Journal of Anxiety Disorders 28 (2014) 310–317
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Journal of Anxiety Disorders
The impact of PTSD symptoms on physical and mental health functioning in returning veterans Anu Asnaani a,b,∗ , Madhavi K. Reddy a,b,c , M. Tracie Shea a,b a b c
Veterans Affairs Medical Center, Providence, RI, United States Alpert Medical School of Brown University, Providence, RI, United States Butler Hospital, Providence, RI, United States
a r t i c l e
i n f o
Article history: Received 10 July 2013 Received in revised form 26 January 2014 Accepted 27 January 2014 Available online 28 February 2014 Keywords: Posttraumatic stress disorder Veterans Physical health Mental health
a b s t r a c t This study aimed to determine the unique impact of PTSD symptoms, beyond other frequently examined factors on physical and mental health functioning in a sample of returning veterans. Assessments of 168 returning OEF/OIF veterans conducted an average of six months following return from deployment included measures of emotional disorders and the Short Form (36) Health Survey. Hierarchical multiple regressions revealed significant, unique contribution of Clinician-Administered PTSD Scale (CAPS) score above all other predictors in the model (demographics, severity of trauma exposure, physical injury, substance abuse and depressive symptoms), for both the physical (8%) and mental (6%) health aggregate scores, along with significant prediction of physical health (4–10%) and mental health (3–7%) subscale scores. The only other significant predictors were age for physical health scores, and depressive symptoms for mental health scores. PTSD criterion B (re-experiencing) symptoms uniquely predicted reduced physical health functioning and higher experience of bodily pain, while criterion D (hyperarousal) symptoms uniquely predicted lower feelings of energy/vitality and poorer perceptions of emotional health. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction As the majority of soldiers have returned from Operation Enduring Freedom (OEF) and Operation Iraqi Freedom (OIF), much of our attention has turned toward ensuring adequate healthcare for those who have served the country upon their return. Indeed, the cost of resources required for the treatment of physical conditions and mental health symptoms incurred during deployment appears formidable, with a median annual cost per patient being in the range of $1500 to $6000 (Hendricks et al., 2012; Taylor et al., 2012). VA hospitals have seen a distinct increase in physical conditions such as traumatic brain injury, chronic musculoskeletal pain, and symptoms stemming from exposure to environmental toxins, which all originate from specific hazards of these conflict zones (Spelman, Hunt, Seal, & Burgo-Black, 2012). In addition, a great deal of focus has been placed on the detection and effects of various mental health concerns, particularly posttraumatic stress
∗ Corresponding author at: Department of Psychiatry, Center for the Treatment and Study of Anxiety, 3535 Market Street, Suite 600, North Philadelphia, PA 19104, United States. Tel.: +1 215 746 3310. E-mail address: [email protected] (A. Asnaani). http://dx.doi.org/10.1016/j.janxdis.2014.01.005 0887-6185/© 2014 Elsevier Ltd. All rights reserved.
disorder (PTSD), following the highly stressful experience of serving in Iraq and Afghanistan (e.g. Shea, Reddy, Tyrka, & Sevin, 2014; Shea, Vujanovic, Mansfield, Sevin, & Liu, 2010). Healthcare burden from those combat veterans screening positive for this disorder alone includes a higher number of missed days from work, a higher public health cost (in the range of $8000 median annual cost per veteran), and higher health care utilization across the medical system (Taylor et al., 2012; Tuerk et al., 2012). Further, veterans with a diagnosis of PTSD present with elevated rates of several medical conditions (e.g. cancer, stroke, non-fatal heart disease, arthritis), greater rates of smoking, and lower frequency of exercise and recommended medical screenings as compared to the age-matched general population (Buckley, Mozley, Bedard, Dewulf, & Greif, 2004). Given these striking and ongoing impacts on the healthcare system, the intersection of mental health symptoms on physical health functioning in the population of returning veterans has become an area in need of further investigation. Numerous studies have highlighted the notable influence of specific mental health issues on physical health in veterans (particularly, depression and substance use), finding a clear association between higher incidence of these psychological disorders and poorer health functioning. For instance, a study by Possemato and colleagues examined the medical records of over 4000 OEF/OIF veterans seeking treatment in
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primary care clinics in the VA and found that a diagnosis of a depressive or substance use disorder was independently associated with higher medical disease burden (as measured by number of health conditions) and higher mental healthcare utilization (Possemato, Wade, Andersen, & Ouimette, 2010). Diagnosis of these two mental disorders was not associated with higher use of medical services for physical conditions, however. Another study found that self-reported hazardous drinking patterns in returning OEF/OIF veterans were associated with poorer self-reported health functioning (McDevitt-Murphy et al., 2010). Other studies in veteran and civilian populations have focused more specifically on the influence of trauma histories on physical health. Studies examining the medical records of large portions of the general population have indicated a significantly increased incidence of a variety of cardiovascular (coronary artery disease, incidence of heart attack, stroke), pulmonary (bronchitis, asthma), and other (arthritis, renal dysfunction) health conditions in individuals with a trauma experience as compared to those without such a history, even after controlling for demographic characteristics, depression, and substance use issues (Glaesmer, Brahler, Gundel, & Riedel-Heller, 2011; Spitzer et al., 2009). Similarly, veterans with a higher reported exposure to war trauma have indicated a significantly higher level of psychological distress and greater number of physical health problems (Maia, McIntyre, Pereira, & Ribeiro, 2011). One particular meta-analysis examined the pooled effect of some 62 studies looking at the impact of PTSD and PTSD symptoms (in both veteran and non-veteran populations) on general health symptoms and specific reported health problems (e.g. cardio-respiratory symptoms, gastrointestinal disorders, and musculoskeletal pain), and found robust evidence for poorer health outcomes for individuals meeting criteria for PTSD and also in the sub-threshold category endorsing high levels of PTSD symptoms (Pacella, Hruska, & Delahanty, 2013). While the authors examined a variety of moderators when this data was available (namely, gender, veteran status, recruitment location, method of assessment, type of comparison group, and scale of measurement), they did not include other variables previously implicated as possible mediators in health functioning such as age, injury, and other mental health conditions (e.g. depression and substance use; see Flood, McDevittMurphy, Weathers, Eakin, & Benson, 2009). However, the authors did find higher effect sizes in veteran samples across the majority of health outcomes, indicating a greater impact of PTSD symptoms in this population. Another related area of concern is health-related quality of life, which is more broadly conceptualized as health functioning and impairment in daily life due to health issues. In line with this, a study conducted by Shiner, Watts, Pomerantz, Young-Xu, and Schnurr (2011) more specifically examined this relationship between PTSD and health functioning in a sample of veterans. The study authors examined changes in health functioning in 167 primarily Vietnam era veterans meeting a threshold score of at least 50 on the PTSD Symptom Checklist (PCL) in a VA primary care clinic over two time points (with an average interval of 300 days), and categorized these individuals according to improvement in PTSD as measured by this self-report scale (“better”: reduction by more than 5 points; “worse”: increase by more than 5 points; or “unchanged”: score 5 points more or less than baseline). The analysis controlled for baseline scores on the PCL and SF-36 Health Survey (SF-36), age, gender, and time to follow-up. Results revealed that those classified as doing “worse” in their PTSD symptoms reported poorer mental health functioning, social functioning, general health, and feelings of vitality than the other groups (Shiner et al., 2011). Another study examining changes in health-related quality of life and health functioning in a sample of 800 OIF veterans before and after a deployment to Iraq found some evidence for a negative relationship between PTSD symptom severity (as measured
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on the PCL) and health functioning immediately following return from deployment (Vasterling et al., 2008). However, while the structural equation modeling analyses showed a direct inverse relationship between PTSD symptoms and daily health functioning prior to deployment, this relationship was only indirect at postdeployment. Specifically, PTSD symptoms seemed to negatively impact physical health symptoms, which in turn were associated with poorer daily health functioning. The analyses only controlled for age, and the authors noted that the absence of a direct relationship at post-deployment assessment might be explained by the influence of other important variables (such as depression) that were not assessed in the investigation. A more recent study built on this prior study by examining the impact of specific clusters of PTSD symptoms on physical health in a sample of tobacco-dependent veterans with chronic PTSD, taking nicotine use, chronic health conditions, substance use and depression into account (Harder et al., 2011). The study authors found a robust, unique contribution of the numbing (criterion C) and hyperarousal (criterion D) symptoms of PTSD on most assessed domains of physical health. This study was, however, restricted to veterans meeting full criteria for PTSD who also had significant nicotine dependence, and the mean age (around 58 years old) of the sample indicated an overall older population. Nevertheless, these studies taken in sum suggest an association between PTSD and poorer health functioning in general, but the relative impact of PTSD symptoms on health in comparison to other mental health issues or trauma exposure remains unclear, particularly in recently returning soldiers (Qureshi, Pyne, Magruder, Schulz, & Kunik, 2009). The current study aims to extend these findings and contribute distinctly to the literature examining the impact of posttraumatic stress symptoms on health functioning in recently returned OEF/OIF veterans. To our knowledge, this is the first comprehensive study to explore unique prediction of health functioning by continuous PTSD symptoms in this population, beyond the effects of other more frequently examined factors (e.g. depressive symptoms, alcohol use disorders, trauma exposure, and demographic characteristics). In addition to the influence of overall PTSD symptom levels, this study aimed to examine the influence of the individual PTSD symptom clusters (i.e. criteria B-D) scores on physical and mental health domains (and each of their constituent subscales) of the SF-36 Health Survey (SF-36), after controlling for other predictors previously implicated in health functioning in this population. The SF-36 is a self-report measure used across the majority of studies examining the impact of PTSD on health-related quality of life (e.g. Harder et al., 2011; Malik et al., 1999; Richardson, Long, Pedlar, & Elhai, 2008; Shiner et al., 2011). We predicted that PTSD symptom scores would significantly and uniquely contribute to OEF/OIF veterans’ report of health functioning within the first year of return from deployment, with higher PTSD symptomatology being uniquely associated with poorer physical and mental health functioning. We limited our exploration to one year postreturn to specifically examine acute risk of PTSD symptoms on perceived health-related quality of life, to better understand the more immediate impacts of PTSD on health functioning. Given previous findings (Shea et al., 2010) that subjective distress was most strongly predicted by hyperarousal (criterion D) relative to re-experiencing (criterion B) and avoidance (criterion C) symptoms in this sample, we predicted that hyperarousal symptoms would significantly and uniquely predict mental health functioning. We also speculated that due to the potential impact of poor sleep and persistent hypervigilance, that hyperarousal symptoms would also predict poorer physical health functioning, consistent with the findings of the few previous studies examining the impact of this subset of symptoms on the physical health in various veteran populations (Harder et al., 2011; Kimerling, Clum, & Wolfe, 2000).
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2. Material and methods 2.1. Participants A total of 238 members of the National Guard and Reserves recently returned from deployment to Iraq (n = 231) or Afghanistan (n = 7) completed initial assessments. Although interview data was complete for these 238 participants, not all participants completed the self-report measures within a year of their return from deployment. The current sample therefore includes 168 participants with complete data collected within the first year following return from deployment. This sample was primarily male (93%), Caucasian (87%), and the mean age of respondents was 34.1 years (SD = 9.9 years). The majority had some post-high school education (69%) and the highest proportion of respondents reported being single (40%). 2.2. Measures 2.2.1. Clinician-administered PTSD scale (CAPS) PTSD symptoms were assessed using the CAPS (Blake et al., 1995) after the assessment of other Axis I disorders to ensure that symptoms better explained by Axis I disorders were not rated as PTSD symptoms. In addition, we also ascertained that symptoms began after one or more Criterion A events that occurred during deployment, to ensure that symptoms reported were related to deployment. Inter-rater reliability for interviewers in the current study was based on 8 audio taped interviews, each rated by a minimum of 3 interviewers. The intraclass correlation coefficient (ICC; Shrout & Fleiss, 1979) for the total PTSD score obtained on the CAPS was .96, and ICCs for individual symptom scores ranged from .47 to 1.0 with a median of .94. 2.2.2. Veterans’ health survey (SF-36V) The SF-36 is a widely used self-report measure assessing for health functioning (Jenkinson, Wright, & Coulter, 1994). The measure consists of 36 items pertaining to level of impairment experienced currently (within the past month) in the two major domains of physical and mental health. Within these two areas, there are four subscales in each domain that are combined to yield the aggregate score (physical or mental health). In the physical health aggregate score, there are sub-scales assessing for physical function, limitations to physical roles, pain, and general health. In the mental health aggregate score, there are sub-scales assessing for social function, limitations to emotional roles, energy/vitality, and emotional health. This measure has been evaluated psychometrically, showing good test–retest reliability, along with good convergent and divergent validity (McHorney, Ware, & Raczek, 1993). The internal consistency of the SF-36 in our sample was very good (˛ = 0.95). 2.2.3. Structured clinical interview (SCID-I/PW/PSY screen) The SCID-I/P W/PSY Screen (First, Spitzer, Gibbon, & Williams, 1996) was used to diagnose current and lifetime substance use disorders according to DSM-IV criteria. Current diagnosis of substance use disorders (alcohol and recreational drugs, excluding nicotine) was entered as a predictor in the analyses. All interviews were conducted by 4 experienced interviewers with a minimum of master’s degree and/or a minimum of two years of diagnostic experience. Interviewers received training to administer both the SCID and CAPS by the Clinical Assessment and Training Unit at Brown University. 2.2.4. Hoge combat experience (item 5) Combat experiences during deployment were assessed by a selfreport measure developed specifically to assess combat and related
experiences in Iraq and Afghanistan (Hoge et al., 2004). For the current study, the total scores on Item 5 of the scale, which consisted of ratings of frequency of exposure to each of 13 events (e.g. “receiving small arms fire,” “seeing dead or seriously injured Americans,” and “being directly responsible for the death of a non-combatant”) rated on a scale from 0 (never) to 4 (10 or more times), was used as the measure of combat experiences and served as a predictor in the analyses. In addition, the last sub-item on this measure specifically assessed for experience of physical injury suffered during deployment (“being wounded or injured”), which was used as an additional predictor in the study analyses. 2.2.5. Brief symptom inventory (BSI) The BSI is a 53-item self-report measure assessing for psychological symptom patterns on 9 subscales of symptoms (Derogatis & Spencer, 1982). All items on this measure are rated using a 5-point distress scale, ranging from 0 (“not at all”) to 4 (extremely). Depression is one of the primary symptom subscales, and is assessed via a total score on 6 items pertaining to symptoms of clinical depression (e.g. “feeling blue,” “feelings of worthlessness,” and “feeling no interest in things”). Thus, total scores on the depression subscale of the BSI can range from 0 to 24, and this subscale total score was used as the measure of depressive symptoms in the analyses. 2.3. Procedure The study was approved by institutional review boards at Brown University, Department of Veterans Affairs, and Department of Defense. Participants provided written informed consent after receiving a complete description of the study. Recruitment occurred at the initial or follow-up Post Deployment Health Assessment (PDHA) or Re-assessment (PDHRA) debriefings, or during drill weekends, between December 2006 and July 2009. All returning personnel were eligible to participate, and we were able to present the study to about 67% of military personnel returning from the units approached. Contact information was obtained for those who gave permission to be contacted, and these individuals were then contacted by phone to schedule an interview. Sixty-six percent of those hearing about the study agreed to be contacted, and the majority of these individuals (70%) participated in the study (i.e. 46% of those hearing about the study enrolled in the study). The initial assessment took place an average of 6 months (range 2 weeks to 10 months) following return from deployment. The assessment battery included a packet of self-report questionnaires (including the self-report measures described above), the CAPS interview and the SCID-I/P W/PSY Screen. All participants were either on annual/compensatory leave or not on active duty status during research participation, and were accordingly each paid $80 for completion of the assessment. Participants reporting significant health symptoms were encouraged to seek treatment, and were provided with the contact information for their local VA hospital. 2.4. Statistical analyses Data were analyzed with IBM SPSS Version 21.0. Separate hierarchical multiple regressions were utilized to examine unique contribution of the hypothesized predictors to variance in the aggregate (physical health and mental health) and 8 sub-scale scores of the SF-36, which served as the dependent variables in the analyses. In the first block, amount of time lapsed (in days) since return from deployment to completion of the SF-36 was entered as the predictor. The second block consisted of demographic predictors (age, ethnicity, and gender), and the third block consisted of two predictors relating to experience of emotional/physical trauma
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(exposure to trauma/life-threatening situations and experience of physical injury). In the fourth block, clinical diagnosis of any current substance use disorder (as measured by the SCID) and total scores on the depression subscale of the BSI at the initial assessment were added as predictors. Finally, the final block consisted of CAPS total score as assessed at return from deployment. Additional supplementary analyses entailed repeating all analyses replacing total CAPS score in the fifth block with the CAPS scores for PTSD diagnosis criteria B (re-experiencing), C (avoidance), and D (hyperarousal), to determine unique contribution of specific DSM-IV PTSD symptom clusters to aggregate and subscale scores on the SF-36. Given the number of planned comparisons (CAPS total score’s prediction of physical and mental health aggregate and 8 subscale scores, and CAPS symptom criteria scores’ prediction of these same 10 dependent variables), the regression models were evaluated using a Holm-Bonferroni correction to adequately control for Type I error (Holm, 1979). This method employs a sequentially rejective version of the Bonferroni correction whereby all p values for each analysis are rank-ordered by magnitude, and then assessed for statistical significance (i.e. using an alpha level of 0.05/20 for the smallest p-value, 0.05/19 for the second smallest p-value, 0.05/18 for the third, and so on).
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3.2. Main analyses: hierarchical multiple regressions Each regression model was first assessed for absence of multicollinearity (VIF < 10), linearity, low incidence of outliers (standard residuals between −3.3 and 3.3), and homogeneity of variance, to ensure none of these assumptions were violated. These indices indicated an appropriate inclusion of all predictors in all regression models conducted. 3.2.1. Prediction of physical health aggregate and subscale scores Analyses revealed that CAPS total score significantly predicted 8% of the physical health aggregate score in the final step of the regression (Table 2). The only other significant predictor of physical health functioning in the full model was age. A similar pattern was observed for three of the four sub-scale scores, with CAPS total significantly predicting scores in the final step of the regression, and uniquely contributing to the variance in these scores (Physical Function: R2 change = 0.10, p < 0.001; Physical Role Limitations: R2 change = 0.04, p < 0.001; Bodily Pain: R2 change = 0.08, p < 0.001), over and beyond all previous steps. The only exception was in the General Health subscale, where depressive symptoms emerged as the only significant predictor over all other predictors in the final step of the model. Analyses of PTSD symptoms revealed that re-experiencing (criterion B) symptoms significantly predicted physical health functioning above all other predictors in the analysis for the aggregate physical health score (B = −0.72, SE = 0.20, t = −3.64, p < 0.001), and the Physical Functioning (B = −0.66, SE = 0.16, t = −4.24, p < 0.001) and Bodily Pain (B = −0.98, SE = 0.30, t = −3.24, p < 0.001) subscale scores in this domain. In contrast, criterion C or D symptom totals did not uniquely predict outcome in these domains (Table 3).
3. Results 3.1. Diagnostic and baseline characteristics of sample Of the 168 participants, 86% reported exposure to a traumatic or life-threatening event during deployment on Hoge Item 5, and 14% reported experiencing a physical injury during deployment on the item of the Hoge scale assessing for physical injury. In addition, while only 15 (9%) of participants in this study sample met current full criteria (A-D) for DSM-IV PTSD, 30.6% currently met Criterion B, 12.7% currently met Criterion C, and 55.5% currently met Criterion D symptoms. The average CAPS score for the whole sample was 20.6 (SD = 19.2) and 59.8 (SD = 18.1) for those meeting full criteria for PTSD. A diagnosis of PTSD was given to those meeting threshold frequency (1 or greater) and severity (2 or greater) on each CAPS item for a sufficient number of criteria in each symptom cluster as defined by the DSM-IV. Further, 21 (12%) met criteria for a current substance use disorder, and 34 (20%) met criteria for a current depressive disorder (major depression, dysthymia, depressive disorder – not otherwise specified) on the SCID. On average, the sample reported mild impairment in both the physical health domain (mean score = 85.1, SD = 13.6) and the mental health domain of the SF-36 (mean score = 77.0, SD = 16.9). Direction and strength of associations among independent and dependent variables are shown in Table 1.
3.2.2. Prediction of mental health aggregate and subscale scores The mental health aggregate score was also significantly predicted by the CAPS score in the final step of the regression, with CAPS score uniquely contributing 6% of the variance in this score (Table 4), over and beyond the other predictors. The CAPS score emerged as a significant predictor of mental health functioning for all four sub-scale scores in the full model (Social Function: R2 change = 0.07, p < 0.001; Emotional Role Limitations: R2 change = 0.04, p < 0.001; Energy/Vitality: R2 change = 0.06, p < 0.001; Emotional Health: R2 change = 0.03, p < 0.001). The mental health aggregate and four subscale scores were also significantly predicted by depressive symptom scores in the final step of the analysis. Follow-up analyses revealed that hyperarousal (criterion D) symptoms of PTSD significantly predicted mental health
Table 1 Zero-order correlations (Pearson’s r) among all predictor and dependent variables in main analysis. Variable
1
2
3
4
5
6
7
8
9
10
11
Physical health Mental health Time Age Ethnicity Gender Hoge item 5 Physical injury SUD Depression CAPS total
– .68** −.03 −.31** −.11 −.05 −.15 −.20* −.20* −.46** −.49**
– −.10 −.17* −.05 .03 −.27** −.21** −.14 −.67** −.61**
– −.01 −.04 .00 .36** .06 .08 .12 .20**
– .07 .12 .14 .06 .02 .24** .11
– .15 .05 .08 .10 .05 .05
– .11 .05 .03 −.02 −.02
– .37** .04 .33** .47**
– .17* .21** .36**
.18* .20**
– .56**
–
Note: * p < 0.05; **p < 0.01. Time = time of SF-36 administration since date of return from deployment (in days); Hoge Item 5 = Hoge Combat Experience Item 5 total; SUD = SCID substance use disorder, current or partial remission; Depression = BSI Depression subscale total; CAPS = Clinician-administered PTSD Scale.
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Table 2 Hierarchical multiple regression analysis using CAPS total score to predict physical health aggregate scores on SF-36. Predictor
Time Age Ethnicity Gender Hoge item 5 Physical injury SUD Depression CAPS total R2 R2 F for change in R2
Block 1
Block 2
Block 3
Block 4
B
SE B
ˇ
B
SE B
ˇ
B
SE B
ˇ
B
.00
.01
−.01
.00 −.43 −4.01 .60
.01 .10 3.00 4.12
−.02 −.31** −.10 .01
.00 −.41 −3.49 1.35 −.09 −4.39
.01 .10 2.97 4.10 .12 2.31
0.02 −.30** −.09 .03 −.06 −.15
.00 −.31 −3.45 1.64 .05 −2.84 −4.47 −8.02
.00 .00 .01
.11 .11 6.62**
.14 .03 3.01
Block 5 SE B .01 .10 2.74 3.76 .12 2.15 2.85 1.57
ˇ
B
SE B
ˇ
.03 −.22** −0.09 .03 .03 −.10 −.11 −.38**
.01 −.33 −3.04 −.11 .20 −1.13 −3.21 −4.53 −.27
.01 .09 2.59 3.57 .12 2.07 2.70 1.67 .06 .37 .08 20.06**
.04 −.24** −.08 .00 .14 −.04 −.08 −.22* −.38**
.29 .15 16.05**
Note: B =unstandardized slope coefficient; SE B = standard error of B; ˇ = standardized slope coefficient; *p < .05; **p < Holm-Bonferroni corrected alpha level. Time = time of SF-36 administration since date of return from deployment (in days); Hoge Item 5 = Hoge Combat Experience Item 5 total; SUD = SCID substance use disorder, current or partial remission; Depression = BSI Depression subscale total; CAPS = Clinician-administered PTSD Scale.
Table 3 Hierarchical multiple regression analysis using CAPS criteria scores to predict physical health aggregate scores on SF-36. Predictor
Time Age Ethnicity Gender Hoge item 5 Physical injury SUD Depression Criterion B Criterion C Criterion D R2 R2 F for change in R2
Block 1
Block 2
Block 3
Block 4
B
SE B
ˇ
B
SE B
ˇ
B
SE B
ˇ
B
.00
.01
−.01
.00 −.43 −4.01 .60
.01 .10 3.00 4.12
−.02 −.31** −.10 .01
.00 −.41 −3.49 1.35 −.09 −4.39
.01 .10 2.97 4.10 .12 2.31
0.02 −.30** −.09 .03 −.06 −.15
.00 −.31 −3.45 1.64 .05 −2.84 −4.47 −8.02
.00 .00 .01
.11 .11 6.62**
.14 .03 3.01
Block 5 SE B .01 .10 2.74 3.76 .12 2.15 2.85 1.57
ˇ
B
SE B
ˇ
.03 −.22** −0.09 .03 .03 −.10 −.11 −.38**
.01 −.31 −3.13 .63 .18 −1.30 −2.09 −4.92 −.72 −.14 −.09
.01 .09 2.56 3.54 .12 2.05 2.72 1.67 .20 .16 .13 .39 .10 8.79**
.07 −.22** −.08 .01 .13 −.05 −.05 −.23* −.32** −.08 −.06
.29 .15 16.05**
Note: B = unstandardized slope coefficient; SE B = standard error of B; ˇ = standardized slope coefficient; *p < 0.05; **p < Holm-Bonferroni corrected alpha level. Time = time of SF-36 administration since date of return from deployment (in days); Hoge Item 5 = Hoge Combat Experience Item 5 total; SUD = SCID substance use disorder, current or partial remission; Depression = BSI Depression subscale total; CAPS = Clinician-administered PTSD Scale. Table 4 Hierarchical multiple regression analysis using CAPS total score to predict mental health aggregate scores on SF-36. Predictor
Block 1 B
Time Age Ethnicity Gender Hoge item 5 Physical injury SUD Depression CAPS total R2 R2 F for change in R2
−.02
Block 2 SE B .02
.01 .01 1.44
Block 3
Block 4
ˇ
B
SE B
ˇ
B
SE B
ˇ
B
−.09
−.02 −.30 −2.32 4.39
.02 .14 3.95 5.88
−.10 −.18* −.05 .06
.00 −.25 −1.43 8.67 −.41 −4.79
.02 .13 3.82 5.80 .16 2.92
0.01 −.15 −.03 .12 −.23* −.13
.00 −.05 −2.45 9.52 −.11 −2.59 −.34 −16.85
.04 .03 1.86
.12 .08 7.02**
Block 5 SE B .01 .10 3.00 4.54 .13 2.32 3.08 1.71 .47 .35 50.53**
ˇ
B
SE B
ˇ
.01 −.03 −0.05 .13 −0.06 −.07 −.01 −.64**
.01 −.06 −1.90 −6.96 .06 −.39 1.11 −12.89 −.31
.01 .10 2.83 4.30 .13 2.23 2.91 1.82 .07 .53 .06 21.10**
.02 −.03 −.04 .09 .03 −.01 −.02 −.49** −.35**
Note: B = unstandardized slope coefficient; SE B = standard error of B; ˇ = standardized slope coefficient; *p < 0.05; **p < Holm-Bonferroni corrected alpha level. Time = time of SF-36 administration since date of return from deployment (in days); Hoge Item 5 = Hoge Combat Experience Item 5 total; SUD = SCID substance use disorder, current or partial remission; Depression = BSI Depression subscale total; CAPS = Clinician-administered PTSD Scale.
functioning above all other predictors in the analysis for the aggregate mental health score (B = −0.49, SE = 0.15, t = −3.26, p < 0.001) and the Energy/Vitality (B = −0.75, SE = 0.22, t = −3.45, p < 0.001) and Emotional Health (B = −0.46, SE = 0.15, t = −3.16, p < 0.001) subscale scores in this domain. Depressive symptoms remained a significant predictor across all analyses in the final step of the regression models. Criterion B or C did not significantly contribute to any of the scores relative to criterion D in the mental health domain (Table 5).
4. Discussion The current study aimed to test the hypothesis that PTSD symptoms uniquely predict health functioning (as measured by the SF-36 Health Survey) in a sample of returning veterans, beyond the effects of other more frequently examined factors (e.g. depressive symptoms, alcohol use disorders, trauma exposure, and demographic characteristics). In addition to the influence of overall PTSD
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Table 5 Hierarchical multiple regression analysis using CAPS criteria scores to predict mental health aggregate scores on SF-36. Predictor
Block 1 B
Time Age Ethnicity Gender Hoge item 5 Physical injury SUD Depression Criterion B Criterion C Criterion D R2 R2 F for change in R2
−.02
Block 2 SE B .02
.01 .01 1.44
Block 3
Block 4
ˇ
B
SE B
ˇ
B
SE B
ˇ
B
−.09
−.02 −.30 −2.32 4.39
.02 .14 3.95 5.88
−.10 −.18* −.05 .06
.00 −.25 −1.43 8.67 −.41 −4.79
.02 .13 3.82 5.80 .16 2.92
0.01 −.15 −.03 .12 −.23* −.13
.00 −.05 −2.45 9.52 −.11 −2.59 −.34 −16.85
.04 .03 1.86
.12 .08 7.02**
Block 5 SE B .01 .10 3.00 4.54 .13 2.32 3.08 1.71
.47 .35 50.53**
ˇ
B
SE B
ˇ
.01 −.03 −.05 .13 −.06 −.07 −.01 −.64**
.01 −.05 −1.32 −6.88 .08 −.35 1.77 −13.24 −.43 .01 −.49
.01 .10 2.82 4.32 .13 2.22 2.94 1.82 .22 .17 .15 .55 .08 8.54**
.02 −.03 −.03 .09 .05 −.01 .04 −.50** −.16* .01 −.26**
Note: B = unstandardized slope coefficient; SE B = standard error of B; ˇ = standardized slope coefficient; *p < 0.05; **p < Holm-Bonferroni corrected alpha level. Time = time of SF-36 administration since date of return from deployment (in days); Hoge Item 5 = Hoge Combat Experience Item 5 total; SUD = SCID substance use disorder, current or partial remission; Depression = BSI Depression subscale total; CAPS = Clinician-administered PTSD Scale.
symptom levels, this study also aimed to explore the influence of the individual PTSD symptoms (i.e. criteria B–D) scores on physical and mental health domains and subscale scores in these domains. Analyses revealed a significant, unique contribution of CAPS scores at return from deployment above all other predictors in the model for both the physical (8%) and mental (6%) health aggregate scores. CAPS scores uniquely predicted 4–10% of the variance in the subscales of the physical health domain, with the only other primary significant variable associated with physical health functioning being age. PTSD and depressive symptoms significantly predicted all four of the mental health subscale scores, with CAPS scores significantly contributing 3–7% of the unique variance. Even though the prevalence rate for full clinical diagnosis of PTSD was a little less than 10% in the sample, participants endorsed significant symptoms meeting threshold for the individual PTSD criteria (ranging from 13% to 56%). Supplementary analyses showed that criterion B (re-experiencing) symptom scores were uniquely associated with physical health functioning and experience of bodily pain, while criterion D (hyperarousal) symptom scores were uniquely associated with feelings of lower energy/vitality and poorer perceptions of emotional health in veterans. Our original hypotheses were mostly supported by these findings; specifically, we expected that PTSD symptom scores would significantly contribute to OEF/OIF veterans’ report of health functioning within the first year of return from deployment, with higher PTSD symptomatology being uniquely associated with poorer physical and mental health functioning. To our knowledge, this was the first study showing a significant contribution of PTSD symptoms beyond symptoms of depression or a substance use diagnosis, trauma exposure or physical injury experience, and demographic factors, in both physical and mental health domains, in a nontreatment seeking, general sample of recently deployed veterans. In addition, the finding of significant contribution of hyperarousal (criterion D) scores to mental health functioning scales is in line with what we expected, given previous findings implicating this cluster of symptoms as being a strong predictor of subjective distress in this sample (Shea et al., 2010). On the other hand, while we predicted that this cluster would also contribute to poorer physical functioning (as a result of poor sleep and persistent hypervigilance), we did not find this to be the case. Rather, re-experiencing symptoms (criterion B) emerged as the only significant variable associated with poorer physical health functioning relative to the other two PTSD diagnostic symptom clusters. Several previous studies (Harder et al., 2011; Lunney & Shnurr, 2007; Polusny et al., 2008; Shea et al., 2010) have found criterion C symptoms (numbing/avoidance) to be a strong predictor
of poorer interpersonal/social functioning, lower health-related quality of life satisfaction, and increased rates of post-trauma healthcare utilization, but this symptom cluster also did not emerge as a significant predictor of any of the subscales of health functioning in the current study. Given the lack of data examining the mechanisms by which individual PTSD symptom clusters influence health-related quality of life (particularly in the physical health domain), it is difficult to ascertain the reason why re-experiencing symptoms might impact physical health. One possible hypothesis is that intrusive thoughts or memories of trauma impact an individual’s ability to complete his/her physical role function because of the high level of interference associated with such symptoms. Alternatively, the physical symptoms associated with trauma memories (e.g. nausea) might contribute to perceptions of poorer physical health. Depressive symptoms emerged as a significant predictor of mental health functioning, and notably explained a greater portion of the variance in this outcome than PTSD symptoms. This is consistent with a previous study finding significant relationships between PTSD and depressive symptoms and mental health-related quality of life, as measured by the mental health subscales of the SF-36 (Richardson et al., 2008). Possible reasons for a stronger relationship of depressive symptoms with the mental health domain of the SF-36 include an actual increased incapacitation/interference from core symptoms of depression (e.g. fatigue, loss of interest), or a stronger influence of depression on self-perception of well-being (e.g. pervasive negative views of the self and world) as reported on the SF-36. Thus, while PTSD symptoms showed a unique relationship with mental health that extends above and beyond the contribution of the depression subscale, depressive symptoms are important to consider in evaluating the mental health of returning veterans. Overall, this study has a number of features that are distinct improvements on the other methodologies used in the study of health functioning in veterans. First, the CAPS is a clinicianadministered measure of PTSD symptoms, which has been shown to more adequately capture the expanse of symptoms for this disorder as compared to self-report (Fokkema, Smits, Kelderman, & Cuijpers, 2013), or categorical diagnosis as captured in primary care settings or using other instruments such as the SCID (Blake et al., 1995; Wright et al., 2012). Further, the CAPS was conducted by trained interviewers who showed good inter-rater reliability. In addition, the SF-36 Health Survey is a widely used validated measure of health that allows us to examine specific areas within physical and mental health functioning, which reveals more meaningful detailed information about health within a set time frame
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than can be afforded by simply using medical diagnoses listed in veterans’ medical records (Glaesmer et al., 2011; Pacella et al., 2013; Possemato et al., 2010; Spitzer et al., 2009). Further, the administration of all measures examined in the current study within a year of return from deployment allowed us to examine the acute risks and impacts on health functioning in this sample, and our range of measures allowed us to control for a more comprehensive set of other possible influencing factors than other studies (McDevittMurphy et al., 2010; Shiner et al., 2011; Vasterling et al., 2008). Finally, given that this was not a treatment-seeking sample, our findings may be more applicable to the larger population of OEF/OIF veterans currently returning from deployment. There are several limitations to the current study that warrant discussion. The study data did not allow us to examine a longitudinal course of health functioning and PTSD symptoms. Other studies examining course of PTSD have indicated that the prevalence rate can actually increase in a subset of individuals after 12 months or more post-return from deployment (Bonanno et al., 2012; Santiago et al., 2013), while the current study assessed participants within a time period that spanned from as soon as 2 weeks up to 12 months after return. Thus, it is possible that the impacts of PTSD on health are ameliorated or exacerbated over time, but the current findings only provide a snapshot of the immediate relationship between these two areas within a year following return from deployment. However, most studies have not examined this longitudinal course of PTSD in relation to health functioning; the only longitudinal study examining PTSD’s influence on health used timepoints before and immediately after deployment, and it did not examine the relative impact of PTSD compared to other key factors such as depression and substance use (Vasterling et al., 2008). In the same vein, such a cross-sectional design does not indicate directionality of the relationship between PTSD symptoms and health functioning (i.e. whether poor health functioning actually exacerbates PTSD or vice versa over time), and this could be more adequately explored using a longitudinal design. Further, continuous measures of psychopathology are typically more robust measures of symptom level (Wright et al., 2012), and while we were able to use a continuous measure of depression (to match the dimensional CAPS score for PTSD), we did not have a similar measure for substance use and had to rely on the categorical SCID diagnosis for this variable. However, our continuous depression score (using the BSI) helped us more adequately capture the influence of depressive symptoms, particularly in the mental health domain. Finally, while the SF-36 is a widely-used and robust measure of health functioning, it still relies on self-report, which is subject to reporting bias, overestimation or over-endorsement, and does not eliminate the possibility that one’s perception of health (as opposed to objectively measured health) is itself negatively influenced by PTSD symptoms. In addition, the overall unique variance explained by the entire model of each regression analysis in this sample was around 40% for physical health functioning and 55% for mental health functioning. Thus, the mental health model certainly explained a larger proportion of the variance, which is expected given that the major predictors of interest are mental health disorders (i.e. substance use, depression and PTSD). There are several other variables that might have increased the explained variance of the regression models, such as other mental health issues outside of depression and substance use, physical conditions such as traumatic brain injury, or social functioning indices such as social support or level of family concern. Such variables should be assessed and incorporated into analyses with similar samples. It was surprising that combat exposure and physical injury did not add more to the physical health aggregate score. In terms of trauma exposure, the results were unchanged even after controlling for number of previous deployments; however, we did not administer a measure of
general trauma exposure. This might reflect a possible limitation to representation of this construct, similar to our measure of physical injury, which assessed only for frequency and not severity of injury. In the same vein, substance use symptoms did not significantly add to the variance in the final step of any of the regression models, which could be due to the relatively recent return from deployment, or general under-reporting of these symptoms. Despite these limitations, this study provides important insight into the relationship between PTSD and health functioning in recently returning veterans, with several key implications. Specifically, while several factors have shown to impact health functioning in this population, providers should be aware of the unique and additional burden of PTSD symptoms in perceptions of lower health-related quality of life in our returning soldiers. This highlights the importance of using effective, empirically based treatments to reduce PTSD symptoms. Further, this study revealed the specificity of effects of PTSD symptom cluster criteria (e.g. re-experiencing symptoms with physical health functioning), which had not been previously examined. Accordingly, clinicians and PTSD researchers alike would benefit from including health-related quality of life measures as a useful indicator of success from treatment. Finally, this study underscored the utility in addressing continuous PTSD symptoms and health functioning, even in veterans not meeting full criteria for the disorder. The present study facilitates a number of avenues for future study of the relationship between PTSD and health functioning in OEF/OIF veterans. It would certainly be important to determine how this relationship changes over time, as we think about the longer-term impacts of PTSD on the healthcare burden of these currently returning veterans. Further, as mentioned previously, it would be useful for future studies to incorporate a dimensional, non-categorical measure of substance use to ensure we fully capture the impact of problematic substance use on health functioning. In addition, while this study provides evidence for a significant, unique impact of PTSD beyond other previously-examined factors on health functioning, we are unclear on the mechanisms through which PTSD symptoms affect physical health functioning in particular. For instance, it would be meaningful to look at whether other mediating or moderating factors play a role in how PTSD symptoms (and specific symptom clusters) result in poorer health functioning. Finally, while there is a significant amount of overlap in the symptoms outlined by DSM-IV versus DSM-5 diagnosis of PTSD, it would be interesting to explore how the added symptom cluster of negative cognitions/negative affect would predict health functioning in each model. Indeed, more study with DSM-5 symptoms is warranted. Such investigations will help us to determine what other problem areas to target, and will inform more effective interventions for PTSD symptoms, with the eventual goal of improving health functioning in returning veterans. Acknowledgments The authors express their appreciation to the participating soldiers, the leadership of the RI National Guard, and to Elizabeth Sevin, Walter Musto, Julianne Voss, Lauren Slater, Melissa Platt, and Sarah Samways for their work on this study. References Blake, D., Weathers, F. W., Nagy, L. M., Kaloupek, D. G., Gusman, F. D., Charney, D. S., et al. (1995). The development of a clinician-administered PTSD scale. Journal of Traumatic Stress, 8(1), 75–90. http://dx.doi.org/10.1002/jts.2490080106 Bonanno, G. A., Mancini, A. D., Horton, J. L., Powell, T. M., LeardMann, C. A., Boyko, E. J., et al. (2012). Trajectories of trauma symptoms and resilience in deployed US military service members: prospective cohort study. British Journal of Psychiatry, 200(4), 317–323. http://dx.doi.org/10.1192/bjp.bp.111.096552
A. Asnaani et al. / Journal of Anxiety Disorders 28 (2014) 310–317 Buckley, T. C., Mozley, S. L., Bedard, M. A., Dewulf, A., & Greif, J. (2004). Preventive health behaviors, health-risk behaviors, physical morbidity, and health-related role functioning impairment in veterans with post-traumatic stress disorder. Military Medicine, 169(7), 536–540. Derogatis, L. R., & Spencer, M. S. (1982). The Brief Symptom Inventory (BSI): administration, scoring, and procedures manual-1. Baltimore: Johns Hopkins University School of Medicine, Clinical Psychometrics Research Unit. First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. (1996). Structured clinical interview for DSM-IV axis I disorders, patient edition (SCID-I/P). New York, NY: New York State Psychiatric Institute, Biometrics Research. Flood, A. M., McDevitt-Murphy, M. E., Weathers, F. W., Eakin, D. E., & Benson, T. A. (2009). Substance use behaviors as a mediator between posttraumatic stress disorder and physical health in trauma-exposed college students. Journal of Behavioral Medicine, 32(3), 234–243. http://dx.doi.org/10.1007/s10865-008-9195-y Fokkema, M., Smits, N., Kelderman, H., & Cuijpers, P. (2013). Response shifts in mental health interventions: an illustration of longitudinal measurement invariance. Psychological Assessment, http://dx.doi.org/10.1037/a0031669 Glaesmer, H., Brahler, E., Gundel, H., & Riedel-Heller, S. G. (2011). The association of traumatic experiences and posttraumatic stress disorder with physical morbidity in old age: a German population-based study. Psychosomatic Medicine, 73(5), 401–406. http://dx.doi.org/10.1097/PSY.0b013e31821b47e8 Harder, L. H., Chen, S., Baker, D. G., Chow, B., McFall, M., Saxon, A., et al. (2011). The influence of posttraumatic stress disorder numbing and hyperarousal symptom clusters in the prediction of physical health status in veterans with chronic tobacco dependence and posttraumatic stress disorder. Journal of Nervous and Mental Disease, 199(12), 940–945. http://dx.doi.org/10.1097/NMD.0b013e3182392bfb Hendricks, A., Krengel, M., Iverson, K. M., Kimerling, R., Tun, C., Amara, J., et al. (2012). Estimating the costs of care. In: J. J. Vasterling, R. A. Bryant, & T. M. Keane (Eds.), PTSD and mild traumatic brain injury (pp. 260–281). New York, NY: Guilford Press. Hoge, C. W., Castro, C. A., Messer, S. C., McGurk, D., Cotting, D. I., & Koffman, R. L. (2004). Combat duty in Iraq and Afghanistan, mental health problems, and barriers to care. New England Journal of Medicine, 351(1), 13–22. http://dx.doi.org/10.1056/NEJMoa040603 Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70. Jenkinson, C. C., Wright, L. L., & Coulter, A. A. (1994). Criterion validity and reliability of the SF-36 in a population sample. Quality Of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care & Rehabilitation, 3(1), 7–12. http://dx.doi.org/10.1007/BF00647843 Kimerling, R., Clum, G. A., & Wolfe, J. (2000). Relationships among trauma exposure, chronic posttraumatic stress disorder symptoms, and self-reported health in women: replication and extension. Journal of Traumatic Stress, 13(1), 115–128. http://dx.doi.org/10.1023/A:1007729116133 Lunney, C. A., & Schnurr, P. P. (2007). Domains of quality of life and symptoms in male veterans treated for posttraumatic stress disorder. Journal Of Traumatic Stress, 20(6), 955–964. http://dx.doi.org/10.1002/jts.20269 Maia, Â., McIntyre, T., Pereira, M., & Ribeiro, E. (2011). War exposure and posttraumatic stress as predictors of Portuguese colonial war veterans’ physical health. Anxiety, Stress & Coping: An International Journal, 24(3), 309–325. http://dx.doi.org/10.1080/10615806.2010.521238 Malik, M. L., Connor, K. M., Sutherland, S. M., Smith, R. D., Davison, R. M., & Davidson, J. T. (1999). Quality of life and posttraumatic stress disorder: a pilot study assessing changes in SF-36 scores before and after treatment in a placebo-controlled trial of fluoxetine. Journal of Traumatic Stress, 12(2), 387–393. http://dx.doi.org/10.1023/A:1024745030140 McDevitt-Murphy, M. E., Williams, J. L., Bracken, K. L., Fields, J. A., Monahan, C. J., & Murphy, J. G. (2010). PTSD symptoms, hazardous drinking, and health functioning among U.S. OEF and OIF veterans presenting to primary care. Journal of Traumatic Stress, 23(1), 108–111.
317
McHorney, C. A., Ware, J. E., & Raczek, A. E. (1993). The MOS 36-Item ShortForm Health Survey (SF-36®): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs. Medical Care, 31(3), 247–263. Pacella, M. L., Hruska, B., & Delahanty, D. L. (2013). The physical health consequences of PTSD and PTSD symptoms: a meta-analytic review. Journal of Anxiety Disorders, 27(1), 33–46. Polusny, M. A., Ries, B. J., Schultz, J. R., Calhoun, P., Clemensen, L., & Johnsen, I. R. (2008). PTSD symptom clusters associated with physical health and health care utilization in rural primary care patients exposed to natural disaster. Journal of Traumatic Stress, 21(1), 75–82. http://dx.doi.org/10.1002/jts.20281 Possemato, K., Wade, M., Andersen, J., & Ouimette, P. (2010). The impact of PTSD, depression, and substance use disorders on disease burden and health care utilization among OEF/OIF veterans. Psychological Trauma: Theory, Research, Practice, and Policy, 2(3), 218–223. http://dx.doi.org/10.1037/a0019236 Qureshi, S. U., Pyne, J. M., Magruder, K. M., Schulz, P. E., & Kunik, M. E. (2009). The link between post-traumatic stress disorder and physical comorbidities: a systematic review. Psychiatric Quarterly, 80(2), 87–97. http://dx.doi.org/10.1007/s11126-009-9096-4 Richardson, J., Long, M. E., Pedlar, D., & Elhai, J. D. (2008). Posttraumatic stress disorder and health-related quality of life among a sample of treatment- and pension-seeking deployed Canadian forces peacekeeping veterans. Canadian Journal of Psychiatry, 53(9), 594–600. Santiago, P. N., Ursano, R. J., Gray, C. L., Pynoos, R. S., Spiegel, D., Lewis-Fernandez, R., et al. (2013). A systematic review of PTSD prevalence and trajectories in DSM-5 defined trauma exposed populations: intentional and non-intentional traumatic events. PLoS ONE, 8(4), e59236. http://dx.doi.org/10.1371/journal.pone.0059236 Shea, M. T., Reddy, M. K., Tyrka, A., & Sevin, E. (2014). Risk factors for postdeployment posttraumatic stress disorder in national guard/reserve service members. Psychiatry Research (in press). Shea, M., Vujanovic, A. A., Mansfield, A. K., Sevin, E., & Liu, F. (2010). Posttraumatic stress disorder symptoms and functional impairment among OEF and OIF National Guard and Reserve veterans. Journal of Traumatic Stress, 23(1), 100–107. Shiner, B., Watts, B. V., Pomerantz, A., Young-Xu, Y., & Schnurr, P. P. (2011). Sensitivity of the SF-36 to PTSD symptom change in veterans. Journal Of Traumatic Stress, 24(1), 111–115. http://dx.doi.org/10.1002/jts.20613 Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin, 86, 420–428. Spelman, J. F., Hunt, S. C., Seal, K. H., & Burgo-Black, A. (2012). Post deployment care for returning combat veterans. Journal of General Internal Medicine, 27(9), 1200–1209. http://dx.doi.org/10.1007/s11606-012-2061-1 Spitzer, C., Barnow, S., Völzke, H., John, U., Freyberger, H. J., & Grabe, H. (2009). Trauma, posttraumatic stress disorder, and physical illness: findings from the general population. Psychosomatic Medicine, 71(9), 1012–1017. http://dx.doi.org/10.1097/PSY.0b013e3181bc76b5 Taylor, B. C., Hagel, E. M., Carlson, K. F., Cifu, D. X., Cutting, A., Bidelspach, D. E., et al. (2012). Prevalence and costs of co-occurring traumatic brain injury with and without psychiatric disturbance and pain among Afghanistan and Iraq war veteran VA users. Medical Care, 50(4), 342–346. http://dx.doi.org/10.1097/MLR.0b013e318245a558 Tuerk, P. W., Wangelin, B., Rauch, S. M., Dismuke, C. E., Yoder, M., Myrick, H., et al. (2012). Health service utilization before and after evidence-based treatment for PTSD. Psychological Services, http://dx.doi.org/10.1037/a0030549 Vasterling, J. J., Schumm, J., Proctor, S. P., Gentry, E., King, D. W., & King, L. A. (2008). Posttraumatic stress disorder and health functioning in a nontreatment-seeking sample of Iraq war veterans: a prospective analysis. Journal of Rehabilitation Research and Development, 45(3), 347–358. Wright, A. C., Krueger, R. F., Hobbs, M. J., Markon, K. E., Eaton, N. R., & Slade, T. (2012). The structure of psychopathology: toward an expanded quantitative empirical model. Journal of Abnormal Psychology, http://dx.doi.org/10.1037/a0030133
Contents lists available at ScienceDirect
Journal of Anxiety Disorders
The impact of PTSD symptoms on physical and mental health functioning in returning veterans Anu Asnaani a,b,∗ , Madhavi K. Reddy a,b,c , M. Tracie Shea a,b a b c
Veterans Affairs Medical Center, Providence, RI, United States Alpert Medical School of Brown University, Providence, RI, United States Butler Hospital, Providence, RI, United States
a r t i c l e
i n f o
Article history: Received 10 July 2013 Received in revised form 26 January 2014 Accepted 27 January 2014 Available online 28 February 2014 Keywords: Posttraumatic stress disorder Veterans Physical health Mental health
a b s t r a c t This study aimed to determine the unique impact of PTSD symptoms, beyond other frequently examined factors on physical and mental health functioning in a sample of returning veterans. Assessments of 168 returning OEF/OIF veterans conducted an average of six months following return from deployment included measures of emotional disorders and the Short Form (36) Health Survey. Hierarchical multiple regressions revealed significant, unique contribution of Clinician-Administered PTSD Scale (CAPS) score above all other predictors in the model (demographics, severity of trauma exposure, physical injury, substance abuse and depressive symptoms), for both the physical (8%) and mental (6%) health aggregate scores, along with significant prediction of physical health (4–10%) and mental health (3–7%) subscale scores. The only other significant predictors were age for physical health scores, and depressive symptoms for mental health scores. PTSD criterion B (re-experiencing) symptoms uniquely predicted reduced physical health functioning and higher experience of bodily pain, while criterion D (hyperarousal) symptoms uniquely predicted lower feelings of energy/vitality and poorer perceptions of emotional health. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction As the majority of soldiers have returned from Operation Enduring Freedom (OEF) and Operation Iraqi Freedom (OIF), much of our attention has turned toward ensuring adequate healthcare for those who have served the country upon their return. Indeed, the cost of resources required for the treatment of physical conditions and mental health symptoms incurred during deployment appears formidable, with a median annual cost per patient being in the range of $1500 to $6000 (Hendricks et al., 2012; Taylor et al., 2012). VA hospitals have seen a distinct increase in physical conditions such as traumatic brain injury, chronic musculoskeletal pain, and symptoms stemming from exposure to environmental toxins, which all originate from specific hazards of these conflict zones (Spelman, Hunt, Seal, & Burgo-Black, 2012). In addition, a great deal of focus has been placed on the detection and effects of various mental health concerns, particularly posttraumatic stress
∗ Corresponding author at: Department of Psychiatry, Center for the Treatment and Study of Anxiety, 3535 Market Street, Suite 600, North Philadelphia, PA 19104, United States. Tel.: +1 215 746 3310. E-mail address: [email protected] (A. Asnaani). http://dx.doi.org/10.1016/j.janxdis.2014.01.005 0887-6185/© 2014 Elsevier Ltd. All rights reserved.
disorder (PTSD), following the highly stressful experience of serving in Iraq and Afghanistan (e.g. Shea, Reddy, Tyrka, & Sevin, 2014; Shea, Vujanovic, Mansfield, Sevin, & Liu, 2010). Healthcare burden from those combat veterans screening positive for this disorder alone includes a higher number of missed days from work, a higher public health cost (in the range of $8000 median annual cost per veteran), and higher health care utilization across the medical system (Taylor et al., 2012; Tuerk et al., 2012). Further, veterans with a diagnosis of PTSD present with elevated rates of several medical conditions (e.g. cancer, stroke, non-fatal heart disease, arthritis), greater rates of smoking, and lower frequency of exercise and recommended medical screenings as compared to the age-matched general population (Buckley, Mozley, Bedard, Dewulf, & Greif, 2004). Given these striking and ongoing impacts on the healthcare system, the intersection of mental health symptoms on physical health functioning in the population of returning veterans has become an area in need of further investigation. Numerous studies have highlighted the notable influence of specific mental health issues on physical health in veterans (particularly, depression and substance use), finding a clear association between higher incidence of these psychological disorders and poorer health functioning. For instance, a study by Possemato and colleagues examined the medical records of over 4000 OEF/OIF veterans seeking treatment in
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primary care clinics in the VA and found that a diagnosis of a depressive or substance use disorder was independently associated with higher medical disease burden (as measured by number of health conditions) and higher mental healthcare utilization (Possemato, Wade, Andersen, & Ouimette, 2010). Diagnosis of these two mental disorders was not associated with higher use of medical services for physical conditions, however. Another study found that self-reported hazardous drinking patterns in returning OEF/OIF veterans were associated with poorer self-reported health functioning (McDevitt-Murphy et al., 2010). Other studies in veteran and civilian populations have focused more specifically on the influence of trauma histories on physical health. Studies examining the medical records of large portions of the general population have indicated a significantly increased incidence of a variety of cardiovascular (coronary artery disease, incidence of heart attack, stroke), pulmonary (bronchitis, asthma), and other (arthritis, renal dysfunction) health conditions in individuals with a trauma experience as compared to those without such a history, even after controlling for demographic characteristics, depression, and substance use issues (Glaesmer, Brahler, Gundel, & Riedel-Heller, 2011; Spitzer et al., 2009). Similarly, veterans with a higher reported exposure to war trauma have indicated a significantly higher level of psychological distress and greater number of physical health problems (Maia, McIntyre, Pereira, & Ribeiro, 2011). One particular meta-analysis examined the pooled effect of some 62 studies looking at the impact of PTSD and PTSD symptoms (in both veteran and non-veteran populations) on general health symptoms and specific reported health problems (e.g. cardio-respiratory symptoms, gastrointestinal disorders, and musculoskeletal pain), and found robust evidence for poorer health outcomes for individuals meeting criteria for PTSD and also in the sub-threshold category endorsing high levels of PTSD symptoms (Pacella, Hruska, & Delahanty, 2013). While the authors examined a variety of moderators when this data was available (namely, gender, veteran status, recruitment location, method of assessment, type of comparison group, and scale of measurement), they did not include other variables previously implicated as possible mediators in health functioning such as age, injury, and other mental health conditions (e.g. depression and substance use; see Flood, McDevittMurphy, Weathers, Eakin, & Benson, 2009). However, the authors did find higher effect sizes in veteran samples across the majority of health outcomes, indicating a greater impact of PTSD symptoms in this population. Another related area of concern is health-related quality of life, which is more broadly conceptualized as health functioning and impairment in daily life due to health issues. In line with this, a study conducted by Shiner, Watts, Pomerantz, Young-Xu, and Schnurr (2011) more specifically examined this relationship between PTSD and health functioning in a sample of veterans. The study authors examined changes in health functioning in 167 primarily Vietnam era veterans meeting a threshold score of at least 50 on the PTSD Symptom Checklist (PCL) in a VA primary care clinic over two time points (with an average interval of 300 days), and categorized these individuals according to improvement in PTSD as measured by this self-report scale (“better”: reduction by more than 5 points; “worse”: increase by more than 5 points; or “unchanged”: score 5 points more or less than baseline). The analysis controlled for baseline scores on the PCL and SF-36 Health Survey (SF-36), age, gender, and time to follow-up. Results revealed that those classified as doing “worse” in their PTSD symptoms reported poorer mental health functioning, social functioning, general health, and feelings of vitality than the other groups (Shiner et al., 2011). Another study examining changes in health-related quality of life and health functioning in a sample of 800 OIF veterans before and after a deployment to Iraq found some evidence for a negative relationship between PTSD symptom severity (as measured
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on the PCL) and health functioning immediately following return from deployment (Vasterling et al., 2008). However, while the structural equation modeling analyses showed a direct inverse relationship between PTSD symptoms and daily health functioning prior to deployment, this relationship was only indirect at postdeployment. Specifically, PTSD symptoms seemed to negatively impact physical health symptoms, which in turn were associated with poorer daily health functioning. The analyses only controlled for age, and the authors noted that the absence of a direct relationship at post-deployment assessment might be explained by the influence of other important variables (such as depression) that were not assessed in the investigation. A more recent study built on this prior study by examining the impact of specific clusters of PTSD symptoms on physical health in a sample of tobacco-dependent veterans with chronic PTSD, taking nicotine use, chronic health conditions, substance use and depression into account (Harder et al., 2011). The study authors found a robust, unique contribution of the numbing (criterion C) and hyperarousal (criterion D) symptoms of PTSD on most assessed domains of physical health. This study was, however, restricted to veterans meeting full criteria for PTSD who also had significant nicotine dependence, and the mean age (around 58 years old) of the sample indicated an overall older population. Nevertheless, these studies taken in sum suggest an association between PTSD and poorer health functioning in general, but the relative impact of PTSD symptoms on health in comparison to other mental health issues or trauma exposure remains unclear, particularly in recently returning soldiers (Qureshi, Pyne, Magruder, Schulz, & Kunik, 2009). The current study aims to extend these findings and contribute distinctly to the literature examining the impact of posttraumatic stress symptoms on health functioning in recently returned OEF/OIF veterans. To our knowledge, this is the first comprehensive study to explore unique prediction of health functioning by continuous PTSD symptoms in this population, beyond the effects of other more frequently examined factors (e.g. depressive symptoms, alcohol use disorders, trauma exposure, and demographic characteristics). In addition to the influence of overall PTSD symptom levels, this study aimed to examine the influence of the individual PTSD symptom clusters (i.e. criteria B-D) scores on physical and mental health domains (and each of their constituent subscales) of the SF-36 Health Survey (SF-36), after controlling for other predictors previously implicated in health functioning in this population. The SF-36 is a self-report measure used across the majority of studies examining the impact of PTSD on health-related quality of life (e.g. Harder et al., 2011; Malik et al., 1999; Richardson, Long, Pedlar, & Elhai, 2008; Shiner et al., 2011). We predicted that PTSD symptom scores would significantly and uniquely contribute to OEF/OIF veterans’ report of health functioning within the first year of return from deployment, with higher PTSD symptomatology being uniquely associated with poorer physical and mental health functioning. We limited our exploration to one year postreturn to specifically examine acute risk of PTSD symptoms on perceived health-related quality of life, to better understand the more immediate impacts of PTSD on health functioning. Given previous findings (Shea et al., 2010) that subjective distress was most strongly predicted by hyperarousal (criterion D) relative to re-experiencing (criterion B) and avoidance (criterion C) symptoms in this sample, we predicted that hyperarousal symptoms would significantly and uniquely predict mental health functioning. We also speculated that due to the potential impact of poor sleep and persistent hypervigilance, that hyperarousal symptoms would also predict poorer physical health functioning, consistent with the findings of the few previous studies examining the impact of this subset of symptoms on the physical health in various veteran populations (Harder et al., 2011; Kimerling, Clum, & Wolfe, 2000).
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2. Material and methods 2.1. Participants A total of 238 members of the National Guard and Reserves recently returned from deployment to Iraq (n = 231) or Afghanistan (n = 7) completed initial assessments. Although interview data was complete for these 238 participants, not all participants completed the self-report measures within a year of their return from deployment. The current sample therefore includes 168 participants with complete data collected within the first year following return from deployment. This sample was primarily male (93%), Caucasian (87%), and the mean age of respondents was 34.1 years (SD = 9.9 years). The majority had some post-high school education (69%) and the highest proportion of respondents reported being single (40%). 2.2. Measures 2.2.1. Clinician-administered PTSD scale (CAPS) PTSD symptoms were assessed using the CAPS (Blake et al., 1995) after the assessment of other Axis I disorders to ensure that symptoms better explained by Axis I disorders were not rated as PTSD symptoms. In addition, we also ascertained that symptoms began after one or more Criterion A events that occurred during deployment, to ensure that symptoms reported were related to deployment. Inter-rater reliability for interviewers in the current study was based on 8 audio taped interviews, each rated by a minimum of 3 interviewers. The intraclass correlation coefficient (ICC; Shrout & Fleiss, 1979) for the total PTSD score obtained on the CAPS was .96, and ICCs for individual symptom scores ranged from .47 to 1.0 with a median of .94. 2.2.2. Veterans’ health survey (SF-36V) The SF-36 is a widely used self-report measure assessing for health functioning (Jenkinson, Wright, & Coulter, 1994). The measure consists of 36 items pertaining to level of impairment experienced currently (within the past month) in the two major domains of physical and mental health. Within these two areas, there are four subscales in each domain that are combined to yield the aggregate score (physical or mental health). In the physical health aggregate score, there are sub-scales assessing for physical function, limitations to physical roles, pain, and general health. In the mental health aggregate score, there are sub-scales assessing for social function, limitations to emotional roles, energy/vitality, and emotional health. This measure has been evaluated psychometrically, showing good test–retest reliability, along with good convergent and divergent validity (McHorney, Ware, & Raczek, 1993). The internal consistency of the SF-36 in our sample was very good (˛ = 0.95). 2.2.3. Structured clinical interview (SCID-I/PW/PSY screen) The SCID-I/P W/PSY Screen (First, Spitzer, Gibbon, & Williams, 1996) was used to diagnose current and lifetime substance use disorders according to DSM-IV criteria. Current diagnosis of substance use disorders (alcohol and recreational drugs, excluding nicotine) was entered as a predictor in the analyses. All interviews were conducted by 4 experienced interviewers with a minimum of master’s degree and/or a minimum of two years of diagnostic experience. Interviewers received training to administer both the SCID and CAPS by the Clinical Assessment and Training Unit at Brown University. 2.2.4. Hoge combat experience (item 5) Combat experiences during deployment were assessed by a selfreport measure developed specifically to assess combat and related
experiences in Iraq and Afghanistan (Hoge et al., 2004). For the current study, the total scores on Item 5 of the scale, which consisted of ratings of frequency of exposure to each of 13 events (e.g. “receiving small arms fire,” “seeing dead or seriously injured Americans,” and “being directly responsible for the death of a non-combatant”) rated on a scale from 0 (never) to 4 (10 or more times), was used as the measure of combat experiences and served as a predictor in the analyses. In addition, the last sub-item on this measure specifically assessed for experience of physical injury suffered during deployment (“being wounded or injured”), which was used as an additional predictor in the study analyses. 2.2.5. Brief symptom inventory (BSI) The BSI is a 53-item self-report measure assessing for psychological symptom patterns on 9 subscales of symptoms (Derogatis & Spencer, 1982). All items on this measure are rated using a 5-point distress scale, ranging from 0 (“not at all”) to 4 (extremely). Depression is one of the primary symptom subscales, and is assessed via a total score on 6 items pertaining to symptoms of clinical depression (e.g. “feeling blue,” “feelings of worthlessness,” and “feeling no interest in things”). Thus, total scores on the depression subscale of the BSI can range from 0 to 24, and this subscale total score was used as the measure of depressive symptoms in the analyses. 2.3. Procedure The study was approved by institutional review boards at Brown University, Department of Veterans Affairs, and Department of Defense. Participants provided written informed consent after receiving a complete description of the study. Recruitment occurred at the initial or follow-up Post Deployment Health Assessment (PDHA) or Re-assessment (PDHRA) debriefings, or during drill weekends, between December 2006 and July 2009. All returning personnel were eligible to participate, and we were able to present the study to about 67% of military personnel returning from the units approached. Contact information was obtained for those who gave permission to be contacted, and these individuals were then contacted by phone to schedule an interview. Sixty-six percent of those hearing about the study agreed to be contacted, and the majority of these individuals (70%) participated in the study (i.e. 46% of those hearing about the study enrolled in the study). The initial assessment took place an average of 6 months (range 2 weeks to 10 months) following return from deployment. The assessment battery included a packet of self-report questionnaires (including the self-report measures described above), the CAPS interview and the SCID-I/P W/PSY Screen. All participants were either on annual/compensatory leave or not on active duty status during research participation, and were accordingly each paid $80 for completion of the assessment. Participants reporting significant health symptoms were encouraged to seek treatment, and were provided with the contact information for their local VA hospital. 2.4. Statistical analyses Data were analyzed with IBM SPSS Version 21.0. Separate hierarchical multiple regressions were utilized to examine unique contribution of the hypothesized predictors to variance in the aggregate (physical health and mental health) and 8 sub-scale scores of the SF-36, which served as the dependent variables in the analyses. In the first block, amount of time lapsed (in days) since return from deployment to completion of the SF-36 was entered as the predictor. The second block consisted of demographic predictors (age, ethnicity, and gender), and the third block consisted of two predictors relating to experience of emotional/physical trauma
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(exposure to trauma/life-threatening situations and experience of physical injury). In the fourth block, clinical diagnosis of any current substance use disorder (as measured by the SCID) and total scores on the depression subscale of the BSI at the initial assessment were added as predictors. Finally, the final block consisted of CAPS total score as assessed at return from deployment. Additional supplementary analyses entailed repeating all analyses replacing total CAPS score in the fifth block with the CAPS scores for PTSD diagnosis criteria B (re-experiencing), C (avoidance), and D (hyperarousal), to determine unique contribution of specific DSM-IV PTSD symptom clusters to aggregate and subscale scores on the SF-36. Given the number of planned comparisons (CAPS total score’s prediction of physical and mental health aggregate and 8 subscale scores, and CAPS symptom criteria scores’ prediction of these same 10 dependent variables), the regression models were evaluated using a Holm-Bonferroni correction to adequately control for Type I error (Holm, 1979). This method employs a sequentially rejective version of the Bonferroni correction whereby all p values for each analysis are rank-ordered by magnitude, and then assessed for statistical significance (i.e. using an alpha level of 0.05/20 for the smallest p-value, 0.05/19 for the second smallest p-value, 0.05/18 for the third, and so on).
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3.2. Main analyses: hierarchical multiple regressions Each regression model was first assessed for absence of multicollinearity (VIF < 10), linearity, low incidence of outliers (standard residuals between −3.3 and 3.3), and homogeneity of variance, to ensure none of these assumptions were violated. These indices indicated an appropriate inclusion of all predictors in all regression models conducted. 3.2.1. Prediction of physical health aggregate and subscale scores Analyses revealed that CAPS total score significantly predicted 8% of the physical health aggregate score in the final step of the regression (Table 2). The only other significant predictor of physical health functioning in the full model was age. A similar pattern was observed for three of the four sub-scale scores, with CAPS total significantly predicting scores in the final step of the regression, and uniquely contributing to the variance in these scores (Physical Function: R2 change = 0.10, p < 0.001; Physical Role Limitations: R2 change = 0.04, p < 0.001; Bodily Pain: R2 change = 0.08, p < 0.001), over and beyond all previous steps. The only exception was in the General Health subscale, where depressive symptoms emerged as the only significant predictor over all other predictors in the final step of the model. Analyses of PTSD symptoms revealed that re-experiencing (criterion B) symptoms significantly predicted physical health functioning above all other predictors in the analysis for the aggregate physical health score (B = −0.72, SE = 0.20, t = −3.64, p < 0.001), and the Physical Functioning (B = −0.66, SE = 0.16, t = −4.24, p < 0.001) and Bodily Pain (B = −0.98, SE = 0.30, t = −3.24, p < 0.001) subscale scores in this domain. In contrast, criterion C or D symptom totals did not uniquely predict outcome in these domains (Table 3).
3. Results 3.1. Diagnostic and baseline characteristics of sample Of the 168 participants, 86% reported exposure to a traumatic or life-threatening event during deployment on Hoge Item 5, and 14% reported experiencing a physical injury during deployment on the item of the Hoge scale assessing for physical injury. In addition, while only 15 (9%) of participants in this study sample met current full criteria (A-D) for DSM-IV PTSD, 30.6% currently met Criterion B, 12.7% currently met Criterion C, and 55.5% currently met Criterion D symptoms. The average CAPS score for the whole sample was 20.6 (SD = 19.2) and 59.8 (SD = 18.1) for those meeting full criteria for PTSD. A diagnosis of PTSD was given to those meeting threshold frequency (1 or greater) and severity (2 or greater) on each CAPS item for a sufficient number of criteria in each symptom cluster as defined by the DSM-IV. Further, 21 (12%) met criteria for a current substance use disorder, and 34 (20%) met criteria for a current depressive disorder (major depression, dysthymia, depressive disorder – not otherwise specified) on the SCID. On average, the sample reported mild impairment in both the physical health domain (mean score = 85.1, SD = 13.6) and the mental health domain of the SF-36 (mean score = 77.0, SD = 16.9). Direction and strength of associations among independent and dependent variables are shown in Table 1.
3.2.2. Prediction of mental health aggregate and subscale scores The mental health aggregate score was also significantly predicted by the CAPS score in the final step of the regression, with CAPS score uniquely contributing 6% of the variance in this score (Table 4), over and beyond the other predictors. The CAPS score emerged as a significant predictor of mental health functioning for all four sub-scale scores in the full model (Social Function: R2 change = 0.07, p < 0.001; Emotional Role Limitations: R2 change = 0.04, p < 0.001; Energy/Vitality: R2 change = 0.06, p < 0.001; Emotional Health: R2 change = 0.03, p < 0.001). The mental health aggregate and four subscale scores were also significantly predicted by depressive symptom scores in the final step of the analysis. Follow-up analyses revealed that hyperarousal (criterion D) symptoms of PTSD significantly predicted mental health
Table 1 Zero-order correlations (Pearson’s r) among all predictor and dependent variables in main analysis. Variable
1
2
3
4
5
6
7
8
9
10
11
Physical health Mental health Time Age Ethnicity Gender Hoge item 5 Physical injury SUD Depression CAPS total
– .68** −.03 −.31** −.11 −.05 −.15 −.20* −.20* −.46** −.49**
– −.10 −.17* −.05 .03 −.27** −.21** −.14 −.67** −.61**
– −.01 −.04 .00 .36** .06 .08 .12 .20**
– .07 .12 .14 .06 .02 .24** .11
– .15 .05 .08 .10 .05 .05
– .11 .05 .03 −.02 −.02
– .37** .04 .33** .47**
– .17* .21** .36**
.18* .20**
– .56**
–
Note: * p < 0.05; **p < 0.01. Time = time of SF-36 administration since date of return from deployment (in days); Hoge Item 5 = Hoge Combat Experience Item 5 total; SUD = SCID substance use disorder, current or partial remission; Depression = BSI Depression subscale total; CAPS = Clinician-administered PTSD Scale.
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Table 2 Hierarchical multiple regression analysis using CAPS total score to predict physical health aggregate scores on SF-36. Predictor
Time Age Ethnicity Gender Hoge item 5 Physical injury SUD Depression CAPS total R2 R2 F for change in R2
Block 1
Block 2
Block 3
Block 4
B
SE B
ˇ
B
SE B
ˇ
B
SE B
ˇ
B
.00
.01
−.01
.00 −.43 −4.01 .60
.01 .10 3.00 4.12
−.02 −.31** −.10 .01
.00 −.41 −3.49 1.35 −.09 −4.39
.01 .10 2.97 4.10 .12 2.31
0.02 −.30** −.09 .03 −.06 −.15
.00 −.31 −3.45 1.64 .05 −2.84 −4.47 −8.02
.00 .00 .01
.11 .11 6.62**
.14 .03 3.01
Block 5 SE B .01 .10 2.74 3.76 .12 2.15 2.85 1.57
ˇ
B
SE B
ˇ
.03 −.22** −0.09 .03 .03 −.10 −.11 −.38**
.01 −.33 −3.04 −.11 .20 −1.13 −3.21 −4.53 −.27
.01 .09 2.59 3.57 .12 2.07 2.70 1.67 .06 .37 .08 20.06**
.04 −.24** −.08 .00 .14 −.04 −.08 −.22* −.38**
.29 .15 16.05**
Note: B =unstandardized slope coefficient; SE B = standard error of B; ˇ = standardized slope coefficient; *p < .05; **p < Holm-Bonferroni corrected alpha level. Time = time of SF-36 administration since date of return from deployment (in days); Hoge Item 5 = Hoge Combat Experience Item 5 total; SUD = SCID substance use disorder, current or partial remission; Depression = BSI Depression subscale total; CAPS = Clinician-administered PTSD Scale.
Table 3 Hierarchical multiple regression analysis using CAPS criteria scores to predict physical health aggregate scores on SF-36. Predictor
Time Age Ethnicity Gender Hoge item 5 Physical injury SUD Depression Criterion B Criterion C Criterion D R2 R2 F for change in R2
Block 1
Block 2
Block 3
Block 4
B
SE B
ˇ
B
SE B
ˇ
B
SE B
ˇ
B
.00
.01
−.01
.00 −.43 −4.01 .60
.01 .10 3.00 4.12
−.02 −.31** −.10 .01
.00 −.41 −3.49 1.35 −.09 −4.39
.01 .10 2.97 4.10 .12 2.31
0.02 −.30** −.09 .03 −.06 −.15
.00 −.31 −3.45 1.64 .05 −2.84 −4.47 −8.02
.00 .00 .01
.11 .11 6.62**
.14 .03 3.01
Block 5 SE B .01 .10 2.74 3.76 .12 2.15 2.85 1.57
ˇ
B
SE B
ˇ
.03 −.22** −0.09 .03 .03 −.10 −.11 −.38**
.01 −.31 −3.13 .63 .18 −1.30 −2.09 −4.92 −.72 −.14 −.09
.01 .09 2.56 3.54 .12 2.05 2.72 1.67 .20 .16 .13 .39 .10 8.79**
.07 −.22** −.08 .01 .13 −.05 −.05 −.23* −.32** −.08 −.06
.29 .15 16.05**
Note: B = unstandardized slope coefficient; SE B = standard error of B; ˇ = standardized slope coefficient; *p < 0.05; **p < Holm-Bonferroni corrected alpha level. Time = time of SF-36 administration since date of return from deployment (in days); Hoge Item 5 = Hoge Combat Experience Item 5 total; SUD = SCID substance use disorder, current or partial remission; Depression = BSI Depression subscale total; CAPS = Clinician-administered PTSD Scale. Table 4 Hierarchical multiple regression analysis using CAPS total score to predict mental health aggregate scores on SF-36. Predictor
Block 1 B
Time Age Ethnicity Gender Hoge item 5 Physical injury SUD Depression CAPS total R2 R2 F for change in R2
−.02
Block 2 SE B .02
.01 .01 1.44
Block 3
Block 4
ˇ
B
SE B
ˇ
B
SE B
ˇ
B
−.09
−.02 −.30 −2.32 4.39
.02 .14 3.95 5.88
−.10 −.18* −.05 .06
.00 −.25 −1.43 8.67 −.41 −4.79
.02 .13 3.82 5.80 .16 2.92
0.01 −.15 −.03 .12 −.23* −.13
.00 −.05 −2.45 9.52 −.11 −2.59 −.34 −16.85
.04 .03 1.86
.12 .08 7.02**
Block 5 SE B .01 .10 3.00 4.54 .13 2.32 3.08 1.71 .47 .35 50.53**
ˇ
B
SE B
ˇ
.01 −.03 −0.05 .13 −0.06 −.07 −.01 −.64**
.01 −.06 −1.90 −6.96 .06 −.39 1.11 −12.89 −.31
.01 .10 2.83 4.30 .13 2.23 2.91 1.82 .07 .53 .06 21.10**
.02 −.03 −.04 .09 .03 −.01 −.02 −.49** −.35**
Note: B = unstandardized slope coefficient; SE B = standard error of B; ˇ = standardized slope coefficient; *p < 0.05; **p < Holm-Bonferroni corrected alpha level. Time = time of SF-36 administration since date of return from deployment (in days); Hoge Item 5 = Hoge Combat Experience Item 5 total; SUD = SCID substance use disorder, current or partial remission; Depression = BSI Depression subscale total; CAPS = Clinician-administered PTSD Scale.
functioning above all other predictors in the analysis for the aggregate mental health score (B = −0.49, SE = 0.15, t = −3.26, p < 0.001) and the Energy/Vitality (B = −0.75, SE = 0.22, t = −3.45, p < 0.001) and Emotional Health (B = −0.46, SE = 0.15, t = −3.16, p < 0.001) subscale scores in this domain. Depressive symptoms remained a significant predictor across all analyses in the final step of the regression models. Criterion B or C did not significantly contribute to any of the scores relative to criterion D in the mental health domain (Table 5).
4. Discussion The current study aimed to test the hypothesis that PTSD symptoms uniquely predict health functioning (as measured by the SF-36 Health Survey) in a sample of returning veterans, beyond the effects of other more frequently examined factors (e.g. depressive symptoms, alcohol use disorders, trauma exposure, and demographic characteristics). In addition to the influence of overall PTSD
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Table 5 Hierarchical multiple regression analysis using CAPS criteria scores to predict mental health aggregate scores on SF-36. Predictor
Block 1 B
Time Age Ethnicity Gender Hoge item 5 Physical injury SUD Depression Criterion B Criterion C Criterion D R2 R2 F for change in R2
−.02
Block 2 SE B .02
.01 .01 1.44
Block 3
Block 4
ˇ
B
SE B
ˇ
B
SE B
ˇ
B
−.09
−.02 −.30 −2.32 4.39
.02 .14 3.95 5.88
−.10 −.18* −.05 .06
.00 −.25 −1.43 8.67 −.41 −4.79
.02 .13 3.82 5.80 .16 2.92
0.01 −.15 −.03 .12 −.23* −.13
.00 −.05 −2.45 9.52 −.11 −2.59 −.34 −16.85
.04 .03 1.86
.12 .08 7.02**
Block 5 SE B .01 .10 3.00 4.54 .13 2.32 3.08 1.71
.47 .35 50.53**
ˇ
B
SE B
ˇ
.01 −.03 −.05 .13 −.06 −.07 −.01 −.64**
.01 −.05 −1.32 −6.88 .08 −.35 1.77 −13.24 −.43 .01 −.49
.01 .10 2.82 4.32 .13 2.22 2.94 1.82 .22 .17 .15 .55 .08 8.54**
.02 −.03 −.03 .09 .05 −.01 .04 −.50** −.16* .01 −.26**
Note: B = unstandardized slope coefficient; SE B = standard error of B; ˇ = standardized slope coefficient; *p < 0.05; **p < Holm-Bonferroni corrected alpha level. Time = time of SF-36 administration since date of return from deployment (in days); Hoge Item 5 = Hoge Combat Experience Item 5 total; SUD = SCID substance use disorder, current or partial remission; Depression = BSI Depression subscale total; CAPS = Clinician-administered PTSD Scale.
symptom levels, this study also aimed to explore the influence of the individual PTSD symptoms (i.e. criteria B–D) scores on physical and mental health domains and subscale scores in these domains. Analyses revealed a significant, unique contribution of CAPS scores at return from deployment above all other predictors in the model for both the physical (8%) and mental (6%) health aggregate scores. CAPS scores uniquely predicted 4–10% of the variance in the subscales of the physical health domain, with the only other primary significant variable associated with physical health functioning being age. PTSD and depressive symptoms significantly predicted all four of the mental health subscale scores, with CAPS scores significantly contributing 3–7% of the unique variance. Even though the prevalence rate for full clinical diagnosis of PTSD was a little less than 10% in the sample, participants endorsed significant symptoms meeting threshold for the individual PTSD criteria (ranging from 13% to 56%). Supplementary analyses showed that criterion B (re-experiencing) symptom scores were uniquely associated with physical health functioning and experience of bodily pain, while criterion D (hyperarousal) symptom scores were uniquely associated with feelings of lower energy/vitality and poorer perceptions of emotional health in veterans. Our original hypotheses were mostly supported by these findings; specifically, we expected that PTSD symptom scores would significantly contribute to OEF/OIF veterans’ report of health functioning within the first year of return from deployment, with higher PTSD symptomatology being uniquely associated with poorer physical and mental health functioning. To our knowledge, this was the first study showing a significant contribution of PTSD symptoms beyond symptoms of depression or a substance use diagnosis, trauma exposure or physical injury experience, and demographic factors, in both physical and mental health domains, in a nontreatment seeking, general sample of recently deployed veterans. In addition, the finding of significant contribution of hyperarousal (criterion D) scores to mental health functioning scales is in line with what we expected, given previous findings implicating this cluster of symptoms as being a strong predictor of subjective distress in this sample (Shea et al., 2010). On the other hand, while we predicted that this cluster would also contribute to poorer physical functioning (as a result of poor sleep and persistent hypervigilance), we did not find this to be the case. Rather, re-experiencing symptoms (criterion B) emerged as the only significant variable associated with poorer physical health functioning relative to the other two PTSD diagnostic symptom clusters. Several previous studies (Harder et al., 2011; Lunney & Shnurr, 2007; Polusny et al., 2008; Shea et al., 2010) have found criterion C symptoms (numbing/avoidance) to be a strong predictor
of poorer interpersonal/social functioning, lower health-related quality of life satisfaction, and increased rates of post-trauma healthcare utilization, but this symptom cluster also did not emerge as a significant predictor of any of the subscales of health functioning in the current study. Given the lack of data examining the mechanisms by which individual PTSD symptom clusters influence health-related quality of life (particularly in the physical health domain), it is difficult to ascertain the reason why re-experiencing symptoms might impact physical health. One possible hypothesis is that intrusive thoughts or memories of trauma impact an individual’s ability to complete his/her physical role function because of the high level of interference associated with such symptoms. Alternatively, the physical symptoms associated with trauma memories (e.g. nausea) might contribute to perceptions of poorer physical health. Depressive symptoms emerged as a significant predictor of mental health functioning, and notably explained a greater portion of the variance in this outcome than PTSD symptoms. This is consistent with a previous study finding significant relationships between PTSD and depressive symptoms and mental health-related quality of life, as measured by the mental health subscales of the SF-36 (Richardson et al., 2008). Possible reasons for a stronger relationship of depressive symptoms with the mental health domain of the SF-36 include an actual increased incapacitation/interference from core symptoms of depression (e.g. fatigue, loss of interest), or a stronger influence of depression on self-perception of well-being (e.g. pervasive negative views of the self and world) as reported on the SF-36. Thus, while PTSD symptoms showed a unique relationship with mental health that extends above and beyond the contribution of the depression subscale, depressive symptoms are important to consider in evaluating the mental health of returning veterans. Overall, this study has a number of features that are distinct improvements on the other methodologies used in the study of health functioning in veterans. First, the CAPS is a clinicianadministered measure of PTSD symptoms, which has been shown to more adequately capture the expanse of symptoms for this disorder as compared to self-report (Fokkema, Smits, Kelderman, & Cuijpers, 2013), or categorical diagnosis as captured in primary care settings or using other instruments such as the SCID (Blake et al., 1995; Wright et al., 2012). Further, the CAPS was conducted by trained interviewers who showed good inter-rater reliability. In addition, the SF-36 Health Survey is a widely used validated measure of health that allows us to examine specific areas within physical and mental health functioning, which reveals more meaningful detailed information about health within a set time frame
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than can be afforded by simply using medical diagnoses listed in veterans’ medical records (Glaesmer et al., 2011; Pacella et al., 2013; Possemato et al., 2010; Spitzer et al., 2009). Further, the administration of all measures examined in the current study within a year of return from deployment allowed us to examine the acute risks and impacts on health functioning in this sample, and our range of measures allowed us to control for a more comprehensive set of other possible influencing factors than other studies (McDevittMurphy et al., 2010; Shiner et al., 2011; Vasterling et al., 2008). Finally, given that this was not a treatment-seeking sample, our findings may be more applicable to the larger population of OEF/OIF veterans currently returning from deployment. There are several limitations to the current study that warrant discussion. The study data did not allow us to examine a longitudinal course of health functioning and PTSD symptoms. Other studies examining course of PTSD have indicated that the prevalence rate can actually increase in a subset of individuals after 12 months or more post-return from deployment (Bonanno et al., 2012; Santiago et al., 2013), while the current study assessed participants within a time period that spanned from as soon as 2 weeks up to 12 months after return. Thus, it is possible that the impacts of PTSD on health are ameliorated or exacerbated over time, but the current findings only provide a snapshot of the immediate relationship between these two areas within a year following return from deployment. However, most studies have not examined this longitudinal course of PTSD in relation to health functioning; the only longitudinal study examining PTSD’s influence on health used timepoints before and immediately after deployment, and it did not examine the relative impact of PTSD compared to other key factors such as depression and substance use (Vasterling et al., 2008). In the same vein, such a cross-sectional design does not indicate directionality of the relationship between PTSD symptoms and health functioning (i.e. whether poor health functioning actually exacerbates PTSD or vice versa over time), and this could be more adequately explored using a longitudinal design. Further, continuous measures of psychopathology are typically more robust measures of symptom level (Wright et al., 2012), and while we were able to use a continuous measure of depression (to match the dimensional CAPS score for PTSD), we did not have a similar measure for substance use and had to rely on the categorical SCID diagnosis for this variable. However, our continuous depression score (using the BSI) helped us more adequately capture the influence of depressive symptoms, particularly in the mental health domain. Finally, while the SF-36 is a widely-used and robust measure of health functioning, it still relies on self-report, which is subject to reporting bias, overestimation or over-endorsement, and does not eliminate the possibility that one’s perception of health (as opposed to objectively measured health) is itself negatively influenced by PTSD symptoms. In addition, the overall unique variance explained by the entire model of each regression analysis in this sample was around 40% for physical health functioning and 55% for mental health functioning. Thus, the mental health model certainly explained a larger proportion of the variance, which is expected given that the major predictors of interest are mental health disorders (i.e. substance use, depression and PTSD). There are several other variables that might have increased the explained variance of the regression models, such as other mental health issues outside of depression and substance use, physical conditions such as traumatic brain injury, or social functioning indices such as social support or level of family concern. Such variables should be assessed and incorporated into analyses with similar samples. It was surprising that combat exposure and physical injury did not add more to the physical health aggregate score. In terms of trauma exposure, the results were unchanged even after controlling for number of previous deployments; however, we did not administer a measure of
general trauma exposure. This might reflect a possible limitation to representation of this construct, similar to our measure of physical injury, which assessed only for frequency and not severity of injury. In the same vein, substance use symptoms did not significantly add to the variance in the final step of any of the regression models, which could be due to the relatively recent return from deployment, or general under-reporting of these symptoms. Despite these limitations, this study provides important insight into the relationship between PTSD and health functioning in recently returning veterans, with several key implications. Specifically, while several factors have shown to impact health functioning in this population, providers should be aware of the unique and additional burden of PTSD symptoms in perceptions of lower health-related quality of life in our returning soldiers. This highlights the importance of using effective, empirically based treatments to reduce PTSD symptoms. Further, this study revealed the specificity of effects of PTSD symptom cluster criteria (e.g. re-experiencing symptoms with physical health functioning), which had not been previously examined. Accordingly, clinicians and PTSD researchers alike would benefit from including health-related quality of life measures as a useful indicator of success from treatment. Finally, this study underscored the utility in addressing continuous PTSD symptoms and health functioning, even in veterans not meeting full criteria for the disorder. The present study facilitates a number of avenues for future study of the relationship between PTSD and health functioning in OEF/OIF veterans. It would certainly be important to determine how this relationship changes over time, as we think about the longer-term impacts of PTSD on the healthcare burden of these currently returning veterans. Further, as mentioned previously, it would be useful for future studies to incorporate a dimensional, non-categorical measure of substance use to ensure we fully capture the impact of problematic substance use on health functioning. In addition, while this study provides evidence for a significant, unique impact of PTSD beyond other previously-examined factors on health functioning, we are unclear on the mechanisms through which PTSD symptoms affect physical health functioning in particular. For instance, it would be meaningful to look at whether other mediating or moderating factors play a role in how PTSD symptoms (and specific symptom clusters) result in poorer health functioning. Finally, while there is a significant amount of overlap in the symptoms outlined by DSM-IV versus DSM-5 diagnosis of PTSD, it would be interesting to explore how the added symptom cluster of negative cognitions/negative affect would predict health functioning in each model. Indeed, more study with DSM-5 symptoms is warranted. Such investigations will help us to determine what other problem areas to target, and will inform more effective interventions for PTSD symptoms, with the eventual goal of improving health functioning in returning veterans. Acknowledgments The authors express their appreciation to the participating soldiers, the leadership of the RI National Guard, and to Elizabeth Sevin, Walter Musto, Julianne Voss, Lauren Slater, Melissa Platt, and Sarah Samways for their work on this study. References Blake, D., Weathers, F. W., Nagy, L. M., Kaloupek, D. G., Gusman, F. D., Charney, D. S., et al. (1995). The development of a clinician-administered PTSD scale. Journal of Traumatic Stress, 8(1), 75–90. http://dx.doi.org/10.1002/jts.2490080106 Bonanno, G. A., Mancini, A. D., Horton, J. L., Powell, T. M., LeardMann, C. A., Boyko, E. J., et al. (2012). Trajectories of trauma symptoms and resilience in deployed US military service members: prospective cohort study. British Journal of Psychiatry, 200(4), 317–323. http://dx.doi.org/10.1192/bjp.bp.111.096552
A. Asnaani et al. / Journal of Anxiety Disorders 28 (2014) 310–317 Buckley, T. C., Mozley, S. L., Bedard, M. A., Dewulf, A., & Greif, J. (2004). Preventive health behaviors, health-risk behaviors, physical morbidity, and health-related role functioning impairment in veterans with post-traumatic stress disorder. Military Medicine, 169(7), 536–540. Derogatis, L. R., & Spencer, M. S. (1982). The Brief Symptom Inventory (BSI): administration, scoring, and procedures manual-1. Baltimore: Johns Hopkins University School of Medicine, Clinical Psychometrics Research Unit. First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. (1996). Structured clinical interview for DSM-IV axis I disorders, patient edition (SCID-I/P). New York, NY: New York State Psychiatric Institute, Biometrics Research. Flood, A. M., McDevitt-Murphy, M. E., Weathers, F. W., Eakin, D. E., & Benson, T. A. (2009). Substance use behaviors as a mediator between posttraumatic stress disorder and physical health in trauma-exposed college students. Journal of Behavioral Medicine, 32(3), 234–243. http://dx.doi.org/10.1007/s10865-008-9195-y Fokkema, M., Smits, N., Kelderman, H., & Cuijpers, P. (2013). Response shifts in mental health interventions: an illustration of longitudinal measurement invariance. Psychological Assessment, http://dx.doi.org/10.1037/a0031669 Glaesmer, H., Brahler, E., Gundel, H., & Riedel-Heller, S. G. (2011). The association of traumatic experiences and posttraumatic stress disorder with physical morbidity in old age: a German population-based study. Psychosomatic Medicine, 73(5), 401–406. http://dx.doi.org/10.1097/PSY.0b013e31821b47e8 Harder, L. H., Chen, S., Baker, D. G., Chow, B., McFall, M., Saxon, A., et al. (2011). The influence of posttraumatic stress disorder numbing and hyperarousal symptom clusters in the prediction of physical health status in veterans with chronic tobacco dependence and posttraumatic stress disorder. Journal of Nervous and Mental Disease, 199(12), 940–945. http://dx.doi.org/10.1097/NMD.0b013e3182392bfb Hendricks, A., Krengel, M., Iverson, K. M., Kimerling, R., Tun, C., Amara, J., et al. (2012). Estimating the costs of care. In: J. J. Vasterling, R. A. Bryant, & T. M. Keane (Eds.), PTSD and mild traumatic brain injury (pp. 260–281). New York, NY: Guilford Press. Hoge, C. W., Castro, C. A., Messer, S. C., McGurk, D., Cotting, D. I., & Koffman, R. L. (2004). Combat duty in Iraq and Afghanistan, mental health problems, and barriers to care. New England Journal of Medicine, 351(1), 13–22. http://dx.doi.org/10.1056/NEJMoa040603 Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70. Jenkinson, C. C., Wright, L. L., & Coulter, A. A. (1994). Criterion validity and reliability of the SF-36 in a population sample. Quality Of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care & Rehabilitation, 3(1), 7–12. http://dx.doi.org/10.1007/BF00647843 Kimerling, R., Clum, G. A., & Wolfe, J. (2000). Relationships among trauma exposure, chronic posttraumatic stress disorder symptoms, and self-reported health in women: replication and extension. Journal of Traumatic Stress, 13(1), 115–128. http://dx.doi.org/10.1023/A:1007729116133 Lunney, C. A., & Schnurr, P. P. (2007). Domains of quality of life and symptoms in male veterans treated for posttraumatic stress disorder. Journal Of Traumatic Stress, 20(6), 955–964. http://dx.doi.org/10.1002/jts.20269 Maia, Â., McIntyre, T., Pereira, M., & Ribeiro, E. (2011). War exposure and posttraumatic stress as predictors of Portuguese colonial war veterans’ physical health. Anxiety, Stress & Coping: An International Journal, 24(3), 309–325. http://dx.doi.org/10.1080/10615806.2010.521238 Malik, M. L., Connor, K. M., Sutherland, S. M., Smith, R. D., Davison, R. M., & Davidson, J. T. (1999). Quality of life and posttraumatic stress disorder: a pilot study assessing changes in SF-36 scores before and after treatment in a placebo-controlled trial of fluoxetine. Journal of Traumatic Stress, 12(2), 387–393. http://dx.doi.org/10.1023/A:1024745030140 McDevitt-Murphy, M. E., Williams, J. L., Bracken, K. L., Fields, J. A., Monahan, C. J., & Murphy, J. G. (2010). PTSD symptoms, hazardous drinking, and health functioning among U.S. OEF and OIF veterans presenting to primary care. Journal of Traumatic Stress, 23(1), 108–111.
317
McHorney, C. A., Ware, J. E., & Raczek, A. E. (1993). The MOS 36-Item ShortForm Health Survey (SF-36®): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs. Medical Care, 31(3), 247–263. Pacella, M. L., Hruska, B., & Delahanty, D. L. (2013). The physical health consequences of PTSD and PTSD symptoms: a meta-analytic review. Journal of Anxiety Disorders, 27(1), 33–46. Polusny, M. A., Ries, B. J., Schultz, J. R., Calhoun, P., Clemensen, L., & Johnsen, I. R. (2008). PTSD symptom clusters associated with physical health and health care utilization in rural primary care patients exposed to natural disaster. Journal of Traumatic Stress, 21(1), 75–82. http://dx.doi.org/10.1002/jts.20281 Possemato, K., Wade, M., Andersen, J., & Ouimette, P. (2010). The impact of PTSD, depression, and substance use disorders on disease burden and health care utilization among OEF/OIF veterans. Psychological Trauma: Theory, Research, Practice, and Policy, 2(3), 218–223. http://dx.doi.org/10.1037/a0019236 Qureshi, S. U., Pyne, J. M., Magruder, K. M., Schulz, P. E., & Kunik, M. E. (2009). The link between post-traumatic stress disorder and physical comorbidities: a systematic review. Psychiatric Quarterly, 80(2), 87–97. http://dx.doi.org/10.1007/s11126-009-9096-4 Richardson, J., Long, M. E., Pedlar, D., & Elhai, J. D. (2008). Posttraumatic stress disorder and health-related quality of life among a sample of treatment- and pension-seeking deployed Canadian forces peacekeeping veterans. Canadian Journal of Psychiatry, 53(9), 594–600. Santiago, P. N., Ursano, R. J., Gray, C. L., Pynoos, R. S., Spiegel, D., Lewis-Fernandez, R., et al. (2013). A systematic review of PTSD prevalence and trajectories in DSM-5 defined trauma exposed populations: intentional and non-intentional traumatic events. PLoS ONE, 8(4), e59236. http://dx.doi.org/10.1371/journal.pone.0059236 Shea, M. T., Reddy, M. K., Tyrka, A., & Sevin, E. (2014). Risk factors for postdeployment posttraumatic stress disorder in national guard/reserve service members. Psychiatry Research (in press). Shea, M., Vujanovic, A. A., Mansfield, A. K., Sevin, E., & Liu, F. (2010). Posttraumatic stress disorder symptoms and functional impairment among OEF and OIF National Guard and Reserve veterans. Journal of Traumatic Stress, 23(1), 100–107. Shiner, B., Watts, B. V., Pomerantz, A., Young-Xu, Y., & Schnurr, P. P. (2011). Sensitivity of the SF-36 to PTSD symptom change in veterans. Journal Of Traumatic Stress, 24(1), 111–115. http://dx.doi.org/10.1002/jts.20613 Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin, 86, 420–428. Spelman, J. F., Hunt, S. C., Seal, K. H., & Burgo-Black, A. (2012). Post deployment care for returning combat veterans. Journal of General Internal Medicine, 27(9), 1200–1209. http://dx.doi.org/10.1007/s11606-012-2061-1 Spitzer, C., Barnow, S., Völzke, H., John, U., Freyberger, H. J., & Grabe, H. (2009). Trauma, posttraumatic stress disorder, and physical illness: findings from the general population. Psychosomatic Medicine, 71(9), 1012–1017. http://dx.doi.org/10.1097/PSY.0b013e3181bc76b5 Taylor, B. C., Hagel, E. M., Carlson, K. F., Cifu, D. X., Cutting, A., Bidelspach, D. E., et al. (2012). Prevalence and costs of co-occurring traumatic brain injury with and without psychiatric disturbance and pain among Afghanistan and Iraq war veteran VA users. Medical Care, 50(4), 342–346. http://dx.doi.org/10.1097/MLR.0b013e318245a558 Tuerk, P. W., Wangelin, B., Rauch, S. M., Dismuke, C. E., Yoder, M., Myrick, H., et al. (2012). Health service utilization before and after evidence-based treatment for PTSD. Psychological Services, http://dx.doi.org/10.1037/a0030549 Vasterling, J. J., Schumm, J., Proctor, S. P., Gentry, E., King, D. W., & King, L. A. (2008). Posttraumatic stress disorder and health functioning in a nontreatment-seeking sample of Iraq war veterans: a prospective analysis. Journal of Rehabilitation Research and Development, 45(3), 347–358. Wright, A. C., Krueger, R. F., Hobbs, M. J., Markon, K. E., Eaton, N. R., & Slade, T. (2012). The structure of psychopathology: toward an expanded quantitative empirical model. Journal of Abnormal Psychology, http://dx.doi.org/10.1037/a0030133
