# Characterizing Trauma Outcomes: From Pre-trauma Risk to Post-trauma Sequelae

> **NIH NIH R01** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2020 · $324,735

## Abstract

Abstract
Background: Trauma is common, but we have little ability to predict who will develop post-trauma psychopathology.
Consistent challenges to our understanding of the etiology of post-trauma psychopathology include: (1) obtaining
unbiased prospective data on risk factors preceding or concurrent with trauma; (2) the inability to model large
comprehensive risk structures with traditional null hypothesis testing methods, despite the knowledge that risk factors do
not operate in isolation; and (3) the almost universal focus on PTSD outcomes to date, while post-trauma
psychopathology likely involves various symptoms spanning multiple disorder categories. The aims of this study are to
(1) use data from a large, prospective population trauma cohort to establish multidimensional classes of post-trauma
psychopathology which include diagnoses from various theoretically derived categories (e.g., stress diagnoses, mood
disorders, personality disorders) and (2) to discover multivariate predictor sets and novel interactions which predict post-
trauma psychopathology class membership and class transitions over time. Given the projected sample size we will also
be able to examine gender differences in psychopathology and resilience, as well as differences by trauma type.
Study Design: This study will make use of national prospective data previously assembled as part of an R21 project (and
augmented with additional trauma data and more years of follow-up) to establish a trauma cohort from 1995 – 2015.
Trauma cohort members will have experienced at least one of 10 traumatic events (i.e., fires/explosions, accidents and
assaults, poisoning, life-threatening illness/injury, pregnancy-related trauma and sudden family deaths). Extensive pre-
trauma and post-trauma data on psychiatric diagnoses, treatment (medication and psychotherapy) and social variables will
be included. We will use latent class analyses to characterize multidimensional post-trauma psychopathology outcomes
(including the absence of psychopathology) and latent transition analyses to examine changes in class membership over
time. Machine learning statistical methods will be applied to the expansive risk factor data to develop multivariate
predictor sets for outcome classes and class transitions over time. Bias analyses will be used to assess the impact of
various forms of systematic error on our results.
Implications: This study fulfills NIMH’s strategic priorities of (1) charting mental illness trajectories to determine when,
where, and how to intervene and (2) strengthening the public health impact of NIMH-supported research. Our approach
will achieve robust and valid risk profiles of post-trauma psychopathology in the most efficient way possible by using pre-
existing prospective data from a full and unselected population. A life course multidimensional approach to trauma
research is a critical next step in this field. In future work, psychopathology classes and multivariate predictor sets
di...

## Key facts

- **NIH application ID:** 9972960
- **Project number:** 5R01MH110453-04
- **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS
- **Principal Investigator:** Jaimie L. Gradus
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $324,735
- **Award type:** 5
- **Project period:** 2017-08-07 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9972960

## Citation

> US National Institutes of Health, RePORTER application 9972960, Characterizing Trauma Outcomes: From Pre-trauma Risk to Post-trauma Sequelae (5R01MH110453-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9972960. Licensed CC0.

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