Different profiles of acute stress disorder differentially predict posttraumatic stress disorder in a large sample of female victims of sexual trauma

Different Profiles of Acute Stress Disorder 1

Published in Psychological Assessment

(pre-published copy)

Different Profiles of Acute Stress Disorder Differentially Predict Posttraumatic Stress

Disorder in a Large Sample of Female Victims of Sexual Trauma

Mark Shevlin, Philip Hyland, and Ask Elklit

Prof. Mark Shevlin – Professor of Psychology University of Ulster

Dr. Philip Hyland – Lecturer in Psychology, National College of Ireland

Prof. Ask Elklit, Professor of Psychology, Southern Danish University


Abstract

This study aimed to test the dimensional structure of acute stress disorder (ASD). Latent

profile analysis was conducted on scores from the Acute Stress Disorder Scale (Bryant,

Moulds, & Guthrie, 2000) using a large sample of female victims of sexual trauma. Four

distinct classes were found. Two of the classes represented high and low levels of ASD,

and the high ASD class was associated with a high probability of subsequent

posttraumatic stress disorder (PTSD). There were two intermediate classes that were

differentiated by the number of arousal symptoms, and the class with high levels of arousal

symptoms had a higher risk of PTSD. The results suggested that ASD is best described by

qualitatively and quantitatively differing subgroups in this sample, whereas previous

research has assumed ASD to be dimensional. This may explain the limited success of

using ASD to predict subsequent PTSD.


Introduction

Acute stress disorder (ASD) is a psychiatric condition characterised by acute stress

responses that may last from two days to four weeks subsequent to the experience of a

traumatic life event. In addition to the experience of a traumatic life event, a diagnosis of

ASD requires that a person experiences an intense emotional reaction to this stressor,

along with a specific constellation of symptoms that lead to significant impairment or

distress. In the newly published Diagnostic and Statistical Manual of Mental Disorders (5th

ed.; DSM-5: American Psychiatric Association, 2013) ASD is no longer listed as an anxiety

disorder but rather as a trauma- or stressor-related disorder. The DSM-5 has

deemphasised the focus placed on the dissociation symptoms highlighted in the previous

edition, and now states that a diagnosis of ASD can be made if a person displays any 9 of

14 symptoms in the areas of intrusions, negative mood, dissociation, avoidance, and

arousal.

ASD was introduced to the DSM-IV (APA, 1994) as a condition characterised

by four symptom classes; dissociation, reexperiencing, avoidance, and arousal. The

introduction of ASD to the DSM-IV was done so for two reasons. The primary purpose was

to describe the acute phase of stress responses experienced by many sufferers of

traumatic events prior to the timeframe in which a diagnosis of PTSD could be made. The

secondary purpose was to identify those trauma survivors who were at a high-risk for

developing PTSD (Koopman, Classen, & Spiegel, 1994). With respect to this secondary

objective of the ASD classification, a substantial body of empirical evidence has called into

question the predictive utility of ASD. Accurately assessing the predictive utility of ASD

necessitates the use of a psychometrically valid measure of the disorder. Unlike the

assessment of PTSD in which a range of reliable and valid measures exist (see Bovin &

Weathers, 2012), the assessment of ASD has been problematic given the absence of a


gold standard method of assessment (see Edmondson, Mills, & Park, 2010). The Acute

Stress Disorder Interview (ASDI; Bryant, Harvey, Dang, & Sackville, 1998) is a structured

clinical interview based upon the diagnostic criteria outlined in DSM-IV. ASDI scores have

been shown to possess satisfactory internal consistency (Cronbach alpha = .90) as well as

test-retest reliability (r = .88). A subsequent confirmatory factor analytic (CFA) study

indicated that the latent structure of the ASDI is best represented by the four factor model

outlined in the DSM-IV (Brooks, Silove, Bryant, O’Donnell, Creamer, & McFarlane, 2008).

In addition to the ASDI, two self-report measures of ASD are commonly

employed. The Stanford Acute Stress Reaction Questionnaire (SASRQCardeña,

Koopman, Classen, Walde, & Spiegel, 2000) has been used in a number of studies (e.g.,

Classen, Koopman, Hales, & Spiegel, 1998; Koopma, Classen, Spiegel, 1994) and the

scores have demonstrated good internal reliability (Cronbach alpha = .90 and .91,

respectively) as well as good concurrent validity with scores on the Impact of Events Scale

(r = .52-.69; see Cardeña et al., 2000). The most recently developed and widely used self-

report measure of ASD is the Acute Stress Disorder Scale (ASDS: Bryant, Moulds, &

Guthrie, 2000) and scores on the scale have been shown to have high test-retest reliability

(r = .94), sensitivity (95%) and specificity (83%). Results from CFA studies to determine

the latent structure of ASDS scores have inconsistent. Wang et al. (2010) found support

for the DSM-IV’s four-factor conceptualisation, whereas Edmondson et al. (2010)

supported a hierarchical model containing a second-order distress factor (with re-

experiencing, arousal, and avoidance first order factors) that was correlated with a

dissociation single first-order factor. Other studies have supported a three-factor model

(dissociation, avoidance, and re-experiencing/arousal; Armour, Elklit, & Shevlin, 2011) and

the DSM-IV’s four-factor model (Hansen, Lasgaard, & Elklit, 2012).


Initially there was evidence that a diagnosis of ASD could be used to

effectively predict the subsequent development of PTSD (Harvey & Bryant, 1998, 1999;

Spiegel, Koopman, Cardefia, & Classen, 1996). However, more recent findings began to

challenge this conclusion. For example, in a longitudinal study of cancer patients, Kangas,

Henry, and Bryant (2005) found that 53% of individuals diagnosed with ASD subsequently

met the criteria for PTSD six months later, however only 36% of those who developed

PTSD satisfied the criteria for ASD during the immediate aftermath of their traumatic

experience. The limited positive predictive power, and poor sensitivity, of ASD in this study

was consistent with other findings (e.g., Brewin, Andrews, & Rose, 2003; Bryant, 2003;

Harvey & Bryant, 2002). Bryant (2011) recently conducted a large-scale meta-analysis to

determine the ability of ASD to predict subsequent PTSD. The review included 22

longitudinal studies consisting of a total of 3,335 participants for whom follow-up data was

available. The results served to further undermine the utility of ASD to predict PTSD. ASD

was found to have modest positive predictive power, with the majority of studies

demonstrating that only 50% of individuals who received a diagnosis of ASD subsequently

developed PTSD. The sensitivity of ASD was also found to be poor with just 48% of those

who eventually received a diagnosis of PTSD initially satisfying the criteria for ASD in the

first month post-trauma. These results served to demonstrate that an ASD diagnosis fails

to identify more than half of all individuals who will subsequently develop PTSD.

A number of plausible explanations have been advanced for the poor

predictive ability of ASD. Bryant’s (2011) review indicated that the sensitivity of ASD could

be improved by adopting a subsyndromal classification in which the dissociation criteria

were ignored. By adopting a conceptualisation of ASD that more closely represents the

PTSD diagnostic model, the predictive ability of ASD was found to be improved. It has also

been suggested that the less restrictive diagnostic criteria for ASD relative to PTSD,


particularly with respect to the arousal and avoidance symptoms, is a cause of the poor

predictive power. The less restrictive diagnostic criteria for ASD means that while a person

could meet the criteria for ASD three weeks post-trauma, that person would not meet the

criteria for PTSD one week later even if their symptoms remained constant (Bryant,

Friedman, Spiegel, Ursano, & Strain, 2011).

In light of the evidence that ASD fails to adequately predict the subsequent

development of PTSD, Bryant and colleagues (2011) have recommended abandoning the

predictive role of ASD and instead solely focusing on ASD as a method of capturing acute

stress responses to traumatic life events. However, the failure of ASD to predict

subsequent PTSD may be due to the underlying assumption that ASD is dimensional in

nature, that is, that people will differ only quantitatively. The assumption that differences in

severity of disorders can only be described quantitatively has been challenged by recent

studies that have reported different ‘types’ of PTSD. For example, Maguen et al. (2013)

conducted a latent class analysis of PTSD symptoms using a sample of Iraq and

Afghanistan Veterans and found three groups that differed quantitatively in symptom

severity (High Symptom, Intermediate Symptom, and Low Symptom groups). The fourth

group was similar to the Intermediate Symptom group but differed qualitatively due to

having very low probabilities of endorsing three emotional numbing symptoms; this group

was labelled ‘Intermediate Symptom with Low Emotional Numbing’. Similarly, Wolf et al.

(2012) conducted a latent class analysis on items from the Clinician-Administered PTSD

Scale (Weathers, Ruscio, & Keane, 1999) using a sample of veterans and argued for a

Dissociative Subtype of PTSD based on a small group that had elevated probabilities of

endorsing the symptoms related to flashbacks, derealization, and depersonalization.

These studies suggest that PTSD may not be dimensional; rather it could be comprised of

groups that differ qualitatively and quantitatively. There have been no similar studies on


ASD to determine if the underlying latent variable is dimensional or categorical. If ASD was

found to be best described by groups that differ qualitatively rather than quantitatively in

overall severity, this may go some way in explaining the poor predictive power of ASD in

predicting PTSD. Particular profiles, or configurations, of symptom severity may confer

greater risk of PTSD than others, even when the overall levels of severity are similar.

The aim of this study is to test the underlying structure of ASD symptoms

using data from a large sample of female sexual victims using latent profile analysis (LPA)

and assess the relationship with PTSD. LPA is a method for identifying homogenous

groups, or latent classes, that share similar symptom profiles. It is predicted that there will

be classes that differ quantitatively, with those classes higher in ASD severity having a

higher probability of subsequently developing PTSD. The relationship between the classes

and the probability of PTSD should be ‘dose-response’ in nature. It is also predicted that

there may be classes that are qualitatively different to the other classes, and that

association with PTSD for these classes would be inconsistent with the ‘dose-response’

relationship of the quantitatively differing classes. This aspect of the study is exploratory as

there are insufficient research findings and theory to guide specific hypotheses about

qualitatively different classes. Large scale meta-analysis (Brewin, Andrews, & Valentine,

2000) and multi-sample studies (Ditlevsen & Elklit, 2010) have indicated that younger age

at exposure to a traumatic event is related to higher levels of posttraumatic stress severity.

As similar findings have been reported for acute stress severity (Cardena, Dennis, Winkel,

& Skitka, 2005; Cohen, 2008) age was used a covariate in the LPA.

Method

Participants

Participants were recruited from the 1,127 female rape victims who contacted

the Centre for Rape Victims (CRV) at the University Hospital of Aarhus (Denmark) from


2002 to 2012. The CRV offers help to victims who contact the centre within 30 days after

being raped. During the initial contact (T1) victims were asked to provide demographic

information and details on a number of assault related issues. Within two weeks after

coming into contact with the centre (T2) the participants were asked to complete a short

battery of questionnaires, one of which assessed Acute Stress Disorder (ASD).

Approximately three months later (T3) participants were asked to fill out another

questionnaire which assessed symptoms of PTSD. Information on age was available for all

participants at T1 (N = 590), and complete data at T2 and T3 was available for 471 and

352 participants respectively. The pairwise percentage of data present was high between

age (T1) and T2 scores for Dissociation (80%), Re-experiencing (82%), Avoidance (82%),

and Arousal (80%). The pairwise percentage of data present was lower between PTSD

scores (T3) and age (60%), Dissociation (43%), Re-experiencing (44%), Avoidance (44%),

and Arousal (43%). Listwise deletion would result in a sample size of 237, however full

information maximum likelihood estimation allows all the available information on 590

participants (for whom data was available at T1 and either T2 or T3) to be used in the

analyses as the missingness is only on the endogenous variables under the assumption of

data being missing at random (Schafer & Graham, 2002).

Most victims (61.5%) had experienced an assault that involved penetration.

Other victims had primarily been exposed to attempted rape (12.3%), molestation (8.7%),

or could not remember the specific type of assault they had experienced (9.2%). The

majority (85.5%) were of Danish origin and their ages ranged from 10 to 71 years. The

mean age of the sample was 22.49 years (SD = 9.2). This sample was significantly older

than the remaining women who visited the CRV, t(1125) = 3.71, p < .001, and were not

included in the study, although the difference was small (2.35 years).


Measures

The Acute Stress Disorder Scale (ASDS; Bryant et al., 2000). The ASDS is a

19-item self-report measure based on the DSM-IV diagnostic criteria of dissociation (5

items), re-experiencing (4 items), avoidance (4 items), and arousal (6 items) symptoms.

The ASDS used a five-point Likert scale ranging from 1 (not at all) to 5 (very much).

Individual item scores were recoded to indicate the absence (score ≤ 3 = 0) or presence

(score ≥ 4 = 1) of a symptom, and a summed total symptom score for dissociation

(possible scores 0-5), re-experiencing (possible scores 0-4), avoidance (possible scores 0-

4), and arousal (possible scores 0-6) was calculated. A DSM-IV diagnosis of ASD requires

at least one symptom to be present for re-experiencing, avoidance, and arousal, and three

symptoms to be present for dissociation. Bryant et al. (2000) reported high levels of

internal consistency (Cronbach’s alpha) for the total scale (.96) and for the subscales

(dissociation = .84, re-experiencing = .87, avoidance = .92, arousal = .93). Alpha

coefficients in this study were lower (total scale = .84, dissociation = .69, re-experiencing =

.66, avoidance = .66, arousal = .75).

The Harvard Trauma Questionnaire Part IV (HTQ: Mollica et al. 1992). The

HTQ can be used to identify participants reporting symptoms consistent with DSM-IV

PTSD diagnostic criteria and also provides a measure of PTSD severity. The first sixteen

items were derived directly from the seventeen DSM-IV criteria for PTSD. The HTQ uses

one item to assess both psychological and physiological reactions to events that symbolize

or resemble aspects of the traumatic event (in accordance with DSM-IV this item is part of

the Re-experiencing cluster). The items are divided into three subscales that correspond to

the three main symptom groups of PTSD: re-experiencing, avoidance, and arousal. The

HTQ requires the respondents to rate how much each symptom has bothered them in the

last week on a four-point Likert-type scale (‘not at all’ (1), to ‘all the time’ (4)). Mollica et al.


(1992) and Elklit and Shevlin (2007) reported high estimates of reliability for each of the

subscales and the scale as a whole. Participants with high likelihood of a diagnosis of

PTSD were identified if they reported at least one re-experiencing symptom, three

avoidance symptoms, and two arousal symptoms as being present. A symptom was rated

as present if the item corresponding to the symptom was scored 3 (‘quite a bit’) or greater.

In this study the internal consistency of the total scale and the three subscales scores

were high (Cronbach’s alpha in parentheses): total scale (.84), re-experiencing (.77),

avoidance (.77), and arousal (.80).

Analysis

Latent profile analysis (LPA) is a statistical method used to identify

homogeneous groups, or classes, from multivariate data. The analysis involved three

linked elements. First, a LPA was conducted to determine the number of classes of ASD,

and assess if they differed qualitatively or quantitatively. The LPA part of the model used

the four total symptom scores for each of the four ASD dimensions. This ensured that

scores captured clinically meaningful responses and also provided the necessary score

variability for the analysis. Second, age was included in the model as a covariate, and third

the PTSD variable was a distal outcome.

The fit of five models (2-class model through to 6-class model) was assessed.

The models were estimated using robust maximum likelihood (Yuan & Bentler, 2000). To

avoid solutions based on local maxima, 500 random sets of starting values were used

initially and 100 final stage optimizations. The relative fit of the models were compared by

using three information theory based fit statistics: the Akaike Information Criterion (AIC;

Akaike, 1987), the Bayesian Information Criterion (BIC; Schwartz, 1978) and sample size

adjusted Bayesian Information Criterion (ssaBIC; Sclove, 1987). The model that produces

the lowest values can be judged the best model. Evidence from simulation studies have


indicated that the BIC was the best information criterion for identifying the correct number

of classes (Nylund, Asparouhov, & Muthén, 2007). In addition the Lo-Mendell-Rubin

adjusted likelihood ratio test (LMR-A; Lo, Mendell, & Rubin, 2001) and the bootstrapped

likelihood ratio test (BLRT; McLachlan & Peel, 2000) were used to compare models with

increasing numbers of latent classes. When a non-significant value (p > .05) occurs this

suggests that the model with one less class should be accepted. All analyses were

conducted using Mplus 7.00 (Muthén &Muthén, 2012).

Results

Most of the participants reported one or more symptoms of dissociation

(77.5%; M = 3.50, SD = 1.36), re-experiencing (93.8%; M = 2.45, SD = 1.21), and

avoidance (91.9%; M = 2.59, SD = 1.25), and three or more symptoms of arousal (97.7%;

M = 4.17, SD = 1.65), while 68.8% reported combined symptoms consistent with ASD

diagnostic criteria. Almost half of the participants (48.3%) who completed the HTQ (N =

352) reported symptoms consistent with PTSD diagnostic criteria. A cross-tabulation of

variables representing ASD and PTSD was statistically significant (N= 237: 2(1)=16.11, p

< .01) and indicated that 79.8% of those with a high likelihood of PTSD initially reported

symptoms consistent with ASD diagnostic criteria, and 45.1% failed to meet the diagnostic

criteria for PTSD and ASD. The percentage of participants with a high likelihood of ASD

but who did not meet the diagnostic criteria for PTSD was 54.9%, and 20.2% of individuals

did not report symptoms consistent with ASD or PTSD.

The fit statistics for the LPA are presented in Table 1. The BIC is lowest for

the 4 class model and the LMR-A indicated that there was no significant improvement in fit

for the 5 class model. However, the AIC and the ssaBIC both decrease for models with 2

through to 6 classes, however the decrease is markedly smaller after 4 classes. The BLRT

values were similar to those for the LMR-A but remained statistically significant for all


models. Although the BLRT has been reported to perform well in a simulation study

(Nylund, Asparouhov, & Muthén, 2007) it also has been reported to be inconsistent with

the LMR-A by remaining statistically non-significant (Statmodel.com, 2013). On the basis

of this the 4 class solution was considered the best fitting model.

Table 1 about here

Figure 1 shows the profile plot for the 4 class solution. Class 4 (N = 299,

50.7%) was the largest, and was characterized by high mean scores on all ASD symptom

clusters. This class was labeled ‘High ASD’. Class 3 (N = 46, 7.8%) was the smallest, and

was characterized by low mean scores on all ASD symptom clusters. This class was

labeled ‘Low ASD’. There were two intermediate classes that were similar in the mean

number of Dissociation and Re-experiencing symptoms. Class 2 (N = 87, 14.7%) differed

from class 1 with a lower number of avoidance symptoms and higher number of arousal

symptoms, and was labeled ‘Low Avoidance, High Arousal’. Class 1 was labeled

‘Intermediate’ class (N = 158, 26.8%).

Figure 1 about here

The association between age and class membership was estimated as a

multinomial logistic regression. Using the ‘Low ASD’ class as a reference category, age

significantly decreased the likelihood of membership of the ‘Intermediate’ class (OR =

0.93, 95%CI = .88 - .99, p < .05) but did not significantly predict membership of the other

two classes.

Table 2 shows the conditional probabilities for having a high likelihood of

PTSD diagnosis based on class membership. For the ‘Low ASD’ group the probability was

not significantly different from zero, and for the ‘High ASD’ group the probability was high

(Pr =. 701). For the two intermediate groups, the ‘Low Avoidance, High Arousal’ group had

a higher probability than the ‘Intermediate’ group, and the difference in the probabilities


equates to the ‘Low Avoidance, High Arousal’ group being almost 3 times more likely to

report symptoms consistent with PTSD diagnostic criteria than the ‘Intermediate’ group

(OR = 2.97, 95%CI = 1.20 - 7.35, p < .05).

Table 2 about here

Discussion

The primary purpose of the current study was to investigate the assumed dimensional

structure of ASD. In order to test our hypothesis of the presence of both quantitatively and

qualitatively distinct latent classes of ASD, LPA was conducted on data from the ASDS

(Bryant et al., 2000) based on a large sample of female sexual trauma victims. Results

indicated the presence of four latent classes. The High ASD class was the largest group

accounting for slightly more than half of the sample. These participants displayed high

levels of dissociation, reexperiencing, avoidance, and arousal symptoms. The Low ASD

class included the fewest number of participants and this class exhibited low levels of each

of the four symptoms groups. Two intermediate classes were observed, one of which was

comprised of a group of individuals who displayed moderate levels of dissociation,

reexperiencing and arousal symptoms, and comparatively higher levels of avoidance

symptoms (Intermediate Class). The second intermediate class (Low Avoidance, High

Arousal) was comparable to the Intermediate class in that participants displayed moderate

levels of dissociation and reexperiencing symptoms, however this class was characterised

by very low level of avoidance symptoms, akin to those observed in the Low ASD class,

and very high levels of arousal symptoms, similar to those observed within the High ASD

class.

The symptom profiles for the High ASD and Low ASD classes were similar

and suggest a quantitative distinction between these classes. Contrastingly, the


Intermediate and Low Avoidance, High Arousal classes displayed qualitatively distinct

symptom profiles from each other, and each class also possessed qualitatively distinct

profiles from the High and Low ASD classes, respectively. These findings are consistent

with the study’s initial hypothesis and provide the first piece of empirical evidence that the

latent structure of ASD is best explained by qualitatively and quantitatively differing

subgroups rather than dimensional scores. Current results are therefore congruent with

recent findings from the wider trauma literature which suggests that PTSD is not

dimensional in nature but is rather comprised of a series of qualitatively distinct classes or

‘subtypes’ (e.g., Maguen et al., 2013; Wolf et al., 2012; Shevlin & Elklit, 2012).

Probabilities of reporting symptoms consistent with PTSD diagnostic criteria

were subsequently investigated based upon membership of each of the four classes. With

respect to the quantitatively distinct classes (the High ASD and Low ASD classes) a clear

‘dose-response’ effect was observed. Individuals belonging to the Low ASD class had a

probability of developing PTSD that was not significantly different from zero whereas

membership of the High ASD class conferred a 70% probability of developing PTSD. Also

in line with predictions, the probabilities of self-reported PTSD based upon membership of

the two qualitatively differing intermediate classes did not follow a traditional ‘dose-

response’ relationship. Individuals belonging to the Intermediate class had a 25%

probability of developing PTSD whereas the probability for the Low Avoidance, High

Arousal class twice as high (49%).

The results indicate that although individuals in each of the two intermediate

classes experience very similar overall levels of distress, a person’s risk of later

developing PTSD is doubled if they report experiencing high levels of arousal. The LPA

showed that it is the combinations of high levels of arousal and low levels of avoidance

that defines members of this class, and variable-centred rather than person-centred


analyses could not have identified this. This suggests that symptoms of arousal, rather

than symptoms of dissociation, may be the most critical factor in the prediction of PTSD.

Previous findings have suggested that improvements in the classification of ASD, and its

predictive power, could be obtained by shifting the emphasis from dissociation to arousal

symptoms (Brewin, Andrews, Rose, & Kirk, 1999; Harvey & Bryant, 1999). Current and

past findings therefore support the recent revisions to the diagnostic criteria in DSM-5

which has eliminated the emphasis placed on dissociation symptoms as the primary

symptom cluster of ASD.

The present study has a number of important implications for clinical practice.

Current findings indicate that increased levels of physiological arousal in the immediate

aftermath of a trauma are the most prominent factor in identifying those who are most

vulnerable to PTSD. Therefore, when performing initial risk assessments clinicians should

be cognizant that simply determining overall symptom severity is not sufficient for

identifying those who are most at risk of developing PTSD. Clinicians would be advised to

instead take a more symptom-specific perspective, particularly with respect to the arousal

symptoms, in order to identify those who have the highest risk of developing PTSD.

Moreover, treatment strategies that serve to reduce levels of arousal in the acute phase

following exposure to trauma would appear prudent as this would serve to substantially

reduce an individual’s likelihood of going on to develop PTSD.

Conclusions drawn from the current study must necessarily be considered in

light of a number of limitations. First, the nature of the sample is limited to a very specific

group of trauma victims who characteristically display high levels of ASD and PTSD

symptomology. Replication of the current analysis with more diverse trauma populations is

clearly warranted. Additionally, participants were recruited from the Danish population

therefore it is unknown whether current results will generalise to other populations.


Second, the analyses for the current study were based upon the use of self-report

measures of ASD (ASDS; Bryant et al., 2000) and PTSD (HTQ; Mollica et al., 1992).

Clinician based measures such as the ASDI (Bryant et al., 1998) and the Clinician-

Administered PTSD scale (Blake et al., 1995) would have been preferable. Third, the

reliability of the ASDS subscales were low compared to those reported in previous ASD

studies. This may be attributable to the varied forms of sexual victimization experienced by

the participants, whereas previous samples have experienced the same event such as

bushfire survivors (Bryant et al., 2000).

In conclusion, this study found that symptom endorsement that is consistent

with ASD has strong predictive power for identifying female sexual trauma victims who will

later have a high likelihood of a diagnosis of PTSD. Additionally, the latent structure of

ASD was found to consist of a series of distinct classes that differed not only quantitatively

but also qualitatively. It was notable that although the two intermediate classes were

comparable in overall severity of ASD, their unique symptom profiles resulted in

substantially different probabilities of developing PTSD. A clear pattern was identified

across the four latent classes which indicated that higher levels of arousal symptoms were

associated with increased probabilities of being diagnosed with PTSD. These results offer

additional evidence that arousal symptoms, rather than dissociation symptoms, are the

most important component of ASD in predicting the subsequent emergence of PTSD.


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Table 1. Fit Statistics for the Latent Profile Analysis of Acute Stress Disorder and Posttraumatic Stress Disorder.

Model Log-

likelihood

AIC BIC ssaBIC LMR-A

p

BLRT

p

2 class -3332.70 6695.41 6761.11 6713.49 406.10

.00

416.71

.00

3 class -3286.98 6615.97 6707.95 6641.28 89.10

.00

91.43

.00

4 class -3249.91 6553.82 6672.08 6586.36 72.26

.01

74.15

.00

5 class -3237.10 6540.21 6684.76 6579.99 24.95

0.58

25.60

.00

6 class -3223.16 6524.33 6695.15 6571.34 27.17

0.23

27.88

.00

Note: AIC = Akaike Information Criterion, BIC = Bayesian Information Criterion, ssaBIC = sample size adjusted Bayesian

Information Criterion, LMR-A Lo-Mendell-Rubin adjusted likelihood ratio test, BLRT = bootstrapped likelihood ratio test.


Table 2. Conditional Probability of PTSD Diagnosis Based on Class Membership.

Class Probability of PTSD

conditional on class

membership

Standard

Error

p

1. Intermediate .247 .062 .00

2. Low Avoidance, High Arousal .493 .073 .00

3. Low ASD .114 .075 .13

4. High ASD .701 .047 .00


Figure 2. Profile Plot Showing Mean ASD Scores and Percentages Meeting Diagnostic Criteria in Each Class.


Dissociation Re-experiencing Avoidance ArousalClass 1 (26.8%) 3.05 1.58 3.08 2.74Class 2 (14.7%) 3.56 2.41 1.05 4.35Class 3 (7.8%) 1.58 1.18 0.93 1.61Class 4 (50.7%) 4.13 3.22 3.31 5.39

0

1

2

3

4

5

6

Num

ber o

f Sym

ptom

s (92.4%)

(14.0%) (100%) .0

(69.4%) (100%) .0

(82.4%) (100%) .0

(100%)

(100%)

(98.3%)

(79.1%)

(100%)

(100%)

(73.3%)

(64.4%)

(100%)

(96.6%)

(86.0%)

(73.3%)

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