A prediction model of military combat and training exposures on VA service-connected disability

a CENC study

B. Eggleston, C. E. Dismuke-Greer, T. K. Pogoda, J. H. Denning, B. C. Eapen, Kathleen Carlson, S. Bhatnagar, R. Nakase-Richardson, M. Troyanskaya, T. Nolen, W. C. Walker

Research output: Contribution to journalArticle

Abstract

Background: Research has shown that number of and blast-related Traumatic Brain Injuries (TBI) are associated with higher levels of service-connected disability (SCD) among US veterans. This study builds and tests a prediction model of SCD based on combat and training exposures experienced during active military service. Methods: Based on 492 US service member and veteran data collected at four Department of Veterans Affairs (VA) sites, traditional and Machine Learning algorithms were used to identify a best set of predictors and model type for predicting %SCD ≥50, the cut-point that allows for veteran access to 0% co-pay for VA health-care services. Results: The final model of predicting %SCD ≥50 in veterans revealed that the best blast/injury exposure-related predictors while deployed or non-deployed were: 1) number of controlled detonations experienced, 2) total number of blast exposures (including controlled and uncontrolled), and 3) the total number of uncontrolled blast and impact exposures. Conclusions and Relevance: We found that the highest blast/injury exposure predictor of %SCD ≥50 was number of controlled detonations, followed by total blasts, controlled or uncontrolled, and occurring in deployment or non-deployment settings. Further research confirming repetitive controlled blast exposure as a mechanism of chronic brain insult should be considered.

Original languageEnglish (US)
JournalBrain Injury
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Veterans
Blast Injuries
Veterans Health
Research
Health Services
Delivery of Health Care
Brain

Keywords

  • concussion and traumatic brain injury
  • disability
  • military
  • potential concussive event
  • Prediction
  • veteran

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Developmental and Educational Psychology
  • Clinical Neurology

Cite this

Eggleston, B., Dismuke-Greer, C. E., Pogoda, T. K., Denning, J. H., Eapen, B. C., Carlson, K., ... Walker, W. C. (Accepted/In press). A prediction model of military combat and training exposures on VA service-connected disability: a CENC study. Brain Injury. https://doi.org/10.1080/02699052.2019.1655793

A prediction model of military combat and training exposures on VA service-connected disability : a CENC study. / Eggleston, B.; Dismuke-Greer, C. E.; Pogoda, T. K.; Denning, J. H.; Eapen, B. C.; Carlson, Kathleen; Bhatnagar, S.; Nakase-Richardson, R.; Troyanskaya, M.; Nolen, T.; Walker, W. C.

In: Brain Injury, 01.01.2019.

Research output: Contribution to journalArticle

Eggleston, B, Dismuke-Greer, CE, Pogoda, TK, Denning, JH, Eapen, BC, Carlson, K, Bhatnagar, S, Nakase-Richardson, R, Troyanskaya, M, Nolen, T & Walker, WC 2019, 'A prediction model of military combat and training exposures on VA service-connected disability: a CENC study', Brain Injury. https://doi.org/10.1080/02699052.2019.1655793
Eggleston, B. ; Dismuke-Greer, C. E. ; Pogoda, T. K. ; Denning, J. H. ; Eapen, B. C. ; Carlson, Kathleen ; Bhatnagar, S. ; Nakase-Richardson, R. ; Troyanskaya, M. ; Nolen, T. ; Walker, W. C. / A prediction model of military combat and training exposures on VA service-connected disability : a CENC study. In: Brain Injury. 2019.
@article{e59bf84ca5284c54a094f5ca611a6059,
title = "A prediction model of military combat and training exposures on VA service-connected disability: a CENC study",
abstract = "Background: Research has shown that number of and blast-related Traumatic Brain Injuries (TBI) are associated with higher levels of service-connected disability (SCD) among US veterans. This study builds and tests a prediction model of SCD based on combat and training exposures experienced during active military service. Methods: Based on 492 US service member and veteran data collected at four Department of Veterans Affairs (VA) sites, traditional and Machine Learning algorithms were used to identify a best set of predictors and model type for predicting {\%}SCD ≥50, the cut-point that allows for veteran access to 0{\%} co-pay for VA health-care services. Results: The final model of predicting {\%}SCD ≥50 in veterans revealed that the best blast/injury exposure-related predictors while deployed or non-deployed were: 1) number of controlled detonations experienced, 2) total number of blast exposures (including controlled and uncontrolled), and 3) the total number of uncontrolled blast and impact exposures. Conclusions and Relevance: We found that the highest blast/injury exposure predictor of {\%}SCD ≥50 was number of controlled detonations, followed by total blasts, controlled or uncontrolled, and occurring in deployment or non-deployment settings. Further research confirming repetitive controlled blast exposure as a mechanism of chronic brain insult should be considered.",
keywords = "concussion and traumatic brain injury, disability, military, potential concussive event, Prediction, veteran",
author = "B. Eggleston and Dismuke-Greer, {C. E.} and Pogoda, {T. K.} and Denning, {J. H.} and Eapen, {B. C.} and Kathleen Carlson and S. Bhatnagar and R. Nakase-Richardson and M. Troyanskaya and T. Nolen and Walker, {W. C.}",
year = "2019",
month = "1",
day = "1",
doi = "10.1080/02699052.2019.1655793",
language = "English (US)",
journal = "Brain Injury",
issn = "0269-9052",
publisher = "Informa Healthcare",

}

TY - JOUR

T1 - A prediction model of military combat and training exposures on VA service-connected disability

T2 - a CENC study

AU - Eggleston, B.

AU - Dismuke-Greer, C. E.

AU - Pogoda, T. K.

AU - Denning, J. H.

AU - Eapen, B. C.

AU - Carlson, Kathleen

AU - Bhatnagar, S.

AU - Nakase-Richardson, R.

AU - Troyanskaya, M.

AU - Nolen, T.

AU - Walker, W. C.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Background: Research has shown that number of and blast-related Traumatic Brain Injuries (TBI) are associated with higher levels of service-connected disability (SCD) among US veterans. This study builds and tests a prediction model of SCD based on combat and training exposures experienced during active military service. Methods: Based on 492 US service member and veteran data collected at four Department of Veterans Affairs (VA) sites, traditional and Machine Learning algorithms were used to identify a best set of predictors and model type for predicting %SCD ≥50, the cut-point that allows for veteran access to 0% co-pay for VA health-care services. Results: The final model of predicting %SCD ≥50 in veterans revealed that the best blast/injury exposure-related predictors while deployed or non-deployed were: 1) number of controlled detonations experienced, 2) total number of blast exposures (including controlled and uncontrolled), and 3) the total number of uncontrolled blast and impact exposures. Conclusions and Relevance: We found that the highest blast/injury exposure predictor of %SCD ≥50 was number of controlled detonations, followed by total blasts, controlled or uncontrolled, and occurring in deployment or non-deployment settings. Further research confirming repetitive controlled blast exposure as a mechanism of chronic brain insult should be considered.

AB - Background: Research has shown that number of and blast-related Traumatic Brain Injuries (TBI) are associated with higher levels of service-connected disability (SCD) among US veterans. This study builds and tests a prediction model of SCD based on combat and training exposures experienced during active military service. Methods: Based on 492 US service member and veteran data collected at four Department of Veterans Affairs (VA) sites, traditional and Machine Learning algorithms were used to identify a best set of predictors and model type for predicting %SCD ≥50, the cut-point that allows for veteran access to 0% co-pay for VA health-care services. Results: The final model of predicting %SCD ≥50 in veterans revealed that the best blast/injury exposure-related predictors while deployed or non-deployed were: 1) number of controlled detonations experienced, 2) total number of blast exposures (including controlled and uncontrolled), and 3) the total number of uncontrolled blast and impact exposures. Conclusions and Relevance: We found that the highest blast/injury exposure predictor of %SCD ≥50 was number of controlled detonations, followed by total blasts, controlled or uncontrolled, and occurring in deployment or non-deployment settings. Further research confirming repetitive controlled blast exposure as a mechanism of chronic brain insult should be considered.

KW - concussion and traumatic brain injury

KW - disability

KW - military

KW - potential concussive event

KW - Prediction

KW - veteran

UR - http://www.scopus.com/inward/record.url?scp=85071729201&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85071729201&partnerID=8YFLogxK

U2 - 10.1080/02699052.2019.1655793

DO - 10.1080/02699052.2019.1655793

M3 - Article

JO - Brain Injury

JF - Brain Injury

SN - 0269-9052

ER -