Research

 

Behavioral Health Research: Populations, Workforce, and Scientific Integrity

The Institute's primary mission is research. Its scholarly work informs continuing education, professional development workshops, and community learning initiatives designed to disseminate evidence-based practices and strengthen the behavioral health workforce.

 

 

Integrated Behavioral Health in Primary Care

Approaches to embedding behavioral health services within primary care settings to address mental health, substance use, and related psychosocial needs alongside physical health conditions in a unified, team-based approach. 

Populations & Patient-Centered Care

Investigating psychosocial, behavioral, and structural mechanisms linking mental health conditions with chronic medical illnesses to develop data-informed strategies to improve prevention, treatment engagement, and health outcomes across the lifespan. 

Behavioral Health Workforce & Training 

BxH Workforce
Studying people, systems, and policies that shape access to and quality of mental health and substance use disorder services.
 

Key Data Sources

We draw on diverse, high-quality data sources, including electronic health records, participant surveys, community-engaged datasets, and national surveillance systems. Across all projects, we prioritize equity, translation, and policy relevance—transforming complex data into insights that support clinicians, policymakers, and communities.

  • Electronic Medical Records (EMR) data from the NIH All of Us Research Program is a diverse dataset to help researchers examine complex health and behavioral health issues.
  • Survey data collected directly from All of Us and community participants.
  • Community data acquired through community-engaged data collection efforts provides crucial context on environmental and social factors influencing mental health and behavioral health services.
  • National datasets such as the CDC's Behavioral Risk Factor Surveillance System (BRFSS) and National Health Interview Survey (NHIS).

Impact and Policy Focus

Researchers in the IIBHR translate evidence into guidance that informs clinical decision-making, workforce planning, and public policy. Through collaboration with academic, governmental, and community partners, findings are disseminated and applied in real-world settings. This implementation-focused scholarship supports data-driven solutions that advance equity, access, and population health.

AoU The future of health begins with you

Featured Research

The All of Us Research Program at CHS

Western University of Health Sciences has a data use and registration agreement in place with the National Institutes of Health (NIH) All of Us Research Program to ensure data security and integrity. WesternU faculty, students, researchers, and postdoctoral fellows can leverage this one-of-a-kind dataset to improve understanding of health and disease, identify opportunities to reduce disparities, and enable more precise approaches to care. For questions, email Dr. Josh Matacotta in the College of Health Sciences at jmatacotta@westernu.edu. 

Learn about our research

Publications by Institute Members

  • Matacotta, J. J., Tran, D., & Yoon, S. (2024). The prevalence of major depressive disorder in people with HIV: Results from the All of Us Research Program. HIV Medicine. https://doi.org/https://doi.org/10.1111/hiv.13653
    Publisher's Version
    HIV Medicine Journal

    Objectives

    The All of Us (AoU) Research Program is a national-scale effort to build a dataset to help transform the future of health research by equipping researchers with comprehensive health data from diverse populations, especially those underrepresented in biomedical research. Our objectives were to evaluate the burden of HIV and major depressive disorder (MDD) in underrepresented groups and the frequency of the HIV/MDD comorbidity.

    Methods

    We conducted a cross-sectional analysis combining collected survey and electronic health record (EHR) data. We ascertained HIV and MDD cases using Observational Medical Outcomes Partnership codes. We used multivariable logistic regression to obtain the odds ratio of HIV in AoU participants and MDD in AoU participants with HIV.

    Results

    The latest AoU data release includes 412 211 participants: 254 700 have at least one medical condition concept in their EHR, of whom 5193 (1.3%) had HIV, and 2238 (43%) of those with HIV had a diagnosis of MDD. Black AoU participants had approximately 4.58 times the odds of having an HIV diagnosis compared with the combined odds of all other racial groups. AoU participants with HIV were more likely to have MDD (p = 0.001) than were participants without HIV.

    Conclusion

    Among AoU participants, Black individuals have a disproportionately high burden of HIV, pointing to underlying factors such as social determinants of health, limited access to healthcare or prevention resources, and potential systemic biases that contribute to these differences. In addition, HIV is a risk factor for mental health issues like MDD. Further data collection from people with HIV will elucidate contributing factors and the need for interventions.

  • Investigating the replicability of the social and behavioural sciences. (2026). Nature.
    Publisher's Version
    Publisher's Version

    Pursuing replicability — independent evidence for previous claims — is important for creating generalizable knowledge1,2. Here we attempted replications of 274 claims of positive results from 164 quantitative papers published from 2009 to 2018 in 54 journals in the social and behavioural sciences. Replications were high powered on average to detect the original effect size (median of 99.6%), used original materials when relevant and available, and were peer reviewed in advance through a standardized internal protocol. Replications showed statistically significant results in the original pattern for 151 of 274 claims (55.1% (95% confidence interval (CI) 49.2–60.9%)) and for 80.8 of 164 papers (49.3% (95% CI 43.8–54.7%)), weighed for replicating multiple claims per paper. We observed modest variation in replication rates across disciplines (42.5–63.1%), although some estimates had high uncertainty. The median Pearson’s r effect size was 0.25 (95% CI 0.21–0.27) for original studies and 0.10 (95% CI 0.09–0.13) for replication studies, an 82.4% (95% CI 67.8–88.2%) reduction in shared variance. Thirteen methods for evaluating replication success provided estimates ranging from 28.6% to 74.8% (median of 49.3%). Some decline in effect size and significance is expected based on power to detect original effects and regression to the mean because we replicated only positive results. We observe that challenges for replicability extend across social–behavioural sciences, illustrating the importance of identifying conditions that promote or inhibit replicability3,4.

  • Hicks, C. M., & Lee, C. S. (2024). Developer Thriving: Four sociocognitive factors that create resilient productivity on software teams. IEEE Software, 41(4), 68-77. (Original work published 2024)
    Publisher's Version
    Publisher's Version

    We present a research-based framework for measuring successful environments on software teams for long-term and sustainable sociocognitive problem-solving. Across 1,282 full-time developers in 12+ industries, we tested the factors of our framework and found it predictive of developers’ self-reported productivity.

    See also: Behavioral Health