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. Author manuscript; available in PMC: 2019 May 1.
Published in final edited form as: Psychol Serv. 2018 May;15(2):181–190. doi: 10.1037/ser0000187

Effect of Social Support and Resilient Coping on Violent Behavior in Military Veterans

Elizabeth E Van Voorhees 1,2, H Ryan Wagner 1,2, Jean C Beckham 1,2, Daniel W Bradford 2,3,4, Lydia C Neal 1, Walter E Penk 5, Eric B Elbogen 1,2,3
PMCID: PMC6398331  NIHMSID: NIHMS1006128  PMID: 29723020

Abstract

Violence toward others has been identified as a serious post-deployment adjustment problem in a subset of Iraq and Afghanistan era veterans. The current study examines the intricate links between posttraumatic stress disorder (PTSD), commonly cited psychosocial risk and protective factors, and violent behavior using a national randomly selected longitudinal sample of Iraq and Afghanistan era U.S. veterans. A total of N=1090 veterans from 50 U.S. states and all U.S. military branches completed two waves of self-report survey data collection one year apart (retention rate=79%). History of severe violence at Wave 1 was the most substantial predictor of subsequent violence. In bivariate analyses high correlations were observed among risk and protective factors, and between risk and protective factors and severe violence at both time points. In multivariate analyses, baseline violence (OR=12.43, p<.001), baseline alcohol misuse (OR=1.06, p<.05), increases in PTSD symptoms between Waves 1 and 2 (OR=1.01, p<.05), and decreases in social support between Waves 1 and 2 (OR=.83, p<.05) were associated with increased risk for violence at Wave 2. Our findings suggest that rather than focusing specifically on PTSD symptoms, alcohol use, resilience or social support in isolation, it may be more useful to consider how these risk and protective factors work in combination to convey how military personnel and veterans are managing the transition from wartime military service to civilian life, and where it might be most effective to intervene.

Keywords: Posttraumatic stress disorder, combat stress disorders, aggression, resilience

Violence is a serious problem among a subset of Iraq- and Afghanistan-era veterans (MacManus et al., 2015). Though several decades of research with Iraq/Afghanistan era veterans (Jakupcak et al., 2007; Milliken, Auchterlonie, & Hoge, 2007; Sayer et al., 2010; Thomas et al., 2010) and Vietnam era veterans (Lasko, Gurvits, Kuhne, Orr, & Pitman, 1994; McFall, Fontana, Raskind, & Rosenheck, 1999; Orcutt, King, & King, 2003; Savarese, Suvak, King, & King, 2001; Taft, Monson, Hebenstreit, King, & King, 2009) has pointed to a set of key factors that increase the risk for violence including posttraumatic stress disorder (PTSD), younger age, alcohol misuse, and degree of combat exposure (Beckham, Moore, & Reynolds, 2000; Elbogen et al., 2010; Jakupcak et al., 2007; MacManus et al., 2015; Taft et al., 2005), there continue to be important gaps in our understanding. First, most research to date has been focused on identifying risk factors for violence, especially PTSD, but has largely overlooked protective factors such as resilience and social support (Pietrzak & Southwick, 2011; Renshaw, 2011). Second, the majority of studies examining violence in military and veteran populations have not enrolled national samples, with only a few exceptions (Elbogen et al., 2014a; Macmanus et al., 2013; Taft et al., 2012). Third, most studies have not controlled for history of violence, which is a robust risk factor (Elbogen et al., 2014b; Monahan et al., 2001). Finally, although exceptions exist (Kachadourian, Quigley, & Leonard, 2014; Makin-Byrd, Bonn-Miller, Drescher, & Timko, 2012; Ramchand, Rudavsky, Grant, Tanielian, & Jaycox, 2015; Shin, Rosen, Greenbaum, & Jain, 2012), extant research has been predominately cross-sectional, prohibiting the possibility of explicating the role of dynamic changes in risk and protective factors over time.

Previous work using the National Post-Deployment Adjustment Survey found that social support and resilient coping at baseline predicted subsequent aggressive behavior (Elbogen et al., 2014a). However, while these findings are useful for clinicians evaluating violence risk, from a rehabilitation perspective it is also critical to understand how (or whether) changes to dynamic risk and protective factors over time are associated with changes in risk for violence. Put differently, clinicians conducting treatment need to know if their efforts to address social support and resilient coping are associated with concurrent reduction of risk. To our knowledge, no study has examined this in military veterans. The current study extends the literature by examining how changes to veterans’ risk and protective factors over the course of one year impact their risk of engaging in severe violence during that time frame. After accounting for demographic and static factors such as age, gender, financial stability, and combat exposure, we hypothesized that changes in PTSD symptomatology and alcohol misuse would be associated with increased risk of severe violence over that time frame, whereas increases in social support and resilience would be associated with decreased risk of severe violence.

Methods

Participants

The sample for this study was taken from the National Post-Deployment Adjustment Survey, which was originally drawn by the U.S. Department of Veterans Affairs (VA) Environmental Epidemiological Service in May 2009. It consisted of a random selection of over one million U.S. military service members who served after September 11, 2001 and who were, at the time of the survey, either separated from active duty or in the Reserves/National Guard. The sample was stratified by gender, and female veterans were oversampled to ensure adequate representation.

After Institutional Review Board approval was obtained, veterans were surveyed using Dillman survey methodology (Dillman, Smyth, & Christian, 2009) involving multiple and varied contacts to optimize response rate. Wave 1 and Wave 2 of data collection involved the same procedures, and participants were reimbursed after completing each wave. Wave 1 was conducted July 2009 to April 2010, yielding a 47% response rate, which is comparable to that achieved in other national surveys of veterans in the United Kingdom (Iversen et al., 2007) and the United States (Tanielian & Jaycox, 2008; Vogt et al., 2011). Wave 2 was conducted from July 2010 to April 2011. In total, 1,090 veterans completed the Wave 2 one-year follow-up survey, yielding a 79% retention rate. Multivariate analyses revealed that younger age and lower income predicted attrition, explaining approximately 4% of the variance in drop-out. None of the other variables, including violent behavior, were significantly associated with attrition.

The proportion of survey responders in each military branch closely approximated the proportion in the U.S. military at both Wave 1 and Wave 2: Army, 54% and 48%, respectively; Air Force, 19% and 22%; Navy, 16% and 17%; Marines, 11% and 11%; and Coast Guard, <1% and 2%. Racial/ethnic groups were representative of those in the military: 71% Caucasian and 29% African American, Hispanic, or other. The final longitudinal sample was representative of 50 states, Washington D.C., and 4 territories in approximately the same proportion as the actual military (FY2009 Annual demographic profile of military members in the Department of Defense and U.S. Coast Guard, 2010).

Median age of the sample was 34 years. Eighty-four percent of the sample was male; 85% of participants reported education beyond high school; and 63% reported being able to meet their basic financial needs. Just under half served in the Reserves or National Guard. Eighty-four percent reported having been stationed in the region of conflict during either Operation Iraqi Freedom (OIF) or Operation Enduring Freedom (OEF; Afghanistan), and a little more than one quarter reported multiple deployments. Twenty-five percent reported their highest rank in service to have been junior enlisted rank (E1-E4); 54% reported they were non-commissioned officers (E5-E9); 2% were Warrant Officers (W1-W5); 8% were junior commissioned officers (O1-O3); and 11% were higher ranking commissioned officers (O4-O10). Time since deployment ranged from one to eight years, with a median of four years. To our knowledge, this national dataset has achieved one of the most representative samples of U.S. Iraq and Afghanistan veterans to date (Elbogen et al., 2014a).

Measures

Violence.

This primary outcome measure of severe violent behavior was measured as present (vs. absent) by endorsement of specific items on the Conflict Tactics Scale (Straus, 1979) (“Used a knife or gun,” “Beat up the other person,” or “Threatened the other person with a knife or gun”) or on the MacArthur Community Violence Scale (Steadman et al., 2000) (“Did you threaten anyone with a gun or knife or other lethal weapon in your hand?,” “Did you use a knife or fire a gun at anyone?,” or “Did you try to physically force anyone to have sex against his or her will?”). To prevent participants in our sample from including in their responses acts that would be appropriate in a military or combat setting, the instructions specifically referenced conflicts in interpersonal relationships or included the specific instructions “Don’t include situations that happened during deployment.” The MacArthur Risk Assessment Study (Monahan et al., 2001) demonstrated that self-report of violence operationalized in this way is equally or more valid than other violence measurement approaches, including extracting data from arrest records (Lidz, Mulvey, & Gardner, 1993; Macmanus et al., 2013; Swanson, Borum, Swartz, & Hiday, 1999).

The following dynamic risk and protective factors were selected based on an empirical review of the literature of violence risk in veterans (Elbogen et al., 2010).

Posttraumatic Stress disorder (PTSD).

PTSD was measured by the Davidson Trauma Scale (DTS) (Davidson et al., 1997). The DTS is a 17-item self-report measure of the frequency and severity of symptoms of PTSD experienced within the past week. Each of the 17 items corresponds to DSM-IV PTSD symptoms. Participants rate frequency and severity separately for each item on a 5-point Likert scale ranging from 0 (not at all/not all distressing) to 4 (every day/extremely distressing). Scores range from 0 to 136. This measure has demonstrated good reliability and validity in military veterans who served in the military after September 11, 2001 (McDonald, Beckham, Morey, & Calhoun, 2009). In the current sample, Cronbach’s alpha was 0.98 for both Waves.

Alcohol Misuse.

Alcohol misuse was measured by Alcohol Use Disorder Identification Test (AUDIT) (Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). The AUDIT is a ten-item measure assessing three factors: alcohol consumption, alcohol dependence, and adverse consequences of alcohol use. The range of possible scores is 0–40 with higher scores indicating increased probability of an alcohol use disorder. The AUDIT has been found to have high test- retest reliability (r = .86) and a high level of agreement with other measures of alcohol use disorders (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001). Cronbach’s alpha in the current sample was 0.86 for Wave 1 and 0.87 for Wave 2.

Social support.

Social support was measured using the combined “Satisfaction” score on two items of the Quality of Life Index (Ferrans & Powers, 1992): “the emotional support you get from your family” and “…from people other than your family”. Participants responded on a 6-point Likert scale ranging from 1 (“Very dissatisfied”) to 6 (“Very satisfied”). Cronbach’s alpha for this measure in the current sample was 0.78 for both Waves. Scores on this measure could range from 2 to 12.

Resilient coping.

Resilient coping was evaluated using the Connor-Davidson Resilience Scale (CD-RISC) (Connor & Davidson, 2003), a 25-item measure that examines an individual’s endorsement of cognitions in response to “problems”, “challenges”, “difficulties” or “failure”. Participants responded on a 5-point Likert scale ranging from “not true at all” to “true nearly all of the time” on questions such as “I can deal with whatever comes my way”; “Having to cope with stress can make me stronger”; “Good or bad, I believe most things happen for a reason”; and “I feel in control of my life”. Scores could range from 0–100. The CD-RISC has demonstrated good psychometric properties (Connor & Davidson, 2003), and it has frequently been used in research with veterans (Elbogen et al., 2014a; Green, Beckham, Youssef, & Elbogen, 2013; Green et al., 2010; Green et al., 2014; Pietrzak, Goldstein, Malley, Rivers, & Southwick, 2010; Pietrzak & Southwick, 2011). In the current sample, Cronbach’s alpha was 0.96 for Wave 1 and 0.97 for Wave 2.

Change scores for psychosocial variables.

To examine how change in psychosocial predictors over time affected severe violence, we computed change scores from Wave 1 to Wave 2 for each of the following variables: DTS total score; AUDIT total score; CD-RISC total score; and total social support score.

Covariates.

Covariates included age, gender, the degree to which basic financial needs were met, and combat exposure. “Basic financial needs met” was indexed by the summed positive response to the question “Do you generally have enough money each month to cover: food; clothing; housing; medical care; traveling around the city for things like shopping, medical appointments, or visiting friends and relatives; and social activities like seeing movies or eating in restaurants,” for a total score ranging from 0 (needs unmet in all categories) to 6 (needs met in all categories). Combat exposure was measured using the 16-item “Combat Experiences” subscale of the Deployment Risk and Resilience Inventory (King, King, & Vogt, 2003). Responses ranged from “never” to “daily or almost daily” on a 5-point Likert scale. Items included, “I went on combat patrols or missions”; I was attacked by terrorists or civilians”; “I killed or think I killed the enemy in combat”; and “I was wounded or injured in combat.”

Analysis

Women constituted 15.6% of the military at the time of data collection (FY2009 Annual demographic profile of military members in the Department of Defense and U.S. Coast Guard, 2010), but due to oversampling they made up 33% of the survey sample. Accordingly, data were weight-adjusted to reflect the current ratio of women in the armed forces, rendering a weight-adjusted total sample size of 866.

Bivariate analyses were used to examine associations among psychosocial risk and protective factors, and between psychosocial risk and protective factors at Waves 1and 2 and severe violent behavior at Waves 1 and 2. Multivariate analyses testing the associations between severe violent behavior reported at Wave 2 and concomitant changes in psychosocial risk and protective factors were modeled using logistic regression procedures. Specifically, a dichotomous proxy variable coded positive for subjects endorsing Wave 2 severe violence was regressed on a series of putative predictive factors and covariates in three separate stages.

At Stage 1, putative predictive factors and covariates were restricted to Wave 1 measurements. At Stage 2, the latter model was extended to include the putative real-time psychosocial risk factors of changes in PTSD severity (change in the DTS score from Wave 1 to Wave 2) and alcohol misuse (change in AUDIT score from Wave 1 to Wave 2). At Stage 3, the model was again extended to include putative real-time psychosocial protective factors of changes to resilient coping (change in CD-RISC score from Wave 1 to Wave 2) and social support (change in score from Wave 1 to Wave 2).

Please note that the joint inclusion of the Wave 1 score and the associated Wave1-Wave2 change score is algebraically equivalent to a model including the Wave 2 score only. We chose to present the data in the former format to elucidate the role of change in altering the probability of violence. Tests of overall effects following the simultaneous inclusion of multiple items were based on comparison of −2 log likelihood statistics. Overall model fit was assessed by the Hosmer-Lemeshow Goodness-of-Fit test and by bootstrap estimates of model bias. SAS v9.2 (Cary, NC) was used for all analyses.

Results

Ten percent of the sample reported violent behavior in the past year at Wave 1, and 9% reported violent behavior in the past year at Wave 2. Wave 1 and 2 means and standard deviations for each of the risk and protective factors, and for the change scores on these variables between Waves 1 and 2, are presented in Table 1.

Table 1.

Mean (SD) of Wave 1 and Wave 2 risk and protective scores, and of change scores

Complete cohort
 No violence at Wave 2
 Violence at Wave 2
Predictor N Mean (SD) N Mean (SD) N Mean (SD)
DTS
 Wave 1 1090 20.77 (29.74) 996 18.78 (27.63) 94 41.46 (42.55)
 Wave 2 1090 20.48 (30.10) 996 17.77 (27.68) 94 48.70 (41.21)
 Δ Wave 1 to Wave 2 1090 −0.29 (24.75) 996 −1.01 (22.04) 94 7.24 (43.89)
AUDIT
 Wave 1 1061 5.54 (5.07) 969 5.09 (4.49) 92 10.27 (8.04)
 Wave 2 1074 5.27 (4.98) 982 4.83 (4.43) 92 9.95 (7.85)
 Δ Wave 1 to Wave 2 1047 −0.20 (3.75) 957 −0.23 (3.35) 90 0.09 (6.68)
CD-RISC
 Wave 1 1060 101.07 (15.47) 967 101.80 (15.05) 93 93.60 (18.06)
 Wave 2 1064 101.42 (16.01) 971 102.71 (15.36) 93 88.29 (17.85)
 Δ Wave 1 to Wave 2 1039 0.51 (12.31) 947 1.07 (11.33) 92 −5.14 (19.15)
Social Support
 Wave 1 1090 9.53 (2.21) 996 9.61 (2.17) 94 8.72 (2.50)
 Wave 2 1079 9.51 (2.21) 985 9.67 (2.14) 94 7.87 (2.41)
 Δ Wave 1 to Wave 2 1067 0.02 (2.08) 974 0.07 (1.97) 93 −0.85 (2.91)
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Note. Sample sizes reported for each measure and wave are unweighted.

Examining the covariates in bivariate analyses, Wave 1 age (r = −.16, p<.0001) and the degree to which one could financially meet basic needs (r = −.20, p<.0001) were negatively associated with severe violence at Wave 2, and Wave 1 combat exposure (r = .22, p<.0001) was positively associated with severe violence at Wave 2. Gender was unrelated to violence at Wave 2.

Table 2 presents bivariate associations among the risk and protective factors, as well as the bivariate associations between each of these risk and protective factors and severe violence at Wave 1 and Wave 2.

Table 2.

Correlations Among Study Measures at Waves 1 and 2

Wave 1
 Wave 2
Variable 1 2 3 4 5 6 7 8 9
Wave 1
 1. Violence -
 2. DTS .25 -
 3. AUDIT .30 .22 -
 4. CD-RISC −.13 −.40 −.27 -
 5. Social support −.15 −.36 −.20 .55 -
Wave 2
 6. Violence .46 .19 .26 −.13 −.10 -
 7. DTS .26 .66 .18 −.37 −.34 .26 -
 8. AUDIT .24 .21 .72 −.27 −.18 .26 .26 -
 9. CD-RISC −.16 −.36 −.24 .69 .46 −.23 −.47 −.31 -
 10. Social support −.15 −.28 −.17 .44 .55 −.21 −.35 −.23 .60
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Note: All correlations significant at the p<.001 level.

Adjusted estimates derived from a series of multivariate logistic regression models are presented in three separate models detailing the associations between Wave 2 violence and 1) baseline factors only (Model 1, Baseline); 2) baseline factors and change in psychosocial risk factors of PTSD and alcohol misuse (Model 2, Δ Risk); and 3) baseline factors, change in psychosocial risk factors of PTSD and alcohol, and change in psychosocial protective factors of resilient coping and social support (Model 3, Δ Protective; see Table 3).

Table 3.

Multivariate Analyses of Risk and Protective Factors in Violence in Veterans

Model 1: Baseline
 Model 2: Δ Risk
 Model 3: Δ Protective
Variable Odds ratio 95% CI Odds ratio 95% CI Odds ratio 95% CI
Age 0.97 0.94–1.01 0.98 0.94–1.01 0.98 0.94–1.01
Female 0.67 0.30–1.50 0.67 0.29–1.53 0.67 0.28–1.56
Basic Needs Met 0.89 0.76–1.04 0.87 0.74–1.03 0.91 0.77–1.09
Combat Exposure 1.02 0.99–1.05 1.01 0.98–1.04 1.01 0.99–1.04
Violence: Wave 1 12.04*** 6.39–22.69 11.26*** 5.88–21.55 12.43*** 6.26–24.68
DTS 1.00 0.99–1.01 1.01† 1.00–1.02 1.01 1.00–1.02
Δ DTS - - 1.01** 1.00–1.02 1.01* 1.00–1.02
AUDIT 1.05* 1.00–1.09 1.07** 1.01–1.12 1.06* 1.00–1.11
Δ AUDIT - - 1.05† 0.99–1.11 1.03 .97–1.10
CD-RISC 0.99 0.97–1.01 1.00 0.98–1.02 0.99 .097–1.01
Δ CD-RISC - - - - 0.98† 0.96–1.00
Social Support 1.06 0.93–1.20 1.06 0.93–1.21 0.98 0.84–1.15
Δ Social Support - - - - 0.83* 0.72–0.97
Model Fit
Pseudo R2 0.334 0.350 0.389
Concordance c (AOC) 0.827 0.840 0.843
HL X2 Probability 0.841 0.690 0.235
−2LL 353.0 337.3 317.7
−2LL X2 na 15.76 19.54
df 9 2 2
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Note.

†

p<.10

*

p<.05

**

p<.01

***

p<.001

In all models, a history of severe violent behavior at Wave 1 was the most substantial predictor of subsequent violence: Participants endorsing severe violence at Wave 1 were at 11 to 12 times greater odds of endorsing severe violence at Wave 2 than were those who did not endorse Wave 1 violence. Of 102 participants endorsing severe violence at Wave 1, exactly half (51) reported a violent incident in the subsequent year. Across models, covariates for age, gender, financial capacity to meet basic needs, and combat exposure were not significantly associated with subsequent violence.

In Model 1, only severe violence at Wave 1 and AUDIT score at Wave 1 were significantly associated with severe violence at Wave 2. In contrast to bivariate findings, Wave 1 PTSD symptoms, resilient coping, and social support were not significantly associated with Wave 2 violence. These results likely reflect high collinearities of the factors with each other and with the covariates.

Model 2 (Δ Risk) included the effects of change in the psychosocial risk factors of PTSD (Δ DTS) and alcohol abuse (Δ AUDIT) on severe violence between Waves 1 and 2. Wave 1 baseline AUDIT score remained significant in the model. Increase in DTS score between Waves 1 and 2 was significantly associated with severe violence, such that for every 1 point increase in DTS score (range 0–136), the risk of severe violence at Wave 2 increased by and odds ratio (OR) of 1.01.

Model 3 (Δ Protective) included the effects of change in the psychosocial protective factors of resilient coping (Δ CD-RISC) and social support (Δ Social Support) between Waves 1 and 2 on Wave 2 severe violence. In Model 3, increases in DTS scores and decreases in social support scores between Waves 1 and 2 significantly predicted severe violence at Wave 2.

A comparison of −2 log likelihood scores indicated that the fits of both Models 2 and 3 were significantly improved by the respective addition of the two risk-based and two protective-based change indices. Failure of Hosmer-Lemeshow derived chi-square tests of model fit to attain significance provided additional evidence of adequacy of fit. Validities of model estimates were assessed using bootstrap sampling procedures (1000 replications). Derived bias estimates for each coefficient were based on the average of the estimated coefficient from each of the 1000 bootstrap estimates less the coefficient value for the original model. Bias was expressed as a proportion of bootstrap-derived standard error for the coefficient; in general derived estimates were substantially less than 10%.

Discussion

This study examined the association of risk and protective factors, and changes to those factors over time, on severe violence in a large, nationally representative, longitudinal sample of U.S. veterans who served in the military after 9/11/2001. Consistent with empirical research in civilian populations (Monahan et al., 2001), history of violence toward others was the most robust predictor of future violence in this veteran sample. Veterans who reported violence at baseline were at an 11- to 12-fold risk of reporting violence over the next year. However, the observed rate of one-year violence in the sample as a whole was 10%, substantially lower than what has been observed in some other veteran samples wherein past four months rates have been reported to be as high as 32% (Hellmuth, Stappenbeck, Hoerster, & Jakupcak, 2012). The discrepancies between our findings and those reported by others may in part reflect other researchers’ use of clinical (vs. nationally representative) samples (Byrne & Riggs, 1996; Hellmuth et al., 2012; Jakupcak et al., 2007), and their inclusion of acts of minor aggression as a part of the operationalized definition of violence (Monahan et al., 2001). Among the other baseline factors tested, alcohol misuse at Wave 1 significantly predicted severe violence at Wave 2 in all three Models. Wave 1 self-reported combat exposure, capacity to meet basic financial needs, PTSD symptom severity, resilient coping, and social support were not significantly associated with severe violence at Wave 2 in any of the models.

When the changes in psychosocial risk and protective factors over the course of the study period were entered into the predictive models with baseline scores, covariates, and Wave 1 violence, the patterns of associations were quite different. In Model 2 (ΔRisk) changes in risk factors between Wave 1 and Wave 2 were added to the baseline model, and it was observed that increases in PTSD symptom scores over the year were associated with a significant increase in the odds of Wave 2 violence. Baseline alcohol misuse continued to significantly predict Wave 2 violence, but the association between change in alcohol misuse over the study year and Wave 2 was not statistically significance (p<.10).

When changes in psychosocial protective factors were added to the model in Model 3 (Δ Protective), a significant negative relationship was observed between odds of Wave 2 severe violence and change in social support over the course of the study year. Drops in social support between Waves 1 and 2 were concurrent with increases in PTSD symptoms, a finding that is consistent with previous research that has found social support to be strongly and bi-directionally related to PTSD symptomatology over time (Charuvastra & Cloitre, 2008; Van Voorhees et al., 2012). The negative association between changes in resilient coping and severe violence (p<.10) did not reach statistical significance.

A limitation to the multivariable modeling of correlated predictors is that effects of those predictors as indicated by estimated regression coefficients may be attenuated when modeled simultaneously. Statistical solutions to this problem have included stepwise variable selection procedures and latent variable approaches. In the current instance, we have approached this by examining incremental improvements in model fit (−2 log likelihood scores) following the simultaneous addition of like factors (chunk tests). Model fit was significantly improved moving from Model 1 to Model 2 by the combined addition of the risk factors of DTS and AUDIT scores. Similarly, the fit of Model 2 was further significantly improved by the addition of the two protective factors, CD-RISC and social support. The latter instance is informative in this regard as CD-RISC, although a significant predictor of violence when modeled as a bivariate, failed to reach significance when modeled simultaneously with support and the two risk factors. We would argue that this cautions against focusing completely on individual coefficients in complex models, and suggest instead that a focus on the entire mosaic of risk and protective factors is at least equally informative and, in most instances, much closer to the decision-making process involved in the clinic.

With the above caveat, we would nonetheless draw attention to decreases in social support as a salient predictor of reduced violence over time. This finding adds to a growing literature on the importance of social support as a protective factor in recovery from stress-related psychopathology (Charuvastra & Cloitre, 2008; Elbogen et al., 2014a), and underscores the importance of focusing on facilitating social support through unit cohesion, family support, and connections within the community for military personnel throughout their service careers and into their transition to civilian life. Similar to what has been reported elsewhere (Pietrzak & Southwick, 2011), social support and resilient coping were particularly highly correlated in this sample, underscoring that it may be questionable to consider these distinct factors in research or therapeutic contexts. Both the Department of Veterans Affairs and the Department of the Army have begun to recognize the importance of social support in contributing to the resilience of military personnel and veterans, and both are developing family-focused initiatives such as the VA’s “Strength at Home” Program (Hayes et al., 2015; Taft et al., 2014; Taft, Macdonald, Creech, Monson, & Murphy, 2016); the VA Caregiver Support Program (http://www.caregiver.va.gov/index.asp); Coaching into Care (http://www.mirecc.va.gov/coaching/); and the Family Skills Component of the U.S. Army’s Comprehensive Soldier Fitness Program (Casey, 2011). With the exception of the Strength at Home Program, however, empirical data on the effectiveness of these programs has yet to be published. We suggest that research on the effectiveness of these programs focusing on social support and resilience should include violence measures among their assessments of functional outcomes.

With respect to “resilience,” the term itself has engendered a great deal of interest and controversy over the past decade, as well as a range of sometimes conflicting definitions of what the construct means. Resilience, broadly speaking, is a multidimensional construct encompassing behavioral, cognitive, physiological, and even educational domains, such that the same individual can be more resilient in one domain and less resilient in another (Luthar, Cicchetti, & Becker, 2000). The measure of resilience we employed in this study, the CD-RISC, examines one, specific aspect of this broader construct, which we have labeled “resilient coping” in this paper. Specifically, the CD-RISC measures the degree to which participants endorse coping-related cognitions of self-efficacy; action-oriented approaches to problem-solving; a sense of control over one’s life; feelings of connectedness to important others and to the community; and a sense of optimism or faith (Connor & Davidson, 2003). As such, resilience in this study should not be construed to reflect a broader range of functioning such as physiological hardiness or behavioral resilience, nor should it be seen as reflective of a fixed personality characteristic of mental toughness, invulnerability, or capacity to “bounce back” from severe or traumatic stress in any global sense. Rather, “resilient coping” as measured in this study refers to the degree to which an individual holds a specific set of beliefs about his or her current capacity to cope with “challenges”, “difficulties” or “failure” typically encountered in day-to-day life (Connor & Davidson, 2003).

In this study we found that after accounting for other correlated factors like social support, resilient coping was no longer significant (p<.10) in predicting violence. Considering, then, the aspect of “resilience” measured in this study, this may suggest that maintaining a sense of personal control, purpose, and optimism over time is inextricably linked to the capacity to draw support from important others (Charuvastra & Cloitre, 2008). This is consistent with core principles of mental health recovery-oriented clinical services that emphasize the importance of relationships and social networks in fostering self-direction (personal control), empowerment (purpose), and hope (optimism) (SAMHSA, 2012). Involvement in clinical programs in the VA that emphasize the engagement with supportive social networks as a central component of mental health recovery could therefore help to increase resilience and decrease violence risk.

It is important to keep in mind, however, that over 80% of this sample served in a war zone, and that others may have served in other military-related emotionally high-risk situations (i.e. processing remains of soldiers returned to the United States (McCarroll, Fullerton, Ursano, & Hermsen, 1996; McCarroll, Ursano, & Fullerton, 1993, 1995; McCarroll, Ursano, Fullerton, & Lundy, 1993; Ursano & McCarroll, 1990); engaging in airstrikes using remotely piloted aircraft or “drones” (Chappelle, Goodman, Reardon, & Thompson, 2014; Otto & Webber, 2013)). Cognitions that are usually adaptive in efforts to manage normal, day-to-day stress may no longer be self-evident for those who have seen combat or who have otherwise been repeatedly exposed to the carnage of war. Exposure to trauma, including the senselessness of suffering and death that inevitably characterizes war, can present a challenge to veterans as they grapple to maintain a sense of meaning, purpose, and optimism in the wake of their experiences. An examination of two of the 23 items on the CD-RISC may illustrate this point. First, according to Cognitive Processing Therapy for PTSD, the CD-RISC item “Good or bad, I believe that most things happen for a reason” may be seen as an endorsement of the “just world hypothesis”, a construct that has been identified as being at the root of some of the most commonly encountered maladaptive cognitions or “stuck points” that maintain the disorder (Resick, Monson, & Chard, 2010). For individuals who have faced uncontrollable trauma and blame themselves or others for senseless, horrific events, the belief that there must have been a reason for what happened can perpetuate hostility toward others or a damaging sense of shame, guilt, and self-doubt (Andrews, Brewin, Rose, & Kirk, 2000; Taylor, 2015). As such, this cognition must be adapted in a way that can accommodate the realities that veterans have experienced to allow recovery to occur. Similarly, the statement “I can deal with whatever comes my way” may be true in most civilian contexts, whereas it may not have been true, for example, in a situation when an individual tried to muster all of her resources but was unsuccessful in saving her friend during a firefight.

In other words, awareness of the stark realities of war may make it particularly difficult for some veterans to reclaim previously held “resilient” coping-related cognitions when they return to civilian life, even when in most day-to-day situations such beliefs are more likely to be adaptive and to hold true. The findings presented here suggest that decreases in resilient coping over time may put individuals at risk for engaging in violence, and that this decline in “resilience” may actually reflect difficulty re-calibrating beliefs about personal control and meaning to allow for effective re-engagement in social relationships in the civilian world. From a clinical standpoint, therapists working with returning veterans may need to keep in mind that the same cognition may be resilient in one context and maladaptive in another. Providing veterans with the opportunity to examine how each of the “resilient” coping-related cognitions does or does not apply in combat vs. civilian contexts may be a powerful approach to facilitating recovery.

There are several study limitations to note. First, the study relied on self-report, leaving open the possibility of underreporting of factors (e.g., PTSD) and outcomes (e.g., violence). Prior research, however, suggests that veterans do not substantially underreport violence. For example, one recent study found that veterans self-reported 80% of all violent incidents supplied by collateral sources (Elbogen, et al., 2013). Second, though retention from Wave 1 to Wave 2 was fairly high at 79%, we did observe that 4% of the variance in drop-out was accounted for by age and income, both of which are factors associated with violence in bivariate analyses. Although it is difficult to ensure perfect sample representativeness, a number of steps were taken to increase generalizability, and our survey sample did reflect the target population with respect to ethnicity, branch, and geography. We suspect that the greater drop-out among younger and lower income participants may be an artifact of the longitudinal study design, because younger and less financially stable participants may maintain less consistent mailing addresses over a one-year time span.

A third limitation is that our measure of social support consisted of only two items drawn from the Satisfaction Score of the Quality of Life Index, rendering it of potentially questionable reliability in measuring this construct. However, the Cronbach’s alpha calculated on this sample for this measure was .78 for both Waves, suggesting adequate internal consistency. Finally, we did not control for the effects of depression on severe violence outcomes, which may be seen as a limitation given previous work documenting an association of depression with aggression particularly in veteran samples with PTSD (Angkaw et al., 2013; Hellmuth et al., 2012; Marshall, Sippel, & Belleau, 2011). However, depression and PTSD are both stress-related disorders that reflect overwhelm of physiological and emotion stress response systems (McEwen, 2000; Tye, Van Voorhees, Hu, & Lineberry, 2015), and it has been argued that depression comorbid with PTSD may be a marker of the severity of posttraumatic psychopathology rather than a separate, phenotypically distinct diagnostic entity (Galatzer-Levy, Nickerson, Litz, & Marmar, 2013; Krueger & Markon, 2006; Tye et al., 2015).

The findings presented here contribute to the understanding of dynamic risk and protective factors for violence in military personnel. Clinicians can use this information to inform their work with individual veterans by collaborating with veterans to help them manage risk factors and bolster protective factors in ways that will facilitate recovery and community reintegration. This may include working with veterans during military-to-civilian transitions to help them to re-examine fundamental elements of their belief systems that are so often disrupted by war and war-related experiences. On a policy level, these data suggest the need for a comprehensive focus on developing both internal and external resources for military personnel, including access to financial resources, support for families, engagement of community social support networks, and access to appropriately-trained mental health professionals. Finally, future research should evaluate whether interventions currently being implemented to increase social support and resilience have a positive impact on violent behavior outcomes.

Acknowledgments

The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs or the National Institutes of Health. Preparation of this manuscript was supported by the National Institute of Mental Health # R01MH080988 (JCB), Clinical Sciences Research and Development Research Career Scientist Award # 11S-RCS-009 (JCB) and Rehabilitation Research and Development Career Development Award # 1IK2RX001298–01A2 (EEV) from the U.S. Department of Veterans Affairs, and the Mid-Atlantic Mental Illness Research, Education and Clinical Center of the Department of Veterans Affairs Office of Mental Health Services and the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Clinical Science Research and Development.

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