions and neural populations involved in reward, motivation, mood, and cognition. Using artificial intelligence-guided behavioral and cellular analyses, we then correlate these neural signatures to mouse behaviors during withdrawal and a cognitive working memory task. We will then test whether two promising emerging treatments, semaglutide and ketamine, can improve cognition, withdrawal symptoms, or mood in mice exposed to fentanyl. Through this, we will contribute to our understanding of how fentanyl exerts its negative effects which can inform the development of more effective therapies for its devastating impact.
From symptom relief to subtype identification: exploring patterns of cannabis use in PTSD
Post-traumatic stress disorder (PTSD) and substance use often go hand in hand, with many people using substances like cannabis to manage their symptoms. This concept, known as the self-medication hypothesis, suggests that people might use cannabis differently depending on the nature of their symptoms. Symptoms are classically split into domains – hyperarousal, emotional numbing, re-experiencing, and avoidance. However, it remains unclear whether different patterns of cannabis use might correspond to specific symptom domains, which could reveal distinct clinical phenotypes of PTSD.
By analyzing data from the PREDICT clinical trial, this study will apply advanced statistical methods to identify unobservable (“latent”) factors that characterize cannabis use in individuals with PTSD and examine their relationship with symptom presentation. In statistics and psychometrics, latent refers to a variable that cannot be directly observed—such as internal motivations, behavioral tendencies, or physiological dependence—but which can be inferred from patterns in observed data (e.g., questionnaire responses). These patterns could offer insights into subgroups of people with PTSD who experience different symptom profiles, also known as phenotypes, and may respond to treatments in unique ways. Ultimately, this research could contribute to more personalized, targeted interventions for individuals living with PTSD.
Substance Use Disorder assessment tools in jails & prisons: a systematic review
Over 65% of people who are incarcerated have a substance use disorder (SUD). Many jails and prisons provide substance use treatment, including behavioral and pharmacotherapy, and SUD identification is the first step. Prisons and jails are a distinct setting for SUD assessment, and tools used for SUD screening in community settings may not perform the same way in carceral settings. This systematic review will identify psychometric evaluation studies in carceral settings of screening and diagnostic tools for SUDs, generally, or specific SUDs (excluding alcohol and nicotine). Additionally, will use and adapt an existing clinical usability scale and develop new metrics to assess acceptability of screening tool use in carceral settings for a person-centered evaluation. This will be guided by further analysis of interviews with prison staff and people with lived experience of incarceration. The additional assessments will capture potential barriers and facilitators to SUD assessment in jails and prisons as they represent resource-limited settings with unique challenges compared to other health systems. The goals for the study are: 1) to help carceral facilities make informed decisions about SUD assessment, 2) to reveal key gaps in the literature, and 3) to inform the development and testing of future tools.
Improving risk-stratification of substance-induced psychosis: incorporating stakeholder perspectives and implementing a standardized assessment of risk factors
First-episode substance-induced psychosis (SIP) presents a clinical challenge in which treatment decisions cannot rely on evidence-based guidelines and long-term outcomes are not well understood. Preliminary findings from our retrospective study of psychiatry inpatients at Harborview Medical Center (HMC) suggest that patients with first-episode SIP had similar rehospitalization rates to those with first-episode psychosis (FEP) but were less likely to receive long-acting injectable antipsychotics (LAIs) even though LAIs may reduce the risk of 30- and 180-day rehospitalization. Our study has also revealed inconsistent assessment of key risk factors for rehospitalization—such as family history of psychosis and patterns of cannabis use—that may be limiting informed decision-making, including appropriate LAI use. This project seeks to improve the risk stratification of first-episode SIP by addressing these gaps. Aim 1 will use qualitative interviews with inpatient attending psychiatrists, psychiatry residents, and patients to explore factors influencing the management of first-episode SIP. Aim 2 will evaluate the acceptability and feasibility of implementing a standardized assessment of cannabis use and family history of psychosis on HMC inpatient psychiatry units. This project will lay the groundwork for future clinical interventions that optimize treatment decisions and improve patient outcomes in psychiatric inpatient settings.
Unraveling the genetics of schizophrenia and bipolar disorder with large-language models
Schizophrenia (SCZ) and bipolar disorder (BPD) are among the most heritable psychiatric conditions, yet their genetic foundations remain poorly understood. Historically, unraveling these disorders’ genetic architectures was limited by inadequate technology. However, breakthroughs in next-generation sequencing have recently produced expansive genomic datasets—including those from the Psychiatric Genomics Consortium (PGC)—for SCZ and BD. Simultaneously, genomic foundation models—advanced large-language models (LLMs) trained on biological data rather than corpuses of text—have emerged as next-generation artificial intelligence platforms that offer unparalleled abilities to predict the functional effects of genetic variants, many of which were previously unclassified. This proposal harnesses these models to analyze publicly available PGC genomic datasets, aiming to annotate both common and rare genetic variants associated with SCZ and BD, pinpoint disease-associated genes, and map the biological pathways they influence. By bridging these recent advances in artificial intelligence with robust genomic data, this proposal seeks to illuminate the genetic underpinnings of SCZ and BPD. The anticipated insights promise to deepen our understanding of heritable psychiatric conditions, laying the groundwork for enhanced diagnostics and novel therapies aimed at biology, rather than nosology
