Jodie Trafton Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

High Impact

Researcher

University of Arkansas at Fayetteville

faculty

45 h-index 199 pubs 9,704 cited

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Biography and Research Information

OverviewAI-generated summary

Jodie Trafton's research focuses on understanding and improving mental health outcomes, particularly within veteran populations. This work includes the development and evaluation of predictive models for suicide risk, such as the Veterans Health Administration REACH VET Program. Trafton has investigated the efficacy of various interventions, including a stepped-wedge cluster randomized controlled trial examining the effect of a predictive analytics-targeted program for patients on opioids. The research also extends to the cost-effectiveness of treatments for opioid use disorder and the role of tele-mental health services, especially during the COVID-19 pandemic.

A significant portion of Trafton's recent publications involve genetic studies, including genome-wide association studies (GWAS) meta-analyses to identify genetic loci associated with suicide attempts and suicidal thoughts and behaviors. These studies aim to uncover genetic risks and their implications for specific health factors among diverse populations, including US military veterans.

Trafton's scholarly contributions are reflected in a high-impact research designation, with an h-index of 45, 199 total publications, and over 9,700 citations. Key collaborators include Matthew Tyler Boden from the University of Arkansas at Fayetteville and researchers from the University of Arkansas for Medical Sciences, such as Sara J. Landes, Karen L. Drummond, and Rajinder Sonia Singh.

Metrics

  • h-index: 45
  • Publications: 199
  • Citations: 9,704

Selected Publications

  • Describing county-level clustering patterns of suicide among Veterans Health Administration patients and all American adults, 2018–2019, using exploratory spatial data analysis (2025) DOI
  • Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality (2025) DOI
  • Combining Machine Learning and Comparative Effectiveness Methodology to Study Primary Care Pharmacotherapy Pathways for Veterans With Depression (2025) DOI
  • A multilevel investigation of potential inequities in the volume of mental health care received by Black Veterans Health Administration patients. (2025) DOI
  • State-level suicide mortality insights: a comparative study of VHA veterans and the whole US population (2025) DOI
  • What do primary care clinicians and patients think about internet-based computerized cognitive behavioral therapy for depression? A qualitative study from the Veterans Health Administration. (2025) DOI
  • Decoding substance use disorder severity from clinical notes using a large language model (2025) DOI
  • VA EDH Data Curation Documentation FY25-Q1 (2024) DOI
  • Geographical Insights into Suicide Mortality Through Spatial Machine Learning (2024) DOI
  • Investigating the importance of social vulnerability in opioid-related mortality across the United States (2024) DOI
  • Describing County-Level Clustering Patterns of Suicide Among Veterans Health Administration Patients and All American Adults, 2018-2019 (2024) DOI
  • VA EDH Data Curation Documentation FY24-Q4 (2024) DOI
  • Providers’ use of pharmacogenetic testing to inform opioid prescribing among veterans (2024) DOI
  • Association of short-term exposure to ambient air pollution and weather conditions with deaths of despair among U.S. Veterans (2024) DOI
  • VA EDH Data Curation Documentation (FY24-Q3) (2024) DOI

Collaborators

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