Match tier
Confirmed
Presence
Current · Arkansas
Last published
2026
Sources
OpenAlex · ORCID
Refreshed
2026-08-06
J
Jodie Trafton
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
High Impact
Researcher
Faculty Researcher
46 h-index
199 pubs
9,797 cited
Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Jodie Trafton's research focuses on understanding and addressing critical health issues, particularly concerning veterans and opioid use disorder. Trafton has investigated the cost-effectiveness of various treatments for opioid use disorder and evaluated programs designed to support veterans, such as the Recovery Engagement and Coordination for Health–Veterans Enhanced Treatment (REACH VET) suicide risk modeling program. This work has involved the use of predictive modeling to identify individuals at risk and to inform clinical interventions.
Further research has delved into the genetic underpinnings of suicidal thoughts and behaviors. Trafton has been involved in large-scale genome-wide association studies, including meta-analyses, within the Million Veterans Program. These studies aim to identify genetic loci associated with suicide attempts and suicidal ideation, seeking to understand the complex interplay of genetic risks and specific health factors. The research has led to the identification of novel, replicable genetic risk loci, contributing to a deeper understanding of the biological factors influencing these serious health outcomes.
Trafton's scholarship metrics include an h-index of 46, with 199 total publications and over 9,797 citations, designating them as a highly cited researcher. Key collaborators include Matthew Tyler Boden and Sara J. Landes, with whom Trafton has co-authored multiple publications.
Metrics
- h-index: 46
- Publications: 199
- Citations: 9,797
Selected Publications
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Association of short-term ambient environmental exposures with suicide and drug overdose deaths among U.S. Veterans (2026)
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A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models (2026)arXiv (Cornell University) OpenAlex
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A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models (2026)
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Leveraging large language models to automate the identification of healthcare access barriers for veterans (2025)
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A modeling study of the opioid epidemic for vulnerable communities in Knoxville, Tennessee (2025)
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Is the Veterans Affairs Suicide Prevention in Emergency Department Intervention associated with improved mental health attendance and all-cause mortality outcomes? (2025)
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Defining suicidality phenotypes for genetic studies: perspectives of the Psychiatric Genomics Consortium Suicide Working Group (2025)
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VA Determinants of Health Data Curation Documentation FY25-Q3 (2025)
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Describing county-level clustering patterns of suicide among Veterans Health Administration patients and all American adults, 2018–2019, using exploratory spatial data analysis (2025)
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VA Determinants of Health Data Curation Documentation FY25-Q2 (2025)
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VA EDH Advanced Software Pipeline Framework Report: Enhancing Automation and Scalability (2025)
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Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality (2025)
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Combining Machine Learning and Comparative Effectiveness Methodology to Study Primary Care Pharmacotherapy Pathways for Veterans With Depression (2025)
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A multilevel investigation of potential inequities in the volume of mental health care received by Black Veterans Health Administration patients. (2025)
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State-level suicide mortality insights: a comparative study of VHA veterans and the whole US population (2025)
Collaboration Network
Top Collaborators
Anuj Kapadia
Oak Ridge National Laboratory (US)
13 shared publications
- Dataset Repository for Investigating Suicide Risk Using Social and Environmental Determinants of Health
- Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality
- Geographical Insights into Suicide Mortality Through Spatial Machine Learning
- State-level suicide mortality insights: a comparative study of VHA veterans and the whole US population
- VA EDH Data Curation Documentation (FY24-Q2)
Showing 5 of 13 shared publications
Susana B. Martins
Oak Ridge National Laboratory (US)
9 shared publications
- High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning
- Question-answering system extracts information on injection drug use from clinical notes
- High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning
- VA EDH Data Curation Documentation (FY24-Q2)
- Executable Library Architecture (XLA): A Knowledge Management Tool for Clinical Concept Computation in Healthcare Operations (Preprint)
Showing 5 of 9 shared publications
Alina Peluso
Oak Ridge National Laboratory (US)
9 shared publications
- Geographical Insights into Suicide Mortality Through Spatial Machine Learning
- State-level suicide mortality insights: a comparative study of VHA veterans and the whole US population
- VA EDH Data Curation Documentation (FY24-Q2)
- Describing county-level clustering patterns of suicide among Veterans Health Administration patients and all American adults, 2018–2019, using exploratory spatial data analysis
- VA EDH Data Curation Documentation (FY24-Q3)
Showing 5 of 9 shared publications
Heidi A. Hanson
Oak Ridge National Laboratory (US)
6 shared publications
- Dataset Repository for Investigating Suicide Risk Using Social and Environmental Determinants of Health
- Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality
- VA EDH Data Curation Documentation (FY24-Q2)
- VA EDH Data Curation Documentation (FY24-Q3)
- VA EDH Data Curation Documentation FY24-Q4
Showing 5 of 6 shared publications
Matthew Tyler Boden
United States Department of Veterans Affairs
5 shared publications
In database
- Mental health treatment and the role of tele-mental health at the veterans health administration during the COVID-19 pandemic.
- Investigation of population-based mental health staffing and efficiency-based mental health productivity using an information-theoretic approach
- Outpatient provider staffing ratios: Binary recursive models associated with quality, access, and satisfaction.
- Veterans Health Administration Outpatient Psychiatry Staffing Model: Longitudinal Analysis on Mental Health Performance
- A multilevel investigation of potential inequities in the volume of mental health care received by Black Veterans Health Administration patients.
Elizabeth M. Oliva
Veterans Health Administration (US)
5 shared publications
- Cost-effectiveness of Treatments for Opioid Use Disorder
- Effect of a Predictive Analytics-Targeted Program in Patients on Opioids: a Stepped-Wedge Cluster Randomized Controlled Trial
- Randomized Policy Evaluation of the Veterans Health Administration Stratification Tool for Opioid Risk Mitigation (STORM)
- Impact of Mandated Case Review Policy on Opioid Discontinuation and Mortality Among High-Risk Long-Term Opioid Therapy Patients: The STORM Stepped-Wedge Cluster Randomized Controlled Trial
- Healthcare costs and use before and after opioid overdose in Veterans Health Administration patients with opioid use disorder
Allison E. Ashley‐Koch
Virginia Commonwealth University (US)
5 shared publications
- GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors
- A genome-wide association study of suicide attempts in the million veterans program identifies evidence of pan-ancestry and ancestry-specific risk loci
- Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans
- Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans
- Genome-wide association study meta-analysis of suicide attempt identifies twelve genome-wide significant loci and implicates genetic risks for specific health factors
David W. Oslin
Philadelphia VA Medical Center (US)
5 shared publications
- A genome-wide association study of suicide attempts in the million veterans program identifies evidence of pan-ancestry and ancestry-specific risk loci
- Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans
- High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning
- High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning
- Providers’ use of pharmacogenetic testing to inform opioid prescribing among veterans
Steven D. Pizer
5 shared publications
- Effect of a Predictive Analytics-Targeted Program in Patients on Opioids: a Stepped-Wedge Cluster Randomized Controlled Trial
- Randomized Policy Evaluation of the Veterans Health Administration Stratification Tool for Opioid Risk Mitigation (STORM)
- A Framework for Inferring and Analyzing Pharmacotherapy Treatment Patterns
- A framework for inferring and analyzing pharmacotherapy treatment patterns
- Combining Machine Learning and Comparative Effectiveness Methodology to Study Primary Care Pharmacotherapy Pathways for Veterans With Depression
Lisa A. Brenner
5 shared publications
- The Veterans Health Administration REACH VET Program: Suicide Predictive Modeling in Practice
- A Framework for Inferring and Analyzing Pharmacotherapy Treatment Patterns
- Impact of Implementation Facilitation on the REACH VET Clinical Program for Veterans at Risk for Suicide
- A framework for inferring and analyzing pharmacotherapy treatment patterns
- Combining Machine Learning and Comparative Effectiveness Methodology to Study Primary Care Pharmacotherapy Pathways for Veterans With Depression
Suzanne Tamang
Stanford University (US)
5 shared publications
- High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning
- Question-answering system extracts information on injection drug use from clinical notes
- Deploying a national clinical text processing infrastructure
- High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning
- Executable Library Architecture (XLA): A Knowledge Management Tool for Clinical Concept Computation in Healthcare Operations (Preprint)
Hilda Klasky
5 shared publications
- Dataset Repository for Investigating Suicide Risk Using Social and Environmental Determinants of Health
- VA EDH Data Curation Documentation (FY24-Q2)
- VA EDH Data Curation Documentation (FY24-Q3)
- VA EDH Data Curation Documentation FY24-Q4
- VA EDH Data Curation Documentation FY25-Q1
K. Sparks
Oak Ridge National Laboratory (US)
5 shared publications
- Dataset Repository for Investigating Suicide Risk Using Social and Environmental Determinants of Health
- VA EDH Data Curation Documentation (FY24-Q2)
- VA EDH Data Curation Documentation (FY24-Q3)
- VA EDH Data Curation Documentation FY24-Q4
- VA EDH Data Curation Documentation FY25-Q1
Jeremy Logan
Oak Ridge National Laboratory (US)
5 shared publications
- VA EDH Data Curation Documentation (FY24-Q2)
- VA EDH Data Curation Documentation (FY24-Q3)
- Association of short-term exposure to ambient air pollution and weather conditions with deaths of despair among U.S. Veterans
- VA EDH Data Curation Documentation FY24-Q4
- VA EDH Data Curation Documentation FY25-Q1
Steven M. Asch
Stanford University (US)
4 shared publications
- Cost-effectiveness of Treatments for Opioid Use Disorder
- Patient Race or Ethnicity, Health Care System Characteristics, and Community Factors Associated with Quality of Antidepressant Medication Management (<scp>AMM</scp>)
- Surveying the Landscape of Quality-of-Care Measures for Mental and Substance Use Disorders
- Healthcare costs and use before and after opioid overdose in Veterans Health Administration patients with opioid use disorder
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