OEF: Relationships among combat exposure, postdeployment stressors, social support, and risk behaviors

Applied and Preventive Psychology 14 (2010) 86–94

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The Army National Guard in OIF/OEF: Relationships among combat exposure, postdeployment stressors, social support, and risk behaviors James Griffith ∗ , Courtney West Army National Guard, United States

a r t i c l e

i n f o

Keywords: Risk behaviors Reservists Combat exposure Postdeployment stressors Buffering effects

a b s t r a c t With the continued operations in Iraq and Afghanistan, studies of the prevalence of posttraumatic stress disorder and related symptoms are now common. However, lacking is how these symptoms relate to precipitating conditions and the mitigating effects of social support on these symptoms. This is particularly relevant for reserve military personnel, who have been shown to be greater at-risk for postdeployment problems. The present study examined questionnaire data obtained from Army National Guard (ARNG) units immediately after their return from deployment to Iraq and Afghanistan during 2010 (N = 4329 soldiers in 50 units). Findings showed few soldiers displayed risk behaviors (i.e., daily alcohol use, use of illicit drugs, suicide thoughts, and physically threatening others) during and after deployment. Those most likely to have more postdeployment risk behaviors were also those who showed more risk behaviors during deployment. A substantial percentage of soldiers reported combat exposure, postdeployment negative emotions, and postdeployment loss of a personal relationship. These reported outcomes were all related to increased risk behaviors after deployment. The buffering effect of social support on postdeployment risk behaviors was equally evident when data were examined individually and when grouped by unit memberships. Implications of findings for future research, practice, and policies are discussed. Published by Elsevier Ltd.

Since 2001, more than 1.7 million U.S. military personnel have served in support of Operation Iraqi Freedom (OIF) and Operation Enduring Freedom (OEF) (Sollinger, Fisher, & Metscher, 2008).One of the unique aspects of these operations has been the deployment of large numbers of U.S. reserve military personnel.1 Reserve forces, in particular, the Army National Guard (ARNG) and Army Reserve (USAR), comprised as high as 30–40% of the ground forces. Not since the Korean War had such a large percentage of the U.S. deployed force been reserves. Changes in force structure in the 1980s and the large-scale operations necessitated the recent mobilization and deployment of reserve military personnel. Enacted in the 1980s, the Total Force policy integrated the “regular” or active duty Army with its reserve components – the ARNG and the USAR (Zapanta, 2005). This was accomplished by moving many of the combat support and combat service support functions to the ARNG and USAR,

∗ Corresponding author at: 10956 Bellehaven Boulevard, Damascus, MD 20872, United States. Tel.: +1 301 452 6026. E-mail address: griffi[email protected] (J. Griffith). 1 The U.S. reserve force consists of seven components, one for each of the five services (Army, Navy, Marine Corps, Air Force, and Coast Guard), in addition to the Air and Army National Guard. The purpose of the reserve components is to provide ready, trained, and qualified personnel who can be called to full-time military service during national emergencies. 0962-1849/$ – see front matter. Published by Elsevier Ltd. doi:10.1016/j.appsy.2011.11.003

respectively. Thus, to conduct major military operations in the future, both active and reserve components would have to be called to action (Whitlock, 2006). This has been particularly relevant in U.S. involvement in Iraq and Afghanistan.

1. Adjustment following deployment Over the last decade, numerous studies have investigated the risks associated with deployment and having participated in combat operations on the physical and mental well-being of soldiers. The First Gulf War gave hints of associations between combat experiences and negative postwar adjustment. Studying a sample of U.S. military personnel during the First Gulf War, Naguen et al. (2011) found soldiers who had killed others were more likely to have posttraumatic stress disorder (PTSD) and to have abused alcohol than soldiers who had not killed. Similarly, Bell, Hunt, Harford, and Kay (2011) reported combat exposure was associated with later adjustment problems. Among U.S. military personnel discharged for health-related disabilities from 1994 to 2007, soldiers deployed to combat zones had significantly more mental health symptoms than soldiers who had not deployed to combat zones. Studies of military personnel deployed to Iraq and Afghanistan have yielded similar results. Periodic surveys of U.S. service personnel deployed to OIF and OEF showed associations between

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deployment (both the number and the length) and probable PTSD and related symptoms (U.S. Department of the Army, 2008). Other studies have noted associations between participation in combat and later physical and psychological adjustment. Studying a sample of returning Army and Marines from deployment to OEF and OIF, Hoge, Auchterlonie, and Milliken (2006) found that combat experiences in Iraq were associated with high utilization of mental health services and attrition from military service after deployment. Smith et al. (2008) studied a cohort of U.S. service personnel deployed to OIF and OEF. After controlling for baseline characteristics including self-reported symptoms of PTSD and various personal and unit characteristics, a threefold increase in PTSD symptoms among deployed personnel who reported combat exposure was found. Fear et al. (2010) studied U.K. military personnel and found deployment to Iraq and Afghanistan was associated with probable PTSD and alcohol abuse. These findings are even more pertinent due to recent observations that reservists may be greater at-risk for PTSD and related symptoms after having returned from deployment. Previously deployed ARNG soldiers were found to be three times more likely to screen positive for PTSD than soldiers with no previous deployments (Kline et al., 2010). In a review of studies of reservists’ deployment adjustment, Griffith (2010) found reserve and active duty personnel had similar rates of PTSD and related symptoms in-theater and shortly after returning from deployment. However, sometime after deployment, reservists had higher rates of PTSD and related symptoms than active duty personnel. Similar trends have been observed in recent large-scale longitudinal studies of reservists (Riviere, Kendall-Robbins, McGurk, Castro, & Hoge, 2011; Thomas, Wilk, Riviere, McGurk, & Castro, 2010), including increased alcohol use (Jacobson et al., 2008a). 2. Trauma as pathogenic Results are consistent with the notion that trauma has a pathogenic effect on the individual. Accordingly, experiences of trauma (e.g., environmental disaster and loss of loved ones) burden an individual’s ability to cope, placing considerable demand on resources to adjust. This disrupts the physical and psychological equilibrium, eventually leading to physical and mental health problems (Dekel, Ein-Dor, & Solomon, 2011). Considerable evidence supports this view. Studies have documented increased rates of PTSD, depression, anxiety, somatization, and alcoholism among those who have experienced trauma (Breslau, Davis, Andreski, & Peterson, 1991; Kessler, Sonnega, Bromet, & Hughes, 1995).This view is also consistent with the stressors-strain model in the health psychology literature (Cohen & Wills, 1985; Hobfoll, 1989) and the demand-resource model in military literature (Bates et al., 2010). Stressors tax individual coping mechanisms, require adjustment, and often result in strain or physical and mental health problems. To the extent additional resources are available, the less likely that stressors will adversely impact individual health and wellbeing. This model is consistent with the “transaction model” of stress, namely, an individual’s perceptions of demands exceeding resources results in negative consequences on well-being (Gallo & Matthews, 2003; Hobfoll, 1989; Jacobson, 1986; Lazarus & Launier, 1978). Often this disparity in needs can be mitigated by social relationships, consistent with the “needs model” of stress (Caplan, 1964), which postulates that satisfying interpersonal relationships are necessary for individual well-being (Weiss, 1974).2 One such

2 The importance of others in stress adaptation is evident in very recent applications of social identity theory to individual health and well-being. Haslam, O’Brien, Jetten, Vormedal, and Penna (2009), for example, have described how identity defined through social relationships often determines stress appraisal and response,

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interpersonal resource is social support, which has been shown to lessen the negative effects of stressors on the individual, called the “buffering effect” (Cohen & Wills, 1985). 3. Mitigating effect of social support Past and current studies have shown that available social support provides soldiers with additional coping mechanisms under stressful circumstances (Belenky, Noy, & Solomon, 1987; Iversen et al., 2010). A related concept is cohesion, which describes the level of task and social/emotional support available to a soldier when needed (Griffith, 1989). In a quasi-experiment, Rom and Mikulincer (2003) found that soldiers in groups having higher levels of cohesion reported higher levels of instrumental and socioemotional functioning during missions compared to soldiers in groups having lower levels of cohesion. Soldiers in the high cohesion groups also reported being less anxious and displayed less avoidant attachment to their groups. In a sample of U.S. soldiers yet to be deployed, Brailey, Vasterling, Proctor, Constans, and Friedman (2007) found that life experiences and cohesion independently predicted PTSD, and cohesion lessened the negative effects of stressful life events on PTSD. Periodic surveys of U.S. military personnel deployed to Iraq and Afghanistan (U.S. Department of the Army, 2008) have shown what might be considered “vertical cohesion” or effective officer leadership (among platoons) moderated the relationship between combat experiences and PTSD symptoms. Less effective unit leadership was associated with a stronger relationship between combat events and mental health problems. Mulligan et al. (2010) reported similar findings when examining U.K. soldiers before and after deployment. Those soldiers who reported lower levels of cohesion and quality of unit leadership in their units were more likely to have higher risk of psychological distress than soldiers with higher levels of cohesion and quality unit leadership. Indeed, Solomon, Mikulincer, and Hobfoll (1986) noted the importance of officer leadership support during the 1982 Lebanon War in lessening the negative effects of stressors on Israeli combat soldiers. 4. Research gaps and the study purpose Several gaps in the current literature are evident when considering the topics of deployment, combat exposure, and physical and mental health problems of reserve military personnel. Despite the seemingly greater vulnerability of reservists to deployment and combat experiences (Griffith, 2010; Jacobson et al., 2008b; Kline et al., 2010; Thomas et al., 2010), there have been few studies that report estimates of combat exposure among Guard/Reserve soldiers and the association with later problem behaviors. Using data from archival personnel and medical systems, large-scale studies characteristically have examined the prevalence of PSTD and related symptoms among deployed military personnel (Milliken, Auchterlonie, & Hoge, 2007; Thomas et al., 2010). Lacking is how such symptoms relate to precipitating conditions and the mitigating effects of social support. Studies of more detailed accounts of combat exposure associated with symptoms and social support have relied primarily on small non-probability samples (Erbes et al., 2008; Vogt, Samper, King, & Martin, 2008). Thus, the first gap in the literature is the lack of reliable estimates of combat exposure and post-deployment stressors for ARNG personnel. The second gap is the absence of information on the extent to which these stressful conditions are associated with later problem behaviors. This includes the extent to which social support can mitigate the

health-related norms and behaviors, social support, coping, and clinical outcomes (pp. 9-14).

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Table 1 Distribution of unit type for the study sample compared to ARNG deployed units. Unit type

Units in study sample %

ARNG units deployed %

Combat arms Combat support Combat service support Other Column N

56.0 28.0 12.0 4.0 50

52.0 33.0 17.0 3.0 2540

negative effects of combat and postdeployment stressors on individual physical and mental well-being. The purpose of this article is to describe the type and extent of problem behaviors among soldiers during and after deployment and the association of problem behaviors with combat exposure and postdeployment stressors, and the extent to which perceived unit support intervenes in these relationships. 5. Method 5.1. Data source The source of data was the Reintegration Unit Risk Inventory (R-URI), a survey administered to members of ARNG units during calendar year 2010. The R-URI is administered and funded by the U.S. Army Substance Abuse Program (ASAP). Commanders are encouraged to offer the inventory to unit members 60–90 days after returning from deployment as a part of their unit reintegration. Data collection, storage, analyses, and reporting are performed routinely in accordance with the American Psychological standards concerning the protection of human samples (American Psychological Association, 2011). Unit members voluntarily completed the inventory. To protect anonymity, individual level responses are recorded with limited demographic information and no personally identifiable information. Responses are concatenated by unit and reported back to unit leaders. The inventory consists of 80 survey items. The survey’s primary purpose is to screen for problem behaviors and attitudes of soldiers, which may have occurred during deployment or postdeployment. Content of items includes: alcohol use, illicit substance use, suicide thoughts, physically harming others, combat experiences (e.g., saw someone wounded or killed, engaged in combat, or killed someone), stressors after deployment (financial difficulties, loss of significant other, etc.), and perceived social support (e.g., work well with others in the unit and trust in unit leaders to share personal problem). In addition, information about the deployment is collected, such as location and length of deployments, and length of time since returning from deployment. 5.2. Sample description A total of 50 units comprised the sample, with 4567 responding soldiers. The survey data provided reasonably good estimates of the units, as most of the unit members completed the survey. The mean response rate of units was 90.2%, with a range of 55–100%. Less certain was how well the survey data represented deployed ARNG personnel in 2009. The inventory did not ask about soldier background and other personal information, which could then be compared with universe information to determine representativeness. Instead, unit type (combat arms, combat service, and combat service support) of units comprising the sample was available. The distribution of unit type for units in the sample was compared to that of deployed units during the same time period. Although there was some variation (see Table 1), units generally represented the distribution of unit types deployed in 2009–2010. Most of the soldiers had returned from OIF (83.5%) and some (15.7%) from OEF.

The remaining soldiers served in other locations and were excluded from analyses as the focus was to examine ARNG soldiers returning from OIF/OEF 5.3. Study variables and scales Scales were derived from soldiers’ responses to survey items. Scales corresponded to study variables. These included: problem behaviors, both during and after deployment; combat exposure, postdeployment stressors; and social support. Each is described below. Risk behaviors in the military, including suicide thoughts, illicit drug use, alcohol abuse, and physical aggression toward others, have negative effects on unit members and unit effectiveness and are thought to be antecedents to more serious psychological and health problems (Ramchand, Acosta, Burns, Jaycox, & Pernin, 2010; U.S. Department of Army, 2010). Accordingly, risk behaviors were assessed by soldier responses to daily alcohol use, illicit drug use, suicide thoughts, and having physically threatened others. Soldiers were asked to respond to the items twice–one set applied to deployment and another set applied to postdeployment. (Alcohol use was not included in deployment questions, due to its explicit non-availability and prohibition.) Combat exposure included items that asked about seeing another individual wounded or killed, having been engaged in combat, having witnessed a trauma, having lost a friend in combat, and having killed anyone. Soldier responses to items showed the scale to have adequate reliability for exploratory research purposes (Cronbach alpha = .60, item-total correlations ranged from .25 to .47) (Nunnally, 1978; Pedhuzar & Schmelkin, 1991). Postdeployment stressors included items that asked about having financial trouble; having lost a significant personal relationship; having feelings of anger and frustration; having feelings of loneliness; and having experienced significant life changes. Soldier responses to items showed the scale to have adequate reliability for exploratory research (Cronbach alpha = .63, item-total correlations ranged from .33 to .45). Social support included items that asked about working well with others in the unit, having people to turn to in time of need, and trusting unit leaders with personal problems. Soldier responses to items showed the scale to have adequate reliability for exploratory research (Cronbach alpha = .55, item-total correlations ranged from .37 to .40). Respondents responded “yes” (coded as 1 s) or “no” (coded as 0 s) to each item. Responses to survey items were then summed to derive cumulative scale scores. 5.4. Analytic approach There were two approaches to the analyses. In the first approach, frequencies were reported for items comprising the study scales (risk behaviors – during and after deployment, combat exposure, postdeployment stressors, and social support). Simple correlations between each item and the two study outcomes, risk behaviors during deployment and risk behaviors after deployment, were also reported. In the second approach, hierarchical linear modeling (HLM by Scientific Software International, 2000) was used to examine relationships of risk behaviors to combat exposure, postdeployment stressors, and social support. This was measured both during and after deployment. HLM is most appropriate for nested data, or in the present study, soldiers having membership in 50 units (Bryk & Raudenbusch, 1992). Level 1 in HLM corresponded to individual soldiers deployed to either OIF or OEF (N = 4329) and Level 2 corresponded to soldiers aggregated by unit (N = 50). The study’s interest was to the extent to which combat exposure and postdeployment stressors were reflected in postdeployment risk behaviors than would be expected based on risk behaviors during deployment. To do this, in Level 1, risk behaviors during deployment were covaried from postdeployment risk behaviors. Next, combat exposure, postdeployment

J. Griffith, C. West / Applied and Preventive Psychology 14 (2010) 86–94 Table 2 Cumulative risk behaviors during deployment and after having returned. Number of risk behaviors

0 1 2 3 4 Column N

% reporting

Diff z-test

Deployment cumulative risk behaviors

Postdeployment cumulative risk behaviors

87.2 11.5 1.2 0.1 – 4329

83.1 14.0 2.3 0.4 0.1 4329

5.35* −3.49* ns ns –

Note. Risk behaviors were the sum of soldier reports of: daily alcohol use, illicit drug use, suicide thoughts, and physically threatened others. During deployment, soldiers were not asked about alcohol consumption. * p < .001, two-tailed.

stressors, and social support were entered as predictor variables. Thus, the outcome – having covaried initial or deployment risk behaviors – represented the extent to which combat exposure and postdeployment stressors were reflected in postdeployment risk behaviors; here, for simplicity, named “changed risk behaviors” (Cohen, Cohen, West, & Aiken, 2003; pp. 570–573). Coefficients of predictor variables showed associations of combat exposure and postdeployment stressors with risk behaviors emergent during postdeployment. Positive coefficients for either combat exposure or postdeployment stressors would then indicate their association with more risk behaviors after deployment than would be expected based on behavior during deployment. Finally, two multiplicative terms (combat exposure × support or postemployment stressors × support) represented the buffering effect of social support (Cohen & Wills, 1985). Level 1 yielded two types of estimates to be used in Level 2 analyses, namely 50 unit (1) means representing postdeployment risk behaviors after partialing out deployment risk behaviors (outcomes-as-intercepts) and (2) slopes corresponding to each predictor represented in the Level 1 equation. Slopes of both combat exposure and postdeployment stressors were of primary interest, because they served as outcomes in Level 2 to determine the crosslevel effect of unit social support on the unit relationship of combat exposure (or postdeployment stressors) with changed risk behaviors (Hofman & Gavin, 1998, p. 636). The phrase, “cross-level effect,” is used in hierarchical linear modeling when referring to a relationship observed at Level 1 (outcomes as slopes), which shows statistically reliable variation when a Level 2 or unit characteristic is considered (Hofman, Griffin, & Gavin, 2000). There were two analytic tests of the buffering effect of social support on the relationship of stressors to risk behaviors. At Level 1, the test of the buffering effect was the coefficient of the interaction term between combat exposure (or postdeployment stressors) and reported social support when predicting changed risk behaviors. A statistically significant negative coefficient would be evidence for the buffering effect. At Level 2, evidence of the buffering effect was indicated by the coefficient of social support when predicting unit slopes or relationships between combat exposure (or postdeployment stressors) and changed risk behaviors. Again, a statistically significant negative coefficient would be evidence for the buffering effect. 6. Results 6.1. Deployment and postdeployment risk behaviors One objective of the study was to describe the type and extent of risk behaviors among soldiers during and after deployment. Table 2

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displays percentages for cumulative risk behaviors during and after deployment. A great majority of soldiers did not engage in risk behaviors during or after deployment, including daily alcohol use, illicit substance use, suicide thoughts, and physically threatened others. About 13–17% of the soldiers, respectively during and after deployment, reported 1 or more risk behaviors. More soldiers reported risk behaviors after deployment than during deployment. During deployment, the primary risk behavior for those with one risk behavior was physically threatening others (72%), whereas after deployment, the primary risk behavior was daily alcohol use (57%). About 1–3% of soldiers, respectively during and after deployment, reported 2 or more risk behaviors, and very few reported more than 2 risk behaviors. During deployment, those with 2 risk behaviors primarily reported suicide thoughts (90%) and physically threatened others (99%). After deployment, those with two risk behaviors primarily reported daily alcohol use (67%) and physically threatening others (52%). Comparisons could be made with estimates of alcohol abuse in another recent study (Jacobson et al., 2008a). Estimated “heavy drinking” (having alcohol drinks more than 4 times a week) of 10.0% in the present sample was very similar to that reported by Jacobson et al. as “heavy weekly drinking” – 9% at postdeployment and 12.5% at a one-to-two-year follow-up. Estimated “binge drinking” was less comparable across the two studies, though survey items were not identical. Of the current sample, 35.2% of the soldiers reported having had 6 or more drinks on one occasion either “monthly,” “weekly,” or “daily.” Jacobson et al. reported a higher estimate of binge drinking – 53.6% at post deployment and 53.0% at follow-up, defined as 5 or more drinks per occasion or per day. 6.2. Deployment experiences An objective of the study was to describe the type and extent of combat exposure, postdeployment stressors, and social support and their relationship with risk behaviors (see Table 3). Ninety-two percent of the soldiers reported having been deployed for 7–12 months. Eighty-eight percent of the soldiers reported having returned from deployment within 3–6 months, equally divided between having been back 1–3 months and 4–6 months. Nearly one-third of the soldiers reported having been deployed twice. Both number of times deployed and length of deployment were slightly correlated with cumulative risk behaviors, though only at postdeployment (both rs = .04, two-tailed). 6.3. Combat exposure Many soldiers reported combat experiences (see Table 3). About one-half reported seeing civilians killed or wounded. About onefifth reported being engaged in combat and having witnessed combat trauma. Fourteen percent reported having lost a close friend, and about 4% reported having killed someone. All combat experiences were positively related to cumulative problem behaviors during and after deployment. Having witnessed combat trauma and having killed someone showed the highest associations with cumulative problem behaviors, both during and after deployment. 6.4. Postdeployment stressors Most soldiers reported postdeployment stressors as negative feelings, such as anger and frustration (36% reported) (see Table 3). Sixteen percent also reported feelings of loneliness after deployment. About one-fifth reported having lost a close personal relationship after having returned from deployment. Financial troubles and significant life changes were reported by about onetenth of the soldiers. All stressors were positively related to

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Table 3 Deployment characteristics, combat exposure, postdeployment stressors and social support and their relationships to cumulative risk behaviors. % reporting

Simple correlation (r) with . . . Cumulative risk behaviors, during deployment

Deployment characteristics Number of deployments 1 time 2 times Length of deployment 7–12 months Time since return 3 months or less 4–6 months Combat exposure See another wounded, killed, etc., including civilians Engaged in combat Witness trauma Lose friend in combat Killed anyone Postdeployment stressors Feelings of anger and frustration Ended significant relationship Feelings of loneliness Having financial troubles Significant life changes Social support Work well with others in the unit People I can turn to Trust unit leaders with personal problems Column N Range 4117–4329

Cumulative risk behaviors, postdeployment

.02

.04**

.02

.04**

.01

.01

48.7 19.2 19.1 14.1 4.2

.06** .07** .19** .03* .08**

.08** .11** .18** .05** .11**

35.9 19.8 15.9 11.5 10.0

.22** .14** .24** .15** .10**

.24** .14** .28** .12** .10**

91.1 89.9 65.5

−.14** −.17** −.18**

−.12** −.15** −.14**

65.3 29.1 91.8 44.6 43.3

Note. Risk behaviors were the sum of soldier reports of: daily alcohol use, illicit drug use, suicide thoughts, and physically threatened others. During deployment, soldiers were not asked about alcohol consumption. *p < .05. ** p < .01, two-tailed.

cumulative risk behaviors, with feelings of anger, frustration, and loneliness showing the highest relationships, both during and after deployment. 6.5. Postdeployment social support Many soldiers reported having available supports (see Table 3). Ninety percent or more of the soldiers reported having people they could call on in time of need or that they worked well with unit members. About two-thirds of the soldiers indicated they trusted unit leaders with their personal problems. In terms of cumulative social support, about eighty-four percent of the soldiers said they had two or more supports. Each type of support was negatively associated with risk behaviors, both during and after deployment. 6.6. Mitigating effects of support The second purpose of the study was to examine whether perceived social support mitigated the negative relationship of combat exposure and postdeployment stressors on changed risk behaviors. To accomplish this, HLM analyses were performed. The first step in HLM is determining whether variability in the primary outcome variable, postdeployment risk behaviors, could reliably be explained by unit membership. An unconditional HLM was performed by including only the outcome variable at Level 1. Results showed that units varied in postdeployment risk behaviors (chi squared = 122.32, p < .001, dfs = 49). The amount of variance explained in postdeployment risk behaviors by the units was small, about two percent. Next, HLM conditional modeling was performed to examine relationships among changed risk behaviors, combat exposure, postdeployment stressors, and social support. The Level 1 or individual soldier regression included as the outcome variables, postdeployment risk behaviors. There were two regressions; one included combat exposure as a predictor variable and the

other included postdeployment stressors (results reported in two separate columns in Table 4). Predictor variables included: deployment risk behaviors (to allow for a change score as the criterion variable), and then, either combat exposure or postdeployment stressors, postdeployment support, and a multiplicative interaction between combat exposure or postdeployment stressors and support. The study interest was examining group variability of combat exposure, postdeployment stressors, and social support in relation to changed risk behaviors. Therefore, the Level 1 predictor variables and the multiplicative interaction terms were group-mean centered. Table 4 shows results for the HLM. The top of the table reports the Level 1 (soldier) results. Both HLM equations (one for combat exposure and another for postdeployment stressors) are represented as two separate columns and each accounted for about 2% of the variance in postdeployment risk behaviors. Not surprisingly, deployment risk behaviors showed strong positive relationships with postdeployment risk behaviors when either combat exposure or postdeployment stressors was included as a predictor variable, respectively, unstandardized regression coefficient = .561 and .520, p < .001. Both combat exposure and postdeployment stressors also had significant and positive relationships with changed risk behaviors, respectively, .072 and .124, p < .001. Social support showed a statistically reliable relationship with changed risk behaviors only in the equation in which combat exposure was included (−.046, p < .001). Evident also was the buffering effect of support on the relationship of stressors on postdeployment risk behaviors. The interaction term for postdeployment stressors was statistically significant and in the predicted direction, −.024, p < .001. The interaction term for combat exposure was in the predicted direction but did not reach a traditional level of statistical significance (−.015, p < .15). To discern the nature of the interactions, regression lines were derived for the moderating variable of support when values of social support equaled ±1 SDs (Aiken & West, 1991). First, postdeployment stressors were regressed on

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Table 4 Prediction of changed risk behaviors (postdeployment risk behavior covarying deployment risk behaviors) by combat exposure, postdeployment stressors, social support, and combat/postdeployment stressors–support interactions. Level 1 (soldier) predictor variables

Criterion: postdeployment risk behaviors Combat exposure

Intercept Deployment risk behaviors Combat exposure Deployment stressors Support 8. Interaction (Support × Combat, or Support × Stressors) Level 2 (unit) predictor variable

Postdeployment stressors

Unstd coeff

SE

Unstd coeff

SE

.135 .561+ .072** – −.046** −.015 p < .15

.011 .034 .029

.142 .520** – .124** −.009 −.024**

.011 .033

.017 .011

.019 .014 .008

Criterion: see center heading Intercept: unit mean changed risk behaviors Unstd coeff

SE

Unstd coeff

SE

1. Mean unit support

−.024

.051

−.008

.054

Level 2 (unit) predictor variable

Slope: B between postdeployment risk and deployment risk behaviors

2. Mean unit support

Level 2 (unit) predictor variable

3/4. Mean unit support Level 2 (unit) predictor variable

Unstd coeff

SE

Unstd coeff

SE

.264 p < .15

.173

.064

.176

Slope: B between changed risk behaviors and combat exposure

Slope: B between changed risk behaviors and postdeployment stressors

Unstd coeff

Unstd coeff

−.051

SE .043

+

.087

SE .046

Slope: B between changed risk behaviors and support Unstd coeff

SE

Unstd coeff

SE

5. Mean unit support

−.089+

.050

−.039

.050

Variance explained Chi-squared (48)

1.9% 119.22**

2.0% 112.63**

Notes. The above represents two HLMs in which combat exposure is entered as a predictor variable and then substituted by postdeployment stressors; see two separate columns. The criterion or outcome variable represented the extent to which combat exposure and postdeployment stressors were reflected in postdeployment risk behaviors. The criterion or outcome variable, “changed risk behaviors,” was derived in the Level 1 equation by having covaried initial or deployment risk behaviors, recognizing that alcohol consumption was not included as deployment questions. Analyses included 50 units and 4147 soldiers. Cases with missing variable values ranged 3895–4147. One unit was dropped from the HLM analyses given that there were too few cases to derive estimates. + p < .10. * p < .05. ** p < .001.

postdeployment risk behaviors, and second, combat exposure was regressed on postdeployment risk behaviors. For soldiers having more support, the relationship of either postdeployment stressors or combat exposure with postdeployment problem behaviors was less positive than for soldiers having less support (for postdeployment stressors, .139 for high support versus .201 for low support; and for combat exposure, .123 for high support versus .177 for low support). In Level 2 analyses, mean unit support was used to predict (1) mean unit changed risk behaviors (Eq. (1) in Table 4); (2) unit slopes of changed risk behaviors (Eq. (2)); and unit slopes between changed risk behaviors and combat exposure or postdeployment stressors (respectively, Eqs. (3) and (4)). Level 2 or unit level analyses are displayed at the bottom of Table 4. The predictor variable, mean unit support, was grand-mean centered. Results showed no statistically reliable relationships of unit support on mean changed risk behaviors or cross-level effects. However, there were three results were just outside traditional levels of statistical significance and are worth mentioning. First, in the combat exposure equation (Eq. (2)), mean unit support moderated relationships between risk behaviors during and after deployment (.264, p < .15). Following

Aiken and West’s (1991) procedure concerning interaction effects, slopes were derived for Level 1 relationships among units having high social support 1 SD above the mean) and among units having low social support (1SD below the mean). The slope of deployment risk behaviors to postdeployment risk behaviors was unexpectedly more positive among units having higher (.738) rather than lower support (.595). Even so, high support units had mean z-score changes in risk behaviors around zero, whereas low support units had mean z-score changes in risk behaviors above zero (respectively, .024 (from .043 to .067) versus .046 (from −.031 to .015), t(47) = −.63, p < .55). Second, also in the combat exposure equation (Eq. (5)), mean unit support moderated relationships between changed risk behaviors and support (−.089, p < .08). The slope of deployment–postdeployment risk behaviors and support was more negative for high support units (−.249) than low support units (−.198).This is the test of the cross-level effect of unit support on combat exposure and changed risk behaviors, or the buffering effect of support at the group level. Third, in the postdeployment stressor equation (Eqs. (3) and (4)), mean unit support moderated relationships between changed risk behaviors and postdeployment stressors, though in an unexpected direction (.087, p < .07). The

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slope of deployment–postdeployment risk behaviors to postdeployment stressors was more positive among high support units (.243) than among low support units (.196). A possible explanation is that soldiers who experience postdeployment stressors, which are more recent than those of combat, draw on needed social support now, in particular, in units offering higher than average levels of support. This explanation is speculative, and too, Level 2 results did not reach traditional levels of statistical significance. 7. Discussion This study reported the type and extent of risk behaviors among soldiers during and after deployment. Associations among postdeployment risk behaviors, combat exposure, and postdeployment stressors were examined, as well as the extent to which social support intervened in these relationships. 7.1. Risk behaviors Few soldiers reported problem behaviors during and after deployment. Findings suggest that risk behaviors are enduring at the individual level. That is, those most likely to have more problem behaviors after deployment were also those who showed more problem behaviors during deployment. Indeed, recent research suggests risky behavior may stem from personality traits. Hong and Paunonen (2009) found that in young adults’ health-related risk behaviors (e.g., smoking, drinking, and speeding) were related to two personality dimensions – low conscientiousness and low agreeableness. Additionally, alcohol consumption and speeding were related to extraversion. The researchers interpreted the unsatisfactory effectiveness of health interventions on risky behaviors (which are based on improving decision-making) largely due to these personality differences, and that in order for interventions to be effective, consideration has to be given to personality differences. Both combat exposure and postdeployment stressors were associated with problem behaviors after returning from deployment. Herman (1992) offered several explanations as to why traumatized victims engage in risky behaviors (summarized in Svetlicky, Solomon, Benbenishty, Levi, & Lubin, 2010). First, risky behaviors may be attempts by soldiers to normalize negative emotions caused by the trauma. That is, excessive alcohol use and illicit drug use may reduce negative emotions and/or reduce hyperarousal associated with trauma symptoms (e.g., PTSD). Second, trauma itself may cause lasting inability to discern dangerous situations and/or to misinterpret threats resulting in further interpersonal difficulties. Third, having survived combat exposure may cause feelings of guilt, shame, and unworthiness, and thus, they do not seek treatment. Finally, feelings caused by the trauma and/or aspects of current situations which are similar to previous trauma may trigger past memories and associated anger and frustration, making aggressive behaviors more likely (Berkowitz, 1990). 7.2. Negative effects of stressors A sizable percentage of soldiers (50%) reported combat exposure, in particular, having seen another wounded, killed, etc. Combat exposure appeared to have a deleterious effect on individual behavior, having an association with increased risk behaviors after deployment. This finding implies further examination of methods to help soldiers dissipate negative emotions related to combat experiences is needed. A recent method being used for recently deployed soldiers is “decompression” during which unit members spend structured time, typically 3–4 days, reflecting on experiences and recognizing what was accomplished during the

deployment (Hacker-Hughes et al., 2008). Proponents of the training have argued this method allows soldiers to gain closure, and be able to better integrate with society. Findings show that a sizable percentage of soldiers experienced postdeployment stressors, with the majority experiencing negative emotions of anger and frustration. It is difficult to know the origin of such feelings. Substantial percentages of soldiers reported not returning to the same civilian job and having lost significant personal relationships after returning from deployment. It is possible that feelings of anger and frustration relate to having lost close personal relationships or having lost civilian jobs (respectively, rs = .23 and .04, p < .01, two-tailed). Circumstances surrounding these losses might be further examined to determine the extent to which the military might intervene for more positive outcomes. Consideration should be given to teaching soldiers skills that develop better coping skills when dealing with combat experiences and associated negative emotions of postdeployment. Fruitful directions are positive psychology and its recent applications in the military, such as resilience fitness training (Reivich, Seligman, & McBride, 2011), as well as interventions that view trauma as the basis for positive change (Tedeschi & Calhoun, 2004). Such approaches equip individuals to view traumatic events as challenges, and while demanding and hurtful, often lead to positive changes of gains in personal strength, intimacy with others, spirituality, and appreciation of life and its possibilities. 7.3. Mitigating effect of social support The buffering effect of social support on the relationship of stressors to risk behaviors was equally evident when data were examined individually and when grouped by unit memberships (cf. Ahronson & Cameron, 2007; Griffith, 2002). Among individual soldiers, social support was associated with decreased risk behaviors as main effects and interaction effects. Additionally, units with higher levels of support moderated relationships among risk behaviors, postdeployment stressors, and support observed within units. Findings imply the need for bolstering both individual and group support, in particular, soldiers trust in officers to confide in them personal problems (Mulligan et al., 2010; Solomon et al., 1986; U.S. Department of the Army, 2008). If soldiers were to return from deployment through decompression locations, consideration should be given to the integral role that small unit officers would play in mitigating the negative effects of deployment, as results suggest here. 7.4. Study limitations Data used in the study have clear strengths as well as limitations. The survey data, collected routinely from soldiers having returned 60–90 days after overseas deployments, provide substantial numbers of cases for analyses. Moreover, survey items covered a broad range of content, which were administered consistently as the same item set. Nonetheless, limitations of the available data were evident. Survey item content was not explicitly designed for the purposes of the present study, and as such, did not always have all the desired data elements. The survey’s anonymity-strategy meant that some relevant soldier background characteristics, such as age, gender, and race/ethnicity, and age, were not available for analysis, and it is possible study results might be different if such characteristics had been included in the analysis. In this study, during-deployment risk behaviors were used as a covariate, judged as being the best proxy measure available for predeployment risk behaviors. Recognition should be given to the fact that such a “pretest” behavior measure, namely deployment risk behaviors, actually was collected during deployment, and very likely included behaviors and experiences that came after respondents’

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combat exposure. Additionally, using during-deployment behaviors as a covariate may have potentially “adjusted away” some of the combat-exposure effect. Alternatively, during-deployment behaviors might be considered a short-term outcome of combat exposure, rather than signs of a preexisting condition. Other study limitations related to the data coming from one measure (questionnaire). Deployment experiences and behaviors, in addition to currently expressed stressors, may have been constructed by the soldier de facto to explain his/her current problem behaviors. To establish more definitive and causal relationships among variables, longitudinal studies of reservists should be considered, tracking individual soldiers in order to gather information on experiences at predeployment, deployment, and postdeployment. With these limitations in mind, results should be interpreted cautiously, and future studies should consider these in their designs. Many of aforementioned limitations are currently being addressed in the Army Study To Assess Risk and Resilience in Service members (STARRS, 2011), which incorporates longitudinal data collection of Army personnel, including soldiers throughout the deployment cycle. Acknowledgements Authors are currently assigned to the Soldier and Family Support Division, National Guard Bureau, Arlington, VA. Special thanks are extended to Sergeant First Class Janet Richards of the Soldier and Family Support Division and Mr. Michael Bigger staff of the Center for Army Substance Abuse Programs who assisted in the transfer and understanding of the data. The findings and views presented here are solely those of the author and do not reflect the position of any entity, public or private. References Ahronson, A., & Cameron, J. E. (2007). The nature and consequences of group cohesion in a military sample. Military Psychology, 19(1), 9–25. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, London: Sage. American Psychological Association. http://www.apa.org/research/responsible/ human/index.aspx, 2011 Bates, M., Bowles, S., Hammermeister, J., Stokes, C., Pinder, E., Moore, M., et al. (2010). Psychological fitness. Military Medicine, 175, 21–38. Belenky, G., Noy, S., & Solomon, Z. (1987). Battle stress, morale, cohesion, combat effectiveness, heroism, and psychiatric casualties: The Israeli experience. In G. Belenky (Ed.), Contemporary studies in combat psychiatry (pp. 11–20). Westport, CT: Greenwood. Bell, N. S., Hunt, P. R., Harford, T. C., & Kay, A. (2011). Deployment to a combat zone and other risk factors for mental health-related disability discharge from the U.S. Army: 1994–2007. Journal of Traumatic Stress, 24(1), 34–43. Berkowitz, L. (1990). On the formation and regulation of anger and aggression. A cognitive-neoassociationistic analysis. American Psychologist, 45, 494–503. Brailey, K., Vasterling, J. J., Proctor, S. P., Constans, J. I., & Friedman, M. J. (2007). PTSD symptoms, life events, and unit cohesion in U.S. soldiers: Baseline findings from the Neurocognition Deployment Health Study. Journal of Traumatic Stress, 20, 495–503. Breslau, N., Davis, G. C., Andreski, P., & Peterson, E. (1991). Traumatic events and posttraumatic stress disorder in urban population of young adults. Archives of General Psychiatry, 48, 216–222. Bryk, A. S., & Raudenbusch, S. W. (1992). Hierarchical linear modeling: Applications and data analysis methods. Newbury Park. CA: Sage. Caplan, G. (1964). Principles of preventive psychiatry. NY: Basic Books. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Erlbaum. Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98, 310–357. Dekel, S., Ein-Dor, T., & Solomon, Z. (2011). Traumatic growth and posttraumatic distress: A longitudinal study. Psychological Trauma: Theory, Research, Practice, and Policy. Erbes, C. R., Arbisi, P. A., Courage, C., Polusny, M. A., Thuras P., & Rath, M. (2008). Contextual predictors of post-deployment symptoms in the RINGS study. Presented at the annual meeting of the 2008 American Psychological Convention, August 14–17, 2008. Fear, N. T., Jones, M., Murphy, D., Hull, S., Iversen, A. C., Coker, B., et al. (2010). What are the consequences of deployment to Iraq and Afghanistan on the mental health of the UK armed forces? A cohort study. The Lancet, 375(9728), 1783–1797.

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