Preinjury Resilience and Mood as Predictors of Early Outcome following Mild Traumatic Brain Injury

Abstract

There is significant heterogeneity in outcomes following mild traumatic brain injury (mTBI). While several host factors (age, gender, and preinjury psychiatric history) have been investigated, the influence of preinjury psychological resilience and mood status in conjunction with mild TBI remains relatively unexplored. Euthymic mood and high resilience are potentially protective against anxiety and postconcussion symptoms, but their relative contributions are currently unknown. This prospective study obtained preinjury estimates of resilience and mood measures in addition to measures of anxiety (Acute Stress Disorder Scale and PTSD-Checklist-Civilian form) and postconcussion symptom severity (Rivermead Post Concussion Symptoms Questionnaire) <24 hours (Baseline), 1 week, and 1 month postinjury in patients with either mTBI (n=46) or a comparison group with orthopedic injuries not involving the head (OI, n=29). The groups did not differ on preinjury resilience or mood status at baseline, but differed significantly on measures of anxiety and postconcussion symptom severity at each subsequent study occasion. Multivariate linear regression analyses were conducted to determine if preinjury resilience and mood were significant contributors to anxiety and postconcussion symptoms during the first month postinjury after accounting for other known host factors (e.g., age at injury, gender, and education). Injury group and preinjury mood status were significant predictors for all three dependent variables at each study occasion (all p<0.007). Preinjury resilience showed a positive trend only for acute stress severity at baseline, but demonstrated significant prediction of all three dependent measures at one week and one month postinjury. These results suggest that preinjury depressed mood and resilience are significant contributors to the severity of postinjury anxiety and postconcussion symptoms, even after accounting for effects of other specific host factors.

Introduction

T raumatic brain injury (TBI) is a significant public health concern, as it is one of the most common neurologic disorders in the United States¹ with an estimated incidence of 1.5 million annually. At least 75% of these TBIs are classified as mild TBI (mTBI).² Economic costs of mTBI have been estimated at $16.5 billion,³ but the direct costs of lost productivity and the indirect costs of burden placed on family and friends and decreased quality of life for the patient have not been clearly established.

Although recovery following mTBI generally is expected to be quite rapid and robust in the majority of these patients,⁴ a small minority (5%–20%) has been shown to have persistent postconcussive problems from months to years postinjury,^5

–11 while the rate can be much higher depending on the specific criteria set employed.^8,9 Additionally, many patients who meet criteria for postconcussion syndrome (PCS)^12,13 or postconcussional disorder (PCD)¹⁴ often have co-morbid psychiatric disorders including, but not limited to, major depression and post-traumatic stress disorder (PTSD),^{8,9,15

–20} in addition to other anxiety disorders.²¹ Postconcussion symptoms are also more severe in patients with mTBI and PTSD than those with mTBI alone,²² which highlights the interplay of both neurogenic and psychogenic factors involved following injury. While it was once thought that physiogenic factors were the immediate precipitant of PCS symptoms and psychogenic factors perpetuate their continued experience,²³ more recent data has suggested that both factors may be involved from the very beginning.²⁴

The incidence of PCS/PCD after civilian TBI ranges from 14% to over 50% depending on the postinjury interval when assessed, and diagnostic criteria used.^8,9,25 Predictors of developing persistent PCS/PCD include above average intelligence,²⁵ female gender,^{8,18,25
–27} poor social support,^18,28 psychosocial adversity,²⁹ postinjury mood,^30,31 preinjury affective or anxiety disorders,²⁵ personality factors,²⁶ and degree of emotional distress following the injury.^{18,27,30,32

–40}

Rates of developing PTSD after civilian TBI have been estimated from 13%¹⁷ to upwards of 40%,^41

–44 which is substantial even before considering the number of mTBIs occurring annually in the United States. The diagnosis of acute stress disorder (ASD) following mTBI is a potent predictor of subsequent development of PTSD⁴⁵ with nearly 80% of those with mTBI diagnosed with ASD within a month of injury going on to develop PTSD within 2 years. Studies investigating risk factors for PTSD following mTBI have been comparatively rare and, until recently with the return of veterans from the wars in Iraq and Afghanistan, had primarily focused on whether the disorder could even be diagnosed in the presence of a TBI if there was amnesia for the traumatic event^{22,33,46

–51} or if PTSD was associated with mTBI at all.⁵²

While the debate in the civilian population continues, several studies have investigated predictors for PTSD in military personnel which have identified risk factors including younger age,^53,54 lower education achievement,⁵⁵ pre-deployment cognitive ability level,⁵⁶ unmarried status,^7,53 low unit morale and cohesion,^55,57 greater combat exposure severity,^54,55,58 sustaining combat-related bodily injury,^59,60 history of pre-deployment assaults,⁶¹ pre-deployment PTSD-like symptoms or negative affect,^57,62 pre-deployment psychiatric status,⁶⁰ and genetic vulnerabilities.^63

–66

In spite of the well-known predictors of co-morbid conditions associated with TBI briefly listed above, outcome prediction following mTBI has proven particularly difficult. Given the large numbers of patients with mTBI treated in emergency departments, the need for efficient screening for those most likely to require referrals for follow-up care is essential. A number of premorbid demographic factors have been investigated to aid outcome prediction following TBI. For example, in a meta-analysis by Brewin et al.,⁶⁷ higher risk of developing acute PTSD in the general population was associated (although inconsistently across studies) with gender, age, socioeconomic status, level of education, psychiatric history, indices of childhood adversity/abuse, social support, and general life stress. It should be noted that this study also found that peri- and post-trauma variables (e.g., trauma severity, lack of social support, life stressors) were often associated with somewhat larger effect sizes than preinjury variables (e.g., gender, age, race), although all of the risk factors demonstrated fairly modest effects. While measures of postinjury mood (depression specifically) have been shown to predict development of ASD following mTBI,⁶⁸ the effect of preinjury mood (as opposed to preinjury episodes of major depression, for instance) has not been well investigated.

Psychological resilience has been difficult to define precisely given its multi-componential nature, but it can be generally conceptualized as the ability to maintain sufficient psychological balance to maintain mental and physical functioning following exposure to aversive stress and/or trauma.⁶⁹ Elements of resilience have been identified as viewing stress as an opportunity for change/growth,⁷⁰ adaptability to change, strong feelings of self-efficacy, and secure attachments to and support from others,⁷¹ and tolerance of negative affect,⁷² While resilience is often thought of as a purely psychological construct, there is evidence from animal research of PTSD to suggest that there may be genetic underpinnings as well.⁷³

In the military setting, Adler and Dolan⁷⁴ found that the level resilience moderated the impact of deployment stress on post-deployment depression levels in service members involved in peacekeeping missions. Studies of veterans of Operations Enduring Freedom and Iraqi Freedom (OEF/OIF) have reported that low resilience was associated with poor unit cohesion and post-deployment social support resulting in depression and PTSD.⁷⁵ Similarly, Dickstein et al.⁷⁶ and Phillips et al.⁷⁷ found that “good unit cohesion” and the number of close friends⁷⁷ increased post-deployment resilience to PTSD and that greater resilience was protective of high combat exposure and was associated with lower depressive symptom severity, fewer health complaints, and lower substance use.⁷⁸ Post-traumatic growth or positive life change following traumatic experiences is another area of keen interest for the military, as evidence exists that resilience can foster good outcomes following combat-related traumatic events.^79,80

Social support, particularly from the family, appears to be a critical factor in protecting against post-deployment onset of PTSD.^81
–83 There is encouraging evidence^84
–86 that pre-deployment resilience training (e.g., the U.S. Army's Battlemind training and the British Marine's Trauma Risk Management training) can reduce post-deployment poor psychological outcomes. These results have led to the development and introduction of the Comprehensive Soldier Fitness (CSF),^87,88 a proactive and preventive approach to minimizing the incidence of negative psychological outcomes following combat, which emphasizes resilience training based on the Battlemind program⁸⁹ and the Master Resilience Training course.⁹⁰ The CSF program continues to undergo evaluation, and validation of the resilience components of the CSF is ongoing.⁹¹ Given this, training elements have been included in the CSF to facilitate posttraumatic growth.⁹²

Psychological resilience has not been well studied, specifically in conjunction with mTBI in civilian populations, and the contribution of preinjury mood status to psychiatric outcome following mTBI remains unknown. Although the study reported here involves a civilian sample, it has implications for military populations as well. The conceptual model used in this study (Fig. 1) illustrates the inclusion of additional host factors (resilience and mood status) to a set of well-known host factors (age, education, gender, injury group) and their relation to postinjury outcome in terms of anxiety and postconcussion symptoms. The design of our study allowed us to assess patients with mTBI within 24 h of injury which made administration of proxy measures of preinjury status feasible and as uncontaminated as possible. Given this unique opportunity, predictors with relevance to not only civilian but also military populations with mTBI were selected including dimensional measures of psychological resilience and mood. We hypothesized that preinjury resilience and mood status would account for a significant portion of the variance in anxiety and postconcussion symptom severity on the day of injury, and at 1 week and 1 month postinjury, even after accounting for other host factors known to influence outcome following mTBI.

FIG. 1.

Resilience and mood as predictors of emotional outcome following mild TBI.

Methods

Participants

A consecutive series of patients were prospectively recruited in the Houston area's two Level-I trauma centers. Inclusion criteria included patients aged 18–50 years who presented, were treated, and released from the Emergency Department (ED) less than 24 h after injury, with fluency in either English or Spanish. Specific inclusion criteria for patients with mTBI included a documented/verified head injury, Glasgow Coma Scale (GCS)⁹³ score of 13–15, loss of consciousness <30 min, post-traumatic amnesia (PTA) <24 h, and no trauma-related abnormalities on CT scan. Patients with mTBI were assessed with the Galveston Orientation and Amnesia Test (GOAT)⁹⁴ to determine if they were in PTA (GOAT score≤75) at the time of study consent. If so, a legally authorized representative was approached to give consent. Participants enrolled using this procedure were reassessed with the GOAT upon the next scheduled study encounter and, if not in PTA at that time, were given the opportunity to give consent and continue or decline further participation.

The definition of mTBI used in this study followed the guidelines of the Department of Defense⁹⁵ and the American Congress of Rehabilitation Medicine.⁹⁶ Inclusion criteria for patients with orthopedic injuries (OI) included injury to extremities or pelvis with an Abbreviated Injury Scale (AIS)⁹⁷ score of <3 in any defined body region, and no evidence of head injury. Patients in both groups were excluded for the following: previous head injury requiring hospitalization (including treatment and discharge from ED), AIS≥3 for any body part, significant history of pre-existing mental disorders (e.g., psychotic disorder, bipolar disorder, and preinjury PTSD diagnosed by psychiatrist/psychologist), Alcohol Use Disorders Identification Test (AUDIT) score ≥8,^98,99 Drug Abuse Screening Test (DAST-10) score ≥3,^100
–102 blood alcohol level >80 mg/dL (or ED documentation of clinical intoxication) at the time of informed consent, left-hand dominant, presence of contraindications for magnetic resonance imaging (MRI; e.g., shrapnel, ferrous metal implants, pacemaker, or claustrophobia), or positive pregnancy test.

Measures

Connor-Davidson Resilience Scale (CD-RISC)

The CD-RISC is a 25-item self-report of psychological resilience¹⁰³ that has been shown to have a factor structure that includes the constructs of personal competence/tenacity, tolerance of negative affect/stress, positive acceptance of change, internal locus of control, and spirituality. Items are rated using a 5-point Likert scale ranging from 0—‘not true at all’ to 4—‘true nearly all of the time.’ At the baseline assessment, participants were asked to rate how often each of the statements was true for them in the previous month as a proxy for their preinjury status. Higher scores indicate greater degrees of resilience. The total score was used as the primary variable in this study.

Center for Epidemiologic Studies Depression Scale (CES-D)

The CES-D¹⁰⁴ is a 20-item self-report measure of depression-related symptom severity rated on a 4-point Likert scale (0—‘rarely or none of the time’ to 3—‘most or all of the time’). Confirmatory factor analysis has demonstrated a factor structure similar to that of the general population (i.e., depressed affect, positive affect, somatic/reduced activity, and interpersonal relationships) in mild to moderate TBI.¹⁰⁵ At the baseline assessment, participants were asked to rate each of the statements as they applied to them in the past week as a proxy for preinjury mood status. Higher scores indicate greater symptom severity. The total score was used as the primary variable in this study.

Acute Stress Disorder Scale (ASDS)

The ASDS is a 19-item self-report of symptoms related to Acute Stress Disorder.¹⁰⁶ Participants were asked to rate their symptom severity (5-point Likert scale from 1—‘not at all’ to 5—‘very much’) since their injury. Higher scores indicate greater symptom severity. The total score was used as the primary variable in this study.

PTSD Checklist—Civilian Form (PCL-C)

The PCL-C^107,108 is the civilian version of a 17-item self-report measure of post-traumatic stress disorder (PTSD) symptom severity comprising required symptoms from the DSM-IV.¹⁴ Participants were asked to rate how much they have been bothered by each of the symptoms (5-point Likert scale from 1—‘not at all’ to 5—‘extremely’) since their injury. Higher scores indicate greater symptom severity. The total score was used as the primary variable in this study.

Rivermead Post Concussion Symptoms Questionnaire (RPCSQ)

The RPCSQ^109
–111 is a 16-item self-report of cognitive, emotional, and somatic complaints that are commonly reported following mTBI. Factor analyses have elicited a 3-factor solution comprising cognitive, somatic, and emotional problems,¹¹¹ although variations have been reported.¹¹² The participants were asked to rate the severity of each symptom (currently compared to preinjury levels) from 0—‘not experienced at all’ to 4—‘severe problem.’ The total score was used as the primary variable in this study.

Procedures

A consecutive series of participants were prospectively screened and recruited from the EDs of the two American College of Surgeons Level-I trauma centers (Memorial Hermann Hospital-Texas Medical Center and Ben Taub General Hospital) in Houston, Texas, by study personnel according to a rotating schedule representing all shifts and days of the week. The diagnosis of TBI was made by ED trauma physicians and Glasgow Coma Scale (GCS) ratings were made by ED trauma physicians and/or staff. Abbreviated Injury Scale (AIS) ratings were made by AIS-certified research nurses based on thorough medical record review, which were used to calculate the Injury Severity Score (ISS). All head CT scans were read and coded by a board-certified neuroradiologist.

Participants were administered a baseline assessment (<24 h postinjury) of their neuropsychological and emotional status. Although the acquisition of proxy preinjury mood and resilience could be contaminated by postinjury factors including emotional reactions and transient cognitive impairments, this procedure would appear to suffer from less bias than collection a week or more postinjury. In-person follow-up assessments were also conducted evaluations at 1 week, and again at 1, 3, and 6 months postinjury. Assessments were conducted by a bachelors-level research associate in the participant's stated preferred language (i.e., English or Spanish). Research associates were not blinded to the participant's injury group status. Only emotional and postconcussion data for the baseline, 1 week, and 1 month assessments are presented here. Informed consent was obtained from the participant through an informed consent form and procedure approved by the Institutional Review Boards of Baylor College of Medicine and the University of Texas Medical School-Houston and their affiliate institutions; no participants were recruited while in post-traumatic amnesia (PTA) which would have required consent by a legally authorized representative.

Data analysis

All analyses were conducted using SAS software for Windows, Version 9.3. Statistical significance was defined as α=0.05 for all analyses unless otherwise specified. Independent variables were analyzed for outliers and no participants were removed from the analysis due to extreme scores. Linear regression analyses were conducted to determine the independent contributions of host factors (i.e., age at injury, gender, years of education) mTBI or OI injury group, and the addition of two additional host factors: preinjury measures of resilience and mood. Separate analyses were performed at each study occasion (baseline, 1 week, and 1 month postinjury) and on three outcome measures (ASDS, PCL-C, and the RPCSQ). Dummy-coding was used for gender (male=0 and female=1) and injury group (OI=0 and mTBI=1). Host factors included in the models were selected based on their demonstrated increased risk (in either the general population or post-TBI) for developing acute stress disorder, PTSD, and/or PCS and PCD, including female gender,^{18,29,67,113

–116} level of education,^{40,67,117

–120} and age.^{29,67,119,121
–123}

Results

Study sample

A total of 75 participants (29 OI and 46 mTBI) have been enrolled in the study at the time of this report (Table 1). The groups were not significantly different in terms of reported preinjury levels of alcohol (p=0.86) and drug use (p=0.09). As expected, the groups differed significantly on mechanism of injury in that the mTBI group was more frequently involved in motor vehicle crashes and falls, and the OI group more often sustained lacerations and other low-velocity injuries of the extremities. The groups also differed for overall injury severity which was expected as the head region was not excluded from the total Injury Severity Score (ISS),⁹⁷ which increases the ISS for the mTBI group due to coding for concussion. With regard to premorbid/host factors, the groups did not differ significantly on age at injury (p=0.07), gender distribution (p=0.75), years of education (p=0.68), preinjury mood (p=0.23), or preinjury psychological resilience (p=0.74).

Table 1.

Demographic and Injury Variables of the Sample

Variable	OI (n=29)	mTBI (n=46)	Statistical comparison
Age at injury (years), mean (SD)	26.7 (7.5)	30.6 (9.6)	F(1,73)=3.44, p=0.07
Gender (female: male)	11:35	6:23	χ²(2)=0.11, p=0.75
SCI, mean (SD)	0.13 (1.0)	−0.22 (0.98)	F(1,72)=2.23, p=0.14
Education (years), mean (SD)	13.0 (2.9)	13.3 (2.9)	F(1,73)=0.17, p=0.69
Race/ethnicity, n (%)
African American	8 (24)	12 (26)	p=1.0^*
Asian	2 (7)	2 (4)
European American	9 (31)	15 (33)
Hispanic	10 (34)	16 (35)
Other	0 (0)	1 (2)
AUDIT	2.4 (1.7)	2.5 (2.2)	F(1,73)=0.03, p=0.86
DAST-10	0.72 (0.8)	0.44 (0.7)	F(1,73)=2.93, p=0.09
VSVT at 1 Week, mean (SD)	46.6 (2.9)	46.1 (3.4)	F(1,69)=0.39, p=0.53
VSVT at 1 Month, mean (SD)	46.8 (1.6)	46.1 (2.6)	F(1,60)=1.1, p=0.30
CES-D	6.1 (5.5)	8.3 (9.0)	F(1,72)=1.45, p=0.23
CD-RISC	82.5 (16.7)	81.0 (21.1)	F(1,72)=0.11, p=0.74
Injury to assessment interval
Baseline (hours), mean (SD)	22.8 (12.5)	19.6 (9.4)	F(1,71)=1.54, p=0.22
1 Week (days), mean (SD)	8.0 (1.1)	7.8 (0.8)	F(1,69)=0.83, p=0.37
1 Month (days), mean (SD)	31.5 (4.2)	31.1 (4.8)	F(1,60)=0.13, p=0.72
ISS	1.8 (1.4)	3.9 (2.1)	F(1,73)=21.4, p<0.0001
Admission GCS	15.0 (0)	14.91 (0.28)	N/A
Mechanism of injury, n (%)
Assault	0 (0)	4 (5.3)	p<0.0001^*
Auto–pedestrian	1 (1.3)	3 (4.0)
Blow to head	0 (0)	2 (2.7)
Fall	5 (6.7)	13 (17.3)
Laceration	11 (14.7)	0 (0)
Motor vehicle crash	1 (1.3)	23 (30.7)
Other	8 (10.7)	0 (0)
Sports	3 (4.0)	1 (1.3)

Fisher's exact test.

SCI, Socioeconomic Composite Index

AUDIT, Alcohol Use Disorders Identification Test; CES-D, Center for Epidemiologic Depression Scale; CD-RISC, Connor-Davidson Resilience Scale; DAST-10, Drug Abuse Screening Test, 10-item version; GCS, Glasgow Coma Scale score; ISS, Injury Severity Score (including head region); VSVT, Victoria Symptom Validity Test, total items raw score.

Effects of secondary gain and concomitant suboptimal effort are well-described in the mTBI literature.^{4,9,109,124

–129} Using the same method as reported by McCauley et al.⁹ participants were queried regarding current involvement in litigation or receipt of compensation one month postinjury. Of the mTBI group, 20.5% reported involvement in litigation compared to 9.1% of the OI group which was not significantly different (Fisher's exact test, p=0.31), and 10.3% of the mTBI group reported receiving some form of injury-related compensation compared to 0% of the OI group which was not significantly different (Fisher's exact test, p=0.29). To further assess suboptimal effort, participants were administered the Victoria Symptom Validity Test¹³⁰ at 1 week and 1 month postinjury. Effort was evaluated using binomial probability scores (e.g., a total number of correct responses exceeding a criterion indicated valid performance). The VSVT manual states that total scores 30–48 (inclusive) suggest nonsuspect effort. All participants produced valid profiles at both time points, reasonably suggesting that adequate effort was deployed toward the assessment tasks presented and the two groups did not differ on this measure at the two time points at which it was administered (Table 1).

Injury group analyses

Comparisons were conducted on the three outcome measures at each study occasion. At baseline, the groups differed significantly on the ASDS, PCL-C, and the RPCSQ (Table 2). Analyses were repeated at 1 week postinjury and similarly, the groups differed significantly on the ASDS, PCL-C, and the RPCSQ. Finally at 1 month postinjury, the groups differed significantly on all three measures. The mTBI group reported significantly greater symptom severity than the OI group on each measure at each study occasion. The groups did not differ on the host factors of resilience (p=0.74) and mood (p=0.23) obtained at the baseline assessment ruling out preinjury differences on these host factors that could have affected the outcome analyses.

Table 2.

Group Comparisons of Outcome Variables at Baseline, 1 Week, and 1 Month Postinjury

Variable	OI (n=29)	mTBI (n=46)	Statistical comparison
Baseline
ASDS	26.1 (8.6)	40.0 (14.7)	F(1,72)=20.6, p<0.0001
PCL-C	22.3 (6.7)	30.4 (11.6)	F(1,72)=11.3, p=0.001
RPCSQ	4.6 (7.3)	15.0 (11.9)	F(1,72)=17.3, p<0.0001
One week
ASDS	27.0 (11.7)	39.8 (18.9)	F(1,69)=10.0, p<0.003
PCL-C	22.3 (7.4)	32.2 (15.4)	F(1,69)=9.8, p<0.003
RPCSQ	2.8 (5.2)	13.7 (14.3)	F(1,69)=14.4, p=0.0003
One month
ASDS	27.1 (11.3)	37.4 (18.3)	F(1,59)=5.7, p<0.02
PCL-C	22.4 (7.1)	30.6 (15.5)	F(1,59)=5.5, p<0.03
RPCSQ	2.0 (4.0)	10.1 (14.6)	F(1,59)=6.5, p<0.02

ASDS, Acute Stress Disorder Scale; PCL-C, PTSD Checklist-Civilian form; RPCSQ, Rivermead Post Concussion Symptoms Questionnaire.

Multivariate linear regression analyses

Although a number of host factors (e.g., age at injury, gender, level of education) have demonstrated robust predictive power for post-mTBI emotional problems, resilience and preinjury mood remain unexplored in civilian mTBI. Euthymic mood and high resilience have the potential to be protective against anxiety and postconcussion symptoms, but this and their relative contributions are currently unknown. To address this, the multivariate linear regression included the independent variables age at injury, gender, education, group, CES-D total score, and CD-RISC total score, which were entered simultaneously into the model. Review of all models revealed that education was not a robust predictor at any study occasion. The regression models were then subsequently re-estimated excluding this variable.

Inter-correlations of the risk factors were conducted for the mTBI and OI groups combined (Table 3) to determine the presence of multicollinearity. There were positive trends between education and age, and an inverse relation between CES-D and education. Only the preinjury proxies of CES-D and CD-RISC were significantly negatively correlated (p<0.0001), suggesting that higher resilience is related to better mood status; however, given the moderate magnitude of the correlation, there is no compelling evidence for multicollinearity, so both variables were considered appropriate for inclusion into the models. Correlations were also explored between the host factors and the outcome measures (Table 4). Preinjury mood demonstrated a significant positive correlation with anxiety and postconcussion measures such that depressed mood (higher CES-D score) was associated with greater symptom severity. Preinjury resilience was correlated negatively with the RPCSQ so that higher resilience was associated with lower PCS symptom severity, but was not related significantly to the anxiety measures.

Table 3.

Spearman Inter-Correlations of Host Factors at Baseline (Day of Injury) for the Combined Groups

	1	2	3	4	5
1. Age at Injury (years)	—
2. Gender	r=0.13	—
3. Education (years)	r=0.22^a	r=0.18	—
4. Group	r=0.18	r=0.04	r=0.07	—
5. CES-D	r=0.00	r=0.04	r=−0.23^a	r=0.09	—
6. CD-RISC	r=−0.04	r=0.07	r=−0.06	r=0.02	r=−0.45^b

0.06; ^b<0.0001.

CD-RISC, Connor-Davidson Resilience Scale obtained at baseline; CES-D, Center for Epidemiologic Studies Depression Scale obtained at baseline.

Table 4.

Spearman Correlations between Host Factors and the Dependent Measures at Baseline (Day of Injury) for the Combined Groups

	ASDS	PCL-C	RPCSQ
CES-D	r=0.43^b	r=0.48^c	r=0.43^b
CD-RISC	r=−0.08	r=−0.15	r=−0.24^a

<0.05, ^b<0.001, ^c<0.0001.

ASDS, Acute Stress Disorder Scale; CD-RISC, Connor-Davidson Resilience Scale obtained at baseline; CES-D, Center for Epidemiologic Studies Depression Scale obtained at baseline; PCL-C, PTSD Checklist-Civilian form; RPCSQ, Rivermead Post Concussion Symptoms Questionnaire.

The effects of preinjury host and risk factors on post injury anxiety and postconcussion symptoms over time are presented in Tables 5 through 7. At the baseline measurement, group was, as expected, a significant factor in the reported severity of anxiety and PCS symptoms (positive β coefficient indicating greater symptom severity in the mTBI group). After accounting for other host factors, preinjury mood (higher level of preinjury depressed mood) was found to make significant contribution and preinjury resilience demonstrated a positive trend. A substantial portion of the variance in the models was found for each dependent measure (adjusted R-square 0.33 to 0.43). At 1 week postinjury (Table 6), gender was a significant predictor of anxiety severity (positive β coefficient indicating greater severity associated with female gender) on the ASDS and a positive trend was found on the PCL-C. Group and preinjury mood status were significant predictors of all three dependent measures (all p<0.007). Preinjury resilience was a significant predictor of the ASDS (p<0.03) and RPCSQ (p=0.01), but only a positive trend was found on the PCL-C (p=0.07), such that higher resilience was associated with greater symptom severity. Explained variance of the dependent measures again was substantial (adjusted R-square 0.33 to 0.37). Finally, the 1 month postinjury model (Table 7) revealed that gender was not a significant predictor for any dependent measure. As an additional assessment of the possible effect of secondary gain, a variable was created encompassing involvement in litigation and compensation payments. This variable was not found to be a significant predictor in any of the regression models, was subsequently removed, and the models were then re-estimated. Group, preinjury mood, and preinjury resilience were significant predictors of all three dependent measures (again, positive β coefficients indicating higher symptom severity associated with greater resilience—a finding that will be addressed later). A substantial portion of the variance in each model was found for each dependent measure (adjusted R-square 0.22 to 0.27).

Table 5.

Baseline/Preinjury Proxy Models—Multivariate Regression Results for Anxiety and Post-Concussion Symptoms

	ASDS				PCL-C				RPCSQ
	B	SE B	t	β	B	SE B	t	β	B	SE B	t	β
Age at injury (years)	−0.14	0.14	−0.99	−0.09	−0.11	0.11	−1.05	−0.18	0.2	0.13	1.6	0.16
Gender	3.96	3.04	1.3	0.12	1.47	2.25	0.65	0.06	3.12	2.61	1.19	0.12
Group	12.3	2.71	4.54	0.42^e	6.65	2.0	3.32	0.30^d	8.16	2.33	3.5	0.35^d
CES-D	1.0	0.19	5.2	0.55^e	0.86	0.14	6.02	0.63^e	0.64	0.17	3.86	0.44^d
CD-RISC	0.14	0.08	1.84	0.19^a	0.07	0.06	1.2	0.13	0.07	0.07	1.05	0.12

Model	F(5,68)=11.71, p<0.0001	F(5,68)=12.10, p<0.0001	F(5,68)=8.24, p<0.0001
R-square	0.46	0.47	0.38
Adjusted R-square	0.42	0.43	0.33

B, Parameter estimate; SE B, Standard error; β, Standardized coefficient.

≤0.07, ^b<0.05, ^c<0.01, ^d<0.001, ^e<0.0001.

Table 6.

One Week Models—Multivariate Regression Results for Anxiety and Postconcussion Symptoms

	ASDS				PCL-C				RPCSQ
	B	SE B	t	β	B	SE B	t	β	B	SE B	t	β
Age at injury (years)	−0.21	0.2	−1.06	−0.11	−0.17	0.15	−1.13	−0.11	.016	0.14	1.12	0.11
Gender	10.31	4.04	2.55	0.25^c	6.2	3.14	1.97	0.19^a	4.39	2.87	1.53	0.15
Group	10.58	3.68	2.88	0.29^c	8.03	2.86	2.81	0.29^c	7.96	2.61	3.05	0.30^c
CES-D	1.15	0.26	4.44	0.52^e	0.96	0.2	4.75	0.56^e	0.88	0.18	4.81	0.55^e
CD-RISC	0.23	0.1	2.25	0.26^b	0.15	0.08	1.86	0.21^a	0.19	0.07	2.62	0.30^c

Model	F(5,65)=7.91, p<0.0001	F(5,65)=8.10, p<0.0001	F(5,65)=9.08, p<0.0001
R-square	0.38	0.38	0.41
Adjusted R-square	0.33	0.34	0.37

B, Parameter estimate; SE B, Standard error; β, Standardized coefficient.

≤0.07, ^b<0.05, c^c<0.01, ^d<0.001, ^e<0.0001

Table 7.

One Month Models—Multivariate Regression Results for Anxiety and Postconcussion Symptoms

	ASDS				PCL-C				RPCSQ
	B	SE B	t	β	B	SE B	t	β	B	SE B	t	β
Age at injury (years)	−0.27	0.21	−1.26	−0.14	−0.23	0.18	−1.28	−0.15	−0.08	0.16	−0.49	−0.06
Gender	7.04	4.31	1.63	0.18	3.71	3.63	1.02	0.12	4.94	3.32	1.49	0.17
Group	9.59	3.93	2.44	0.28^b	7.72	3.31	2.34	0.27^b	7.33	3.03	2.42	0.28^b
CES-D	1.16	0.28	4.11	0.52^e	0.86	0.24	3.62	0.48^d	0.75	0.22	3.44	0.45^c
CD-RISC	0.27	0.11	2.41	0.31^b	0.2	0.1	2.13	0.28^b	0.2	0.09	2.32	0.30^b

Model	F(5,55)=5.46, p=0.0004	F(5,55)=4.31, p<0.003	F(5,55)=4.37, p=0.002
R-square	0.33	0.28	0.29
Adjusted R-square	0.27	0.22	0.22

B, Parameter estimate; SE B, Standard error; β, Standardized coefficient.

≤0.07, ^b<0.05, ^c<0.01, ^d<0.001, ^e<0.0001.

Discussion

Support was found for our hypothesis that preinjury resilience and mood status were significantly related to outcome following mTBI; depressed mood demonstrated an adverse effect on postinjury outcome, and resilience also was related to outcome. This was true even after accounting for variance attributable to other known robust predictors. It would appear from the results of this early analysis of participants in an on-going study that the effect of preinjury mood has an earlier and greater effect on outcome in terms of anxiety and postconcussive symptom severity than resilience (see Tables 5 –7). No similar studies could be identified for comparison with civilian populations; however, these findings generally are consistent with those from military studies that have demonstrated a protective effect of pre-deployment resilience to post-deployment PTSD,^{76
–78,81

–86} although the time scale of assessments, traumatic exposures, and a number of other obvious procedural differences exist. To our knowledge, this is the first study to find a link, albeit a paradoxical one, between resilience and the severity of postconcussive symptoms following mTBI. It remains to be seen if this effect lasts beyond the first month postinjury and ultimately whether it actually offers protection from persistent PCS/PCD symptoms or if it exacerbates these symptoms, at least in the short term.

This is the first known study to measure preinjury mood status and its effect on outcomes in mTBI, although a number of studies have investigated the role of preinjury psychiatric disorders or postinjury mood and their impact on post-mTBI outcomes.^{18,25,27,30

–40} It is not surprising that mood has such a significant impact on symptom presentation. In a sample of 1502 OEF/OIF veterans, depression was more strongly associated with PCS symptoms such as headaches and sleep disturbance that a mTBI itself.¹³¹ Psychological distress (e.g., depression and anxiety) measured within 2 weeks post-mTBI have been shown to significantly increase the odds of developing persistent PCS at 6 months postinjury.¹²⁶ These findings offer an important opportunity as they represent modifiable host factors that could be targeted to significantly improve outcome.

There are a number of possible reasons for the strong effect of depressed preinjury mood. Given the relatively small sample size, it is conceivable that this effect is strong only for this particular sample of participants and would be altered with a larger sample. It is also plausible that depressed preinjury mood is acting as a substitute for preinjury psychiatric status, as this characteristic was not specifically included. Finally, the possibility that the participants' estimates of preinjury emotional status were influenced by their postinjury emotional reactions and concomitant cognitive difficulties cannot be ruled out conclusively. For instance, Hutchison et al.¹³² reported that psychological distress is measurably demonstrable within hours following injury in collegiate athletes. Although preinjury proxy reports may be arguably less contaminated (having been collected less than 24 h postinjury as opposed to later time points as in other studies), other methods of collecting these specific quantified data before their injuries appear unfeasible. Given that this is an early analysis, these questions will not be fully addressed until the study's completion.

Review of the standardized β coefficient reveals that higher preinjury resilience is actually associated with higher anxiety and postconcussion severity which was an unexpected finding. One explanation is that in the first month postinjury, those with higher resilience have not had sufficient time to bounce back or experience a period of posttraumatic growth. It is also possible that there is a missing mediator variable that could be included into the model which would better explain this paradoxical relation (e.g., social support); while it is beyond the current data to determine this, a more complex model may be needed to explain the interplay between resilience and social support in the near-term following mTBI. These are questions that may be addressed with a larger sample at the end of our ongoing study.

Several limitations of this study need to be acknowledged. First, the relatively small sample size is a reflection of the early stage of this project. With this type of preliminary analysis, our ability to include a number of other variables which could have altered the results was limited. Recognizing this, we elected to include variables with well-known significant effects on developing anxiety disorders and PCS/PCD following mTBI to determine whether resilience and mood could account for variance above these well-established predictors. Another important limitation is the use of preinjury proxies collected on the day of injury. We fully acknowledge the potential for emotional reactions less than 24 h postinjury to influence the participants' estimates of preinjury resilience and mood status. However, it should be noted that quantified estimates of these constructs are not likely available in preinjury medical records and these measures did not differ significantly between groups suggesting that both groups had similar reactions to their injuries in spite of differences in circumstances and mechanisms of injury. The study was limited by a relatively low n/k ratio (15 participants for each independent variable) which also could have altered the findings. Given that this was an early analysis of participants enrolled thus far in an on-going project, we will have to opportunity to validate and expand these results with a larger sample at the conclusion of the study, and this will allow us to verify the structure of the multivariate regression models. Finally, as the research associates conducting the postinjury assessments were not blinded to the participant's group status or other predictor scores, there is the possibility that this may have introduced bias and is a threat to the internal validity of this study; however, the risk would appear small as the participant-completed measures present little opportunity for influence from well-trained and experienced examiners or scoring bias.

To summarize, this study has shown that levels of preinjury depressed mood and low resilience are related to postinjury anxiety and postconcussion symptoms even after accounting for effects of other known host factors including age, gender, and injury group. While preliminary, they suggest that acute interventions directed at reducing depressed mood and bolstering resilience may prove beneficial in improving outcome from mTBI. Future work from our group will attempt to replicate these results at the study's conclusion and explore structural models to extend our present findings.

Footnotes

Acknowledgments

We sincerely thank the participants for their interest and willingness to take part in this research. This work was supported by W81XWH-08-2-0131 (McCarthy-PI), W81XWH-08-0132 (Robertson-PI), and W81XWH-08-2-0133 (Levin-PI) from the Congressionally Directed Medical Research Programs and the Department of Defense. The information in this article and the article itself have never been published either electronically or in print. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Department of Defense.

Author Disclosure Statement

None of the authors have any financial or other relationship(s) that could be construed as a conflict of interest with respect to the content of this article.

References

National Center for Injury Prevention and Control. 2003. Report to Congress on Mild Traumatic Brain Injury in the United States: Steps to Prevent a Serious Public Health Problem. Centers for Disease Control and Prevention: Atlanta.

Sosin

, Sniezek

, Thurman

. 1996. Incidence of mild and moderate brain injury in the United States, 1991. Brain Inj, 10:47–54.

Thurman

. 2001. The epidemiology and economics of head trauma. Head Trauma: Basic, Preclinical, and Clinical Directions. Miller

, Hayes

. John Wiley and Sons: New York.

Carroll

, Cassidy

, Peloso

et al. 2004. Prognosis for mild traumatic brain injury: Results of the WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. J Rehabil Med, 43:84–105.

Alexander

. 1995. Mild traumatic brain injury: Pathophysiology, natural history, and clinical management. Neurology, 45:1253–1260.

Cassidy

, Carroll

, Peloso

et al. 2004. Incidence, risk factors and prevention of mild traumatic brain injury: Results of the WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. J Rehab Med, 43:28–60.

Iversen

, van Staden

, Hughes

et al. 2009. The prevalence of common mental disorders and PTSD in the UK military: Using data from a clinical interview-based study. BMC Psychiatry, 9:68.

McCauley

, Boake

, Pedroza

, Brown

, Levin

, Goodman

, Merritt

. 2005. Postconcussional disorder: Are the DSM-IV criteria an improvement over the ICD-10? J Nerv Ment Dis, 193:540–550.

McCauley

, Boake

, Pedroza

, Brown

, Levin

, Goodman

, Merritt

. 2008. Correlates of persistent postconcussional disorder: DSM-IV criteria versus ICD-10. J Clin Exp Neuropsychol., 30:360–379.

10.

Ruff

, Camenzuli

, Mueller

. 1996. Miserable minority: Emotional risk factors that influence the outcome of a mild traumatic brain injury. Brain Injury, 10:551–565.

11.

Wood

. 2004. Understanding the 'miserable minority': A diathesis-stress paradigm for postconcussional syndrome. Brain Injury, 18:1135–1153.

12.

World Health Organization. 1992. The ICD-10 classification of mental and behavioural disorders: Clinical descriptions and diagnostic guidelines. World Health Organization: Geneva.

13.

World Health Organization. 1993. The ICD-10 classification of mental and behavioural disorders: Diagnostic criteria for research. World Health Organization: Geneva.

14.

American Psychiatric Association. 1994. Diagnostic and Statistical Manual of Mental Disorders. 4th 4th. American Psychiatric Association: Washington, D.C.

15.

Garden

, Sullivan

. 2010. An examination of the base rates of postconcussion symptoms: The influence of demographics and depression. Appl Neuropsychol, 17:1–7.

16.

Konrad

, Geburek

, Rist

, Blumenroth

, Fischer

, Husstedt

, Arolt

, Schiffbauer

, Lohmann

. 2010. Long-term cognitive and emotional consequences of mild traumatic brain injury. Psychol Medl, 15.

17.

Levin

, Brown

, Song

et al. 2001. Depression and posttraumatic stress disorder at three months after mild to moderate traumatic brain injury. J Clin Exp Neuropsychol., 23:754–769.

18.

McCauley

, Boake

, Levin

, Contant

, Song

. 2001. Postconcussional disorder following mild to moderate traumatic brain injury: Anxiety, depression, and social support as risk factors and comorbidities. J Clin Exp Neuropsychol, 23:792–808.

19.

Rao

, Bertrand

, Rosenberg

, Makley

, Schretlen

, Brandt

, Mielke

. 2010. Predictors of new-onset depression after mild traumatic brain injury. J Neuropsychiatry Clin Neurosci, 22:100–104.

20.

Vanderploeg

, Curtiss

, Luis

, Salazar

. 2007. Long-term morbidities following self-reported mild traumatic brain injury. J Clin Exp Neuropsychol., 29:585–598.

21.

Moore

, Terryberry-Spohr

, Hope

. 2006. Mild traumatic brain injury and anxiety sequelae: A review of the literature. Brain Injury, 20:117–132.

22.

Bryant

, Creamer

, O'Donnell

, Silove

, Clark

, McFarlane

. 2009. Post-traumatic amnesia and the nature of post-traumatic stress disorder after mild traumatic brain injury. J Int Neuropsychol Soc, 15:862–867.

23.

Lishman

. 1988. Physiogenesis and psychogenesis in the ‘postconcussional syndrome.’ Brit J Psychiatry, 153:460–469.

24.

Silverberg

, Iverson

. 2011. Etiology of the postconcussion syndrome: Physiogenesis and psychogenesis revisited. NeuroRehabilitation, 29:317–329.

25.

Meares

, Shores

, Taylor

et al. 2008. Mild traumatic brain injury does not predict acute postconcussion syndrome. J Neurol Neurosurg Psychiatry, 79:300–306.

26.

Cattelani

, Gugliotta

, Maravita

, Mazzucchi

. 1996. Post-concussive syndrome: paraclinical signs, subjective symptoms, cognitive functions and MMPI profiles. Brain Injury, 10:187–195.

27.

Dischinger

, Ryb

, Kufera

, Auman

. 2009. Early predictors of postconcussive syndrome in a population of trauma patients with mild traumatic brain injury. J Trauma, 66:289–296discussion 296–287.

28.

Luis

, Vanderploeg

, Curtiss

. 2003. Predictors of postconcussion symptom complex in community dwelling male veterans. J Int Neuropsychol Soc, 9:1001–1015.

29.

Fenton

, McClelland

, Montgomery

, MacFlynn

, Rutherford

. 1993. The postconcussional syndrome: Social antecedents and psychological sequelae. Br J Psychiatry, 162:493–497.

30.

King

. 1996. Emotional, neuropsychological, and organic factors: Their use in the prediction of persisting postconcussion symptoms after moderate and mild head injuries. J Neurol Neurosurg Psychiatry, 61:75–81.

31.

Mooney

, Speed

, Sheppard

. 2005. Factors related to recovery after mild traumatic brain injury. Brain Injury, 19:975–987.

32.

Bohnen

, Jolles

, Twijnstra

, Mellink

, Sulon

. 1992. Coping styles, cortisol reactivity, and performance in a vigilance task of patients with persistent postconcussive symptoms after a mild head injury. Int J Neurosci, 64:97–105.

33.

Bryant

. 2001. Posttraumatic stress disorder and mild brain injury: Controversies, causes and consequences. J Clin Exp Neuropsychol, 23:718–728.

34.

Evered

, Ruff

, Baldo

, Isomura

. 2003. Emotional risk factors and postconcussional disorder. Assessment, 10:420–427.

35.

Fann

, Katon

, Uomoto

, Esselman

. 1995. Psychiatric disorders and functional disability in outpatients with traumatic brain injuries. Am J Psychiatry, 152:1493–1499.

36.

Gasquoine

. 1997. Postconcussion symptoms. Neuropsychol Rev, 7:77–85.

37.

Hanna-Pladdy

, Berry

, Bennett

, Phillips

, Gouvier

. 2001. Stress as a diagnostic challenge for postconcussive symptoms: Sequelae of mild traumatic brain injury or physiological stress response. Clin Neuropsychol, 15:289–304.

38.

Karzmark

, Hall

, Englander

. 1995. Late-onset postconcussion symptoms after mild brain injury: The role of premorbid, injury-related, environmental, and personality factors. Brain Injury, 9:21–26.

39.

King

, Crawford

, Wenden

, Caldwell

, Wade

. 1999. Early prediction of persisting postconcussion symptoms following mild and moderate head injuries. Br J Clin Psychol, 38:15–25.

40.

Stulemeijer

, van der Werf

, Borm

, Vos

. 2008. Early prediction of favourable recovery 6 months after mild traumatic brain injury. J Neurol Neurosurg Psychiatry, 79:936–942.

41.

Bryant

, Harvey

. 1995. Acute stress response: A comparison of head injured and non-head injured patients. Psychol Med, 25:869–873.

42.

Bryant

, Harvey

. 1998. Relationship between acute stress disorder and posttraumatic stress disorder following mild traumatic brain injury. Am J Psychiatry, 155:625–629.

43.

Hickling

, Gillen

, Blanchard

, Buckley

, Taylor

. 1998. Traumatic brain injury and posttraumatic stress disorder: A preliminary investigation of neuropsychological test results in PTSD secondary to motor vehicle accidents. Brain Injury, 12:265–274.

44.

Ohry

, Rattok

, Solomon

. 1996. Post-traumatic stress disorder in brain injury patients. Brain Injury, 10:687–695.

45.

Harvey

, Bryant

. 2000. Two-year prospective evaluation of the relationship between acute stress disorder and posttraumatic stress disorder following mild traumatic brain injury. Am J Psychiatry, 157:626–628.

46.

Creamer

, O'Donnell

, Pattison

. 2005. Amnesia, traumatic brain injury, and posttraumatic stress disorder: A methodological inquiry. Behav Res Ther, 43:1383–1389.

47.

Gil

, Caspi

, Ben-Ari

, Koren

, Klein

. 2005. Does memory of a traumatic event increase the risk for posttraumatic stress disorder in patients with traumatic brain injury? A prospective study. Am J Psychiatry, 162:963–969.

48.

McNeil

. 1996. Can PTSD occur with amnesia for the precipitating event? Cog Neuropsychiatry, 1:239–246.

49.

Parker

. 2002. Recommendations for the revision of DSM-IV diagnostic categories for co-morbid posttraumatic stress disorder and traumatic brain injury. NeuroRehabilitation, 17:131–143.

50.

Sbordone

, Liter

. 1995. Mild traumatic brain injury does not produce post-traumatic stress disorder. Brain Injury, 9:405–412.

51.

Sbordone

, Ruff

. 2010. Re-examination of the controversial coexistence of traumatic brain injury and posttraumatic stress disorder: Misdiagnosis and self-report measures. Psychol Injury Law, 3:63–76.

52.

Hesdorffer

, Rauch

, Tamminga

. 2009. Long-term psychiatric outcomes following traumatic brain injury: A review of the literature. J Head Trauma Rehabil, 24:452–459.

53.

Richardson

, Naifeh

, Elhai

. 2007. Posttraumatic stress disorder and associated risk factors in Canadian peacekeeping veterans with health-related disabilities. Can J Psychiatry, 52:510–518.

54.

Seal

, Metzler

, Gima

, Bertenthal

, Maguen

, Marmar

. 2009. Trends and risk factors for mental health diagnoses among Iraq and Afghanistan veterans using Department of Veterans Affairs health care, 2002–2008. Am J Public Health, 99:1651–1658.

55.

Iversen

, Fear

, Ehlers

et al. 2008. Risk factors for post-traumatic stress disorder among UK Armed Forces personnel. Psychol Med, 38:511–522.

56.

Kremen

, Koenen

, Boake

et al. 2007. Pretrauma cognitive ability and risk for posttraumatic stress disorder: A twin study. Arch Gen Psychiatry, 64:361–368.

57.

Brailey

, Vasterling

, Proctor

, Constans

, Friedman

. 2007. PTSD symptoms, life events, and unit cohesion in U.S. soldiers: Baseline findings from the neurocognition deployment health study. J Trauma Stress, 20:495–503.

58.

Smith

, Ryan

, Wingard

, Slymen

, Sallis

, Kritz-Silverstein

. 2008a. New onset and persistent symptoms of post-traumatic stress disorder self reported after deployment and combat exposures: Prospective population based US military cohort study. BMJ, 336:366–371.

59.

Koren

, Norman

, Cohen

, Berman

, Klein

. 2005. Increased PTSD risk with combat-related injury: A matched comparison study of injured and uninjured soldiers experiencing the same combat events. Am J Psychiatry, 162:276–282.

60.

Sandweiss

, Slymen

, Leardmann

et al. 2011. Preinjury psychiatric status, injury severity, and postdeployment posttraumatic stress disorder. Arch Gen Psychiatry, 68:496–504.

61.

Smith

, Wingard

, Ryan

, Kritz-Silverstein

, Slymen

, Sallis

. 2008b. Prior assault and posttraumatic stress disorder after combat deployment. Epidemiology, 19:505–512.

62.

Maguen

, Turcotte

, Peterson

et al. 2008. Description of risk and resilience factors among military medical personnel before deployment to Iraq. Mil Med, 173:1–9.

63.

McLeod

, Koenen

, Meyer

, Lyons

, Eisen

, True

, Goldberg

. 2001. Genetic and environmental influences on the relationship among combat exposure, posttraumatic stress disorder symptoms, and alcohol use. J Trauma Stress, 14:259–275.

64.

Scherrer

, Xian

, Lyons

et al. 2008. Posttraumatic stress disorder; combat exposure; and nicotine dependence, alcohol dependence, and major depression in male twins. Compr Psychiatry, 49:297–304.

65.

van Zuiden

, Geuze

, Willemen

et al. 2011. Pre-existing high glucocorticoid receptor number predicting development of posttraumatic stress symptoms after military deployment. Am J Psychiatry, 168:89–96.

66.

Young

, Lawford

, Noble

et al. 2002. Harmful drinking in military veterans with post-traumatic stress disorder: Association with the D2 dopamine receptor A1 allele. Alcohol Alcohol, 37:451–456.

67.

Brewin

, Andrews

, Valentine

. 2000. Meta-analysis of risk factors for posttraumatic stress disorder in trauma-exposed adults. J Consult Clin Psychol, 68:748–766.

68.

Harvey

, Bryant

. 1998. Predictors of acute stress following mild traumatic brain injury. Brain Injury, 12:147–154.

69.

Bonanno

. 2004. Loss, trauma, and human resilience: Have we underestimated the human capacity to thrive after extremely aversive events? Am Psychol, 59:20–28.

70.

Lyons

. 1991. Strategies for assessing the potential for positive adjustment following trauma. J Traum Stress, 4:93–111.

71.

Rutter

. 1985. Resilience in the face of adversity. Protective factors and resistance to psychiatric disorder. Br J Psychiatry, 147:598–611.

72.

Kobasa

. 1979. Stressful life events, personality, and health: An inquiry into hardiness. J Personality Soc Psychol, 37:1–11.

73.

Kozlovsky

, Matar

, Kaplan

, Kotler

, Zohar

, Cohen

. 2008. The immediate early gene Arc is associated with behavioral resilience to stress exposure in an animal model of posttraumatic stress disorder. Eur Neuropsychopharmacol, 18:107–116.

74.

Adler

, Dolan

. 2006. Military hardiness as a buffer of psychological health on return from deployment. Mil Med, 171:93–98.

75.

Pietrzak

, Johnson

, Goldstein

, Malley

, Southwick

. 2009a. Posttraumatic stress disorder mediates the relationship between mild traumatic brain injury and health and psychosocial functioning in veterans of Operations Enduring Freedom and Iraqi Freedom. J Nerv Ment Dis, 197:748–753.

76.

Dickstein

, McLean

, Mintz

et al. 2010. Unit cohesion and PTSD symptom severity in Air Force medical personnel. Mil Med, 175:482–486.

77.

Phillips

, Leardmann

, Gumbs

, Smith

. 2010. Risk factors for posttraumatic stress disorder among deployed US male marines. BMC Psychiatry, 10:52.

78.

Green

, Calhoun

, Dennis

, Beckham

. 2010. Exploration of the resilience construct in posttraumatic stress disorder severity and functional correlates in military combat veterans who have served since September 11, 2001. J Clin Psychiatry, 71:823–830.

79.

Schok

, Kleber

, Lensvelt-Mulders

. 2010. A model of resilience and meaning after military deployment: Personal resources in making sense of war and peacekeeping experiences. Aging Ment Health, 14:328–338.

80.

Seligman

, Fowler

. 2011. Comprehensive soldier ritness and the future of psychology. Am Psychol, 66:82–86.

81.

Pietrzak

, Johnson

, Goldstein

, Malley

, Southwick

. 2009b. Psychological resilience and postdeployment social support protect against traumatic stress and depressive symptoms in soldiers returning from Operations Enduring Freedom and Iraqi Freedom. Depress Anxiety, 26:745–751.

82.

Pietrzak

, Southwick

. 2011. Psychological resilience in OEF-OIF Veterans: Application of a novel classification approach and examination of demographic and psychosocial correlates. J Affect Disord, 133:560–568.

83.

Polusny

, Erbes

, Murdoch

, Arbisi

, Thuras

, Rath

. 2011. Prospective risk factors for new-onset post-traumatic stress disorder in National Guard soldiers deployed to Iraq. Psychol Med, 41:687–698.

84.

Adler

, Bliese

, McGurk

, Hoge

, Castro

. 2009a. Battlemind debriefing and battlemind training as early interventions with soldiers returning from Iraq: Randomization by platoon. J Consult Clin Psychol, 77:928–940.

85.

Adler

, Castro

, McGurk

. 2009b. Time-driven battlemind psychological debriefing: A group-level early intervention in combat. Mil Med, 174:21–28.

86.

Frappell-Cooke

, Gulina

, Green

, Hacker Hughes

, Greenberg

. 2010. Does trauma risk management reduce psychological distress in deployed troops? Occup Med (Lond), 60:645–650.

87.

Casey

Jr.

2011. Comprehensive soldier fitness: A vision for psychological resilience in the U.S. Army. Am Psychol, 66:1–3.

88.

Cornum

, Matthews

, Seligman

. 2011. Comprehensive soldier fitness: Building resilience in a challenging institutional context. Am Psychol, 66:4–9.

89.

Wells

, Miller

, Adler

, Engel

, Smith

, Fairbank

. 2011. Mental health impact of the Iraq and Afghanistan conflicts: A review of US research, service provision, and programmatic responses. Int Rev Psychiatry, 23:144–152.

90.

Reivich

, Seligman

, McBride

. 2011. Master resilience training in the U.S. Army. Am Psychol, 66:25–34.

91.

Lester

, McBride

, Bliese

, Adler

. 2011. Bringing science to bear: An empirical assessment of the Comprehensive Soldier Fitness program. Am Psychol, 66:77–81.

92.

Tedeschi

, McNally

. 2011. Can we facilitate posttraumatic growth in combat veterans? Am Psychol, 66:19–24.

93.

Teasdale

, Jennett

. 1974. Assessment of coma and impaired consciousness. A practical scale. Lancet, 2:81–84.

94.

Levin

, O'Donnell

, Grossman

. 1979. The Galveston Orientation and Amnesia Test. A practical scale to assess cognition after head injury. J Nervous Mental Dis, 167:675–684.

95.

Assistant Secretary of Defense for Health Affairs. 2007. Health Affairs Memorandum. October 1, 2007. Traumatic Brain Injury: Definition and Reporting.

96.

Kay

. 1993. Mild traumatic brain injury committee of the head injury interdisciplinary special interest group of the American Congress of Rehabilitation Medicine. Definition of mild traumatic brain injury. J Head Trauma Rehabil, 8:86–87.

97.

Committee on Injury Scaling. 1998. Abbreviated Injury Scale. Association for the Advancement of Automotive Medicine: Arlington Heights, IL.

98.

Bohn

, Babor

, Kranzler

. 1995. The Alcohol Use Disorders Identification Test (AUDIT): Validation of a screening instrument for use in medical settings. J Stud Alcohol, 56:423–432.

99.

Saunders

, Aasland

, Babor

, de la Fuente

, Grant

. 1993. Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption–II. Addiction, 88:791–804.

100.

Bohn

, Babor

, Kranzler

. 1991. Validity of the Drug Abuse Screening Test (DAST-10) in inpatient substance abusers. Proceedings of the 53rd Annual Scientific Meeting, The Committee on Problems of Drug Dependence. Services DoHaH. NIDA Research Monograph: Rockville, MD, 233.

101.

Skinner

. 1982. The drug abuse screening test. Addict Behav, 7:363–371.

102.

Yudko

, Lozhkina

, Fouts

. 2007. A comprehensive review of the psychometric properties of the Drug Abuse Screening Test. J Subst Abuse Treat, 32:189–198.

103.

Connor

, Davidson

. 2003. Development of a new resilience scale: The Connor-Davidson Resilience Scale (CD-RISC) Depress Anxiety, 18:76–82.

104.

Radloff

. 1977. The CES-D Scale: A self-report depression scale for research in the general population. Appl Psychol Meas, 1:385–401.

105.

McCauley

, Pedroza

, Brown

, Boake

, Levin

, Goodman

, Merritt

. 2006. Confirmatory factor structure of the Center for Epidemiologic Studies-Depression scale (CES-D) in mild-to-moderate traumatic brain injury. Brain Injury, 20:519–527.

106.

Bryant

, Moulds

, Guthrie

. 2000. Acute Stress Disorder Scale: A self-report measure of acute stress disorder. Psychol Assess, 12:61–68.

107.

Blanchard

, Jones-Alexander

, Buckley

, Forneris

. 1996. Psychometric properties of the PTSD Checklist (PCL) Behav Res Ther, 34:669–673.

108.

Weathers

, Litz

, Herman

, Huska

, Keane

. 1993. The PTSD Checklist (PCL): Reliability, Validity, and Diagnostic Utility. 9th Annual Convention of the International Society for Traumatic Stress Studies: San Antonio, TX.

109.

Eyres

, Carey

, Gilworth

, Neumann

, Tennant

. 2005. Construct validity and reliability of the Rivermead Postconcussion Symptoms Questionnaire. Clin Rehabil, 19:878–887.

110.

King

, Crawford

, Wenden

, Moss

, Wade

. 1995. The Rivermead Post Concussion Symptoms Questionnaire: A measure of symptoms commonly experienced after head injury and its reliability. J Neurol, 242:587–592.

111.

Potter

, Leigh

, Wade

, Fleminger

. 2006. The Rivermead Post Concussion Symptoms Questionnaire: A confirmatory factor analysis. J Neurol, 253:1603–1614.

112.

Herrmann

, Rapoport

, Rajaram

et al. 2009. Factor analysis of the Rivermead Postconcussion Symptoms Questionnaire in mild-to-moderate traumatic brain injury patients. J Neuropsychiatry Clin Neu, 21:181–188.

113.

Bazarian

, Wong

, Harris

, Leahey

, Mookerjee

, Dombovy

. 1999. Epidemiology and predictors of post-concussive syndrome after minor head injury in an emergency population. Brain Injury, 13:173–189.

114.

McClelland

, Fenton

, Rutherford

. 1994. The postconcussional syndrome revisited. J R Soc Med, 87:508–510.

115.

Ponsford

, Willmott

, Rothwell

et al. 2000. Factors influencing outcome following mild traumatic brain injury in adults. J Int Neuropsychol Soc, 6:568–579.

116.

Zlotnick

, Zimmerman

, Wolfsdorf

, Mattia

. 2001. Gender differences in patients with posttraumatic stress disorder in a general psychiatric practice. Am J Psychiatry, 158:1923–1925.

117.

Breslau

, Davis

, Andreski

. 1995. Risk factors for PTSD-related traumatic events: A prospective analysis. Am J Psychiatry, 152:529–535.

118.

Evans

. 1992. The postconcussion syndrome and the sequelae of mild head injury. Neurol Clin, 10:815–847.

119.

Stogner

. 2000. Prediction of postconcussional syndrome from personality and health psychology variables. Dissertation Abstracts International. Section B: The Sciences & Engineering, 61:550.

120.

Stulemeijer

, Vos

, Bleijenberg

, van der Werf

. 2007. Cognitive complaints after mild traumatic brain injury: Things are not always what they seem. J Psychosom Res, 63:637–645.

121.

Dikmen

, Temkin

, Armsden

. 1989. Neuropsychological recovery: Relationship to psychosocial functioning and postconcussional complaints. Mild Head Injury. Levin

, Eisenberg

, Benton

. Oxford University Press: New York, 229–241.

122.

Russell

. 1971. The Traumatic Amnesias. Oxford University Press: London.

123.

Rutherford

, Merrett

, McDonald

. 1979. Symptoms at one year following concussion from mild head injuries. Injury, 10:225–230.

124.

Binder

, Rohling

. 1996. Money matters: A meta-analytic review of the effects of financial incentives on recovery after closed-head injury. Am J Psychiatry, 153:7–10.

125.

Cato

, Brewster

, Ryan

, Giuliano

. 2002. Coaching and the ability to simulate mild traumatic brain injury symptoms. Clin Neuropsychol, 16:524–535.

126.

Hou

, Moss-Morris

, Peveler

, Mogg

, Bradley

, Belli

. 2012. When a minor head injury results in enduring symptoms: A prospective investigation of risk factors for postconcussional syndrome after mild traumatic brain injury. J Neurol Neurosurg Psychiatry, 83:217–223.

127.

Larrabee

. 1997. Neuropsychological outcome, post concussion symptoms, and rorensic considerations in mild closed head trauma. Semin Clin Neuropsychiatry, 2:196–206.

128.

Miller

. 2001. Not just malingering: Syndrome diagnosis in traumatic brain injury litigation. NeuroRehabilitation, 16:109–122.

129.

Paniak

, Reynolds

, Toller-Lobe

, Melnyk

, Nagy

, Schmidt

. 2002. A longitudinal study of the relationship between financial compensation and symptoms after treated mild traumatic brain injury. J Clin Exp Neuropsychol, 24:187–193.

130.

Slick

, Hopp

, Strauss

, Thompson

. 1997. Victoria Symptom Validity Test. Psychological Assessment Resources: Odessa, FL.

131.

Wilk

, Herrell

, Wynn

, Riviere

, Hoge

. 2012. Mild traumatic brain injury (concussion), posttraumatic stress disorder, and depression in U.S. soldiers involved in combat Ddployments: Association with postdeployment symptoms. Psychosom Med, 74:249–257.

132.

Hutchison

, Mainwaring

, Comper

, Richards

, Bisschop

. 2009. Differential emotional responses of varsity athletes to concussion and musculoskeletal injuries. Clin J Sport Med, 19:13–19.