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Posttraumatic Symptom Structure Across Age Groups a

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d

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Liat Helpman , Lilach Rachamim , Idan M. Aderka , Ayala Gabai-Daie , Inbal Schindel-Allon & Eva Gilboa-Schechtman

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Psychology Department and Gonda Brain Research Center, Bar-Ilan University

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Psychology Department, Bar-Ilan University

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Schneider Children's Medical Center of Israel, School of Psychology, Interdisciplinary Center (IDC) of Herzliya, Israel d

Psychology Department, University of Haifa Published online: 17 Mar 2014.

Click for updates To cite this article: Liat Helpman, Lilach Rachamim, Idan M. Aderka, Ayala Gabai-Daie, Inbal Schindel-Allon & Eva GilboaSchechtman (2015) Posttraumatic Symptom Structure Across Age Groups, Journal of Clinical Child & Adolescent Psychology, 44:4, 630-639, DOI: 10.1080/15374416.2014.883928 To link to this article: http://dx.doi.org/10.1080/15374416.2014.883928

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Journal of Clinical Child & Adolescent Psychology, 44(4), 630–639, 2015 Copyright # Taylor & Francis Group, LLC ISSN: 1537-4416 print=1537-4424 online DOI: 10.1080/15374416.2014.883928

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Posttraumatic Symptom Structure Across Age Groups Liat Helpman Psychology Department and Gonda Brain Research Center, Bar-Ilan University

Lilach Rachamim Downloaded by [New York University] at 02:49 13 June 2015

Psychology Department, Bar-Ilan University and Schneider Children’s Medical Center of Israel, School of Psychology, Interdisciplinary Center (IDC) of Herzliya, Israel

Idan M. Aderka Psychology Department, University of Haifa

Ayala Gabai-Daie and Inbal Schindel-Allon Psychology Department, Bar-Ilan University

Eva Gilboa-Schechtman Psychology Department and Gonda Brain Research Center, Bar-Ilan University

The applicability of diagnostic criteria of Posttraumatic Stress Disorder to the pediatric population has been a focus of much debate (e.g., Carrion, Weems, Ray, & Reiss, 2002), informing changes in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM–5). The current study examined the factor structure of posttraumatic distress among adult versus pediatric samples using confirmatory factor analysis. The analysis was performed on the DSM–IV-adherent Posttraumatic Diagnostic Scale (Foa, Cashman, Jaycox, & Perry, 1997) and Child Posttraumatic Symptom Scale (Foa, Johnson, Feeny, & Treadwell, 2001). The sample included 378 adult and 204 child and adolescent victims of diverse single-event traumas. A series of models based on previous findings and DSM–IV specification were evaluated. A 4-factor model (Intrusions, Avoidance, Dysphoria, and Hyperarousal), similar to the DSM–5 model, best fit the data among adults, and a different 4-factor model (Intrusion, Avodiance, Numbing, and Hyperarousal) best fit the data among children and adolescents. Despite some similarity, the posttraumatic symptom profiles of pediatric and adult samples may differ. These differences are not fully incorporated into the DSM–5, and warrant further examination.

Posttraumatic Stress Disorder (PTSD) is a potentially severe and debilitating disorder, with dire social, familial, and occupational consequences (e.g., La Greca, Silverman, Vernberg, & Prinstein, 1996; Read et al., 2012). Until This article is based on data obtained by Lilach Rachamim, Inbal Schindel-Allon, and Ayala Gabai-Daie at Schneider Children’s Medical Center of Israel, and at Bar Ilan University. Correspondence should be addressed to Liat Helpman, 24 Hovevei Zion st., Tel Aviv 63364, Israel. E-mail: [email protected]

recently, the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev. [DSM-IV-TR]; American Psychiatric Association [APA], 2000) specifies this disorder as comprising 17 possible symptoms, split into three categories: Intrusion, or reexperiencing (I); avoidance (A); and hyperarousal (H). However, there has been much debate regarding the suitability of this model in describing PTSD. Researchers exploring the symptom structure of posttraumatic distress suggested different two- (e.g.,

PTSD ACROSS AGE GROUPS

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Taylor, Kuch, Koch, Crockett, & Passey, 1998), three(e.g., Anthony, Lonigan, & Hecht, 1999), and four-factor (e.g., Sack, Seeley, & Clarke, 1997) models. In addition, the developmental adequacy of these diagnostic criteria, based mostly on research in adult samples, for the pediatric populations has been questioned (e.g., Scheeringa, Zeanah, Drell, & Larrieu, 1995). These findings have advised the four-factor structure included in DSM–5 (APA, 2013; Miller et al., 2012), and the developmental modifications for symptomatology (Scheeringa & Myers, 2012). Yet symptom structure, thus far, is postulated to be similar across human development. In the following, we attempt to challenge this assumption by comparing structural models, which have been empirically tested and are presented shortly, in both adult and pediatric samples, using comparable, but developmentally appropriate, assessment tools.

THEORETICAL ASPECTS OF DEVELOPMENTAL DIFFERENCES IN POSTTRAUMATIC SYMPTOMATOLOGY Potential developmental differences in posttraumatic symptoms have long been discussed (see Salmon & Bryant, 2002). The theoretical framework of posttraumatic symptoms stipulates certain neurological, cognitive, emotional, and behavioral factors that stem from an overgeneralized ‘‘fear network,’’ linking various, seemingly innocuous, cues to danger (Foa & Kozak, 1986). This theory is based on adult models of processing and may not be fully applicable to pediatric populations: The developmental trajectory implies potentially major differences in language, memory, encoding of information, knowledge base, and the ability to adequately make threat appraisals, emotional regulation, and neurological development between age groups (see Salmon & Bryant, 2002). Children are also more dependent on others; thus the extent to which caregivers are able to shield the children from difficult experiences and their aftermath, as well as to convey information and model coping in a developmentally appropriate manner, may impact the sequelae of children’s trauma (Pynoos et al., 2009; Salmon & Bryant, 2002). Although some attempts at a theoretically driven, developmentally sensitive understanding of pediatric PTSD have been made (e.g., Pynoos, Steinberg, & Piacentini, 1999), these are not reflected in past or current diagnostic criteria, nor in standardized, widely used assessment methods. In fact, particularly for young children, more complex methods of assessment are needed, taking into account the reports of the children themselves and of significant others (Scheeringa, Wright, Hunt, & Zeanah, 2006). Thus, when examining symptom structure of PTSD, there are insufficient tools to encompass and examine theoretical aspects of developmental differences. Also, theoretically

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driven models of pediatric PTSD have, thus far, not been stringently empirically examined. In the following sections, we review models that have been examined in this manner.

Two-Factor Models of PTSD Shortly after the publication of the DSM–IV, Taylor et al. (1998) first challenged the three-factor model presented there. They conducted an exploratory factor analysis (EFA) on data obtained from a sample of United Nation peacekeepers and motor vehicle accident victims using the Posttraumatic Diagnostic Scale (PDS; Foa, Cashman, Jaycox, & Perry, 1997) and the scheduled clinical interview for the DSM–IV (SCID; First & Gibbon, 2004), both adhering to the DSM-IV symptom criteria for PTSD. This factor analysis resulted in a two-factor model: intrusion=active avoidance and arousal=numbing. This model, the two-factor numbing model, has since been compared to additional models and has been found to fit the data less well within both adult (Palmieri & Fitzgerald, 2005; Simms, Watson, & Doebbelling, 2002) and pediatric (Anthony et al., 1999) samples. Therefore, it is not included in our analyses.

Three-Factor Models of PTSD The most extensively studied three-factor model specifies factors of intrusion=active avoidance, numbing, and arousal (three-factor numbing model). This model was found to best fit the data in a large pediatric sample of hurricane Hugo survivors (Anthony et al., 1999). In that study, the model fared better than other two-, three-, and four-factor models with numbing, as well as the DSM-IV model. This three-factor numbing model also provided a good fit with an additional pediatric sample (Anthony et al., 2005), without comparison to additional models. This model was not chosen as the best-fitting model in later adult (Simms et al., 2000) and pediatric (Saul, Grant, & Carter, 2008) samples. Therefore, we do not include it in our comparison. A more recent three-factor model was suggested following an examination of the self-reported posttraumatic symptoms in a sample of German adult trauma victims (Griesel, Wessa, & Flor, 2006). The model supported by EFA was a three-factor dysphoria model, comprising of intrusion=active avoidance, dysphoria, and arousal. This model has been compared to several other models (see Table 1) using a confirmatory factor analysis (CFA), in an adult sample responding to a PTSD symptom checklist (PCL), and results have supported the three-factor dysphoria model as the best-fitting model (Lancaster, Melka, & Rodriguez, 2009). As this model has not been thoroughly examined within a pediatric population, we chose to include it in our comparison.

632

Varied traumas

524

Combat stressors

1,896 deployed and 1,799 non-deployed 419 UN, 103 MVA

Combat stressors and MVA

Varied traumas

1,581

Combat stress

194

PDS, SCID

Modified Diagnostic Interview Schedule PCL

IES, SCID

CAPS, PCL

PCL

Sexual harassment

Terrorist attack of 9=11

PCL

PCL

PCL

CAPS

PDS

RI

RI

PCL

Assessment Tool

Community violence

2,960

299 English speaking and 120 Spanish speaking. 1,218

Varied traumas

IPV

Varied traumas

143

396 patients, 405 treatment seekers for IPV 344

Natural disaster

Natural disaster

Medical problems

Trauma Types

396

5,664

349

N

DSM-SR, DSM-CA

DSM-SR

DSM-CA

Quasi DSM-SR, DSM-CA

DSM-SR, DSM-CA

DSM-SR

DSM-SR

DSM-SR

DSM-SR

DSM-CA

DSM-SR

Quasi DSM-SR

Quasi DSM-SR

DSM-SR

Assessment Tool

DSM model, 4- and 3-factor numbing models 2-, 3-, and 4-factor numbing models, 4-factor dysphoria Factors derived by EFA

2- and 4-factor numbing, 4-factor dysphoria, DSM 4-factor numbing only

1-, 2-, and 4-factor numbing, DSM, and 4-factor dysphoria

2-factor and 4-factor numbing, 4-factor dysphoria, DSM 2-, 3-, and 4-factor numbing, 3- and 4-factor dysphoria 3- and 4-factor numbing

2-factor, 4-factor, numbing

Factors derived by EFA

2-factor, 3-factor and 4-factor numbing, DSM 2-, 3-, and 4- factor numbing, DSM, theory-driven models 3-factor numbing

Models Compared

2-factor numbing

4-factor dysphoria

4-factor numbing

4-factor numbing

4-factor dysphoria

4-factor numbing

4-factor numbing

3-factor dysphoria

4-factor dysphoria

4-factor numbing

3-factor dysphoria

3-factor numbing

3-factor numbing

4-factor numbing

Best Fitting Model

Note: DSM–IV ¼ Diagnostic and Statistical Manual of Mental Disorders (4th ed.); IES ¼ Impact of Events Scale; PCL ¼ Posttraumatic Symptom Checklist; PDS ¼ Posttraumatic Diagnostic Scale; CAPS ¼ Clinician Administered Posttraumatic Stress Disorder scale; IPV ¼ interpersonal violence; SCID ¼ Scheduled Clinical Interview for the DSM–IV; RI ¼ Reaction index; UN ¼ United Nations; MVA ¼ motor vehicle accident; DSM-SR ¼ DSM-IV adherent self report measure; DSM-CA ¼ DSM-IV adherent clinician administered measure; EFA ¼ exploratory factor analysis.

Taylor et al., 1998

Saul et al., 2008 Simms et al., 2000

Sack et al., 2007

Deployed and non-deployed military personnel UN peacekeepers in Bosnia, MVA victims

Women who experienced workplace sexual harassment Utility workers at the World Trade Center Ground Zero site Adolescent and young adult refugees in Cambodia Adolescents

Palmieri et al. 2005

Palmieri et al., 2007

Adult survivors of community violence

College students

Marshall, 2004

Lancaster et al., 2009

Krause et al., 2007

Treatment seeking male military veterans Adult women victims of IPV

Pediatric sample of hurricane survivors Adult trauma survivors

Anthony et al., 2005 Griesel et al., 2006 King et al., 1998

Anthony et al. 1999

Community sample of adults in a medical clinic Pediatric sample of hurricane survivors

Sample

Asmundson et al., 2000

Study

TABLE 1 Previous Examinations of Symptom Structure

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PTSD ACROSS AGE GROUPS

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Four-Factor Models of PTSD Sack et al. (1997) used EFA on data from a sample of adolescent and young adult refugees in Cambodia responding to the SCID and to a self-report scale (the Impact of Events scale; Horowitz, Wilner, & Alvarez, 1979) and found a four-factor model to best fit the data: intrusion, active avoidance, passive avoidance= numbing, and hyperarousal (four-factor numbing model; see Table 2). King and colleagues (1998) conducted a CFA on the responses to the Clinician Administered PTSD Scale (Blake et al., 1990) administered to a sample of treatment seeking male military veterans (see Table 1), supporting the superiority of the four-factor numbing model. A later study by Asmundson et al. (2000) replicated this conclusion in a community sample of adults in a medical clinic, who were administered a PCL (see Table 1). The four-factor numbing model once again prevailed in a similar study by Marshall (2004). Saul et al. (2008) compared the DSM–IV model, as well as four- and three-factor numbing models, in data including clinical interviews of adolescent trauma victims. The four-factor model best fit the data. Simms et al. (2000) administered a PCL to a large sample of deployed and nondeployed Gulf War veterans. A CFA was conducted, comparing two-factor, three-factor, and four-factor numbing models, with a new, theoretically derived model: This model, which we refer to as the four-factor dysphoria model, included factors of intrusion, avoidance, dysphoria, and arousal (see Table 2). This new model was found to best fit the TABLE 2 Models Examined in the Present Study Model

Item

DSM–IV

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Intrusion Intrusion Intrusion Intrusion Intrusion Avoidance Avoidance Avoidance Avoidance Avoidance Avoidance Avoidance Arousal Arousal Arousal Arousal Arousal

4-Factor Numbing

4-Factor Dysphoria

3-Factor Dysphoria

Intrusion Intrusion Intrusion Intrusion Intrusion Avoidance Avoidance Avoidance Numbing Numbing Numbing Numbing Arousal Arousal Arousal Arousal Arousal

Intrusion Intrusion Intrusion Intrusion Intrusion Avoidance Avoidance Avoidance Dysphoria Dysphoria Dysphoria Dysphoria Dysphoria Dysphoria Dysphoria Arousal Arousal

Intrusion=Avoidance Intrusion=Avoidance Intrusion=Avoidance Intrusion=Avoidance Intrusion=Avoidance Intrusion=Avoidance Intrusion=Avoidance Intrusion=Avoidance Dysphoria Dysphoria Dysphoria Dysphoria Dysphoria Dysphoria Dysphoria Arousal Arousal

Note: DSM–IV ¼ Diagnostic and Statistical Manual of Mental Disorders (4th ed.).

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data in this study. This model fared better than two-factor and four-factor numbing models, as well as the DSM–IV three-factor model, in a study by Palmieri, Weathers, Difede, and King (2007), which examined the symptoms of almost 3,000 Ground Zero disaster workers using the Clinician Administered PTSD Scale and the PCL. These results were replicated using only the PCL in a sample of adult women victims of interpersonal violence (Krause, Kaltman, Goodman, & Dutton, 2007). This model has yet to be examined in pediatric populations.

Summary and Study Aims Multiple models of posttraumatic symptom structure have been examined in both pediatric and adult samples, with inconsistent results. These inconsistencies may be attributed to several factors. First, many studies examined symptoms brought on by a single trauma such as war (e.g., Taylor et al., 1997), a natural disaster (e.g., Anthony et al., 1999), or a terror attack (e.g., Palmieri et al., 2007). It may be that different traumas are associated with distinct trauma types. Second, symptom assessment methods may account for these differences. Some studies used questionnaires that adhere to the specific 17 symptoms stipulated by the DSM–IV (e.g., Lancaster et al., 2009; Palmieri et al., 2007), whereas others used questionnaires that do not subscribe to this list of symptoms (e.g., Anthony et al., 1999; Anthony et al., 2005; King et al., 1998). Finally, differences in symptom structure between pediatric and adult samples may be inherent to manifestation of PTSD across the developmental span, as discussed. Controversy withstanding, two models emerge in the literature as leading ‘‘contestants’’: The four-factor numbing and the four-factor dysphoria models. These two models were consistently found as fitting different data sets, using diverse assessment tools (see Table 1). The numbing model was previously found suitable for both pediatric and adult samples. The dysphoria model has yet to be examined in a pediatric sample. This examination is of particular importance due to the symptom structure proposed in DSM–5, which has, for children over the age of 6, adolescents, and adults, incorporated clusters with the well-known symptoms of intrusion and active avoidance; an expanded dysphoria cluster; and a cluster including symptoms of arousal, anger and aggression (see Miller et al., 2012). An additional model remains insufficiently examined: the three-factor dysphoria model. Therefore, we attempt to examine and compare these three models and the current DSM–IV model (see Table 2). Addressing some of the unanswered questions and methodological gaps in the literature to date, we compare these models in both

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HELPMAN ET AL.

adult and pediatric samples, exposed to diverse trauma types, using DSM–IV-adhering assessment tools.

TABLE 3 Demographic and Clinical Measures for Children and Adults Variable

METHODS

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Participants and Procedure The pediatric sample consisted of 207 children and adolescents from two groups: (a) consecutively admitted children seeking treatment at the Posttrauma Clinic at the Schneider Children’s Medical Center of Israel (SCMC; N ¼ 161), and (b) volunteers (N ¼ 46) reporting a traumatic event. Mean age was 12.9, and 55.9% were female. All participants were between the ages of 8 and 19. The adult sample included 378 trauma victims (225 women) and comprised three groups: (a) volunteers who had experienced a traumatic event (N ¼ 134); (b) parents seeking treatment for their children following trauma, who were traumatized themselves (N ¼ 101); and (c) ER patients seeking medical attention following a traumatic incident (N ¼ 143). All participants signed informed consent forms prior to participation. Both adult and child volunteers were recruited using a snowball technique starting from the research assistants and staff of the SCMC, and were approached by telephone prior to coming in for participation. Children seeking treatment at the PTSD clinic at SCMC, as well as their parents, were approached and offered participation there, being told that participation in the study would not bear on the children’s treatment course. Questionnaires were then completed at the clinic. ER patients were approached at the hospital following the trauma. Those who chose to participate were mailed study questionnaires 3 months later, along with a return envelope. In all groups, only individuals who had been exposed to a traumatic event that had occurred at least 3 months before questionnaires were to be filled were approached regarding participation (because approach was immediately following the traumatic event in the ER sample, mailing of questionnaires to participants was timed at 3 months after the initial approach). Those with known cognitive impairment or psychosis were not approached. Three of the pediatric volunteers were suspected as needing clinical attention at initial phone screening, referred to treatment, and were not invited to complete questionnaires; thus 204 individuals composed the final pediatric sample. Demographic, clinical, and traumarelated measures are presented in Table 3. Measures PDS (Foa et al., 1997). The PDS is a widely used and well-validated self-report questionnaire. It includes

Demographics Age (Years) Female Time Since Trauma (Months) Type of Trauma MVA Sexual Assault Terror Other Assault Other Traumas Total CPSS Score Total PDS Score

Childrena

Adultsb

12.68 (3.18) 55.9% 22.81 (25.29)c

34.65 (12.16) 59.5% 26.99 (59.71)c

28.9% 12.7% 23.5% 7.8% 27.1% 19.42 (12.18) —

59.4% 4.7% 8.5% 5.7% 21.7% — 10.64 (11.43)

Note: MVA ¼ motor vehicle accidents; CPSS ¼ Children’s Posttraumatic Symptom Scale; PSD ¼ Posttraumatic Diagnostic Scale. a n ¼ 204. b n ¼ 378. c Range was 3 to 77 months for adults and 3 to 168 months for the pediatric sample.

items closely matching the 17 DSM–IV posttraumatic symptoms, with each item on the PDS measuring a single PTSD symptom. For each item, individuals report the frequency of symptom occurrence during the past month on a 4-point Likert scale. Foa et al. (1997) reported high reliability (a ¼ .92). This measure was administered to adults only (18 and older). We used the Hebrew version of the PDS (as in DeKeyser Ganz, Raz, Gothelf, Yaniv, & Buchval, 2010). In our sample, reliability of the PDS was 0.94. Child PTSD symptom scale (CPSS; Foa, Johnson, Feeny, & Treadwell, 2001). The CPSS is a self-report inventory, developed based on the PDS, with similar items modified to developmentally suit pediatric respondents, such as providing examples for abstract questions and replacing the references to work with references to school. We used the Hebrew version of the CPSS (Rachamim, Helpman, Foa, Aderka, & Gilboa-Schechtman, 2011), which evinced very high reliability (a ¼ .91). Analytic Strategy As in many previously reported samples, our data were positively skewed and significantly differed from the normal distribution (all Shapiro-Wilk statistics >0.65, and all ps < .001). Lower values were more frequently reported by participants on both questionnaires in comparison to the expectations derived from the normal distribution. This has been previously found in studies examining posttrauma among nonclinical populations (e.g., Su & Chen, 2008). Thus, we performed a CFA

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PTSD ACROSS AGE GROUPS

using robust maximum-likelihood estimation as it adjusts for data distributed non-normally (Ullman, 2006). Missing values were excluded from the analyses. To assess model fit we used four goodness-of-fit indexes: the SatorraBentler scaled v2(S-B v2; Bentler & Bonett, 1980), the non-normed fit index (NNFI; Bentler & Bonnet, 1980), the comparative fit index (CFI; Bentler, 1990), and the root mean square error of approximation (RMSEA; Steiger, 1980). Models are thought to fit the data well when the CFI and NNFI are greater than .90 (Bentler, 1990), and the RMSEA is less than .06 (Hu & Bentler, 1999; Kline, 1998). In all CFA analyses, correlations between errors were constrained to zero, items were constrained to load on only one factor, and factors were free to correlate (Ullman, 2006). Comparisons between nested models were conducted using the S-B v2 test (Satorra & Bentler, 2001). Comparisons between non-nested models were conducted by comparing the Akaike Information Criterion (AIC; Akaike, 1987). In both cases lower values indicate better fitting models (Tabachnick & Fidell, 2007). For both children and adults, we began by assessing model fit for each of the five models examined in this study. We then compared nested models followed by non-nested models. After arriving at the best-fitting model, we modified it removing items with low loadings and assessed model fit again. RESULTS Posttraumatic Stress Symptom Structure Among Children We assessed model fit for each of the models among children (see Table 4). We then compared nested models. The four-factor numbing model and the DSM–IV model TABLE 4 Model Fit Among Children and Adults

Model Children DSM-IV 4-Factor Numbing 4-Factor Dysphoria 3-Factor Dysphoria Adults DSM-IV 4-Factor Numbing 4-Factor Dysphoria 3-Factor Dysphoria

S-B Scaled v2

NNFI

CFI

RMSEA

AIC

167.44 147.26 163.74 168.43

0.95 0.97 0.95 0.95

0.96 0.97 0.96 0.96

0.05 0.04 0.05 0.05

64.56 78.74 62.26 63.57

246.05 200.93 189.40 198.52

0.92 0.94 0.95 0.95

0.93 0.95 0.96 0.96

0.06 0.05 0.04 0.04

14.05 25.07 36.60 33.48

Note: DSM-IV ¼ Diagnostic and Statistical Manual of Mental Disorders (4th ed.); S-B ¼ Satorra-Bentler; NNFI ¼ non-normed fit index; CFI ¼ comparative fit index; RMSEA ¼ root mean square error of approximation; AIC ¼ Akaike Information Criterion.

635

are nested, as the numbing and avoidance factors from the four-factor model are combined to form the avoidance factor in the DSM–IV model. Thus, we compared them using the S-B scaled v2 difference test. Results indicated a significant difference between the models, S-B scaled v2(3) ¼ 20.17, p ¼ .00. This indicates that the four-factor numbing model, S-B scaled v2(113) ¼ 147.26, provides a better fit compared to the DSM–IV model, S-B scaled v2(116) ¼ 162.67. We then compared the four-factor dysphoria model and the three-factor dysphoria model. These models are nested as well, as intrusion and avoidance from the four-factor dysphoria model combine to form the intrusion=avoidance factor in the three-factor dysphoria model. No significant difference was found between these models, S-B scaled v2(3) ¼ 4.67, p ¼ .20. In summation, by comparing nested models, we found the four-factor numbing model to be superior to the DSM–IV model. We then compared the remaining models (four-factor numbing, four-factor dysphoria, three-factor dysphoria) using the AIC goodness-of-fit index. The four-factor numbing model had the lowest value of the three models (AIC ¼ 78.74) followed by the three-factor dysphoria model (AIC ¼ 63.57), and the four-factor dysphoria model (AIC ¼ 62.26). Thus we found the best fitting model among children to be the four-factor numbing model. Two items in the four-factor numbing model had low factor loadings: Item 8 (memory problems) had a loading of 0.28 on the avoidance factor, and Item 12 (foreshortened future) had a loading of 0.38 on the numbing factor. All other factors had loadings of 0.57 or more. Items 8 and 12 were also found to have poor loadings in many previous studies (e.g., Anthony et al., 1999; Anthony et al., 2005). Thus we removed these items and assessed model fit again. The final model included four factors: intrusion (Items 1–5), avoidance (Items 6–7), numbing (Items 9–11), and arousal (Items 13–17). This model fit the data well, S-B scaled v2(84) ¼ 111.70, p ¼ .02 (NFI ¼ 0.92, CFI ¼ 0.98, RMSEA ¼ 0.04), and all factor loadings were higher than 0.57. Posttraumatic Stress Symptom Structure Among Adults We assessed model fit for the five models among adults (see Table 4). When comparing nested models, results indicated a significant difference between the four-factor numbing model and the DSM–IV model, S-B scaled v2(3) ¼ 33.42, p ¼ .00: The four-factor numbing model, S-B scaled v2(113) ¼ 200.93, provided a better fit than the DSM–IV model, S-B scaled v2(116) ¼ 246.05. We also found a significant difference between the fourfactor dysphoria model and the three-factor dysphoria model, S-B scaled v2(3) ¼ 8.69, p ¼ .03: The four-factor

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HELPMAN ET AL.

dysphoria model, S-B scaled v2(113) ¼ 189.40, provided a better fit compared to the three-factor dysphoria model, S-B scaled v2(116) ¼ 198.52. In summary, by comparing nested models we found the four-factor numbing model to be superior to the DSM–IV model, and the four-factor dysphoria model to be superior to the three-factor dysphoria model. We then compared between the two models which emerged as the strongest (four-factor numbing and four-factor dysphoria) using the AIC goodness-of-fit index. The four-factor dysphoria model had lower AIC values (AIC ¼ 36.60) compared to the three-factor dysphoria model (AIC ¼ 25.07). Thus we found the best-fitting model among adults to be the four-factor dysphoria model. Item 8 in the four-factor dysphoria model had a low factor loading (0.32) on the avoidance factor. All other factors had loadings of 0.64 or more. This item was also found to have poor loadings in many previous studies (e.g., Palmieri & Fitzgerald, 2005; Taylor et al., 1998). Thus we removed this item and assessed model fit again. The final model included four factors: intrusion (Items 1–5), avoidance (Items 6–7), dysphoria (Items 9–15), and arousal (Items 16–17). This model fit the data well, S-B scaled v2(84) ¼ 164.14, p ¼ .00 (NNFI ¼ 0.96, CFI ¼ 0.96, RMSEA ¼ 0.04) and all factor loadings were higher than 0.65.

DISCUSSION In this study, we aimed at expanding the existing literature on posttraumatic symptom structure by including individuals who vary in both age and trauma type, and who were recruited through clinical and community means. Such examination opens the possibility of discussing not only nosological but developmental aspects of this disturbance.

Commonalities Between Child and Adult Posttraumatic Symptomatology For adults and youngsters alike, four-factor models have been found as best describing the data. These models distinguish between active and passive avoidance, which have been postulated to reflect separate regulatory mechanisms. Foa, Zinbarg, and Rothbaum (1992) proposed that, although both provide escape from distressing emotions, numbing represents an automatic or physiological process, while avoidance may be a primarily psychological process. Such models, in recent years, have garnered increasing support from studies of both adults and youths (Anthony et al., 2005; Foa et al., 1995; Sack et al., 1997) are reflected in DSM–5 diagnostic criteria.

An additional finding common to the younger and older samples in our study is the low loading for the item gauging memory problems on all the models examined. This item is meant to reflect posttraumatic dissociation, once considered one of the core features of traumatic reaction (e.g., Ehlers, Mayou, & Bryant, 2003). The centrality of this feature in posttraumatic disturbance has been brought under intense scrutiny, due to low loading of this item in previous studies (e.g., Anthony et al., 1999; Anthony et al., 2005, for youth, and King et al., 1998; Palmieri & Fitzgerald, 2005; Taylor et al., 1998, for adults). The DSM–5, in fact, acknowledges this specificity of dissociation as characterizing only a subtype of PTSD. Developmental Differences in Posttraumatic Symptomatology Although the previous conclusions may be applied to the features of posttraumatic distress in the general population, regardless of age, several differences emerge between adults and younger trauma victims. First, one item pertaining to a sense of foreshortened future (Item 12) has demonstrated low loading within the different models in pediatric samples, both in this study and previous ones (e.g., Anthony et al., 2005). This may reflect a fundamental developmental difference in the perception of time (Carelli, Forman, & Ma¨ntyla¨, 2007), rendering this item irrelevant for youngsters. Indeed, it has been removed from the criteria for PTSD in DSM–5. A final difference between the two age groups examined was found in the actual symptom structure: The four-factor-numbing model fit the data slightly better for children and adolescents, and the four-factordysphoria model fit slightly better for adults. Differences in structure may have to do with differences in development: Symptoms such as trouble sleeping, trouble concentrating, and irritability were more associated with the arousal factor among children in our sample and more with the dysphoria factor among adults. These findings concur with literature showing that, although PTSD is closely associated with depression among adults (Foa et al., 1997), children may be more prone to reacting to stressful life events with anxiety and behavioral disturbance rather than depression (Famularo, Fenton, Kinscherff, & Augustyn, 1996). Among adults, traumatic events may be deleterious to self-concepts and self-worth, as they may contradict beliefs at their very core (Ehlers & Clark, 2000), promoting depression. Children, on the other hand, may not have such firmly set beliefs and appraisals and may be more worried about concrete issues such as perceived immediate danger to themselves or to caregivers (Salmon & Bryant, 2002). On the other hand, pediatric depression itself presents often with

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irritable features (Birmaher et al., 2007).Therefore, posttraumatic reactions among children may be of a more anxious-behavioral type, with a more discernibly depressive type emerging toward adulthood, or rather, the depression itself may take on irritable features, thus being less discernible from arousal in early developmental stages. These two possibilities warrant further investigation, and although some changes (i.e., developmental adaptation of several items, lowering the threshold) appear in the DSM–5, the specifics suggested by the differences in structure are not. Also, these developmental adaptations are applicable, under the new diagnostic criteria, to children 6 years old or younger. Our data, pertaining to older children and adolescents, suggest otherwise.

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Limitations and Future Directions Our study has several limitations. First, we gauged only the diagnostic criteria for PTSD symptoms at the time of data collection and did not include questions regarding additional symptoms, which are not included in the DSM-IV. These may include somatization, which has been underscored as a prominent feature of pediatric anxiety (Eminson, 2001), and additional behavioral symptoms. In fact, it has been suggested that non-selfreport methods, such as observation of play with various toys and drawing in younger children, observation of reactions in naturalistic settings, as well as collection of information from parents and teachers, may prove critical in fully capturing the scope of pediatric posttraumatic reactions (Pynoos et al., 2009; Salmon & Bryant, 2002; Scheeringa & Myers, 2012). Therefore, we may have overlooked unique and developmentally accurate symptomatology, as well as methods that could bridge the memory, language, and coding gaps represented in early stages of development. Examining such symptoms and employing such methods may contribute to achieving a more comprehensive and age-appropriate symptom structure. Our sample was also limited by the age range represented by it, and our conclusions cannot be generalized to additional age groups. Our adult and child samples were heterogeneous with respect to trauma, time of assessment, subgroups, and demographics. Due to statistical considerations, we were not able to gauge differences in symptom structure depending on symptomsalient factors such as developmental stage (e.g., young children vs. adolescents) and type of trauma, which has been repeatedly demonstrated as related to symptom manifestation (Fairbank, 2008; Pynoos et al., 1999; Salmon & Bryant, 2002; Scheeringa, Wright, Hunt, & Zeanah, 2006). Finally, although our sample included only individuals exposed to DSM-IV-defined traumatic events and adhered to the 3-month time minimum since trauma there specified, we did include a broad range of

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symptom severity, encompassing clinical and subclinical levels. Thus, our results may inform further studies with clinical samples but may not be fully generalizable to this population.

CONCLUSIONS AND CLINICAL IMPLICATIONS Our results support several of the changes proposed for DSM–5 PTSD diagnosis: four-factor symptom structure, attenuation of the centrality of dissociation, and developmental adaptations. Also, they offer unique insight into how pediatric and adult PTSD symptom structure may differ in reflecting human development, informing additional possible modifications to diagnosis. Developmentally accurate PTSD diagnosis should, perhaps, reflect the anxious-behavioral nature of pediatric PTSD and include multiple modalities of assessment, such as clinical observation and reports of significant others. Stage-specific developmental adaptations may be incorporated, which take into account cognitive, linguistic, regulatory, and neurological maturation. For example, separation anxiety may be apparent in younger children but increased risk-taking behavior among adolescents (Pynoos et al., 2009). Also, overt symptoms may not be apparent at certain stages, but developmental arrest may occur: Although changes in startle are apparent later in life, small children may not initially display a change in startle, but then fail to achieve the regulatory increase on such responses (Pynoos et al., 2013). Such issues are only partially addressed by the modifications in the DSM–5 and addressing them may lead to the more accurate identification of the sequelae of trauma among youths now given other anxiety and behavioral diagnoses (see Fairbank, 2008), making appropriate treatment more readily available to those suffering posttraumatic reactions.

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