Psychosocial and Functional Predictors of Depression and Anxiety Symptoms in Veterans and Service Members With TBI: A VA TBI Model Systems Study

Predictors of Psychiatric Hospitalization After Discharge From Inpatient Neurorehabilitation for Traumatic Brain Injury

OBJECTIVE

To examine, among persons discharged from inpatient rehabilitation for traumatic brain injury (TBI), the degree to which pre-TBI factors were associated with post-TBI hospitalization for psychiatric reasons. The authors hypothesized that pre-TBI psychiatric hospitalization and other pre-TBI mental health treatment would predict post-TBI psychiatric hospitalization following rehabilitation discharge, up to 5 years post-TBI.

SETTING

Five Veterans Affairs Polytrauma Rehabilitation Centers.

PARTICIPANTS

Participants with nonmissing rehospitalization status and reason, who were followed at 1 year (N = 1006), 2 years (N = 985), and 5 years (N = 772) post-TBI.

DESIGN

A secondary analysis of the Veterans Affairs TBI Model Systems, a multicenter, longitudinal study of veterans and active-duty service members with a history of mild, moderate, or severe TBI previously admitted to comprehensive inpatient medical rehabilitation. This study examined participants cross-sectionally at 3 follow-up timepoints.

MAIN MEASURES

Psychiatric Rehospitalization was classified according to Healthcare Cost and Utilization Project multilevel Clinical Classifications diagnosis terminology (Category 5).

RESULTS

Rates of post-TBI psychiatric hospitalization at years 1, 2, and 5 were 4.3%, 4.7%, and 4.1%, respectively. While bivariate comparisons identified pre-TBI psychiatric hospitalization and pre-TBI mental health treatment as factors associated with psychiatric rehospitalization after TBI across all postinjury timepoints, these factors were statistically nonsignificant when examined in a multivariate model across all timepoints. In the multivariable analysis, pre-TBI psychiatric hospitalization was significantly associated with increased odds of post-TBI psychiatric hospitalization only at 1-year post-TBI (adjusted odds ratio = 2.65; 95% confidence interval, 1.07-6.55, P = .04). Posttraumatic amnesia duration was unrelated to psychiatric rehospitalization.

CONCLUSIONS

Study findings suggest the limited utility of age, education, and pre-TBI substance use and mental health utilization in predicting post-TBI psychiatric hospitalization. Temporally closer social and behavior factors, particularly those that are potentially modifiable, should be considered in future research.

Branching out: Feasibility of examining the effects of greenspace on mental health after traumatic brain injury

Aim

This pilot study's aim was to determine the feasibility of examining the effects of an environmental variable (i.e., tree canopy coverage) on mental health after sustaining a brain injury.

Methods

A secondary data analysis was conducted leveraging existing information on mental health after moderate to severe traumatic brain injury (TBI) from the TBI Model System. Mental health was measured using PHQ-9 (depression) and GAD-7 (anxiety) scores. The data were compared with data on tree canopy coverage in the state of Texas that was obtained from the Multi-Resolution Land Characteristics (MRLC) Consortium using GIS analysis. Tree canopy coverage as an indicator of neighborhood socioeconomic status was also examined using the Neighborhood SES Index.

Results

Tree canopy coverage had weak and non-significant correlations with anxiety and depression scores, as well as neighborhood socioeconomic status. Data analysis was limited by small sample size. However, there is a higher percentage (18.8%) of participants who reported moderate to severe depression symptoms in areas with less than 30% tree canopy coverage, compared with 6.6% of participants who endorsed moderate to severe depression symptoms and live in areas with more than 30% tree canopy coverage (there was no difference in anxiety scores).

Conclusion

Our work confirms the feasibility of measuring the effects of tree canopy coverage on mental health after brain injury and warrants further investigation into examining tree canopy coverage and depression after TBI. Future work will include nationwide analyses to potentially detect significant relationships, as well as examine differences in geographic location.

Identifying depression in the United States veterans using deep learning algorithms, NHANES 2005-2018

BACKGROUND

Depression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of risk factors for depression are still severely limited. This study used a deep learning algorithm to identify depression in veterans and its factors associated with clinical manifestations.

METHODS

Our data originated from the National Health and Nutrition Examination Survey (2005-2018). A dataset of 2,546 veterans was identified using deep learning and five traditional machine learning algorithms with 10-fold cross-validation. Model performance was assessed by examining the area under the subject operating characteristic curve (AUC), accuracy, recall, specificity, precision, and F1 score.

RESULTS

Deep learning had the highest AUC (0.891, 95%CI 0.869-0.914) and specificity (0.906) in identifying depression in veterans. Further study on depression among veterans of different ages showed that the AUC values for deep learning were 0.929 (95%CI 0.904-0.955) in the middle-aged group and 0.924(95%CI 0.900-0.948) in the older age group. In addition to general health conditions, sleep difficulties, memory impairment, work incapacity, income, BMI, and chronic diseases, factors such as vitamins E and C, and palmitic acid were also identified as important influencing factors.

CONCLUSIONS

Compared with traditional machine learning methods, deep learning algorithms achieved optimal performance, making it conducive for identifying depression and its risk factors among veterans.

Exploring racial/ethnic disparities in rehabilitation outcomes after TBI: A veterans affairs model systems study

BACKGROUND

Almost one-third of the U.S. military population is comprised of service members and veterans (SMVs) of color. Research suggests poorer functional and psychosocial outcomes among Black and Hispanic/Latine vs. White civilians following traumatic brain injury (TBI).

OBJECTIVE

This study examined racial/ethnic differences in 5-year functional independence and life satisfaction trajectories among SMVs who had undergone acute rehabilitation at one of five VA TBI Model Systems (TBIMS) Polytrauma Rehabilitation Centers (PRCs).

METHODS

Differences in demographic and injury-related factors were assessed during acute rehabilitation among White (n = 663), Black (n = 89) and Hispanic/Latine (n = 124) groups. Functional Independence Measure (FIM) Motor, FIM Cognitive, and Satisfaction with Life Scale (SWLS) scores were collected at 1, 2, and 5 years after injury. Racial/ethnic comparisons in these outcome trajectories were made using hierarchical linear modeling.

RESULTS

Black SMVs were less likely than White and Hispanic/Latine SMVs to have been deployed to a combat zone; there were no other racial/ethnic differences in any demographic or injury-related variable assessed. In terms of outcomes, no racial/ethnic differences emerged in FIM Motor, FIM cognitive, or SWLS trajectories.

CONCLUSION

The absence of observable racial/ethnic differences in 5-year outcome trajectories after TBI among SMVs from VA TBIMS PRCs contrasts sharply with previous research identifying disparities in these same outcomes and throughout the larger VA health care system. Individuals enrolled in VA PRCs are likely homogenized on key social determinants of health that would otherwise contribute to racial/ethnic disparities in outcome trajectories.

Traumatic brain injury-induced submissive behavior in rats: link to depression and anxiety

Traumatic brain injury (TBI) affects millions of people worldwide, many of whom are affected with post-TBI mood disorders or behavioral changes, including aggression or social withdrawal. Diminished functionality can persist for decades after TBI and delay rehabilitation and resumption of employment. It has been established that there is a relationship between these mental disorders and brain injury. However, the etiology and causal relationships behind these conditions are poorly understood. Rodent models provide a helpful tool for researching mood disorders and social impairment due to their natural tendencies to form social hierarchies. Here, we present a rat model of mental complications after TBI using a suite of behavioral tests to examine the causal relationships between changes in social behavior, including aggressive, hierarchical, depressive, and anxious behavior. For this purpose, we used multivariate analysis to identify causal relationships between the above post-TBI psychiatric sequelae. We performed statistical analysis using principal component analysis, discriminant analysis, and correlation analysis, and built a model to predict dominant-submissive behavior based on the behavioral tests. This model displayed a predictive accuracy of 93.3% for determining dominant-submissive behavior in experimental groups. Machine learning algorithms determined that in rats, aggression is not a principal prognostic factor for dominant-submissive behavior. Alternatively, dominant-submissive behavior is determined solely by the rats' depressive-anxious state and exploratory activity. We expect the causal approach used in this study will guide future studies into mood conditions and behavioral changes following TBI.

Two-Step Resilience-Oriented Intervention for Veterans with Traumatic Brain Injury: A Pilot Randomized Controlled Trial

OBJECTIVE

The present randomized parallel two-arm pilot study aimed to compare the efficacy of two-step resilience-oriented intervention with treatment as usual in veterans with mild to moderate traumatic brain injury.

METHOD

Two-step Resilience-Oriented Intervention (TROI) is a brief psychological intervention that targets cognitive (step 1) and emotional (step 2) factors of resilience and consists of six 1-hour sessions. Overall, 70 Ukrainian veterans serviced in Anti-Terrorist Operation / Joint Forces Operation were randomly assigned to an intervention group (TROI group) or a control group that underwent treatment as usual (TAU group). For pre- (T1) and post-treatment (T2) assessment the Connor-Davidson Resilience Scale (CD-RISC), Hospital Anxiety and Depression Scale (HADS), Montreal Cognitive Assessment Scale (MoCA), Neurobehavioral Symptom Inventory (NSI), Posttraumatic Stress Disorder Checklist 5 (PCL-5), Chaban Quality of Life Scale (CQLS), Positive and Negative Affect Scale (PANAS) were used.

RESULTS

Multivariable linear regression with the treatment group, gender, baseline cognitive performance level and TBI severity as the independent variables revealed statistically significant improvements in the TROI group in resilience (CD-RISC), cognitive performance (MoCA), postconcussive symptoms (NSI), posttraumatic symptoms (PCL-5), positive affect (PANAS) and quality of life (CQLS) comparing to such in TAU group. We found no statistically significant differences between groups in depression, anxiety (HADS) and negative affect (PANAS) outcomes. Additionally, Wilcoxon signed-rank test revealed that participants who completed two-step resilience-oriented intervention had significantly improved scores for all outcomes compared to the baseline (p < 0.05).

CONCLUSIONS

In summary, we can tentatively conclude that adding TROI to the standard treatment measures may improve the resilience and sustainable symptoms in veterans with TBI when compared with standard treatment. Targeting cognitive and emotional factors like problem-solving, decision-making, positive thinking can promote resilience in veterans with TBI and be useful in facilitating recovery from injury. Results of this pilot study are promising, but the intervention needs to be studied in a larger trial.