Hire our professional essay experts at Gradehunters.net who are available online 24/7 for an essay paper written to a high standard at an affordable cost.

Order a Similar Paper Order a Different Paper

Literature Review

: You will do 6 literature reviews pretaining to your specific course and topic. Reviews literature related to the unifying/overarching them within the core specializations identified. The focus of the literature review is to determine how the topic has been addressed in the available research literature, extending knowledge in the field by synthesizing research literature from the core areas within the primary topic. Provide a current state of accumulated knowledge as it relates to the specific topic, integrating the core specializations. Summarize the general state of the literature on the topic.

The literature review section should begin with a description of the

literature search strategy

including (not limited to):

  • Keywords used
  • Databases searched
  • Years included
  • Results yielded
  • Results excluded

The following should be included in the literature review section:

  • Research studies should be summarized with detail, including the findings, how they were obtained, and any biases and limitations affecting the findings.
  • Significant or noteworthy similarities and differences among core areas and the unifying theme should be highlighted.
  • Provide

    critical analyses

    of available research literature.
  • Conclusion that summarizes the section.


: Articulate the importance of the findings of the literature reviewed;

explain why these findings are important to the field of psychology

. Make recommendations for future research based on the literature reviewed and explain the rationale for the recommendations.

This section should include:

  • Synthesis of the research literature, redeveloping conceptualizations of existing paradigms, or proposing new paradigms.
  • Extending of knowledge through the integration of the literature review findings.
  • Supported recommendations for future research.
  • Conclusion summarizing the major elements of the project

emotional abuse predicted increases in depressive symptoms among Caucasian adolescents with more overgeneral memories (t= 4.10, P = .0001), but not among those with fewer overgeneral memories (t=−0.39, P = .70). This 2-way interaction was not significant for African–American adolescents. For adolescents of both races there was also a significant 2-way interaction between overgeneral memories and emotional neglect in the same direction. Conclusion:These results support the hypothesis that emotional maltreatment is particularly deleterious among adolescents with overgeneral autobiograph- ical memories, and that some of these mechanisms may be specific to Caucasian adolescents. http://dx.doi.org/10.1016/j.comppsych.2012.07.047 Current domestic violent exposure among resettling refugee populations C.L. Striley, S. LeLaurin St. Louis, MO Service providers funded by the United States (US) Department of State resettle international refugees deemed at highest risk by the United Nations High Commissioner for Refugees in cities across the U.S. Of the 10.5 million international refugees, those resettled in the US constitute half of the 1% at highest risk of persecution (US State Department, 2010). Many of these refugees have lived through horrendous conditions, including war and famine; all are undergoing upheaval and dislocation. Some of those being resettled will experience violence in their own homes. Efforts to help these refugees are complicated by cultural and language barriers. The National Immigrant Family Violence Institute (NIFVI) was created by Suzanne LeLaurin, Senior Vice President For Individuals And Families at the International Institute of St. Louis and Catherine Striley, now Assistant Professor of epidemiology at the University of Florida, to help “provide individualized technical assistance and training, as well as specialized resource materials on the unique issues faced by immigrant communities in combating domestic violence”(NIFVI, undated). NIFVI member agencies from San Francisco to the State of New Jersey, work with refugee communities to help reduce the risk of domestic or interpersonal partner violence among those being resettled. We present findings from funded services provided January 2010 through September 2011 by the 6 NIFVI agencies. Seven hundred and twenty eight adult refugees and immigrants from 103 countries of origin resettling in the Kansas City, St. Louis, Boston, San Francisco, and Philadelphia areas and in the State of New Jersey received services. Among these 154 (21%) were deemed by the professionals to be at medium or high risk of domestic violence at baseline; record review 6 months later showed that 92 (13%) were judged to have remained at medium or high risk. Thirty two percent of the sample whole sample (230) consisted of female heads of households. Service provision required professional interpreter service 34% of the time (250 people). The top languages spoken other than English, in order, were Spanish, Arabic, Nepali, Somali, French, Tigrigna (from Eritrea and Ethiopia), Haitian Creole, Karen (from Burma) and Amharic (from Ethiopia). NIFVI data reveal that resettling refugees are at risk for current interpersonal partner or domestic violence. Mental health service providers need to assess current as well as past violence exposure. Providers also need to be prepared to offer culturally appropriate services and translation. The needs of these families and their children are often overlooked by existing service systems (American Psychological Association, 2010). http://dx.doi.org/10.1016/j.comppsych.2012.07.048 The effect of trauma on risk of PTSD is modified by parental psychopathology K. Tahaney a,P.Xi a, N. Delgado a, M. Grant a,W.Kremen b, C. Franz b, M. Lyons a a Boston, MAbLa JollaThe etiology of post-traumatic stress disorder (PTSD) is multifaceted and involves complex genetic and environmental components including childhood experiences, preexisting mood and anxiety disorders, substance use disorders, and conduct disorder. It is important to identify familial risk factors, as they can provide more information about the roles of genes and environmental experiences in the development of PTSD. Specific risk factors have been identified, particularly among veterans exposed to combat trauma. Past studies of the Vietnam Era Twin Registry (VETR) found various forms of parental psychopathology to significantly increase the risk for developing PTSD in twin pairs following exposure to trauma. Our sample included 6744 male twins from the VETR. In our analyses we included the 1276 individuals who reported during a structured interview that they had experienced combat trauma. The interview also included questions about parental psychopathology (maternal and paternal depression and alcohol problems). Among this group with combat trauma, 30.0% metDSM-III-Rdiagnostic criteria for PTSD. Level of combat exposure was quantified using the 18 item Combat Experiences Scale. We used generalized estimating equations to examine the main effects of level of combat exposure and parental psychopathology and their interaction on risk of PTSD while controlling for non-independent observations within twin pairs. As expected, the level of combat exposure was significantly associated with PTSD. When level of combat exposure was controlled, maternal and paternal depression and maternal and paternal alcohol problems significantly predicted risk of PTSD. The only significant interaction was between maternal alcohol problems and level of combat exposure. These results indicate that both internalizing and externalizing disorders among both fathers and mothers are associated with risk of PTSD in the offspring, but only maternal alcohol problems alters the relationship between combat exposure and PTSD in the offspring. These results are equally consistent with two different mechanisms for the effect of maternal alcohol problems on sensitivity to environmental trauma. The first possibility is that differential sensitivity is genetically transmitted from mother to son. The alternative possibility is that alcohol problems in the mother produce an environment for the son that alters his response to exposure to combat trauma. One or both of these mechanisms of intergenerational influence may have been operating. http://dx.doi.org/10.1016/j.comppsych.2012.07.049 Genetic moderators of the associations between prenatal nicotine exposure and offspring psychopathology A. Talati a, S.E. Hodge a, P.J. Wickramaratne a, S.P. Hamilton b,M.M.Weissman a a New York, NYbSan Francisco, CA Objective:Smoking by mothers during pregnancy has been associated with a range of adverse outcomes among their offspring. Not all exposed offspring however go on to develop these problems, suggesting a moderating role for other biological or environmental factors. We examined brain-derived neurotrophic factor (BDNF) in this context, given the growth factor’s critical role in fetal development of brain circuits and recent evidence for modulation by nicotine. We hypothesized here that offspring who were exposed to nicotinein uteroand had lower-functioning BDNF variants would have greater psychopathology than offspring with one or neither of these risks. Method:Exposure was based on whether or not the mother smoked≥10 cigarettes daily/almost daily while pregnant. Offspring outcomes were assessed independently using the SADS interview across multiple waves, and blind to exposure. Multiple BDNF polymorphisms, including the functionally alteringval66met,were genotyped. Results:Among offspring withva/valgenotypes, prenatal exposure was not related to any adverse outcome. Among offspring with≥1copyofthelower functioningmet-allele, exposure resulted in three-to-five fold increases of drug, alcohol, and conduct disorders, but notanxiety or depression. BDNF-genotypes were not independently associated with any outcome or with prenatal exposure, and also did not moderate effects of maternal postnatal substance use. Conclusions:BDNF plays an important prenatal role in guiding the formation of neural circuits.Metcarrying fetuses–already having suboptimal BDNFE11 Abstracts / Comprehensive Psychiatry 54 (2013) E1–E14
The dimensional structure of posttraumatic stress symptomatology in 323,903 U.S. veterans Ilan Harpaz-Rotem a,*, Jack Tsai b, Robert H. Pietrzak a, Rani Hoff c aNational Center for PTSD, VA Connecticut Healthcare System and Yale Department of Psychiatry, United StatesbThe New England Mental Illness Research Education and Clinical Center, VA Connecticut Healthcare System and Yale Department of Psychiatry, United States cThe Northeast Evaluation Program (NEPEC), the National Center for PTSD, VA Connecticut Healthcare System and Yale Department of Psychiatry, United States article info Article history: Received 4 October 2013 Received in revised form 28 October 2013 Accepted 31 October 2013 Keywords: PTSD Depression Anxiety Substance use disorder Psychopathology abstract There is ongoing debate regarding the optimal dimensional structure of posttraumatic stress disorder symptomatology. A better understanding of this structure has significant implications, as it can provide more refined phenotypic measures for use in studies of the etiology and neurobiology of PTSD, as well as for use as endpoints in treatment studies of this disorder. In this study we analyzed the dimensional structure of PTSD symptomatology, as assessed using the PTSD Symptom Checklist-Military Version in 323,903 Veterans. Confirmatory factor analyses were used to compare two 4-factor models and a newly proposed 5-factor model to the 3-factor DSM-IV model of PTSD symptom dimensionality. To evaluate the external validity of the best-fitting model, we then conducted a structural equation model examining how the symptom dimensions of this model related to diagnoses of depression, anxiety, and substance use disorder. Results indicated that a newly proposed 5-factor‘dysphoric arousal’model comprised of separate re-experiencing, avoidance, numbing, dysphoric arousal, and anxious arousal symptom clusters provided a significantly betterfit to the data compared to the DSM-IV and the two alternative four-factor models. External validity analyses revealed that numbing symptoms were most strongly related to di- agnoses of depression and substance use disorder, and that dysphoric arousal symptoms were most strongly related to a diagnosis of anxiety disorder. Thus the dimensional structure of PTSD may be best represented byfive symptom dimensions. The clinical implications of these results and implications for further refinement of extant PTSD assessment instruments are discussed. Published by Elsevier Ltd. In recent years, there has been ongoing debate regarding the optimal characterization of the structure of posttraumatic stress disorder (PTSD) symptomatology (Armour et al., 2013a; Elhai et al., 2011;Elhai and Palmieri, 2011; Friedman et al., 2011; King et al., 1998; Shevlin et al., 2009; Simms et al., 2002). This debate was relevant to the reformulation of diagnostic criteria for PTSD in the recently published 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V), and will likely continue to inform the evolving conceptualization of the component symptom clusters that comprise the heterogeneous phenotype that charac- terizes this disorder (Friedman et al., 2011). PTSD is one of the most prevalent and disabling psychiatric disorders among U.S. Veterans (Harpaz-Rotem and Rosenheck,2011; Tanielian and Jaycox, 2008; Thomas et al., 2010). It is also prevalent among the general adult population, with 20% of the US adult population experiencing a traumatic event in a given year (Kessler et al., 2005; Kessler et al., 1995). A recent examination of the delivery of psychotherapy among the privately insured US population found that individuals diagnosed with PTSD were the most likely to receive psychotherapy compared to individuals diagnosed with other psychiatric disorders, thereby highlighting the psychological burden of this disorder (Harpaz-Rotem et al., 2012). To date, however, all studies that have examined the struc- ture of PTSD symptomatology in Veterans have employed relatively small sample sizes and thus, the generalizability of these results to the broader population of U.S. Veterans is unknown. Several studies have challenged that the structure of PTSD symptoms is comprised of three symptom clusters, as specified in DSM-IV: re-experiencing (Criterion B), avoidance/numbing (Crite- rion C), and hyperarousal (Criterion D). Two alternative four-factor models have been proposed and confirmatory factor analytic (CFA) * Corresponding author. The National Center for PTSD and Yale Department of Psychiatry, VACHS/116B, 950 Campbell, West Haven, CT 06511, United States. E-mail address:[email protected](I. Harpaz-Rotem). Contents lists available atScienceDirect Journal of Psychiatric Research journal homepage: www.elsevier.com/locate/psychires 0022-3956/$esee front matter Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.jpsychires.2013.10.020 Journal of Psychiatric Research 49 (2014) 31e36 studies have suggested that they provide superiorfit to symptom- level PTSD data than the 3-factor DSM-IV model (King et al., 1998; Simms et al., 2002). These two four-factor models include the dysphoria model (Simms et al., 2002) and the emotional numbing model (King et al., 1998). In the dysphoria model, PTSD symptoms are separated into distinct four factors of re-experiencing, avoid- ance, dysphoria and hyperarousal symptoms. In the emotional numbing model, PTSD symptoms are separated into distinct four factors of re-experiencing, avoidance, emotional numbing and hy- perarousal symptoms (seeTable 1for details). Several CFA studies have demonstrated the superiority of the two 4-factor models of PTSD symptoms over the DSM-IV 3-factor model (Elhai et al., 2009; Elhai et al., 2008; Elhai et al., 2011; Engdahl et al., 2011; Grubaugh et al., 2010; Mansfield et al., 2010; Palmieri et al., 2007; Yufik and Simms, 2010). A recent meta-analysis suggested that the dysphoria model is only marginally superior than the numbing model in characterizing PTSD symptom structure, irrespective of the sample studied or PTSD assessment instrument employed (Yufik and Simms, 2010). Given that each of the two 4-factors models have demonstrated superiority that varied based on sam- ple characteristics, testing conditions and instruments used, it is yet premature to determine which of these models better represents the latent structure of PTSD. The DSM-5 PTSD symptom clusters, however, more closely resemble the emotional numbing model in its inclusion of 4 symptom dimensions: intrusion symptoms, avoidance, negative alterations in cognitions and mood, and alter- ations in arousal and reactivity (Friedman et al., 2011). One of the debates surrounding the latent structure of PTSD symptomatology involves three specific Hyperarousal symptoms (D1eD3) and whether they represent one of two constructse Dysphoria or Hyperarousal. In an attempt to address this issue, Shevlin et al. (2009)analyzed data from a large, nationally repre- sentative sample of civilian U.S. adults, and found that symptoms D1-D3 were not clear indicators of either the Dysphoria or the Hyperarousal factors but rather that they cross-loaded on both factors, thereby supporting the 4-factor dysphoria model. More recently,Elhai et al. (2011)have attempted to reconcile differences between the numbing and dysphoria models. In their investigation, they found support for a novel 5-factor model that separates fear- based panic symptoms (i.e., hypervigilance, exaggerated startle; “anxious arousal”) from hyperarousal symptoms characterized by dysphoria-related arousal abnormalities (symptoms D1eD3) rep- resented by anger/irritability, sleep difficulties, and concentrationproblems (i.e.,“dysphoric arousal”). This solution is in line with a theoretical model proposed byWatson (2005), which separates symptoms that involve restlessness and agitation (i.e., irritability) from more fear-based, panic-like symptoms (i.e., exaggerated startle response). A growing number of CFA studies, which have been conducted in nationally representative civilian samples, general adult samples of medical patients, survivors of domestic violence, natural di- sasters, a violent riot, and military veterans have found that this newly proposed 5-factor model provides a significantly better representation of PTSD symptom structure than the DSM-IV and both of the four-factor models (Armour et al., 2012, 2013a, 2013b; Elhai et al., 2011; Pietrzak et al., 2012a; Pietrzak et al., 2012b; Wang et al., 2011a, 2011c). Some studies have also examined how the 5-factor model relates to external measures of psychopathol- ogy, such as depression and anxiety (Pietrzak et al., 2012b; Wang et al., 2012; Wang et al., 2011b). Results of these studies have demonstrated that re-experiencing, avoidance, and anxious arousal symptoms are most strongly linked to anxiety, numbing symptoms to depression, and dysphoric arousal symptoms to both anxiety and depression. Results of these studies provide preliminary support for the external validity of the 5-factor PTSD model. In the current study, we examined the dimensional structure of PTSD symptoms from more than 320,000 U.S. veterans who pre- sented for treatment at any VA medical center in the United States. To our knowledge, this is the largest dataset of symptom-level PTSD data ever assembled and thus provides a unique opportunity to assess the dimensional structure of PTSD symptomatology. Our primary aim was to evaluate the potential robustness of the 5- factor model in characterizing PTSD symptom dimensionality relative to two 4- factor models (numbing and dysphoria) and the 3-factor DSM-IV model. Based on a growing body of CFA research that has highlighted the superiority of the 5-factor model relative to the two 4- and 3-factor DSM-IV models in characterizing the structure of PTSD symptoms (Armour et al., 2013a; Elhai et al., 2011; Pietrzak et al., 2012a), we hypothesized that the 5-factor model would provide the best structural representation of PTSD symp- tomatology in this sample. We then repeated the analyses with two restricted subsamples,first with women only and then with only veterans diagnosed with PTSD to evaluate the stability of the factor structure among these important veteran subsamples. As a sec- ondary aim, we evaluated how the 5-factor Dysphoric Arousal model relates to other common psychiatric conditions in veter- ansddepression, other anxiety disorder, and substance use disorder. 1. Method Since the beginning of Fiscal Year 2008, the U.S. Department of Veterans Affairs (VA) has mandated mental health providers to assess PTSD symptoms using the PTSD Checklist Military Version (PCL-M; (Weathers et al., 1991) during their initial contact with Veterans who have served in the conflicts in Iraq and/or Afghanistan and who present for mental health assessment or treatment at a VA medical center. In 2010, the required reporting expanded to all patients with a diagnosis of PTSD. The PCL-M is a 17-item self-report instrument developed to assess the presence and severity of military-related PTSD symptoms that is based on the DSM-IV diagnostic criteria for PTSD. The PCL-M screening re- sults are collected by the VA mental health provider and are entered by the provider into the electronic medical record. Using the VA electronic medical record databases that capture outpatient care and test results, we identified PCL-M scores that were completed for every Veteran who received mental health care be- tween October 1, 2008 and September 31, 2012. The data were Table 1 Item mappings for each of the PTSD factor models. PTSD symptom Model DSM-IV Dysphoria Numbing 5-Factor B1. Intrusive thoughts R R R R B2. Recurrent dreams R R R R B3. Flashbacks R R R R B4. Emotional reactivity R R R R B5. Physiological reactivity R R R R C1. Avoiding thoughts of trauma A A A A C2. Avoiding reminders of trauma A A A A C3. Inability to recall aspects of trauma A D N N C4. Loss of Interest A D N N C5. Detachment A D N N C6. Restricted affect A D N N C7. Sense of foreshortened future A D N N D1. Sleep disturbance H D H DA D2. Irritability/anger H D H DA D3. Difficulties concentrating H D H DA D4. Hypervigilance H H H AA D5. Exaggerated startle response H H H AA R¼Re-experiencing; A¼Avoidance; H¼Hyperarousal; D¼Dysphoria; N¼Emotional numbing; DA¼Dysphoric Arousal; AA¼Anxious Arousal. I. Harpaz-Rotem et al. / Journal of Psychiatric Research 49 (2014) 31e36 32 unduplicated to include only thefirst PCL-M on record, so that there are no repeated measurements for any individual veteran included in the current study. All PCL-M assessments with incom- plete data were also excluded. Thefinal sample included PCL-M scores from 323,903 unique Veterans. From the electronic medi- cal record, we additionally recorded participants’demographic data and all mental health diagnoses given to each individual within 365 days from the initial administration of the PCL-M. These diagnoses however, represent providers’clinical impression and judgment, and are not based on a structural diagnostic interview. 2. Sample characteristics Table 2shows the general demographic and clinical character- istics of our sample. The mean age of the sample was 44.2 and 59.2% were white. As expected, the majority of veterans were men (90.3%). The most commonly assigned clinician-assigned diagnoses within 12 months of veterans’initial PCL-M screening were PTSD (40.1%) and mood disorder (42.9%; Major Depressive disorder 12.5% and 30.4% dysthymia). Mean annual income of participants was $25,337 49,941. The majority of Veteran (58.5%) did not receive any disability income within 12 months of their initial PCL-M assessment. Only 12,851 (4%) of the veterans in the sample were classified as unemployable and were receiving 100% disability compensation from the VA. 65% of the veterans in the sample resided in a large urban area and 40.2% had served in the recent conflict in Iraq and/or Afghanistan. 3. Data analysis PCL-M scores were non-normally distributed, as evidenced by Mardia’s coefficient for multivariate kurtosis>1.96. Thus, CFAs were conducted using robust maximum likelihood estimation with the Satorra and Bentler (SeB) c2scaling correction (Satorra and Bentler, 2001). This procedure estimates standard errors under conditions of multivariate non-normality and calculates other c2- dependentfit statistics based on the SeB c2statistic. In the CFA models, we specified PCL-M items to load only on one of the pro- posed factors each, all factors were allowed to correlate, all errorcovariances werefixed to zero, and all tests were 2-tailed. In addition to the SeB c2, modelfit was evaluated using the comparativefit index (CFI), Tucker Lewis Index (TLI), Akaike In- formation Criterion (AIC), Bayesian Information Criterion (BIC), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR) values. Higher CFI and TLI values and lower SeB c2, AIC, BIC, RMSEA, and SRMR values indicate a betterfitting model. Fit was also determined by empirically- defined benchmarks, as follows: CFI and TLI 0.90 indicate adequatefit and 0.95 an excellentfit; RMSEA 0.08 as an adequatefit and 0.06 as indicative of excellentfit; and SRMR 0.08 generally considered as goodfit(Hu and Bentler, 1999). Finally, we calculated c2difference tests for nested models with a correction factor (given the use of the SeB c2statistic) to compare the relativefit of thefive-factor model to the four-factor dysphoria and numbing models and the three-factor DSM-IV model (Fan and Sivo, 2009). Last, we evaluated the external validity of thefive-factor model in relation to clinician-assigned diagnoses of depression, other anxiety disorder, and substance use disorder, which were diag- nosed within 365 days from the administration of the PCL-M. Because the factors that comprise the 5-factor model are inher- ently highly correlated, we conducted these analyses using a structural equation model (SEM; seeFig. 1). 4. Results Table 3shows the results of the CFAs for the three-factor DSM-IV model, the four-factor dysphoria and numbing models, and the five-factor dysphoric arousal model. As indicated by lower SeB c2, AIC, BIC, RMSEA, and SRMR values, as well as higher CFI and TLI values, thefive-factor model provided a betterfit to the data than the three- and four-factor models. Most notably and relevant for the comparisons was thefive-factor lower BIC score compared to the other models. Compared to empirically defined benchmarks, thefive-factor model indicated excellentfit (CFI and TLI 0.95; RMSEA 0.06; SRMR 0.08). c2difference tests further demon- strated that thefive-factor modelfit significantly better than the three-factor DSM-IV model, Dc 2(7)¼11110 0 . 9 1,p<.001; the four- factor dysphoria model, Dc 2(4)¼34150.88,p<.001; and the four- factor numbing model, Dc 2(4)¼39172.59,p<.001. Moreover, Table 3demonstrates that thefive-factor’s CFI difference from the alternative models is greater than 0.01 indicating a betterfit(Fan and Sivo, 2009). To assess the factor structure among important veteran sub- samples, we repeated the CFA analyses in two subgroups, women and veterans with a diagnosis of PTSD. When these CFAs were repeated on only female veterans (n¼31,259), the results remained the same. Thefive-factor model provided a betterfit than the three- and four-factor models as shown by lower SeB c2, AIC, BIC, RMSEA, and SRMR values; higher CFI and TLI values (CFI and TLI 0.95); and significant c2difference tests. When analyses were repeated on only veterans with PTSD (n¼129,808), the results also remained the same. Thefive-factor model provided a betterfit than the three- and four-factor models, as evidenced by lower SeB c2, AIC, BIC, RMSEA, and SRMR values; higher CFI and TLI values (CFI and TLI 0.95); and significant c2difference tests. To assess the external validity of thefive-factor model while accounting for the high correlations among thefive factors, we conducted a structural equation model to assess association be- tween thefive-factor model and clinician-assigned diagnoses of depression, other anxiety disorder, and substance use disorder, which highly prevalent among veterans diagnosed with PTSD (see Fig. 1). Results indicated a unique and significant contribution of Table 2 Characteristics of study sample (n¼323,903). Variable Mean (SD) orn(%) Mean age (SD) 44.16 (15.50) Male 292,644 (90.35%) Married 156,132 (48.20%) Race White (Non-Hispanic) 191,641(59.17%) Black 50,379 (15.55%) Hispanic 27,128 (8.38%) Other 54,755 (16.90%) Urban (vs. Rural) 210,632 (65.03%) Service-connection 0% or not service-connected 189,682 (58.56%) 1e99% 121,370 (37.47%) 100% 12,851 (3.97%) Psychiatric diagnoses PTSD 129,808 (40.08%) Dysthymia 98,484 (30.41%) Other anxiety disorder 58,584 (18.09%) Alcohol use disorder 41,771 (12.90%) Major depression 40,436 (12.48%) Adjustment disorder 32,147 (9.92%) Drug use disorder 28,114 (8.68%) Bipolar disorder 11,066 (3.42%) Personality disorder 6339 (1.96%) Other psychotic disorder 4295 (1.33%) Schizophrenia 4970 (1.53%) OEF/OIF status 130,263 (40.22%) I. Harpaz-Rotem et al. / Journal of Psychiatric Research 49 (2014) 31e3633 each factor to each of these diagnoses. The Model’s c2(148)¼222,294, CFI¼0.95 and RMSEA¼0.031 indicated a relatively goodfit. Standardized estimates are presented inTable 4. The results indicated that while each of thefive factors were significantly associated with depression, other anxiety disorder, and substance use disorder, the strongest associations were observed between numbing symptoms and depression and sub- stance use disorder; and between dysphoric arousal and other anxiety disorder. 5. Discussion In this study, we evaluated the dimensional structure of PTSD in more than 300,000 U.S. veterans, one of the largest samples to have ever been studied in the CFA literature on PTSD. We further eval- uated the external validity of thefive-factor model by examining its association with common comorbid diagnoses using structural equation modeling, which accounts for the high intercorrelations among thefive factors. Results of CFAs demonstrated the superi- ority of the 5-factor model of re-experiencing, avoidance, numbing, dysphoric arousal, and anxious arousal (Elhai et al., 2011) in com- parison to the three-factor DSM-IV and 4-factor numbing and dysphoria models in characterizing PTSD symptom dimensionality. Thesefindings extend prior studies that have recently beenpublished from relatively smaller samples of OIF/OEF/OND veterans (Pietrzak et al., 2012a), survivors of domestic violence (Elhai et al., 2011), an earthquake (Wang et al., 2011a), a violent riot (Wang et al., 2011a), and of the general population (Armour et al., 2013a), which similarly found that the dimensional structure of PTSD symptom- atology is best represented by the 5-factor dysphoric arousal model. External validity analysis using SEM revealed significant asso- ciations between each of thefive factors, and clinician-based di- agnoses of depression, other anxiety disorder, and substance use disorder. Numbing symptoms were most strongly related to di- agnoses of depression and substance use disorder, while dysphoric arousal symptoms were most strongly related to a diagnosis of other anxiety disorder. Thesefindings extend on work, which has highlighted the association between PTSD numbing symptoms and depression and, hyperarousal symptoms and anxiety (Pietrzak et al., 2010; Wang et al., 2011a, 2011b) to suggest unique associa- tions between numbing symptoms and substance use disorder, and between dysphoric arousal and other anxiety disorder. Results of our study provide further empirical support for Wat- son’s theoretical model where general distress/dysphoric symp- toms are described as being distinct from more fear-based, panic symptoms that characterize this disorder (Watson, 2005). In this respect, symptoms associated with the dysphoric arousal cluster, Fig. 1.SEM model assessing external correlates of the PTSD 5-factor model.*All 5 factors were allowed to correlate with each other in the model. Table 3 Fit indices of confirmatory factor analyses of the 17-item PTSD Checklist (N¼323,903). Model SeB c2 df CFI TLI RMSEA AIC BIC SRMR 3-factor DSM-IV 196,842.06 116 0.93 0.92 0.07 15,555,200.23 15,555,777.39 0.04 4-factor dysphoria 107,435.52 113 0.96 0.96 0.05 15,435,737.75 15,436346.97 0.03 4-factor numbing 112,457.23 113 0.96 0.95 0.06 15,442,706.92 15,443,316.15 0.03 5-factor 73,284.64 109 0.98 0.97 0.05 15,390,605.02 15,391,257.00 0.02 I. Harpaz-Rotem et al. / Journal of Psychiatric Research 49 (2014) 31e36 34 which are primarily characterized by restlessness and agitation (e.g., irritability) are considered to be conceptually distinct from the numbing cluster symptoms, which are characterized by numbing of responsiveness and anhedonia. The symptoms of the dysphoric arousal cluster are further considered distinct from the two other symptoms that comprise the DSM-IV hyperarousal clustere hypervigilance and exaggerated startle, which are physiological and fear-based symptoms (Elhai et al., 2011). Ourfindings have several potential clinical implications. First, they suggest that the dysphoric and anxious arousal symptoms may constitute independent, theoretically distinct symptom clus- ters. This separation of the hyperarousal symptom cluster is important, as it helps to inform understanding of the optimal phenotypic model of this aspect of PTSD and accordingly, can inform studies of the underlying neurobiology of PTSD. Second, characterization of the optimal symptom structure of PTSD can also increase the reliability of clinical evaluation and assessment of component elements of PTSD, and provide more refined endpoints for use as treatment outcome measures. Third, hyperarousal symptoms that are considered to play a major role in the persis- tence of PTSD (Marshall et al., 2006; Schell et al., 2004; Solomon et al., 2009). Thus, better understanding of which aspect of hy- perarousaleanxious arousal or dysphoric arousaleis most strongly related to the development and maintenance of other, more disabling symptom clusters, such as emotional numbing, may help guide the development of more refined prevention and treatment approaches. Fourth, the proposedfive-factor model of PTSD symptom classification may also provide a more nuanced understanding of the nature and etiology of the high levels of co- morbid psychopathology associated with PTSD (Biehn et al., 2013). The distinction of dysphoric and anxious arousal may also assist researchers and clinicians in better assessing the effectiveness of PTSD treatment by providing more refined domains to be used in assessing treatment outcomes. Both prolonged exposure therapy (PE) and cognitive processing therapy (CPT) have demonstrated promising results in mitigating PTSD symptoms, primarily reex- periencing, hypervigilance, avoidance, and numbing symptoms, but has had only limited success in reducing the dysphoric arousal symptoms, particularly sleep problems. Thisfinding provides clinical support to the independence nature of this symptom cluster (Galovski et al., 2009; Resick et al., 2012). For instance, when assessing the efficacy of CPT and PE on sleep, it was documented that although participants in the two treatment groups experi- enced improvements in their sleep, irrespective of the treatment condition and treatment gains, participants never reached what authors defined as“good sleeper status”or a“normal sleep func- tioning”(Galovski et al., 2009). Interestingly, CPT non-responsive clients were found to be presenting with significantly higherhyperarousal symptoms then responders (Stein et al., 2012), thereby suggesting potential relationships between the distinct anxious arousal cluster and the mechanism of change in CPT. Additional research is needed to evaluate this possibility. Given the VA’s use of PCL-Ms to screen new veterans presenting for services, the five-factor model may also be useful in providing recommendations for specific targeted areas for treatment for veterans who screen positive for PTSD. Currently, scores above a total threshold PCL-M score trigger a positive screen, but subscale scores from a more refined phenotypic model could yield more detailed, patient-specific clinical information that may be useful for treatment providers. This study has several limitations. First, thefinal factor solution was based on comparingfit statistics between models using confir- matory factor analysis, and the differences, while consistent with emerging literature, were not dramatic, which may be due to vari- ability in measurement and administration of the PCL-M in a national healthcare system. Moreover, there was high inter-factor correlation which is not a problem in confirmatory factor analysis but multi- collinearity needs to be considered if the factors are used together to predict other variables. Second, all study participants were U.S. Armed Forces personnel or veterans of the U.S. Armed Forces pre- senting for treatment or evaluation and thus it is not a representative sample of the broader U.S. veteran population. In addition, it is un- clear which veterans in our sample were compensation-seeking versus treatment-seeking. Replication of these results with other samples is needed. Nevertheless, one of the strength associated with the collection of PCL scores in the VA Healthcare System is that veterans completed PCL-Ms with their mental health providers and thus, the PCL-Ms were not based purely on self-report. Third, both the avoidance and anxious arousal factors are comprised of only two PCL-M items. Thus it is difficult to determine their validity and reli- ability in assessing these broader latent constructs. 6. Conclusion This study utilized data from over 320,000 veterans who responded to a widely used self-report measure of military-related PTSD during their initial VA mental health appointment. The data provide strong support for a theory-drivenfive-factor model of PTSD symptoms. Additional research is needed to evaluate the clinical utility of this model; its relation to neurobiological and genetic markers; and how this model may help to inform the refinement of PTSD assessment instruments. Conflict of interest none to report. Contributors Harpaz-Rotem, IlaneDesign, data collection, data analyses and writing. Tsai, Jack: data analyses and writing. Pietrzak, Robert: Assist in data analyses interpretation and writing. Hoff Rani: Data collection and writing. Role of funding source No funding was provided. Disclosures and acknowledgments None to disclose. Table 4 Standardized coefficients in SEM of 5-factor model and co-occurring psychopathologies. Factors Depression Anxiety SUD B (95% CI) B (95% CI) B (95% CI) Reexperiencing 0.143 (0.141e0.145) 0.080 (0.077e0.083) 0.107 (0.104e0.110) Avoidance 0.145 (0.143e0.147) 0.073 (0.070e0.076) 0.095 (0.092e0.098) Numbing 0.219 (0.217e0.221) 0.085 (0.082e0.088) 0.146 (0.143e0.149) Dysphoric arousal0.171 (0.169e0.173) 0.102 (0.099e0.105) 0.095 (0.092e0.098) Anxious arousal0.113 (0.111e0.115) 0.091 (0.088e0.094) 0.098 (0.095e0.101) p<.0001 in all correlations between the 5 factors and diagnosis. 95%CI¼95% confidence interval. I. Harpaz-Rotem et al. / Journal of Psychiatric Research 49 (2014) 31e3635 Acknowledgment None to report References Armour C, Carragher N, Elhai JD. Assessing thefit of the Dysphoric Arousal model across two nationally representative epidemiological surveys: the Australian NSMHWB and the United States NESARC. J Anxiety Disord 2013a;27:109e15. Armour C, Elhai JD, Richardson D, Ractliffe K, Wang L, Elklit A. Assessing afive factor model of PTSD: is dysphoric arousal a unique PTSD construct showing differ- ential relationships with anxiety and depression? J Anxiety Disord 2012;26: 368e76. Armour C, Raudzah Ghazali S, Elklit A. PTSD’s latent structure in Malaysian tsunami victims: assessing the newly proposed Dysphoric Arousal model. Psychiatry Res 2013b;206:26e32. Biehn TL, Contractor A, Elhai JD, Tamburrino M, Fine TH, Prescott MR, et al. Relations between the underlying dimensions of PTSD and major depression using an epidemiological survey of deployed Ohio National Guard soldiers. J Affect Dis- ord 2013;144:106e11. Elhai JD, Biehn TL, Armour C, Klopper JJ, Frueh BC, Palmieri PA. Evidence for a unique PTSD construct represented by PTSD’s D1-D3 symptoms. J Anxiety Disord 2011;25:340e5. Elhai JD, Engdahl RM, Palmieri PA, Naifeh JA, Schweinle A, Jacobs GA. Assessing posttraumatic stress disorder with or without reference to a single, worst traumatic event: examining differences in factor structure. Psychol Assess 2009;21:629e34. Elhai JD, Grubaugh AL, Kashdan TB, Frueh BC. Empirical examination of a pro- posed refinement to DSM-IV posttraumatic stress disorder symptom criteria using the National Comorbidity Survey Replication data. J Clin Psychiatry 2008;69:597e602. Elhai JD, Palmieri PA. The factor structure of posttraumatic stress disorder: a liter- ature update, critique of methodology, and agenda for future research. J Anxiety Disord 2011;25:849e54. Engdahl RM, Elhai JD, Richardson JD, Frueh BC. Comparing posttraumatic stress disorder’s symptom structure between deployed and nondeployed veterans. Psychol Assess 2011;23:1e6. Fan X, Sivo SA. Using goodness-of-fit indexes in assessing mean structure invari- ance. Struc Equat Model 2009;16:54e67. Friedman MJ, Resick PA, Bryant RA, Brewin CR. Considering PTSD for DSM-5. Depress Anxiety 2011;28:750e69. Galovski TE, Monson C, Bruce SE, Resick PA. Does cognitive-behavioral therapy for PTSD improve perceived health and sleep impairment? J Trauma Stress 2009;22:197e204. Grubaugh AL, Long ME, Elhai JD, Frueh BC, Magruder KM. An examination of the construct validity of posttraumatic stress disorder with veterans using a revised criterion set. Behav Res Ther 2010;48:909e14. Harpaz-Rotem I, Libby D, Rosenheck RA. Psychotherapy use in a privately insured population of patients diagnosed with a mental disorder. Soc Psychiatry Psy- chiatr Epidemiol 2012;47:1837e44. Harpaz-Rotem I, Rosenheck RA. Serving those who served: retention of newly returning veterans from Iraq and Afghanistan in mental health treatment. Psychiatr Serv 2011;62:22e 7. Hu L, Bentler PM. Cutoff criteria forfit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equat Model 1999;6:1e55. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005;62:593e602. Kessler RC, Sonnega A, Bromet E, Hughes M, Nelson CB. Posttraumatic stress disorder in the National Comorbidity Survey. Arch Gen Psychiatry 1995;52: 10 4 8e60. King DW, Leskin GA, King LA, Weathers FW. Confirmatory factor analysis of the Clinician-Administered PTSD Scale: evidence for the dimensionality of post- traumatic stress disorder. Psychol Assess 1998;10:90e6.Mansfield AJ, Williams J, Hourani LL, Babeu LA. Measurement invariance of post- traumatic stress disorder symptoms among U.S. military personnel. J Trauma Stress 2010;23:91e9. Marshall GN, Schell TL, Glynn SM, Shetty V. The role of hyperarousal in the mani- festation of posttraumatic psychological distress following injury. J Abnorm Psychol 2006;115:624e8. Palmieri PA, Weathers FW, Difede J, King DW. Confirmatory factor analysis of the PTSD Checklist and the clinician-administered PTSD Scale in disaster workers exposed to the World Trade Center Ground Zero. J Abnorm Psychol 2007;116: 329e41. Pietrzak RH, Goldstein MB, Malley JC, Rivers AJ, Southwick SM. Structure of posttraumatic stress disorder symptoms and psychosocial functioning in veterans of operations enduring freedom and Iraqi freedom. Psychiatry Res 2010;178:323e9. Pietrzak RH, Tsai J, Harpaz-Rotem I, Whealin JM, Southwick SM. Support for a novel five-factor model of posttraumatic stress symptoms in three independent samples of Iraq/Afghanistan veterans: a confirmatory factor analytic study. J Psychiatr Res 2012a;46:317e22. Pietrzak RH, Van Ness PH, Fried TR, Galea S, Norris F. Diagnostic utility and factor structure of the PTSD Checklist in older adults. Int Psychogeriatr 2012b;24: 1684e96. Resick PA, Williams LF, Suvak MK, Monson CM, Gradus JL. Long-term outcomes of cognitive-behavioral treatments for posttraumatic stress disorder among fe- male rape survivors. J Consult Clin Psychol 2012;80:201e10. Satorra A, Bentler PM. A scaled difference chi-square test statistic for moment structure analysis. Psychometrika 2001;66:507e14. Schell TL, Marshall GN, Jaycox LH. All symptoms are not created equal: the prom- inent role of hyperarousal in the natural course of posttraumatic psychological distress. J Abnorm Psychol 2004;113:189e97. Shevlin M, McBride O, Armour C, Adamson G. Reconciling the differences between the King et al. (1998) and Simms, et al. (2002) factor models of PTSD. J Anxiety Disord 2009;23:995e10 01. Simms LJ, Watson D, Doebbeling BN. Confirmatory factor analyses of posttraumatic stress symptoms in deployed and nondeployed veterans of the Gulf War. J Abnorm Psychol 2002;111:637 e47. Solomon Z, Horesh D, Ein-Dor T. The longitudinal course of posttraumatic stress disorder symptom clusters among war veterans. J Clin Psychiatry 2009;70: 837e43. Stein NR, Dickstein BD, Schuster J, Litz BT, Resick PA. Trajectories of response to treatment for posttraumatic stress disorder. Behav Ther 2012;43:790e800. Tanielian T, Jaycox LH. Invisible wounds of war: psychological and cognitive injuries, their consequences, and services to assist recovery. Santa Monica, CA: The RAND Center for Military Health Policy Research; 2008. Thomas JL, Wilk JE, Riviere LA, McGurk D, Castro CA, Hoge CW. Prevalence of mental health problems and functional impairment among active component and National Guard soldiers 3 and 12 months following combat in Iraq. Arch Gen Psychiatry 2010;67:614e23. Wang L, Cao C, Wang R, Zhang J, Li Z. The dimensionality of PTSD symptoms and their relationship to health-related quality of life in Chinese earthquake sur- vivors. J Anxiety Disord 2012;26:711e8. Wang L, Li Z, Shi Z, Zhang J, Zhang K, Liu Z, et al. Testing the dimensionality of posttraumatic stress responses in young Chinese adult earthquake survivors: further evidence for“dysphoric arousal”as a unique PTSD construct. Depress Anxiety 2011a;28:1097e10 4. Wang L, Long D, Li Z, Armour C. Posttraumatic stress disorder symptom structure in Chinese adolescents exposed to a deadly earthquake. J Abnormal Child Psychol 2011:749e58. Wang L, Zhang J, Shi Z, Zhou M, Li Z, Zhang K, et al. Comparing alternative factor models of PTSD symptoms across earthquake victims and violent riot witnesses in China: evidence for afive-factor model proposed by Elhai et al. (2011). J Anxiety Disord 2011c;25:771e6. Watson D. Rethinking the mood and anxiety disorders: a quantitative hierarchical model for DSM-V. J Abnorm Psychol 2005;114:522e36. Weathers FW, Litz BT, Huska JA, Keane TM. The PTSD checklist (PCL). p. Boston, Massachusetts: National Center for PTSD; 1991. Yufik T, Simms LJ. A meta-analytic investigation of the structure of posttraumatic stress disorder symptoms. J Abnorm Psychol 2010;119:764e76. I. Harpaz-Rotem et al. / Journal of Psychiatric Research 49 (2014) 31e36 36
Research report Exploring the relationship between underlying dimensions of posttraumatic stress disorder and depression in a national, trauma-exposed military sample Jon D. Elhai a,⁎, Ateka A. Contractor a, Patrick A. Palmieri b, David Forbes c, J. Don Richardson d,e aDepartment of Psychology, University of Toledo, Ohio, United StatesbCenter for the Treatment and Study of Traumatic Stress, Department of Psychiatry, Summa Health System, Ohio, United StatescAustralian Centre for Posttraumatic Mental Health and Department of Psychiatry, University of Melbourne, Melbourne, AustraliadOperational Stress Injury Clinic, St. Joseph’s Health Care London, Parkwood Hospital, London, Ontario, CanadaeVeterans Affairs Canada, Hamilton, Ontario, Canada article info abstract Article history: Received 30 March 2011 Received in revised form 25 April 2011 Accepted 27 April 2011 Available online 19 May 2011 Background: Posttraumatic stress disorder (PTSD) and depression are highly comorbid and intercorrelated. Yet little research has examined the underlying processes explaining their interrelationship. Method:In the present survey study, the investigators assessed the combined symptom structure of PTSD and depression symptoms, to examine shared, underlying psychopatholog- ical processes. Participants included 740 Canadian military veterans from a national, epidemiological survey, previously deployed on peacekeeping missions and administered the PTSD Checklist and Center for Epidemiological Studies-Depression Scale (CES-D). Results:An eight-factor PTSD/depression model fit adequately. In analyses validating the structure, PTSD’s dysphoria factor was more related to depressive affect than to several other PTSD and depression factors. Somatic problems were more related to dysphoria than to other PTSD factors. Limitations:Only military veterans were sampled, and without the use of structured diagnostic interviews. Conclusions:Results highlight a set of interrelationships that PTSD’s dysphoria factor shares with specific depression factors, shedding light on the underlying psychopathology of PTSD that emphasizes dysphoric mood. © 2011 Elsevier B.V. All rights reserved. Keywords: Posttraumatic stress disorder Depression Confirmatory factor analysis Military veterans PTSD Checklist Center for Epidemiological Studies-Depression Scale 1. Introduction Studies demonstrate that major depressive disorder (MDD) and PTSD are highly comorbid and statistically correlated, despite some symptom overlap. Studies have notexamined the individual, underlying structural dimensions of both PTSD and depression to explore their associations. PTSD has clear conceptual and empirical ties to depressive disorders. Based on nationally representative studies, 48–55% of individuals diagnosed with a lifetime history of PTSD were also diagnosed with lifetime major depression (Elhai et al., 2008; Kessler et al., 1995). The substantial PTSD-depression comorbidity persists, even after removing items that overlap between the disorders, in large-scale epidemiological studies of adult civilians (Elhai et al., 2008) and military veterans (Grubaugh et al., 2010). Furthermore, among the most widely used PTSD instruments, relationships with depression Journal of Affective Disorders 133 (2011) 477–480 ⁎Corresponding author at: Department of Psychology, University of Toledo, Mail Stop #948, 2801 W. Bancroft St., Toledo, Ohio 43606-3390, United States. Tel.: + 1 419 530 2829; fax: + 1 419 530 8479. URL:http://www.jon-elhai.com(J.D. Elhai). 0165-0327/$–see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jad.2011.04.035 Contents lists available atScienceDirect Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad severity range from .61 to .75 for the Clinician-Administered PTSD Scale (CAPS; reviewed inWeathers et al., 2001), and from .63 to .67 for the Posttraumatic Stress Disorder Checklist (PCL; e.g.,Adkins et al., 2008). The most empirically supported structural PTSD models (Elhai and Palmieri, 2011; Yufik and Simms, 2010) were developed byKing et al. (1998) and Simms et al. (2002). King et al.’s emotional numbing model involves four intercorre- lated factors, separating theDSM-IVPTSD model’s avoidance and numbing factors, in addition to reexperiencing and hyperarousal (King et al., 1998). Simms et al.’s dysphoria model revises King et al.’s model by moving PTSD’s sleep disturbance, irritability, and impaired concentration symp- toms from the hyperarousal to emotional numbing factor, reconceptualizing this factor as general dysphoria or distress (Simms et al., 2002). We focus here on the dysphoria model, given its large dysphoria factor which is conceptually and empirically related to depression. Regarding depression’s factor structure, we focus on the original 20-item Center for Epidemiological Studies-Depression Scale (CES-D) (Radloff, 1977), used in the present study. The four-factor CES-D model includes depressive affect, positive affect, somatic complaints and retarded activity, and interper- sonal problems (Radloff, 1977). Research studies have most consistently supported this CES-D model (e.g.,Nguyen et al., 2004; Sheehan et al., 1995). Some studies have found unique associations for PTSD’s dysphoria factor with depression (Simms et al., 2002) and emotional distress (Forbes et al., 2010); others failed to replicate thisfinding (Elklit et al., 2010; Marshall et al., 2010; Miller et al., 2010). Examining this issue by using latent dimensions for both PTSD and depression represents a unique contribution in providing improved precision in measuring the disorders’ factors, further refining our understanding of the shared psychopathological processes behind PTSD and depression. The current study aimed to test the joint factor structure of PTSD and depression using the the PCL and CES-D, with a sample of war-zone exposed military veterans. Specifically, it was hypothesized that based on analyses validating the symptom structure, depression’s depressive affect and so- matic complaint factors would be more related to PTSD’s dysphoria factor than to other PTSD or depression factors, given the relationship between dysphoria (which includes somatic distress) with depressive affect (Simms et al., 2002). 2. Method 2.1. Participants and procedure We used archival data from Veterans Affairs Canada (VAC). VAC anonymously mailed self-administered questionnaires to 2760 Canadian veterans in 1999, with research ethics board approval. This target sample was randomly selected and nationally stratified from 18,443 individuals identified with health conditions after serving in the Canadian armed forces, receiving or eligible for a VAC disability pension (described in Richardson et al., 2006). Informed consent was implied based upon completion and return of the materials, with a response rate of 71.3% (n= 1968).Among those who returned the surveys, 1106 veterans served since 1990, of which 784 reported being deployed on at least one peacekeeping mission. We only report on these 784 respondents deployed since 1990, coinciding with an era of more stressful training and hazardous war-zone deploy- ments (reviewed inSareen et al., 2010). Among the 784 respondents with valid data, 95.7% (n= 749) were men. Age ranged from 20 to 65 years (M= 44.92,SD= 9.67). The majority completed high school/secondary education (n= 224, 29.8%), or had attended college (n= 199, 26.5%) or completed college education (n= 189, 25.1%). Canadian forces service duration ranged fromb1 year to 45 years (M= 19.08,SD= 10.42). Concerning unique deployments, 54.1% (n= 424) had been deployed once, 31.1% (n= 244) were deployed twice, 11.1% (n= 87) were deployed three times, and 2.3% (n= 18) were deployed four or more times. Although race/ethnicity data are not typically collected in Canada, the vast majority of the larger veteran pool sampled was Caucasian. 2.2. Instrumentation The PTSD Checklist (PCL)-military version was used to measure PTSD symptoms. The PCL (Weathers et al., 1993)isa 17-item self-report measure of PTSD severity, mirroringDSM- IV’s PTSD symptoms, using afive-point Likert scale (1 =“not at all”to 5 =“extremely”). The PCL has excellent internal consistency, test retest reliability, and diagnostic validity (reviewed inMcDonald and Calhoun, 2010). The four-factor PTSD CFA model bySimms et al. (2002)has been supported with the PCL, including with military veterans (e.g.,Naifeh et al., 2010; Pietrzak et al., 2010; Simms et al., 2002). The CES-D (Radloff, 1977)isa20-item,self-report depression instrument with a four-point Likert response format (0 =“rare or none of the time”to 3 =“most or all of the time”). Excellent internal consistency, test-retest reliabil- ity and convergent validity have been established (Knight et al., 1997; Radloff, 1977). Several studies support a four-factor CES-D model (e.g.,Nguyen et al., 2004; Sheehan et al., 1995). 2.3. Analysis Forty-four participants were eliminated for missing at least 20% of responses on the PCL or CES-D. Among the resulting sample of 740 subjects, 119 respondents on the PCL and 59 respondents on the CES-D were missing 1– 2 items, distributed completely randomly; we used Mplus 6.1 soft- ware (Muthén and Muthén, 2010a) to derive parameter estimates using full information maximum likelihood (ML) procedures with a pairwise present estimation (Muthén and Muthén, 2010b). Significant non-normality was found for the PCL. There- fore, ML estimation with a mean-adjusted Satorra–Bentler (S–B) chi-square statistic was used for the PCL CFA, which is robust to non-normality (Satorra and Bentler, 2001), treating PCL items as continuously-scaled. For CFAs including the CES- D items, we treated CES-D items as ordinal rather than continuous variables; as a result, those models used poly- choric covariances and probit regression coefficients, robust weighted least squares estimation with a mean- and 478J.D. Elhai et al. / Journal of Affective Disorders 133 (2011) 477–480 variance-adjusted chi-square (WLSMV) (Flora and Curran, 2004; Wirth and Edwards, 2007). Wefirst conducted two CFAs—a four-factor PCL dysphoria model, and four-factor CES-D model, as specified above. We subsequently tested a joint, eight-factor, inter- correlated PCL/CES-D CFA. Residual error covariances were fixed to zero, and correlations between all latent factors were freely estimated. In addition to chi-square values, robust versions of goodness offit indices were examined, including the comparativefit index (CFI), Tucker–Lewis Index (TLI), root mean square error of approximation (RMSEA) (and for the PCL model, given its continuously-scaled nature, the stan- dardized root mean square residual, or SRMR). Models thatfit very well (or adequately) are indicated by CFI and TLI≥.95 (.90–.94), RMSEAb.06 (to .08), and SRMRb.08 (to .10) (Hu and Bentler, 1999). We also present Bayesian Information Criterion (BIC) values for comparing non-nested models with the same sets of variables; chi-square difference testing is not possible between models not nested within one another. BIC values are only computable using an ML estimator; therefore, since we used the WLSMV estimator, we re-computed analyses using ML estimation to derive BIC values. In comparing BIC values between models, a 10-point BIC difference represents a 150:1 likelihood and“very strong” (pb.05) support that the model with the smaller BIC valuefits best (Kass and Raftery, 1995). Using the eight-factor PCL/CES-D CFA, we implemented validation analyses to test our hypotheses about the specific associations between PTSD’s dysphoria factor and particular depression factors, relative to other PTSD factors. We tested these hypotheses by conducting individual Wald chi-square tests, testing the null hypothesis that the difference between two correlations would be zero (using an alpha level of .01). 3. Results Total PCL scores averaged 30.95 (SD= 16.77); a cutoff score of 50 in military veterans best discriminates between those with and without PTSD (McDonald and Calhoun, 2010). Total CES-D scores averaged 13.99 (SD= 12.31); a score of 16 or higher indicates significant depression (Radloff, 1977). Internal consistency was excellent for the PCL (alpha = .96), and CES-D (alpha = .93). A CFA for the four-factor dysphoria PTSD modelfit the data very well, S–Bχ 2(113,N= 740) = 282.98,pb.001, CFI = .97, TLI = .96, RMSEA = .05 (90% CI: .039–.052),SRMR = .03. A CFA for the four-factor CES-D model alsofit well, robustχ 2(164,N= 740) = 705.67,pb.001, CFI = .98, TLI = .97, RMSEA = .07 (90% CI: .062–.072). 1,2 The eight- factor combined modelfit the data reasonably well, robustχ 2 (601,N= 740) = 1660.02,pb.001, CFI = .92, TLI = .91, RMSEA = .05 (90% CI: .046–.052).Table 1displays factor intercorrelations. Next we validated the factor structure by testing specific hypotheses about dysphoria’s relationships with PTSD and depression factors. First, the CES-D’s depressive affect factor was more related to PTSD’s dysphoria (r= .77) than to PTSD factors involving reexperiencing (r=.62, Waldχ 2(1, N= 740) = 42.55,pb.001), avoidance (r= .57, Waldχ2(1, N= 740) = 53.28,pb.001), and hyperarousal (r= .67, Wald χ 2(1,N= 740) = 20.01,pb.001). Second, PTSD’s dysphoria factor was more related to the CES-D’s depressive affect factor (r= .77) than to the positive affect factor (r=−.66, Waldχ 2 (1,N= 740) = 694.78,pb.001) and interpersonal problems factor (r= .66, Waldχ 2(1,N= 740) = 15.74,pb.001); how- ever, dysphoria was more related to somatic problems (r= .84, Waldχ 2(1,N= 740) = 22.22,pb.001) than to depressive affect. Next, the CES-D’s interpersonal problems factor was more related to PTSD’s dysphoria (r= .65) than to effortful avoidance (r= .49, Waldχ 2(1,N= 740) = 27.54,pb.001). Finally, the CES-D’s somatic problems factor was more related to PTSD’s dysphoria factor (r= .84) than to reexperiencing (r= .66, Waldχ 2(1,N= 740) = 68.83,pb.001), avoidance (r= .60, Waldχ2(1,N= 740) = 76.36,pb.001), and hyper- arousal (r= .72, Waldχ 2(1,N= 740) = 31.97,pb.001). 4. Discussion We found well-fitting models of PTSD and depression, using the PCL and CES-D, respectively, supporting previous factor analytic literature. When testing a combined eight- factor PTSD/depression model, this modelfit fairly well. Validation analyses demonstrated that dysphoria was most closely related to the CES-D’s depressive affect and somatic problems factors, suggesting that the shared variance between PTSD and depression may be due to dysphoric symptoms present in the PTSD diagnosis. PTSD’s dysphoria factor is a diverse construct that involves symptoms characterized by a numbing of general respon- siveness and depressed mood (PTSD’s C3–C7), but also involves an agitated or restless form of dysphoria that includes features of both anxiety and depression (D1–D3) 1We were concerned that perhaps if the positive affect factor’s items were reverse-coded, it would be redundant with the depressive affect factor. We therefore reverse-coded positive affect factor items, and using a chi- square difference test (Muthén and Muthén, 2010b), a three-factor CES-D model (BIC = 24,085.71) that merged these affect factors resulted in a significantly worsefit than the four-factor model (BIC = 23,624.01),χ 2 diff (3, N= 740) = 294.30,pb.001. Thus these factors did not appear redundant, supporting previous research (Sheehan et al., 1995). 2Because we found a large correlation between depressive affect and somatic complaints (r= .93), we tested whether a three-factor model that merged these two factors resulted in a significantly worsefit than the four- factor model. The three-factor model had a worsefit (BIC = 23,943.74) than the four-factor model (BIC = 23,624.01),χ 2 diff (3,N= 740) = 87.82,pb.001, suggesting that these factors are distinct, supporting previous research (Nguyen et al., 2004). Table 1 Correlations among PTSD and depression factors (N = 740). 1 2345678 1. Reexperiencing– 2. Avoidance .88– 3. Dysphoria .83 .80– 4. Hyperarousal .86 .77 .89– 5. Depressive affect .62 .57 .77 .67– 6. Positive affect−.40−.38−.45−.37−.66– 7. Somatic complaints.66 .60 .84 .72 .93−.54– 8. Interpersonal problems.50 .49 .65 .60 .79−.51 .74–479 J.D. Elhai et al. / Journal of Affective Disorders 133 (2011) 477–480 (Elhai et al., 2011). Thus it is not too surprising that dysphoria was more related to somatic problems than to depressed affect, given overlap between PTSD’s D1–D3 symptoms with the CES-D’s somatic complaints that too involve a similar construct. Results are generalizable primarily to the CES-D and PCL, but suggest that there are non-specific dimensions of PTSD (i.e., dysphoria) that are substantially related to dimensions of depression. Thesefindings provide more support for some authors’ contention that PTSD has diminished construct validity (Rosen and Lilienfield, 2008; Spitzer et al., 2007). Several limitations apply to the present study. First, we only sampled military veterans, and thus generalizability to civilian victims of psychological trauma is not known. Furthermore, the sample was not representative of military veterans in general, given their Canadian nationality, and selection based on experiencing a health-related disability. Additionally, our lack of structured diagnostic interviews limits generalizability to individuals assessed with self-report instruments. Nonetheless, despite these limitations, the present study adds to previous literature by providing a better understand- ing of dysphoria’s relation to depressive affect and somatic depression. Given that dysphoria is a diverse construct within PTSD, future research should further test whether dysphoria has unique components that are differentially related to external measures of distress. Role of funding source The authors received no funding related to this project. Conflict of interest The authors have no perceived or actual conflicts of interest related to this project. However, Dr. Richardson is a paid consultant for VA Canada. Acknowledgements None. References Adkins, J.W., Weathers, F.W., McDevitt-Murphy, M.E., Daniels, J.B., 2008. Psychometric properties of seven self-report measures of posttraumatic stress disorder in college students with mixed civilian trauma exposure. J. Anxiety Disord 22, 1393–1402. Elhai, J.D., Palmieri, P.A., 2011. The factor structure of posttraumatic stress disorder: A literature update, critique of methodology, and agenda for future research. J. Anxiety Disord 25, 849–854. Elhai, J.D., Grubaugh, A.L., Kashdan, T.B., Frueh, B.C., 2008. Empirical examination of a proposed refinement to DSM-IV posttraumatic stress disorder symptom criteria using the National Comorbidity Survey Replication data. J. Clin. Psychiatry 69, 597–602. Elhai, J.D., Biehn, T.L., Armour, C., Klopper, J.J., Frueh, B.C., Palmieri, P.A., 2011. Evidence for a unique PTSD construct represented by PTSD’s D1–D3 symptoms. J. Anxiety Disord 25, 340–345. Elklit, A., Armour, C., Shevlin, M., 2010. Testing alternative factor models of PTSD and the robustness of the dysphoria factor. J. Anxiety Disord 24, 147–154. Flora, D.B., Curran, P.J., 2004. An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychol Methods 9, 466–491. Forbes, D., Parslow, R., Creamer, M., O’Donnell, M., Bryant, R., McFarlane, A., Silove, D., Shalev, A., 2010. A longitudinal analysis of posttraumatic stressdisorder symptoms and their relationship with fear and anxious-misery disorders: implications for DSM-V. J. Affect. Disord. 127, 147–152. Grubaugh, A.L., Long, M.E., Elhai, J.D., Frueh, B.C., Magruder, K.M., 2010. An examination of the construct validity of posttraumatic stress disorder with veterans using a revised criterion set. Behav. Res. Ther 48, 909–914. Hu, L., Bentler, P.M., 1999. Cutoff criteria forfit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling 6, 1–55. Kass, R.E., Raftery, A.E., 1995. Bayes factors. J Am Stat Assoc 90, 773–795. Kessler, R.C., Sonnega, A., Bromet, E., Hughes, M., Nelson, C.B., 1995. Posttraumatic stress disorder in the National Comorbidity Survey. Arch. Gen. Psychiatry 52, 1048–1060. King, D.W., Leskin, G.A., King, L.A., Weathers, F.W., 1998. Confirmatory factor analysis of the Clinician-Administered PTSD Scale: evidence for the dimensionality of posttraumatic stress disorder. Psychol Assess 10, 90–96. Knight, R.G., Williams, S., McGee, R., Olaman, S., 1997. Psychometric properties of the Centre for Epidemiologic Studies Depression Scale (CES-D) in a sample of women in middle life. Behav. Res. Ther 35, 373–380. Marshall, G.N., Schell, T.L., Miles, J.N.V., 2010. All PTSD symptoms are highly associated with general distress: ramifications for the dysphoria symptom cluster. J. Abnorm. Psychol 119, 126–135. McDonald, S.D., Calhoun, P.S., 2010. The diagnostic accuracy of the PTSD Checklist: a critical review. Clin. Psychol. Rev 30, 976–987. Miller, M.W., Wolf, E.J., Harrington, K.M., Brown, T.A., Kaloupek, D.G., Keane, T.M., 2010. An evaluation of competing models for the structure of PTSD symptoms using external measures of comorbidity. J. Trauma. Stress 23, 631–638. Muthén, B.O., Muthén, L.K., 2010a. Mplus. Los Angeles, California. Muthén, B.O., Muthén, L.K., 2010b. Technical Appendices. Los Angeles, California. Naifeh, J.A., Richardson, J.D., Del Ben, K.S., Elhai, J.D., 2010. Heterogeneity in the latent structure of PTSD symptoms among Canadian veterans. Psychol Assess 22, 666–674. Nguyen, H.A., Kitner-Triolo, M., Evans, M.K., Zonderman, A.B., 2004. Factorial invariance of the CES-D in low socioeconomic status African Americans compared with a nationally representative sample. Psychiatry Res 126, 177–187. Pietrzak, R.H., Goldstein, M.B., Malley, J.C., Rivers, A.J., Southwick, S.M., 2010. Structure of posttraumatic stress disorder symptoms and psychosocial functioning in Veterans of Operations Enduring Freedom and Iraqi Freedom. Psychiatry Res 178, 323–329. Radloff, L.S., 1977. The CES-D Scale: a self-report depression scale for research in the general population. Applied Psychological Measurement 1, 385–401. Richardson, J.D., Elhai, J.D., Pedlar, D., 2006. Association of PTSD and depression with medical and specialist care utilization in modern peacekeeping veterans in Canada with health-related disabilities. J. Clin. Psychiatry 67, 1240–1245. Rosen, G.M., Lilienfield, S.O., 2008. Posttraumatic stress disorder: an empirical evaluation of core assumptions. Clin. Psychol. Rev 28, 837–868. Sareen, J., Stein, M.B., Thoresen, S., Belik, S.-L., Zamorski, M., Asmundson, G.J.G., 2010. Is peacekeeping peaceful? A systematic review. Can J Psychiatry 55, 464–472. Satorra, A., Bentler, P.M., 2001. A scaled difference chi-square test statistic for moment structure analysis. Psychometrika 66, 507–514. Sheehan, T.J., Fifield, J., Reisine, S., Tennen, H., 1995. The measurement structure of the Center for Epidemiologic Studies Depression Scale. J. Pers. Assess 64, 507–521. Simms, L.J., Watson, D., Doebbeling, B.N., 2002. Confirmatory factor analyses of posttraumatic stress symptoms in deployed and nondeployed veterans of the Gulf War. J. Abnorm. Psychol 111, 637–647. Spitzer, R.L., First, M.B., Wakefield, J.C., 2007. Saving PTSD from itself in DSM-V. J. Anxiety Disord 21, 233–241. Weathers, F.W., Litz, B.T., Herman, D.S., Huska, J.A., Keane, T.M., 1993. The PTSD Checklist: Reliability, Validity, & Diagnostic Utility. Annual Meeting of the International Society for Traumatic Stress Studies, San Antonio, Texas. Weathers, F.W., Keane, T.M., Davidson, J.R., 2001. Clinician-administered PTSD scale: a review of thefirst ten years of research. Depress. Anxiety 13, 132–156. Wirth, R.J., Edwards, M.C., 2007. Item factor analysis: current approaches and future directions. Psychol Methods 12, 58–79. Yufik, T., Simms, L.J., 2010. A meta-analytic investigation of the structure of posttraumatic stress disorder symptoms. J. Abnorm. Psychol 119, 764–776. 480J.D. Elhai et al. / Journal of Affective Disorders 133 (2011) 477–480


Everyone needs a little help with academic work from time to time. Hire the best essay writing professionals working for us today!

Get a 15% discount for your first order

Order a Similar Paper Order a Different Paper