# Predictor Profiles of Opioid Use Disorders and Overdose Among Post-9/11 Veterans

> **NIH VA IK2** · VA BOSTON HEALTH CARE SYSTEM · 2022 · —

## Abstract

The overall aim of this proposed study is to use machine learning prediction models to evaluate the
multifaceted, additive and multiplicative interactions of known and novel risk factors for opioid use disorder
(OUD) and overdose in Post-9/11 Veterans. The proposed study will also investigate the short- and long-term
impact of the coronavirus disease 2019 (COVID-19) pandemic on the risk of OUD and overdose.
 TRAINING PLAN: The CDA-2 training plan will facilitate the applicant’s primary career goal of becoming a
fully funded, independent epidemiologic researcher at the Department of Veterans Affairs (VA), with a focus on
addiction and suicidal behavior. The CDA-2 will provide additional training necessary to lead an independent
program of research investigating the multifaceted sociodemographic, physical, psychological, and behavioral
factors mediating and moderating the risk of addiction and suicidal behavior. The first step of achieving this
goal is to complete the following training aims: 1) gaining expertise in the biological and behavioral basis of
addiction; 2) gaining expertise in the assessment of the problems of TBI and blast exposure, psychiatric
disorders, and suicidal behavior, which is pervasive in this generation of Veterans; 3) gaining expertise in
advanced analytic techniques employed in health data science, including machine learning algorithms; and 4)
professional development to achieve career independence as a VA funded epidemiologic researcher.
 RESEARCH DESIGN & METHODS: The proposed study will use Veterans Health Administration (VHA)
electronic medical records to develop models predicting OUD and overdose risk. The sample will include Post-
9/11 Veterans who are aged 18-65, receive care in the VHA, and will have completed the VA primary TBI
screen between October 2007 and February 2020 (n~1,267,000). We will assess the risk of incident and
recurrent OUD and overdose events, as separate outcomes, using machine learning algorithmic models. We
will examine whether overdose was 1) fatal and non-fatal and 2) intentional and unintentional. For Aims 1 and
2, we will examine the risk of OUD and overdose events between October 1, 2007 and February 29, 2020. For
Exploratory Aim 3, we will examine the risk of OUD and overdose events between March 1, 2020 and
September 30, 2025. We will use several machine learning classification-tree modeling approaches, including
classification and regression trees, random forest, and gradient boosting, to develop predictor profiles of OUD
and overdose incorporating important risk factors and interactions. The validity (sensitivity and specificity) and
prediction accuracy (area under the curve) will be assessed for all prediction profile models. OBJECTIVES:
Aim 1: Develop and evaluate the performance of predictor profiles incorporating known and novel risk factors
and interactions for OUD and overdose over proximal (30, 60, and 90 days) and distal (180, 365, 730, 1095
and >1460 days) prediction interva...

## Key facts

- **NIH application ID:** 10363000
- **Project number:** 1IK2CX002192-01A2
- **Recipient organization:** VA BOSTON HEALTH CARE SYSTEM
- **Principal Investigator:** Jennifer R Fonda
- **Activity code:** IK2 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2022-04-01 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10363000

## Citation

> US National Institutes of Health, RePORTER application 10363000, Predictor Profiles of Opioid Use Disorders and Overdose Among Post-9/11 Veterans (1IK2CX002192-01A2). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10363000. Licensed CC0.

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